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EleutherAI/lambada_openai

EleutherAI

--- pretty_name: LAMBADA OpenAI language_creators: - machine-generated license: mit multilinguality: - translation task_ids: - language-modeling source_datasets: - lambada size_categories: - 1K<n<10K language: - de - en - es - fr - it dataset_info: - config_name: default features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: de features: - name: text dtype: string splits: - name: test num_bytes: 1904576 num_examples: 5153 download_size: 1985231 dataset_size: 1904576 - config_name: en features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: es features: - name: text dtype: string splits: - name: test num_bytes: 1821735 num_examples: 5153 download_size: 1902349 dataset_size: 1821735 - config_name: fr features: - name: text dtype: string splits: - name: test num_bytes: 1948795 num_examples: 5153 download_size: 2028703 dataset_size: 1948795 - config_name: it features: - name: text dtype: string splits: - name: test num_bytes: 1813420 num_examples: 5153 download_size: 1894613 dataset_size: 1813420 --- ## Dataset Description - **Repository:** [openai/gpt2](https://github.com/openai/gpt-2) - **Paper:** Radford et al. [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) ### Dataset Summary This dataset is comprised of the LAMBADA test split as pre-processed by OpenAI (see relevant discussions [here](https://github.com/openai/gpt-2/issues/131#issuecomment-497136199) and [here](https://github.com/huggingface/transformers/issues/491)). It also contains machine translated versions of the split in German, Spanish, French, and Italian. LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole text, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. ### Languages English, German, Spanish, French, and Italian. ### Source Data For non-English languages, the data splits were produced by Google Translate. See the [`translation_script.py`](translation_script.py) for more details. ## Additional Information ### Hash Checksums For data integrity checks we leave the following checksums for the files in this dataset: | File Name | Checksum (SHA-256) | |--------------------------------------------------------------------------|------------------------------------------------------------------| | lambada_test_de.jsonl | 51c6c1795894c46e88e4c104b5667f488efe79081fb34d746b82b8caa663865e | | [openai/lambada_test.jsonl](https://openaipublic.blob.core.windows.net/gpt-2/data/lambada_test.jsonl) | 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 | | lambada_test_en.jsonl | 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 | | lambada_test_es.jsonl | ffd760026c647fb43c67ce1bc56fd527937304b348712dce33190ea6caba6f9c | | lambada_test_fr.jsonl | 941ec6a73dba7dc91c860bf493eb66a527cd430148827a4753a4535a046bf362 | | lambada_test_it.jsonl | 86654237716702ab74f42855ae5a78455c1b0e50054a4593fb9c6fcf7fad0850 | ### Licensing License: [Modified MIT](https://github.com/openai/gpt-2/blob/master/LICENSE) ### Citation ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` ```bibtex @misc{ author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel}, title={The LAMBADA dataset}, DOI={10.5281/zenodo.2630551}, publisher={Zenodo}, year={2016}, month={Aug} } ``` ### Contributions Thanks to Sid Black ([@sdtblck](https://github.com/sdtblck)) for translating the `lambada_openai` dataset into the non-English languages. Thanks to Jonathan Tow ([@jon-tow](https://github.com/jon-tow)) for adding this dataset.

Matthijs/cmu-arctic-xvectors

Matthijs

--- pretty_name: CMU ARCTIC X-Vectors task_categories: - text-to-speech - audio-to-audio license: mit --- # Speaker embeddings extracted from CMU ARCTIC There is one `.npy` file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors. The [CMU ARCTIC](http://www.festvox.org/cmu_arctic/) dataset divides the utterances among the following speakers: - bdl (US male) - slt (US female) - jmk (Canadian male) - awb (Scottish male) - rms (US male) - clb (US female) - ksp (Indian male) The X-vectors were extracted using [this script](https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py), which uses the `speechbrain/spkrec-xvect-voxceleb` model. Usage: ```python from datasets import load_dataset embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = embeddings_dataset[7306]["xvector"] speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0) ```

k4d3/by_blotch

k4d3

--- license: wtfpl ---

andyvhuynh/NatureMultiView

andyvhuynh

--- dataset_info: features: - dtype: string name: observation_uuid - dtype: float32 name: latitude - dtype: float32 name: longitude - dtype: int64 name: positional_accuracy - dtype: int64 name: taxon_id - dtype: string name: quality_grade - dtype: string name: gl_image_date - dtype: string name: ancestry - dtype: string name: rank - dtype: string name: name - dtype: string name: gl_inat_id - dtype: int64 name: gl_photo_id - dtype: string name: license - dtype: string name: observer_id - dtype: bool name: rs_classification - dtype: string name: ecoregion - dtype: bool name: supervised - dtype: string name: rs_image_date - dtype: bool name: finetune_0.25percent - dtype: bool name: finetune_0.5percent - dtype: bool name: finetune_1.0percent - dtype: bool name: finetune_2.5percent - dtype: bool name: finetune_5.0percent - dtype: bool name: finetune_10.0percent - dtype: bool name: finetune_20.0percent - dtype: bool name: finetune_100.0percent - dtype: image name: gl_image - name: rs_image sequence: sequence: sequence: int64 ---  # Nature Multi-View (NMV) Dataset Datacard To encourage development of better machine learning methods for operating with diverse, unlabeled natural world imagery, we introduce Nature Multi-View (NMV), a multi-view dataset of over 3 million ground-level and aerial image pairs from over 1.75 million citizen science observations for over 6,000 native and introduced plant species across California. ## Characteristics and Challenges - Long-Tail Distribution: The dataset exhibits a long-tail distribution common in natural world settings, making it a realistic benchmark for machine learning applications. - Geographic Bias: The dataset reflects the geographic bias of citizen science data, with more observations from densely populated and visited regions like urban areas and National Parks. - Many-to-One Pairing: There are instances in the dataset where multiple ground-level images are paired to the same aerial image. ## Splits - Training Set: - Full Training Set: 1,755,602 observations, 3,307,025 images - Labeled Training Set: - 20%: 334,383 observations, 390,908 images - 5%: 93,708 observations, 97,727 images - 1%: 19,371 observations, 19,545 images - 0.25%: 4,878 observations, 4,886 images - Validation Set: 150,555 observations, 279,114 images - Test Set: 182,618 observations, 334,887 images ## Acquisition - Ground-Level Images: - Sourced from iNaturalist open data on AWS. - Filters applied: - Vascular plants - Within California state boundaries - Observations dated from January 1, 2011, to September 27, 2023 - Geographic uncertainty < 120 meters - Research-grade or in need of ID (excluding casual observations) - Availability of corresponding remote sensing imagery - Overlap with bio-climatic variables - Aerial Images: - Sourced from the 2018 National Agriculture Imagery Program (NAIP). - RGB-Infrared images, 256x256 pixels, 60 cm-per-pixel resolution. - Centered on the latitude and longitude of the iNaturalist observation. ## Features - observation_uuid (string): Unique identifier for each observation in the dataset. - latitude (float32): Latitude coordinate of the observation. - longitude (float32): Longitude coordinate of the observation. - positional_accuracy (int64): Accuracy of the geographical position. - taxon_id (int64): Identifier for the taxonomic classification of the observed species. - quality_grade (string): Quality grade of the observation, indicating its verification status (e.g., research-grade, needs ID). - gl_image_date (string): Date when the ground-level image was taken. - ancestry (string): Taxonomic ancestry of the observed species. - rank (string): Taxonomic rank of the observed species (e.g., species, genus). - name (string): Scientific name of the observed species. - gl_inat_id (string): iNaturalist identifier for the ground-level observation. - gl_photo_id (int64): Identifier for the ground-level photo. - license (string): License type under which the image is shared (e.g., CC-BY). - observer_id (string): Identifier for the observer who recorded the observation. - rs_classification (bool): Indicates if remote sensing classification data is available. - ecoregion (string): Ecoregion where the observation was made. - supervised (bool): Indicates if the observation is part of the supervised dataset. - rs_image_date (string): Date when the remote sensing (aerial) image was taken. - finetune_0.25percent (bool): Indicates if the observation is included in the 0.25% finetuning subset. - finetune_0.5percent (bool): Indicates if the observation is included in the 0.5% finetuning subset. - finetune_1.0percent (bool): Indicates if the observation is included in the 1.0% finetuning subset. - finetune_2.5percent (bool): Indicates if the observation is included in the 2.5% finetuning subset. - finetune_5.0percent (bool): Indicates if the observation is included in the 5.0% finetuning subset. - finetune_10.0percent (bool): Indicates if the observation is included in the 10.0% finetuning subset. - finetune_20.0percent (bool): Indicates if the observation is included in the 20.0% finetuning subset. - finetune_100.0percent (bool): Indicates if the observation is included in the 100.0% finetuning subset. - gl_image (image): Ground-level image associated with the observation. - rs_image (sequence of sequences of int64): Aerial image data associated with the observation, represented as a sequence of pixel values. ## References - iNaturalist: www.inaturalist.org - United States Department of Agriculture: NAIP Imagery. www.naip-usdaonline.hub.arcgis.com.

fixie-ai/common_voice_17_0

fixie-ai

--- dataset_info: - config_name: ar features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 300234489.0 num_examples: 10470 - name: test num_bytes: 311234035.0 num_examples: 10480 - name: train num_bytes: 718845895.0 num_examples: 28369 download_size: 1250028526 dataset_size: 1330314419.0 - config_name: de features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 23759438592.6 num_examples: 589100 - name: test num_bytes: 715601886.0 num_examples: 16183 - name: validation num_bytes: 710830645.0 num_examples: 16183 download_size: 24582787064 dataset_size: 25185871123.6 - config_name: en features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: test num_bytes: 9329520290.338 num_examples: 16393 - name: validation num_bytes: 9434608798.338 num_examples: 16393 - name: train num_bytes: 44987747251.6 num_examples: 1101170 - name: validated num_bytes: 68921650062.024 num_examples: 1799288 download_size: 128219063641 dataset_size: 132673526402.3 - config_name: es features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 13216214878.31 num_examples: 336846 - name: test num_bytes: 748084507.0 num_examples: 15857 - name: validation num_bytes: 770184703.0 num_examples: 15857 download_size: 14415677901 dataset_size: 14734484088.309998 - config_name: fr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 20630346378.228 num_examples: 558054 - name: test num_bytes: 684908439.0 num_examples: 16159 - name: validation num_bytes: 703910244.0 num_examples: 16159 download_size: 21981003249 dataset_size: 22019165061.228 - config_name: frold features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 20616364930.228 num_examples: 558054 - name: test num_bytes: 674959025.258 num_examples: 16159 - name: validation num_bytes: 703829746.38 num_examples: 16159 download_size: 21972606682 dataset_size: 21995153701.866 - config_name: hi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 275394930.996 num_examples: 9378 - name: validation num_bytes: 145392985.176 num_examples: 4856 - name: test num_bytes: 220164125.264 num_examples: 6308 - name: other num_bytes: 253400896.056 num_examples: 8088 - name: invalidated num_bytes: 53706876.0 num_examples: 1550 - name: validated num_bytes: 721036368.28 num_examples: 20658 download_size: 1481543483 dataset_size: 1669096181.7719998 - config_name: it features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 6137402083.638 num_examples: 169771 - name: validation num_bytes: 701042124.0 num_examples: 15149 - name: test num_bytes: 741163579.0 num_examples: 15155 download_size: 7600033249 dataset_size: 7579607786.638 - config_name: ja features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 186515137.0 num_examples: 6261 - name: test num_bytes: 199063298.0 num_examples: 6261 - name: train num_bytes: 307772889.0 num_examples: 10039 download_size: 684220424 dataset_size: 693351324.0 - config_name: pt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 290319070.0 num_examples: 9464 - name: test num_bytes: 304560776.0 num_examples: 9467 - name: train num_bytes: 624494986.0 num_examples: 21968 download_size: 1188978689 dataset_size: 1219374832.0 - config_name: ru features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 393037777.0 num_examples: 10203 - name: test num_bytes: 397099376.0 num_examples: 10203 - name: train num_bytes: 977625337.0 num_examples: 26377 download_size: 1734268016 dataset_size: 1767762490.0 - config_name: sv-SE features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 201604157.344 num_examples: 7744 - name: validation num_bytes: 145407584.16 num_examples: 5210 - name: test num_bytes: 168456898.744 num_examples: 5259 - name: other num_bytes: 182626841.121 num_examples: 6759 - name: invalidated num_bytes: 43666692.56 num_examples: 1428 - name: validated num_bytes: 1302439008.81 num_examples: 40770 download_size: 1772780355 dataset_size: 2044201182.7389998 - config_name: tr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 854586956.976 num_examples: 35147 - name: validation num_bytes: 265450510.268 num_examples: 11258 - name: test num_bytes: 363424742.28 num_examples: 11290 - name: other num_bytes: 4238883.0 num_examples: 117 - name: invalidated num_bytes: 152949072.07 num_examples: 4530 - name: validated num_bytes: 2694662410.926 num_examples: 114056 download_size: 4038924157 dataset_size: 4335312575.5199995 - config_name: uk features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 824014245.552 num_examples: 25137 - name: validation num_bytes: 338351263.068 num_examples: 10007 - name: test num_bytes: 363575667.839 num_examples: 10011 - name: other num_bytes: 211123163.846 num_examples: 7851 - name: invalidated num_bytes: 141986802.304 num_examples: 3204 - name: validated num_bytes: 2579348540.4549994 num_examples: 75489 download_size: 4037277320 dataset_size: 4458399683.063999 configs: - config_name: ar data_files: - split: validation path: ar/validation-* - split: test path: ar/test-* - split: train path: ar/train-* - config_name: de data_files: - split: validation path: de/validation-* - split: test path: de/test-* - split: train path: de/train-* - config_name: en data_files: - split: test path: en/test-* - split: validation path: en/validation-* - split: train path: en/train-* - split: validated path: en/validated-* - config_name: es data_files: - split: validation path: es/validation-* - split: test path: es/test-* - split: train path: es/train-* - config_name: fr data_files: - split: validation path: fr/validation-* - split: train path: frnew/train-* - split: test path: fr/test-* - config_name: frold data_files: - split: train path: fr/train-* - split: test path: fr/test-* - split: validation path: fr/validation-* - config_name: hi data_files: - split: train path: hi/train/** - split: validation path: hi/validation/** - split: test path: hi/test/** - split: other path: hi/other/** - split: invalidated path: hi/invalidated/** - split: validated path: hi/validated/** - config_name: it data_files: - split: validation path: it/validation-* - split: test path: it/test-* - split: train path: it/train-* - config_name: ja data_files: - split: validation path: ja/validation-* - split: test path: ja/test-* - split: train path: ja/train-* - config_name: pt data_files: - split: validation path: pt/validation-* - split: test path: pt/test-* - split: train path: pt/train-* - config_name: ru data_files: - split: validation path: ru/validation-* - split: test path: ru/test-* - split: train path: ru/train-* - config_name: sv-SE data_files: - split: train path: sv-SE/train/** - split: validation path: sv-SE/validation/** - split: test path: sv-SE/test/** - split: other path: sv-SE/other/** - split: invalidated path: sv-SE/invalidated/** - split: validated path: sv-SE/validated/** - config_name: tr data_files: - split: train path: tr/train/** - split: validation path: tr/validation/** - split: test path: tr/test/** - split: other path: tr/other/** - split: invalidated path: tr/invalidated/** - split: validated path: tr/validated/** - config_name: uk data_files: - split: train path: uk/train/** - split: validation path: uk/validation/** - split: test path: uk/test/** - split: other path: uk/other/** - split: invalidated path: uk/invalidated/** - split: validated path: uk/validated/** ---

TempoFunk/tempofunk-sdance

TempoFunk

--- task_categories: - text-to-video - text-to-image - video-classification - image-classification language: - en size_categories: - 1K<n<10K license: agpl-3.0 --- # TempoFunk S(mall)Dance 10k samples of metadata and encoded latents & prompts of videos themed around **dance**. ## Data format - Video frame latents - Numpy arrays - 120 frames, 512x512 source size - Encoded shape (120, 4, 64, 64) - CLIP (openai) encoded prompts - Video description (as seen in metadata) - Encoded shape (77,768) - Video metadata as JSON (description, tags, categories, source URLs, etc.)

mlfoundations/dclm-baseline-1.0-parquet

mlfoundations

--- language: - en license: cc-by-4.0 --- ## DCLM-baseline ***Note: this is an identical copy of https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0, where all the files have been mapped to a parquet format.*** DCLM-baseline is a 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks. Below are comparisions of model trained on DCLM-baseline with other models in the 7B regime. | Model | Params | Tokens | Open dataset? | CORE | MMLU | EXTENDED | |---------------|--------|--------|---------------|----------|----------|----------| | **Open weights, closed datasets** | | | | | | | | Llama2 | 7B | 2T | ✗ | 49.2 | 45.8 | 34.1 | | DeepSeek | 7B | 2T | ✗ | 50.7 | 48.5 | 35.3 | | Mistral-0.3 | 7B | ? | ✗ | 57.0 | 62.7 | 45.1 | | QWEN-2 | 7B | ? | ✗ | 57.5 | **71.9** | 50.5 | | Llama3 | 8B | 15T | ✗ | 57.6 | 66.2 | 46.3 | | Gemma | 8B | 6T | ✗ | 57.8 | 64.3 | 44.6 | | Phi-3 | 7B | ? | ✗ | **61.0** | 69.9 | **57.9** | | **Open weights, open datasets** | | | | | | | | Falcon | 7B | 1T | ✓ | 44.1 | 27.4 | 25.1 | | Amber | 7B | 1.2T | ✓ | 39.8 | 27.9 | 22.3 | | Crystal | 7B | 1.2T | ✓ | 48.0 | 48.2 | 33.2 | | OLMo-1.7 | 7B | 2.1T | ✓ | 47.0 | 54.0 | 34.2 | | MAP-Neo | 7B | 4.5T | ✓ | **50.2** | **57.1** | **40.4** | | **Models we trained** | | | | | | | | FineWeb edu | 7B | 0.14T | ✓ | 38.7 | 26.3 | 22.1 | | FineWeb edu | 7B | 0.28T | ✓ | 41.9 | 37.3 | 24.5 | | **DCLM-BASELINE** | 7B | 0.14T | ✓ | 44.1 | 38.3 | 25.0 | | **DCLM-BASELINE** | 7B | 0.28T | ✓ | 48.9 | 50.8 | 31.8 | | **DCLM-BASELINE** | 7B | 2.6T | ✓ | **57.1** | **63.7** | **45.4** | ## Dataset Details ### Dataset Description - **Curated by:** The DCLM Team - **Language(s) (NLP):** English - **License:** CC-by-4.0 ### Dataset Sources - **Repository:** https://datacomp.ai/dclm - **Paper:**: https://arxiv.org/abs/2406.11794 - **Construction Code**: https://github.com/mlfoundations/dclm ## Uses ### Direct Use DCLM-Baseline is intended to be used as a research baseline for the DCLM benchmark. It demonstrates the importance of data curation in training performant language models. ### Out-of-Scope Use DCLM-Baseline is not intended for training production-ready models or for specific domains such as code and math. It may not perform as well as domain-specific datasets for these tasks. Due to these limitations, the dataset is intended for research use only. DCLM-Baseline is a subset of the DCLM-Pool, which is a corpus of 240 trillion tokens derived from Common Crawl. The dataset is in plain text format. ## Dataset Creation ### Curation Rationale DCLM-Baseline was created to demonstrate the effectiveness of the DCLM testbed in developing high-quality training sets for language models. It serves as a proof of concept for the data curation strategies enabled by DCLM and is designed to be a research baseline for the benchmark. ### Source Data #### Data Collection and Processing DCLM-Baseline was created by applying a series of cleaning, filtering, and deduplication steps to the raw Common Crawl data (DCLM-Pool). The key steps include: 1. Heuristic cleaning and filtering (reproduction of RefinedWeb) 2. Deduplication using a Bloom filter 3. Model-based filtering using a fastText classifier trained on instruction-formatted data (OpenHermes 2.5 and r/ExplainLikeImFive) #### Who are the source data producers? The source data is from Common Crawl, which is a repository of web crawl data. ### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations The dataset may contain biases present in the Common Crawl data. The dataset's performance on code and math tasks is limited compared to its performance on language understanding tasks. DCLM-Baseline is designed for research purposes only. ### Recommendations Users should be aware of the potential biases and limitations of the dataset, especially when using it for specific domains like code and math. The dataset should only be used for research purposes in the context of the DCLM benchmark. ## Citation ```bibtex @misc{li2024datacomplm, title={DataComp-LM: In search of the next generation of training sets for language models}, author={Jeffrey Li and Alex Fang and Georgios Smyrnis and Maor Ivgi and Matt Jordan and Samir Gadre and Hritik Bansal and Etash Guha and Sedrick Keh and Kushal Arora and Saurabh Garg and Rui Xin and Niklas Muennighoff and Reinhard Heckel and Jean Mercat and Mayee Chen and Suchin Gururangan and Mitchell Wortsman and Alon Albalak and Yonatan Bitton and Marianna Nezhurina and Amro Abbas and Cheng-Yu Hsieh and Dhruba Ghosh and Josh Gardner and Maciej Kilian and Hanlin Zhang and Rulin Shao and Sarah Pratt and Sunny Sanyal and Gabriel Ilharco and Giannis Daras and Kalyani Marathe and Aaron Gokaslan and Jieyu Zhang and Khyathi Chandu and Thao Nguyen and Igor Vasiljevic and Sham Kakade and Shuran Song and Sujay Sanghavi and Fartash Faghri and Sewoong Oh and Luke Zettlemoyer and Kyle Lo and Alaaeldin El-Nouby and Hadi Pouransari and Alexander Toshev and Stephanie Wang and Dirk Groeneveld and Luca Soldaini and Pang Wei Koh and Jenia Jitsev and Thomas Kollar and Alexandros G. Dimakis and Yair Carmon and Achal Dave and Ludwig Schmidt and Vaishaal Shankar}, year={2024}, eprint={2406.11794}, archivePrefix={arXiv}, primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'} ```

stanfordnlp/snli

stanfordnlp

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|other-flicker-30k - extended|other-visual-genome task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification paperswithcode_id: snli pretty_name: Stanford Natural Language Inference dataset_info: config_name: plain_text features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: test num_bytes: 1258904 num_examples: 10000 - name: validation num_bytes: 1263036 num_examples: 10000 - name: train num_bytes: 65884386 num_examples: 550152 download_size: 20439300 dataset_size: 68406326 configs: - config_name: plain_text data_files: - split: test path: plain_text/test-* - split: validation path: plain_text/validation-* - split: train path: plain_text/train-* --- # Dataset Card for SNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://nlp.stanford.edu/projects/snli/ - **Repository:** [More Information Needed] - **Paper:** https://aclanthology.org/D15-1075/ - **Paper:** https://arxiv.org/abs/1508.05326 - **Leaderboard:** https://nlp.stanford.edu/projects/snli/ - **Point of Contact:** [Samuel Bowman](mailto:bowman@nyu.edu) - **Point of Contact:** [Gabor Angeli](mailto:angeli@stanford.edu) - **Point of Contact:** [Chris Manning](manning@stanford.edu) ### Dataset Summary The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). ### Supported Tasks and Leaderboards Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is the task of determining the inference relation between two (short, ordered) texts: entailment, contradiction, or neutral ([MacCartney and Manning 2008](https://aclanthology.org/C08-1066/)). See the [corpus webpage](https://nlp.stanford.edu/projects/snli/) for a list of published results. ### Languages The language in the dataset is English as spoken by users of the website Flickr and as spoken by crowdworkers from Amazon Mechanical Turk. The BCP-47 code for English is en. ## Dataset Structure ### Data Instances For each instance, there is a string for the premise, a string for the hypothesis, and an integer for the label. Note that each premise may appear three times with a different hypothesis and label. See the [SNLI corpus viewer](https://huggingface.co/datasets/viewer/?dataset=snli) to explore more examples. ``` {'premise': 'Two women are embracing while holding to go packages.' 'hypothesis': 'The sisters are hugging goodbye while holding to go packages after just eating lunch.' 'label': 1} ``` The average token count for the premises and hypotheses are given below: | Feature | Mean Token Count | | ---------- | ---------------- | | Premise | 14.1 | | Hypothesis | 8.3 | ### Data Fields - `premise`: a string used to determine the truthfulness of the hypothesis - `hypothesis`: a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise - `label`: an integer whose value may be either _0_, indicating that the hypothesis entails the premise, _1_, indicating that the premise and hypothesis neither entail nor contradict each other, or _2_, indicating that the hypothesis contradicts the premise. Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`. ### Data Splits The SNLI dataset has 3 splits: _train_, _validation_, and _test_. All of the examples in the _validation_ and _test_ sets come from the set that was annotated in the validation task with no-consensus examples removed. The remaining multiply-annotated examples are in the training set with no-consensus examples removed. Each unique premise/caption shows up in only one split, even though they usually appear in at least three different examples. | Dataset Split | Number of Instances in Split | | ------------- |----------------------------- | | Train | 550,152 | | Validation | 10,000 | | Test | 10,000 | ## Dataset Creation ### Curation Rationale The [SNLI corpus (version 1.0)](https://nlp.stanford.edu/projects/snli/) was developed as a benchmark for natural langauge inference (NLI), also known as recognizing textual entailment (RTE), with the goal of producing a dataset large enough to train models using neural methodologies. ### Source Data #### Initial Data Collection and Normalization The hypotheses were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015. Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015). The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://aclanthology.org/Q14-1006/), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). The premises from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted. #### Who are the source language producers? A large portion of the premises (160k) were produced in the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators. The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers. An additional 4,000 premises come from the pilot study of the [VisualGenome corpus](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html). Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour. ### Annotations #### Annotation process 56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015). The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors. | Label | Fleiss κ | | --------------- |--------- | | _contradiction_ | 0.77 | | _entailment_ | 0.72 | | _neutral_ | 0.60 | | overall | 0.70 | #### Who are the annotators? The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples. ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. (It should be noted that the truth conditions of a hypothesis given a premise does not necessarily match the truth conditions of the hypothesis in the real world.) Systems that are successful at such a task may be more successful in modeling semantic representations. ### Discussion of Biases The language reflects the content of the photos collected from Flickr, as described in the [Data Collection](#initial-data-collection-and-normalization) section. [Rudinger et al (2017)](https://aclanthology.org/W17-1609/) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses. ### Other Known Limitations [Gururangan et al (2018)](https://aclanthology.org/N18-2017/), [Poliak et al (2018)](https://aclanthology.org/S18-2023/), and [Tsuchiya (2018)](https://aclanthology.org/L18-1239/) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time. ## Additional Information ### Dataset Curators The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the [Stanford NLP group](https://nlp.stanford.edu/). It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109. ### Licensing Information The Stanford Natural Language Inference Corpus by The Stanford NLP Group is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/). The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/), also released under an Attribution-ShareAlike licence. ### Citation Information The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: ```bibtex @inproceedings{bowman-etal-2015-large, title = "A large annotated corpus for learning natural language inference", author = "Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher and Manning, Christopher D.", editor = "M{\`a}rquez, Llu{\'\i}s and Callison-Burch, Chris and Su, Jian", booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2015", address = "Lisbon, Portugal", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D15-1075", doi = "10.18653/v1/D15-1075", pages = "632--642", } ``` The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/), which can be cited by way of this paper: ```bibtex @article{young-etal-2014-image, title = "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions", author = "Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia", editor = "Lin, Dekang and Collins, Michael and Lee, Lillian", journal = "Transactions of the Association for Computational Linguistics", volume = "2", year = "2014", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q14-1006", doi = "10.1162/tacl_a_00166", pages = "67--78", } ``` ### Contact Information For any comments or questions, please email [Samuel Bowman](mailto:bowman@nyu.edu), [Gabor Angeli](mailto:angeli@stanford.edu) and [Chris Manning](manning@stanford.edu). ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.

DL3DV/DL3DV-ALL-960P

DL3DV

--- tags: - 3D Vision - NeRF - 3D Gaussian - Dataset - Novel View Synthesis - Text to 3D - Image to 3D pretty_name: Dl3DV-Dataset size_categories: - n>1T --- # DL3DV-Dataset This repo has all the 960P frames with camera poses of DL3DV-10K Dataset. We are working hard to review all the dataset to avoid sensitive information. Thank you for your patience. # Download If you have enough space, you can use git to download a dataset from huggingface. See this [link](https://huggingface.co/docs/hub/en/datasets-downloading). [480P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P)/[960P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-960P) versions should satisfies most needs. If you do not have enough space, we further provide a [download script](https://github.com/DL3DV-10K/Dataset/blob/main/scripts/download.py) here to download a subset. The usage: ```Bash usage: download.py [-h] --odir ODIR --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} --resolution {4K,2K,960P,480P} --file_type {images+poses,video,colmap_cache} [--hash HASH] [--clean_cache] optional arguments: -h, --help show this help message and exit --odir ODIR output directory --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} The subset of the benchmark to download --resolution {4K,2K,960P,480P} The resolution to donwnload --file_type {images+poses,video,colmap_cache} The file type to download --hash HASH If set subset=hash, this is the hash code of the scene to download --clean_cache If set, will clean the huggingface cache to save space ``` Here are some examples: ```Bash # Make sure you have applied for the access. # Use this to download the download.py script wget https://raw.githubusercontent.com/DL3DV-10K/Dataset/main/scripts/download.py # Download 960P resolution images and poses, 0~1K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 1K --resolution 960P --file_type images+poses --clean_cache # Download 960P resolution images and poses, 1K~2K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 2K --resolution 960P --file_type images+poses --clean_cache ``` You can also download a specific scene with its hash. The scene-hash pair visualization can be found [here](https://htmlpreview.github.io/?https://github.com/DL3DV-10K/Dataset/blob/main/visualize/index.html). ```Bash python download.py --odir DL3DV-10K --subset 2K --resolution 960P --file_type images+poses --hash e2cedefea8a0ed2d0ffbd5bdc08acbe7e1f85c96f72f7b790e9dfe1c98963047 --clean_cache ``` # News - [x] DL3DV-1K, 2K, 3K, 4K - [ ] DL3DV-5K ~ 10K

bop-benchmark/datasets

bop-benchmark

--- task_categories: - image-segmentation - object-detection - robotics - zero-shot-object-detection size_categories: - n>1T configs: - config_name: MegaPose-ShapeNetCore data_files: MegaPose-ShapeNetCore/*.tar - config_name: MegaPose-GSO data_files: MegaPose-GSO/*.tar --- # BOP: Benchmark for 6D Object Pose Estimation The goal of BOP is to capture the state of the art in estimating the 6D pose, i.e. 3D translation and 3D rotation, of rigid objects from RGB/RGB-D images. An accurate, fast, robust, scalable and easy-to-train method that solves this task will have a big impact in application fields such as robotics or augmented reality.  Homepage: https://bop.felk.cvut.cz/home/ Toolkit: https://github.com/thodan/bop_toolkit ## Downloading datasets #### Option 1: Using `huggingface_hub`: <details><summary>Click to expand</summary> a. Install the library: ``` pip install --upgrade huggingface_hub ``` b. Download the dataset: ``` from huggingface_hub import snapshot_download dataset_name = "hope" local_dir = "./datasets" snapshot_download(repo_id="bop-benchmark/datasets", allow_patterns=f"{dataset_name}/*zip", repo_type="dataset", local_dir=local_dir) ``` If you want to download the entire BOP datasets (~3TB), please remove the `allow_patterns` argument. More options are available in the [official documentation](https://huggingface.co/docs/huggingface_hub/main/en/guides/download). </details> #### Option 2: Using `huggingface_hub[cli]`: <details><summary>Click to expand</summary> a. Install the library: ``` pip install -U "huggingface_hub[cli]" ``` b. Download the dataset: ``` export LOCAL_DIR=./datasets export DATASET_NAME=hope huggingface-cli download bop-benchmark/datasets --include "$DATASET_NAME/*.zip" --local-dir $LOCAL_DIR --repo-type=dataset ``` Please remove this argument `--include "$DATASET_NAME/*.zip"` to download entire BOP datasets (~3TB). More options are available in the [official documentation](https://huggingface.co/docs/huggingface_hub/main/en/guides/download). </details> #### Option 3: Using `wget`: <details><summary>Click to expand</summary> Similar `wget` command as in [BOP website](https://bop.felk.cvut.cz/datasets/) can be used to download the dataset from huggingface hub: ``` export SRC=https://huggingface.co/datasets/bop-benchmark/datasets/resolve/main wget $SRC/lm/lm_base.zip # Base archive wget $SRC/lm/lm_models.zip # 3D object models wget $SRC/lm/lm_test_all.zip # All test images ("_bop19" for a subset) wget $SRC/lm/lm_train_pbr.zip # PBR training images ``` </details> Datasets are stored in `.zip` format. You can extract them using the following command: ``` bash scripts/extract_bop.sh ``` If you are running on a machine with high bandwidth, you can increase your download speed by adding the following environment variable: ``` pip install huggingface_hub[hf_transfer] export HF_HUB_ENABLE_HF_TRANSFER=1 ``` ## Uploading datasets You create a new dataset and want to share it with BOP community. Here is a step-by-step guide to upload the dataset and create a pull request to [our huggingface hub](https://huggingface.co/datasets/bop-benchmark/datasets/). Feel free to reach out to vanngn.nguyen@gmail.com if you have any questions. Similar to the download process, you can upload the dataset using the `huggingface_hub` library or `huggingface_hub[cli]`. We recommend using `huggingface_hub[cli]` for its simplicity. #### Option 1: Using `huggingface_hub[cli]`: <details><summary>Click to expand</summary> a. Install the library: ``` pip install -U "huggingface_hub[cli]" ``` b. Log-in and create a token ``` huggingface-cli login ``` Then go to [this link](https://huggingface.co/settings/tokens) and generate a token. IMPORTANT: the token should have write access as shown below: <img src="./media/token_hf.png" alt="image" width="300"> Make sure you are in the bop-benchmark group by running: ``` huggingface-cli whoami ``` c. Upload dataset: The command is applied for both folders and specific files: ``` # Usage: huggingface-cli upload bop-benchmark/datasets [local_path] [path_in_repo] --repo-type=dataset --create-pr ``` For example, to upload hope dataset: ``` export LOCAL_FOLDER=./datasets/hope export HF_FOLDER=/hope huggingface-cli upload bop-benchmark/datasets $LOCAL_FOLDER $HF_FOLDER --repo-type=dataset --create-pr ``` </details> #### Option 2: Using `huggingface_hub`: <details><summary>Click to expand</summary> a. Install the library: ``` pip install --upgrade huggingface_hub ``` b. Creating a pull-request: We recommend organizing the dataset in a folder and then uploading it to the huggingface hub. For example, to upload `lmo`: ``` from huggingface_hub import HfApi, CommitOperationAdd dataset_name = "lmo" local_dir = "./datasets/lmo" operations = [] for file in local_dir.glob("*"): add_commit = CommitOperationAdd( path_in_repo=f"/{dataset_name}", path_or_fileobj=local_dir, ) operations.append(add_commit) api = HfApi() MY_TOKEN = # get from https://huggingface.co/settings/tokens api.create_commit(repo_id="bop-benchmark/datasets", repo_type="dataset", commit_message=f"adding {dataset_name} dataset", token=MY_TOKEN, operations=operations, create_pr=True) ``` If your dataset is large (> 500 GB), you can upload it in chunks by adding the `multi_commits=True, multi_commits_verbose=True,` argument. More options are available in the [official documentation](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/package_reference/hf_api#huggingface_hub.HfApi.create_pull_request). </details> ## FAQ #### 1. How to upload a large file > 50 GB? Note that HuggingFace limits the size of each file to 50 GB. If your dataset is larger, you can split it into smaller files: ``` zip -s 50g input.zip --out output.zip ``` This command will split the `input.zip` into multiple files of 50GB size `output.zip`, `output.z01`, `output.z01`, ... You can then extract them using one of the following commands: ``` # option 1: combine zip -s0 output.zip --out input.zip # option 2: using 7z to unzip directly 7z x output.zip ``` #### 2. How to increase download speed? If you are running on a machine with high bandwidth, you can increase your download speed by adding the following environment variable: ``` pip install huggingface_hub[hf_transfer] export HF_HUB_ENABLE_HF_TRANSFER=1 ``` ## Publications - [**BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects**](https://arxiv.org/pdf/2403.09799.pdf) - T. Hodaň, M. Sundermeyer, Y. Labbé, V. N. Nguyen, G. Wang, E. Brachmann, B. Drost, V. Lepetit, C. Rother, J. Matas - IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW, [CV4MR workshop](https://cv4mr.github.io/)) 2024, Seattle - [PDF](https://arxiv.org/pdf/2403.09799.pdf), [SLIDES](https://cmp.felk.cvut.cz/sixd/workshop_2023/slides/bop_challenge_2023_results.pdf), [VIDEO](https://www.youtube.com/watch?v=PcDszFANcDQ), [BIB](https://cmp.felk.cvut.cz/~hodanto2/data/hodan2023bop.bib) - [**BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects**](https://arxiv.org/pdf/2302.13075.pdf) - M. Sundermeyer, T. Hodaň, Y. Labbé, G. Wang, E. Brachmann, B. Drost, C. Rother, J. Matas - IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW, [CV4MR workshop](https://cv4mr.github.io/)) 2023, Vancouver - [PDF](https://arxiv.org/pdf/2302.13075.pdf), [SLIDES](https://cmp.felk.cvut.cz/sixd/workshop_2022/slides/bop_challenge_2022_results.pdf), [VIDEO 1](https://vimeo.com/showcase/9946695/video/768457697), [VIDEO 2](https://vimeo.com/showcase/9946695/video/768458355), [BIB](https://cmp.felk.cvut.cz/~hodanto2/data/sundermeyer2022bop.bib) - [**BOP Challenge 2020 on 6D Object Localization**](https://arxiv.org/pdf/2009.07378.pdf) - T. Hodaň, M. Sundermeyer, B. Drost, Y. Labbé, E. Brachmann, F. Michel, C. Rother, J. Matas - European Conference on Computer Vision Workshops (ECCVW) 2020, Glasgow - [PDF](https://arxiv.org/pdf/2009.07378.pdf), [SLIDES](https://bop.felk.cvut.cz/media/bop_challenge_2020_results.pdf), [BIB](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2020bop.bib) - [**BOP: Benchmark for 6D Object Pose Estimation**](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop.pdf) - T. Hodaň, F. Michel, E. Brachmann, W. Kehl, A. G. Buch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X. Zabulis, C. Sahin, F. Manhardt, F. Tombari, T.-K. Kim, J. Matas, C. Rother - European Conference on Computer Vision (ECCV) 2018, Munich - [PDF](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop.pdf), [SLIDES](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop_slides_eccv.pdf), [POSTER](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop_poster.pdf), [BIB](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop.bib) The online evaluation system has been developed by [T. Hodaň](http://www.hodan.xyz) and [A. Melenovský](https://www.linkedin.com/in/anton%C3%ADn-melenovsk%C3%BD-09907b151/).

togethercomputer/RedPajama-Data-1T-Sample

togethercomputer

--- task_categories: - text-generation language: - en pretty_name: Red Pajama 1T Sample --- # Dataset Card for Dataset Name ### Dataset Summary RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This HuggingFace repo contains a 1B-token sample of the RedPajama dataset. The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T): | Dataset | Token Count | |---------------|-------------| | Commoncrawl | 878 Billion | | C4 | 175 Billion | | GitHub | 59 Billion | | Books | 26 Billion | | ArXiv | 28 Billion | | Wikipedia | 24 Billion | | StackExchange | 20 Billion | | Total | 1.2 Trillion | A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ``` { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...} } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use simhash to remove near duplicates. #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score. <!-- ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed] -->

mlfoundations/MINT-1T-PDF-CC-2023-50

mlfoundations

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-50`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c).  ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

hexuan21/weak-to-strong-backup

hexuan21

--- license: apache-2.0 ---

codeShare/text-to-image-prompts

codeShare

--- license: mit task_categories: - text-to-image - image-classification language: - en pretty_name: fusionn-t2i size_categories: - 100K<n<1M --- If you have questions about this dataset , feel free to ask them on the fusion-discord : [https://discord.gg/8TVHPf6Edn](https://discord.gg/8TVHPf6Edn) This collection contains sets from the fusion-t2i-ai-generator on perchance. This datset is used in this notebook: https://huggingface.co/datasets/codeShare/text-to-image-prompts/tree/main/Google%20Colab%20Notebooks To see the full sets, please use the url "https://perchance.org/" + url , where the urls are listed below: ``` _generator gen_e621 fusion-t2i-e621-tags-1 fusion-t2i-e621-tags-2 fusion-t2i-e621-tags-3 fusion-t2i-e621-tags-4 fusion-t2i-e621-tags-5 fusion-t2i-e621-tags-6 fusion-t2i-e621-tags-7 fusion-t2i-e621-tags-8 fusion-t2i-e621-tags-9 fusion-t2i-e621-tags-10 fusion-t2i-e621-tags-11 fusion-t2i-e621-tags-12 fusion-t2i-e621-tags-13 fusion-t2i-e621-tags-14 fusion-t2i-e621-tags-15 fusion-t2i-e621-tags-16 fusion-t2i-e621-tags-17 fusion-t2i-e621-tags-18 fusion-t2i-e621-tags-19 fusion-t2i-e621-tags-20 fusion-t2i-e621-tags-21 fusion-t2i-e621-tags-22 fusion-t2i-e621-tags-23 //--NEW STUFF clipartist //fusion-t2i-clip-artists-1 //fusion-t2i-clip-artists-2 //fusion-t2i-clip-artists-3 //fusion-t2i-clip-artists-4 //fusion-t2i-clip-artists-5 //fusion-t2i-clip-artists-6 fusion-t2i-clip-artists-7 //<--Only this set of SDXL artists for now (test to see if better) clipflavour fusion-t2i-clip-flavours-1 fusion-t2i-clip-flavours-2 fusion-t2i-clip-flavours-3 fusion-t2i-clip-flavours-3 //4 no exist? fusion-t2i-clip-flavours-5 fusion-t2i-clip-flavours-6 fusion-t2i-clip-flavours-7 fusion-t2i-clip-flavours-8 fusion-t2i-clip-flavours-9 fusion-t2i-clip-flavours-10 fusion-t2i-clip-flavours-10 //10 too fusion-t2i-clip-flavours-12 fusion-t2i-clip-flavours-13 fusion-t2i-clip-flavours-14 fusion-t2i-clip-flavours-15 fusion-t2i-clip-flavours-16 fusion-t2i-clip-flavours-16 //17? fusion-t2i-clip-flavours-18 fusion-t2i-clip-flavours-19 fusion-t2i-clip-flavours-20 fusion-t2i-clip-flavours-21 fusion-t2i-clip-flavours-22 fusion-t2i-clip-flavours-23 fusion-t2i-clip-flavours-24 fusion-t2i-clip-flavours-24 //25 fusion-t2i-clip-flavours-26 fusion-t2i-clip-flavours-27 fusion-t2i-clip-flavours-28 fusion-t2i-clip-flavours-29 fusion-t2i-clip-flavours-30 //-----// ypf fusion-t2i-civitai-21-30-chars-mix-1 fusion-t2i-civitai-21-30-chars-mix-2 fusion-t2i-civitai-21-30-chars-mix-3 fusion-t2i-civitai-21-30-chars-mix-4 fusion-t2i-civitai-21-30-chars-mix-5 fusion-t2i-civitai-21-30-chars-mix-6 fusion-t2i-civitai-21-30-chars-mix-7 fusion-t2i-civitai-21-30-chars-mix-8 bpf fusion-t2i-civitai-0-20-chars-mix-1 fusion-t2i-civitai-0-20-chars-mix-2 fusion-t2i-civitai-0-20-chars-mix-3 fusion-t2i-civitai-0-20-chars-mix-4 fusion-t2i-civitai-0-20-chars-mix-5 fusion-t2i-civitai-0-20-chars-mix-6 fusion-t2i-civitai-0-20-chars-mix-7 fusion-t2i-civitai-0-20-chars-mix-8 fusion-t2i-civitai-0-20-chars-mix-9 fusion-t2i-civitai-0-20-chars-mix-10 fusion-t2i-civitai-0-20-chars-mix-11 fusion-t2i-civitai-0-20-chars-mix-12 fusion-t2i-civitai-0-20-chars-mix-13 fusion-t2i-civitai-0-20-chars-mix-14 fusion-t2i-civitai-0-20-chars-mix-15 fusion-t2i-civitai-0-20-chars-mix-16 fusion-t2i-civitai-0-20-chars-mix-17 fusion-t2i-civitai-0-20-chars-mix-18 fusion-t2i-civitai-0-20-chars-mix-19 fusion-t2i-civitai-0-20-chars-mix-20 fusion-t2i-civitai-0-20-chars-mix-21 fusion-t2i-civitai-0-20-chars-mix-22 fusion-t2i-civitai-0-20-chars-mix-23 fusion-t2i-civitai-0-20-chars-mix-24 fusion-t2i-civitai-0-20-chars-mix-25 fusion-t2i-civitai-0-20-chars-mix-26 fusion-t2i-civitai-0-20-chars-mix-27 fusion-t2i-civitai-0-20-chars-mix-28 fusion-t2i-civitai-0-20-chars-mix-29 fusion-t2i-civitai-0-20-chars-mix-30 fusion-t2i-civitai-0-20-chars-mix-31 fusion-t2i-civitai-0-20-chars-mix-32 fusion-t2i-civitai-0-20-chars-mix-33 fusion-t2i-civitai-0-20-chars-mix-34 fusion-t2i-civitai-0-20-chars-mix-35 fusion-t2i-civitai-0-20-chars-mix-36 fusion-t2i-civitai-0-20-chars-mix-37 fusion-t2i-civitai-0-20-chars-mix-38 fusion-t2i-civitai-0-20-chars-mix-39 fusion-t2i-civitai-0-20-chars-mix-40 fusion-t2i-civitai-0-20-chars-mix-41 fusion-t2i-civitai-0-20-chars-mix-42 fusion-t2i-civitai-0-20-chars-mix-43 fusion-t2i-civitai-0-20-chars-mix-44 fusion-t2i-civitai-0-20-chars-mix-45 fusion-t2i-civitai-0-20-chars-mix-46 fmc fusion-t2i-female-movie-characters-2 fusion-t2i-female-movie-characters-3 fusion-t2i-female-movie-characters-4 fusion-t2i-female-movie-characters-5 fusion-t2i-female-movie-characters-6 nationalities fusion-t2i-nationality-1 fusion-t2i-nationality-1 artby fusion-t2i-art-by-prompts-1 fusion-t2i-art-by-prompts-1 emojis fusion-t2i-emojis-2 fusion-t2i-emojis-2 moviegenres fusion-t2i-moviegenre-1 fusion-t2i-moviegenre-1 movietitles fusion-t2i-movietitle-4 fusion-t2i-movietitle-5 fusion-t2i-movietitle-6 fusion-t2i-movietitle-7 fusion-t2i-movietitle-8 fusion-t2i-movietitle-9 fusion-t2i-movietitle-10 fusion-t2i-movietitle-11 fusion-t2i-movietitle-12 fusion-t2i-movietitle-13 fusion-t2i-movietitle-14 fusion-t2i-movietitle-15 fusion-t2i-movietitle-16 fusion-t2i-movietitle-17 fusion-t2i-movietitle-18 fusion-t2i-movietitle-19 fusion-t2i-movietitle-20 videogametitles fusion-t2i-videogame-title-1 fusion-t2i-videogame-title-2 fusion-t2i-videogame-title-3 tvseries fusion-t2i-tv-series-2 fusion-t2i-tv-series-3 moviestudios fusion-t2i-moviestudios-1 fusion-t2i-moviestudios-1 lingerie //fusion-t2i-lingerie-1 fusion-t2i-lingerie-1 fusion-t2i-lingerie-2 //With brands apadj //apparel adjective fusion-t2i-apparel-adjective-1 fusion-t2i-apparel-adjective-1 movies fusion-t2i-movies-1 fusion-t2i-movies-2 fusion-t2i-movies-3 fantasycreatures fusion-t2i-fantasy-creature-1 fusion-t2i-fantasy-creature-1 fantasyclasses fusion-t2i-fantasy-class-1 fusion-t2i-fantasy-class-1 unicodes fusion-t2i-unicode-2 fusion-t2i-unicode-2 unicode_prefix fusion-t2i-unicode-prefix-1 fusion-t2i-unicode-prefix-1 unicode_suffix fusion-t2i-unicode-suffix-1 fusion-t2i-unicode-suffix-1 gen_r34tags fusion-t2i-rule34-tags-1 fusion-t2i-rule34-tags-2 fusion-t2i-rule34-tags-3 fusion-t2i-rule34-tags-4 fusion-t2i-rule34-tags-5 r34artists fusion-t2i-rule34-artists-1 fusion-t2i-rule34-artists-1 nsfwpromptfeatures fusion-t2i-nsfw-prompt-features-1 fusion-t2i-nsfw-prompt-features-2 youngcelebs fusion-t2i-young-celebrities-1 fusion-t2i-young-celebrities-1 //New set gfn fusion-t2i-girl-firstname-1 fusion-t2i-girl-firstname-2 fusion-t2i-girl-firstname-3 fusion-t2i-girl-firstname-4 fusion-t2i-girl-firstname-5 fusion-t2i-girl-firstname-6 fusion-t2i-girl-firstname-7 fusion-t2i-girl-firstname-8 fusion-t2i-girl-firstname-9 fusion-t2i-girl-firstname-10 fusion-t2i-girl-firstname-11 fusion-t2i-girl-firstname-12 fusion-t2i-girl-firstname-13 fusion-t2i-girl-firstname-14 fusion-t2i-girl-firstname-15 fusion-t2i-girl-firstname-16 fusion-t2i-girl-firstname-17 fusion-t2i-girl-firstname-18 fusion-t2i-girl-firstname-19 fusion-t2i-girl-firstname-20 fusion-t2i-girl-firstname-21 fusion-t2i-girl-firstname-22 fusion-t2i-girl-firstname-23 fusion-t2i-girl-firstname-24 fusion-t2i-girl-firstname-25 fusion-t2i-girl-firstname-26 fusion-t2i-girl-firstname-27 fusion-t2i-girl-firstname-28 fusion-t2i-girl-firstname-29 fusion-t2i-girl-firstname-30 animals fusion-t2i-animals-1 fusion-t2i-animals-1 //Old set lastNames fusion-t2i-lastnames-19 fusion-t2i-lastnames-1 fusion-t2i-lastnames-2 fusion-t2i-lastnames-3 fusion-t2i-lastnames-4 fusion-t2i-lastnames-5 fusion-t2i-lastnames-6 fusion-t2i-lastnames-7 fusion-t2i-lastnames-8 fusion-t2i-lastnames-9 fusion-t2i-lastnames-10 fusion-t2i-lastnames-11 fusion-t2i-lastnames-12 fusion-t2i-lastnames-13 fusion-t2i-lastnames-14 fusion-t2i-lastnames-15 fusion-t2i-lastnames-16 fusion-t2i-lastnames-17 fusion-t2i-lastnames-18 fusion-t2i-lastnames-20 media fusion-t2i-media-outlets-1 fusion-t2i-media-outlets-1 unused yada gen_danbooru fusion-t2i-danbooru-tags-1 fusion-t2i-danbooru-tags-2 fusion-t2i-danbooru-tags-3 fusion-t2i-danbooru-tags-4 fusion-t2i-danbooru-tags-5 fusion-t2i-danbooru-tags-6 fusion-t2i-danbooru-tags-7 fusion-t2i-danbooru-tags-8 fusion-t2i-danbooru-tags-9 fusion-t2i-danbooru-tags-10 fusion-t2i-danbooru-tags-11 fusion-t2i-danbooru-tags-12 fusion-t2i-danbooru-tags-13 fusion-t2i-danbooru-tags-14 fusion-t2i-danbooru-tags-15 fusion-t2i-danbooru-tags-16 fusion-t2i-danbooru-tags-17 fusion-t2i-danbooru-tags-18 fusion-t2i-danbooru-tags-19 fusion-t2i-danbooru-tags-20 fusion-t2i-danbooru-tags-21 fusion-t2i-danbooru-tags-22 fusion-t2i-danbooru-tags-23 fusion-t2i-danbooru-tags-24 fusion-t2i-danbooru-tags-25 fusion-t2i-danbooru-tags-26 fusion-t2i-danbooru-tags-27 fusion-t2i-danbooru-tags-28 fusion-t2i-danbooru-tags-29 fusion-t2i-danbooru-tags-30 fusion-t2i-danbooru-tags-31 fusion-t2i-danbooru-tags-32 fusion-t2i-danbooru-tags-33 gen_lyrics fusion-t2i-lyrics-letterwords-1 fusion-t2i-lyrics-letterwords-2 fusion-t2i-lyrics-letterwords-3 fusion-t2i-lyrics-letterwords-4 fusion-t2i-lyrics-letterwords-5 fusion-t2i-lyrics-letterwords-6 fusion-t2i-lyrics-letterwords-7 fusion-t2i-lyrics-letterwords-8 fusion-t2i-lyrics-letterwords-9 fusion-t2i-lyrics-letterwords-10 //new edits gen_nsfw fusion-t2i-nsfw-terms-1 fusion-t2i-nsfw-terms-2 //fusion-t2i-nsfw-terms-3 gen_nsfwtags fusion-t2i-nsfw-terms-1 fusion-t2i-nsfw-terms-2 nsfwtagscommon fusion-t2i-nsfw-tags-common-1 fusion-t2i-nsfw-tags-common-1 /// //unused old_nsfw fusion-t2i-nsfw-terms-3 fusion-t2i-nsfw-terms-4 fusion-t2i-nsfw-terms-5 fusion-t2i-nsfw-terms-6 fusion-t2i-nsfw-terms-7 fusion-t2i-nsfw-terms-8 fusion-t2i-nsfw-terms-9 fusion-t2i-nsfw-terms-10 fusion-t2i-nsfw-terms-11 fusion-t2i-nsfw-terms-12 fusion-t2i-nsfw-terms-13 fusion-t2i-nsfw-terms-14 fusion-t2i-nsfw-terms-15 fusion-t2i-nsfw-terms-16 fusion-t2i-nsfw-terms-17 fusion-t2i-nsfw-terms-18 fusion-t2i-nsfw-tags-2 fusion-t2i-nsfw-tags-3 fusion-t2i-nsfw-tags-4 fusion-t2i-nsfw-tags-5 fusion-t2i-nsfw-tags-6 fusion-t2i-nsfw-tags-7 fusion-t2i-nsfw-tags-8 fusion-t2i-nsfw-tags- flagnames fusion-t2i-names-from-flag-1 fusion-t2i-names-from-flag-1 common_prefix fusion-t2i-sd15-clip-tokens-common-prefix-1 fusion-t2i-sd15-clip-tokens-common-prefix-2 fusion-t2i-sd15-clip-tokens-common-prefix-3 average_prefix fusion-t2i-sd15-clip-tokens-average-prefix-1 fusion-t2i-sd15-clip-tokens-average-prefix-2 fusion-t2i-sd15-clip-tokens-average-prefix-3 rare_prefix fusion-t2i-sd15-clip-tokens-rare-prefix-1 fusion-t2i-sd15-clip-tokens-rare-prefix-2 fusion-t2i-sd15-clip-tokens-rare-prefix-3 weird_prefix fusion-t2i-sd15-clip-tokens-weird-prefix-1 fusion-t2i-sd15-clip-tokens-weird-prefix-2 fusion-t2i-sd15-clip-tokens-weird-prefix-3 exotic_prefix fusion-t2i-sd15-clip-tokens-exotic-prefix-1 fusion-t2i-sd15-clip-tokens-exotic-prefix-2 fusion-t2i-sd15-clip-tokens-exotic-prefix-3 common_suffix fusion-t2i-sd15-clip-tokens-common-suffix-1 fusion-t2i-sd15-clip-tokens-common-suffix-2 fusion-t2i-sd15-clip-tokens-common-suffix-3 fusion-t2i-sd15-clip-tokens-common-suffix-4 fusion-t2i-sd15-clip-tokens-common-suffix-5 fusion-t2i-sd15-clip-tokens-common-suffix-6 fusion-t2i-sd15-clip-tokens-common-suffix-7 average_suffix fusion-t2i-sd15-clip-tokens-average-suffix-1 fusion-t2i-sd15-clip-tokens-average-suffix-2 fusion-t2i-sd15-clip-tokens-average-suffix-3 fusion-t2i-sd15-clip-tokens-average-suffix-4 fusion-t2i-sd15-clip-tokens-average-suffix-5 fusion-t2i-sd15-clip-tokens-average-suffix-6 fusion-t2i-sd15-clip-tokens-average-suffix-7 rare_suffix fusion-t2i-sd15-clip-tokens-rare-suffix-1 fusion-t2i-sd15-clip-tokens-rare-suffix-2 fusion-t2i-sd15-clip-tokens-rare-suffix-3 fusion-t2i-sd15-clip-tokens-rare-suffix-4 fusion-t2i-sd15-clip-tokens-rare-suffix-5 fusion-t2i-sd15-clip-tokens-rare-suffix-6 fusion-t2i-sd15-clip-tokens-rare-suffix-7 weird_suffix fusion-t2i-sd15-clip-tokens-weird-suffix-1 fusion-t2i-sd15-clip-tokens-weird-suffix-2 fusion-t2i-sd15-clip-tokens-weird-suffix-3 fusion-t2i-sd15-clip-tokens-weird-suffix-4 fusion-t2i-sd15-clip-tokens-weird-suffix-5 fusion-t2i-sd15-clip-tokens-weird-suffix-6 fusion-t2i-sd15-clip-tokens-weird-suffix-7 exotic_suffix fusion-t2i-sd15-clip-tokens-exotic-suffix-1b fusion-t2i-sd15-clip-tokens-exotic-suffix-2 fusion-t2i-sd15-clip-tokens-exotic-suffix-3 fusion-t2i-sd15-clip-tokens-exotic-suffix-4 fusion-t2i-sd15-clip-tokens-exotic-suffix-5 fusion-t2i-sd15-clip-tokens-exotic-suffix-6 fusion-t2i-sd15-clip-tokens-exotic-suffix-7 celebs fusion-t2i-v2-celeb-1 fusion-t2i-v2-celeb-2 fusion-t2i-v2-celeb-3 //fusion-t2i-celebs-1 Old version promptfeatures fusion-t2i-prompt-features-1 fusion-t2i-prompt-features-2 fusion-t2i-prompt-features-3 fusion-t2i-prompt-features-4 fusion-t2i-prompt-features-5 fusion-t2i-prompt-features-6 fusion-t2i-prompt-features-7 fusion-t2i-prompt-features-8 fusion-t2i-prompt-features-9 fusion-t2i-prompt-features-10 fusion-t2i-prompt-features-11 fusion-t2i-prompt-features-12 fusion-t2i-prompt-features-13 fusion-t2i-prompt-features-14 fusion-t2i-prompt-features-15 fusion-t2i-prompt-features-16 fusion-t2i-prompt-features-17 fusion-t2i-prompt-features-18 fusion-t2i-prompt-features-19 fusion-t2i-prompt-features-20 fusion-t2i-prompt-features-21 fusion-t2i-prompt-features-22 fusion-t2i-prompt-features-23 fusion-t2i-prompt-features-24 fusion-t2i-prompt-features-25 fusion-t2i-prompt-features-26 fusion-t2i-prompt-features-27 fusion-t2i-prompt-features-28 fusion-t2i-prompt-features-29 fusion-t2i-prompt-features-30 fusion-t2i-prompt-features-31 fusion-t2i-prompt-features-32 fusion-t2i-prompt-features-33 fusion-t2i-prompt-features-34 nsfwtexts fusion-t2i-nsfw-texting-1 fusion-t2i-nsfw-texting-1 studios fusion-t2i-nsfw-studios-1 fusion-t2i-nsfw-studios-2 fusion-t2i-nsfw-studios-3 fusion-t2i-nsfw-studios-4 fusion-t2i-nsfw-studios-5 fusion-t2i-nsfw-studios-6 fusion-t2i-nsfw-studios-7 fusion-t2i-nsfw-studios-8 fusion-t2i-nsfw-studios-9 fusion-t2i-nsfw-studios-10 fusion-t2i-nsfw-studios-11 fusion-t2i-nsfw-studios-12 fusion-t2i-nsfw-studios-13 fusion-t2i-nsfw-studios-14 fusion-t2i-nsfw-studios-15 fusion-t2i-nsfw-studios-16 perspectives fusion-t2i-perspectives-1 fusion-t2i-perspectives-1 artstyles fusion-t2i-original-artstyles-1 fusion-t2i-original-artstyles-1 e621artists fusion-t2i-e621-artists-1 fusion-t2i-e621-artists-1 gen_bodyfeatures fusion-t2i-bodyfeatures-1 fusion-t2i-bodyfeatures-1 mangart fusion-t2i-manga-artist-1 fusion-t2i-manga-artist-2 //fusion-t2i-manga-artist-3 //fusion-t2i-manga-artist-4 nsfwpromptfeatures fusion-t2i-nsfw-prompt-features-1 fusion-t2i-nsfw-prompt-features-2 fusion-t2i-nsfw-prompt-features-3 fusion-t2i-nsfw-prompt-features-4 fusion-t2i-nsfw-prompt-features-5 fusion-t2i-nsfw-prompt-features-6 fusion-t2i-nsfw-prompt-features-7 fusion-t2i-nsfw-prompt-features-8 fusion-t2i-nsfw-prompt-features-9 fusion-t2i-nsfw-prompt-features-10 fusion-t2i-nsfw-prompt-features-11 fusion-t2i-nsfw-prompt-features-12 fusion-t2i-nsfw-prompt-features-13 fusion-t2i-nsfw-prompt-features-14 fusion-t2i-nsfw-prompt-features-15 gen_names fusion-t2i-nsfw-names-1 fusion-t2i-nsfw-names-2 fusion-t2i-nsfw-names-3 fusion-t2i-nsfw-names-4 fusion-t2i-nsfw-names-5 fusion-t2i-nsfw-names-6 fusion-t2i-nsfw-names-7 fusion-t2i-nsfw-names-8 fusion-t2i-nsfw-names-9 nsfwnews fusion-t2i-fake-nsfw-headlines-1 fusion-t2i-fake-nsfw-headlines-1 tsundere fusion-t2i-tsundere-quotes-1 fusion-t2i-tsundere-quotes-1 ```

jacobbieker/eumetsat-cloudmask-rss

jacobbieker

--- license: mit ---

agkphysics/AudioSet

agkphysics

--- language: - en license: cc-by-4.0 size_categories: - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - audio-classification paperswithcode_id: audioset pretty_name: AudioSet config_names: - balanced - unbalanced tags: - audio dataset_info: - config_name: balanced features: - name: video_id dtype: string - name: audio dtype: audio - name: labels sequence: string - name: human_labels sequence: string splits: - name: train num_bytes: 26016210987 num_examples: 18685 - name: test num_bytes: 23763682278 num_examples: 17142 download_size: 49805654900 dataset_size: 49779893265 - config_name: unbalanced features: - name: video_id dtype: string - name: audio dtype: audio - name: labels sequence: string - name: human_labels sequence: string splits: - name: train num_bytes: 2408656417541 num_examples: 1738788 - name: test num_bytes: 23763682278 num_examples: 17142 download_size: 2433673104977 dataset_size: 2432420099819 --- # Dataset Card for AudioSet ## Dataset Description - **Homepage**: https://research.google.com/audioset/index.html - **Paper**: https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/45857.pdf - **Leaderboard**: https://paperswithcode.com/sota/audio-classification-on-audioset ### Dataset Summary [AudioSet](https://research.google.com/audioset/dataset/index.html) is a dataset of 10-second clips from YouTube, annotated into one or more sound categories, following the AudioSet ontology. ### Supported Tasks and Leaderboards - `audio-classification`: Classify audio clips into categories. The leaderboard is available [here](https://paperswithcode.com/sota/audio-classification-on-audioset) ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances Example instance from the dataset: ```python { 'video_id': '--PJHxphWEs', 'audio': { 'path': 'audio/bal_train/--PJHxphWEs.flac', 'array': array([-0.04364824, -0.05268681, -0.0568949 , ..., 0.11446512, 0.14912748, 0.13409865]), 'sampling_rate': 48000 }, 'labels': ['/m/09x0r', '/t/dd00088'], 'human_labels': ['Speech', 'Gush'] } ``` ### Data Fields Instances have the following fields: - `video_id`: a `string` feature containing the original YouTube ID. - `audio`: an `Audio` feature containing the audio data and sample rate. - `labels`: a sequence of `string` features containing the labels associated with the audio clip. - `human_labels`: a sequence of `string` features containing the human-readable forms of the same labels as in `labels`. ### Data Splits The distribuion of audio clips is as follows: #### `balanced` configuration | |train|test | |-----------|----:|----:| |# instances|18685|17142| #### `unbalanced` configuration | |train |test | |-----------|------:|----:| |# instances|1738788|17142| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? The labels are from the AudioSet ontology. Audio clips are from YouTube. ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations 1. The YouTube videos in this copy of AudioSet were downloaded in March 2023, so not all of the original audios are available. The number of clips able to be downloaded is as follows: - Balanced train: 18685 audio clips out of 22160 originally. - Unbalanced train: 1738788 clips out of 2041789 originally. - Evaluation: 17142 audio clips out of 20371 originally. 2. Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at 44.1 kHz 24 bit. Audio files are stored in the FLAC format. ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The AudioSet data is licensed under CC-BY-4.0 ## Citation ```bibtex @inproceedings{jort_audioset_2017, title = {Audio Set: An ontology and human-labeled dataset for audio events}, author = {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter}, year = {2017}, booktitle = {Proc. IEEE ICASSP 2017}, address = {New Orleans, LA} } ```

Idavidrein/gpqa

Idavidrein

--- license: cc-by-4.0 viewer: true extra_gated_prompt: >- You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora. extra_gated_fields: I accept these terms: checkbox configs: - config_name: gpqa_extended data_files: gpqa_extended.csv - config_name: gpqa_main data_files: gpqa_main.csv - config_name: gpqa_diamond data_files: gpqa_diamond.csv - config_name: gpqa_experts data_files: gpqa_experts.csv task_categories: - question-answering - text-generation language: - en tags: - open-domain-qa - open-book-qa - multiple-choice-qa pretty_name: GPQA size_categories: - n<1K --- # Dataset Card for GPQA <!-- Provide a quick summary of the dataset. --> GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google. We request that you **do not reveal examples from this dataset in plain text or images online**, to reduce the risk of leakage into foundation model training corpora. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are "Google-proof"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities. - **Curated by:** David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman - **License:** CC BY 4.0 ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** https://github.com/idavidrein/gpqa - **Paper:** https://arxiv.org/abs/2311.12022 ## Uses The dataset is primarily intended to be used for scalable oversight experiments, although it can also be used for more general LLM capabilities benchmarking. ## Dataset Card Contact David Rein: idavidrein@gmail.com --- Submit corrections to examples in GPQA via this form: https://forms.gle/iTY4zMETNsPhJq8R9 ---

dair-ai/emotion

dair-ai

--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: emotion pretty_name: Emotion tags: - emotion-classification dataset_info: - config_name: split features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 1741533 num_examples: 16000 - name: validation num_bytes: 214695 num_examples: 2000 - name: test num_bytes: 217173 num_examples: 2000 download_size: 1287193 dataset_size: 2173401 - config_name: unsplit features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 45444017 num_examples: 416809 download_size: 26888538 dataset_size: 45444017 configs: - config_name: split data_files: - split: train path: split/train-* - split: validation path: split/validation-* - split: test path: split/test-* default: true - config_name: unsplit data_files: - split: train path: unsplit/train-* train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "emotion" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 16.13 MB - **Size of the generated dataset:** 47.62 MB - **Total amount of disk used:** 63.75 MB ### Dataset Summary Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances An example looks as follows. ``` { "text": "im feeling quite sad and sorry for myself but ill snap out of it soon", "label": 0 } ``` ### Data Fields The data fields are: - `text`: a `string` feature. - `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5). ### Data Splits The dataset has 2 configurations: - split: with a total of 20_000 examples split into train, validation and split - unsplit: with a total of 416_809 examples in a single train split | name | train | validation | test | |---------|-------:|-----------:|-----:| | split | 16000 | 2000 | 2000 | | unsplit | 416809 | n/a | n/a | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset should be used for educational and research purposes only. ### Citation Information If you use this dataset, please cite: ``` @inproceedings{saravia-etal-2018-carer, title = "{CARER}: Contextualized Affect Representations for Emotion Recognition", author = "Saravia, Elvis and Liu, Hsien-Chi Toby and Huang, Yen-Hao and Wu, Junlin and Chen, Yi-Shin", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1404", doi = "10.18653/v1/D18-1404", pages = "3687--3697", abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.", } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

HuggingFaceTB/smollm-corpus

HuggingFaceTB

--- license: odc-by dataset_info: - config_name: cosmopedia-v2 features: - name: prompt dtype: string - name: text dtype: string - name: token_length dtype: int64 - name: audience dtype: string - name: format dtype: string - name: seed_data dtype: string splits: - name: train num_bytes: 212503640747 num_examples: 39134000 download_size: 122361137711 dataset_size: 212503640747 - config_name: fineweb-edu-dedup features: - name: text dtype: string - name: id dtype: string - name: metadata struct: - name: dump dtype: string - name: url dtype: string - name: date dtype: timestamp[s] - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 splits: - name: train num_bytes: 957570164451 num_examples: 190168005 download_size: 550069279849 dataset_size: 957570164451 - config_name: python-edu features: - name: blob_id dtype: string - name: repo_name dtype: string - name: path dtype: string - name: length_bytes dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 splits: - name: train num_bytes: 989334135 num_examples: 7678448 download_size: 643903049 dataset_size: 989334135 configs: - config_name: cosmopedia-v2 data_files: - split: train path: cosmopedia-v2/train-* - config_name: fineweb-edu-dedup data_files: - split: train path: fineweb-edu-dedup/train-* - config_name: python-edu data_files: - split: train path: python-edu/train-* language: - en --- # SmolLM-Corpus This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models. You can find more details about the models trained on this dataset in our [SmolLM blog post](https://huggingface.co/blog/smollm). # Dataset subsets ## Cosmopedia v2 Cosmopedia v2 is an enhanced version of Cosmopedia, the largest synthetic dataset for pre-training, consisting of over 39 million textbooks, blog posts, and stories generated by [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). Most of the samples are generated by prompting the model to generate content on specific topics using a web page referred to as a "seed sample," as shown in Figure 1. We use web samples to increase diversity and expand the range of prompts. You can find more details in this [blog post](https://huggingface.co/blog/smollm). ### Dataset Features * `prompt (string)`: The input prompt used to generate the text. * `text (string)`: The generated text content. * `token_length (int64)`: The length of the text in tokens (Mistral-7B tokenizer). * `audience (string)`: The intended audience for the content. * `format (string)`: The format of the content (e.g., textbook, story). * `seed_data (string)`: The seed sample used to generate the text. ### Loading the dataset ```python from datasets import load_dataset ds = load_dataset("HuggingFaceTB/smollm-corpus", "cosmopedia-v2", split="train", num_proc=16) print(ds[0]) ``` ## Python-Edu The `python-edu` subset consists of Python files that were scored 4 or more by the [educational code model](https://huggingface.co/HuggingFaceTB/python-edu-scorer). The files were extracted from the [`stack-v2-train`](https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids) dataset. ### Dataset Features * `blob_id (string)`: Software Heritage (SWH) ID of the file on AWS S3. * `repo_name (string)`: Repository name on GitHub. * `path (string)`: The file path within the repository. * `length_bytes (int64)`: Length of the file content in UTF-8 bytes. * `score (float32)`: The output of the educational scoring model. * `int_score (uint8)`: The rounded educational score. ### Downloading the data The file contents are downloaded from Software Heritage's S3 bucket to ensure data compliance. Please refer to [the-stack-v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids) for the data license. When running on a 16-core AWS `us-east-1` instance, this script takes ~6 hours to download the files: ```python import boto3 import gzip from datasets import load_dataset from botocore.exceptions import ClientError num_proc = 16 s3 = boto3.client('s3') bucket_name = "softwareheritage" def download_contents(blob_id): key = f"content/{blob_id}" try: obj = s3.get_object(Bucket=bucket_name, Key=key) with gzip.GzipFile(fileobj=obj['Body']) as fin: content = fin.read().decode("utf-8", errors="ignore") return {"text": content, "download_success": True} except ClientError as e: if e.response['Error']['Code'] == 'NoSuchKey': print(f"File not found: {key}") return {"text": "", "download_success": False} else: raise ds = load_dataset("HuggingFaceTB/smollm-corpus", "python-edu", split="train", num_proc=num_proc) ds = ds.map(download_contents, input_columns="blob_id", num_proc=num_proc) # Filter out failed downloads ds = ds.filter(lambda x: x['download_success']) # Optionally, print the first example to verify the data print(ds[0]) ``` ## FineWeb-Edu (deduplicated) FineWeb-Edu-Dedup is a deduplicated subset of the [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) dataset, containing 220 billion tokens of educational web pages. The source dataset was filtered using an educational quality classifier to retain only the highest quality educational content. For more information refer to the [FineWeb-v1 blog post](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1) ### Dataset Features * `text (string)`: The web page's text content. * `id (string)`: Unique ID of the web page. * `metadata (struct)`: Metadata about the web page, including: * `dump (string)`: The source CommonCrawl dump. * `url (string)`: The URL of the web page. * `date (timestamp[s])`: The date the web page was captured. * `file_path (string)`: The file path of the commoncrawl snapshot. * `language (string)`: The language of the web page. * `language_score (float64)`: The language probability. * `token_count (int64)`: The token count of the web page (gpt2 tokenizer). * `score (float64)`: The educational quality score. * `int_score (int64)`: The rounded educational quality score. ### Loading the dataset ```python from datasets import load_dataset ds = load_dataset("HuggingFaceTB/smollm-corpus", "fineweb-edu-dedup", split="train", num_proc=16) print(ds[0]) ``` ## Citation ``` @software{benallal2024smollmcorpus, author = {Ben Allal, Loubna and Lozhkov, Anton and Penedo, Guilherme and Wolf, Thomas and von Werra, Leandro}, title = {SmolLM-Corpus}, month = July, year = 2024, url = {https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus} } ```

Open-Orca/FLAN

Open-Orca

--- license: cc-by-4.0 language: - en library_name: transformers pipeline_tag: text-generation datasets: - Open-Orca/OpenOrca size_categories: - 1B<n<10B --- <p><h1>🍮 The WHOLE FLAN Collection! 🍮</h1></p>  # Overview This repository includes the full dataset from the [FLAN Collection](https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html), totalling ~300GB as parquets. Generated using the official seqio templating from the [Google FLAN Collection GitHub repo](https://github.com/google-research/FLAN/tree/main/flan/v2). The data is subject to all the same licensing of the component datasets. To keep up with our continued work on OpenOrca and other exciting research, find our Discord here: https://AlignmentLab.ai # Motivation This work was done as part of the requirements for the OpenOrca project. There was not a large enough subset of FLAN Collection generated publicly to subsample from to complete the work. So, we opted to process the entire collection ourselves. Generating this requires an understanding of seqio and a Linux server with 512GB of CPU ram, as well as fast drives and custom limits for many parameters beyond what is default on Linux server distributions (e.g., requiring up to 45,000 threads running at once). It takes downloading over 400GB of datasets, working around tfds bugs, and then processing the datasets over the course of several days. We provide this repo as a resource to other ML researchers, as it saves these time consuming and laborious steps to getting the data into a more accessible format for further consumption. # Data ## Organization * JSON files at top level are used for subsampling in OpenOrca * Parquets in subdirectories contain the entire FLAN collection in Dask-sharded folders by submix fractions ## Zero-Shot vs Few-Shot and Options vs No-Options The core sub-collections of FLAN are `CoT`, `Dialog`, `NIv2`, `T0`, and `flan2021`. Within those sub-collections are four "remixes" of the data that are templated differently: * `Zero-Shot` and `Few-Shot` * `Zero-Shot` provides a prompt, question, or challenge without any exemplaries prior * `Few-Shot` provides exemplaries first * `Options` and `No-Options` * `Options` provides a question or challenge with multiple-choice (e.g. A/B/C/D) answer options provided to select from * `No-Options` requires a free-form answer For every sub-collection, only some of the "remixes" may officially be provided. All available have been generated in full without any redaction or sub-sampling. An example: `t0_fsopt_data` folder contains the sub-collection `T0`'s Few-Shot (FS), Options (OPT) remix set. Notably, this is the largest "remix" and the one that necessitates 512GB CPU ram to generate. The raw json output is nearly 200GB. ## Parquet Sizes Each sub-collection's individual remixes are provided as [Parquet](https://huggingface.co/docs/datasets/loading#parquet) files which have been sharded by [Dask](https://huggingface.co/docs/datasets/main/en/filesystems#dask) into ~160MB chunks (starting from 256MB blocks of the source jsonl files). The folder structure along with size sums is provided below. ``` $ du -h --max-depth=1 ./ 9.1G ./niv2_fsopt_data 2.4G ./niv2_zsopt_data 59G ./flan_fsopt_data 984M ./dialog_zsopt_data 11G ./flan_zsopt_data 8.6G ./dialog_fsopt_data 16G ./t0_zsnoopt_data 149M ./cot_fsopt_data 20M ./cot_zsopt_data 17G ./t0_zsopt_data 11G ./flan_zsnoopt_data 101G ./t0_fsopt_data 25G ./flan_fsnoopt_data 39G ./t0_fsnoopt_data 296G ./ ``` # Citations ```bibtex @misc{goodson2023huggyflan title={Fine FLAN: Seqio to Parquet So You Don't Have To}, author={Bleys Goodson}, year={2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/datasets/Open-Orca/FLAN}, } ``` ```bibtex @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ```bibtex @misc{wei2022finetuned, title={Finetuned Language Models Are Zero-Shot Learners}, author={Jason Wei and Maarten Bosma and Vincent Y. Zhao and Kelvin Guu and Adams Wei Yu and Brian Lester and Nan Du and Andrew M. Dai and Quoc V. Le}, year={2022}, eprint={2109.01652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{sanh2022multitask, title={Multitask Prompted Training Enables Zero-Shot Task Generalization}, author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Tali Bers and Stella Biderman and Leo Gao and Thomas Wolf and Alexander M. Rush}, year={2022}, eprint={2110.08207}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ```bibtex @misc{wang2022supernaturalinstructions, title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}, author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi}, year={2022}, eprint={2204.07705}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

trl-internal-testing/zen

trl-internal-testing

--- dataset_info: - config_name: conversational_implicit_prompt_preference features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2810.3684210526317 num_examples: 17 - name: test num_bytes: 253 num_examples: 2 download_size: 7230 dataset_size: 3063.3684210526317 - config_name: conversational_language_modeling features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1439.6315789473683 num_examples: 17 - name: test num_bytes: 169.3684210526316 num_examples: 2 download_size: 3950 dataset_size: 1609.0 - config_name: conversational_preference features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2116.0526315789475 num_examples: 17 - name: test num_bytes: 248.94736842105263 num_examples: 2 download_size: 9108 dataset_size: 2365.0 - config_name: conversational_prompt_completion features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1507.6315789473683 num_examples: 17 - name: test num_bytes: 177.3684210526316 num_examples: 2 download_size: 6364 dataset_size: 1685.0 - config_name: conversational_prompt_only features: - name: prompt list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 830.3157894736842 num_examples: 17 - name: test num_bytes: 97.6842105263158 num_examples: 2 download_size: 3676 dataset_size: 928.0 - config_name: conversational_unpaired_preference features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string - name: label dtype: bool splits: - name: train num_bytes: 1484.3684210526317 num_examples: 17 - name: test num_bytes: 174.6315789473684 num_examples: 2 download_size: 7196 dataset_size: 1659.0 - config_name: standard_implicit_prompt_preference features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 1606.0526315789473 num_examples: 17 - name: test num_bytes: 188.94736842105263 num_examples: 2 download_size: 4688 dataset_size: 1795.0 - config_name: standard_language_modeling features: - name: text dtype: string splits: - name: train num_bytes: 787.3684210526316 num_examples: 17 - name: test num_bytes: 92.63157894736842 num_examples: 2 download_size: 2626 dataset_size: 880.0 - config_name: standard_preference features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 1268.7368421052631 num_examples: 17 - name: test num_bytes: 149.26315789473685 num_examples: 2 download_size: 4953 dataset_size: 1418.0 - config_name: standard_prompt_completion features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 855.3684210526316 num_examples: 17 - name: test num_bytes: 100.63157894736842 num_examples: 2 download_size: 3473 dataset_size: 956.0 - config_name: standard_prompt_only features: - name: prompt dtype: string splits: - name: train num_bytes: 473.3157894736842 num_examples: 17 - name: test num_bytes: 55.68421052631579 num_examples: 2 download_size: 2160 dataset_size: 529.0 - config_name: standard_unpaired_preference features: - name: prompt dtype: string - name: completion dtype: string - name: label dtype: bool splits: - name: train num_bytes: 867.8947368421053 num_examples: 17 - name: test num_bytes: 102.10526315789474 num_examples: 2 download_size: 4364 dataset_size: 970.0 configs: - config_name: conversational_implicit_prompt_preference data_files: - split: train path: conversational_implicit_prompt_preference/train-* - split: test path: conversational_implicit_prompt_preference/test-* - config_name: conversational_language_modeling data_files: - split: train path: conversational_language_modeling/train-* - split: test path: conversational_language_modeling/test-* - config_name: conversational_preference data_files: - split: train path: conversational_preference/train-* - split: test path: conversational_preference/test-* - config_name: conversational_prompt_completion data_files: - split: train path: conversational_prompt_completion/train-* - split: test path: conversational_prompt_completion/test-* - config_name: conversational_prompt_only data_files: - split: train path: conversational_prompt_only/train-* - split: test path: conversational_prompt_only/test-* - config_name: conversational_unpaired_preference data_files: - split: train path: conversational_unpaired_preference/train-* - split: test path: conversational_unpaired_preference/test-* - config_name: standard_implicit_prompt_preference data_files: - split: train path: standard_implicit_prompt_preference/train-* - split: test path: standard_implicit_prompt_preference/test-* - config_name: standard_language_modeling data_files: - split: train path: standard_language_modeling/train-* - split: test path: standard_language_modeling/test-* - config_name: standard_preference data_files: - split: train path: standard_preference/train-* - split: test path: standard_preference/test-* - config_name: standard_prompt_completion data_files: - split: train path: standard_prompt_completion/train-* - split: test path: standard_prompt_completion/test-* - config_name: standard_prompt_only data_files: - split: train path: standard_prompt_only/train-* - split: test path: standard_prompt_only/test-* - config_name: standard_unpaired_preference data_files: - split: train path: standard_unpaired_preference/train-* - split: test path: standard_unpaired_preference/test-* ---

allenai/social_i_qa

allenai

--- language: - en paperswithcode_id: social-iqa pretty_name: Social Interaction QA dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answerA dtype: string - name: answerB dtype: string - name: answerC dtype: string - name: label dtype: string splits: - name: train num_bytes: 6389954 num_examples: 33410 - name: validation num_bytes: 376508 num_examples: 1954 download_size: 2198056 dataset_size: 6766462 --- # Dataset Card for "social_i_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://leaderboard.allenai.org/socialiqa/submissions/get-started](https://leaderboard.allenai.org/socialiqa/submissions/get-started) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.20 MB - **Size of the generated dataset:** 6.76 MB - **Total amount of disk used:** 8.97 MB ### Dataset Summary We introduce Social IQa: Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people’s actions and their social implications. For example, given an action like "Jesse saw a concert" and a question like "Why did Jesse do this?", humans can easily infer that Jesse wanted "to see their favorite performer" or "to enjoy the music", and not "to see what's happening inside" or "to see if it works". The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models’ abilities to reason about the social implications of everyday events and situations. (Less) ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 2.20 MB - **Size of the generated dataset:** 6.76 MB - **Total amount of disk used:** 8.97 MB An example of 'validation' looks as follows. ``` { "answerA": "sympathetic", "answerB": "like a person who was unable to help", "answerC": "incredulous", "context": "Sydney walked past a homeless woman asking for change but did not have any money they could give to her. Sydney felt bad afterwards.", "label": "1", "question": "How would you describe Sydney?" } ``` ### Data Fields The data fields are the same among all splits. #### default - `context`: a `string` feature. - `question`: a `string` feature. - `answerA`: a `string` feature. - `answerB`: a `string` feature. - `answerC`: a `string` feature. - `label`: a `string` feature. ### Data Splits | name |train|validation| |-------|----:|---------:| |default|33410| 1954| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

laion/strategic_game_chess

laion

--- tags: - game pretty_name: The Chess Dataset license: cc-by-4.0 --- # Chess > Recent advancements in artificial intelligence (AI) underscore the progress of reasoning and planning shown by recent generalist machine learning (ML) models. The progress can be boosted by datasets that can further boost these generic capabilities when used for training foundation models of various kind. This research initiative has generated extensive synthetic datasets from complex games — chess, Rubik's Cube, and mazes — to study facilitation and the advancement of these critical generic skills in AI models. This dataset contains 3.2 billion games, equating to approximately 608 billion individual moves. it is generated through self-play by Stockfish engine using Fugaku and we add initial moves to expand its diversity. Each game has three columns: 'Moves', 'Termination' and 'Result', - 'Move': recorded chess moves of the whole game. - 'Termination': include CHECKMATE, INSUFFICIENT_MATERIAL, ... etc. - Please check this for detail information https://python-chess.readthedocs.io/en/latest/core.html#chess.Outcome.termination - 'Result': result of this game, 1-0, 1/2-1/2, 0-1. ### Call for Collaboration We invite interested researchers and ML practitioners to explore these datasets' potential. Whether training GPT models from scratch or fine-tuning pre-existing models, we encourage the exploration of various pre-training and fine-tuning strategies using these game-based datasets standalone or as enhancement of other already composed large-scale data. Our team is prepared to assist in securing necessary GPU resources for these explorations. We are particularly interested in collaborators eager to pre-train models of small to medium scale on our game data, subsequently transition to standard text-based training, and then perform comparative analyses against models of similar architecture trained exclusively on text data. Conclusively, this initiative marks a significant stride toward intricate problem-solving and strategic planning in AI, extending an open invitation to the research community for collaborative advancement in this domain.

HuggingFaceM4/OBELICS

HuggingFaceM4

--- language: - en license: cc-by-4.0 size_categories: - 100M<n<1B pretty_name: OBELICS configs: - config_name: default data_files: - split: train path: data/train-* - config_name: opt_out_docs_removed_2023_07_12 data_files: - split: train path: opt_out_docs_removed_2023_07_12/train-* dataset_info: - config_name: default features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 715724717192 num_examples: 141047697 download_size: 71520629655 dataset_size: 715724717192 - config_name: opt_out_docs_removed_2023_07_12 features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 684638314215 num_examples: 134648855 download_size: 266501092920 dataset_size: 684638314215 --- # Dataset Card for OBELICS ## Dataset Description - **Visualization of OBELICS web documents:** https://huggingface.co/spaces/HuggingFaceM4/obelics_visualization - **Paper:** [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://arxiv.org/abs/2306.16527) - **Repository:** https://github.com/huggingface/OBELICS - **Point of Contact: hugo@huggingface.co** `OBELICS` is an open, massive, and curated collection of interleaved image-text web documents, containing 141M English documents, 115B text tokens, and 353M images, extracted from Common Crawl dumps between February 2020 and February 2023. The collection and filtering steps are described in our [paper](https://huggingface.co/papers/2306.16527). Interleaved image-text web documents are a succession of text paragraphs interleaved by images, such as web pages that contain images. Models trained on these web documents outperform vision and language models trained solely on image-text pairs on various benchmarks. They can also generate long and coherent text about a set of multiple images. As an example, we trained [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b), a visual language model that accepts arbitrary sequences of image and text inputs and produces text outputs. We provide an [interactive visualization](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) of OBELICS that allows exploring the content of OBELICS. The map shows a subset of 11M of the 141M documents. [](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) ## Data Fields An example of a sample looks as follows: ``` # The example has been cropped { 'images': [ 'https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg', None ], 'metadata': '[{"document_url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "unformatted_src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "formatted_filename": "lamborghini urus original carbon fiber accessories", "alt_text": "VW Group Allegedly Receives Offer To Sell Lamborghini For $9.2 Billion", "original_width": 1920, "original_height": 1080, "format": "jpeg"}, null]', 'general_metadata': '{"url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "warc_filename": "crawl-data/CC-MAIN-2021-25/segments/1623488528979.69/warc/CC-MAIN-20210623011557-20210623041557-00312.warc.gz", "warc_record_offset": 322560850, "warc_record_length": 17143}', 'texts': [ None, 'The buyer would get everything, including Lambo\'s headquarters.\n\nThe investment groupQuantum Group AG has submitted a€7.5 billion ($9.2 billion at current exchange rates) offer to purchase Lamborghini from Volkswagen Group, Autocar reports. There\'s no info yet about whether VW intends to accept the offer or further negotiate the deal.\n\nQuantum ... Group Chief Executive Herbert Diess said at the time.' ] } ``` Each sample is composed of the same 4 fields: `images`, `texts`, `metadata`, and `general_metadata`. `images` and `texts` are two lists of the same size, where for each index, one element and only one is not `None`. For example, for the interleaved web document `<image_1>text<image_2>`, we would find `[image_1, None, image_2]` in `images` and `[None, text, None]` in `texts`. The images are replaced by their URLs, and the users need to download the images, for instance, with the library [img2dataset](https://github.com/rom1504/img2dataset). `metadata` is the string representation of a list containing information about each of the images. It has the same length as `texts` and `images` and logs for each image relevant information such as original source document, unformatted source, alternative text if present, etc. `general_metadata` is the string representation of a dictionary containing the URL of the document, and information regarding the extraction from Common Crawl snapshots. ## Size and Data Splits There is only one split, `train`, that contains 141,047,697 documents. `OBELICS` with images replaced by their URLs weighs 666.6 GB (😈) in arrow format and 377 GB in the uploaded `parquet` format. ## Considerations for Using the Data ### Discussion of Biases A subset of this dataset `train`, of ~50k was evaluated using the Data Measurements Tool, with a particular focus on the nPMI metric > nPMI scores for a word help to identify potentially problematic associations, ranked by how close the association is. > nPMI bias scores for paired words help to identify how word associations are skewed between the selected selected words (Aka et al., 2021). > You can select from gender and sexual orientation identity terms that appear in the dataset at least 10 times. > The resulting ranked words are those that co-occur with both identity terms. > The more positive the score, the more associated the word is with the first identity term. The more negative the score, the more associated the word is with the second identity term. While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively.  We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations. ## Opted-out content To respect the preferences of content creators, we removed from OBELICS all images for which creators explicitly opted out of AI model training. We used the [Spawning API](https://api.spawning.ai/spawning-api) to verify that the images in the dataset respect the original copyright owners’ choices. However, due to an error on our side, we did not remove entire documents (i.e., URLs) that opted out of AI model training. As of July 12, 2023, it represents 4.25% of the totality of OBELICS. The config `opt_out_docs_removed_2023_07_12` applies the correct filtering at the web document level as of July 2023: `ds = load_dataset("HuggingFaceM4/OBELICS", "opt_out_docs_removed_2023_07_12")`. We recommend users of OBELICS to regularly check every document against the API. ## Content warnings Despite our efforts in filtering, OBELICS contains a small proportion of documents that are not suitable for all audiences. For instance, while navigating the interactive map, you might find the cluster named "Sex" which predominantly contains descriptions of pornographic movies along with pornographic images. Other clusters would contain advertising for sex workers or reports of violent shootings. In our experience, these documents represent a small proportion of all the documents. ## Terms of Use By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model. ### Licensing Information License CC-BY-4.0. ### Citation Information If you are using this dataset, please cite ``` @misc{laurencon2023obelics, title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents}, author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh}, year={2023}, eprint={2306.16527}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```

locuslab/TOFU

locuslab

--- annotations_creators: - machine-generated language: - en language_creators: - machine-generated license: mit multilinguality: - monolingual pretty_name: TOFU size_categories: - 1K<n<10K source_datasets: - original tags: - unlearning - question answering - TOFU - NLP - LLM task_categories: - question-answering task_ids: - closed-domain-qa configs: - config_name: full data_files: full.json default: true - config_name: forget01 data_files: forget01.json - config_name: forget05 data_files: forget05.json - config_name: forget10 data_files: forget10.json - config_name: retain90 data_files: retain90.json - config_name: retain95 data_files: retain95.json - config_name: retain99 data_files: retain99.json - config_name: world_facts data_files: world_facts.json - config_name: real_authors data_files: real_authors.json - config_name: forget01_perturbed data_files: forget01_perturbed.json - config_name: forget05_perturbed data_files: forget05_perturbed.json - config_name: forget10_perturbed data_files: forget10_perturbed.json - config_name: retain_perturbed data_files: retain_perturbed.json - config_name: world_facts_perturbed data_files: world_facts_perturbed.json - config_name: real_authors_perturbed data_files: real_authors_perturbed.json --- # TOFU: Task of Fictitious Unlearning 🍢 The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set. ## Quick Links - [**Website**](https://locuslab.github.io/tofu): The landing page for TOFU - [**arXiv Paper**](http://arxiv.org/abs/2401.06121): Detailed information about the TOFU dataset and its significance in unlearning tasks. - [**GitHub Repository**](https://github.com/locuslab/tofu): Access the source code, fine-tuning scripts, and additional resources for the TOFU dataset. - [**Dataset on Hugging Face**](https://huggingface.co/datasets/locuslab/TOFU): Direct link to download the TOFU dataset. - [**Leaderboard on Hugging Face Spaces**](https://huggingface.co/spaces/locuslab/tofu_leaderboard): Current rankings and submissions for the TOFU dataset challenges. - [**Summary on Twitter**](https://x.com/_akhaliq/status/1745643293839327268): A concise summary and key takeaways from the project. ## Applicability 🚀 The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 chat, and Phi-1.5 models, but can be easily adapted to other models. ## Loading the Dataset To load the dataset, use the following code: ```python from datasets import load_dataset dataset = load_dataset("locuslab/TOFU", "full") ``` ### Available forget sets are: - `forget01`: Forgetting 1% of the original dataset, all entries correspond to a single author. - `forget05`: Forgetting 5% of the original dataset, all entries correspond to a single author. - `forget10`: Forgetting 10% of the original dataset, all entries correspond to a single author. Retain sets corresponding to each forget set are also available, which can be used to train an Oracle model. ## Codebase The code for training the models and the availability of all fine-tuned models can be found at our [GitHub repository](https://github.com/locuslab/tofu). ## Citing Our Work If you find our codebase and dataset beneficial, please cite our work: ``` @misc{tofu2024, title={TOFU: A Task of Fictitious Unlearning for LLMs}, author={Pratyush Maini and Zhili Feng and Avi Schwarzschild and Zachary C. Lipton and J. Zico Kolter}, year={2024}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

mlfoundations/MINT-1T-PDF-CC-2024-18

mlfoundations

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T configs: - config_name: default data_files: - split: train path: CC-MAIN-*/* --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2024-18`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c).  ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

McGill-NLP/WebLINX-full

McGill-NLP

--- language: - en size_categories: - 10K<n<100K config_names: - chat configs: - config_name: chat default: true data_files: - split: train path: chat/train.csv - split: validation path: chat/valid.csv - split: test path: chat/test_iid.csv - split: test_geo path: chat/test_geo.csv - split: test_vis path: chat/test_vis.csv - split: test_cat path: chat/test_cat.csv - split: test_web path: chat/test_web.csv tags: - conversational - image-to-text - vision - convAI --- # WebLINX: Real-World Website Navigation with Multi-Turn Dialogue WARNING: This is not the main WebLINX data card! You might want to use the main WebLINX data card instead: > **[WebLINX: Real-World Website Navigation with Multi-Turn Dialogue](https://huggingface.co/datasets/mcgill-nlp/weblinx)**

mteb/banking77

mteb

--- language: - en ---

Antreas/TALI

Antreas

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: image_url dtype: string - name: item_idx dtype: int64 - name: wit_features struct: - name: attribution_passes_lang_id sequence: bool - name: caption_alt_text_description sequence: string - name: caption_reference_description sequence: string - name: caption_title_and_reference_description sequence: string - name: context_page_description sequence: string - name: context_section_description sequence: string - name: hierarchical_section_title sequence: string - name: is_main_image sequence: bool - name: language sequence: string - name: page_changed_recently sequence: bool - name: page_title sequence: string - name: page_url sequence: string - name: section_title sequence: string - name: wit_idx dtype: int64 - name: youtube_title_text dtype: string - name: youtube_description_text dtype: string - name: youtube_video_content dtype: binary - name: youtube_video_starting_time dtype: string - name: youtube_subtitle_text dtype: string - name: youtube_video_size dtype: int64 - name: youtube_video_file_path dtype: string splits: - name: train num_bytes: 1902638101655.625 num_examples: 1052915 - name: val num_bytes: 104485442867.25 num_examples: 57958 - name: test num_bytes: 111107332347.375 num_examples: 61389 download_size: 2058391040534 dataset_size: 2118230876870.25 license: cc-by-4.0 task_categories: - zero-shot-classification tags: - video - audio - text - image - tetramodal - multimodal - youtube - wikipedia pretty_name: TALI size_categories: - 1M<n<10M --- # Dataset Card for "TALI" ## Table of Contents 1. Dataset Description 1. Abstract 2. Brief Description 2. Dataset Information 1. Modalities 2. Dataset Variants 3. Dataset Statistics 4. Data Fields 5. Data Splits 3. Dataset Creation 4. Dataset Use 5. Additional Information ## Dataset Description ### Abstract TALI is a large-scale, tetramodal dataset designed to facilitate a shift from unimodal and duomodal to tetramodal research in deep learning. It aligns text, video, images, and audio, providing a rich resource for innovative self-supervised learning tasks and multimodal research. TALI enables exploration of how different modalities and data/model scaling affect downstream performance, with the aim of inspiring diverse research ideas and enhancing understanding of model capabilities and robustness in deep learning. ### Brief Description TALI (Temporally and semantically Aligned Audio, Language and Images) is a dataset that uses the Wikipedia Image Text (WIT) captions and article titles to search Youtube for videos that match the captions. It then downloads the video, audio, and subtitles from these videos. The result is a rich multimodal dataset that has multiple caption types related to both the WiT Images, and the Youtube videos. This enables learning to take place between either temporally or semantically aligned text, images, audio and video. ## Dataset Information ### Modalities The TALI dataset consists of the following modalities: 1. Image: 1. Wikipedia caption image 2. Randomly sampled image from youtube video 2. Text 1. Wikipedia Caption Text 2. Wikipedia Title Text 3. Wikipedia Main Body Text 4. YouTube Subtitle Text 5. YouTube Description Text 6. YouTube Title Text 3. Audio 1. YouTube Content Audio 4. Video 1. YouTube Content Video ## Usage: To get started with TALI, you can load the dataset via Hugging Face's `datasets` library through our helper functions. The reason we don't use `datasets` directly is because we found huggingface_hub downloads much faster and reliable. For a full set of possible configurations look at [examples.py](examples.py). Here's a basic usage example: First install the tali package: ### Installation For the default install use: ```bash pip install git+https://github.com/AntreasAntoniou/TALI ``` For the dev install use: ```bash pip install git+https://github.com/AntreasAntoniou/TALI[dev] ``` Then use the dataset using: ### Examples Import relevant helper functions ```python import pathlib from enum import Enum import torch from tqdm.auto import tqdm from tali.data import ( SubModalityTypes, TALIBaseTransform, TALIBaseTransformConfig, VideoFramesFormat, default_transforms, load_dataset_via_hub, ) ``` #### TALI with default transforms (CLIP and Whisper) and no streaming ```python def tali_with_transforms_no_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI" )["train"] ( image_transforms, text_transforms, audio_transforms, video_transforms, ) = default_transforms() preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=text_transforms, image_tokenizer=image_transforms, audio_tokenizer=audio_transforms, video_tokenizer=video_transforms, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with no transforms and no streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats ```python def tali_without_transforms_no_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI" )["train"] preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=None, image_tokenizer=None, audio_tokenizer=None, video_tokenizer=None, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with default transforms and streaming ```python def tali_with_transforms_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI", streaming=True )["train"] ( image_transforms, text_transforms, audio_transforms, video_transforms, ) = default_transforms() preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=text_transforms, image_tokenizer=image_transforms, audio_tokenizer=audio_transforms, video_tokenizer=video_transforms, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with no transforms and streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats ```python def tali_without_transforms_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI", streaming=True )["train"] preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=None, image_tokenizer=None, audio_tokenizer=None, video_tokenizer=None, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` ### Dataset Statistics TBA ## Dataset Creation The TALI dataset was created by starting from the WiT dataset and using either the context_page_description or page_title as a source-query to search YouTube for video that were creative commons opted-in, and, not age restricted. The top 100 result titles were returned and compared with the source-query using the CLIP text embeddings of the largest CLIP model available. The top-1 title’s video based on the CLIP ranking was chosen and downloaded. The video was broken into 30-second segments and the top-10 segments for eachvideo were chosen based on the distance between the CLIP image embedding of the first image of each segment and the video’s title text. The image, audio, and subtitle frames were extracted from these segments. At sampling time, one of these 10 segments is randomly selected, and a 10-second segment is chosen out of the 30-second clip. The result is 200 video frames (spread throughout the 10-second segment), and 160000 audio frames (10 seconds). ## Dataset Use TALI is designed for use in a wide range of multimodal research tasks, including but not limited to: - Multimodal understanding and reasoning - Self-supervised learning - Multimodal alignment and translation - Multimodal summarization - Multimodal question answering ## Dataset Curators: Antreas Antoniou Citation Information: TBA Contributions: Thanks to all contributors including data curators, annotators, and software developers. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

roneneldan/TinyStories

roneneldan

--- license: cdla-sharing-1.0 task_categories: - text-generation language: - en --- Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary. Described in the following paper: https://arxiv.org/abs/2305.07759. The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M. Additional resources: tinystories_all_data.tar.gz - contains a superset of the stories together with metadata and the prompt that was used to create each story. TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger). Evaluation_prompts.yaml: List of prompts used to evaluate our models (see paper)

ylacombe/cml-tts

ylacombe

--- language: - nl - fr - de - it - pl - pt - es license: cc-by-4.0 size_categories: - 1M<n<10M task_categories: - text-to-speech - text-to-audio pretty_name: CML-TTS dataset_info: - config_name: dutch features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 186374683541.98 num_examples: 309785 - name: dev num_bytes: 2912063172.928 num_examples: 4834 - name: test num_bytes: 2757891736.78 num_examples: 4570 download_size: 132987704971 dataset_size: 192044638451.68802 - config_name: french features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 64984002840.768 num_examples: 107598 - name: dev num_bytes: 2257393207.796 num_examples: 3739 - name: test num_bytes: 2281630546.306 num_examples: 3763 download_size: 48345998335 dataset_size: 69523026594.87 - config_name: german features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 369052038020.872 num_examples: 608296 - name: dev num_bytes: 3197115278.604 num_examples: 5314 - name: test num_bytes: 3288183839.092 num_examples: 5466 download_size: 280438261836 dataset_size: 375537337138.568 - config_name: italian features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 30242801015.92 num_examples: 50345 - name: dev num_bytes: 938644924.81 num_examples: 1765 - name: test num_bytes: 979116355.51 num_examples: 1835 download_size: 21996805791 dataset_size: 32160562296.239998 - config_name: polish features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 11127461686.356 num_examples: 18719 - name: dev num_bytes: 356048249 num_examples: 853 - name: test num_bytes: 367796887 num_examples: 814 download_size: 8114633186 dataset_size: 11851306822.356 - config_name: portuguese features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 20722423371.0 num_examples: 34265 - name: dev num_bytes: 622824524.224 num_examples: 1134 - name: test num_bytes: 673141068.9 num_examples: 1297 download_size: 14421097659 dataset_size: 22018388964.124 - config_name: spanish features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 101377452063.176 num_examples: 168524 - name: dev num_bytes: 1882729515.184 num_examples: 3148 - name: test num_bytes: 1851592818.0 num_examples: 3080 download_size: 73687756096 dataset_size: 105111774396.36 configs: - config_name: dutch data_files: - split: train path: dutch/train-* - split: dev path: dutch/dev-* - split: test path: dutch/test-* - config_name: french data_files: - split: train path: french/train-* - split: dev path: french/dev-* - split: test path: french/test-* - config_name: german data_files: - split: train path: german/train-* - split: dev path: german/dev-* - split: test path: german/test-* - config_name: italian data_files: - split: train path: italian/train-* - split: dev path: italian/dev-* - split: test path: italian/test-* - config_name: polish data_files: - split: train path: polish/train-* - split: dev path: polish/dev-* - split: test path: polish/test-* - config_name: portuguese data_files: - split: train path: portuguese/train-* - split: dev path: portuguese/dev-* - split: test path: portuguese/test-* - config_name: spanish data_files: - split: train path: spanish/train-* - split: dev path: spanish/dev-* - split: test path: spanish/test-* --- # Dataset Card for CML-TTS ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Data Statistics](#data-statistics) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [MultiLingual LibriSpeech ASR corpus](https://www.openslr.org/146/) - **Repository:** [CML-TTS-Dataset](https://github.com/freds0/CML-TTS-Dataset) - **Paper:** [CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages](https://arxiv.org/abs/2306.10097) ### Dataset Summary CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/146) to make it easier to stream. ### Supported Tasks - `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS). ### Languages The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz. ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German): ```python from datasets import load_dataset mls = load_dataset("ylacombe/cml-tts", "german", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True) print(next(iter(mls))) ``` #### *Bonus* You can create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). **Local:** ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler mls = load_dataset("ylacombe/cml-tts", "german", split="train") batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False) dataloader = DataLoader(mls, batch_sampler=batch_sampler) ``` **Streaming:** ```python from datasets import load_dataset from torch.utils.data import DataLoader mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True) dataloader = DataLoader(mls, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'audio': {'path': '6892_8912_000729.wav', 'array': array([-1.52587891e-...7344e-05]), 'sampling_rate': 24000}, 'wav_filesize': 601964, 'text': 'Proszę pana, tu pano... zdziwiony', 'transcript_wav2vec': 'proszę pana tu panow... zdziwiony', 'levenshtein': 0.96045197740113, 'duration': 13.648979591836737, 'num_words': 29, 'speaker_id': 6892} ``` ### Data Fields - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - transcript_wav2vec: the transcription of the audio file using the wav2vec model. Has been used to curate the dataset. - wav_filesize: The size of the audio waveform file. Has been used to curate the dataset. - levenshtein: The [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between the wav2vec transcription and the original transcription. Has been used to curate the dataset. - duration: The duration of the audio in seconds. - num_words: The number of words of the transcription. ### Data Splits | # Samples | Train | Dev | Test | |------------|--------|------|------| | german | 608296 | 5314 | 5466 | | dutch | 309785 | 4834 | 4570 | | french | 107598 | 3739 | 3763 | | spanish | 168524 | 3148 | 3080 | | italian | 50345 | 1765 | 1835 | | portuguese | 34265 | 1134 | 1297 | | polish | 18719 | 853 | 814 | ### Data Statistics | Language | Duration (Train) | Duration (Test) | Duration (Dev) | Speakers (Train) | Speakers (Test) | Speakers (Dev) | |------------|-------------------|------------------|----------------|------------------|-----------------|----------------| | | M | F | M | F | M | F | M | F | M | F | M | F | | Dutch | 482.82 | 162.17 | 2.46 | 1.29 | 2.24 | 1.67 | 8 | 27 | 3 | 3 | 2 | 4 | | French | 260.08 | 24.04 | 2.48 | 3.55 | 3.31 | 2.72 | 25 | 20 | 8 | 9 | 10 | 8 | | German | 1128.96 | 436.64 | 3.75 | 5.27 | 4.31 | 5.03 | 78 | 90 | 13 | 17 | 13 | 15 | | Italian | 73.78 | 57.51 | 1.47 | 0.85 | 0.40 | 1.52 | 23 | 38 | 5 | 5 | 4 | 6 | | Polish | 30.61 | 8.32 | 0.70 | 0.90 | 0.56 | 0.80 | 4 | 4 | 2 | 2 | 2 | 2 | | Portuguese | 23.14 | 44.81 | 0.28 | 0.24 | 0.68 | 0.20 | 20 | 10 | 5 | 4 | 6 | 3 | | Spanish | 279.15 | 164.08 | 2.77 | 2.06 | 3.40 | 2.34 | 35 | 42 | 10 | 8 | 11 | 9 | | Total | 3,176.13| | 28.11 | | 29.19 | | 424 | | 94 | | 95 | | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) ### Citation Information ``` @misc{oliveira2023cmltts, title={CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages}, author={Frederico S. Oliveira and Edresson Casanova and Arnaldo Cândido Júnior and Anderson S. Soares and Arlindo R. Galvão Filho}, year={2023}, eprint={2306.10097}, archivePrefix={arXiv}, primaryClass={eess.AS} } ``` ### Contributions Thanks to [@ylacombe](https://github.com/ylacombe) for adding this dataset.

poloclub/diffusiondb

poloclub

--- layout: default title: Home nav_order: 1 has_children: false annotations_creators: - no-annotation language: - en language_creators: - found license: - cc0-1.0 multilinguality: - multilingual pretty_name: DiffusionDB size_categories: - n>1T source_datasets: - original tags: - stable diffusion - prompt engineering - prompts - research paper task_categories: - text-to-image - image-to-text task_ids: - image-captioning --- # DiffusionDB <img width="100%" src="https://user-images.githubusercontent.com/15007159/201762588-f24db2b8-dbb2-4a94-947b-7de393fc3d33.gif"> ## Table of Contents - [DiffusionDB](#diffusiondb) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Two Subsets](#two-subsets) - [Key Differences](#key-differences) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Dataset Metadata](#dataset-metadata) - [Metadata Schema](#metadata-schema) - [Data Splits](#data-splits) - [Loading Data Subsets](#loading-data-subsets) - [Method 1: Using Hugging Face Datasets Loader](#method-1-using-hugging-face-datasets-loader) - [Method 2. Use the PoloClub Downloader](#method-2-use-the-poloclub-downloader) - [Usage/Examples](#usageexamples) - [Downloading a single file](#downloading-a-single-file) - [Downloading a range of files](#downloading-a-range-of-files) - [Downloading to a specific directory](#downloading-to-a-specific-directory) - [Setting the files to unzip once they've been downloaded](#setting-the-files-to-unzip-once-theyve-been-downloaded) - [Method 3. Use `metadata.parquet` (Text Only)](#method-3-use-metadataparquet-text-only) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [DiffusionDB homepage](https://poloclub.github.io/diffusiondb) - **Repository:** [DiffusionDB repository](https://github.com/poloclub/diffusiondb) - **Distribution:** [DiffusionDB Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb) - **Paper:** [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://arxiv.org/abs/2210.14896) - **Point of Contact:** [Jay Wang](mailto:jayw@gatech.edu) ### Dataset Summary DiffusionDB is the first large-scale text-to-image prompt dataset. It contains **14 million** images generated by Stable Diffusion using prompts and hyperparameters specified by real users. DiffusionDB is publicly available at [🤗 Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb). ### Supported Tasks and Leaderboards The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. ### Languages The text in the dataset is mostly English. It also contains other languages such as Spanish, Chinese, and Russian. ### Two Subsets DiffusionDB provides two subsets (DiffusionDB 2M and DiffusionDB Large) to support different needs. |Subset|Num of Images|Num of Unique Prompts|Size|Image Directory|Metadata Table| |:--|--:|--:|--:|--:|--:| |DiffusionDB 2M|2M|1.5M|1.6TB|`images/`|`metadata.parquet`| |DiffusionDB Large|14M|1.8M|6.5TB|`diffusiondb-large-part-1/` `diffusiondb-large-part-2/`|`metadata-large.parquet`| ##### Key Differences 1. Two subsets have a similar number of unique prompts, but DiffusionDB Large has much more images. DiffusionDB Large is a superset of DiffusionDB 2M. 2. Images in DiffusionDB 2M are stored in `png` format; images in DiffusionDB Large use a lossless `webp` format. ## Dataset Structure We use a modularized file structure to distribute DiffusionDB. The 2 million images in DiffusionDB 2M are split into 2,000 folders, where each folder contains 1,000 images and a JSON file that links these 1,000 images to their prompts and hyperparameters. Similarly, the 14 million images in DiffusionDB Large are split into 14,000 folders. ```bash # DiffusionDB 2M ./ ├── images │   ├── part-000001 │   │   ├── 3bfcd9cf-26ea-4303-bbe1-b095853f5360.png │   │   ├── 5f47c66c-51d4-4f2c-a872-a68518f44adb.png │   │   ├── 66b428b9-55dc-4907-b116-55aaa887de30.png │   │   ├── [...] │   │   └── part-000001.json │   ├── part-000002 │   ├── part-000003 │   ├── [...] │   └── part-002000 └── metadata.parquet ``` ```bash # DiffusionDB Large ./ ├── diffusiondb-large-part-1 │   ├── part-000001 │   │   ├── 0a8dc864-1616-4961-ac18-3fcdf76d3b08.webp │   │   ├── 0a25cacb-5d91-4f27-b18a-bd423762f811.webp │   │   ├── 0a52d584-4211-43a0-99ef-f5640ee2fc8c.webp │   │   ├── [...] │   │   └── part-000001.json │   ├── part-000002 │   ├── part-000003 │   ├── [...] │   └── part-010000 ├── diffusiondb-large-part-2 │   ├── part-010001 │   │   ├── 0a68f671-3776-424c-91b6-c09a0dd6fc2d.webp │   │   ├── 0a0756e9-1249-4fe2-a21a-12c43656c7a3.webp │   │   ├── 0aa48f3d-f2d9-40a8-a800-c2c651ebba06.webp │   │   ├── [...] │   │   └── part-000001.json │   ├── part-010002 │   ├── part-010003 │   ├── [...] │   └── part-014000 └── metadata-large.parquet ``` These sub-folders have names `part-0xxxxx`, and each image has a unique name generated by [UUID Version 4](https://en.wikipedia.org/wiki/Universally_unique_identifier). The JSON file in a sub-folder has the same name as the sub-folder. Each image is a `PNG` file (DiffusionDB 2M) or a lossless `WebP` file (DiffusionDB Large). The JSON file contains key-value pairs mapping image filenames to their prompts and hyperparameters. ### Data Instances For example, below is the image of `f3501e05-aef7-4225-a9e9-f516527408ac.png` and its key-value pair in `part-000001.json`. <img width="300" src="https://i.imgur.com/gqWcRs2.png"> ```json { "f3501e05-aef7-4225-a9e9-f516527408ac.png": { "p": "geodesic landscape, john chamberlain, christopher balaskas, tadao ando, 4 k, ", "se": 38753269, "c": 12.0, "st": 50, "sa": "k_lms" }, } ``` ### Data Fields - key: Unique image name - `p`: Prompt - `se`: Random seed - `c`: CFG Scale (guidance scale) - `st`: Steps - `sa`: Sampler ### Dataset Metadata To help you easily access prompts and other attributes of images without downloading all the Zip files, we include two metadata tables `metadata.parquet` and `metadata-large.parquet` for DiffusionDB 2M and DiffusionDB Large, respectively. The shape of `metadata.parquet` is (2000000, 13) and the shape of `metatable-large.parquet` is (14000000, 13). Two tables share the same schema, and each row represents an image. We store these tables in the Parquet format because Parquet is column-based: you can efficiently query individual columns (e.g., prompts) without reading the entire table. Below are three random rows from `metadata.parquet`. | image_name | prompt | part_id | seed | step | cfg | sampler | width | height | user_name | timestamp | image_nsfw | prompt_nsfw | |:-----------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------:|-----------:|-------:|------:|----------:|--------:|---------:|:-----------------------------------------------------------------|:--------------------------|-------------:|--------------:| | 0c46f719-1679-4c64-9ba9-f181e0eae811.png | a small liquid sculpture, corvette, viscous, reflective, digital art | 1050 | 2026845913 | 50 | 7 | 8 | 512 | 512 | c2f288a2ba9df65c38386ffaaf7749106fed29311835b63d578405db9dbcafdb | 2022-08-11 09:05:00+00:00 | 0.0845108 | 0.00383462 | | a00bdeaa-14eb-4f6c-a303-97732177eae9.png | human sculpture of lanky tall alien on a romantic date at italian restaurant with smiling woman, nice restaurant, photography, bokeh | 905 | 1183522603 | 50 | 10 | 8 | 512 | 768 | df778e253e6d32168eb22279a9776b3cde107cc82da05517dd6d114724918651 | 2022-08-19 17:55:00+00:00 | 0.692934 | 0.109437 | | 6e5024ce-65ed-47f3-b296-edb2813e3c5b.png | portrait of barbaric spanish conquistador, symmetrical, by yoichi hatakenaka, studio ghibli and dan mumford | 286 | 1713292358 | 50 | 7 | 8 | 512 | 640 | 1c2e93cfb1430adbd956be9c690705fe295cbee7d9ac12de1953ce5e76d89906 | 2022-08-12 03:26:00+00:00 | 0.0773138 | 0.0249675 | #### Metadata Schema `metadata.parquet` and `metatable-large.parquet` share the same schema. |Column|Type|Description| |:---|:---|:---| |`image_name`|`string`|Image UUID filename.| |`prompt`|`string`|The text prompt used to generate this image.| |`part_id`|`uint16`|Folder ID of this image.| |`seed`|`uint32`| Random seed used to generate this image.| |`step`|`uint16`| Step count (hyperparameter).| |`cfg`|`float32`| Guidance scale (hyperparameter).| |`sampler`|`uint8`| Sampler method (hyperparameter). Mapping: `{1: "ddim", 2: "plms", 3: "k_euler", 4: "k_euler_ancestral", 5: "k_heun", 6: "k_dpm_2", 7: "k_dpm_2_ancestral", 8: "k_lms", 9: "others"}`. |`width`|`uint16`|Image width.| |`height`|`uint16`|Image height.| |`user_name`|`string`|The unique discord ID's SHA256 hash of the user who generated this image. For example, the hash for `xiaohk#3146` is `e285b7ef63be99e9107cecd79b280bde602f17e0ca8363cb7a0889b67f0b5ed0`. "deleted_account" refer to users who have deleted their accounts. None means the image has been deleted before we scrape it for the second time.| |`timestamp`|`timestamp`|UTC Timestamp when this image was generated. None means the image has been deleted before we scrape it for the second time. Note that timestamp is not accurate for duplicate images that have the same prompt, hypareparameters, width, height.| |`image_nsfw`|`float32`|Likelihood of an image being NSFW. Scores are predicted by [LAION's state-of-art NSFW detector](https://github.com/LAION-AI/LAION-SAFETY) (range from 0 to 1). A score of 2.0 means the image has already been flagged as NSFW and blurred by Stable Diffusion.| |`prompt_nsfw`|`float32`|Likelihood of a prompt being NSFW. Scores are predicted by the library [Detoxicy](https://github.com/unitaryai/detoxify). Each score represents the maximum of `toxicity` and `sexual_explicit` (range from 0 to 1).| > **Warning** > Although the Stable Diffusion model has an NSFW filter that automatically blurs user-generated NSFW images, this NSFW filter is not perfect—DiffusionDB still contains some NSFW images. Therefore, we compute and provide the NSFW scores for images and prompts using the state-of-the-art models. The distribution of these scores is shown below. Please decide an appropriate NSFW score threshold to filter out NSFW images before using DiffusionDB in your projects. <img src="https://i.imgur.com/1RiGAXL.png" width="100%"> ### Data Splits For DiffusionDB 2M, we split 2 million images into 2,000 folders where each folder contains 1,000 images and a JSON file. For DiffusionDB Large, we split 14 million images into 14,000 folders where each folder contains 1,000 images and a JSON file. ### Loading Data Subsets DiffusionDB is large (1.6TB or 6.5 TB)! However, with our modularized file structure, you can easily load a desirable number of images and their prompts and hyperparameters. In the [`example-loading.ipynb`](https://github.com/poloclub/diffusiondb/blob/main/notebooks/example-loading.ipynb) notebook, we demonstrate three methods to load a subset of DiffusionDB. Below is a short summary. #### Method 1: Using Hugging Face Datasets Loader You can use the Hugging Face [`Datasets`](https://huggingface.co/docs/datasets/quickstart) library to easily load prompts and images from DiffusionDB. We pre-defined 16 DiffusionDB subsets (configurations) based on the number of instances. You can see all subsets in the [Dataset Preview](https://huggingface.co/datasets/poloclub/diffusiondb/viewer/all/train). ```python import numpy as np from datasets import load_dataset # Load the dataset with the `large_random_1k` subset dataset = load_dataset('poloclub/diffusiondb', 'large_random_1k') ``` #### Method 2. Use the PoloClub Downloader This repo includes a Python downloader [`download.py`](https://github.com/poloclub/diffusiondb/blob/main/scripts/download.py) that allows you to download and load DiffusionDB. You can use it from your command line. Below is an example of loading a subset of DiffusionDB. ##### Usage/Examples The script is run using command-line arguments as follows: - `-i` `--index` - File to download or lower bound of a range of files if `-r` is also set. - `-r` `--range` - Upper bound of range of files to download if `-i` is set. - `-o` `--output` - Name of custom output directory. Defaults to the current directory if not set. - `-z` `--unzip` - Unzip the file/files after downloading - `-l` `--large` - Download from Diffusion DB Large. Defaults to Diffusion DB 2M. ###### Downloading a single file The specific file to download is supplied as the number at the end of the file on HuggingFace. The script will automatically pad the number out and generate the URL. ```bash python download.py -i 23 ``` ###### Downloading a range of files The upper and lower bounds of the set of files to download are set by the `-i` and `-r` flags respectively. ```bash python download.py -i 1 -r 2000 ``` Note that this range will download the entire dataset. The script will ask you to confirm that you have 1.7Tb free at the download destination. ###### Downloading to a specific directory The script will default to the location of the dataset's `part` .zip files at `images/`. If you wish to move the download location, you should move these files as well or use a symbolic link. ```bash python download.py -i 1 -r 2000 -o /home/$USER/datahoarding/etc ``` Again, the script will automatically add the `/` between the directory and the file when it downloads. ###### Setting the files to unzip once they've been downloaded The script is set to unzip the files _after_ all files have downloaded as both can be lengthy processes in certain circumstances. ```bash python download.py -i 1 -r 2000 -z ``` #### Method 3. Use `metadata.parquet` (Text Only) If your task does not require images, then you can easily access all 2 million prompts and hyperparameters in the `metadata.parquet` table. ```python from urllib.request import urlretrieve import pandas as pd # Download the parquet table table_url = f'https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/metadata.parquet' urlretrieve(table_url, 'metadata.parquet') # Read the table using Pandas metadata_df = pd.read_parquet('metadata.parquet') ``` ## Dataset Creation ### Curation Rationale Recent diffusion models have gained immense popularity by enabling high-quality and controllable image generation based on text prompts written in natural language. Since the release of these models, people from different domains have quickly applied them to create award-winning artworks, synthetic radiology images, and even hyper-realistic videos. However, generating images with desired details is difficult, as it requires users to write proper prompts specifying the exact expected results. Developing such prompts requires trial and error, and can often feel random and unprincipled. Simon Willison analogizes writing prompts to wizards learning “magical spells”: users do not understand why some prompts work, but they will add these prompts to their “spell book.” For example, to generate highly-detailed images, it has become a common practice to add special keywords such as “trending on artstation” and “unreal engine” in the prompt. Prompt engineering has become a field of study in the context of text-to-text generation, where researchers systematically investigate how to construct prompts to effectively solve different down-stream tasks. As large text-to-image models are relatively new, there is a pressing need to understand how these models react to prompts, how to write effective prompts, and how to design tools to help users generate images. To help researchers tackle these critical challenges, we create DiffusionDB, the first large-scale prompt dataset with 14 million real prompt-image pairs. ### Source Data #### Initial Data Collection and Normalization We construct DiffusionDB by scraping user-generated images on the official Stable Diffusion Discord server. We choose Stable Diffusion because it is currently the only open-source large text-to-image generative model, and all generated images have a CC0 1.0 Universal Public Domain Dedication license that waives all copyright and allows uses for any purpose. We choose the official [Stable Diffusion Discord server](https://discord.gg/stablediffusion) because it is public, and it has strict rules against generating and sharing illegal, hateful, or NSFW (not suitable for work, such as sexual and violent content) images. The server also disallows users to write or share prompts with personal information. #### Who are the source language producers? The language producers are users of the official [Stable Diffusion Discord server](https://discord.gg/stablediffusion). ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information The authors removed the discord usernames from the dataset. We decide to anonymize the dataset because some prompts might include sensitive information: explicitly linking them to their creators can cause harm to creators. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better understanding of large text-to-image generative models. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. It should note that we collect images and their prompts from the Stable Diffusion Discord server. The Discord server has rules against users generating or sharing harmful or NSFW (not suitable for work, such as sexual and violent content) images. The Stable Diffusion model used in the server also has an NSFW filter that blurs the generated images if it detects NSFW content. However, it is still possible that some users had generated harmful images that were not detected by the NSFW filter or removed by the server moderators. Therefore, DiffusionDB can potentially contain these images. To mitigate the potential harm, we provide a [Google Form](https://forms.gle/GbYaSpRNYqxCafMZ9) on the [DiffusionDB website](https://poloclub.github.io/diffusiondb/) where users can report harmful or inappropriate images and prompts. We will closely monitor this form and remove reported images and prompts from DiffusionDB. ### Discussion of Biases The 14 million images in DiffusionDB have diverse styles and categories. However, Discord can be a biased data source. Our images come from channels where early users could use a bot to use Stable Diffusion before release. As these users had started using Stable Diffusion before the model was public, we hypothesize that they are AI art enthusiasts and are likely to have experience with other text-to-image generative models. Therefore, the prompting style in DiffusionDB might not represent novice users. Similarly, the prompts in DiffusionDB might not generalize to domains that require specific knowledge, such as medical images. ### Other Known Limitations **Generalizability.** Previous research has shown a prompt that works well on one generative model might not give the optimal result when used in other models. Therefore, different models can need users to write different prompts. For example, many Stable Diffusion prompts use commas to separate keywords, while this pattern is less seen in prompts for DALL-E 2 or Midjourney. Thus, we caution researchers that some research findings from DiffusionDB might not be generalizable to other text-to-image generative models. ## Additional Information ### Dataset Curators DiffusionDB is created by [Jay Wang](https://zijie.wang), [Evan Montoya](https://www.linkedin.com/in/evan-montoya-b252391b4/), [David Munechika](https://www.linkedin.com/in/dmunechika/), [Alex Yang](https://alexanderyang.me), [Ben Hoover](https://www.bhoov.com), [Polo Chau](https://faculty.cc.gatech.edu/~dchau/). ### Licensing Information The DiffusionDB dataset is available under the [CC0 1.0 License](https://creativecommons.org/publicdomain/zero/1.0/). The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE). ### Citation Information ```bibtex @article{wangDiffusionDBLargescalePrompt2022, title = {{{DiffusionDB}}: {{A}} Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models}, author = {Wang, Zijie J. and Montoya, Evan and Munechika, David and Yang, Haoyang and Hoover, Benjamin and Chau, Duen Horng}, year = {2022}, journal = {arXiv:2210.14896 [cs]}, url = {https://arxiv.org/abs/2210.14896} } ``` ### Contributions If you have any questions, feel free to [open an issue](https://github.com/poloclub/diffusiondb/issues/new) or contact [Jay Wang](https://zijie.wang).

sal4ahm/RealCQA

sal4ahm

--- license: mit --- # RealCQA: Real-World Complex Question Answering Dataset This repository contains the dataset used in the paper "[RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic](https://arxiv.org/pdf/2308.01979)" (ICDAR 2023). The dataset is designed to facilitate research in complex question answering, involving a diverse set of real-world images and associated textual question-answer pairs. ## Dataset Overview The RealCQA dataset consists of 28,266 images, and corresponding 2 million question-answer pairs organized into three complementary subsets. Each image is accompanied by a JSON file containing one or more question blocks. The dataset is structured to address a range of question-answering tasks that require an understanding of the visual content. ### Dataset Structure The dataset is organized into the following folders: - **Images** - `images`: Contains the first 10,000 images. - `images2`: Contains the next 10,000 images. - `images3`: Contains the remaining 8,266 images. - **JSON Files** - `jsons`: Contains the JSON files corresponding to the images in the `images` folder. - `jsons2`: Contains the JSON files corresponding to the images in the `images2` folder. - `jsons3`: Contains the JSON files corresponding to the images in the `images3` folder. - **QA Files** These are the QA created in our proposed dataset. - `qa`: Contains the QA files corresponding to the images in the `images` folder. - `qa2`: Contains the QA files corresponding to the images in the `images2` folder. - `qa3`: Contains the QA files corresponding to the images in the `images3` folder. ### File Details - **Images**: JPEG files named in the format `PMCxxxxxx_abc.jpg`, where `xxxxxx` represents the PubMed Central ID and `abc` represents an identifier specific to the image. - **JSON Files**: JSON files named in the same format as the images. These are groundtruth annotations from the https://chartinfo.github.io challenge, they provide annotations for chart type, text(OCR), text location, text type (axis/tick/legend), data used to plot the chart. - **QA Files**: QA files named in the same format as the images. Each QA file is a list of question blocks associated with the corresponding image we created in our proposed dataset. #### QA Structure Each QA file contains a list of question blocks in the following format: ```json [ { "taxonomy id": "2j", "QID": "16", "question": "Are all the bars in the chart visually horizontal?", "answer": "no", "answer_type": "Binary", "qa_id": "XbUzFtjqsEOF", "PMC_ID": "PMC8439477___g003" }, { "taxonomy id": "1a", "QID": "7a", "question": "What is the type of chart?", "answer": "Vertical Bar chart", "answer_type": "String", "qa_id": "wzcdDijkrHtt", "PMC_ID": "PMC8439477___g003" } ] ``` ### Dataset Loader To facilitate loading and using the dataset, we provide a custom dataset loader script, `dataset.py`. This script defines a PyTorch `Dataset` class to handle loading, preprocessing, and batching of the images and question-answer pairs. #### How to Use the Dataset Loader 1. **Setup and Requirements** Ensure you have the following Python packages installed: ```bash pip install torch torchvision Pillow ``` 2. **Dataset Loader Script** Use the provided `dataset.py` to load the dataset. The script is designed to load the dataset efficiently and handle both training and testing cases. ```python from dataset import RQADataset from torch.utils.data import DataLoader dataset = RQADataset(data_dir='.', split='train') # split='test' for RQA9357 split used in the paper # Test loading a single item print(f"Number of samples in dataset: {len(dataset)}") sample = dataset[0] print("Sample data:", sample) # Initialize DataLoader dataloader = DataLoader(dataset, batch_size=4, collate_fn=RQADataset.custom_collate) # Test DataLoader for batch in dataloader: print("Batch data:", batch) break # Load only one batch for testing ``` ### Citation If you use this dataset in your research, please cite the following paper: ```bibtex @InProceedings{10.1007/978-3-031-41682-8_5, author="Ahmed, Saleem and Jawade, Bhavin and Pandey, Shubham and Setlur, Srirangaraj and Govindaraju, Venu", editor="Fink, Gernot A. and Jain, Rajiv and Kise, Koichi and Zanibbi, Richard", title="RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic", booktitle="Document Analysis and Recognition - ICDAR 2023", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="66--83", abstract="We present a comprehensive study of chart visual question-answering(QA) task, to address the challenges faced in comprehending and extracting data from chart visualizations within documents. Despite efforts to tackle this problem using synthetic charts, solutions are limited by the shortage of annotated real-world data. To fill this gap, we introduce a benchmark and dataset for chart visual QA on real-world charts, offering a systematic analysis of the task and a novel taxonomy for template-based chart question creation. Our contribution includes the introduction of a new answer type, `list', with both ranked and unranked variations. Our study is conducted on a real-world chart dataset from scientific literature, showcasing higher visual complexity compared to other works. Our focus is on template-based QA and how it can serve as a standard for evaluating the first-order logic capabilities of models. The results of our experiments, conducted on a real-world out-of-distribution dataset, provide a robust evaluation of large-scale pre-trained models and advance the field of chart visual QA and formal logic verification for neural networks in general. Our code and dataset is publicly available (https://github.com/cse-ai-lab/RealCQA).", isbn="978-3-031-41682-8" } } ``` ### License This dataset is licensed under the [MIT License](LICENSE). By using this dataset, you agree to abide by its terms and conditions. ### Contact For any questions or issues, please contact the authors of the paper or open an issue in this repository.

kamilakesbi/transformers_image_doc

kamilakesbi

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 406434.0 num_examples: 2 download_size: 381914 dataset_size: 406434.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ptb-text-only/ptb_text_only

ptb-text-only

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - other license_details: LDC User Agreement for Non-Members multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Penn Treebank dataset_info: features: - name: sentence dtype: string config_name: penn_treebank splits: - name: train num_bytes: 5143706 num_examples: 42068 - name: test num_bytes: 453710 num_examples: 3761 - name: validation num_bytes: 403156 num_examples: 3370 download_size: 5951345 dataset_size: 6000572 --- # Dataset Card for Penn Treebank ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://catalog.ldc.upenn.edu/LDC99T42 - **Repository:** 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt' - **Paper:** https://www.aclweb.org/anthology/J93-2004.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. The rare words in this version are already replaced with <unk> token. The numbers are replaced with <N> token. ### Supported Tasks and Leaderboards Language Modelling ### Languages The text in the dataset is in American English ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Dataset provided for research purposes only. Please check dataset license for additional information. ### Citation Information @article{marcus-etal-1993-building, title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank", author = "Marcus, Mitchell P. and Santorini, Beatrice and Marcinkiewicz, Mary Ann", journal = "Computational Linguistics", volume = "19", number = "2", year = "1993", url = "https://www.aclweb.org/anthology/J93-2004", pages = "313--330", } ### Contributions Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.

bigcode/the-stack-v2

bigcode

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: The-Stack-v2 size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: [] extra_gated_prompt: |- ## Terms of Use for The Stack v2 The Stack v2 dataset is a collection of source code in over 600 programming languages. We ask that you read and acknowledge the following points before using the dataset: 1. Downloading the dataset in bulk requires a an agreement with SoftwareHeritage and INRIA. Contact [datasets@softwareheritage.org](mailto:datasets@softwareheritage.org?subject=TheStackV2%20request%20for%20dataset%20access%20information) for more information. 2. If you are using the dataset to train models you must adhere to the SoftwareHeritage [principles for language model training](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/). 3. The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 4. The Stack v2 is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack v2 to the most recent usable version. By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox dataset_info: features: - name: blob_id dtype: string - name: directory_id dtype: string - name: path dtype: string - name: content_id dtype: string - name: detected_licenses sequence: string - name: license_type dtype: string - name: repo_name dtype: string - name: snapshot_id dtype: string - name: revision_id dtype: string - name: branch_name dtype: string - name: visit_date dtype: timestamp[ns] - name: revision_date dtype: timestamp[ns] - name: committer_date dtype: timestamp[ns] - name: github_id dtype: int64 - name: star_events_count dtype: int64 - name: fork_events_count dtype: int64 - name: gha_license_id dtype: string - name: gha_event_created_at dtype: timestamp[ns] - name: gha_created_at dtype: timestamp[ns] - name: gha_language dtype: string - name: src_encoding dtype: string - name: language dtype: string - name: is_vendor dtype: bool - name: is_generated dtype: bool - name: length_bytes dtype: int64 - name: extension dtype: string configs: - config_name: default default: true data_files: - split: train path: "data/*/*.parquet" - config_name: "1C_Enterprise" data_files: - split: train path: "data/1C_Enterprise/*.parquet" - config_name: "2-Dimensional_Array" data_files: - split: train path: "data/2-Dimensional_Array/*.parquet" - config_name: "4D" data_files: - split: train path: "data/4D/*.parquet" - config_name: "ABAP" data_files: - split: train path: "data/ABAP/*.parquet" - config_name: "ABAP_CDS" data_files: - split: train path: "data/ABAP_CDS/*.parquet" - config_name: "ABNF" data_files: - split: train path: "data/ABNF/*.parquet" - config_name: "AGS_Script" data_files: - split: train path: "data/AGS_Script/*.parquet" - config_name: "AIDL" data_files: - split: train path: "data/AIDL/*.parquet" - config_name: "AL" data_files: - split: train path: "data/AL/*.parquet" - config_name: "AMPL" data_files: - split: train path: "data/AMPL/*.parquet" - config_name: "ANTLR" data_files: - split: train path: "data/ANTLR/*.parquet" - config_name: "API_Blueprint" data_files: - split: train path: "data/API_Blueprint/*.parquet" - config_name: "APL" data_files: - split: train path: "data/APL/*.parquet" - config_name: "ASL" data_files: - split: train path: "data/ASL/*.parquet" - config_name: "ASN.1" data_files: - split: train path: "data/ASN.1/*.parquet" - config_name: "ASP.NET" data_files: - split: train path: "data/ASP.NET/*.parquet" - config_name: "ATS" data_files: - split: train path: "data/ATS/*.parquet" - config_name: "ActionScript" data_files: - split: train path: "data/ActionScript/*.parquet" - config_name: "Ada" data_files: - split: train path: "data/Ada/*.parquet" - config_name: "Adobe_Font_Metrics" data_files: - split: train path: "data/Adobe_Font_Metrics/*.parquet" - config_name: "Agda" data_files: - split: train path: "data/Agda/*.parquet" - config_name: "Alloy" data_files: - split: train path: "data/Alloy/*.parquet" - config_name: "Alpine_Abuild" data_files: - split: train path: "data/Alpine_Abuild/*.parquet" - config_name: "Altium_Designer" data_files: - split: train path: "data/Altium_Designer/*.parquet" - config_name: "AngelScript" data_files: - split: train path: "data/AngelScript/*.parquet" - config_name: "Ant_Build_System" data_files: - split: train path: "data/Ant_Build_System/*.parquet" - config_name: "Antlers" data_files: - split: train path: "data/Antlers/*.parquet" - config_name: "ApacheConf" data_files: - split: train path: "data/ApacheConf/*.parquet" - config_name: "Apex" data_files: - split: train path: "data/Apex/*.parquet" - config_name: "Apollo_Guidance_Computer" data_files: - split: train path: "data/Apollo_Guidance_Computer/*.parquet" - config_name: "AppleScript" data_files: - split: train path: "data/AppleScript/*.parquet" - config_name: "Arc" data_files: - split: train path: "data/Arc/*.parquet" - config_name: "AsciiDoc" data_files: - split: train path: "data/AsciiDoc/*.parquet" - config_name: "AspectJ" data_files: - split: train path: "data/AspectJ/*.parquet" - config_name: "Assembly" data_files: - split: train path: "data/Assembly/*.parquet" - config_name: "Astro" data_files: - split: train path: "data/Astro/*.parquet" - config_name: "Asymptote" data_files: - split: train path: "data/Asymptote/*.parquet" - config_name: "Augeas" data_files: - split: train path: "data/Augeas/*.parquet" - config_name: "AutoHotkey" data_files: - split: train path: "data/AutoHotkey/*.parquet" - config_name: "AutoIt" data_files: - split: train path: "data/AutoIt/*.parquet" - config_name: "Avro_IDL" data_files: - split: train path: "data/Avro_IDL/*.parquet" - config_name: "Awk" data_files: - split: train path: "data/Awk/*.parquet" - config_name: "BASIC" data_files: - split: train path: "data/BASIC/*.parquet" - config_name: "Ballerina" data_files: - split: train path: "data/Ballerina/*.parquet" - config_name: "Batchfile" data_files: - split: train path: "data/Batchfile/*.parquet" - config_name: "Beef" data_files: - split: train path: "data/Beef/*.parquet" - config_name: "Befunge" data_files: - split: train path: "data/Befunge/*.parquet" - config_name: "Berry" data_files: - split: train path: "data/Berry/*.parquet" - config_name: "BibTeX" data_files: - split: train path: "data/BibTeX/*.parquet" - config_name: "Bicep" data_files: - split: train path: "data/Bicep/*.parquet" - config_name: "Bikeshed" data_files: - split: train path: "data/Bikeshed/*.parquet" - config_name: "Bison" data_files: - split: train path: "data/Bison/*.parquet" - config_name: "BitBake" data_files: - split: train path: "data/BitBake/*.parquet" - config_name: "Blade" data_files: - split: train path: "data/Blade/*.parquet" - config_name: "BlitzBasic" data_files: - split: train path: "data/BlitzBasic/*.parquet" - config_name: "BlitzMax" data_files: - split: train path: "data/BlitzMax/*.parquet" - config_name: "Bluespec" data_files: - split: train path: "data/Bluespec/*.parquet" - config_name: "Boo" data_files: - split: train path: "data/Boo/*.parquet" - config_name: "Boogie" data_files: - split: train path: "data/Boogie/*.parquet" - config_name: "Brainfuck" data_files: - split: train path: "data/Brainfuck/*.parquet" - config_name: "BrighterScript" data_files: - split: train path: "data/BrighterScript/*.parquet" - config_name: "Brightscript" data_files: - split: train path: "data/Brightscript/*.parquet" - config_name: "Browserslist" data_files: - split: train path: "data/Browserslist/*.parquet" - config_name: "C" data_files: - split: train path: "data/C/*.parquet" - config_name: "C++" data_files: - split: train path: "data/C++/*.parquet" - config_name: "C-ObjDump" data_files: - split: train path: "data/C-ObjDump/*.parquet" - config_name: "C-Sharp" data_files: - split: train path: "data/C-Sharp/*.parquet" - config_name: "C2hs_Haskell" data_files: - split: train path: "data/C2hs_Haskell/*.parquet" - config_name: "CAP_CDS" data_files: - split: train path: "data/CAP_CDS/*.parquet" - config_name: "CIL" data_files: - split: train path: "data/CIL/*.parquet" - config_name: "CLIPS" data_files: - split: train path: "data/CLIPS/*.parquet" - config_name: "CMake" data_files: - split: train path: "data/CMake/*.parquet" - config_name: "COBOL" data_files: - split: train path: "data/COBOL/*.parquet" - config_name: "CODEOWNERS" data_files: - split: train path: "data/CODEOWNERS/*.parquet" - config_name: "COLLADA" data_files: - split: train path: "data/COLLADA/*.parquet" - config_name: "CSON" data_files: - split: train path: "data/CSON/*.parquet" - config_name: "CSS" data_files: - split: train path: "data/CSS/*.parquet" - config_name: "CSV" data_files: - split: train path: "data/CSV/*.parquet" - config_name: "CUE" data_files: - split: train path: "data/CUE/*.parquet" - config_name: "CWeb" data_files: - split: train path: "data/CWeb/*.parquet" - config_name: "Cabal_Config" data_files: - split: train path: "data/Cabal_Config/*.parquet" - config_name: "Cadence" data_files: - split: train path: "data/Cadence/*.parquet" - config_name: "Cairo" data_files: - split: train path: "data/Cairo/*.parquet" - config_name: "CameLIGO" data_files: - split: train path: "data/CameLIGO/*.parquet" - config_name: "Cap-n_Proto" data_files: - split: train path: "data/Cap-n_Proto/*.parquet" - config_name: "CartoCSS" data_files: - split: train path: "data/CartoCSS/*.parquet" - config_name: "Ceylon" data_files: - split: train path: "data/Ceylon/*.parquet" - config_name: "Chapel" data_files: - split: train path: "data/Chapel/*.parquet" - config_name: "Charity" data_files: - split: train path: "data/Charity/*.parquet" - config_name: "Checksums" data_files: - split: train path: "data/Checksums/*.parquet" - config_name: "ChucK" data_files: - split: train path: "data/ChucK/*.parquet" - config_name: "Cirru" data_files: - split: train path: "data/Cirru/*.parquet" - config_name: "Clarion" data_files: - split: train path: "data/Clarion/*.parquet" - config_name: "Clarity" data_files: - split: train path: "data/Clarity/*.parquet" - config_name: "Classic_ASP" data_files: - split: train path: "data/Classic_ASP/*.parquet" - config_name: "Clean" data_files: - split: train path: "data/Clean/*.parquet" - config_name: "Click" data_files: - split: train path: "data/Click/*.parquet" - config_name: "Clojure" data_files: - split: train path: "data/Clojure/*.parquet" - config_name: "Closure_Templates" data_files: - split: train path: "data/Closure_Templates/*.parquet" - config_name: "Cloud_Firestore_Security_Rules" data_files: - split: train path: "data/Cloud_Firestore_Security_Rules/*.parquet" - config_name: "CoNLL-U" data_files: - split: train path: "data/CoNLL-U/*.parquet" - config_name: "CodeQL" data_files: - split: train path: "data/CodeQL/*.parquet" - config_name: "CoffeeScript" data_files: - split: train path: "data/CoffeeScript/*.parquet" - config_name: "ColdFusion" data_files: - split: train path: "data/ColdFusion/*.parquet" - config_name: "ColdFusion_CFC" data_files: - split: train path: "data/ColdFusion_CFC/*.parquet" - config_name: "Common_Lisp" data_files: - split: train path: "data/Common_Lisp/*.parquet" - config_name: "Common_Workflow_Language" data_files: - split: train path: "data/Common_Workflow_Language/*.parquet" - config_name: "Component_Pascal" data_files: - split: train path: "data/Component_Pascal/*.parquet" - config_name: "Cool" data_files: - split: train path: "data/Cool/*.parquet" - config_name: "Coq" data_files: - split: train path: "data/Coq/*.parquet" - config_name: "Creole" data_files: - split: train path: "data/Creole/*.parquet" - config_name: "Crystal" data_files: - split: train path: "data/Crystal/*.parquet" - config_name: "Csound" data_files: - split: train path: "data/Csound/*.parquet" - config_name: "Csound_Document" data_files: - split: train path: "data/Csound_Document/*.parquet" - config_name: "Csound_Score" data_files: - split: train path: "data/Csound_Score/*.parquet" - config_name: "Cuda" data_files: - split: train path: "data/Cuda/*.parquet" - config_name: "Cue_Sheet" data_files: - split: train path: "data/Cue_Sheet/*.parquet" - config_name: "Curry" data_files: - split: train path: "data/Curry/*.parquet" - config_name: "Cycript" data_files: - split: train path: "data/Cycript/*.parquet" - config_name: "Cython" data_files: - split: train path: "data/Cython/*.parquet" - config_name: "D" data_files: - split: train path: "data/D/*.parquet" - config_name: "DIGITAL_Command_Language" data_files: - split: train path: "data/DIGITAL_Command_Language/*.parquet" - config_name: "DM" data_files: - split: train path: "data/DM/*.parquet" - config_name: "DNS_Zone" data_files: - split: train path: "data/DNS_Zone/*.parquet" - config_name: "DTrace" data_files: - split: train path: "data/DTrace/*.parquet" - config_name: "Dafny" data_files: - split: train path: "data/Dafny/*.parquet" - config_name: "Darcs_Patch" data_files: - split: train path: "data/Darcs_Patch/*.parquet" - config_name: "Dart" data_files: - split: train path: "data/Dart/*.parquet" - config_name: "DataWeave" data_files: - split: train path: "data/DataWeave/*.parquet" - config_name: "Debian_Package_Control_File" data_files: - split: train path: "data/Debian_Package_Control_File/*.parquet" - config_name: "DenizenScript" data_files: - split: train path: "data/DenizenScript/*.parquet" - config_name: "Dhall" data_files: - split: train path: "data/Dhall/*.parquet" - config_name: "Diff" data_files: - split: train path: "data/Diff/*.parquet" - config_name: "DirectX_3D_File" data_files: - split: train path: "data/DirectX_3D_File/*.parquet" - config_name: "Dockerfile" data_files: - split: train path: "data/Dockerfile/*.parquet" - config_name: "Dogescript" data_files: - split: train path: "data/Dogescript/*.parquet" - config_name: "Dylan" data_files: - split: train path: "data/Dylan/*.parquet" - config_name: "E" data_files: - split: train path: "data/E/*.parquet" - config_name: "E-mail" data_files: - split: train path: "data/E-mail/*.parquet" - config_name: "EBNF" data_files: - split: train path: "data/EBNF/*.parquet" - config_name: "ECL" data_files: - split: train path: "data/ECL/*.parquet" - config_name: "ECLiPSe" data_files: - split: train path: "data/ECLiPSe/*.parquet" - config_name: "EJS" data_files: - split: train path: "data/EJS/*.parquet" - config_name: "EQ" data_files: - split: train path: "data/EQ/*.parquet" - config_name: "Eagle" data_files: - split: train path: "data/Eagle/*.parquet" - config_name: "Earthly" data_files: - split: train path: "data/Earthly/*.parquet" - config_name: "Easybuild" data_files: - split: train path: "data/Easybuild/*.parquet" - config_name: "Ecere_Projects" data_files: - split: train path: "data/Ecere_Projects/*.parquet" - config_name: "EditorConfig" data_files: - split: train path: "data/EditorConfig/*.parquet" - config_name: "Edje_Data_Collection" data_files: - split: train path: "data/Edje_Data_Collection/*.parquet" - config_name: "Eiffel" data_files: - split: train path: "data/Eiffel/*.parquet" - config_name: "Elixir" data_files: - split: train path: "data/Elixir/*.parquet" - config_name: "Elm" data_files: - split: train path: "data/Elm/*.parquet" - config_name: "Elvish" data_files: - split: train path: "data/Elvish/*.parquet" - config_name: "Emacs_Lisp" data_files: - split: train path: "data/Emacs_Lisp/*.parquet" - config_name: "EmberScript" data_files: - split: train path: "data/EmberScript/*.parquet" - config_name: "Erlang" data_files: - split: train path: "data/Erlang/*.parquet" - config_name: "Euphoria" data_files: - split: train path: "data/Euphoria/*.parquet" - config_name: "F-Sharp" data_files: - split: train path: "data/F-Sharp/*.parquet" - config_name: "F-Star" data_files: - split: train path: "data/F-Star/*.parquet" - config_name: "FIGlet_Font" data_files: - split: train path: "data/FIGlet_Font/*.parquet" - config_name: "FLUX" data_files: - split: train path: "data/FLUX/*.parquet" - config_name: "Factor" data_files: - split: train path: "data/Factor/*.parquet" - config_name: "Fancy" data_files: - split: train path: "data/Fancy/*.parquet" - config_name: "Fantom" data_files: - split: train path: "data/Fantom/*.parquet" - config_name: "Faust" data_files: - split: train path: "data/Faust/*.parquet" - config_name: "Fennel" data_files: - split: train path: "data/Fennel/*.parquet" - config_name: "Filebench_WML" data_files: - split: train path: "data/Filebench_WML/*.parquet" - config_name: "Filterscript" data_files: - split: train path: "data/Filterscript/*.parquet" - config_name: "Fluent" data_files: - split: train path: "data/Fluent/*.parquet" - config_name: "Formatted" data_files: - split: train path: "data/Formatted/*.parquet" - config_name: "Forth" data_files: - split: train path: "data/Forth/*.parquet" - config_name: "Fortran" data_files: - split: train path: "data/Fortran/*.parquet" - config_name: "Fortran_Free_Form" data_files: - split: train path: "data/Fortran_Free_Form/*.parquet" - config_name: "FreeBasic" data_files: - split: train path: "data/FreeBasic/*.parquet" - config_name: "FreeMarker" data_files: - split: train path: "data/FreeMarker/*.parquet" - config_name: "Frege" data_files: - split: train path: "data/Frege/*.parquet" - config_name: "Futhark" data_files: - split: train path: "data/Futhark/*.parquet" - config_name: "G-code" data_files: - split: train path: "data/G-code/*.parquet" - config_name: "GAML" data_files: - split: train path: "data/GAML/*.parquet" - config_name: "GAMS" data_files: - split: train path: "data/GAMS/*.parquet" - config_name: "GAP" data_files: - split: train path: "data/GAP/*.parquet" - config_name: "GCC_Machine_Description" data_files: - split: train path: "data/GCC_Machine_Description/*.parquet" - config_name: "GDB" data_files: - split: train path: "data/GDB/*.parquet" - config_name: "GDScript" data_files: - split: train path: "data/GDScript/*.parquet" - config_name: "GEDCOM" data_files: - split: train path: "data/GEDCOM/*.parquet" - config_name: "GLSL" data_files: - split: train path: "data/GLSL/*.parquet" - config_name: "GN" data_files: - split: train path: "data/GN/*.parquet" - config_name: "GSC" data_files: - split: train path: "data/GSC/*.parquet" - config_name: "Game_Maker_Language" data_files: - split: train path: "data/Game_Maker_Language/*.parquet" - config_name: "Gemfile.lock" data_files: - split: train path: "data/Gemfile.lock/*.parquet" - config_name: "Gemini" data_files: - split: train path: "data/Gemini/*.parquet" - config_name: "Genero" data_files: - split: train path: "data/Genero/*.parquet" - config_name: "Genero_Forms" data_files: - split: train path: "data/Genero_Forms/*.parquet" - config_name: "Genie" data_files: - split: train path: "data/Genie/*.parquet" - config_name: "Genshi" data_files: - split: train path: "data/Genshi/*.parquet" - config_name: "Gentoo_Ebuild" data_files: - split: train path: "data/Gentoo_Ebuild/*.parquet" - config_name: "Gentoo_Eclass" data_files: - split: train path: "data/Gentoo_Eclass/*.parquet" - config_name: "Gerber_Image" data_files: - split: train path: "data/Gerber_Image/*.parquet" - config_name: "Gettext_Catalog" data_files: - split: train path: "data/Gettext_Catalog/*.parquet" - config_name: "Gherkin" data_files: - split: train path: "data/Gherkin/*.parquet" - config_name: "Git_Attributes" data_files: - split: train path: "data/Git_Attributes/*.parquet" - config_name: "Git_Config" data_files: - split: train path: "data/Git_Config/*.parquet" - config_name: "Git_Revision_List" data_files: - split: train path: "data/Git_Revision_List/*.parquet" - config_name: "Gleam" data_files: - split: train path: "data/Gleam/*.parquet" - config_name: "Glyph" data_files: - split: train path: "data/Glyph/*.parquet" - config_name: "Glyph_Bitmap_Distribution_Format" data_files: - split: train path: "data/Glyph_Bitmap_Distribution_Format/*.parquet" - config_name: "Gnuplot" data_files: - split: train path: "data/Gnuplot/*.parquet" - config_name: "Go" data_files: - split: train path: "data/Go/*.parquet" - config_name: "Go_Checksums" data_files: - split: train path: "data/Go_Checksums/*.parquet" - config_name: "Go_Module" data_files: - split: train path: "data/Go_Module/*.parquet" - config_name: "Golo" data_files: - split: train path: "data/Golo/*.parquet" - config_name: "Gosu" data_files: - split: train path: "data/Gosu/*.parquet" - config_name: "Grace" data_files: - split: train path: "data/Grace/*.parquet" - config_name: "Gradle" data_files: - split: train path: "data/Gradle/*.parquet" - config_name: "Grammatical_Framework" data_files: - split: train path: "data/Grammatical_Framework/*.parquet" - config_name: "GraphQL" data_files: - split: train path: "data/GraphQL/*.parquet" - config_name: "Graph_Modeling_Language" data_files: - split: train path: "data/Graph_Modeling_Language/*.parquet" - config_name: "Graphviz_(DOT)" data_files: - split: train path: "data/Graphviz_(DOT)/*.parquet" - config_name: "Groovy" data_files: - split: train path: "data/Groovy/*.parquet" - config_name: "Groovy_Server_Pages" data_files: - split: train path: "data/Groovy_Server_Pages/*.parquet" - config_name: "HAProxy" data_files: - split: train path: "data/HAProxy/*.parquet" - config_name: "HCL" data_files: - split: train path: "data/HCL/*.parquet" - config_name: "HLSL" data_files: - split: train path: "data/HLSL/*.parquet" - config_name: "HOCON" data_files: - split: train path: "data/HOCON/*.parquet" - config_name: "HTML" data_files: - split: train path: "data/HTML/*.parquet" - config_name: "HTML+ECR" data_files: - split: train path: "data/HTML+ECR/*.parquet" - config_name: "HTML+EEX" data_files: - split: train path: "data/HTML+EEX/*.parquet" - config_name: "HTML+ERB" data_files: - split: train path: "data/HTML+ERB/*.parquet" - config_name: "HTML+PHP" data_files: - split: train path: "data/HTML+PHP/*.parquet" - config_name: "HTML+Razor" data_files: - split: train path: "data/HTML+Razor/*.parquet" - config_name: "HTTP" data_files: - split: train path: "data/HTTP/*.parquet" - config_name: "HXML" data_files: - split: train path: "data/HXML/*.parquet" - config_name: "Hack" data_files: - split: train path: "data/Hack/*.parquet" - config_name: "Haml" data_files: - split: train path: "data/Haml/*.parquet" - config_name: "Handlebars" data_files: - split: train path: "data/Handlebars/*.parquet" - config_name: "Harbour" data_files: - split: train path: "data/Harbour/*.parquet" - config_name: "Haskell" data_files: - split: train path: "data/Haskell/*.parquet" - config_name: "Haxe" data_files: - split: train path: "data/Haxe/*.parquet" - config_name: "HiveQL" data_files: - split: train path: "data/HiveQL/*.parquet" - config_name: "HolyC" data_files: - split: train path: "data/HolyC/*.parquet" - config_name: "Hy" data_files: - split: train path: "data/Hy/*.parquet" - config_name: "HyPhy" data_files: - split: train path: "data/HyPhy/*.parquet" - config_name: "IDL" data_files: - split: train path: "data/IDL/*.parquet" - config_name: "IGOR_Pro" data_files: - split: train path: "data/IGOR_Pro/*.parquet" - config_name: "INI" data_files: - split: train path: "data/INI/*.parquet" - config_name: "IRC_log" data_files: - split: train path: "data/IRC_log/*.parquet" - config_name: "Idris" data_files: - split: train path: "data/Idris/*.parquet" - config_name: "Ignore_List" data_files: - split: train path: "data/Ignore_List/*.parquet" - config_name: "ImageJ_Macro" data_files: - split: train path: "data/ImageJ_Macro/*.parquet" - config_name: "Inform_7" data_files: - split: train path: "data/Inform_7/*.parquet" - config_name: "Inno_Setup" data_files: - split: train path: "data/Inno_Setup/*.parquet" - config_name: "Io" data_files: - split: train path: "data/Io/*.parquet" - config_name: "Ioke" data_files: - split: train path: "data/Ioke/*.parquet" - config_name: "Isabelle" data_files: - split: train path: "data/Isabelle/*.parquet" - config_name: "Isabelle_ROOT" data_files: - split: train path: "data/Isabelle_ROOT/*.parquet" - config_name: "J" data_files: - split: train path: "data/J/*.parquet" - config_name: "JAR_Manifest" data_files: - split: train path: "data/JAR_Manifest/*.parquet" - config_name: "JFlex" data_files: - split: train path: "data/JFlex/*.parquet" - config_name: "JSON" data_files: - split: train path: "data/JSON/*.parquet" - config_name: "JSON5" data_files: - split: train path: "data/JSON5/*.parquet" - config_name: "JSONLD" data_files: - split: train path: "data/JSONLD/*.parquet" - config_name: "JSON_with_Comments" data_files: - split: train path: "data/JSON_with_Comments/*.parquet" - config_name: "JSONiq" data_files: - split: train path: "data/JSONiq/*.parquet" - config_name: "Janet" data_files: - split: train path: "data/Janet/*.parquet" - config_name: "Jasmin" data_files: - split: train path: "data/Jasmin/*.parquet" - config_name: "Java" data_files: - split: train path: "data/Java/*.parquet" - config_name: "JavaScript" data_files: - split: train path: "data/JavaScript/*.parquet" - config_name: "JavaScript+ERB" data_files: - split: train path: "data/JavaScript+ERB/*.parquet" - config_name: "Java_Properties" data_files: - split: train path: "data/Java_Properties/*.parquet" - config_name: "Java_Server_Pages" data_files: - split: train path: "data/Java_Server_Pages/*.parquet" - config_name: "Jest_Snapshot" data_files: - split: train path: "data/Jest_Snapshot/*.parquet" - config_name: "JetBrains_MPS" data_files: - split: train path: "data/JetBrains_MPS/*.parquet" - config_name: "Jinja" data_files: - split: train path: "data/Jinja/*.parquet" - config_name: "Jison" data_files: - split: train path: "data/Jison/*.parquet" - config_name: "Jison_Lex" data_files: - split: train path: "data/Jison_Lex/*.parquet" - config_name: "Jolie" data_files: - split: train path: "data/Jolie/*.parquet" - config_name: "Jsonnet" data_files: - split: train path: "data/Jsonnet/*.parquet" - config_name: "Julia" data_files: - split: train path: "data/Julia/*.parquet" - config_name: "Jupyter_Notebook" data_files: - split: train path: "data/Jupyter_Notebook/*.parquet" - config_name: "KRL" data_files: - split: train path: "data/KRL/*.parquet" - config_name: "Kaitai_Struct" data_files: - split: train path: "data/Kaitai_Struct/*.parquet" - config_name: "KakouneScript" data_files: - split: train path: "data/KakouneScript/*.parquet" - config_name: "KiCad_Layout" data_files: - split: train path: "data/KiCad_Layout/*.parquet" - config_name: "KiCad_Legacy_Layout" data_files: - split: train path: "data/KiCad_Legacy_Layout/*.parquet" - config_name: "KiCad_Schematic" data_files: - split: train path: "data/KiCad_Schematic/*.parquet" - config_name: "Kit" data_files: - split: train path: "data/Kit/*.parquet" - config_name: "Kotlin" data_files: - split: train path: "data/Kotlin/*.parquet" - config_name: "Kusto" data_files: - split: train path: "data/Kusto/*.parquet" - config_name: "LFE" data_files: - split: train path: "data/LFE/*.parquet" - config_name: "LLVM" data_files: - split: train path: "data/LLVM/*.parquet" - config_name: "LOLCODE" data_files: - split: train path: "data/LOLCODE/*.parquet" - config_name: "LSL" data_files: - split: train path: "data/LSL/*.parquet" - config_name: "LTspice_Symbol" data_files: - split: train path: "data/LTspice_Symbol/*.parquet" - config_name: "LabVIEW" data_files: - split: train path: "data/LabVIEW/*.parquet" - config_name: "Lark" data_files: - split: train path: "data/Lark/*.parquet" - config_name: "Lasso" data_files: - split: train path: "data/Lasso/*.parquet" - config_name: "Latte" data_files: - split: train path: "data/Latte/*.parquet" - config_name: "Lean" data_files: - split: train path: "data/Lean/*.parquet" - config_name: "Less" data_files: - split: train path: "data/Less/*.parquet" - config_name: "Lex" data_files: - split: train path: "data/Lex/*.parquet" - config_name: "LigoLANG" data_files: - split: train path: "data/LigoLANG/*.parquet" - config_name: "LilyPond" data_files: - split: train path: "data/LilyPond/*.parquet" - config_name: "Limbo" data_files: - split: train path: "data/Limbo/*.parquet" - config_name: "Linker_Script" data_files: - split: train path: "data/Linker_Script/*.parquet" - config_name: "Linux_Kernel_Module" data_files: - split: train path: "data/Linux_Kernel_Module/*.parquet" - config_name: "Liquid" data_files: - split: train path: "data/Liquid/*.parquet" - config_name: "Literate_Agda" data_files: - split: train path: "data/Literate_Agda/*.parquet" - config_name: "Literate_CoffeeScript" data_files: - split: train path: "data/Literate_CoffeeScript/*.parquet" - config_name: "Literate_Haskell" data_files: - split: train path: "data/Literate_Haskell/*.parquet" - config_name: "LiveScript" data_files: - split: train path: "data/LiveScript/*.parquet" - config_name: "Logos" data_files: - split: train path: "data/Logos/*.parquet" - config_name: "Logtalk" data_files: - split: train path: "data/Logtalk/*.parquet" - config_name: "LookML" data_files: - split: train path: "data/LookML/*.parquet" - config_name: "LoomScript" data_files: - split: train path: "data/LoomScript/*.parquet" - config_name: "Lua" data_files: - split: train path: "data/Lua/*.parquet" - config_name: "M" data_files: - split: train path: "data/M/*.parquet" - config_name: "M4" data_files: - split: train path: "data/M4/*.parquet" - config_name: "M4Sugar" data_files: - split: train path: "data/M4Sugar/*.parquet" - config_name: "MATLAB" data_files: - split: train path: "data/MATLAB/*.parquet" - config_name: "MAXScript" data_files: - split: train path: "data/MAXScript/*.parquet" - config_name: "MLIR" data_files: - split: train path: "data/MLIR/*.parquet" - config_name: "MQL4" data_files: - split: train path: "data/MQL4/*.parquet" - config_name: "MQL5" data_files: - split: train path: "data/MQL5/*.parquet" - config_name: "MTML" data_files: - split: train path: "data/MTML/*.parquet" - config_name: "MUF" data_files: - split: train path: "data/MUF/*.parquet" - config_name: "Macaulay2" data_files: - split: train path: "data/Macaulay2/*.parquet" - config_name: "Makefile" data_files: - split: train path: "data/Makefile/*.parquet" - config_name: "Mako" data_files: - split: train path: "data/Mako/*.parquet" - config_name: "Markdown" data_files: - split: train path: "data/Markdown/*.parquet" - config_name: "Marko" data_files: - split: train path: "data/Marko/*.parquet" - config_name: "Mask" data_files: - split: train path: "data/Mask/*.parquet" - config_name: "Mathematica" data_files: - split: train path: "data/Mathematica/*.parquet" - config_name: "Maven_POM" data_files: - split: train path: "data/Maven_POM/*.parquet" - config_name: "Max" data_files: - split: train path: "data/Max/*.parquet" - config_name: "Mercury" data_files: - split: train path: "data/Mercury/*.parquet" - config_name: "Meson" data_files: - split: train path: "data/Meson/*.parquet" - config_name: "Metal" data_files: - split: train path: "data/Metal/*.parquet" - config_name: "Microsoft_Developer_Studio_Project" data_files: - split: train path: "data/Microsoft_Developer_Studio_Project/*.parquet" - config_name: "Microsoft_Visual_Studio_Solution" data_files: - split: train path: "data/Microsoft_Visual_Studio_Solution/*.parquet" - config_name: "MiniD" data_files: - split: train path: "data/MiniD/*.parquet" - config_name: "MiniYAML" data_files: - split: train path: "data/MiniYAML/*.parquet" - config_name: "Mint" data_files: - split: train path: "data/Mint/*.parquet" - config_name: "Mirah" data_files: - split: train path: "data/Mirah/*.parquet" - config_name: "Modelica" data_files: - split: train path: "data/Modelica/*.parquet" - config_name: "Modula-2" data_files: - split: train path: "data/Modula-2/*.parquet" - config_name: "Modula-3" data_files: - split: train path: "data/Modula-3/*.parquet" - config_name: "Module_Management_System" data_files: - split: train path: "data/Module_Management_System/*.parquet" - config_name: "Monkey" data_files: - split: train path: "data/Monkey/*.parquet" - config_name: "Monkey_C" data_files: - split: train path: "data/Monkey_C/*.parquet" - config_name: "Moocode" data_files: - split: train path: "data/Moocode/*.parquet" - config_name: "MoonScript" data_files: - split: train path: "data/MoonScript/*.parquet" - config_name: "Motoko" data_files: - split: train path: "data/Motoko/*.parquet" - config_name: "Motorola_68K_Assembly" data_files: - split: train path: "data/Motorola_68K_Assembly/*.parquet" - config_name: "Move" data_files: - split: train path: "data/Move/*.parquet" - config_name: "Muse" data_files: - split: train path: "data/Muse/*.parquet" - config_name: "Mustache" data_files: - split: train path: "data/Mustache/*.parquet" - config_name: "Myghty" data_files: - split: train path: "data/Myghty/*.parquet" - config_name: "NASL" data_files: - split: train path: "data/NASL/*.parquet" - config_name: "NCL" data_files: - split: train path: "data/NCL/*.parquet" - config_name: "NEON" data_files: - split: train path: "data/NEON/*.parquet" - config_name: "NL" data_files: - split: train path: "data/NL/*.parquet" - config_name: "NPM_Config" data_files: - split: train path: "data/NPM_Config/*.parquet" - config_name: "NSIS" data_files: - split: train path: "data/NSIS/*.parquet" - config_name: "NWScript" data_files: - split: train path: "data/NWScript/*.parquet" - config_name: "Nasal" data_files: - split: train path: "data/Nasal/*.parquet" - config_name: "Nearley" data_files: - split: train path: "data/Nearley/*.parquet" - config_name: "Nemerle" data_files: - split: train path: "data/Nemerle/*.parquet" - config_name: "NetLinx" data_files: - split: train path: "data/NetLinx/*.parquet" - config_name: "NetLinx+ERB" data_files: - split: train path: "data/NetLinx+ERB/*.parquet" - config_name: "NetLogo" data_files: - split: train path: "data/NetLogo/*.parquet" - config_name: "NewLisp" data_files: - split: train path: "data/NewLisp/*.parquet" - config_name: "Nextflow" data_files: - split: train path: "data/Nextflow/*.parquet" - config_name: "Nginx" data_files: - split: train path: "data/Nginx/*.parquet" - config_name: "Nim" data_files: - split: train path: "data/Nim/*.parquet" - config_name: "Ninja" data_files: - split: train path: "data/Ninja/*.parquet" - config_name: "Nit" data_files: - split: train path: "data/Nit/*.parquet" - config_name: "Nix" data_files: - split: train path: "data/Nix/*.parquet" - config_name: "Nu" data_files: - split: train path: "data/Nu/*.parquet" - config_name: "NumPy" data_files: - split: train path: "data/NumPy/*.parquet" - config_name: "Nunjucks" data_files: - split: train path: "data/Nunjucks/*.parquet" - config_name: "OCaml" data_files: - split: train path: "data/OCaml/*.parquet" - config_name: "ObjDump" data_files: - split: train path: "data/ObjDump/*.parquet" - config_name: "ObjectScript" data_files: - split: train path: "data/ObjectScript/*.parquet" - config_name: "Object_Data_Instance_Notation" data_files: - split: train path: "data/Object_Data_Instance_Notation/*.parquet" - config_name: "Objective-C" data_files: - split: train path: "data/Objective-C/*.parquet" - config_name: "Objective-C++" data_files: - split: train path: "data/Objective-C++/*.parquet" - config_name: "Objective-J" data_files: - split: train path: "data/Objective-J/*.parquet" - config_name: "Odin" data_files: - split: train path: "data/Odin/*.parquet" - config_name: "Omgrofl" data_files: - split: train path: "data/Omgrofl/*.parquet" - config_name: "Opa" data_files: - split: train path: "data/Opa/*.parquet" - config_name: "Opal" data_files: - split: train path: "data/Opal/*.parquet" - config_name: "OpenCL" data_files: - split: train path: "data/OpenCL/*.parquet" - config_name: "OpenEdge_ABL" data_files: - split: train path: "data/OpenEdge_ABL/*.parquet" - config_name: "OpenQASM" data_files: - split: train path: "data/OpenQASM/*.parquet" - config_name: "OpenRC_runscript" data_files: - split: train path: "data/OpenRC_runscript/*.parquet" - config_name: "OpenSCAD" data_files: - split: train path: "data/OpenSCAD/*.parquet" - config_name: "OpenStep_Property_List" data_files: - split: train path: "data/OpenStep_Property_List/*.parquet" - config_name: "OpenType_Feature_File" data_files: - split: train path: "data/OpenType_Feature_File/*.parquet" - config_name: "Open_Policy_Agent" data_files: - split: train path: "data/Open_Policy_Agent/*.parquet" - config_name: "Org" data_files: - split: train path: "data/Org/*.parquet" - config_name: "Ox" data_files: - split: train path: "data/Ox/*.parquet" - config_name: "Oxygene" data_files: - split: train path: "data/Oxygene/*.parquet" - config_name: "Oz" data_files: - split: train path: "data/Oz/*.parquet" - config_name: "P4" data_files: - split: train path: "data/P4/*.parquet" - config_name: "PEG.js" data_files: - split: train path: "data/PEG.js/*.parquet" - config_name: "PHP" data_files: - split: train path: "data/PHP/*.parquet" - config_name: "PLSQL" data_files: - split: train path: "data/PLSQL/*.parquet" - config_name: "PLpgSQL" data_files: - split: train path: "data/PLpgSQL/*.parquet" - config_name: "POV-Ray_SDL" data_files: - split: train path: "data/POV-Ray_SDL/*.parquet" - config_name: "Pan" data_files: - split: train path: "data/Pan/*.parquet" - config_name: "Papyrus" data_files: - split: train path: "data/Papyrus/*.parquet" - config_name: "Parrot" data_files: - split: train path: "data/Parrot/*.parquet" - config_name: "Parrot_Assembly" data_files: - split: train path: "data/Parrot_Assembly/*.parquet" - config_name: "Parrot_Internal_Representation" data_files: - split: train path: "data/Parrot_Internal_Representation/*.parquet" - config_name: "Pascal" data_files: - split: train path: "data/Pascal/*.parquet" - config_name: "Pawn" data_files: - split: train path: "data/Pawn/*.parquet" - config_name: "Pep8" data_files: - split: train path: "data/Pep8/*.parquet" - config_name: "Perl" data_files: - split: train path: "data/Perl/*.parquet" - config_name: "Pic" data_files: - split: train path: "data/Pic/*.parquet" - config_name: "Pickle" data_files: - split: train path: "data/Pickle/*.parquet" - config_name: "PicoLisp" data_files: - split: train path: "data/PicoLisp/*.parquet" - config_name: "PigLatin" data_files: - split: train path: "data/PigLatin/*.parquet" - config_name: "Pike" data_files: - split: train path: "data/Pike/*.parquet" - config_name: "PlantUML" data_files: - split: train path: "data/PlantUML/*.parquet" - config_name: "Pod" data_files: - split: train path: "data/Pod/*.parquet" - config_name: "Pod_6" data_files: - split: train path: "data/Pod_6/*.parquet" - config_name: "PogoScript" data_files: - split: train path: "data/PogoScript/*.parquet" - config_name: "Pony" data_files: - split: train path: "data/Pony/*.parquet" - config_name: "Portugol" data_files: - split: train path: "data/Portugol/*.parquet" - config_name: "PostCSS" data_files: - split: train path: "data/PostCSS/*.parquet" - config_name: "PostScript" data_files: - split: train path: "data/PostScript/*.parquet" - config_name: "PowerBuilder" data_files: - split: train path: "data/PowerBuilder/*.parquet" - config_name: "PowerShell" data_files: - split: train path: "data/PowerShell/*.parquet" - config_name: "Prisma" data_files: - split: train path: "data/Prisma/*.parquet" - config_name: "Processing" data_files: - split: train path: "data/Processing/*.parquet" - config_name: "Procfile" data_files: - split: train path: "data/Procfile/*.parquet" - config_name: "Proguard" data_files: - split: train path: "data/Proguard/*.parquet" - config_name: "Prolog" data_files: - split: train path: "data/Prolog/*.parquet" - config_name: "Promela" data_files: - split: train path: "data/Promela/*.parquet" - config_name: "Propeller_Spin" data_files: - split: train path: "data/Propeller_Spin/*.parquet" - config_name: "Protocol_Buffer" data_files: - split: train path: "data/Protocol_Buffer/*.parquet" - config_name: "Protocol_Buffer_Text_Format" data_files: - split: train path: "data/Protocol_Buffer_Text_Format/*.parquet" - config_name: "Public_Key" data_files: - split: train path: "data/Public_Key/*.parquet" - config_name: "Pug" data_files: - split: train path: "data/Pug/*.parquet" - config_name: "Puppet" data_files: - split: train path: "data/Puppet/*.parquet" - config_name: "PureBasic" data_files: - split: train path: "data/PureBasic/*.parquet" - config_name: "PureScript" data_files: - split: train path: "data/PureScript/*.parquet" - config_name: "Pure_Data" data_files: - split: train path: "data/Pure_Data/*.parquet" - config_name: "Python" data_files: - split: train path: "data/Python/*.parquet" - config_name: "Python_traceback" data_files: - split: train path: "data/Python_traceback/*.parquet" - config_name: "Q-Sharp" data_files: - split: train path: "data/Q-Sharp/*.parquet" - config_name: "QML" data_files: - split: train path: "data/QML/*.parquet" - config_name: "QMake" data_files: - split: train path: "data/QMake/*.parquet" - config_name: "Qt_Script" data_files: - split: train path: "data/Qt_Script/*.parquet" - config_name: "Quake" data_files: - split: train path: "data/Quake/*.parquet" - config_name: "R" data_files: - split: train path: "data/R/*.parquet" - config_name: "RAML" data_files: - split: train path: "data/RAML/*.parquet" - config_name: "RDoc" data_files: - split: train path: "data/RDoc/*.parquet" - config_name: "REALbasic" data_files: - split: train path: "data/REALbasic/*.parquet" - config_name: "REXX" data_files: - split: train path: "data/REXX/*.parquet" - config_name: "RMarkdown" data_files: - split: train path: "data/RMarkdown/*.parquet" - config_name: "RPC" data_files: - split: train path: "data/RPC/*.parquet" - config_name: "RPGLE" data_files: - split: train path: "data/RPGLE/*.parquet" - config_name: "RPM_Spec" data_files: - split: train path: "data/RPM_Spec/*.parquet" - config_name: "RUNOFF" data_files: - split: train path: "data/RUNOFF/*.parquet" - config_name: "Racket" data_files: - split: train path: "data/Racket/*.parquet" - config_name: "Ragel" data_files: - split: train path: "data/Ragel/*.parquet" - config_name: "Raku" data_files: - split: train path: "data/Raku/*.parquet" - config_name: "Rascal" data_files: - split: train path: "data/Rascal/*.parquet" - config_name: "Raw_token_data" data_files: - split: train path: "data/Raw_token_data/*.parquet" - config_name: "ReScript" data_files: - split: train path: "data/ReScript/*.parquet" - config_name: "Readline_Config" data_files: - split: train path: "data/Readline_Config/*.parquet" - config_name: "Reason" data_files: - split: train path: "data/Reason/*.parquet" - config_name: "ReasonLIGO" data_files: - split: train path: "data/ReasonLIGO/*.parquet" - config_name: "Rebol" data_files: - split: train path: "data/Rebol/*.parquet" - config_name: "Record_Jar" data_files: - split: train path: "data/Record_Jar/*.parquet" - config_name: "Red" data_files: - split: train path: "data/Red/*.parquet" - config_name: "Redcode" data_files: - split: train path: "data/Redcode/*.parquet" - config_name: "Redirect_Rules" data_files: - split: train path: "data/Redirect_Rules/*.parquet" - config_name: "Regular_Expression" data_files: - split: train path: "data/Regular_Expression/*.parquet" - config_name: "Ren-Py" data_files: - split: train path: "data/Ren-Py/*.parquet" - config_name: "RenderScript" data_files: - split: train path: "data/RenderScript/*.parquet" - config_name: "Rich_Text_Format" data_files: - split: train path: "data/Rich_Text_Format/*.parquet" - config_name: "Ring" data_files: - split: train path: "data/Ring/*.parquet" - config_name: "Riot" data_files: - split: train path: "data/Riot/*.parquet" - config_name: "RobotFramework" data_files: - split: train path: "data/RobotFramework/*.parquet" - config_name: "Roff" data_files: - split: train path: "data/Roff/*.parquet" - config_name: "Roff_Manpage" data_files: - split: train path: "data/Roff_Manpage/*.parquet" - config_name: "Rouge" data_files: - split: train path: "data/Rouge/*.parquet" - config_name: "RouterOS_Script" data_files: - split: train path: "data/RouterOS_Script/*.parquet" - config_name: "Ruby" data_files: - split: train path: "data/Ruby/*.parquet" - config_name: "Rust" data_files: - split: train path: "data/Rust/*.parquet" - config_name: "SAS" data_files: - split: train path: "data/SAS/*.parquet" - config_name: "SCSS" data_files: - split: train path: "data/SCSS/*.parquet" - config_name: "SELinux_Policy" data_files: - split: train path: "data/SELinux_Policy/*.parquet" - config_name: "SMT" data_files: - split: train path: "data/SMT/*.parquet" - config_name: "SPARQL" data_files: - split: train path: "data/SPARQL/*.parquet" - config_name: "SQF" data_files: - split: train path: "data/SQF/*.parquet" - config_name: "SQL" data_files: - split: train path: "data/SQL/*.parquet" - config_name: "SQLPL" data_files: - split: train path: "data/SQLPL/*.parquet" - config_name: "SRecode_Template" data_files: - split: train path: "data/SRecode_Template/*.parquet" - config_name: "SSH_Config" data_files: - split: train path: "data/SSH_Config/*.parquet" - config_name: "STAR" data_files: - split: train path: "data/STAR/*.parquet" - config_name: "STL" data_files: - split: train path: "data/STL/*.parquet" - config_name: "STON" data_files: - split: train path: "data/STON/*.parquet" - config_name: "SVG" data_files: - split: train path: "data/SVG/*.parquet" - config_name: "SWIG" data_files: - split: train path: "data/SWIG/*.parquet" - config_name: "Sage" data_files: - split: train path: "data/Sage/*.parquet" - config_name: "SaltStack" data_files: - split: train path: "data/SaltStack/*.parquet" - config_name: "Sass" data_files: - split: train path: "data/Sass/*.parquet" - config_name: "Scala" data_files: - split: train path: "data/Scala/*.parquet" - config_name: "Scaml" data_files: - split: train path: "data/Scaml/*.parquet" - config_name: "Scheme" data_files: - split: train path: "data/Scheme/*.parquet" - config_name: "Scilab" data_files: - split: train path: "data/Scilab/*.parquet" - config_name: "Self" data_files: - split: train path: "data/Self/*.parquet" - config_name: "ShaderLab" data_files: - split: train path: "data/ShaderLab/*.parquet" - config_name: "Shell" data_files: - split: train path: "data/Shell/*.parquet" - config_name: "ShellCheck_Config" data_files: - split: train path: "data/ShellCheck_Config/*.parquet" - config_name: "ShellSession" data_files: - split: train path: "data/ShellSession/*.parquet" - config_name: "Shen" data_files: - split: train path: "data/Shen/*.parquet" - config_name: "Sieve" data_files: - split: train path: "data/Sieve/*.parquet" - config_name: "Singularity" data_files: - split: train path: "data/Singularity/*.parquet" - config_name: "Slash" data_files: - split: train path: "data/Slash/*.parquet" - config_name: "Slice" data_files: - split: train path: "data/Slice/*.parquet" - config_name: "Slim" data_files: - split: train path: "data/Slim/*.parquet" - config_name: "SmPL" data_files: - split: train path: "data/SmPL/*.parquet" - config_name: "Smali" data_files: - split: train path: "data/Smali/*.parquet" - config_name: "Smalltalk" data_files: - split: train path: "data/Smalltalk/*.parquet" - config_name: "Smarty" data_files: - split: train path: "data/Smarty/*.parquet" - config_name: "Solidity" data_files: - split: train path: "data/Solidity/*.parquet" - config_name: "Soong" data_files: - split: train path: "data/Soong/*.parquet" - config_name: "SourcePawn" data_files: - split: train path: "data/SourcePawn/*.parquet" - config_name: "Spline_Font_Database" data_files: - split: train path: "data/Spline_Font_Database/*.parquet" - config_name: "Squirrel" data_files: - split: train path: "data/Squirrel/*.parquet" - config_name: "Stan" data_files: - split: train path: "data/Stan/*.parquet" - config_name: "Standard_ML" data_files: - split: train path: "data/Standard_ML/*.parquet" - config_name: "Starlark" data_files: - split: train path: "data/Starlark/*.parquet" - config_name: "Stata" data_files: - split: train path: "data/Stata/*.parquet" - config_name: "StringTemplate" data_files: - split: train path: "data/StringTemplate/*.parquet" - config_name: "Stylus" data_files: - split: train path: "data/Stylus/*.parquet" - config_name: "SubRip_Text" data_files: - split: train path: "data/SubRip_Text/*.parquet" - config_name: "SugarSS" data_files: - split: train path: "data/SugarSS/*.parquet" - config_name: "SuperCollider" data_files: - split: train path: "data/SuperCollider/*.parquet" - config_name: "Svelte" data_files: - split: train path: "data/Svelte/*.parquet" - config_name: "Swift" data_files: - split: train path: "data/Swift/*.parquet" - config_name: "SystemVerilog" data_files: - split: train path: "data/SystemVerilog/*.parquet" - config_name: "TI_Program" data_files: - split: train path: "data/TI_Program/*.parquet" - config_name: "TLA" data_files: - split: train path: "data/TLA/*.parquet" - config_name: "TOML" data_files: - split: train path: "data/TOML/*.parquet" - config_name: "TSQL" data_files: - split: train path: "data/TSQL/*.parquet" - config_name: "TSV" data_files: - split: train path: "data/TSV/*.parquet" - config_name: "TSX" data_files: - split: train path: "data/TSX/*.parquet" - config_name: "TXL" data_files: - split: train path: "data/TXL/*.parquet" - config_name: "Talon" data_files: - split: train path: "data/Talon/*.parquet" - config_name: "Tcl" data_files: - split: train path: "data/Tcl/*.parquet" - config_name: "Tcsh" data_files: - split: train path: "data/Tcsh/*.parquet" - config_name: "TeX" data_files: - split: train path: "data/TeX/*.parquet" - config_name: "Tea" data_files: - split: train path: "data/Tea/*.parquet" - config_name: "Terra" data_files: - split: train path: "data/Terra/*.parquet" - config_name: "Texinfo" data_files: - split: train path: "data/Texinfo/*.parquet" - config_name: "Text" data_files: - split: train path: "data/Text/*.parquet" - config_name: "TextMate_Properties" data_files: - split: train path: "data/TextMate_Properties/*.parquet" - config_name: "Textile" data_files: - split: train path: "data/Textile/*.parquet" - config_name: "Thrift" data_files: - split: train path: "data/Thrift/*.parquet" - config_name: "Turing" data_files: - split: train path: "data/Turing/*.parquet" - config_name: "Turtle" data_files: - split: train path: "data/Turtle/*.parquet" - config_name: "Twig" data_files: - split: train path: "data/Twig/*.parquet" - config_name: "TypeScript" data_files: - split: train path: "data/TypeScript/*.parquet" - config_name: "Type_Language" data_files: - split: train path: "data/Type_Language/*.parquet" - config_name: "Unified_Parallel_C" data_files: - split: train path: "data/Unified_Parallel_C/*.parquet" - config_name: "Unity3D_Asset" data_files: - split: train path: "data/Unity3D_Asset/*.parquet" - config_name: "Unix_Assembly" data_files: - split: train path: "data/Unix_Assembly/*.parquet" - config_name: "Uno" data_files: - split: train path: "data/Uno/*.parquet" - config_name: "UnrealScript" data_files: - split: train path: "data/UnrealScript/*.parquet" - config_name: "UrWeb" data_files: - split: train path: "data/UrWeb/*.parquet" - config_name: "V" data_files: - split: train path: "data/V/*.parquet" - config_name: "VBA" data_files: - split: train path: "data/VBA/*.parquet" - config_name: "VBScript" data_files: - split: train path: "data/VBScript/*.parquet" - config_name: "VCL" data_files: - split: train path: "data/VCL/*.parquet" - config_name: "VHDL" data_files: - split: train path: "data/VHDL/*.parquet" - config_name: "Vala" data_files: - split: train path: "data/Vala/*.parquet" - config_name: "Valve_Data_Format" data_files: - split: train path: "data/Valve_Data_Format/*.parquet" - config_name: "Velocity_Template_Language" data_files: - split: train path: "data/Velocity_Template_Language/*.parquet" - config_name: "Verilog" data_files: - split: train path: "data/Verilog/*.parquet" - config_name: "Vim_Help_File" data_files: - split: train path: "data/Vim_Help_File/*.parquet" - config_name: "Vim_Script" data_files: - split: train path: "data/Vim_Script/*.parquet" - config_name: "Vim_Snippet" data_files: - split: train path: "data/Vim_Snippet/*.parquet" - config_name: "Visual_Basic_.NET" data_files: - split: train path: "data/Visual_Basic_.NET/*.parquet" - config_name: "Volt" data_files: - split: train path: "data/Volt/*.parquet" - config_name: "Vue" data_files: - split: train path: "data/Vue/*.parquet" - config_name: "Vyper" data_files: - split: train path: "data/Vyper/*.parquet" - config_name: "Wavefront_Material" data_files: - split: train path: "data/Wavefront_Material/*.parquet" - config_name: "Wavefront_Object" data_files: - split: train path: "data/Wavefront_Object/*.parquet" - config_name: "WebAssembly" data_files: - split: train path: "data/WebAssembly/*.parquet" - config_name: "WebIDL" data_files: - split: train path: "data/WebIDL/*.parquet" - config_name: "WebVTT" data_files: - split: train path: "data/WebVTT/*.parquet" - config_name: "Web_Ontology_Language" data_files: - split: train path: "data/Web_Ontology_Language/*.parquet" - config_name: "Wget_Config" data_files: - split: train path: "data/Wget_Config/*.parquet" - config_name: "Whiley" data_files: - split: train path: "data/Whiley/*.parquet" - config_name: "Wikitext" data_files: - split: train path: "data/Wikitext/*.parquet" - config_name: "Win32_Message_File" data_files: - split: train path: "data/Win32_Message_File/*.parquet" - config_name: "Windows_Registry_Entries" data_files: - split: train path: "data/Windows_Registry_Entries/*.parquet" - config_name: "Witcher_Script" data_files: - split: train path: "data/Witcher_Script/*.parquet" - config_name: "Wollok" data_files: - split: train path: "data/Wollok/*.parquet" - config_name: "World_of_Warcraft_Addon_Data" data_files: - split: train path: "data/World_of_Warcraft_Addon_Data/*.parquet" - config_name: "Wren" data_files: - split: train path: "data/Wren/*.parquet" - config_name: "X10" data_files: - split: train path: "data/X10/*.parquet" - config_name: "XC" data_files: - split: train path: "data/XC/*.parquet" - config_name: "XCompose" data_files: - split: train path: "data/XCompose/*.parquet" - config_name: "XML" data_files: - split: train path: "data/XML/*.parquet" - config_name: "XML_Property_List" data_files: - split: train path: "data/XML_Property_List/*.parquet" - config_name: "XPages" data_files: - split: train path: "data/XPages/*.parquet" - config_name: "XProc" data_files: - split: train path: "data/XProc/*.parquet" - config_name: "XQuery" data_files: - split: train path: "data/XQuery/*.parquet" - config_name: "XS" data_files: - split: train path: "data/XS/*.parquet" - config_name: "XSLT" data_files: - split: train path: "data/XSLT/*.parquet" - config_name: "X_BitMap" data_files: - split: train path: "data/X_BitMap/*.parquet" - config_name: "X_Font_Directory_Index" data_files: - split: train path: "data/X_Font_Directory_Index/*.parquet" - config_name: "X_PixMap" data_files: - split: train path: "data/X_PixMap/*.parquet" - config_name: "Xojo" data_files: - split: train path: "data/Xojo/*.parquet" - config_name: "Xonsh" data_files: - split: train path: "data/Xonsh/*.parquet" - config_name: "Xtend" data_files: - split: train path: "data/Xtend/*.parquet" - config_name: "YAML" data_files: - split: train path: "data/YAML/*.parquet" - config_name: "YANG" data_files: - split: train path: "data/YANG/*.parquet" - config_name: "YARA" data_files: - split: train path: "data/YARA/*.parquet" - config_name: "YASnippet" data_files: - split: train path: "data/YASnippet/*.parquet" - config_name: "Yacc" data_files: - split: train path: "data/Yacc/*.parquet" - config_name: "Yul" data_files: - split: train path: "data/Yul/*.parquet" - config_name: "ZAP" data_files: - split: train path: "data/ZAP/*.parquet" - config_name: "ZIL" data_files: - split: train path: "data/ZIL/*.parquet" - config_name: "Zeek" data_files: - split: train path: "data/Zeek/*.parquet" - config_name: "ZenScript" data_files: - split: train path: "data/ZenScript/*.parquet" - config_name: "Zephir" data_files: - split: train path: "data/Zephir/*.parquet" - config_name: "Zig" data_files: - split: train path: "data/Zig/*.parquet" - config_name: "Zimpl" data_files: - split: train path: "data/Zimpl/*.parquet" - config_name: "cURL_Config" data_files: - split: train path: "data/cURL_Config/*.parquet" - config_name: "desktop" data_files: - split: train path: "data/desktop/*.parquet" - config_name: "dircolors" data_files: - split: train path: "data/dircolors/*.parquet" - config_name: "eC" data_files: - split: train path: "data/eC/*.parquet" - config_name: "edn" data_files: - split: train path: "data/edn/*.parquet" - config_name: "fish" data_files: - split: train path: "data/fish/*.parquet" - config_name: "hoon" data_files: - split: train path: "data/hoon/*.parquet" - config_name: "jq" data_files: - split: train path: "data/jq/*.parquet" - config_name: "kvlang" data_files: - split: train path: "data/kvlang/*.parquet" - config_name: "mIRC_Script" data_files: - split: train path: "data/mIRC_Script/*.parquet" - config_name: "mcfunction" data_files: - split: train path: "data/mcfunction/*.parquet" - config_name: "mupad" data_files: - split: train path: "data/mupad/*.parquet" - config_name: "nanorc" data_files: - split: train path: "data/nanorc/*.parquet" - config_name: "nesC" data_files: - split: train path: "data/nesC/*.parquet" - config_name: "ooc" data_files: - split: train path: "data/ooc/*.parquet" - config_name: "q" data_files: - split: train path: "data/q/*.parquet" - config_name: "reStructuredText" data_files: - split: train path: "data/reStructuredText/*.parquet" - config_name: "robots.txt" data_files: - split: train path: "data/robots.txt/*.parquet" - config_name: "sed" data_files: - split: train path: "data/sed/*.parquet" - config_name: "wdl" data_files: - split: train path: "data/wdl/*.parquet" - config_name: "wisp" data_files: - split: train path: "data/wisp/*.parquet" - config_name: "xBase" data_files: - split: train path: "data/xBase/*.parquet" --- # The Stack v2 <center> <img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/thestackv2_banner.png" alt="Stackv2" width="900" height="600"> </center> ## Dataset Description - **Homepage:** https://www.bigcode-project.org/ - **Repository:** https://github.com/bigcode-project - **Paper:** [Link](https://huggingface.co/papers/2402.19173) - **Point of Contact:** contact@bigcode-project.org The dataset consists of 4 versions: - [`bigcode/the-stack-v2`](https://huggingface.co/datasets/bigcode/the-stack-v2): the full "The Stack v2" dataset **<-- you are here** - [`bigcode/the-stack-v2-dedup`](https://huggingface.co/datasets/bigcode/the-stack-v2-dedup): based on the `bigcode/the-stack-v2` but further near-deduplicated - [`bigcode/the-stack-v2-train-full-ids`](https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids): based on the `bigcode/the-stack-v2-dedup` dataset but further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories. - [`bigcode/the-stack-v2-train-smol-ids`](https://huggingface.co/datasets/bigcode/the-stack-v2-train-smol-ids): based on the `bigcode/the-stack-v2-dedup` dataset but further filtered with heuristics and spanning 17 programming languages. The data is grouped into repositories. **These datasets only contain the SWHIDs to download the code files and not the content of the files itself. See examples below to see how to download content. We are working on making the training datasets available in the coming weeks.** The Stack v2 is significantly larger than v1: ||The Stack v1|The Stack v2| |-|-|-| | full | 6.4TB | 67.5TB | | dedup | 2.9TB | 32.1TB | | train (full) | ~200B tokens | ~900B tokens | ### Changelog |Release|Description| |-|-| | v2.1.0 | Removed repositories that opted out before 2024-04-09. Removed unreachable/private repositories (according to SWH) | | v2.0.1 | Removed repositories that opted out before 2023-10-20. StarCoder2 was trained on this version | | v2.0 | Initial release of the Stack v2 | ### Dataset Summary The Stack v2 contains over 3B files in 600+ programming and markup languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. This dataset is derived from the Software Heritage archive, the largest public archive of software source code and accompanying development history. Software Heritage is an open, non profit initiative to collect, preserve, and share the source code of all publicly available software, launched by Inria, in partnership with UNESCO. We acknowledge Software Heritage for providing access to this invaluable resource. For more details, visit the [Software Heritage website](https://www.softwareheritage.org). ### Languages The dataset contains 658 languages. The full list can be found in the [language stats table](https://huggingface.co/datasets/bigcode/the-stack-v2/blob/main/language_stats.csv). ### How to use it ```python from datasets import load_dataset # full dataset (file IDs only) ds = load_dataset("bigcode/the-stack-v2", split="train") # specific language (e.g. Dockerfiles) ds = load_dataset("bigcode/the-stack-v2", "Dockerfile", split="train") # dataset streaming (will only download the data as needed) ds = load_dataset("bigcode/the-stack-v2", streaming=True, split="train") for sample in iter(ds): print(sample) ``` #### Downloading the file contents The file contents are stored in the Software Heritage S3 bucket to ensure data compliance. Downloading data in bulk requires an agreement with SoftwareHeritage and INRIA as stated in the dataset agreement. Make sure to configure your environment with your [AWS credentials](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/configure/index.html#examples). ```bash pip install smart_open[s3] ``` ```python import os import boto3 from smart_open import open from datasets import load_dataset session = boto3.Session( aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"], aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"]) s3 = session.client("s3") def download_contents(blob_id, src_encoding): s3_url = f"s3://softwareheritage/content/{blob_id}" with open(s3_url, "rb", compression=".gz", transport_params={"client": s3}) as fin: content = fin.read().decode(src_encoding) return {"content": content} ds = load_dataset("bigcode/the-stack-v2", split="train", streaming=True) ds = ds.map(lambda row: download_contents(row["blob_id"], row["src_encoding"])) for row in ds: print(row["content"]) break ``` ## Dataset Structure ### Data Fields * `blob_id` (`string`): Software Heritage (SWH) ID of the file on AWS S3. * `directory_id` (`string`): SWH ID of the root directory of the repository. * `path` (`string`): The file path within the repository. * `content_id` (`string`): SWH content ID. * `detected_licenses` (`string[]`): List of licenses (SPDX) detected by ScanCode. * `license_type` (`string`): Inferred license type (`permissive` or `no_license`). * `repo_name` (`string`): Repository name on GitHub. * `snapshot_id` (`string`): SWH snapshot ID. * `revision_id` (`string`): SWH revision (commit) ID. * `branch_name` (`string`): Repository branch name. * `visit_date` (`timestamp[ns]`): SWH crawl (snapshot) timestamp. * `revision_date` (`timestamp[ns]`): SWH revision (commit) timestamp. * `committer_date` (`timestamp[ns]`): SWH revision (commit) timestamp reported by the committer. * `github_id` (`int64`): GitHub identifier for the repository. * `star_events_count` (`int64`): number of stars calculated from GHArchive events. * `fork_events_count` (`int64`): number of forks calculated from GHArchive events. * `gha_license_id` (`string`): GHArchive SPDX license identifier, `None` if the repo is missing. * `gha_event_created_at` (`timestamp[ns]`): Timestamp of the latest event on GHArchive for this repository. * `gha_created_at` (`timestamp[ns]`): Timestamp of repository creation on GitHub, `None` if the repo is missing. * `gha_language` (`string`): Repository's primary programming language on GitHub, `None` if the repo is missing. * `src_encoding` (`string`): Original encoding of the file content befre converting to UTF-8. * `language` (`string`): Programming language of the file, detected by `go-enry / linguist`. * `is_vendor` (`bool`): Indicator of vendor file (external library), detected by `go-enry`. * `is_generated` (`bool`): Indicator of generated file (external library), detected by `go-enry`. * `length_bytes` (`int64`): Length of the file content in UTF-8 bytes. * `extension` (`string`): File extension. ### Data Splits The dataset has no splits and all data is loaded as train split by default. If you want to setup a custom train-test split beware that dataset contains a lot of near-duplicates which can cause leakage into the test split. ## Dataset Creation For more information on the dataset creation pipeline please refer to the [technical report](https://huggingface.co/papers/2402.19173). ### Curation Rationale One of the challenges faced by researchers working on code LLMs is the lack of openness and transparency around the development of these systems. Most prior works described the high-level data collection process but did not release the training data. It is therefore difficult for other researchers to fully reproduce these models and understand what kind of pre-training data leads to high-performing code LLMs. By releasing an open large-scale code dataset we hope to make training of code LLMs more reproducible. ### Source Data #### Data Collection 3.28B unique files belonging to 104.2M github repositories were collected by traversing the Software Heritage [2023-09-06](https://docs.softwareheritage.org/devel/swh-dataset/graph/dataset.html#graph-dataset-2023-09-06) graph dataset. Additional repository-level metadata was collected from [GitHub Archive](https://www.gharchive.org/) data up to 2023-09-14. The total uncompressed size of all files is 67.53TB. Near-deduplication was implemented in the pre-processing pipeline on top of exact deduplication. Roughly 40% of permissively licensed files were (near-)duplicates. The following are not stored: * Files that cannot contribute to training code: binary, empty, could not be decoded * Files larger than 10MB **Training Datasets**: For the training datasets the programming languages were filtered further to 17 and 600+ for the `the-stack-v2-smol-ids` and `the-stack-v2-full-ids` dataset, respecively. In addition, heuristics were applied to further increase the quality of the dataset. The code files are also grouped into repositories to allow to pretrain with full repository context. For more details see the [technical report](https://drive.google.com/file/d/17iGn3c-sYNiLyRSY-A85QOzgzGnGiVI3/view?usp=sharing). ##### License detection We extract repository-level license information from [GH Archive](https://www.gharchive.org/) for all repositories with matching names in the SWH dataset. When the repo-level license is not available, i.e., for 96.93\% of repositories, we use the [ScanCode Toolkit](https://github.com/nexB/scancode-toolkit) to detect file-level licenses as follows: * Find all filenames that could contain a license (e.g., LICENSE, MIT.txt, Apache2.0) or contain a reference to the license (e.g., README.md, GUIDELINES); * Apply ScanCode's license detection to the matching files and gather the SPDX IDs of the detected licenses; * Propagate the detected licenses to all files that have the same base path within the repository as the license file. The licenses we consider permissive are listed [here](https://huggingface.co/datasets/bigcode/the-stack-v2/blob/main/license_stats.csv). This list was compiled from the licenses approved by the [Blue Oak Council](https://blueoakcouncil.org/list), as well as licenses categorized as "Permissive" or "Public Domain" by [ScanCode](https://scancode-licensedb.aboutcode.org/). #### Who are the source language producers? The source (code) language producers are users of GitHub that created unique repository names up until 2023-09-06 (cutoff date). ### Personal and Sensitive Information The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. Deduplication has helped to reduce the amount of sensitive data that may exist. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their [open-access](https://en.wikipedia.org/wiki/Open_access) research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. Complaints, removal requests, and "do not contact" requests can be sent to contact@bigcode-project.org. ### Opting out of The Stack v2 We are giving developers the ability to have their code removed from the dataset upon request. The process for submitting and enacting removal requests will keep evolving throughout the project as we receive feedback and build up more data governance tools. You can check if your code is in The Stack v2 with the following ["Am I In The Stack?" Space](https://huggingface.co/spaces/bigcode/in-the-stack). If you'd like to have your data removed from the dataset follow the [instructions on GitHub](https://github.com/bigcode-project/opt-out-v2). ## Considerations for Using the Data ### Social Impact of Dataset The Stack v2 is an output of the BigCode Project. BigCode aims to be responsible by design and by default. The project is conducted in the spirit of Open Science, focused on the responsible development of LLMs for code. With the release of The Stack v2, we aim to increase access, reproducibility, and transparency of code LLMs in the research community. Work to de-risk and improve on the implementation of ethical best practices of code LLMs is conducted in various BigCode working groups. The Legal, Ethics, and Governance working group has explored topics such as licensing (including copyleft and the intended use of permissively licensed code), attribution of generated code to original code, rights to restrict processing, the inclusion of Personally Identifiable Information (PII), and risks of malicious code, among other topics. This work is ongoing as of October 25th, 2022. We expect code LLMs to enable people from diverse backgrounds to write higher quality code and develop low-code applications. Mission-critical software could become easier to maintain as professional developers are guided by code-generating systems on how to write more robust and efficient code. While the social impact is intended to be positive, the increased accessibility of code LLMs comes with certain risks such as over-reliance on the generated code and long-term effects on the software development job market. A broader impact analysis relating to Code LLMs can be found in section 7 of this [paper](https://arxiv.org/abs/2107.03374). An in-depth risk assessments for Code LLMs can be found in section 4 of this [paper](https://arxiv.org/abs/2207.14157). ### Discussion of Biases The code collected from GitHub does not contain demographic information or proxy information about the demographics. However, it is not without risks, as the comments within the code may contain harmful or offensive language, which could be learned by the models. Widely adopted programming languages like C and Javascript are overrepresented compared to niche programming languages like Julia and Scala. Some programming languages such as SQL, Batchfile, TypeScript are less likely to be permissively licensed (4% vs the average 10%). This may result in a biased representation of those languages. Permissively licensed files also tend to be longer. The majority of natural language present in code from GitHub is English. ### Other Known Limitations One of the current limitations of The Stack v2 is that scraped HTML for websites may not be compliant with Web Content Accessibility Guidelines ([WCAG](https://www.w3.org/WAI/standards-guidelines/wcag/)). This could have an impact on HTML-generated code that may introduce web accessibility issues. The training dataset could contain malicious code and/or the model could be used to generate malware or ransomware. To the best of our knowledge, all files contained in the dataset are licensed with one of the permissive licenses (see list in [Licensing information](#licensing-information)) or no license. The accuracy of license attribution is limited by the accuracy of GHArchive and ScanCode Toolkit. Any mistakes should be reported to BigCode Project for review and follow-up as needed. ## Additional Information ### Dataset Curators 1. Harm de Vries, ServiceNow Research, harm.devries@servicenow.com 2. Leandro von Werra, Hugging Face, leandro@huggingface.co ### Licensing Information The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. The list of [SPDX license identifiers](https://spdx.org/licenses/) included in the dataset can be found [here](https://huggingface.co/datasets/bigcode/the-stack-v2/blob/main/license_stats.csv). ### Citation Information ```bash @misc{lozhkov2024starcoder, title={StarCoder 2 and The Stack v2: The Next Generation}, author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries}, year={2024}, eprint={2402.19173}, archivePrefix={arXiv}, primaryClass={cs.SE} } ```

shuaishuaicdp/GUI-World

shuaishuaicdp

--- task_categories: - question-answering - text-generation language: - en pretty_name: GUI-World size_categories: - 10K<n<100K --- <div align="center"> <h1>GUI-World: A Dataset for GUI-Orientated Multimodal Large Language Models [](https://arxiv.org/abs/2406.10819) [](https://huggingface.co/shuaishuaicdp/GUI-Vid) [](https://gui-world.github.io/) <img src="figures/GUI_overview.png"> <img src="figures/radar.jpg"> <p align="center"> </p> </div> ## Dataset: GUI-World ### Overview GUI-World introduces a comprehensive benchmark for evaluating MLLMs in dynamic and complex GUI environments. It features extensive annotations covering six GUI scenarios and eight types of GUI-oriented questions. The dataset assesses state-of-the-art ImageLLMs and VideoLLMs, highlighting their limitations in handling dynamic and multi-step tasks. It provides valuable insights and a foundation for future research in enhancing the understanding and interaction capabilities of MLLMs with dynamic GUI content. This dataset aims to advance the development of robust GUI agents capable of perceiving and interacting with both static and dynamic GUI elements. ### How to use GUI-World See [Github](https://github.com/Dongping-Chen/GUI-World) for further details. Based on GUI-World, we train the first VideoLLM [**GUI-Vid**](https://huggingface.co/shuaishuaicdp/GUI-Vid) with powerful GUI understanding capability. ## License This work is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). ## Citation ``` @article{chen2024gui, title={GUI-WORLD: A Dataset for GUI-Orientated Multimodal Large Language Models}, author={GUI-World Team}, year={2024} } ```

common-canvas/commoncatalog-cc-by-nc-sa

common-canvas

--- license: cc-by-nc-sa-4.0 dataset_info: features: - name: jpg dtype: image - name: blip2_caption dtype: string - name: caption dtype: string - name: licensename dtype: string - name: licenseurl dtype: string - name: width dtype: int32 - name: height dtype: int32 - name: original_width dtype: int32 - name: original_height dtype: int32 - name: photoid dtype: int64 - name: uid dtype: string - name: unickname dtype: string - name: datetaken dtype: timestamp[us] - name: dateuploaded dtype: int64 - name: capturedevice dtype: string - name: title dtype: string - name: usertags dtype: string - name: machinetags dtype: string - name: longitude dtype: float64 - name: latitude dtype: float64 - name: accuracy dtype: int64 - name: pageurl dtype: string - name: downloadurl dtype: string - name: serverid dtype: int64 - name: farmid dtype: int64 - name: secret dtype: string - name: secretoriginal dtype: string - name: ext dtype: string - name: url dtype: string - name: key dtype: string - name: status dtype: string - name: error_message dtype: string - name: exif dtype: string - name: sha256 dtype: string - name: description dtype: string task_categories: - text-to-image language: - en --- # Dataset Card for CommonCatalog CC-BY-NC-SA This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 2014 from users of Yahoo Flickr. The dataset contains images of up to 4k resolution, making this one of the highest resolution captioned image datasets. ## Dataset Details ### Dataset Description We provide captions synthetic captions to approximately 100 million high resolution images collected from Yahoo Flickr Creative Commons (YFCC). - **Curated by:** Aaron Gokaslan - **Language(s) (NLP):** en - **License:** See relevant yaml tag / dataset name. ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** https://github.com/mosaicml/diffusion - **Paper:** https://arxiv.org/abs/2310.16825 - **Demo:** See CommonCanvas Gradios ## Uses We use CommonCatalog to train a family latent diffusion models called CommonCanvas. The goal is to produce a model that is competitive with Stable Diffusion 2, but to do so using an easily accessible dataset of known provenance. Doing so makes replicating the model significantly easier, and provides a clearer mechanism for applying training-data attribution techniques. ### Direct Use Training text-to-image models Training image-to-text models ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> * Commercial use * Crafting content that is offensive or injurious towards individuals, including negative portrayals of their living conditions, cultural backgrounds, religious beliefs, etc. * Deliberately creating or spreading content that is discriminatory or reinforces harmful stereotypes. * Falsely representing individuals without their permission. * Generating sexual content that may be seen by individuals without their consent. * Producing or disseminating false or misleading information. * Creating content that depicts extreme violence or bloodshed. * Distributing content that modifies copyrighted or licensed material in a way that breaches its usage terms. ## Dataset Structure The dataset is divided into 10 subsets each containing parquets about 4GB each. Each subfolder within contains a resolution range of the images and their respective aspect ratios. The dataset is also divided along images licensed for commercial use (C) and those that are not (NC). ## Dataset Creation ### Curation Rationale Creating a standardized, accessible dataset with synthetic caption and releasing it so other people can train on a common dataset for open source image generation. ### Source Data Yahoo Flickr Creative Commons 100M Dataset and Synthetically Generated Caption Data. #### Data Collection and Processing All synthetic captions were generated with BLIP2. See paper for more details. #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> Users of Flickr ## Bias, Risks, and Limitations See Yahoo Flickr Creative Commons 100M dataset for more information. The information was collected circa 2014 and known to have a bias towards internet connected Western countries. Some areas such as the global south lack representation. ## Citation **BibTeX:** ``` @article{gokaslan2023commoncanvas, title={CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images}, author={Gokaslan, Aaron and Cooper, A Feder and Collins, Jasmine and Seguin, Landan and Jacobson, Austin and Patel, Mihir and Frankle, Jonathan and Stephenson, Cory and Kuleshov, Volodymyr}, journal={arXiv preprint arXiv:2310.16825}, year={2023} } ``` ## Dataset Card Authors [Aaron Gokaslan](https://huggingface.co/Skylion007) ## Dataset Card Contact [Aaron Gokaslan](https://huggingface.co/Skylion007)

HuggingFaceFV/finevideo

HuggingFaceFV

--- language: - en license: cc size_categories: - 10K<n<100K task_categories: - visual-question-answering - video-text-to-text dataset_info: features: - name: mp4 dtype: binary - name: json struct: - name: content_fine_category dtype: string - name: content_metadata struct: - name: characterList list: - name: characterId dtype: string - name: description dtype: string - name: name dtype: string - name: description dtype: string - name: fps dtype: float64 - name: qAndA list: - name: answer dtype: string - name: question dtype: string - name: scenes list: - name: activities list: - name: description dtype: string - name: timestamp struct: - name: end_timestamp dtype: string - name: start_timestamp dtype: string - name: audioVisualCorrelation dtype: float64 - name: cast sequence: string - name: characterInteraction list: - name: characters sequence: string - name: description dtype: string - name: contextualRelevance dtype: string - name: dynamismScore dtype: float64 - name: mood struct: - name: description dtype: string - name: keyMoments list: - name: changeDescription dtype: string - name: timestamp dtype: string - name: narrativeProgression list: - name: description dtype: string - name: timestamp dtype: string - name: props list: - name: name dtype: string - name: timestamp struct: - name: end_timestamp dtype: string - name: start_timestamp dtype: string - name: sceneId dtype: int64 - name: thematicElements dtype: string - name: timestamps struct: - name: end_timestamp dtype: string - name: start_timestamp dtype: string - name: title dtype: string - name: videoEditingDetails list: - name: description dtype: string - name: timestamps struct: - name: end_timestamp dtype: string - name: start_timestamp dtype: string - name: storylines struct: - name: climax struct: - name: description dtype: string - name: timestamp dtype: string - name: description dtype: string - name: scenes sequence: int64 - name: title dtype: string - name: trimmingSuggestions list: - name: description dtype: string - name: timestamps struct: - name: end_timestamp dtype: string - name: start_timestamp dtype: string - name: content_parent_category dtype: string - name: duration_seconds dtype: int64 - name: original_json_filename dtype: string - name: original_video_filename dtype: string - name: resolution dtype: string - name: text_to_speech dtype: string - name: text_to_speech_word_count dtype: int64 - name: timecoded_text_to_speech list: - name: end dtype: string - name: start dtype: string - name: text dtype: string - name: youtube_age_limit dtype: int64 - name: youtube_categories sequence: string - name: youtube_channel dtype: string - name: youtube_channel_follower_count dtype: int64 - name: youtube_comment_count dtype: int64 - name: youtube_description dtype: string - name: youtube_like_count dtype: int64 - name: youtube_tags sequence: string - name: youtube_title dtype: string - name: youtube_upload_date dtype: string - name: youtube_view_count dtype: int64 splits: - name: train num_bytes: 678002078273 num_examples: 43751 download_size: 673393341968 dataset_size: 678002078273 configs: - config_name: default data_files: - split: train path: data/train-* extra_gated_prompt: '## Terms of Use for FineVideo FineVideo dataset is a collection of over 43.000 YouTube videos. We ask that you read and acknowledge the following points before using the dataset: 1. FineVideo is a collection of Creative Commons videos. Any use of all or part of the videos must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 2. FineVideo is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of FineVideo to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/HuggingFaceFV/finevideo/discussions/2). If you have questions about dataset versions and allowed uses, please also ask them in the dataset''s [community discussions](https://huggingface.co/datasets/HuggingFaceFV/finevideo/discussions/3). We will also notify users via email when the latest usable version changes. 3. To host, share, or otherwise provide access to FineVideo, you must include [these Terms of Use](https://huggingface.co/datasets/HuggingFaceFV/finevideo#terms-of-use-for-finevideo) and require users to agree to it. By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.' extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox tags: - video --- # FineVideo <center> <img src="https://huggingface.co/datasets/HuggingFaceFV/images/resolve/main/logo.png" alt="FineVideo"> </center> - [FineVideo](#finevideo) * [Description](#description) + [Dataset Explorer](#dataset-explorer) + [Revisions](#revisions) + [Dataset Distribution](#dataset-distribution) * [How to download and use FineVideo](#how-to-download-and-use-finevideo) + [Using `datasets`](#using-datasets) + [Using `huggingface_hub`](#using-huggingface_hub) + [Load a subset of the dataset](#load-a-subset-of-the-dataset) * [Dataset Structure](#dataset-structure) + [Data Instances](#data-instances) + [Data Fields](#data-fields) * [Dataset Creation](#dataset-creation) * [License CC-By](#license-cc-by) * [Considerations for Using the Data](#considerations-for-using-the-data) + [Social Impact of Dataset](#social-impact-of-dataset) + [Discussion of Biases](#discussion-of-biases) * [Additional Information](#additional-information) + [Credits](#credits) + [Future Work](#future-work) + [Opting out of FineVideo](#opting-out-of-finevideo) + [Citation Information](#citation-information) * [Terms of use for FineVideo](#terms-of-use-for-finevideo) ## Description This dataset opens up new frontiers in video understanding, with special focus on the tricky tasks of mood analysis, storytelling and media edition in multimodal settings. It's packed with detailed notes on scenes, characters, plot twists, and how audio and visuals play together, making it a versatile tool for everything from beefing up pre-trained models to fine-tuning AI for specific video tasks. What sets this dataset apart is its focus on capturing the emotional journey and narrative flow of videos - areas where current multimodal datasets fall short - giving researchers the ingredients to cook up more context-savvy video analysis models. ### Dataset Explorer You can explore the dataset directly from your browser in the [FineVideo Space](https://huggingface.co/spaces/HuggingFaceFV/FineVideo-Explorer). <center> <a href="https://huggingface.co/spaces/HuggingFaceFV/FineVideo-Explorer"> <img src="https://huggingface.co/datasets/HuggingFaceFV/images/resolve/main/finevideo.gif" alt="FineVideo Explorer" style="width:50%;"> </a> </center> ### Revisions | Date | Changes | |----------|-----------------------------------------| | Sept '24 | Initial release of FineVideo | | Nov '24 | Addition of time-coded speech-to-text | ### Dataset Distribution This comprehensive dataset includes: - 43,751 videos - An average video length of 4.7 minutes with approximately 3,425 hours of content - Content from 122 categories with 358.61 videos per category on average <center> <img src="https://huggingface.co/datasets/HuggingFaceFV/images/resolve/main/categories_plot.png" alt="Content categories"> </center> The videos were originally shared on YouTube under Creative Commons Attribution (CC-BY) licenses. FineVideo obtained these videos along with their speech-to-text transcriptions from [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons), a project that aggregates audio transcripts of CC-BY licensed YouTube videos. ## How to download and use FineVideo ### Using `datasets` ```python from datasets import load_dataset import os #full dataset (600GB of data) dataset = load_dataset("HuggingFaceFV/finevideo", split="train") print(dataset[0]['json'] # Access the metadata and speech to text of the first sample dataset['0']['mp4'] # Access the video #dataset streaming (will only download the data as needed) dataset = load_dataset("HuggingFaceFV/finevideo", split="train", streaming=True) sample = next(iter(dataset)) print(sample['json']) with open('sample.mp4', 'wb') as video_file: video_file.write(sample['mp4']) ``` ### Using `huggingface_hub` ```python from huggingface_hub import snapshot_download folder = snapshot_download('HuggingFaceFV/finevideo', repo_type='dataset', local_dir='./finevideo/') ``` ### Load a subset of the dataset To load just a subset from a given ```content_parent_category``` such as 'Sports' you may use the following script: ```python from datasets import load_dataset import json import os # Load the dataset in streaming mode dataset = load_dataset("HuggingFaceFV/finevideo", split="train", streaming=True) # Define the category you want to filter by desired_category = 'Your_Category_Here' # Replace with your desired category def is_desired_category(sample): return sample['json']['content_parent_category'] == desired_category filtered_dataset = filter(is_desired_category, dataset) # Create directories to save videos and metadata os.makedirs("videos", exist_ok=True) os.makedirs("metadata", exist_ok=True) for idx, sample in enumerate(filtered_dataset): video_filename = f"videos/sample_{idx}.mp4" with open(video_filename, 'wb') as video_file: video_file.write(sample['mp4']) json_filename = f"metadata/sample_{idx}.json" with open(json_filename, 'w') as json_file: json.dump(sample['json'], json_file) ``` ## Dataset Structure ### Data Instances Each data instance has a video and a metadata part. In metadata we can find different collections of metadata: - technical metadata (i.e. resolution, duration) - title level metadata (content fine / parent categories) - youtube details (i.e. channel, title, view count) - speech to text of the full video - timecode-level metadata (i.e. beginning / end of scenes, activities, object appearances) ```json { "content_fine_category": "Engineering Projects", "content_metadata": { "characterList": [ { "characterId": "1", "description": "A young woman with long blonde hair, wearing a grey shirt and an orange safety vest. She is a participant in the heavy equipment operators course.", "name": "Sara Paynton" } // ... (other characters omitted for brevity) ], "description": "A video highlighting the Heavy Equipment Operators course, focusing on its benefits, collaboration between institutions, and testimonials from clients and coordinators.", "fps": 23.976024615513296, "scenes": [ { "activities": [ { "description": "Sara stands in front of a 'Heavy Equipment Operator Training Centre' sign and talks about the course.", "timestamp": { "end_timestamp": "00:00:09.009", "start_timestamp": "00:00:00.000" } } // ... (other activities omitted for brevity) ], "audioVisualCorrelation": 0.8, "cast": ["Sara Paynton"], "characterInteraction": [], "contextualRelevance": "The visuals of heavy equipment in action create a sense of excitement and potential for those interested in this field.", "dynamismScore": 0.7, "mood": { "description": "Excited", "keyMoments": [] }, "narrativeProgression": [ { "description": "Introduction to the training center and Sara's presence.", "timestamp": "00:00:00.000" } // ... (other narrative progression points omitted for brevity) ], "props": [ { "name": "'Heavy Equipment Operator Training Centre' sign, construction site in the background.", "timestamp": { "end_timestamp": "00:00:09.009", "start_timestamp": "00:00:00.000" } } // ... (other props omitted for brevity) ], "sceneId": 1, "thematicElements": "Importance of training, career opportunities, personal growth.", "timestamps": { "end_timestamp": "00:00:28.779", "start_timestamp": "00:00:00.000" }, "title": "Introductory Scenes", "videoEditingDetails": [ { "description": "Fade in from black, slow zoom into the sign.", "timestamps": { "end_timestamp": "00:00:09.009", "start_timestamp": "00:00:00.000" } } // ... (other video editing details omitted for brevity) ] } // ... (other scenes omitted for brevity) ], "storylines": { "climax": { "description": "High success and employment rates emphasized by Bill Everitt.", "timestamp": "00:01:45.981" }, "description": "Stories surrounding the Heavy Equipment Operators Course, featuring its success, training benefits, and client experiences.", "scenes": [1, 2, 3, 4, 5] }, "title": "Heavy Equipment Operators Course Promo" }, "content_parent_category": "Education", "duration_seconds": 208, "resolution": "640x360", "youtube_title": "Training Heavy Equipment Operators", "youtube_upload_date": "20160511", "youtube_view_count": 89462 } ``` ### Data Fields ```python { "resolution": "string", # Video resolution, e.g. "640x360" "duration_seconds": int, # Duration of the video in seconds "content_parent_category": "string", # Broad category of the content "content_fine_category": "string", # Specific category of the content "youtube_title": "string", # Title of the YouTube video "youtube_description": "string", # Description of the YouTube video "text_to_speech_word_count": int, # Word count of the text-to-speech content "youtube_categories": ["string"], # List of YouTube categories "youtube_tags": ["string"], # List of YouTube tags "youtube_channel": "string", # Name of the YouTube channel "youtube_view_count": int, # Number of views on the video "youtube_comment_count": int, # Number of comments on the video "youtube_like_count": int, # Number of likes on the video "youtube_channel_follower_count": int, # Number of followers for the channel "youtube_upload_date": "string", # Upload date in YYYYMMDD format "youtube_age_limit": int, # Age limit for the video (0 if none) "content_metadata": { "title": "string", # Generated title "description": "string", # Generated description "characterList": [ # Full list of characters that appear in the video { "characterId": "string", "name": "string", # Descriptive name or real name of the character "description": "string" # Description that should allow a person or a model recognize them } ], "scenes": [ { "sceneId": int, "title": "string", "timestamps": { "start_timestamp": "string", "end_timestamp": "string" }, "cast": ["string"], # Characters from characterList that appear in this specific scene "activities": [ # List of activities happening in the scene { "description": "string", "timestamp": { "start_timestamp": "string", "end_timestamp": "string" } } ], "props": [ # List of objects / props that appear in the scene { "name": "string", "timestamp": { "start_timestamp": "string", "end_timestamp": "string" } } ], "videoEditingDetails": [ # Editing work in the scene such as transitions or effects { "description": "string", "timestamps": { "start_timestamp": "string", "end_timestamp": "string" } } ], "mood": { # General mood of the scene "description": "string", "keyMoments": [ # If mood transitions within the scene, we annotate a key moment { "timestamp": "string", "changeDescription": "string" } ] }, "narrativeProgression": [ # How the story unfolds over time { "description": "string", "timestamp": "string" } ], "characterInteraction": [ # Describes which characters from Cast interact within the scene { "characters": ["string"], "description": "string" } ], "thematicElements": "string", # Main ideas or messages in a story that give it deeper meaning beyond just the events that happen. "contextualRelevance": "string", # Analyzes if information, ideas, or actions are appropriate and useful for the particular circumstances at hand "dynamismScore": float, # Score [0,1] that measures the dynamism of the scene "audioVisualCorrelation": float # Score [0,1] that measures the correlation between what we see and what we hear } ], "storylines": { # Storyline and list of scenes that contributed to it "description": "string", "scenes": [int], "climax": { # If applies, climax of the story "description": "string", "timestamp": "string" } }, "qAndA": [ # Collection of five Q&A about the video that focus on specific timestamp question as well as overall video understanding { "question": "string", "answer": "string" } ], "trimmingSuggestions": [ # Overall suggestions that could help make the video more dynamic { "description": "string", # Type of trimming and why "timestamps": { "start_timestamp": "string", "end_timestamp": "string" } } ], "fps": float # Video frames per second }, "text_to_speech": "string" # Full text-to-speech content "timecoded_text_to_speech": [ # List of time-coded text segments with start and end timestamps { "start": "string", # Start timestamp of the segment, e.g., "00:00:00.000" "end": "string", # End timestamp of the segment, e.g., "00:00:04.546" "text": "string" # Text content for the specific segment, e.g., "We're in West Bank, BC, in the heart of the reserve." }, ... ] } ``` ## Dataset Creation From an initial pool of 1.8M videos, we distilled a dynamic and diverse selection suitable to be meaningfully temporally annotated <center> <img src="https://huggingface.co/datasets/HuggingFaceFV/images/resolve/main/dataset-creation.png" alt="Dataset Creation"> </center> ## License CC-By The videos and transcripts provided are derived from [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons). All the transcripts are part of a video shared under a CC-By license and, in accordance with that license, every YouTube channel is fully credited. The timecode-level metadata has been generated with Google’s Gemini API and structured with OpenAI’s GPT-4o. While content under a free license can be lawfully reproduced in any setting, we recommend that this set be preferably used for open research. Along with the requirements of proper attribution of the license, we encourage full release of data sources used for training models, extensive open documentation and responsible use of the dataset. ## Considerations for Using the Data ### Social Impact of Dataset With the release of this dataset we aim to make model training more accessible to the machine learning community at large. While multiple open-weights models with strong performance have been publicly released in the past, more often than not these releases are not accompanied by the corresponding training dataset. This is unfortunate as the dataset specificities and characteristics have been demonstrated to have a very large impact and role in the performances of the models. As the creation of a high quality training dataset is a fundamental requirement to training an LLM capable of excelling at downstream tasks, with FineVideo we (a) not only make the dataset creation process more transparent, by documenting our entire processing setup, we also (b) help alleviate the costs of dataset curation, both in time and in compute, for model creators by publicly releasing our dataset with the community. ### Discussion of Biases Efforts were made to minimize the amount of NSFW and toxic content present in the dataset by employing metadata and visual filters. However, there are still a significant number of videos present in the final dataset that could be considered toxic or contain harmful content. As FineVideo was sourced from diverse content creators from YouTube as a whole, any harmful biases typically present in it may be reproduced on our dataset. ## Additional Information ### Credits Created by: Miquel Farré, Andi Marafioti, Lewis Tunstall, Leandro Von Werra and Thomas Wolf With the expertise and support of the 🤗 crew: Abubakar Abid, Charles Bensimon, Eliott Coyac, Merve Enoyan, Hynek Kydlíček, Quentin Lhoest, Omar Sanseviero, Apolinário Passos, Guilherme Penedo, Bruna Trevelin, Ross Wightman Thanks to: Mara Lucien and Romann Weber for their inputs on narrative aspects and taxonomies. Kavya Srinet and Francisco Massa for their inputs on video data loaders and multimodal LLMs. Marc Pampols for the FineVideo promo video. ### Future Work We plan to release the code for the data pipeline used to create FineVideo. In future iterations, we aim to expand the dataset's size and increase the range of annotated aspects. ### Opting out of FineVideo In addition to selecting videos with permissive licenses, we are giving content creators the ability to have their videos removed from the dataset upon request. The process for submitting and enacting removal requests will keep evolving throughout the project as we receive feedback and build up more data governance tools. If you have videos that include your personal data, you may use the following form to request its removal from the dataset submit [the following form](https://forms.gle/cdpapYnCqg4wWk5e7). We may follow up for additional information. We will then work on excluding the videos in the next iteration of FineVideo as we keep updating the dataset. ### Citation Information ```python @misc{Farré2024FineVideo, title={FineVideo}, author={Farré, Miquel and Marafioti, Andi and Tunstall, Lewis and Von Werra, Leandro and Wolf, Thomas}, year={2024}, howpublished={\url{https://huggingface.co/datasets/HuggingFaceFV/finevideo}}, } ``` ## Terms of use for FineVideo FineVideo dataset is a collection of over 43.000 YouTube videos. We ask that you read and acknowledge the following points before using the dataset: 1. FineVideo is a collection of Creative Commons videos. Any use of all or part of the videos must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 2. FineVideo is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of FineVideo to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/HuggingFaceFV/finevideo/discussions/2). If you have questions about dataset versions and allowed uses, please also ask them in the dataset's [community discussions](https://huggingface.co/datasets/HuggingFaceFV/finevideo/discussions/3). We will also notify users via email when the latest usable version changes. 3. To host, share, or otherwise provide access to FineVideo, you must include these Terms of Use.

Cohere/wikipedia-2023-11-embed-multilingual-v3

Cohere

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split: train path: ka/* - config_name: "kaa" data_files: - split: train path: kaa/* - config_name: "kab" data_files: - split: train path: kab/* - config_name: "kbd" data_files: - split: train path: kbd/* - config_name: "kbp" data_files: - split: train path: kbp/* - config_name: "kcg" data_files: - split: train path: kcg/* - config_name: "kg" data_files: - split: train path: kg/* - config_name: "ki" data_files: - split: train path: ki/* - config_name: "kk" data_files: - split: train path: kk/* - config_name: "kl" data_files: - split: train path: kl/* - config_name: "km" data_files: - split: train path: km/* - config_name: "kn" data_files: - split: train path: kn/* - config_name: "ko" data_files: - split: train path: ko/* - config_name: "koi" data_files: - split: train path: koi/* - config_name: "krc" data_files: - split: train path: krc/* - config_name: "ks" data_files: - split: train path: ks/* - config_name: "ksh" data_files: - split: train path: ksh/* - config_name: "ku" data_files: - split: train path: ku/* - config_name: "kv" data_files: - split: train path: kv/* - config_name: "kw" data_files: - split: train path: kw/* - config_name: "ky" data_files: - split: train path: ky/* - config_name: "la" data_files: - split: train path: la/* - config_name: "lad" data_files: - split: train path: lad/* - config_name: "lb" data_files: - split: train path: lb/* - config_name: "lbe" data_files: - split: train path: lbe/* - config_name: "lez" data_files: - split: train path: lez/* - config_name: "lfn" data_files: - split: train path: lfn/* - config_name: "lg" data_files: - split: train path: lg/* - config_name: "li" data_files: - split: train path: li/* - config_name: "lij" data_files: - split: train path: lij/* - config_name: "lld" data_files: - split: train path: lld/* - config_name: "lmo" data_files: - split: train path: lmo/* - config_name: "ln" data_files: - split: train path: ln/* - config_name: "lo" data_files: - split: train path: lo/* - config_name: "lt" data_files: - split: train path: lt/* - config_name: "ltg" data_files: - split: train path: ltg/* - config_name: "lv" data_files: - split: train path: lv/* - config_name: "mad" data_files: - split: train path: mad/* - config_name: "mai" data_files: - split: train path: mai/* - config_name: "map-bms" data_files: - split: train path: map-bms/* - config_name: "mdf" data_files: - split: train path: mdf/* - config_name: "mg" data_files: - split: train path: mg/* - config_name: "mhr" data_files: - split: train path: mhr/* - config_name: "mi" data_files: - split: train path: mi/* - config_name: "min" data_files: - split: train path: min/* - config_name: "mk" data_files: - split: train path: mk/* - config_name: "ml" data_files: - split: train path: ml/* - config_name: "mn" data_files: - split: train path: mn/* - config_name: "mni" data_files: - split: train path: mni/* - config_name: "mnw" data_files: - split: train path: mnw/* - config_name: "mr" data_files: - split: train path: mr/* - config_name: "mrj" data_files: - split: train path: mrj/* - config_name: "ms" data_files: - split: train path: ms/* - config_name: "mt" data_files: - split: train path: mt/* - config_name: "mwl" data_files: - split: train path: mwl/* - config_name: "my" data_files: - split: train path: my/* - config_name: "myv" data_files: - split: train path: myv/* - config_name: "mzn" data_files: - split: train path: mzn/* - config_name: "nah" data_files: - split: train path: nah/* - config_name: "nap" data_files: - split: train path: nap/* - config_name: "nds" data_files: - split: train path: nds/* - config_name: "nds-nl" data_files: - split: train path: nds-nl/* - config_name: "ne" data_files: - split: train path: ne/* - config_name: "new" data_files: - split: train path: new/* - config_name: "nia" data_files: - split: train path: nia/* - config_name: "nl" data_files: - split: train path: nl/* - config_name: "nn" data_files: - split: train path: nn/* - config_name: "no" data_files: - split: train path: no/* - config_name: "nov" data_files: - split: train path: nov/* - config_name: "nqo" data_files: - split: train path: nqo/* - config_name: "nrm" data_files: - split: train path: nrm/* - config_name: "nso" data_files: - split: train path: nso/* - config_name: "nv" data_files: - split: train path: nv/* - config_name: "ny" data_files: - split: train path: ny/* - config_name: "oc" data_files: - split: train path: oc/* - config_name: "olo" data_files: - split: train path: olo/* - config_name: "om" data_files: - split: train path: om/* - config_name: "or" data_files: - split: train path: or/* - config_name: "os" data_files: - split: train path: os/* - config_name: "pa" data_files: - split: train path: pa/* - config_name: "pag" data_files: - split: train path: pag/* - config_name: "pam" data_files: - split: train path: pam/* - config_name: "pap" data_files: - split: train path: pap/* - config_name: "pcd" data_files: - split: train path: pcd/* - config_name: "pcm" data_files: - split: train path: pcm/* - config_name: "pdc" data_files: - split: train path: pdc/* - config_name: "pfl" data_files: - split: train path: pfl/* - config_name: "pi" data_files: - split: train path: pi/* - config_name: "pih" data_files: - split: train path: pih/* - config_name: "pl" data_files: - split: train path: pl/* - config_name: "pms" data_files: - split: train path: pms/* - config_name: "pnb" data_files: - split: train path: pnb/* - config_name: "pnt" data_files: - split: train path: pnt/* - config_name: "ps" data_files: - split: train path: ps/* - config_name: "pt" data_files: - split: train path: pt/* - config_name: "pwn" data_files: - split: train path: pwn/* - config_name: "qu" data_files: - split: train path: qu/* - config_name: "rm" data_files: - split: train path: rm/* - config_name: "rmy" data_files: - split: train path: rmy/* - config_name: "rn" data_files: - split: train path: rn/* - config_name: "ro" data_files: - split: train path: ro/* - config_name: "roa-rup" data_files: - split: train path: roa-rup/* - config_name: "roa-tara" data_files: - split: train path: roa-tara/* - config_name: "ru" data_files: - split: train path: ru/* - config_name: "rue" data_files: - split: train path: rue/* - config_name: "rw" data_files: - split: train path: rw/* - config_name: "sa" data_files: - split: train path: sa/* - config_name: "sah" data_files: - split: train path: sah/* - config_name: "sat" data_files: - split: train path: sat/* - config_name: "sc" data_files: - split: train path: sc/* - config_name: "scn" data_files: - split: train path: scn/* - config_name: "sco" data_files: - split: train path: sco/* - config_name: "sd" data_files: - split: train path: sd/* - config_name: "se" data_files: - split: train path: se/* - config_name: "sg" data_files: - split: train path: sg/* - config_name: "sh" data_files: - split: train path: sh/* - config_name: "shi" data_files: - split: train path: shi/* - config_name: "shn" data_files: - split: train path: shn/* - config_name: "si" data_files: - split: train path: si/* - config_name: "simple" data_files: - split: train path: simple/* - config_name: "sk" data_files: - split: train path: sk/* - config_name: "skr" data_files: - split: train path: skr/* - config_name: "sl" data_files: - split: train path: sl/* - config_name: "sm" data_files: - split: train path: sm/* - config_name: "smn" data_files: - split: train path: smn/* - config_name: "sn" data_files: - split: train path: sn/* - config_name: "so" data_files: - split: train path: so/* - config_name: "sq" data_files: - split: train path: sq/* - config_name: "sr" data_files: - split: train path: sr/* - config_name: "srn" data_files: - split: train path: srn/* - config_name: "ss" data_files: - split: train path: ss/* - config_name: "st" data_files: - split: train path: st/* - config_name: "stq" data_files: - split: train path: stq/* - config_name: "su" data_files: - split: train path: su/* - config_name: "sv" data_files: - split: train path: sv/* - config_name: "sw" data_files: - split: train path: sw/* - config_name: "szl" data_files: - split: train path: szl/* - config_name: "szy" data_files: - split: train path: szy/* - config_name: "ta" data_files: - split: train path: ta/* - config_name: "tay" data_files: - split: train path: tay/* - config_name: "tcy" data_files: - split: train path: tcy/* - config_name: "te" data_files: - split: train path: te/* - config_name: "tet" data_files: - split: train path: tet/* - config_name: "tg" data_files: - split: train path: tg/* - config_name: "th" data_files: - split: train path: th/* - config_name: "ti" data_files: - split: train path: ti/* - config_name: "tk" data_files: - split: train path: tk/* - config_name: "tl" data_files: - split: train path: tl/* - config_name: "tly" data_files: - split: train path: tly/* - config_name: "tn" data_files: - split: train path: tn/* - config_name: "to" data_files: - split: train path: to/* - config_name: "tpi" data_files: - split: train path: tpi/* - config_name: "tr" data_files: - split: train path: tr/* - config_name: "trv" data_files: - split: train path: trv/* - config_name: "ts" data_files: - split: train path: ts/* - config_name: "tt" data_files: - split: train path: tt/* - config_name: "tum" data_files: - split: train path: tum/* - config_name: "tw" data_files: - split: train path: tw/* - config_name: "ty" data_files: - split: train path: ty/* - config_name: "tyv" data_files: - split: train path: tyv/* - config_name: "udm" data_files: - split: train path: udm/* - config_name: "ug" data_files: - split: train path: ug/* - config_name: "uk" data_files: - split: train path: uk/* - config_name: "ur" data_files: - split: train path: ur/* - config_name: "uz" data_files: - split: train path: uz/* - config_name: "ve" data_files: - split: train path: ve/* - config_name: "vec" data_files: - split: train path: vec/* - config_name: "vep" data_files: - split: train path: vep/* - config_name: "vi" data_files: - split: train path: vi/* - config_name: "vls" data_files: - split: train path: vls/* - config_name: "vo" data_files: - split: train path: vo/* - config_name: "wa" data_files: - split: train path: wa/* - config_name: "war" data_files: - split: train path: war/* - config_name: "wo" data_files: - split: train path: wo/* - config_name: "wuu" data_files: - split: train path: wuu/* - config_name: "xal" data_files: - split: train path: xal/* - config_name: "xh" data_files: - split: train path: xh/* - config_name: "xmf" data_files: - split: train path: xmf/* - config_name: "yi" data_files: - split: train path: yi/* - config_name: "yo" data_files: - split: train path: yo/* - config_name: "za" data_files: - split: train path: za/* - config_name: "zea" data_files: - split: train path: zea/* - config_name: "zh" data_files: - split: train path: zh/* - config_name: "zh-classical" data_files: - split: train path: zh-classical/* - config_name: "zh-min-nan" data_files: - split: train path: zh-min-nan/* - config_name: "zh-yue" data_files: - split: train path: zh-yue/* - config_name: "zu" data_files: - split: train path: zu/* --- # Multilingual Embeddings for Wikipedia in 300+ Languages This dataset contains the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset dump from 2023-11-01 from Wikipedia in all 300+ languages. The individual articles have been chunked and embedded with the state-of-the-art multilingual [Cohere Embed V3 embedding model](https://txt.cohere.com/introducing-embed-v3/). This enables an easy way to semantically search across all of Wikipedia or to use it as a knowledge source for your RAG application. In total is it close to 250M paragraphs / embeddings. You can also use the model to perform cross-lingual search: Enter your search query in any language and get the most relevant results back. ## Loading the dataset ### Loading the document embeddings You can either load the dataset like this: ```python from datasets import load_dataset lang = "simple" #Use the Simple English Wikipedia subset docs = load_dataset("Cohere/wikipedia-2023-11-embed-multilingual-v3", lang, split="train") ``` Or you can also stream it without downloading it before: ```python from datasets import load_dataset lang = "simple" #Use the Simple English Wikipedia subset docs = load_dataset("Cohere/wikipedia-2023-11-embed-multilingual-v3", lang, split="train", streaming=True) for doc in docs: doc_id = doc['_id'] title = doc['title'] text = doc['text'] emb = doc['emb'] ``` Note, depending on the language, the download can be quite large. ## Search A full search example (on the first 1,000 paragraphs): ```python #Run: pip install cohere datasets numpy from datasets import load_dataset import numpy as np import cohere co = cohere.Client("YOUR_COHERE_API_KEY") # Add your cohere API key from www.cohere.com lang = "simple" top_k = 3 #Load at max 1000 chunks + embeddings max_docs = 1000 docs_stream = load_dataset(f"Cohere/wikipedia-2023-11-embed-multilingual-v3", lang, split="train", streaming=True) docs = [] doc_embeddings = [] for doc in docs_stream: docs.append(doc) doc_embeddings.append(doc['emb']) if len(docs) >= max_docs: break doc_embeddings = np.asarray(doc_embeddings) query = 'Who was Alan Turing' response = co.embed(texts=[query], model='embed-multilingual-v3.0', input_type="search_query") query_embedding = response.embeddings query_embedding = np.asarray(query_embedding) # Compute dot score between query embedding and document embeddings dot_scores = np.matmul(query_embedding, doc_embeddings.transpose())[0] top_k_hits = np.argpartition(dot_scores, -top_k)[-top_k:].tolist() # Sort top_k_hits by dot score top_k_hits.sort(key=lambda x: dot_scores[x], reverse=True) # Print results print("Query:", query) for doc_id in top_k_hits: print(docs[doc_id]['title']) print(docs[doc_id]['text']) print(docs[doc_id]['url'], "\n") ``` ## Overview The following table contains all language codes together with the total numbers of passages. | Language | #Docs | |---|:---:| | en | 41,488,110 | | de | 20,772,081 | | fr | 17,813,768 | | ru | 13,734,543 | | es | 12,905,284 | | it | 10,462,162 | | ceb | 9,818,657 | | uk | 6,901,192 | | ja | 6,626,537 | | nl | 6,101,353 | | pl | 5,973,650 | | pt | 5,637,930 | | sv | 4,911,480 | | ca | 4,156,889 | | ar | 3,691,451 | | cs | 3,118,524 | | he | 2,948,882 | | hu | 2,924,609 | | vi | 2,835,049 | | zh | 2,775,260 | | fi | 2,427,097 | | id | 2,358,335 | | no | 2,211,270 | | sr | 2,154,631 | | fa | 2,073,154 | | tr | 1,775,036 | | ro | 1,770,527 | | el | 1,599,770 | | ko | 1,513,291 | | bg | 1,455,765 | | hy | 1,386,140 | | eu | 1,327,579 | | da | 1,224,982 | | eo | 1,216,706 | | war | 1,185,097 | | sh | 1,139,309 | | tt | 1,119,546 | | arz | 1,089,164 | | gl | 1,056,990 | | et | 1,054,770 | | ce | 1,013,217 | | ast | 1,010,445 | | sl | 984,855 | | hr | 910,923 | | sk | 874,014 | | ms | 869,579 | | be | 857,033 | | th | 839,712 | | az | 817,631 | | uz | 811,028 | | mk | 784,576 | | lt | 770,069 | | bn | 767,965 | | cy | 762,338 | | ta | 685,975 | | simple | 646,424 | | te | 634,778 | | kk | 627,085 | | ka | 595,401 | | hi | 541,822 | | nn | 530,590 | | lv | 484,957 | | af | 461,674 | | ba | 434,939 | | ur | 434,269 | | bs | 396,692 | | sq | 388,788 | | ml | 384,795 | | min | 373,156 | | la | 340,521 | | pnb | 335,958 | | be-x-old | 314,600 | | kn | 309,085 | | azb | 294,248 | | oc | 283,297 | | zh-min-nan | 278,547 | | fy | 248,075 | | my | 241,025 | | lb | 216,558 | | ky | 216,344 | | als | 206,387 | | mr | 203,479 | | br | 200,486 | | pa | 188,091 | | is | 177,272 | | mg | 171,947 | | sw | 171,650 | | ha | 167,807 | | tl | 166,907 | | nds | 166,019 | | an | 143,163 | | jv | 142,104 | | ps | 138,240 | | ig | 132,250 | | new | 128,696 | | tg | 128,237 | | ga | 125,456 | | lld | 125,094 | | su | 124,390 | | cv | 122,671 | | ckb | 120,886 | | si | 119,223 | | mn | 114,878 | | lmo | 103,836 | | io | 101,713 | | gu | 99,450 | | vec | 95,072 | | zh-yue | 89,145 | | bar | 88,238 | | sco | 83,906 | | ne | 83,598 | | ku | 82,935 | | hyw | 82,343 | | pms | 77,834 | | as | 76,093 | | km | 74,177 | | sah | 71,599 | | li | 69,267 | | or | 65,510 | | mt | 64,038 | | szl | 56,836 | | yi | 55,375 | | ht | 55,079 | | dag | 53,343 | | sa | 51,735 | | nv | 49,355 | | bpy | 47,757 | | vo | 47,375 | | ug | 44,764 | | sat | 43,500 | | ia | 42,012 | | bo | 41,438 | | mwl | 41,273 | | sd | 40,395 | | bcl | 39,967 | | mnw | 39,578 | | hsb | 39,560 | | avk | 39,001 | | scn | 38,359 | | rm | 37,436 | | diq | 34,743 | | vep | 33,654 | | xmf | 33,238 | | ban | 32,320 | | wa | 32,132 | | ilo | 31,046 | | nds-nl | 30,918 | | qu | 30,529 | | so | 29,936 | | mhr | 29,619 | | vls | 29,227 | | sc | 28,977 | | fo | 28,809 | | gd | 28,149 | | rw | 28,037 | | gom | 27,792 | | yo | 27,789 | | tum | 26,743 | | wuu | 26,532 | | frr | 26,010 | | sn | 25,941 | | tk | 24,269 | | blk | 24,194 | | mzn | 23,837 | | co | 23,065 | | szy | 22,854 | | am | 22,467 | | shn | 22,432 | | skr | 21,081 | | lfn | 20,781 | | tyv | 20,762 | | lij | 20,553 | | ie | 19,994 | | rue | 19,916 | | crh | 19,016 | | gor | 18,146 | | ary | 17,463 | | dv | 16,941 | | lg | 16,751 | | roa-tara | 16,572 | | bjn | 16,429 | | tw | 16,304 | | bh | 15,938 | | pam | 15,134 | | os | 15,096 | | myv | 15,062 | | gn | 14,983 | | lez | 14,152 | | mai | 13,806 | | kv | 13,534 | | pcd | 13,057 | | zh-classical | 12,791 | | zea | 12,528 | | lo | 12,525 | | gv | 12,074 | | stq | 11,890 | | zu | 11,680 | | smn | 11,672 | | kw | 11,539 | | bat-smg | 11,240 | | hif | 11,215 | | ext | 10,967 | | ace | 10,821 | | trv | 10,546 | | ami | 10,538 | | tcy | 10,531 | | lad | 10,386 | | alt | 10,256 | | pap | 10,187 | | kab | 10,179 | | fur | 10,148 | | nap | 10,079 | | mrj | 9,771 | | kaa | 9,548 | | nqo | 9,153 | | glk | 9,120 | | pfl | 8,790 | | fiu-vro | 8,757 | | nso | 8,635 | | jbo | 8,577 | | bxr | 8,549 | | wo | 8,549 | | olo | 8,530 | | map-bms | 8,393 | | ksh | 8,226 | | csb | 8,085 | | av | 7,873 | | mni | 7,740 | | udm | 7,730 | | mi | 7,643 | | kbp | 7,616 | | dsb | 7,536 | | frp | 7,294 | | om | 7,045 | | ang | 7,023 | | hak | 6,866 | | gur | 6,761 | | se | 6,733 | | anp | 6,704 | | tay | 6,434 | | mdf | 6,351 | | gcr | 6,347 | | koi | 6,300 | | krc | 6,293 | | ay | 5,985 | | cdo | 5,917 | | nrm | 5,786 | | xh | 5,756 | | tn | 5,712 | | tly | 5,598 | | shi | 5,179 | | pcm | 5,076 | | fat | 4,968 | | nia | 4,795 | | dty | 4,728 | | kbd | 4,667 | | gpe | 4,289 | | cbk-zam | 4,224 | | ff | 4,166 | | dz | 4,117 | | guw | 3,982 | | eml | 3,979 | | ln | 3,774 | | inh | 3,768 | | nah | 3,720 | | ab | 3,465 | | ks | 3,255 | | mad | 3,236 | | haw | 3,227 | | gag | 3,076 | | tet | 3,030 | | ny | 2,933 | | pag | 2,727 | | guc | 2,454 | | roa-rup | 2,409 | | jam | 2,387 | | awa | 2,242 | | pdc | 2,239 | | to | 2,165 | | za | 2,132 | | st | 2,051 | | ltg | 2,005 | | atj | 1,967 | | nov | 1,916 | | ss | 1,904 | | pwn | 1,881 | | ee | 1,819 | | sm | 1,659 | | ts | 1,645 | | gan | 1,626 | | xal | 1,619 | | kcg | 1,555 | | cu | 1,477 | | srn | 1,395 | | got | 1,280 | | fon | 1,247 | | din | 1,214 | | arc | 1,167 | | fj | 1,164 | | rmy | 1,113 | | ady | 1,040 | | rn | 1,033 | | bm | 1,017 | | tpi | 957 | | ve | 919 | | ki | 798 | | pnt | 796 | | chr | 788 | | kl | 770 | | lbe | 766 | | bi | 718 | | ti | 706 | | kg | 609 | | pih | 606 | | ch | 513 | | bug | 429 | | ty | 297 | | ik | 275 | | iu | 263 | | pi | 260 | | sg | 204 | | chy | 57 | | cr | 41 | | Total | 247,154,006 |

cis-lmu/Glot500

cis-lmu

--- license: other license_name: license license_link: LICENSE configs: - config_name: knv_Latn data_files: - split: train path: "knv_Latn/train/*.arrow" - config_name: tgk_Latn data_files: - split: train path: "tgk_Latn/train/*.arrow" - config_name: ton_Latn data_files: - split: train path: "ton_Latn/train/*.arrow" - config_name: nld_Latn data_files: - split: train path: "nld_Latn/train/*.arrow" - config_name: tzo_Latn data_files: - split: train path: "tzo_Latn/train/*.arrow" - config_name: cuk_Latn data_files: - split: train path: "cuk_Latn/train/*.arrow" - config_name: fil_Latn data_files: - split: train path: "fil_Latn/train/*.arrow" - config_name: hau_Arab data_files: - split: train path: "hau_Arab/train/*.arrow" - config_name: uzb_Cyrl data_files: - split: train path: "uzb_Cyrl/train/*.arrow" - config_name: jav_Latn data_files: - split: train path: "jav_Latn/train/*.arrow" - config_name: rap_Latn data_files: - split: train path: "rap_Latn/train/*.arrow" - config_name: bak_Cyrl data_files: - split: train path: "bak_Cyrl/train/*.arrow" - config_name: por_Latn data_files: - split: train path: "por_Latn/train/*.arrow" - config_name: hbo_Hebr data_files: - split: train path: "hbo_Hebr/train/*.arrow" - config_name: quy_Latn data_files: - split: train path: "quy_Latn/train/*.arrow" - config_name: hnj_Latn data_files: - split: train path: "hnj_Latn/train/*.arrow" - config_name: ast_Latn data_files: - split: train path: "ast_Latn/train/*.arrow" - config_name: cos_Latn data_files: - split: train path: "cos_Latn/train/*.arrow" - config_name: fon_Latn data_files: - split: train path: "fon_Latn/train/*.arrow" - config_name: sna_Latn data_files: - split: train path: "sna_Latn/train/*.arrow" - config_name: dzo_Tibt data_files: - split: train path: "dzo_Tibt/train/*.arrow" - config_name: nob_Latn data_files: - split: train path: "nob_Latn/train/*.arrow" - config_name: nch_Latn data_files: - split: train path: "nch_Latn/train/*.arrow" - config_name: che_Cyrl data_files: - split: train path: "che_Cyrl/train/*.arrow" - config_name: ext_Latn data_files: - split: train path: "ext_Latn/train/*.arrow" - config_name: dtp_Latn data_files: - split: train path: "dtp_Latn/train/*.arrow" - config_name: yue_Hani data_files: - split: train path: "yue_Hani/train/*.arrow" - config_name: kbd_Cyrl data_files: - split: train path: "kbd_Cyrl/train/*.arrow" - config_name: mar_Deva data_files: - split: train path: "mar_Deva/train/*.arrow" - config_name: ron_Latn data_files: - split: train path: "ron_Latn/train/*.arrow" - config_name: acr_Latn data_files: - split: train path: "acr_Latn/train/*.arrow" - config_name: afb_Arab data_files: - split: train path: "afb_Arab/train/*.arrow" - config_name: sqi_Latn data_files: - split: train path: "sqi_Latn/train/*.arrow" - config_name: eng_Latn data_files: - split: train path: "eng_Latn/train/*.arrow" - config_name: ksd_Latn data_files: - split: train path: "ksd_Latn/train/*.arrow" - config_name: bcl_Latn data_files: - split: train path: "bcl_Latn/train/*.arrow" - config_name: ksh_Latn data_files: - split: train path: "ksh_Latn/train/*.arrow" - config_name: hin_Latn data_files: - split: train path: "hin_Latn/train/*.arrow" - config_name: myv_Cyrl data_files: - split: train path: "myv_Cyrl/train/*.arrow" - config_name: kjh_Cyrl data_files: - split: train path: "kjh_Cyrl/train/*.arrow" - config_name: sah_Cyrl data_files: - split: train path: "sah_Cyrl/train/*.arrow" - config_name: naq_Latn data_files: - split: train path: "naq_Latn/train/*.arrow" - config_name: tdt_Latn data_files: - split: train path: "tdt_Latn/train/*.arrow" - config_name: kac_Latn data_files: - split: train path: "kac_Latn/train/*.arrow" - config_name: cak_Latn data_files: - split: train path: "cak_Latn/train/*.arrow" - config_name: kir_Cyrl data_files: - split: train path: "kir_Cyrl/train/*.arrow" - config_name: mps_Latn data_files: - split: train path: "mps_Latn/train/*.arrow" - config_name: yid_Hebr data_files: - split: train path: "yid_Hebr/train/*.arrow" - config_name: srn_Latn data_files: - split: train path: "srn_Latn/train/*.arrow" - config_name: div_Thaa data_files: - split: train path: "div_Thaa/train/*.arrow" - config_name: mkd_Cyrl data_files: - split: train path: "mkd_Cyrl/train/*.arrow" - config_name: bre_Latn data_files: - split: train path: "bre_Latn/train/*.arrow" - config_name: tvl_Latn data_files: - split: train path: "tvl_Latn/train/*.arrow" - config_name: ven_Latn data_files: - split: train path: "ven_Latn/train/*.arrow" - config_name: wuu_Hani data_files: - split: train path: "wuu_Hani/train/*.arrow" - config_name: mwl_Latn data_files: - split: train path: "mwl_Latn/train/*.arrow" - config_name: miq_Latn data_files: - split: train path: "miq_Latn/train/*.arrow" - config_name: slv_Latn data_files: - split: train path: "slv_Latn/train/*.arrow" - config_name: hrv_Latn data_files: - split: train path: "hrv_Latn/train/*.arrow" - config_name: hmo_Latn data_files: - split: train path: "hmo_Latn/train/*.arrow" - config_name: som_Latn data_files: - split: train path: "som_Latn/train/*.arrow" - config_name: bod_Tibt data_files: - split: train path: "bod_Tibt/train/*.arrow" - config_name: pls_Latn data_files: - split: train path: "pls_Latn/train/*.arrow" - config_name: ile_Latn data_files: - split: train path: "ile_Latn/train/*.arrow" - config_name: luo_Latn data_files: - split: train path: "luo_Latn/train/*.arrow" - config_name: pus_Arab data_files: - split: train path: "pus_Arab/train/*.arrow" - config_name: fao_Latn data_files: - split: train path: "fao_Latn/train/*.arrow" - config_name: ces_Latn data_files: - split: train path: "ces_Latn/train/*.arrow" - config_name: fas_Arab data_files: - split: train path: "fas_Arab/train/*.arrow" - config_name: swa_Latn data_files: - split: train path: "swa_Latn/train/*.arrow" - config_name: ary_Arab data_files: - split: train path: "ary_Arab/train/*.arrow" - config_name: tbz_Latn data_files: - split: train path: "tbz_Latn/train/*.arrow" - config_name: hus_Latn data_files: - split: train path: "hus_Latn/train/*.arrow" - config_name: ote_Latn data_files: - split: train path: "ote_Latn/train/*.arrow" - config_name: ilo_Latn data_files: - split: train path: "ilo_Latn/train/*.arrow" - config_name: abk_Cyrl data_files: - split: train path: "abk_Cyrl/train/*.arrow" - config_name: bqc_Latn data_files: - split: train path: "bqc_Latn/train/*.arrow" - config_name: hil_Latn data_files: - split: train path: "hil_Latn/train/*.arrow" - config_name: pon_Latn data_files: - split: train path: "pon_Latn/train/*.arrow" - config_name: zul_Latn data_files: - split: train path: "zul_Latn/train/*.arrow" - config_name: als_Latn data_files: - split: train path: "als_Latn/train/*.arrow" - config_name: pes_Arab data_files: - split: train path: "pes_Arab/train/*.arrow" - config_name: bpy_Beng data_files: - split: train path: "bpy_Beng/train/*.arrow" - config_name: bos_Latn data_files: - split: train path: "bos_Latn/train/*.arrow" - config_name: sot_Latn data_files: - split: train path: "sot_Latn/train/*.arrow" - config_name: lin_Latn data_files: - split: train path: "lin_Latn/train/*.arrow" - config_name: tuk_Cyrl data_files: - split: train path: "tuk_Cyrl/train/*.arrow" - config_name: gla_Latn data_files: - split: train path: "gla_Latn/train/*.arrow" - config_name: wln_Latn data_files: - split: train path: "wln_Latn/train/*.arrow" - config_name: apc_Arab data_files: - split: train path: "apc_Arab/train/*.arrow" - config_name: hin_Deva data_files: - split: train path: "hin_Deva/train/*.arrow" - config_name: hye_Armn data_files: - split: train path: "hye_Armn/train/*.arrow" - config_name: tir_Ethi data_files: - split: train path: "tir_Ethi/train/*.arrow" - config_name: pap_Latn data_files: - split: train path: "pap_Latn/train/*.arrow" - config_name: gcf_Latn data_files: - split: train path: "gcf_Latn/train/*.arrow" - config_name: cjk_Latn data_files: - split: train path: "cjk_Latn/train/*.arrow" - config_name: pcd_Latn data_files: - split: train path: "pcd_Latn/train/*.arrow" - config_name: tur_Latn data_files: - split: train path: "tur_Latn/train/*.arrow" - config_name: kon_Latn data_files: - split: train path: "kon_Latn/train/*.arrow" - config_name: csy_Latn data_files: - split: train path: "csy_Latn/train/*.arrow" - config_name: bul_Cyrl data_files: - split: train path: "bul_Cyrl/train/*.arrow" - config_name: xho_Latn data_files: - split: train path: "xho_Latn/train/*.arrow" - config_name: guc_Latn data_files: - split: train path: "guc_Latn/train/*.arrow" - config_name: aka_Latn data_files: - split: train path: "aka_Latn/train/*.arrow" - config_name: kea_Latn data_files: - split: train path: "kea_Latn/train/*.arrow" - config_name: bar_Latn data_files: - split: train path: "bar_Latn/train/*.arrow" - config_name: sme_Latn data_files: - split: train path: "sme_Latn/train/*.arrow" - config_name: csb_Latn data_files: - split: train path: "csb_Latn/train/*.arrow" - config_name: bak_Latn data_files: - split: train path: "bak_Latn/train/*.arrow" - config_name: djk_Latn data_files: - split: train path: "djk_Latn/train/*.arrow" - config_name: xav_Latn data_files: - split: train path: "xav_Latn/train/*.arrow" - config_name: oci_Latn data_files: - split: train path: "oci_Latn/train/*.arrow" - config_name: acm_Arab data_files: - split: train path: "acm_Arab/train/*.arrow" - config_name: rmy_Cyrl data_files: - split: train path: "rmy_Cyrl/train/*.arrow" - config_name: krc_Cyrl data_files: - split: train path: "krc_Cyrl/train/*.arrow" - config_name: cym_Latn data_files: - split: train path: "cym_Latn/train/*.arrow" - config_name: lus_Latn data_files: - split: train path: "lus_Latn/train/*.arrow" - config_name: ngu_Latn data_files: - split: train path: "ngu_Latn/train/*.arrow" - config_name: yom_Latn data_files: - split: train path: "yom_Latn/train/*.arrow" - config_name: tam_Taml data_files: - split: train path: "tam_Taml/train/*.arrow" - config_name: ajp_Arab data_files: - split: train path: "ajp_Arab/train/*.arrow" - config_name: epo_Latn data_files: - split: train path: "epo_Latn/train/*.arrow" - config_name: fra_Latn data_files: - split: train path: "fra_Latn/train/*.arrow" - config_name: ita_Latn data_files: - split: train path: "ita_Latn/train/*.arrow" - config_name: seh_Latn data_files: - split: train path: "seh_Latn/train/*.arrow" - config_name: hbs_Latn data_files: - split: train path: "hbs_Latn/train/*.arrow" - config_name: uzn_Cyrl data_files: - split: train path: "uzn_Cyrl/train/*.arrow" - config_name: ksw_Mymr data_files: - split: train path: "ksw_Mymr/train/*.arrow" - config_name: pms_Latn data_files: - split: train path: "pms_Latn/train/*.arrow" - config_name: zlm_Latn data_files: - split: train path: "zlm_Latn/train/*.arrow" - config_name: qub_Latn data_files: - split: train path: "qub_Latn/train/*.arrow" - config_name: arg_Latn data_files: - split: train path: "arg_Latn/train/*.arrow" - config_name: enm_Latn data_files: - split: train path: "enm_Latn/train/*.arrow" - config_name: kaa_Cyrl data_files: - split: train path: "kaa_Cyrl/train/*.arrow" - config_name: toj_Latn data_files: - split: train path: "toj_Latn/train/*.arrow" - config_name: spa_Latn data_files: - split: train path: "spa_Latn/train/*.arrow" - config_name: pol_Latn data_files: - split: train path: "pol_Latn/train/*.arrow" - config_name: kos_Latn data_files: - split: train path: "kos_Latn/train/*.arrow" - config_name: kab_Latn data_files: - split: train path: "kab_Latn/train/*.arrow" - config_name: pan_Guru data_files: - split: train path: "pan_Guru/train/*.arrow" - config_name: nan_Latn data_files: - split: train path: "nan_Latn/train/*.arrow" - config_name: aze_Latn data_files: - split: train path: "aze_Latn/train/*.arrow" - config_name: ara_Arab data_files: - split: train path: "ara_Arab/train/*.arrow" - config_name: meu_Latn data_files: - split: train path: "meu_Latn/train/*.arrow" - config_name: som_Arab data_files: - split: train path: "som_Arab/train/*.arrow" - config_name: lvs_Latn data_files: - split: train path: "lvs_Latn/train/*.arrow" - config_name: nbl_Latn data_files: - split: train path: "nbl_Latn/train/*.arrow" - config_name: crh_Latn data_files: - split: train path: "crh_Latn/train/*.arrow" - config_name: kbp_Latn data_files: - split: train path: "kbp_Latn/train/*.arrow" - config_name: tgl_Latn data_files: - split: train path: "tgl_Latn/train/*.arrow" - config_name: kmb_Latn data_files: - split: train path: "kmb_Latn/train/*.arrow" - config_name: hun_Latn data_files: - split: train path: "hun_Latn/train/*.arrow" - config_name: yao_Latn data_files: - split: train path: "yao_Latn/train/*.arrow" - config_name: arn_Latn data_files: - split: train path: "arn_Latn/train/*.arrow" - config_name: jbo_Latn data_files: - split: train path: "jbo_Latn/train/*.arrow" - config_name: mzn_Arab data_files: - split: train path: "mzn_Arab/train/*.arrow" - config_name: lzh_Hani data_files: - split: train path: "lzh_Hani/train/*.arrow" - config_name: heb_Hebr data_files: - split: train path: "heb_Hebr/train/*.arrow" - config_name: bjn_Latn data_files: - split: train path: "bjn_Latn/train/*.arrow" - config_name: gug_Latn data_files: - split: train path: "gug_Latn/train/*.arrow" - config_name: swc_Latn data_files: - split: train path: "swc_Latn/train/*.arrow" - config_name: yor_Latn data_files: - split: train path: "yor_Latn/train/*.arrow" - config_name: ban_Latn data_files: - split: train path: "ban_Latn/train/*.arrow" - config_name: tlh_Latn data_files: - split: train path: "tlh_Latn/train/*.arrow" - config_name: chv_Cyrl data_files: - split: train path: "chv_Cyrl/train/*.arrow" - config_name: sin_Sinh data_files: - split: train path: "sin_Sinh/train/*.arrow" - config_name: ind_Latn data_files: - split: train path: "ind_Latn/train/*.arrow" - config_name: amh_Ethi data_files: - split: train path: "amh_Ethi/train/*.arrow" - config_name: zea_Latn data_files: - split: train path: "zea_Latn/train/*.arrow" - config_name: kpg_Latn data_files: - split: train path: "kpg_Latn/train/*.arrow" - config_name: glk_Arab data_files: - split: train path: "glk_Arab/train/*.arrow" - config_name: crh_Cyrl data_files: - split: train path: "crh_Cyrl/train/*.arrow" - config_name: nyu_Latn data_files: - split: train path: "nyu_Latn/train/*.arrow" - config_name: ibo_Latn data_files: - split: train path: "ibo_Latn/train/*.arrow" - config_name: msa_Latn data_files: - split: train path: "msa_Latn/train/*.arrow" - config_name: prs_Arab data_files: - split: train path: "prs_Arab/train/*.arrow" - config_name: nap_Latn data_files: - split: train path: "nap_Latn/train/*.arrow" - config_name: bik_Latn data_files: - split: train path: "bik_Latn/train/*.arrow" - config_name: srp_Cyrl data_files: - split: train path: "srp_Cyrl/train/*.arrow" - config_name: lao_Laoo data_files: - split: train path: "lao_Laoo/train/*.arrow" - config_name: kom_Cyrl data_files: - split: train path: "kom_Cyrl/train/*.arrow" - config_name: nde_Latn data_files: - split: train path: "nde_Latn/train/*.arrow" - config_name: hui_Latn data_files: - split: train path: "hui_Latn/train/*.arrow" - config_name: uig_Latn data_files: - split: train path: "uig_Latn/train/*.arrow" - config_name: new_Deva data_files: - split: train path: "new_Deva/train/*.arrow" - config_name: kur_Arab data_files: - split: train path: "kur_Arab/train/*.arrow" - config_name: sco_Latn data_files: - split: train path: "sco_Latn/train/*.arrow" - config_name: ayr_Latn data_files: - split: train path: "ayr_Latn/train/*.arrow" - config_name: suz_Deva data_files: - split: train path: "suz_Deva/train/*.arrow" - config_name: wal_Latn data_files: - split: train path: "wal_Latn/train/*.arrow" - config_name: mlt_Latn data_files: - split: train path: "mlt_Latn/train/*.arrow" - config_name: asm_Beng data_files: - split: train path: "asm_Beng/train/*.arrow" - config_name: san_Deva data_files: - split: train path: "san_Deva/train/*.arrow" - config_name: kaz_Cyrl data_files: - split: train path: "kaz_Cyrl/train/*.arrow" - config_name: iba_Latn data_files: - split: train path: "iba_Latn/train/*.arrow" - config_name: tuk_Latn data_files: - split: train path: "tuk_Latn/train/*.arrow" - config_name: nso_Latn data_files: - split: train path: "nso_Latn/train/*.arrow" - config_name: run_Latn data_files: - split: train path: "run_Latn/train/*.arrow" - config_name: ctu_Latn data_files: - split: train path: "ctu_Latn/train/*.arrow" - config_name: bam_Latn data_files: - split: train path: "bam_Latn/train/*.arrow" - config_name: fin_Latn data_files: - split: train path: "fin_Latn/train/*.arrow" - config_name: gor_Latn data_files: - split: train path: "gor_Latn/train/*.arrow" - config_name: kmr_Latn data_files: - split: train path: "kmr_Latn/train/*.arrow" - config_name: pag_Latn data_files: - split: train path: "pag_Latn/train/*.arrow" - config_name: niu_Latn data_files: - split: train path: "niu_Latn/train/*.arrow" - config_name: xmf_Geor data_files: - split: train path: "xmf_Geor/train/*.arrow" - config_name: ekk_Latn data_files: - split: train path: "ekk_Latn/train/*.arrow" - config_name: lmo_Latn data_files: - split: train path: "lmo_Latn/train/*.arrow" - config_name: ceb_Latn data_files: - split: train path: "ceb_Latn/train/*.arrow" - config_name: mhr_Cyrl data_files: - split: train path: "mhr_Cyrl/train/*.arrow" - config_name: plt_Latn data_files: - split: train path: "plt_Latn/train/*.arrow" - config_name: qvi_Latn data_files: - split: train path: "qvi_Latn/train/*.arrow" - config_name: roh_Latn data_files: - split: train path: "roh_Latn/train/*.arrow" - config_name: aln_Latn data_files: - split: train path: "aln_Latn/train/*.arrow" - config_name: mah_Latn data_files: - split: train path: "mah_Latn/train/*.arrow" - config_name: npi_Deva data_files: - split: train path: "npi_Deva/train/*.arrow" - config_name: tok_Latn data_files: - split: train path: "tok_Latn/train/*.arrow" - config_name: mgh_Latn data_files: - split: train path: "mgh_Latn/train/*.arrow" - config_name: eml_Latn data_files: - split: train path: "eml_Latn/train/*.arrow" - config_name: pnb_Arab data_files: - split: train path: "pnb_Arab/train/*.arrow" - config_name: nav_Latn data_files: - split: train path: "nav_Latn/train/*.arrow" - config_name: cat_Latn data_files: - split: train path: "cat_Latn/train/*.arrow" - config_name: gym_Latn data_files: - split: train path: "gym_Latn/train/*.arrow" - config_name: sat_Olck data_files: - split: train path: "sat_Olck/train/*.arrow" - config_name: snd_Arab data_files: - split: train path: "snd_Arab/train/*.arrow" - config_name: isl_Latn data_files: - split: train path: "isl_Latn/train/*.arrow" - config_name: kal_Latn data_files: - split: train path: "kal_Latn/train/*.arrow" - config_name: aoj_Latn data_files: - split: train path: "aoj_Latn/train/*.arrow" - config_name: zai_Latn data_files: - split: train path: "zai_Latn/train/*.arrow" - config_name: guj_Gujr data_files: - split: train path: "guj_Gujr/train/*.arrow" - config_name: min_Latn data_files: - split: train path: "min_Latn/train/*.arrow" - config_name: grc_Grek data_files: - split: train path: "grc_Grek/train/*.arrow" - config_name: hmn_Latn data_files: - split: train path: "hmn_Latn/train/*.arrow" - config_name: ido_Latn data_files: - split: train path: "ido_Latn/train/*.arrow" - config_name: khm_Khmr data_files: - split: train path: "khm_Khmr/train/*.arrow" - config_name: quh_Latn data_files: - split: train path: "quh_Latn/train/*.arrow" - config_name: ikk_Latn data_files: - split: train path: "ikk_Latn/train/*.arrow" - config_name: iku_Cans data_files: - split: train path: "iku_Cans/train/*.arrow" - config_name: tat_Latn data_files: - split: train path: "tat_Latn/train/*.arrow" - config_name: bel_Cyrl data_files: - split: train path: "bel_Cyrl/train/*.arrow" - config_name: dyu_Latn data_files: - split: train path: "dyu_Latn/train/*.arrow" - config_name: que_Latn data_files: - split: train path: "que_Latn/train/*.arrow" - config_name: quw_Latn data_files: - split: train path: "quw_Latn/train/*.arrow" - config_name: wol_Latn data_files: - split: train path: "wol_Latn/train/*.arrow" - config_name: hne_Deva data_files: - split: train path: "hne_Deva/train/*.arrow" - config_name: zho_Hani data_files: - split: train path: "zho_Hani/train/*.arrow" - config_name: tum_Latn data_files: - split: train path: "tum_Latn/train/*.arrow" - config_name: swh_Latn data_files: - split: train path: "swh_Latn/train/*.arrow" - config_name: kua_Latn data_files: - split: train path: "kua_Latn/train/*.arrow" - config_name: ncj_Latn data_files: - split: train path: "ncj_Latn/train/*.arrow" - config_name: ewe_Latn data_files: - split: train path: "ewe_Latn/train/*.arrow" - config_name: hat_Latn data_files: - split: train path: "hat_Latn/train/*.arrow" - config_name: ina_Latn data_files: - split: train path: "ina_Latn/train/*.arrow" - config_name: deu_Latn data_files: - split: train path: "deu_Latn/train/*.arrow" - config_name: ahk_Latn data_files: - split: train path: "ahk_Latn/train/*.arrow" - config_name: srm_Latn data_files: - split: train path: "srm_Latn/train/*.arrow" - config_name: lug_Latn data_files: - split: train path: "lug_Latn/train/*.arrow" - config_name: ach_Latn data_files: - split: train path: "ach_Latn/train/*.arrow" - config_name: rmy_Latn data_files: - split: train path: "rmy_Latn/train/*.arrow" - config_name: smo_Latn data_files: - split: train path: "smo_Latn/train/*.arrow" - config_name: mos_Latn data_files: - split: train path: "mos_Latn/train/*.arrow" - config_name: srd_Latn data_files: - split: train path: "srd_Latn/train/*.arrow" - config_name: ltz_Latn data_files: - split: train path: "ltz_Latn/train/*.arrow" - config_name: srp_Latn data_files: - split: train path: "srp_Latn/train/*.arrow" - config_name: azb_Arab data_files: - split: train path: "azb_Arab/train/*.arrow" - config_name: aze_Arab data_files: - split: train path: "aze_Arab/train/*.arrow" - config_name: ori_Orya data_files: - split: train path: "ori_Orya/train/*.arrow" - config_name: mzh_Latn data_files: - split: train path: "mzh_Latn/train/*.arrow" - config_name: kur_Latn data_files: - split: train path: "kur_Latn/train/*.arrow" - config_name: wbm_Latn data_files: - split: train path: "wbm_Latn/train/*.arrow" - config_name: crs_Latn data_files: - split: train path: "crs_Latn/train/*.arrow" - config_name: ada_Latn data_files: - split: train path: "ada_Latn/train/*.arrow" - config_name: hif_Latn data_files: - split: train path: "hif_Latn/train/*.arrow" - config_name: jpn_Japn data_files: - split: train path: "jpn_Japn/train/*.arrow" - config_name: pcm_Latn data_files: - split: train path: "pcm_Latn/train/*.arrow" - config_name: tso_Latn data_files: - split: train path: "tso_Latn/train/*.arrow" - config_name: nor_Latn data_files: - split: train path: "nor_Latn/train/*.arrow" - config_name: bsb_Latn data_files: - split: train path: "bsb_Latn/train/*.arrow" - config_name: gaa_Latn data_files: - split: train path: "gaa_Latn/train/*.arrow" - config_name: ukr_Cyrl data_files: - split: train path: "ukr_Cyrl/train/*.arrow" - config_name: mon_Latn data_files: - split: train path: "mon_Latn/train/*.arrow" - config_name: nep_Deva data_files: - split: train path: "nep_Deva/train/*.arrow" - config_name: guj_Deva data_files: - split: train path: "guj_Deva/train/*.arrow" - config_name: pis_Latn data_files: - split: train path: "pis_Latn/train/*.arrow" - config_name: lhu_Latn data_files: - split: train path: "lhu_Latn/train/*.arrow" - config_name: nya_Latn data_files: - split: train path: "nya_Latn/train/*.arrow" - config_name: poh_Latn data_files: - split: train path: "poh_Latn/train/*.arrow" - config_name: nnb_Latn data_files: - split: train path: "nnb_Latn/train/*.arrow" - config_name: grn_Latn data_files: - split: train path: "grn_Latn/train/*.arrow" - config_name: mco_Latn data_files: - split: train path: "mco_Latn/train/*.arrow" - config_name: ory_Orya data_files: - split: train path: "ory_Orya/train/*.arrow" - config_name: ful_Latn data_files: - split: train path: "ful_Latn/train/*.arrow" - config_name: diq_Latn data_files: - split: train path: "diq_Latn/train/*.arrow" - config_name: sag_Latn data_files: - split: train path: "sag_Latn/train/*.arrow" - config_name: afr_Latn data_files: - split: train path: "afr_Latn/train/*.arrow" - config_name: haw_Latn data_files: - split: train path: "haw_Latn/train/*.arrow" - config_name: umb_Latn data_files: - split: train path: "umb_Latn/train/*.arrow" - config_name: hsb_Latn data_files: - split: train path: "hsb_Latn/train/*.arrow" - config_name: fij_Latn data_files: - split: train path: "fij_Latn/train/*.arrow" - config_name: hbs_Cyrl data_files: - split: train path: "hbs_Cyrl/train/*.arrow" - config_name: san_Latn data_files: - split: train path: "san_Latn/train/*.arrow" - config_name: vls_Latn data_files: - split: train path: "vls_Latn/train/*.arrow" - config_name: zsm_Latn data_files: - split: train path: "zsm_Latn/train/*.arrow" - config_name: lij_Latn data_files: - split: train path: "lij_Latn/train/*.arrow" - config_name: quc_Latn data_files: - split: train path: "quc_Latn/train/*.arrow" - config_name: mam_Latn data_files: - split: train path: "mam_Latn/train/*.arrow" - config_name: tls_Latn data_files: - split: train path: "tls_Latn/train/*.arrow" - config_name: tuc_Latn data_files: - split: train path: "tuc_Latn/train/*.arrow" - config_name: dan_Latn data_files: - split: train path: "dan_Latn/train/*.arrow" - config_name: rue_Cyrl data_files: - split: train path: "rue_Cyrl/train/*.arrow" - config_name: ace_Latn data_files: - split: train path: "ace_Latn/train/*.arrow" - config_name: bem_Latn data_files: - split: train path: "bem_Latn/train/*.arrow" - config_name: kam_Latn data_files: - split: train path: "kam_Latn/train/*.arrow" - config_name: kaa_Latn data_files: - split: train path: "kaa_Latn/train/*.arrow" - config_name: ndo_Latn data_files: - split: train path: "ndo_Latn/train/*.arrow" - config_name: oss_Cyrl data_files: - split: train path: "oss_Cyrl/train/*.arrow" - config_name: lit_Latn data_files: - split: train path: "lit_Latn/train/*.arrow" - config_name: frr_Latn data_files: - split: train path: "frr_Latn/train/*.arrow" - config_name: yap_Latn data_files: - split: train path: "yap_Latn/train/*.arrow" - config_name: bzj_Latn data_files: - split: train path: "bzj_Latn/train/*.arrow" - config_name: gom_Latn data_files: - split: train path: "gom_Latn/train/*.arrow" - config_name: swe_Latn data_files: - split: train path: "swe_Latn/train/*.arrow" - config_name: lfn_Latn data_files: - split: train path: "lfn_Latn/train/*.arrow" - config_name: cmn_Hani data_files: - split: train path: "cmn_Hani/train/*.arrow" - config_name: mon_Cyrl data_files: - split: train path: "mon_Cyrl/train/*.arrow" - config_name: vep_Latn data_files: - split: train path: "vep_Latn/train/*.arrow" - config_name: ixl_Latn data_files: - split: train path: "ixl_Latn/train/*.arrow" - config_name: gil_Latn data_files: - split: train path: "gil_Latn/train/*.arrow" - config_name: mau_Latn data_files: - split: train path: "mau_Latn/train/*.arrow" - config_name: tsn_Latn data_files: - split: train path: "tsn_Latn/train/*.arrow" - config_name: aym_Latn data_files: - split: train path: "aym_Latn/train/*.arrow" - config_name: vec_Latn data_files: - split: train path: "vec_Latn/train/*.arrow" - config_name: gom_Deva data_files: - split: train path: "gom_Deva/train/*.arrow" - config_name: fur_Latn data_files: - split: train path: "fur_Latn/train/*.arrow" - config_name: kin_Latn data_files: - split: train path: "kin_Latn/train/*.arrow" - config_name: gcr_Latn data_files: - split: train path: "gcr_Latn/train/*.arrow" - config_name: sgs_Latn data_files: - split: train path: "sgs_Latn/train/*.arrow" - config_name: bih_Deva data_files: - split: train path: "bih_Deva/train/*.arrow" - config_name: vie_Latn data_files: - split: train path: "vie_Latn/train/*.arrow" - config_name: tha_Thai data_files: - split: train path: "tha_Thai/train/*.arrow" - config_name: pau_Latn data_files: - split: train path: "pau_Latn/train/*.arrow" - config_name: est_Latn data_files: - split: train path: "est_Latn/train/*.arrow" - config_name: lue_Latn data_files: - split: train path: "lue_Latn/train/*.arrow" - config_name: rug_Latn data_files: - split: train path: "rug_Latn/train/*.arrow" - config_name: kjb_Latn data_files: - split: train path: "kjb_Latn/train/*.arrow" - config_name: kik_Latn data_files: - split: train path: "kik_Latn/train/*.arrow" - config_name: mri_Latn data_files: - split: train path: "mri_Latn/train/*.arrow" - config_name: ber_Latn data_files: - split: train path: "ber_Latn/train/*.arrow" - config_name: ssw_Latn data_files: - split: train path: "ssw_Latn/train/*.arrow" - config_name: cab_Latn data_files: - split: train path: "cab_Latn/train/*.arrow" - config_name: quz_Latn data_files: - split: train path: "quz_Latn/train/*.arrow" - config_name: arb_Arab data_files: - split: train path: "arb_Arab/train/*.arrow" - config_name: mai_Deva data_files: - split: train path: "mai_Deva/train/*.arrow" - config_name: bew_Cyrl data_files: - split: train path: "bew_Cyrl/train/*.arrow" - config_name: tat_Cyrl data_files: - split: train path: "tat_Cyrl/train/*.arrow" - config_name: mya_Mymr data_files: - split: train path: "mya_Mymr/train/*.arrow" - config_name: alt_Cyrl data_files: - split: train path: "alt_Cyrl/train/*.arrow" - config_name: nno_Latn data_files: - split: train path: "nno_Latn/train/*.arrow" - config_name: hrx_Latn data_files: - split: train path: "hrx_Latn/train/*.arrow" - config_name: hau_Latn data_files: - split: train path: "hau_Latn/train/*.arrow" - config_name: gsw_Latn data_files: - split: train path: "gsw_Latn/train/*.arrow" - config_name: pam_Latn data_files: - split: train path: "pam_Latn/train/*.arrow" - config_name: sun_Latn data_files: - split: train path: "sun_Latn/train/*.arrow" - config_name: lat_Latn data_files: - split: train path: "lat_Latn/train/*.arrow" - config_name: bis_Latn data_files: - split: train path: "bis_Latn/train/*.arrow" - config_name: udm_Cyrl data_files: - split: train path: "udm_Cyrl/train/*.arrow" - config_name: tca_Latn data_files: - split: train path: "tca_Latn/train/*.arrow" - config_name: uig_Arab data_files: - split: train path: "uig_Arab/train/*.arrow" - config_name: glg_Latn data_files: - split: train path: "glg_Latn/train/*.arrow" - config_name: tah_Latn data_files: - split: train path: "tah_Latn/train/*.arrow" - config_name: ckb_Arab data_files: - split: train path: "ckb_Arab/train/*.arrow" - config_name: gle_Latn data_files: - split: train path: "gle_Latn/train/*.arrow" - config_name: lim_Latn data_files: - split: train path: "lim_Latn/train/*.arrow" - config_name: slk_Latn data_files: - split: train path: "slk_Latn/train/*.arrow" - config_name: nds_Latn data_files: - split: train path: "nds_Latn/train/*.arrow" - config_name: kor_Hang data_files: - split: train path: "kor_Hang/train/*.arrow" - config_name: uzb_Latn data_files: - split: train path: "uzb_Latn/train/*.arrow" - config_name: pfl_Latn data_files: - split: train path: "pfl_Latn/train/*.arrow" - config_name: azj_Latn data_files: - split: train path: "azj_Latn/train/*.arrow" - config_name: tgk_Cyrl data_files: - split: train path: "tgk_Cyrl/train/*.arrow" - config_name: glv_Latn data_files: - split: train path: "glv_Latn/train/*.arrow" - config_name: jam_Latn data_files: - split: train path: "jam_Latn/train/*.arrow" - config_name: kat_Geor data_files: - split: train path: "kat_Geor/train/*.arrow" - config_name: fry_Latn data_files: - split: train path: "fry_Latn/train/*.arrow" - config_name: kat_Latn data_files: - split: train path: "kat_Latn/train/*.arrow" - config_name: twi_Latn data_files: - split: train path: "twi_Latn/train/*.arrow" - config_name: eus_Latn data_files: - split: train path: "eus_Latn/train/*.arrow" - config_name: toi_Latn data_files: - split: train path: "toi_Latn/train/*.arrow" - config_name: mlg_Latn data_files: - split: train path: "mlg_Latn/train/*.arrow" - config_name: tyv_Cyrl data_files: - split: train path: "tyv_Cyrl/train/*.arrow" - config_name: arz_Arab data_files: - split: train path: "arz_Arab/train/*.arrow" - config_name: hyw_Armn data_files: - split: train path: "hyw_Armn/train/*.arrow" - config_name: chk_Latn data_files: - split: train path: "chk_Latn/train/*.arrow" - config_name: vol_Latn data_files: - split: train path: "vol_Latn/train/*.arrow" - config_name: kek_Latn data_files: - split: train path: "kek_Latn/train/*.arrow" - config_name: teo_Latn data_files: - split: train path: "teo_Latn/train/*.arrow" - config_name: ell_Grek data_files: - split: train path: "ell_Grek/train/*.arrow" - config_name: kan_Knda data_files: - split: train path: "kan_Knda/train/*.arrow" - config_name: tpi_Latn data_files: - split: train path: "tpi_Latn/train/*.arrow" - config_name: rop_Latn data_files: - split: train path: "rop_Latn/train/*.arrow" - config_name: lua_Latn data_files: - split: train path: "lua_Latn/train/*.arrow" - config_name: mad_Latn data_files: - split: train path: "mad_Latn/train/*.arrow" - config_name: top_Latn data_files: - split: train path: "top_Latn/train/*.arrow" - config_name: scn_Latn data_files: - split: train path: "scn_Latn/train/*.arrow" - config_name: war_Latn data_files: - split: train path: "war_Latn/train/*.arrow" - config_name: ngl_Latn data_files: - split: train path: "ngl_Latn/train/*.arrow" - config_name: mal_Mlym data_files: - split: train path: "mal_Mlym/train/*.arrow" - config_name: szl_Latn data_files: - split: train path: "szl_Latn/train/*.arrow" - config_name: orm_Latn data_files: - split: train path: "orm_Latn/train/*.arrow" - config_name: urd_Arab data_files: - split: train path: "urd_Arab/train/*.arrow" - config_name: cbk_Latn data_files: - split: train path: "cbk_Latn/train/*.arrow" - config_name: tgk_Arab data_files: - split: train path: "tgk_Arab/train/*.arrow" multilinguality: - multilingual pinned: true tags: - multilingual language: - abk - ace - ach - acm - acr - ada - afb - afr - ahk - ajp - aka - aln - als - alt - amh - aoj - apc - ara - arb - arg - arn - ary - arz - asm - ast - aym - ayr - azb - aze - azj - bak - bam - ban - bar - bcl - bel - bem - ber - bew - bih - bik - bis - bjn - bod - bos - bpy - bqc - bre - bsb - bul - bzj - cab - cak - cat - cbk - ceb - ces - che - chk - chv - cjk - ckb - cmn - cos - crh - crs - csb - csy - ctu - cuk - cym - dan - deu - diq - div - djk - dtp - dyu - dzo - ekk - ell - eml - eng - enm - epo - est - eus - ewe - ext - fao - fas - fij - fil - fin - fon - fra - frr - fry - ful - fur - gaa - gcf - gcr - gil - gla - gle - glg - glk - glv - gom - gor - grc - grn - gsw - guc - gug - guj - gym - hat - hau - haw - hbo - hbs - heb - hif - hil - hin - hmn - hmo - hne - hnj - hrv - hrx - hsb - hui - hun - hus - hye - hyw - iba - ibo - ido - ikk - iku - ile - ilo - ina - ind - isl - ita - ixl - jam - jav - jbo - jpn - kaa - kab - kac - kal - kam - kan - kat - kaz - kbd - kbp - kea - kek - khm - kik - kin - kir - kjb - kjh - kmb - kmr - knv - kom - kon - kor - kos - kpg - krc - ksd - ksh - ksw - kua - kur - lao - lat - lfn - lhu - lij - lim - lin - lit - lmo - ltz - lua - lue - lug - luo - lus - lvs - lzh - mad - mah - mai - mal - mam - mar - mau - mco - meu - mgh - mhr - min - miq - mkd - mlg - mlt - mon - mos - mps - mri - msa - mwl - mya - myv - mzh - mzn - nan - nap - naq - nav - nbl - nch - ncj - nde - ndo - nds - nep - new - ngl - ngu - niu - nld - nnb - nno - nob - nor - npi - nso - nya - nyu - oci - ori - orm - ory - oss - ote - pag - pam - pan - pap - pau - pcd - pcm - pes - pfl - pis - pls - plt - pms - pnb - poh - pol - pon - por - prs - pus - qub - quc - que - quh - quw - quy - quz - qvi - rap - rmy - roh - ron - rop - rue - rug - run - sag - sah - san - sat - scn - sco - seh - sgs - sin - slk - slv - sme - smo - sna - snd - som - sot - spa - sqi - srd - srm - srn - srp - ssw - sun - suz - swa - swc - swe - swh - szl - tah - tam - tat - tbz - tca - tdt - teo - tgk - tgl - tha - tir - tlh - tls - toi - toj - tok - ton - top - tpi - tsn - tso - tuc - tuk - tum - tur - tvl - twi - tyv - tzo - udm - uig - ukr - umb - urd - uzb - uzn - vec - ven - vep - vie - vls - vol - wal - war - wbm - wln - wol - wuu - xav - xho - xmf - yao - yap - yid - yom - yor - yue - zai - zea - zho - zlm - zsm - zul pretty_name: Glot500 Corpus --- # Glot500 Corpus A dataset of natural language data collected by putting together more than 150 existing mono-lingual and multilingual datasets together and crawling known multilingual websites. The focus of this dataset is on 500 extremely low-resource languages. (More Languages still to be uploaded here) This dataset is used to train the [Glot500](https://huggingface.co/cis-lmu/glot500-base) model. - **Homepage:** [homepage](https://github.com/cisnlp/Glot500) - **Repository:** [github](https://github.com/cisnlp/Glot500) - **Paper:** [acl](https://aclanthology.org/2023.acl-long.61/), [arxiv](https://arxiv.org/abs/2305.12182) This dataset has the identical data format as the [Taxi1500 Raw Data](https://huggingface.co/datasets/cis-lmu/Taxi1500-RawData) dataset, so that both datasets can be used in parallel seamlessly. Parts of the original Glot500 dataset cannot be published publicly. Please fill out [thi form]{https://docs.google.com/forms/d/1FHto_4wWYvEF3lz7DDo3P8wQqfS3WhpYfAu5vM95-qU/viewform?edit_requested=true} to get access to these parts. ## Usage Replace `nbl_Latn` with your specific language. ```python from datasets import load_dataset dataset = load_dataset('cis-lmu/Glot500', 'nbl_Latn', split='train') print(dataset['train'][0]) # First row of nbl_Latn ``` <details> <summary>Click to show supported languages:</summary> ``` ton_Latn nld_Latn tzo_Latn leh_Latn cuk_Latn ibg_Latn uzb_Cyrl jav_Latn rap_Latn zpa_Latn bak_Cyrl por_Latn quy_Latn ast_Latn cos_Latn fon_Latn sna_Latn dzo_Tibt nob_Latn nch_Latn ish_Latn che_Cyrl ext_Latn ldi_Latn dtp_Latn yue_Hani kbd_Cyrl mar_Deva ron_Latn acr_Latn afb_Arab sqi_Latn eng_Latn ksd_Latn rus_Cyrl bcl_Latn ksh_Latn hin_Latn myv_Cyrl kjh_Cyrl sah_Cyrl gkp_Latn naq_Latn tdt_Latn rmn_Cyrl kac_Latn cak_Latn kir_Cyrl mps_Latn yid_Hebr dhv_Latn srn_Latn div_Thaa mkd_Cyrl idu_Latn bre_Latn bas_Latn ven_Latn pxm_Latn wuu_Hani mwl_Latn miq_Latn kss_Latn wes_Latn slv_Latn hrv_Latn hmo_Latn som_Latn bod_Tibt pls_Latn ile_Latn luo_Latn pus_Arab fao_Latn fas_Arab swa_Latn ifb_Latn ary_Arab tbz_Latn hus_Latn ote_Latn ilo_Latn ctd_Latn abk_Cyrl bqc_Latn hil_Latn pon_Latn zul_Latn als_Latn pes_Arab bpy_Beng bos_Latn sot_Latn lin_Latn tuk_Cyrl gla_Latn wln_Latn apc_Arab hin_Deva hye_Armn tir_Ethi pap_Latn gcf_Latn cjk_Latn pcd_Latn tur_Latn kon_Latn mwn_Latn izz_Latn xho_Latn lam_Latn guc_Latn aka_Latn kea_Latn sme_Latn fat_Latn csb_Latn bak_Latn djk_Latn xav_Latn oci_Latn acm_Arab rmy_Cyrl bim_Latn mck_Latn krc_Cyrl cym_Latn lus_Latn ncx_Latn ngu_Latn yom_Latn tam_Taml ajp_Arab epo_Latn fra_Latn ita_Latn seh_Latn sxn_Latn pdt_Latn hbs_Latn uzn_Cyrl bhw_Latn ksw_Mymr pms_Latn zlm_Latn ami_Latn qub_Latn twx_Latn tsz_Latn kaa_Cyrl toj_Latn toh_Latn kos_Latn ogo_Latn kab_Latn pan_Guru nan_Latn aze_Latn prk_Latn ara_Arab meu_Latn nba_Latn lvs_Latn nbl_Latn loz_Latn crh_Latn bci_Latn kbp_Latn tgl_Latn kmb_Latn hun_Latn nzi_Latn yao_Latn arn_Latn hyw_Cyrl vmw_Latn jbo_Latn mzn_Arab lzh_Hani heb_Hebr cce_Latn bjn_Latn gug_Latn yor_Latn ban_Latn tlh_Latn chv_Cyrl sin_Sinh ind_Latn dua_Latn sid_Latn amh_Ethi zea_Latn kpg_Latn crh_Cyrl nyu_Latn dln_Latn ibo_Latn tih_Latn msa_Latn nap_Latn mgr_Latn bik_Latn srp_Cyrl lao_Laoo guw_Latn kom_Cyrl sop_Latn nde_Latn hui_Latn cfm_Latn new_Deva kur_Arab sco_Latn nyk_Latn lun_Latn suz_Deva wal_Latn asm_Beng rar_Latn san_Deva kaz_Cyrl tog_Latn iba_Latn tuk_Latn nso_Latn run_Latn ctu_Latn bam_Latn fin_Latn gor_Latn kmr_Latn ben_Beng pag_Latn niu_Latn xmf_Geor ekk_Latn tsc_Latn lmo_Latn mhr_Cyrl plt_Latn qvi_Latn roh_Latn oke_Latn mah_Latn tok_Latn mgh_Latn eml_Latn urh_Latn pnb_Arab yua_Latn nav_Latn zne_Latn bin_Latn cat_Latn gym_Latn sat_Olck snd_Arab isl_Latn rmn_Grek bba_Latn kal_Latn aoj_Latn qug_Latn zai_Latn guj_Gujr min_Latn tob_Latn grc_Grek hmn_Latn ido_Latn khm_Khmr ikk_Latn iku_Cans tat_Latn bel_Cyrl dyu_Latn que_Latn efi_Latn quw_Latn nyn_Latn wol_Latn hne_Deva zho_Hani swh_Latn bum_Latn kua_Latn ncj_Latn ewe_Latn hat_Latn ina_Latn mfe_Latn ahk_Latn srm_Latn lug_Latn ach_Latn rmy_Latn tpm_Latn smo_Latn mos_Latn srd_Latn srp_Latn azb_Arab ori_Orya mzh_Latn kur_Latn phm_Latn kwn_Latn crs_Latn ada_Latn ttj_Latn hif_Latn tzh_Latn tdx_Latn bbc_Latn cnh_Latn pcm_Latn tso_Latn nor_Latn bsb_Latn kqn_Latn gaa_Latn ukr_Cyrl lav_Latn nep_Deva kmr_Cyrl ige_Latn pis_Latn lhu_Latn nya_Latn tiv_Latn mny_Latn kri_Latn nyy_Latn poh_Latn nnb_Latn grn_Latn mco_Latn ory_Orya ful_Latn diq_Latn sag_Latn tel_Telu afr_Latn haw_Latn umb_Latn hsb_Latn fij_Latn hbs_Cyrl san_Latn vls_Latn zsm_Latn lij_Latn quc_Latn mam_Latn tuc_Latn dan_Latn rue_Cyrl ace_Latn bem_Latn kam_Latn ndo_Latn mbb_Latn mrw_Latn ajg_Latn oss_Cyrl her_Latn lit_Latn frr_Latn yap_Latn bzj_Latn gom_Latn swe_Latn lfn_Latn cmn_Hani mon_Cyrl vep_Latn ixl_Latn gil_Latn mau_Latn aym_Latn gom_Deva fur_Latn cgg_Latn chw_Latn kin_Latn alz_Latn ndc_Latn gcr_Latn rmn_Latn sgs_Latn bih_Deva skg_Latn bts_Latn vie_Latn tha_Thai tcf_Latn pau_Latn est_Latn lue_Latn rug_Latn gur_Latn kik_Latn mri_Latn ber_Latn ssw_Latn cab_Latn quz_Latn arb_Arab mai_Deva tat_Cyrl mya_Mymr alt_Cyrl nno_Latn nse_Latn hrx_Latn hau_Latn koo_Latn gsw_Latn pam_Latn sun_Latn lat_Latn bis_Latn btx_Latn udm_Cyrl xmv_Latn tca_Latn uig_Arab glg_Latn tah_Latn llb_Latn ckb_Arab gle_Latn lim_Latn slk_Latn nds_Latn kor_Hang uzb_Latn gkn_Latn pfl_Latn azj_Latn glv_Latn jam_Latn kat_Geor abn_Latn fry_Latn kat_Latn twi_Latn eus_Latn toi_Latn mlg_Latn ifa_Latn tyv_Cyrl arz_Arab chk_Latn vol_Latn kek_Latn teo_Latn ell_Grek kan_Knda rng_Latn tpi_Latn mdy_Ethi lua_Latn mad_Latn top_Latn scn_Latn ngl_Latn mal_Mlym szl_Latn orm_Latn nia_Latn urd_Arab mxv_Latn cbk_Latn ``` </details> ## License We don't own any part of the data. The original source of each sentence of the data is indicated in dataset field. To see the copyright license of the original datasets visit [here](https://github.com/cisnlp/Glot500#glot500-c). We license the actual packaging, the metadata and the annotations of these data under the cc0-1.0. If you are a website/dataset owner and do not want your data to be included in this corpra, please send us an email at glot500@cis.lmu.de. ## Ethical Considerations **1. Biases:** The text corpus may reflect the perspectives, opinions, or demographics of its sources or creators. It is important for users to critically evaluate the text in context especially for news sources and social medias. **2. Representativeness:** While we have aimed for diversity and inclusivity, the text corpus may not fully represent all native speakers. Users should be mindful of any potential underrepresentation. **3. Ethics:** We acknowledge that the collection and use of text data can have ethical implications. We have strived to handle the data responsibly, but we encourage users to consider the broader ethical implications of their own research or applications. ## Citation If you use any part of this code and data in your research, please cite it using the following BibTeX entry. ``` @inproceedings{imanigooghari-etal-2023-glot500, title = "Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages", author = {ImaniGooghari, Ayyoob and Lin, Peiqin and Kargaran, Amir Hossein and Severini, Silvia and Jalili Sabet, Masoud and Kassner, Nora and Ma, Chunlan and Schmid, Helmut and Martins, Andr{\'e} and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich}, editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.61", doi = "10.18653/v1/2023.acl-long.61", pages = "1082--1117", abstract = "The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, {``}help{''} from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should notlimit NLP to a small fraction of the world{'}s languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at \url{https://github.com/cisnlp/Glot500}.", } ```

nvidia/HelpSteer2

nvidia

--- license: cc-by-4.0 language: - en pretty_name: HelpSteer2 size_categories: - 10K<n<100K tags: - human-feedback --- # HelpSteer2: Open-source dataset for training top-performing reward models HelpSteer2 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses. This dataset has been created in partnership with [Scale AI](https://scale.com/). When used to tune a [Llama 3.1 70B Instruct Model](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct), we achieve 94.1% on RewardBench, which makes it the best Reward Model as of 1 Oct 2024. This reward model is available on HuggingFace in both .nemo format at [Llama-3.1-Nemotron-70B-Reward](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward) or HF-compatible format at [Llama-3.1-Nemotron-70B-Reward-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward-HF) Using this reward model for RLHF (specifically, REINFORCE), we were able to align a Llama-3.1-70B-Instruct model to reach [AlpacaEval 2 LC](https://tatsu-lab.github.io/alpaca_eval/) of 57.6, [Arena Hard](https://github.com/lmarena/arena-hard-auto) of 85.0 and [GPT-4-Turbo MT-Bench](https://github.com/lm-sys/FastChat/pull/3158) of 8.98, which are known to be predictive of [LMSys Chatbot Arena Elo](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) This Instruct model is available at [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) as .nemo model and [Llama-3.1-Nemotron-70B-Instruct-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) as a HF Transformers model. As of 1 Oct 2024, this aligned model is #1 on all three automatic alignment benchmarks, edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet. See details on HelpSteer2-Preference paper at [https://arxiv.org/abs/2410.01257](https://arxiv.org/abs/2410.01257) - as a preview, this model can correctly the question ```How many r in strawberry?``` without specialized prompting or additional reasoning tokens: ``` A sweet question! Let’s count the “R”s in “strawberry”: 1. S 2. T 3. R 4. A 5. W 6. B 7. E 8. R 9. R 10. Y There are **3 “R”s** in the word “strawberry”. ``` Reward Models was trained using the open-source [NeMo Aligner](https://github.com/NVIDIA/NeMo-Aligner). HelpSteer2 is a follow-up to the popular [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) dataset and we recommend using HelpSteer2 instead of HelpSteer. HelpSteer2 Paper : [HelpSteer2: Open-source dataset for training top-performing reward models](http://arxiv.org/abs/2406.08673) ## RewardBench Primary Dataset LeaderBoard As of 1 Oct 2024, Llama-3.1-Nemotron-70B-Reward performs best Overall on RewardBench as well as with strong performance in Chat, Safety and Reasoning categories among the models below. | Model | Type of Data Used For Training | Overall | Chat | Chat-Hard | Safety | Reasoning | |:-----------------------------|:----------------|:-----|:----------|:-------|:----------|:-----------------------| | _**Llama-3.1-Nemotron-70B-Reward**_ |Permissive Licensed Data Only (CC-BY-4.0) | **94.1** | **97.5** | 85.7 | **95.1** | **98.1** | | Skywork-Reward-Gemma-2-27B | Includes GPT4 Generated Data| 93.8 | 95.8 | **91.4** | 91.9 | 96.1 | | TextEval-Llama3.1-70B | Not disclosed | 93.5 | 94.1 | 90.1 | 93.2 | 96.4 | | Skywork-Critic-Llama-3.1-70B | Not fully disclosed | 93.3 | 96.6 | 87.9 | 93.1 | 95.5 | | SFR-LLaMa-3.1-70B-Judge-r | Not fully disclosed | 92.7 | 96.9 | 84.8 | 91.6 | 97.6 | Nemotron-4-340B-Reward | Permissive Licensed Data Only (CC-BY-4.0) | 92.0 | 95.8 | 87.1 | 91.5 | 93.7 | | ArmoRM-Llama3-8B-v0.1 | Includes GPT4 Generated Data | 90.8 | 96.9 | 76.8 | 92.2 | 97.3 | | Cohere May 2024 | Not disclosed | 89.5 | 96.4 | 71.3 | 92.7 | 97.7 | | Llama3-70B-SteerLM-RM | Permissive Licensed Data Only (CC-BY-4.0) | 88.8 | 91.3 | 80.3 | 92.8 | 90.7 | | Google Gemini Pro 1.5 | Not disclosed | 88.1 | 92.3 | 80.6 | 87.5 | 92.0 | | GPT-4o-2024-08-06 |Not disclosed | 86.7 | 96.1 | 76.1 | 88.1 | 86.6 | | claude-3-5-sonnet-20240620 | Not disclosed | 84.2 | 96.4 | 74.0 | 81.6 | 84.7 | | Meta-Llama-3.1-70B-Instruct | Not fully disclosed | 84.0 | 97.2 | 70.2 | 82.8 | 86.0 | To better understand why Llama-3.1-Nemotron-70B-Reward does less well in the Chat-Hard category, we analyze the scores for each consistutent subset under the Chat-Hard category. We find that on categories that uses human annotations as ground truth, Llama-3.1-Nemotron-70B-Reward performs similar to Skywork-Reward-Gemma-2-27B (<= 2.2% difference). On the other hand, when GPT-4 annotations are used as Ground-Truth, Llama-3.1-Nemotron-70B-Reward trails substantially behind Skywork-Reward-Gemma-2-27B (by 10.8 to 19.2%). This suggests that Skywork-Reward-Gemma-2-27B can better modelling GPT-4 preferences (but not human-annotated preferences), likely contributed by the inclusion of GPT-4 annotated training data used to train it found in the [OffSetBias dataset](https://huggingface.co/datasets/NCSOFT/offsetbias) as part of the [Skywork-Reward-Preference-80k](https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.1). | Model | Type of Data Used For Training | Chat-Hard | LLMBar-Adversarial-Manual | LLMBar-Adversarial-Neighbour | LLMBar-Natural | LLMBar-Adversarial-GPTInst | LLMBar-Adversarial-GPTOut | MT-Bench-Hard| |:-----------------------------|:----------------|:-----|:----------|:-------|:----------|:-----------------------|:-----------------------|:-----------------------| |||| Human as Ground Truth | Human as Ground Truth | Human as Ground Truth | _GPT-4 as Ground Truth_ |_GPT-4 as Ground Truth_ | _GPT-4 as Ground Truth_ | | Llama-3.1-Nemotron-70B-Reward |Permissive Licensed Data Only (CC-BY-4.0) | 85.7 | 76.1 | 88.8 | 95.0 | 87.0 | 72.3 | 75.7 | Skywork-Reward-Gemma-2-27B | Includes GPT4 Generated Data | 91.4 | 78.3 | 89.6 | 96.0 | 97.8 | 91.5 | 86.5| ## Dataset Description HelpSteer contains 21, 362 samples, each containing a prompt, a response as well as five human-annotated attributes of the response, each ranging between 0 and 4 where higher means better for each attribute. Consecutive samples (e.g. sample 1 with 2, 3 with 4 ...) share the same prompt so this can be used for preference pairs based on the helpfulness score (e.g. training DPO or Preference RM), in addition to training SteerLM Regression RM. About 29% of all prompts used are multi-turn. In this case, the prompt consists of all of the user turns and all but the last assistant turn, which is contained in the response field. This is done because the attribute values only assessed only for the last assistant turn. For multi-turn prompts, the structure of prompts look like below. This is how we used it for training SteerLM Regression Reward Models, but you need to use it in other settings, please adjust as required. ```json {user turn 1} <extra_id_1>Assistant {assistant turn 1} <extra_id_1>User {user turn 2} ``` These attributes are: 1. **Helpfulness**: Overall helpfulness of the response to the prompt. 2. **Correctness**: Inclusion of all pertinent facts without errors. 3. **Coherence**: Consistency and clarity of expression. 4. **Complexity**: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise). 5. **Verbosity**: Amount of detail included in the response, relative to what is asked for in the prompt. An example looks like ```json { "prompt": "c#", "response": "C# is a high-level, object-oriented programming language developed by Microsoft as part of its .NET initiative. It was created as a modern alternative to Java and supports a variety of programming paradigms, including imperative, functional, and event-driven. C# is primarily used for Windows application development, but it can also be used for web, mobile, and game development. The language is designed to be safe, secure, and efficient, and it provides developers with a rich set of libraries and tools for building robust and scalable applications. C# is also widely used in the game development industry, particularly in the development of games for the Xbox 360 and Xbox One consoles.", "helpfulness": 3, "correctness": 4, "coherence": 4, "complexity": 2, "verbosity": 1 } ``` ## Preferences (NEW - 1 Oct 2024) We release the preference annotations between the responses. While existing HelpSteer2 can be use for Bradley-Terry Modelling (by taking difference in helpfulness), we also collected dedicated annotation for which among two responses they prefer. We recommend using this set of annotation for Bradley-Terry Modelling because it accounts for the situation that one response can be preferred over the other even when they have the same helpfulness score (e.g. 4) For each sample, we provide: **Preference Strength** (one of the following - score: meaning) -3: Response 1 is much better than Response 2 -2: Response 1 is better than Response 2 -1: Response 1 is slightly better than Response 2 0: Response 1 is about the same as Response 2 1: Response 2 is slightly better than Response 1 2: Response 2 is better than Response 1 3: Response 2 is much better than Response 1 **Human-written Preference Justification** This has been post-processed by us into a Preference statement and a Preference elaboration. **Three Most Similar Preferences** In calculating overall preference, we only consider the most similar preferences in order to filter outliers. **All Preferences Unprocessed** We did not use these in our experiments but we release all preferences in case someone finds them helpful. **Split** For clarity, the ```train``` subset of the ```preference``` annotations only contains prompts and responses from the original HelpSteer2 ```train``` set, as is for the validation subset. This represents our best attempt at doing a data matched comparison of the two data collection approaches. An example looks like ```json { "split": "train", "prompt": "Define Signal Discuss its various properties with the help of diagram", "response_1": "A signal is a form of energy that is used to transmit information from one place to another. It can be in the form of sound, light, radio waves, or any other form of energy that can be detected by a sensor or receiver.\n\nThe properties of a signal can be described by its amplitude, frequency, and phase.\n\nAmplitude: The amplitude of a signal is the measure of its strength or intensity. It is usually measured in volts or watts.\n\nFrequency: The frequency of a signal is the measure of how many times it repeats itself in a given period of time. It is usually measured in Hertz (Hz).\n\nPhase: The phase of a signal is the measure of its position in time. It is usually measured in degrees or radians.\n\nA signal can be represented by a graph that shows its amplitude or intensity over time. This graph is called a waveform. The shape of the waveform can provide information about the type of signal and the information it carries.", "response_2": "A signal is a message that is conveyed from a sender to a receiver through a communication channel. The message can be in the form of a sound, light, electrical, or any other form that can be transmitted and received. The properties of a signal can vary depending on the type of signal and the communication channel used. Here are some common properties of signals:\n\nAmplitude: This is the maximum value that a signal can take. It represents the strength of the signal. For example, in an electrical signal, amplitude can be measured in volts.\n\nFrequency: This is the number of cycles or oscillations of a signal per unit time. It is measured in hertz (Hz). For example, a sound signal with a frequency of 20 Hz vibrates 20 times per second.\n\nWavelength: This is the distance between two successive crests or troughs of a signal. It is measured in meters (m). For example, in a light signal, wavelength can be measured in nanometers (nm).\n\nPhase: This is the position of a signal with respect to a reference signal. It is measured in degrees (\u00b0) or radians (rad). For example, in an electrical signal, phase can be measured in relation to a reference signal.\n\nEnvelope: This is the curve that bounds the amplitude of a signal. It can be used to represent the shape of a signal. For example, in an audio signal, the envelope can represent the shape of a musical note.\n\nNoise: This is any unwanted or random variation in a signal that can affect its quality. It can be caused by various sources such as interference, interference, or natural phenomena.\n\nHere is a diagram that represents the properties of a signal:\n\n\nAmplitude\nFrequency\nWavelength\nPhase\nEnvelope\nNoise\n\n\nThe diagram shows how each property can affect the shape and quality of a signal. The presence of noise can degrade the quality of a signal and make it difficult to interpret. The properties of a signal can be controlled and optimized to ensure that the message is conveyed accurately and efficiently.", "preference_strength": 1, "preference_statement": "@Response 2 is better than @Response 1 because it provides a comprehensive insightful explanation of signanal and its properties.", "preference_elaboration": "It is complete, clear and correct as it discuss all the the poperties of signal while @Response 1 only discusses three properties of signal. It does not diuscuss important properties like noise, phase and envelope. @Response 2 follows all the instruction but @Response 1 does not follow all the instruction. For instance the instruction requires an explanation of signal and its properties with an aid of a diagram but @Response 1 does not provide the diagram.", "three_most_similar_preferences": [ { "statement": "@Response 2 is better than @Response 1 because it provides a comprehensive insightful explanation of signanal and its properties.", "elaboration": "It is complete, clear and correct as it discuss all the the poperties of signal while @Response 1 only discusses three properties of signal. It does not diuscuss important properties like noise, phase and envelope. @Response 2 follows all the instruction but @Response 1 does not follow all the instruction. For instance the instruction requires an explanation of signal and its properties with an aid of a diagram but @Response 1 does not provide the diagram.", "strength": 1 }, { "statement": "@Response 2 is slightly better than @Response 1.", "elaboration": "@Response 2 goes into detail about the different types of signals that can be used for transmittal. Providing these topics gives a full overview of Signal Discuss. That makes this prompt complete, extremely helpful, and it is well-written. This response uses a paragraph format which breaks up the change in topic. @Response 1 covers a signal in less detail. It leaves out wavelengths, noise, and envelop as a way to transmit information from one network to another. This is not necessarily bad, but it is not in full detail.", "strength": 1 }, { "statement": "@Response 2 is slightly better than @Response 1 because it includes the diagram as requested by the prompt, which @Response 1 does not.", "elaboration": "However, @Response 2 does have issues with **correctness**: irrelevant terms like \"envelope\" are typically properties of the diagram, not the signal. **Formatting** could also be improved for @Response 2. While the diagram is included, it does not display correctly and the word \"interference\" is erroneously repeated twice.", "strength": 1 } ], "all_preferences_unprocessed": [ { "strength": 1, "justification": "@Response 2 is better than @Response 1 because it provides a comprehensive insightful explanation of signanal and its properties. It is complete, clear and correct as it discuss all the the poperties of signal while @Response 1 only discusses three properties of signal. It does not diuscuss important properties like noise, phase and envelope. @Response 2 follows all the instruction but @Response 1 does not follow all the instruction. For instance the instruction requires an explanation of signal and its properties with an aid of a diagram but @Response 1 does not provide the diagram." }, { "strength": 1, "justification": "@Response 2 is slightly better than @Response 1. @Response 2 goes into detail about the different types of signals that can be used for transmittal. Providing these topics gives a full overview of Signal Discuss. That makes this prompt complete, extremely helpful, and it is well-written. This response uses a paragraph format which breaks up the change in topic. @Response 1 covers a signal in less detail. It leaves out wavelengths, noise, and envelop as a way to transmit information from one network to another. This is not necessarily bad, but it is not in full detail." }, { "strength": 1, "justification": "@Response 2 is slightly better than @Response 1 because it includes the diagram as requested by the prompt, which @Response 1 does not. However, @Response 2 does have issues with **correctness**: irrelevant terms like \"envelope\" are typically properties of the diagram, not the signal. **Formatting** could also be improved for @Response 2. While the diagram is included, it does not display correctly and the word \"interference\" is erroneously repeated twice. Although @Response 1 is more succinct and the writing style flows better, it falls short in **instructions following** and therefore @Response 2 is slightly better." } ] } ``` ## Disagreements (NEW - 1 Oct 2024) We release the individual annotations that were used to calculate the mean score (after filtering outliers) for the five HelpSteer attributes. The schema is similar to HelpSteer2, except that each entry is a list of labels given by various annotators. We believe this resource support studies on how/why annotators disagree with one another as well as transparency on the data collection process. Note: A small proportion of samples only annotations by 2 annotators - this is because one or more of the annotations were labelled as not-ratable by annotators or did not have a valid annotation. Here's an example below: ```json { "prompt": "c#", "response": "C# is a high-level, object-oriented programming language developed by Microsoft as part of its .NET initiative. It was created as a modern alternative to Java and supports a variety of programming paradigms, including imperative, functional, and event-driven. C# is primarily used for Windows application development, but it can also be used for web, mobile, and game development. The language is designed to be safe, secure, and efficient, and it provides developers with a rich set of libraries and tools for building robust and scalable applications. C# is also widely used in the game development industry, particularly in the development of games for the Xbox 360 and Xbox One consoles.", "helpfulness": [ 3, 3, 4 ], "correctness": [ 3, 4, 4 ], "coherence": [ 4, 3, 4 ], "complexity": [ 2, 2, 2 ], "verbosity": [ 2, 1, 1 ] } ``` ## Using the Huggingface Datasets ```python from datasets import load_dataset ds = load_dataset("nvidia/HelpSteer2") train = ds['train'] # len(train) = 20324 (95%) val = ds['validation'] # len(val) = 1038 (5%) preference = load_dataset("nvidia/HelpSteer2", data_dir="preference")['train'] # despite the name, this contains both train and val, which you can use split to distinguish disagreements = load_dataset("nvidia/HelpSteer2", data_dir="disagreements")['train'] ``` ## Source 1. Prompts are collected based on mostly user-contributed ShareGPT prompts and with a small proportion (~5%) that are human generated by Scale AI. 2. Responses are generated by early versions of a mix of 10 different inhouse LLMs (note: none from properitary LLM providers such as OpenAI). We generate 2 responses per prompts (each from a different model) using sampling techniques to give diverse yet reasonable responses. 3. Annotations of various attributes were done by Scale AI. Annotators rated each response on a Likert 5 scale (between 0 and 4) for each attribute (helpfulness, correctness, coherence, complexity and verbosity). ## Annotation methodology (short) 1. We engaged a select group of contractors via Scale AI. These contractors were provided with comprehensive guidelines that defined each attribute and the criteria for every rating level, together with some annotated examples. These guidelines and examples are detailed in the Appendix of the accompanying paper. 2. The annotation process involved approximately 1000 U.S.-based human annotators. Candidates first underwent preliminary assignments, including assessments of English proficiency, to determine eligibility for working on the project. Subsequently, they participated in an introductory training course on the task which ended with a test that involved annotating 35 sample responses. This process ensured not only a thorough understanding of the task requirements but also the delivery of high-quality annotations. 3. Every sample was independently annotated by a minimum of three annotators and up to five annotators, if the initial annotators do not agree with each other sufficiently (2 points or less on helpfulness). The final annotations (mean of 3.41 annotators) were obtain by taking the mean of the three annotators who agree with each other most, rounded to the nearest integer. 4. Post-annotations, Scale AI performed extensive quality assurance, with each annotation reaching a minimum of two human reviews in addition to automated checks. After receiving the annotations from Scale AI, we conducted our independent quality assurance to make sure that the quality of the annotations was up to our expectations. As a result, many annotations were filtered away to retain only 20, 324 samples. ## Ethical statement Annotators for the dataset were contracted through Scale AI. Scale AI engages the Anker Methodology, GISC Impact Sourcing Standard, and UN Sustainable Development Goals to provide a fair and competitive pay. The specific pay is calculated based on many factors, including the specific project, the specialized skillset and expertise required, regional costs of living and then transparently listed on Scale AI platform. Scale AI also provides multiple channels for questions and support, including 24/7 support teams, community discussion channels with specially trained moderators, and a “speak up” hotline where contractors can report concerns anonymously. Worker concerns can be submitted to and are reviewed by our Remotasks support team, and pay disputes are reviewed by support specialists trained in this area. ## Contact E-Mail: [Zhilin Wang](mailto:zhilinw@nvidia.com) ## Citation If you find this dataset useful, please cite the following works ```bibtex @misc{wang2024helpsteer2preferencecomplementingratingspreferences, title={HelpSteer2-Preference: Complementing Ratings with Preferences}, author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong}, year={2024}, eprint={2410.01257}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2410.01257}, } @misc{wang2024helpsteer2, title={HelpSteer2: Open-source dataset for training top-performing reward models}, author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev}, year={2024}, eprint={2406.08673}, archivePrefix={arXiv}, primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'} } ```

tau/commonsense_qa

tau

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: commonsenseqa pretty_name: CommonsenseQA dataset_info: features: - name: id dtype: string - name: question dtype: string - name: question_concept dtype: string - name: choices sequence: - name: label dtype: string - name: text dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 2207794 num_examples: 9741 - name: validation num_bytes: 273848 num_examples: 1221 - name: test num_bytes: 257842 num_examples: 1140 download_size: 1558570 dataset_size: 2739484 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "commonsense_qa" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.tau-nlp.org/commonsenseqa - **Repository:** https://github.com/jonathanherzig/commonsenseqa - **Paper:** https://arxiv.org/abs/1811.00937 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 4.68 MB - **Size of the generated dataset:** 2.18 MB - **Total amount of disk used:** 6.86 MB ### Dataset Summary CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers. The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation split, and "Question token split", see paper for details. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The dataset is in English (`en`). ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 4.68 MB - **Size of the generated dataset:** 2.18 MB - **Total amount of disk used:** 6.86 MB An example of 'train' looks as follows: ``` {'id': '075e483d21c29a511267ef62bedc0461', 'question': 'The sanctions against the school were a punishing blow, and they seemed to what the efforts the school had made to change?', 'question_concept': 'punishing', 'choices': {'label': ['A', 'B', 'C', 'D', 'E'], 'text': ['ignore', 'enforce', 'authoritarian', 'yell at', 'avoid']}, 'answerKey': 'A'} ``` ### Data Fields The data fields are the same among all splits. #### default - `id` (`str`): Unique ID. - `question`: a `string` feature. - `question_concept` (`str`): ConceptNet concept associated to the question. - `choices`: a dictionary feature containing: - `label`: a `string` feature. - `text`: a `string` feature. - `answerKey`: a `string` feature. ### Data Splits | name | train | validation | test | |---------|------:|-----------:|-----:| | default | 9741 | 1221 | 1140 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under the MIT License. See: https://github.com/jonathanherzig/commonsenseqa/issues/5 ### Citation Information ``` @inproceedings{talmor-etal-2019-commonsenseqa, title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge", author = "Talmor, Alon and Herzig, Jonathan and Lourie, Nicholas and Berant, Jonathan", booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N19-1421", doi = "10.18653/v1/N19-1421", pages = "4149--4158", archivePrefix = "arXiv", eprint = "1811.00937", primaryClass = "cs", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

lmms-lab/DocVQA

lmms-lab

--- license: apache-2.0 dataset_info: - config_name: DocVQA features: - name: questionId dtype: string - name: question dtype: string - name: question_types sequence: string - name: image dtype: image - name: docId dtype: int64 - name: ucsf_document_id dtype: string - name: ucsf_document_page_no dtype: string - name: answers sequence: string - name: data_split dtype: string splits: # - name: train # num_bytes: 5659006943.631 # num_examples: 39463 - name: validation num_bytes: 2532447207.066 num_examples: 5349 - name: test num_bytes: 2500408525.732 num_examples: 5188 download_size: 9555791945 dataset_size: 10691862676.428999 - config_name: InfographicVQA features: - name: questionId dtype: string - name: question dtype: string - name: answers sequence: string - name: answer_type sequence: string - name: image dtype: image - name: image_url dtype: string - name: operation/reasoning sequence: string - name: ocr dtype: string - name: data_split dtype: string splits: # - name: train # num_bytes: 11559694546.32 # num_examples: 23946 - name: validation num_bytes: 1863177404.253 num_examples: 2801 - name: test num_bytes: 1851304047.712 num_examples: 3288 download_size: 2544892079 dataset_size: 15274175998.285 configs: - config_name: DocVQA data_files: # - split: train # path: DocVQA/train-* - split: validation path: DocVQA/validation-* - split: test path: DocVQA/test-* - config_name: InfographicVQA data_files: # - split: train # path: InfographicVQA/train-* - split: validation path: InfographicVQA/validation-* - split: test path: InfographicVQA/test-* --- <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [DocVQA](https://arxiv.org/abs/2007.00398). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{mathew2020docvqa, title={DocVQA: A Dataset for VQA on Document Images. CoRR abs/2007.00398 (2020)}, author={Mathew, Minesh and Karatzas, Dimosthenis and Manmatha, R and Jawahar, CV}, journal={arXiv preprint arXiv:2007.00398}, year={2020} } ```

DL3DV/DL3DV-ALL-480P

DL3DV

--- tags: - 3D Vision - NeRF - 3D Gaussian - Dataset - Novel View Synthesis - Text to 3D - Image to 3D pretty_name: Dl3DV-Dataset size_categories: - 100B<n<1T --- # DL3DV-Dataset This repo has all the 480P frames with camera poses of DL3DV-10K Dataset. We are working hard to review all the dataset to avoid sensitive information. Thank you for your patience. # Download If you have enough space, you can use git to download a dataset from huggingface. See this [link](https://huggingface.co/docs/hub/en/datasets-downloading). [480P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P)/[960P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-960P) versions should satisfies most needs. If you do not have enough space, we further provide a [download script](https://github.com/DL3DV-10K/Dataset/blob/main/scripts/download.py) here to download a subset. The usage: ```Bash usage: download.py [-h] --odir ODIR --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} --resolution {4K,2K,960P,480P} --file_type {images+poses,video,colmap_cache} [--hash HASH] [--clean_cache] optional arguments: -h, --help show this help message and exit --odir ODIR output directory --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} The subset of the benchmark to download --resolution {4K,2K,960P,480P} The resolution to donwnload --file_type {images+poses,video,colmap_cache} The file type to download --hash HASH If set subset=hash, this is the hash code of the scene to download --clean_cache If set, will clean the huggingface cache to save space ``` Here are some examples: ```Bash # Make sure you have applied for the access. # Use this to download the download.py script wget https://raw.githubusercontent.com/DL3DV-10K/Dataset/main/scripts/download.py # Download 480P resolution images and poses, 0~1K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 1K --resolution 480P --file_type images+poses --clean_cache # Download 480P resolution images and poses, 1K~2K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 2K --resolution 480P --file_type images+poses --clean_cache ``` You can also download a specific scene with its hash. The scene-hash pair visualization can be found [here](https://htmlpreview.github.io/?https://github.com/DL3DV-10K/Dataset/blob/main/visualize/index.html). ```Bash # Download 480P resolution images and poses, 1K~2K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 2K --resolution 480P --file_type images+poses --hash e2cedefea8a0ed2d0ffbd5bdc08acbe7e1f85c96f72f7b790e9dfe1c98963047 --clean_cache ``` # News - [x] DL3DV-1K, 2K, 3K, 4K - [ ] DL3DV-5K ~ 10K

fixie-ai/librispeech_asr

fixie-ai

--- language: - en dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: test num_bytes: 623948478.48 num_examples: 2620 - name: validation num_bytes: 622190064.956 num_examples: 2703 - name: train.360 num_bytes: 41953890926.124 num_examples: 104014 - name: train.100 num_bytes: 11606313661.774 num_examples: 28539 download_size: 53886816833 dataset_size: 54806343131.334 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: train.500 num_bytes: 57330687390.808 num_examples: 148688 - name: validation num_bytes: 591511495.496 num_examples: 2864 - name: test num_bytes: 616939198.113 num_examples: 2939 download_size: 57019309170 dataset_size: 58539138084.417 configs: - config_name: clean data_files: - split: test path: clean/test-* - split: validation path: clean/validation-* - split: train.360 path: clean/train.360-* - split: train.100 path: clean/train.100-* - config_name: other data_files: - split: train.500 path: other/train.500-* - split: validation path: other/validation-* - split: test path: other/test-* ---

ai4bharat/sangraha

ai4bharat

--- license: cc-by-4.0 task_categories: - text-generation language: - as - bn - gu - en - hi - kn - ks - ml - mr - ne - or - pa - sa - sd - ta - te - ur tags: - language-modeling - casual-lm - llm pretty_name: sangraha dataset_info: - config_name: verified features: - name: doc_id dtype: string - name: type dtype: string - name: text dtype: string splits: - name: asm - name: ben - name: brx - name: doi - name: eng - name: gom - name: guj - name: hin - name: kan - name: kas - name: mai - name: mal - name: mar - name: mni - name: nep - name: ori - name: pan - name: san - name: sat - name: snd - name: tam - name: tel - name: urd - config_name: unverified features: - name: doc_id dtype: string - name: text dtype: string splits: - name: asm - name: ben - name: guj - name: hin - name: kan - name: mal - name: mar - name: nep - name: ori - name: pan - name: san - name: tam - name: tel - name: urd - config_name: synthetic features: - name: doc_id dtype: string - name: text dtype: string splits: - name: asm_Beng - name: asm_Latn - name: ben_Beng - name: ben_Latn - name: guj_Gujr - name: guj_Latn - name: hin_Deva - name: hin_Latn - name: kan_Knda - name: kan_Latn - name: mal_Mlym - name: mal_Latn - name: mar_Deva - name: mar_Latn - name: npi_Deva - name: npi_Latn - name: ory_Orya - name: ory_Latn - name: pan_Guru - name: pan_Latn - name: san_Deva - name: san_Latn - name: tam_Taml - name: tam_Latn - name: tel_Telu - name: tel_Latn - name: urd_Arab - name: urd_Latn configs: - config_name: verified data_files: - split: asm path: verified/asm/*.parquet - split: ben path: verified/ben/*.parquet - split: brx path: verified/brx/*.parquet - split: doi path: verified/doi/*.parquet - split: eng path: verified/eng/*.parquet - split: gom path: verified/gom/*.parquet - split: guj path: verified/guj/*.parquet - split: hin path: verified/hin/*.parquet - split: kan path: verified/kan/*.parquet - split: kas path: verified/kas/*.parquet - split: mai path: verified/mai/*.parquet - split: mal path: verified/mal/*.parquet - split: mar path: verified/mar/*.parquet - split: mni path: verified/mni/*.parquet - split: nep path: verified/nep/*.parquet - split: ori path: verified/ori/*.parquet - split: pan path: verified/pan/*.parquet - split: san path: verified/san/*.parquet - split: sat path: verified/sat/*.parquet - split: snd path: verified/snd/*.parquet - split: tam path: verified/tam/*.parquet - split: tel path: verified/tel/*.parquet - split: urd path: verified/urd/*.parquet - config_name: unverified data_files: - split: asm path: unverified/asm/*.parquet - split: ben path: unverified/ben/*.parquet - split: guj path: unverified/guj/*.parquet - split: hin path: unverified/hin/*.parquet - split: kan path: unverified/kan/*.parquet - split: mal path: unverified/mal/*.parquet - split: mar path: unverified/mar/*.parquet - split: nep path: unverified/nep/*.parquet - split: ori path: unverified/ori/*.parquet - split: pan path: unverified/pan/*.parquet - split: san path: unverified/san/*.parquet - split: tam path: unverified/tam/*.parquet - split: tel path: unverified/tel/*.parquet - split: urd path: unverified/urd/*.parquet - config_name: synthetic data_files: - split: asm_Beng path: synthetic/asm_Beng/*.parquet - split: asm_Latn path: synthetic/asm_Latn/*.parquet - split: ben_Beng path: synthetic/ben_Beng/*.parquet - split: ben_Latn path: synthetic/ben_Latn/*.parquet - split: guj_Gujr path: synthetic/guj_Gujr/*.parquet - split: guj_Latn path: synthetic/guj_Latn/*.parquet - split: hin_Deva path: synthetic/hin_Deva/*.parquet - split: hin_Latn path: synthetic/hin_Latn/*.parquet - split: kan_Knda path: synthetic/kan_Knda/*.parquet - split: kan_Latn path: synthetic/kan_Latn/*.parquet - split: mal_Mlym path: synthetic/mal_Mlym/*.parquet - split: mal_Latn path: synthetic/mal_Latn/*.parquet - split: mar_Deva path: synthetic/mar_Deva/*.parquet - split: mar_Latn path: synthetic/mar_Latn/*.parquet - split: npi_Deva path: synthetic/npi_Deva/*.parquet - split: npi_Latn path: synthetic/npi_Latn/*.parquet - split: ory_Orya path: synthetic/ory_Orya/*.parquet - split: ory_Latn path: synthetic/ory_Latn/*.parquet - split: pan_Guru path: synthetic/pan_Guru/*.parquet - split: pan_Latn path: synthetic/pan_Latn/*.parquet - split: san_Deva path: synthetic/san_Deva/*.parquet - split: san_Latn path: synthetic/san_Latn/*.parquet - split: tam_Taml path: synthetic/tam_Taml/*.parquet - split: tam_Latn path: synthetic/tam_Latn/*.parquet - split: tel_Telu path: synthetic/tel_Telu/*.parquet - split: tel_Latn path: synthetic/tel_Latn/*.parquet - split: urd_Arab path: synthetic/urd_Arab/*.parquet - split: urd_Latn path: synthetic/urd_Latn/*.parquet size_categories: - 100B<n<1T --- # Sangraha <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63ef3cd11e695b35aa48bebc/nDnyidcqIOLAP9dTw9GrK.png" /> </p> Sangraha is the largest high-quality, cleaned Indic language pretraining data containing 251B tokens summed up over 22 languages, extracted from curated sources, existing multilingual corpora and large scale translations. **Coming Soon**: - Sangraha Synthetic - Translated and Romanised English Wikimedia data. - Sangraha Verified - Hindi YouTube transcribed data. **More information**: - For detailed information on the curation and cleaning process of Sangraha, please checkout our paper [on Arxiv](https://arxiv.org/abs/2403.06350); - Check out the scraping and cleaning pipelines used to curate Sangraha [on GitHub](https://github.com/AI4Bharat/IndicLLMSuite); ## Getting Started For downloading the entire Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha") ``` For downloading a subset (Verified/Unverified) of Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha", data_dir="<subset_name>") # for example: dataset = load_dataset("ai4bharat/sangraha", data_dir="verified") ``` For downloading one language from a subset of Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha", data_dir="<subset_name>/<lang_code>") # for example: dataset = load_dataset("ai4bharat/sangraha", data_dir="verified/asm") ``` ## Background Sangraha contains three broad components: - **Sangraha Verified**: Containing scraped data from "human-verified" Websites, OCR-extracted data from high quality Indic language PDFs, transcribed data from various Indic language videos, podcasts, movies, courses, etc. - **Sangraha Unverfied**: High quality Indic language data extracted from existing multilingual corpora employing perplexity filtering using n-gram language models trained on Sangraha Verified. - **Sangraha Synthetic**: WikiMedia English translated to 14 Indic languages and further "romanised" from 14 languages by transliteration to English. ## Data Statistics | **Lang Code** | **Verified** | **Synthetic** | **Unverified** | **Total Tokens (in Millions)** | | ------------- | ------------ | ------------- | -------------- | ------------------------------ | | asm | 292.1 | 11,696.4 | 17.5 | 12,006.0 | | ben | 10,604.4 | 13,814.1 | 5,608.8 | 30,027.5 | | brx | 1.5 | - | - | 1.5 | | doi | 0.06 | - | - | 0.06 | | eng | 12,759.9 | - | - | 12,759.9 | | gom | 10.1 | - | - | 10.1 | | guj | 3,647.9 | 12,934.5 | 597.0 | 17,179.4 | | hin | 12,617.3 | 9,578.7 | 12,348.3 | 34,544.3 | | kan | 1,778.3 | 12,087.4 | 388.8 | 14,254.5 | | kas | 0.5 | - | - | 0.5 | | mai | 14.6 | - | - | 14.6 | | mal | 2,730.8 | 13,130.0 | 547.8 | 16,408.6 | | mar | 2,827.0 | 10,816.7 | 652.1 | 14,295.8 | | mni | 7.4 | - | - | 7.4 | | npi | 1,822.5 | 10,588.7 | 485.5 | 12,896.7 | | ori | 1,177.1 | 11,338.0 | 23.7 | 12,538.8 | | pan | 1,075.3 | 9,969.6 | 136.9 | 11,181.8 | | san | 1,329.0 | 13,553.5 | 9.8 | 14,892.3 | | sat | 0.3 | - | - | 0.3 | | snd | 258.2 | - | - | 258.2 | | tam | 3,985.1 | 11,859.3 | 1,515.9 | 17,360.3 | | urd | 3,658.1 | 9,415.8 | 1,328.2 | 14,402.1 | | tel | 3,706.8 | 11,924.5 | 647.4 | 16,278.7 | | **Total** | **64,306.1** | **162,707.9** | **24,307.7** | **251,321.0** | To cite Sangraha, please use: ``` @article{khan2024indicllmsuite, title = {IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages}, author = {Mohammed Safi Ur Rahman Khan and Priyam Mehta and Ananth Sankar and Umashankar Kumaravelan and Sumanth Doddapaneni and Suriyaprasaad G and Varun Balan G and Sparsh Jain and Anoop Kunchukuttan and Pratyush Kumar and Raj Dabre and Mitesh M. Khapra}, year = {2024}, journal = {arXiv preprint arXiv: 2403.06350} } ```

cfilt/IITB-IndicMonoDoc

cfilt

--- license: cc-by-4.0 task_categories: - text-generation language: - hi - mr - gu - sa - ta - te - ml - ne - as - bn - ks - or - pa - ur - sd - kn size_categories: - 10B<n<100B tags: - language-modeling - llm - clm viewer: false --- IITB Document level Monolingual Corpora for Indian languages. 22 scheduled languages of India + English (1) Assamese, (2) Bengali, (3) Gujarati, (4) Hindi, (5) Kannada, (6) Kashmiri, (7) Konkani, (8) Malayalam, (9) Manipuri, (10) Marathi, (11) Nepali, (12) Oriya, (13) Punjabi, (14) Sanskrit, (15) Sindhi, (16) Tamil, (17) Telugu, (18) Urdu (19) Bodo, (20) Santhali, (21) Maithili and (22) Dogri. | Language | Total (#Mil Tokens) | |:---------:|:--------------------:| | bn | 5258.47 | | en | 11986.53 | | gu | 887.18 | | hi | 11268.33 | | kn | 567.16 | | ml | 845.32 | | mr | 1066.76 | | ne | 1542.39 | | pa | 449.61 | | ta | 2171.92 | | te | 767.18 | | ur | 2391.79 | | as | 57.64 | | brx | 2.25 | | doi | 0.37 | | gom | 2.91 | | kas | 1.27 | | mai | 1.51 | | mni | 0.99 | | or | 81.96 | | sa | 80.09 | | sat | 3.05 | | sd | 83.81 | | Total= | 39518.51 | To cite this dataset: ``` @misc{doshi2024worry, title={Do Not Worry if You Do Not Have Data: Building Pretrained Language Models Using Translationese}, author={Meet Doshi and Raj Dabre and Pushpak Bhattacharyya}, year={2024}, eprint={2403.13638}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

k4d3/pixel_art

k4d3

--- license: wtfpl ---

alfredplpl/artbench-pd-256x256

alfredplpl

--- language: - en license: mit size_categories: - 10K<n<100K task_categories: - text-to-image - image-to-text dataset_info: features: - name: image dtype: image - name: caption dtype: string - name: caption_jp dtype: string - name: artist dtype: string - name: url dtype: string splits: - name: train num_bytes: 1494472790.952 num_examples: 46298 download_size: 1517381098 dataset_size: 1494472790.952 configs: - config_name: default data_files: - split: train path: data/train-* tags: - art --- # Dataset Card for ArtBench Public Domain 256x256  [日本語はこちら](README_JA.md) This repository is the subset of ArtBench. [ArtBench](https://github.com/liaopeiyuan/artbench) is the dataset for historical arts such as Art Nouveau and Ukiyo-e. I picked up public domain images from ArtBench. Then, I create new dataset. # Usage You can use huggingface datasets to download the dataset. You can also download [the tar file](artbench-pd-256x256.tar). ```python from datasets import load_dataset dataset = load_dataset("alfredplpl/artbench-pd-256x256") ``` # Intended Use - Research Vision & Language - Develop text-to-image model or image-to-text model. # Procedure - caption We captioned the images by Florence-2-large. We gave the model DETAILED_CAPTION task. - caption_jp We captioned the images by Qwen2-VL-2B-Instruct. # Captioning Cost - 30 GPUhours (RTX 4090) # Citation ```bibtex @article{liao2022artbench, title={The ArtBench Dataset: Benchmarking Generative Models with Artworks}, author={Liao, Peiyuan and Li, Xiuyu and Liu, Xihui and Keutzer, Kurt}, journal={arXiv preprint arXiv:2206.11404}, year={2022} } ``` # Acknowledgement We appreciate [Peiyuan Liao](https://liaopeiyuan.com/) to create the great dataset. This dataset leads futher researchs and development.

nyu-mll/blimp

nyu-mll

--- annotations_creators: - crowdsourced language_creators: - machine-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - acceptability-classification paperswithcode_id: blimp pretty_name: BLiMP dataset_info: - config_name: adjunct_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 165894 num_examples: 1000 download_size: 62231 dataset_size: 165894 - config_name: anaphor_gender_agreement features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 130918 num_examples: 1000 download_size: 39201 dataset_size: 130918 - config_name: anaphor_number_agreement features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 139879 num_examples: 1000 download_size: 41547 dataset_size: 139879 - config_name: animate_subject_passive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 144423 num_examples: 1000 download_size: 47282 dataset_size: 144423 - config_name: animate_subject_trans features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 127798 num_examples: 1000 download_size: 49651 dataset_size: 127798 - config_name: causative features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 122772 num_examples: 1000 download_size: 48963 dataset_size: 122772 - config_name: complex_NP_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 198972 num_examples: 1000 download_size: 78211 dataset_size: 198972 - config_name: coordinate_structure_constraint_complex_left_branch features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 210912 num_examples: 1000 download_size: 67908 dataset_size: 210912 - config_name: coordinate_structure_constraint_object_extraction features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171655 num_examples: 1000 download_size: 51584 dataset_size: 171655 - config_name: determiner_noun_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 156120 num_examples: 1000 download_size: 49893 dataset_size: 156120 - config_name: determiner_noun_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 156204 num_examples: 1000 download_size: 49527 dataset_size: 156204 - config_name: determiner_noun_agreement_irregular_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 164473 num_examples: 1000 download_size: 47274 dataset_size: 164473 - config_name: determiner_noun_agreement_irregular_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 161074 num_examples: 1000 download_size: 47422 dataset_size: 161074 - config_name: determiner_noun_agreement_with_adj_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 179666 num_examples: 1000 download_size: 56346 dataset_size: 179666 - config_name: determiner_noun_agreement_with_adj_irregular_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 184529 num_examples: 1000 download_size: 54405 dataset_size: 184529 - config_name: determiner_noun_agreement_with_adj_irregular_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 184396 num_examples: 1000 download_size: 54064 dataset_size: 184396 - config_name: determiner_noun_agreement_with_adjective_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 185126 num_examples: 1000 download_size: 55682 dataset_size: 185126 - config_name: distractor_agreement_relational_noun features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 191473 num_examples: 1000 download_size: 59641 dataset_size: 191473 - config_name: distractor_agreement_relative_clause features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 216756 num_examples: 1000 download_size: 77897 dataset_size: 216756 - config_name: drop_argument features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 109806 num_examples: 1000 download_size: 39961 dataset_size: 109806 - config_name: ellipsis_n_bar_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 217590 num_examples: 1000 download_size: 92776 dataset_size: 217590 - config_name: ellipsis_n_bar_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 233161 num_examples: 1000 download_size: 98882 dataset_size: 233161 - config_name: existential_there_object_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 223741 num_examples: 1000 download_size: 76641 dataset_size: 223741 - config_name: existential_there_quantifiers_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 162931 num_examples: 1000 download_size: 51576 dataset_size: 162931 - config_name: existential_there_quantifiers_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 164826 num_examples: 1000 download_size: 52092 dataset_size: 164826 - config_name: existential_there_subject_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 200063 num_examples: 1000 download_size: 59519 dataset_size: 200063 - config_name: expletive_it_object_raising features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 238615 num_examples: 1000 download_size: 88607 dataset_size: 238615 - config_name: inchoative features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 104319 num_examples: 1000 download_size: 39842 dataset_size: 104319 - config_name: intransitive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 111097 num_examples: 1000 download_size: 42387 dataset_size: 111097 - config_name: irregular_past_participle_adjectives features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 144661 num_examples: 1000 download_size: 36654 dataset_size: 144661 - config_name: irregular_past_participle_verbs features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 125692 num_examples: 1000 download_size: 37297 dataset_size: 125692 - config_name: irregular_plural_subject_verb_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 165584 num_examples: 1000 download_size: 50725 dataset_size: 165584 - config_name: irregular_plural_subject_verb_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 153843 num_examples: 1000 download_size: 42707 dataset_size: 153843 - config_name: left_branch_island_echo_question features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 147840 num_examples: 1000 download_size: 50481 dataset_size: 147840 - config_name: left_branch_island_simple_question features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 150060 num_examples: 1000 download_size: 50293 dataset_size: 150060 - config_name: matrix_question_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 153262 num_examples: 1000 download_size: 51899 dataset_size: 153262 - config_name: npi_present_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 138465 num_examples: 1000 download_size: 51981 dataset_size: 138465 - config_name: npi_present_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 127636 num_examples: 1000 download_size: 51661 dataset_size: 127636 - config_name: only_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 148516 num_examples: 1000 download_size: 51361 dataset_size: 148516 - config_name: only_npi_scope features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 208902 num_examples: 1000 download_size: 84970 dataset_size: 208902 - config_name: passive_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 145882 num_examples: 1000 download_size: 53931 dataset_size: 145882 - config_name: passive_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 113960 num_examples: 1000 download_size: 40499 dataset_size: 113960 - config_name: principle_A_c_command features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 188490 num_examples: 1000 download_size: 67867 dataset_size: 188490 - config_name: principle_A_case_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 170398 num_examples: 1000 download_size: 61092 dataset_size: 170398 - config_name: principle_A_case_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 170412 num_examples: 1000 download_size: 56430 dataset_size: 170412 - config_name: principle_A_domain_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171170 num_examples: 1000 download_size: 59120 dataset_size: 171170 - config_name: principle_A_domain_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 165333 num_examples: 1000 download_size: 58464 dataset_size: 165333 - config_name: principle_A_domain_3 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 158998 num_examples: 1000 download_size: 52859 dataset_size: 158998 - config_name: principle_A_reconstruction features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 152104 num_examples: 1000 download_size: 44480 dataset_size: 152104 - config_name: regular_plural_subject_verb_agreement_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 158819 num_examples: 1000 download_size: 49466 dataset_size: 158819 - config_name: regular_plural_subject_verb_agreement_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 153609 num_examples: 1000 download_size: 43365 dataset_size: 153609 - config_name: sentential_negation_npi_licensor_present features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 171864 num_examples: 1000 download_size: 54830 dataset_size: 171864 - config_name: sentential_negation_npi_scope features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 232098 num_examples: 1000 download_size: 90157 dataset_size: 232098 - config_name: sentential_subject_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 172432 num_examples: 1000 download_size: 56666 dataset_size: 172432 - config_name: superlative_quantifiers_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 159290 num_examples: 1000 download_size: 48453 dataset_size: 159290 - config_name: superlative_quantifiers_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 159340 num_examples: 1000 download_size: 50480 dataset_size: 159340 - config_name: tough_vs_raising_1 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 148636 num_examples: 1000 download_size: 44779 dataset_size: 148636 - config_name: tough_vs_raising_2 features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 169684 num_examples: 1000 download_size: 61465 dataset_size: 169684 - config_name: transitive features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 133104 num_examples: 1000 download_size: 55090 dataset_size: 133104 - config_name: wh_island features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 142340 num_examples: 1000 download_size: 52808 dataset_size: 142340 - config_name: wh_questions_object_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 193045 num_examples: 1000 download_size: 70049 dataset_size: 193045 - config_name: wh_questions_subject_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 195593 num_examples: 1000 download_size: 71632 dataset_size: 195593 - config_name: wh_questions_subject_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 268270 num_examples: 1000 download_size: 98913 dataset_size: 268270 - config_name: wh_vs_that_no_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 188872 num_examples: 1000 download_size: 71710 dataset_size: 188872 - config_name: wh_vs_that_no_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 247039 num_examples: 1000 download_size: 95504 dataset_size: 247039 - config_name: wh_vs_that_with_gap features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 173386 num_examples: 1000 download_size: 60291 dataset_size: 173386 - config_name: wh_vs_that_with_gap_long_distance features: - name: sentence_good dtype: string - name: sentence_bad dtype: string - name: field dtype: string - name: linguistics_term dtype: string - name: UID dtype: string - name: simple_LM_method dtype: bool - name: one_prefix_method dtype: bool - name: two_prefix_method dtype: bool - name: lexically_identical dtype: bool - name: pair_id dtype: int32 splits: - name: train num_bytes: 231595 num_examples: 1000 download_size: 84147 dataset_size: 231595 configs: - config_name: adjunct_island data_files: - split: train path: adjunct_island/train-* - config_name: anaphor_gender_agreement data_files: - split: train path: anaphor_gender_agreement/train-* - config_name: anaphor_number_agreement data_files: - split: train path: anaphor_number_agreement/train-* - config_name: animate_subject_passive data_files: - split: train path: animate_subject_passive/train-* - config_name: animate_subject_trans data_files: - split: train path: animate_subject_trans/train-* - config_name: causative data_files: - split: train path: causative/train-* - config_name: complex_NP_island data_files: - split: train path: complex_NP_island/train-* - config_name: coordinate_structure_constraint_complex_left_branch data_files: - split: train path: coordinate_structure_constraint_complex_left_branch/train-* - config_name: coordinate_structure_constraint_object_extraction data_files: - split: train path: coordinate_structure_constraint_object_extraction/train-* - config_name: determiner_noun_agreement_1 data_files: - split: train path: determiner_noun_agreement_1/train-* - config_name: determiner_noun_agreement_2 data_files: - split: train path: determiner_noun_agreement_2/train-* - config_name: determiner_noun_agreement_irregular_1 data_files: - split: train path: determiner_noun_agreement_irregular_1/train-* - config_name: determiner_noun_agreement_irregular_2 data_files: - split: train path: determiner_noun_agreement_irregular_2/train-* - config_name: determiner_noun_agreement_with_adj_2 data_files: - split: train path: determiner_noun_agreement_with_adj_2/train-* - config_name: determiner_noun_agreement_with_adj_irregular_1 data_files: - split: train path: determiner_noun_agreement_with_adj_irregular_1/train-* - config_name: determiner_noun_agreement_with_adj_irregular_2 data_files: - split: train path: determiner_noun_agreement_with_adj_irregular_2/train-* - config_name: determiner_noun_agreement_with_adjective_1 data_files: - split: train path: determiner_noun_agreement_with_adjective_1/train-* - config_name: distractor_agreement_relational_noun data_files: - split: train path: distractor_agreement_relational_noun/train-* - config_name: distractor_agreement_relative_clause data_files: - split: train path: distractor_agreement_relative_clause/train-* - config_name: drop_argument data_files: - split: train path: drop_argument/train-* - config_name: ellipsis_n_bar_1 data_files: - split: train path: ellipsis_n_bar_1/train-* - config_name: ellipsis_n_bar_2 data_files: - split: train path: ellipsis_n_bar_2/train-* - config_name: existential_there_object_raising data_files: - split: train path: existential_there_object_raising/train-* - config_name: existential_there_quantifiers_1 data_files: - split: train path: existential_there_quantifiers_1/train-* - config_name: existential_there_quantifiers_2 data_files: - split: train path: existential_there_quantifiers_2/train-* - config_name: existential_there_subject_raising data_files: - split: train path: existential_there_subject_raising/train-* - config_name: expletive_it_object_raising data_files: - split: train path: expletive_it_object_raising/train-* - config_name: inchoative data_files: - split: train path: inchoative/train-* - config_name: intransitive data_files: - split: train path: intransitive/train-* - config_name: irregular_past_participle_adjectives data_files: - split: train path: irregular_past_participle_adjectives/train-* - config_name: irregular_past_participle_verbs data_files: - split: train path: irregular_past_participle_verbs/train-* - config_name: irregular_plural_subject_verb_agreement_1 data_files: - split: train path: irregular_plural_subject_verb_agreement_1/train-* - config_name: irregular_plural_subject_verb_agreement_2 data_files: - split: train path: irregular_plural_subject_verb_agreement_2/train-* - config_name: left_branch_island_echo_question data_files: - split: train path: left_branch_island_echo_question/train-* - config_name: left_branch_island_simple_question data_files: - split: train path: left_branch_island_simple_question/train-* - config_name: matrix_question_npi_licensor_present data_files: - split: train path: matrix_question_npi_licensor_present/train-* - config_name: npi_present_1 data_files: - split: train path: npi_present_1/train-* - config_name: npi_present_2 data_files: - split: train path: npi_present_2/train-* - config_name: only_npi_licensor_present data_files: - split: train path: only_npi_licensor_present/train-* - config_name: only_npi_scope data_files: - split: train path: only_npi_scope/train-* - config_name: passive_1 data_files: - split: train path: passive_1/train-* - config_name: passive_2 data_files: - split: train path: passive_2/train-* - config_name: principle_A_c_command data_files: - split: train path: principle_A_c_command/train-* - config_name: principle_A_case_1 data_files: - split: train path: principle_A_case_1/train-* - config_name: principle_A_case_2 data_files: - split: train path: principle_A_case_2/train-* - config_name: principle_A_domain_1 data_files: - split: train path: principle_A_domain_1/train-* - config_name: principle_A_domain_2 data_files: - split: train path: principle_A_domain_2/train-* - config_name: principle_A_domain_3 data_files: - split: train path: principle_A_domain_3/train-* - config_name: principle_A_reconstruction data_files: - split: train path: principle_A_reconstruction/train-* - config_name: regular_plural_subject_verb_agreement_1 data_files: - split: train path: regular_plural_subject_verb_agreement_1/train-* - config_name: regular_plural_subject_verb_agreement_2 data_files: - split: train path: regular_plural_subject_verb_agreement_2/train-* - config_name: sentential_negation_npi_licensor_present data_files: - split: train path: sentential_negation_npi_licensor_present/train-* - config_name: sentential_negation_npi_scope data_files: - split: train path: sentential_negation_npi_scope/train-* - config_name: sentential_subject_island data_files: - split: train path: sentential_subject_island/train-* - config_name: superlative_quantifiers_1 data_files: - split: train path: superlative_quantifiers_1/train-* - config_name: superlative_quantifiers_2 data_files: - split: train path: superlative_quantifiers_2/train-* - config_name: tough_vs_raising_1 data_files: - split: train path: tough_vs_raising_1/train-* - config_name: tough_vs_raising_2 data_files: - split: train path: tough_vs_raising_2/train-* - config_name: transitive data_files: - split: train path: transitive/train-* - config_name: wh_island data_files: - split: train path: wh_island/train-* - config_name: wh_questions_object_gap data_files: - split: train path: wh_questions_object_gap/train-* - config_name: wh_questions_subject_gap data_files: - split: train path: wh_questions_subject_gap/train-* - config_name: wh_questions_subject_gap_long_distance data_files: - split: train path: wh_questions_subject_gap_long_distance/train-* - config_name: wh_vs_that_no_gap data_files: - split: train path: wh_vs_that_no_gap/train-* - config_name: wh_vs_that_no_gap_long_distance data_files: - split: train path: wh_vs_that_no_gap_long_distance/train-* - config_name: wh_vs_that_with_gap data_files: - split: train path: wh_vs_that_with_gap/train-* - config_name: wh_vs_that_with_gap_long_distance data_files: - split: train path: wh_vs_that_with_gap_long_distance/train-* --- # Dataset Card for "blimp" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/alexwarstadt/blimp - **Paper:** [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://doi.org/10.1162/tacl_a_00321) - **Paper:** https://arxiv.org/abs/1912.00582 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 29.58 MB - **Size of the generated dataset:** 11.45 MB - **Total amount of disk used:** 41.03 MB ### Dataset Summary BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### adjunct_island - **Size of downloaded dataset files:** 0.36 MB - **Size of the generated dataset:** 0.17 MB - **Total amount of disk used:** 0.52 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### anaphor_gender_agreement - **Size of downloaded dataset files:** 0.44 MB - **Size of the generated dataset:** 0.14 MB - **Total amount of disk used:** 0.57 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### anaphor_number_agreement - **Size of downloaded dataset files:** 0.45 MB - **Size of the generated dataset:** 0.14 MB - **Total amount of disk used:** 0.59 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### animate_subject_passive - **Size of downloaded dataset files:** 0.46 MB - **Size of the generated dataset:** 0.15 MB - **Total amount of disk used:** 0.61 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` #### animate_subject_trans - **Size of downloaded dataset files:** 0.43 MB - **Size of the generated dataset:** 0.13 MB - **Total amount of disk used:** 0.57 MB An example of 'train' looks as follows. ``` { "UID": "tough_vs_raising_1", "field": "syntax_semantics", "lexically_identical": false, "linguistics_term": "control_raising", "one_prefix_method": false, "pair_id": 2, "sentence_bad": "Benjamin's tutor was certain to boast about.", "sentence_good": "Benjamin's tutor was easy to boast about.", "simple_LM_method": true, "two_prefix_method": false } ``` ### Data Fields The data fields are the same among all splits. #### adjunct_island - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### anaphor_gender_agreement - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### anaphor_number_agreement - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### animate_subject_passive - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. #### animate_subject_trans - `sentence_good`: a `string` feature. - `sentence_bad`: a `string` feature. - `field`: a `string` feature. - `linguistics_term`: a `string` feature. - `UID`: a `string` feature. - `simple_LM_method`: a `bool` feature. - `one_prefix_method`: a `bool` feature. - `two_prefix_method`: a `bool` feature. - `lexically_identical`: a `bool` feature. - `pair_id`: a `int32` feature. ### Data Splits | name |train| |------------------------|----:| |adjunct_island | 1000| |anaphor_gender_agreement| 1000| |anaphor_number_agreement| 1000| |animate_subject_passive | 1000| |animate_subject_trans | 1000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information BLiMP is distributed under a [CC-BY](https://creativecommons.org/licenses/by/4.0/) license. Source: https://github.com/alexwarstadt/blimp#license ### Citation Information ``` @article{warstadt2020blimp, author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.}, title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {377-392}, year = {2020}, doi = {10.1162/tacl\_a\_00321}, URL = {https://doi.org/10.1162/tacl_a_00321}, eprint = {https://doi.org/10.1162/tacl_a_00321}, abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. } } ``` #### Errata Some results were misreported in the published TACL version. Please refer to the corrected version on arXiv: https://arxiv.org/abs/1912.00582 ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

legacy-datasets/mc4

legacy-datasets

--- pretty_name: mC4 annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - he - hi - hmn - ht - hu - hy - id - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - 'no' - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu language_bcp47: - bg-Latn - el-Latn - hi-Latn - ja-Latn - ru-Latn - zh-Latn license: - odc-by multilinguality: - multilingual size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M - 10M<n<100M - 100M<n<1B - 1B<n<10B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: mc4 viewer: false --- <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "mc4" is deprecated and will be deleted. Use "<a href="https://huggingface.co/datasets/allenai/c4">allenai/c4</a>" instead.</p> </div> # Dataset Card for mC4 ## Table of Contents - [Dataset Card for mC4](#dataset-card-for-mc4) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://huggingface.co/datasets/allenai/c4 - **Paper:** https://arxiv.org/abs/1910.10683 ### Dataset Summary A multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4 108 languages are available and are reported in the table below. Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script. | language code | language name | |:----------------|:---------------------| | af | Afrikaans | | am | Amharic | | ar | Arabic | | az | Azerbaijani | | be | Belarusian | | bg | Bulgarian | | bg-Latn | Bulgarian (Latin) | | bn | Bangla | | ca | Catalan | | ceb | Cebuano | | co | Corsican | | cs | Czech | | cy | Welsh | | da | Danish | | de | German | | el | Greek | | el-Latn | Greek (Latin) | | en | English | | eo | Esperanto | | es | Spanish | | et | Estonian | | eu | Basque | | fa | Persian | | fi | Finnish | | fil | Filipino | | fr | French | | fy | Western Frisian | | ga | Irish | | gd | Scottish Gaelic | | gl | Galician | | gu | Gujarati | | ha | Hausa | | haw | Hawaiian | | hi | Hindi | | hi-Latn | Hindi (Latin script) | | hmn | Hmong, Mong | | ht | Haitian | | hu | Hungarian | | hy | Armenian | | id | Indonesian | | ig | Igbo | | is | Icelandic | | it | Italian | | iw | former Hebrew | | ja | Japanese | | ja-Latn | Japanese (Latin) | | jv | Javanese | | ka | Georgian | | kk | Kazakh | | km | Khmer | | kn | Kannada | | ko | Korean | | ku | Kurdish | | ky | Kyrgyz | | la | Latin | | lb | Luxembourgish | | lo | Lao | | lt | Lithuanian | | lv | Latvian | | mg | Malagasy | | mi | Maori | | mk | Macedonian | | ml | Malayalam | | mn | Mongolian | | mr | Marathi | | ms | Malay | | mt | Maltese | | my | Burmese | | ne | Nepali | | nl | Dutch | | no | Norwegian | | ny | Nyanja | | pa | Punjabi | | pl | Polish | | ps | Pashto | | pt | Portuguese | | ro | Romanian | | ru | Russian | | ru-Latn | Russian (Latin) | | sd | Sindhi | | si | Sinhala | | sk | Slovak | | sl | Slovenian | | sm | Samoan | | sn | Shona | | so | Somali | | sq | Albanian | | sr | Serbian | | st | Southern Sotho | | su | Sundanese | | sv | Swedish | | sw | Swahili | | ta | Tamil | | te | Telugu | | tg | Tajik | | th | Thai | | tr | Turkish | | uk | Ukrainian | | und | Unknown language | | ur | Urdu | | uz | Uzbek | | vi | Vietnamese | | xh | Xhosa | | yi | Yiddish | | yo | Yoruba | | zh | Chinese | | zh-Latn | Chinese (Latin) | | zu | Zulu | You can load the mC4 subset of any language like this: ```python from datasets import load_dataset en_mc4 = load_dataset("mc4", "en") ``` And if you can even specify a list of languages: ```python from datasets import load_dataset mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"]) ``` ### Supported Tasks and Leaderboards mC4 is mainly intended to pretrain language models and word representations. ### Languages The dataset supports 108 languages. ## Dataset Structure ### Data Instances An example form the `en` config is: ``` {'timestamp': '2018-06-24T01:32:39Z', 'text': 'Farm Resources in Plumas County\nShow Beginning Farmer Organizations & Professionals (304)\nThere are 304 resources serving Plumas County in the following categories:\nMap of Beginning Farmer Organizations & Professionals serving Plumas County\nVictoria Fisher - Office Manager - Loyalton, CA\nAmy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\nShow Farm Income Opportunities Organizations & Professionals (353)\nThere are 353 resources serving Plumas County in the following categories:\nFarm Ranch And Forest Retailers (18)\nMap of Farm Income Opportunities Organizations & Professionals serving Plumas County\nWarner Valley Wildlife Area - Plumas County\nShow Farm Resources Organizations & Professionals (297)\nThere are 297 resources serving Plumas County in the following categories:\nMap of Farm Resources Organizations & Professionals serving Plumas County\nThere are 57 resources serving Plumas County in the following categories:\nMap of Organic Certification Organizations & Professionals serving Plumas County', 'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'} ``` ### Data Fields The data have several fields: - `url`: url of the source as a string - `text`: text content as a string - `timestamp`: timestamp as a string ### Data Splits To build mC4, the authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table: | config | train | validation | |:---------|:--------|:-------------| | af | ? | ? | | am | ? | ? | | ar | ? | ? | | az | ? | ? | | be | ? | ? | | bg | ? | ? | | bg-Latn | ? | ? | | bn | ? | ? | | ca | ? | ? | | ceb | ? | ? | | co | ? | ? | | cs | ? | ? | | cy | ? | ? | | da | ? | ? | | de | ? | ? | | el | ? | ? | | el-Latn | ? | ? | | en | ? | ? | | eo | ? | ? | | es | ? | ? | | et | ? | ? | | eu | ? | ? | | fa | ? | ? | | fi | ? | ? | | fil | ? | ? | | fr | ? | ? | | fy | ? | ? | | ga | ? | ? | | gd | ? | ? | | gl | ? | ? | | gu | ? | ? | | ha | ? | ? | | haw | ? | ? | | hi | ? | ? | | hi-Latn | ? | ? | | hmn | ? | ? | | ht | ? | ? | | hu | ? | ? | | hy | ? | ? | | id | ? | ? | | ig | ? | ? | | is | ? | ? | | it | ? | ? | | iw | ? | ? | | ja | ? | ? | | ja-Latn | ? | ? | | jv | ? | ? | | ka | ? | ? | | kk | ? | ? | | km | ? | ? | | kn | ? | ? | | ko | ? | ? | | ku | ? | ? | | ky | ? | ? | | la | ? | ? | | lb | ? | ? | | lo | ? | ? | | lt | ? | ? | | lv | ? | ? | | mg | ? | ? | | mi | ? | ? | | mk | ? | ? | | ml | ? | ? | | mn | ? | ? | | mr | ? | ? | | ms | ? | ? | | mt | ? | ? | | my | ? | ? | | ne | ? | ? | | nl | ? | ? | | no | ? | ? | | ny | ? | ? | | pa | ? | ? | | pl | ? | ? | | ps | ? | ? | | pt | ? | ? | | ro | ? | ? | | ru | ? | ? | | ru-Latn | ? | ? | | sd | ? | ? | | si | ? | ? | | sk | ? | ? | | sl | ? | ? | | sm | ? | ? | | sn | ? | ? | | so | ? | ? | | sq | ? | ? | | sr | ? | ? | | st | ? | ? | | su | ? | ? | | sv | ? | ? | | sw | ? | ? | | ta | ? | ? | | te | ? | ? | | tg | ? | ? | | th | ? | ? | | tr | ? | ? | | uk | ? | ? | | und | ? | ? | | ur | ? | ? | | uz | ? | ? | | vi | ? | ? | | xh | ? | ? | | yi | ? | ? | | yo | ? | ? | | zh | ? | ? | | zh-Latn | ? | ? | | zu | ? | ? | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. ### Citation Information ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` ### Contributions Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

Samsung/samsum

Samsung

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-nc-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: samsum-corpus pretty_name: SAMSum Corpus tags: - conversations-summarization dataset_info: features: - name: id dtype: string - name: dialogue dtype: string - name: summary dtype: string config_name: samsum splits: - name: train num_bytes: 9479141 num_examples: 14732 - name: test num_bytes: 534492 num_examples: 819 - name: validation num_bytes: 516431 num_examples: 818 download_size: 2944100 dataset_size: 10530064 train-eval-index: - config: samsum task: summarization task_id: summarization splits: eval_split: test col_mapping: dialogue: text summary: target --- # Dataset Card for SAMSum Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://arxiv.org/abs/1911.12237v2 - **Repository:** [Needs More Information] - **Paper:** https://arxiv.org/abs/1911.12237v2 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger convesations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person. The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances The created dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people The first instance in the training set: {'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"} ### Data Fields - dialogue: text of dialogue. - summary: human written summary of the dialogue. - id: unique id of an example. ### Data Splits - train: 14732 - val: 818 - test: 819 ## Dataset Creation ### Curation Rationale In paper: > In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typ- ically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assis- tant and a client buying petrol. As a consequence, we decided to create a chat dialogue dataset by constructing such conversa- tions that would epitomize the style of a messenger app. ### Source Data #### Initial Data Collection and Normalization In paper: > We asked linguists to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. It includes chit-chats, gossiping about friends, arranging meetings, discussing politics, consulting university assignments with colleagues, etc. Therefore, this dataset does not contain any sensitive data or fragments of other corpora. #### Who are the source language producers? linguists ### Annotations #### Annotation process In paper: > Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one ref- erence summary. #### Who are the annotators? language experts ### Personal and Sensitive Information None, see above: Initial Data Collection and Normalization ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information non-commercial licence: CC BY-NC-ND 4.0 ### Citation Information ``` @inproceedings{gliwa-etal-2019-samsum, title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization", author = "Gliwa, Bogdan and Mochol, Iwona and Biesek, Maciej and Wawer, Aleksander", booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-5409", doi = "10.18653/v1/D19-5409", pages = "70--79" } ``` ### Contributions Thanks to [@cccntu](https://github.com/cccntu) for adding this dataset.

MMMU/MMMU

MMMU

--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering - visual-question-answering - multiple-choice pretty_name: mmmu dataset_info: - config_name: Accounting features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262599.0 num_examples: 5 - name: validation num_bytes: 1598285.0 num_examples: 30 - name: test num_bytes: 22135625.0 num_examples: 380 download_size: 37363379 dataset_size: 23996509.0 - config_name: Agriculture features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 22082656.0 num_examples: 5 - name: validation num_bytes: 119217558.0 num_examples: 30 - name: test num_bytes: 993664077.0 num_examples: 287 download_size: 1158036990 dataset_size: 1134964291.0 - config_name: Architecture_and_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 137750.0 num_examples: 5 - name: validation num_bytes: 721378.0 num_examples: 30 - name: test num_bytes: 16054607.0 num_examples: 551 download_size: 48763955 dataset_size: 16913735.0 - config_name: Art features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 6241184.0 num_examples: 5 - name: validation num_bytes: 29934534.0 num_examples: 30 - name: test num_bytes: 237801390.0 num_examples: 231 download_size: 585798641 dataset_size: 273977108.0 - config_name: Art_Theory features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 7435106.0 num_examples: 5 - name: validation num_bytes: 33481558.0 num_examples: 30 - name: test num_bytes: 553174647.0 num_examples: 429 download_size: 930525695 dataset_size: 594091311.0 - config_name: Basic_Medical_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 814310.0 num_examples: 5 - name: validation num_bytes: 4125930.0 num_examples: 30 - name: test num_bytes: 48125891.0 num_examples: 326 download_size: 84666454 dataset_size: 53066131.0 - config_name: Biology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 574342.0 num_examples: 5 - name: validation num_bytes: 8491863.0 num_examples: 30 - name: test num_bytes: 132966151.0 num_examples: 345 download_size: 410242502 dataset_size: 142032356.0 - config_name: Chemistry features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262397.0 num_examples: 5 - name: validation num_bytes: 1518573.0 num_examples: 30 - name: test num_bytes: 37219529.0 num_examples: 603 download_size: 108345562 dataset_size: 39000499.0 - config_name: Clinical_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1467945.0 num_examples: 5 - name: validation num_bytes: 10882484.0 num_examples: 30 - name: test num_bytes: 98201863.0 num_examples: 325 download_size: 160611488 dataset_size: 110552292.0 - config_name: Computer_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 440523.0 num_examples: 5 - name: validation num_bytes: 2072018.0 num_examples: 30 - name: test num_bytes: 32047381.0 num_examples: 371 download_size: 55640991 dataset_size: 34559922.0 - config_name: Design features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2259873.0 num_examples: 5 - name: validation num_bytes: 17923120.0 num_examples: 30 - name: test num_bytes: 77676331.0 num_examples: 169 download_size: 142866617 dataset_size: 97859324.0 - config_name: Diagnostics_and_Laboratory_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2056117.0 num_examples: 5 - name: validation num_bytes: 37106233.0 num_examples: 30 - name: test num_bytes: 157003069.0 num_examples: 162 download_size: 603957093 dataset_size: 196165419.0 - config_name: Economics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 171434.0 num_examples: 5 - name: validation num_bytes: 1487048.0 num_examples: 30 - name: test num_bytes: 11852300.0 num_examples: 267 download_size: 20777635 dataset_size: 13510782.0 - config_name: Electronics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - 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name: validation num_bytes: 1641935.0 num_examples: 30 - name: test num_bytes: 14748428.0 num_examples: 432 download_size: 35246567 dataset_size: 16495369.0 - config_name: Finance features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 296124.0 num_examples: 5 - name: validation num_bytes: 1071060.0 num_examples: 30 - name: test num_bytes: 12065803.0 num_examples: 355 download_size: 29551521 dataset_size: 13432987.0 - config_name: Geography features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1494060.0 num_examples: 5 - name: validation num_bytes: 6671316.0 num_examples: 30 - name: test num_bytes: 137218400.0 num_examples: 565 download_size: 374766631 dataset_size: 145383776.0 - config_name: History features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - 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config_name: Manage features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 449514.0 num_examples: 5 - name: validation num_bytes: 3277436.0 num_examples: 30 - name: test num_bytes: 29963963.0 num_examples: 245 download_size: 51186888 dataset_size: 33690913.0 - config_name: Marketing features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 116960.0 num_examples: 5 - name: validation num_bytes: 1472981.0 num_examples: 30 - name: test num_bytes: 7732976.0 num_examples: 181 download_size: 13146078 dataset_size: 9322917.0 - config_name: Materials features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 239632.0 num_examples: 5 - name: validation num_bytes: 2305223.0 num_examples: 30 - name: test num_bytes: 25256854.0 num_examples: 458 download_size: 105773156 dataset_size: 27801709.0 - config_name: Math features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 175839.0 num_examples: 5 - name: validation num_bytes: 1444496.0 num_examples: 30 - name: test num_bytes: 27701845.0 num_examples: 505 download_size: 174098418 dataset_size: 29322180.0 - config_name: Mechanical_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 152542.0 num_examples: 5 - name: validation num_bytes: 874988.0 num_examples: 30 - name: test num_bytes: 15093746.0 num_examples: 429 download_size: 30450114 dataset_size: 16121276.0 - config_name: Music features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1417615.0 num_examples: 5 - name: validation num_bytes: 9359372.0 num_examples: 30 - name: test num_bytes: 134096770.0 num_examples: 334 download_size: 174725052 dataset_size: 144873757.0 - config_name: Pharmacy features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 207924.0 num_examples: 5 - name: validation num_bytes: 1656342.0 num_examples: 30 - name: test num_bytes: 31866248.0 num_examples: 430 download_size: 62721263 dataset_size: 33730514.0 - config_name: Physics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 233734.0 num_examples: 5 - name: validation num_bytes: 1114130.0 num_examples: 30 - name: test num_bytes: 15905705.0 num_examples: 408 download_size: 35238571 dataset_size: 17253569.0 - config_name: Psychology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 600864.0 num_examples: 5 - name: validation num_bytes: 4403886.0 num_examples: 30 - name: test num_bytes: 53813915.0 num_examples: 305 download_size: 102466671 dataset_size: 58818665.0 - config_name: Public_Health features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 234781.0 num_examples: 5 - name: validation num_bytes: 1508761.0 num_examples: 30 - name: test num_bytes: 32150088.0 num_examples: 509 download_size: 48231609 dataset_size: 33893630.0 - config_name: Sociology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 3769220.0 num_examples: 5 - name: validation num_bytes: 18455336.0 num_examples: 30 - name: test num_bytes: 144301123.0 num_examples: 252 download_size: 310313826 dataset_size: 166525679.0 configs: - config_name: Accounting data_files: - split: dev path: Accounting/dev-* - split: validation path: Accounting/validation-* - split: test path: Accounting/test-* - config_name: Agriculture data_files: - split: dev path: Agriculture/dev-* - split: validation path: Agriculture/validation-* - split: test path: Agriculture/test-* - config_name: Architecture_and_Engineering data_files: - split: dev path: Architecture_and_Engineering/dev-* - split: validation path: Architecture_and_Engineering/validation-* - split: test path: Architecture_and_Engineering/test-* - config_name: Art data_files: - split: dev path: Art/dev-* - split: validation path: Art/validation-* - split: test path: Art/test-* - config_name: Art_Theory data_files: - split: dev path: Art_Theory/dev-* - split: validation path: Art_Theory/validation-* - split: test path: Art_Theory/test-* - config_name: Basic_Medical_Science data_files: - split: dev path: Basic_Medical_Science/dev-* - split: validation path: Basic_Medical_Science/validation-* - split: test path: Basic_Medical_Science/test-* - config_name: Biology data_files: - split: dev path: Biology/dev-* - split: validation path: Biology/validation-* - split: test path: Biology/test-* - config_name: Chemistry data_files: - split: dev path: Chemistry/dev-* - split: validation path: Chemistry/validation-* - split: test path: Chemistry/test-* - config_name: Clinical_Medicine data_files: - split: dev path: Clinical_Medicine/dev-* - split: validation path: Clinical_Medicine/validation-* - split: test path: Clinical_Medicine/test-* - config_name: Computer_Science data_files: - split: dev path: Computer_Science/dev-* - split: validation path: Computer_Science/validation-* - split: test path: Computer_Science/test-* - config_name: Design data_files: - split: dev path: Design/dev-* - split: validation path: Design/validation-* - split: test path: Design/test-* - config_name: Diagnostics_and_Laboratory_Medicine data_files: - split: dev path: Diagnostics_and_Laboratory_Medicine/dev-* - split: validation path: Diagnostics_and_Laboratory_Medicine/validation-* - split: test path: Diagnostics_and_Laboratory_Medicine/test-* - config_name: Economics data_files: - split: dev path: Economics/dev-* - split: validation path: Economics/validation-* - split: test path: Economics/test-* - config_name: Electronics data_files: - split: dev path: Electronics/dev-* - split: validation path: Electronics/validation-* - split: test path: Electronics/test-* - config_name: Energy_and_Power data_files: - split: dev path: Energy_and_Power/dev-* - split: validation path: Energy_and_Power/validation-* - split: test path: Energy_and_Power/test-* - config_name: Finance data_files: - split: dev path: Finance/dev-* - split: validation path: Finance/validation-* - split: test path: Finance/test-* - config_name: Geography data_files: - split: dev path: Geography/dev-* - split: validation path: Geography/validation-* - split: test path: Geography/test-* - config_name: History data_files: - split: dev path: History/dev-* - split: validation path: History/validation-* - split: test path: History/test-* - config_name: Literature data_files: - split: dev path: Literature/dev-* - split: validation path: Literature/validation-* - split: test path: Literature/test-* - config_name: Manage data_files: - split: dev path: Manage/dev-* - split: validation path: Manage/validation-* - split: test path: Manage/test-* - config_name: Marketing data_files: - split: dev path: Marketing/dev-* - split: validation path: Marketing/validation-* - split: test path: Marketing/test-* - config_name: Materials data_files: - split: dev path: Materials/dev-* - split: validation path: Materials/validation-* - split: test path: Materials/test-* - config_name: Math data_files: - split: dev path: Math/dev-* - split: validation path: Math/validation-* - split: test path: Math/test-* - config_name: Mechanical_Engineering data_files: - split: dev path: Mechanical_Engineering/dev-* - split: validation path: Mechanical_Engineering/validation-* - split: test path: Mechanical_Engineering/test-* - config_name: Music data_files: - split: dev path: Music/dev-* - split: validation path: Music/validation-* - split: test path: Music/test-* - config_name: Pharmacy data_files: - split: dev path: Pharmacy/dev-* - split: validation path: Pharmacy/validation-* - split: test path: Pharmacy/test-* - config_name: Physics data_files: - split: dev path: Physics/dev-* - split: validation path: Physics/validation-* - split: test path: Physics/test-* - config_name: Psychology data_files: - split: dev path: Psychology/dev-* - split: validation path: Psychology/validation-* - split: test path: Psychology/test-* - config_name: Public_Health data_files: - split: dev path: Public_Health/dev-* - split: validation path: Public_Health/validation-* - split: test path: Public_Health/test-* - config_name: Sociology data_files: - split: dev path: Sociology/dev-* - split: validation path: Sociology/validation-* - split: test path: Sociology/test-* tags: - biology - medical - finance - chemistry - music - art - art_theory - design - music - business - accounting - economics - finance - manage - marketing - health - medicine - basic_medical_science - clinical - pharmacy - public_health - humanities - social_science - history - literature - sociology - psychology - science - biology - chemistry - geography - math - physics - engineering - agriculture - architecture - computer_science - electronics - energy_and_power - materials - mechanical_engineering --- # MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI) [**🌐 Homepage**](https://mmmu-benchmark.github.io/) | [**🏆 Leaderboard**](https://mmmu-benchmark.github.io/#leaderboard) | [**🤗 Dataset**](https://huggingface.co/datasets/MMMU/MMMU/) | [**🤗 Paper**](https://huggingface.co/papers/2311.16502) | [**📖 arXiv**](https://arxiv.org/abs/2311.16502) | [**GitHub**](https://github.com/MMMU-Benchmark/MMMU) ## 🔔News - **🛠️[2024-05-30]: Fixed duplicate option issues in Materials dataset items (validation_Materials_25; test_Materials_17, 242) and content error in validation_Materials_25.** - **🛠️[2024-04-30]: Fixed missing "-" or "^" signs in Math dataset items (dev_Math_2, validation_Math_11, 12, 16; test_Math_8, 23, 43, 113, 164, 223, 236, 287, 329, 402, 498) and corrected option errors in validation_Math_2. If you encounter any issues with the dataset, please contact us promptly!** - **🚀[2024-01-31]: We added Human Expert performance on the [Leaderboard](https://mmmu-benchmark.github.io/#leaderboard)!🌟** - **🔥[2023-12-04]: Our evaluation server for test set is now availble on [EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview). We welcome all submissions and look forward to your participation! 😆** ## Dataset Details ### Dataset Description We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes **11.5K meticulously collected multimodal questions** from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span **30 subjects** and **183 subfields**, comprising **30 highly heterogeneous image types**, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI). 🎯 **We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.** The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the **test set** on **[EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview)**.  ### Dataset Creation MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning. The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials. - **Content:** The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects. - **Image Types:** The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text.  ## 🏆 Mini-Leaderboard We show a mini-leaderboard here and please find more information in our paper or [**homepage**](https://mmmu-benchmark.github.io/). | Model | Val (900) | Test (10.5K) | |--------------------------------|:---------:|:------------:| | Expert (Best) | 88.6 | - | | Expert (Medium) | 82.6 | - | | Expert (Worst) | 76.2 | - | | GPT-4o* | **69.1** | - | | Gemini 1.5 Pro* | 62.2 | - | | InternVL2-Pro* | 62.0 | **55.7** | | Gemini 1.0 Ultra* | 59.4 | - | | Claude 3 Opus* | 59.4 | - | | GPT-4V(ision) (Playground) | 56.8 | **55.7** | | Reka Core* | 56.3 | - | | Gemini 1.5 Flash* | 56.1 | - | | SenseChat-Vision-0423-Preview* | 54.6 | 50.3 | | Reka Flash* | 53.3 | - | | Claude 3 Sonnet* | 53.1 | - | | HPT Pro* | 52.0 | - | | VILA1.5* | 51.9 | 46.9 | | Qwen-VL-MAX* | 51.4 | 46.8 | | InternVL-Chat-V1.2* | 51.6 | 46.2 | | Skywork-VL* | 51.4 | 46.2 | | LLaVA-1.6-34B* | 51.1 | 44.7 | | Claude 3 Haiku* | 50.2 | - | | Adept Fuyu-Heavy* | 48.3 | - | | Gemini 1.0 Pro* | 47.9 | - | | Marco-VL-Plus* | 46.2 | 44.3 | | Yi-VL-34B* | 45.9 | 41.6 | | Qwen-VL-PLUS* | 45.2 | 40.8 | | HPT Air* | 44.0 | - | | Reka Edge* | 42.8 | - | | Marco-VL* | 41.2 | 40.4 | | OmniLMM-12B* | 41.1 | 40.4 | | Bunny-8B* | 43.3 | 39.0 | | Bunny-4B* | 41.4 | 38.4 | | Weitu-VL-1.0-15B* | - | 38.4 | | InternLM-XComposer2-VL* | 43.0 | 38.2 | | Yi-VL-6B* | 39.1 | 37.8 | | InfiMM-Zephyr-7B* | 39.4 | 35.5 | | InternVL-Chat-V1.1* | 39.1 | 35.3 | | Math-LLaVA-13B* | 38.3 | 34.6 | | SVIT* | 38.0 | 34.1 | | MiniCPM-V* | 37.2 | 34.1 | | MiniCPM-V-2* | 37.1 | - | | Emu2-Chat* | 36.3 | 34.1 | | BLIP-2 FLAN-T5-XXL | 35.4 | 34.0 | | InstructBLIP-T5-XXL | 35.7 | 33.8 | | LLaVA-1.5-13B | 36.4 | 33.6 | | Bunny-3B* | 38.2 | 33.0 | | Qwen-VL-7B-Chat | 35.9 | 32.9 | | SPHINX* | 32.9 | 32.9 | | mPLUG-OWL2* | 32.7 | 32.1 | | BLIP-2 FLAN-T5-XL | 34.4 | 31.0 | | InstructBLIP-T5-XL | 32.9 | 30.6 | | Gemini Nano2* | 32.6 | - | | CogVLM | 32.1 | 30.1 | | Otter | 32.2 | 29.1 | | LLaMA-Adapter2-7B | 29.8 | 27.7 | | MiniGPT4-Vicuna-13B | 26.8 | 27.6 | | Adept Fuyu-8B | 27.9 | 27.4 | | Kosmos2 | 24.4 | 26.6 | | OpenFlamingo2-9B | 28.7 | 26.3 | | Frequent Choice | 22.1 | 23.9 | | Random Choice | 26.8 | 25.8 | *: results provided by the authors. ## Limitations Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases. And the focus on college-level subjects might not fully be a sufficient test for Expert AGI. However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities. In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI. ## Disclaimers The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution. Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed. ## Contact - Xiang Yue: xiangyue.work@gmail.com - Yu Su: su.809@osu.edu - Wenhu Chen: wenhuchen@uwaterloo.ca ## Citation **BibTeX:** ```bibtex @inproceedings{yue2023mmmu, title={MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI}, author={Xiang Yue and Yuansheng Ni and Kai Zhang and Tianyu Zheng and Ruoqi Liu and Ge Zhang and Samuel Stevens and Dongfu Jiang and Weiming Ren and Yuxuan Sun and Cong Wei and Botao Yu and Ruibin Yuan and Renliang Sun and Ming Yin and Boyuan Zheng and Zhenzhu Yang and Yibo Liu and Wenhao Huang and Huan Sun and Yu Su and Wenhu Chen}, booktitle={Proceedings of CVPR}, year={2024}, } ```

EleutherAI/hendrycks_math

EleutherAI

--- license: mit ---

lmms-lab/MMMU

lmms-lab

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 57719107.0 num_examples: 150 - name: validation num_bytes: 347519954.0 num_examples: 900 - name: test num_bytes: 3271046267.0 num_examples: 10500 download_size: 3377778136 dataset_size: 3676285328.0 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: validation path: data/validation-* - split: test path: data/test-* --- This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated. <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [MMMU](https://github.com/MMMU-Benchmark/MMMU). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{yue2023mmmu, title={Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi}, author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Stevens, Samuel and Jiang, Dongfu and Ren, Weiming and Sun, Yuxuan and others}, journal={arXiv preprint arXiv:2311.16502}, year={2023} } ```

PromptEval/PromptEval_MMLU_full

PromptEval

--- language: - en license: mit task_categories: - question-answering pretty_name: MMLU_PromptEval_full dataset_info: - config_name: format_0 features: - name: question dtype: string - name: subject dtype: string - name: example dtype: int32 - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D '4': E - name: input_formatted dtype: string - name: model_output dtype: string - name: correctness dtype: int8 splits: - name: meta_llama_llama_3_8b num_bytes: 40967634 num_examples: 14042 - name: meta_llama_llama_3_8b_instruct num_bytes: 40967594 num_examples: 14042 - name: meta_llama_llama_3_70b_instruct num_bytes: 40965182 num_examples: 14042 - name: codellama_codellama_34b_instruct num_bytes: 40827221 num_examples: 14042 - name: google_flan_t5_xl num_bytes: 40729214 num_examples: 14042 - name: google_flan_t5_xxl num_bytes: 40728930 num_examples: 14042 - name: google_flan_ul2 num_bytes: 40728928 num_examples: 14042 - name: ibm_mistralai_merlinite_7b num_bytes: 40820070 num_examples: 14042 - name: mistralai_mixtral_8x7b_instruct_v01 num_bytes: 40827213 num_examples: 14042 - name: mistralai_mistral_7b_instruct_v0_2 num_bytes: 40828810 num_examples: 14042 - name: google_gemma_7b num_bytes: 54217882 num_examples: 14042 - name: google_gemma_7b_it num_bytes: 50624184 num_examples: 14042 - name: tiiuae_falcon_40b num_bytes: 40827222 num_examples: 14042 - name: mistralai_mistral_7b_v0_1 num_bytes: 40827221 num_examples: 14042 - name: tiiuae_falcon_180b num_bytes: 40827222 num_examples: 14042 download_size: 157447067 dataset_size: 635714527 - config_name: format_104 features: - name: question dtype: string - name: subject dtype: string - name: example dtype: int32 - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D '4': E - name: input_formatted dtype: string - name: model_output dtype: string - name: correctness dtype: int8 splits: - name: meta_llama_llama_3_8b num_bytes: 41711868 num_examples: 14042 - name: meta_llama_llama_3_8b_instruct num_bytes: 41711864 num_examples: 14042 - name: meta_llama_llama_3_70b_instruct num_bytes: 41711812 num_examples: 14042 - name: codellama_codellama_34b_instruct num_bytes: 42245461 num_examples: 14042 - name: google_flan_t5_xl num_bytes: 42133203 num_examples: 14042 - name: google_flan_t5_xxl num_bytes: 42133166 num_examples: 14042 - name: google_flan_ul2 num_bytes: 42133151 num_examples: 14042 - name: ibm_mistralai_merlinite_7b num_bytes: 42231264 num_examples: 14042 - name: mistralai_mixtral_8x7b_instruct_v01 num_bytes: 41571413 num_examples: 14042 - name: mistralai_mistral_7b_instruct_v0_2 num_bytes: 41571963 num_examples: 14042 - name: google_gemma_7b num_bytes: 55994487 num_examples: 14042 - name: google_gemma_7b_it num_bytes: 49139088 num_examples: 14042 - name: tiiuae_falcon_40b num_bytes: 42231421 num_examples: 14042 - name: mistralai_mistral_7b_v0_1 num_bytes: 42245466 num_examples: 14042 - name: tiiuae_falcon_180b num_bytes: 42231422 num_examples: 14042 download_size: 157480740 dataset_size: 650997049 - config_name: format_110 features: - 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config_name: format_96 features: - name: question dtype: string - name: subject dtype: string - name: example dtype: int32 - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D '4': E - name: input_formatted dtype: string - name: model_output dtype: string - name: correctness dtype: int8 splits: - name: meta_llama_llama_3_8b num_bytes: 41711868 num_examples: 14042 - name: meta_llama_llama_3_8b_instruct num_bytes: 41711805 num_examples: 14042 - name: meta_llama_llama_3_70b_instruct num_bytes: 41710979 num_examples: 14042 - name: codellama_codellama_34b_instruct num_bytes: 41571447 num_examples: 14042 - name: google_flan_t5_xl num_bytes: 41459116 num_examples: 14042 - name: google_flan_t5_xxl num_bytes: 41459113 num_examples: 14042 - name: google_flan_ul2 num_bytes: 41459137 num_examples: 14042 - name: ibm_mistralai_merlinite_7b num_bytes: 41566175 num_examples: 14042 - name: mistralai_mixtral_8x7b_instruct_v01 num_bytes: 41571433 num_examples: 14042 - name: mistralai_mistral_7b_instruct_v0_2 num_bytes: 41571736 num_examples: 14042 - name: google_gemma_7b num_bytes: 55609065 num_examples: 14042 - name: google_gemma_7b_it num_bytes: 47476186 num_examples: 14042 - name: tiiuae_falcon_40b num_bytes: 41557405 num_examples: 14042 - name: mistralai_mistral_7b_v0_1 num_bytes: 41571448 num_examples: 14042 - name: tiiuae_falcon_180b num_bytes: 41557406 num_examples: 14042 download_size: 156737430 dataset_size: 643564319 - config_name: format_97 features: - name: question dtype: string - name: subject dtype: string - name: example dtype: int32 - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D '4': E - name: input_formatted dtype: string - name: model_output dtype: string - name: correctness dtype: int8 splits: - name: meta_llama_llama_3_8b num_bytes: 41711868 num_examples: 14042 - name: meta_llama_llama_3_8b_instruct num_bytes: 41711860 num_examples: 14042 - name: meta_llama_llama_3_70b_instruct num_bytes: 41711335 num_examples: 14042 - name: codellama_codellama_34b_instruct num_bytes: 41571445 num_examples: 14042 - name: google_flan_t5_xl num_bytes: 41459126 num_examples: 14042 - name: google_flan_t5_xxl num_bytes: 41459114 num_examples: 14042 - name: google_flan_ul2 num_bytes: 41459135 num_examples: 14042 - name: ibm_mistralai_merlinite_7b num_bytes: 41561220 num_examples: 14042 - name: mistralai_mixtral_8x7b_instruct_v01 num_bytes: 41571382 num_examples: 14042 - name: mistralai_mistral_7b_instruct_v0_2 num_bytes: 41571983 num_examples: 14042 - name: google_gemma_7b num_bytes: 55595994 num_examples: 14042 - name: google_gemma_7b_it num_bytes: 47270289 num_examples: 14042 - name: tiiuae_falcon_40b num_bytes: 41557405 num_examples: 14042 - name: mistralai_mistral_7b_v0_1 num_bytes: 41571452 num_examples: 14042 - name: tiiuae_falcon_180b num_bytes: 41557406 num_examples: 14042 download_size: 156606916 dataset_size: 643341014 configs: - config_name: format_0 data_files: - split: meta_llama_llama_3_8b path: format_0/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_0/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_0/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_0/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_0/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_0/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_0/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_0/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_0/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_0/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_0/google_gemma_7b-* - split: google_gemma_7b_it path: format_0/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_0/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_0/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_0/tiiuae_falcon_180b-* - config_name: format_104 data_files: - split: meta_llama_llama_3_8b path: format_104/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_104/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_104/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_104/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_104/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_104/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_104/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_104/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_104/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_104/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_104/google_gemma_7b-* - split: google_gemma_7b_it path: format_104/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_104/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_104/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_104/tiiuae_falcon_180b-* - config_name: format_110 data_files: - split: meta_llama_llama_3_8b path: format_110/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_110/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_110/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_110/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_110/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_110/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_110/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_110/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_110/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_110/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_110/google_gemma_7b-* - split: google_gemma_7b_it path: format_110/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_110/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_110/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_110/tiiuae_falcon_180b-* - config_name: format_111 data_files: - split: meta_llama_llama_3_8b path: format_111/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_111/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_111/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_111/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_111/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_111/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_111/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_111/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_111/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_111/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_111/google_gemma_7b-* - split: google_gemma_7b_it path: format_111/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_111/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_111/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_111/tiiuae_falcon_180b-* - config_name: format_112 data_files: - split: meta_llama_llama_3_8b path: format_112/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_112/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_112/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_112/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_112/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_112/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_112/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_112/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_112/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_112/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_112/google_gemma_7b-* - split: google_gemma_7b_it path: format_112/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_112/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_112/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_112/tiiuae_falcon_180b-* - config_name: format_113 data_files: - split: meta_llama_llama_3_8b path: format_113/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_113/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_113/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_113/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_113/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_113/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_113/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_113/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_113/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_113/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_113/google_gemma_7b-* - split: google_gemma_7b_it path: format_113/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_113/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_113/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_113/tiiuae_falcon_180b-* - config_name: format_120 data_files: - split: meta_llama_llama_3_8b path: format_120/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_120/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_120/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_120/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_120/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_120/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_120/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_120/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_120/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_120/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_120/google_gemma_7b-* - split: google_gemma_7b_it path: format_120/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_120/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_120/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_120/tiiuae_falcon_180b-* - config_name: format_122 data_files: - split: meta_llama_llama_3_8b path: format_122/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_122/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_122/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_122/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_122/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_122/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_122/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_122/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_122/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_122/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_122/google_gemma_7b-* - split: google_gemma_7b_it path: format_122/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_122/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_122/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_122/tiiuae_falcon_180b-* - config_name: format_123 data_files: - split: meta_llama_llama_3_8b path: format_123/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_123/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_123/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_123/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_123/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_123/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_123/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_123/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_123/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_123/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_123/google_gemma_7b-* - split: google_gemma_7b_it path: format_123/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_123/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_123/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_123/tiiuae_falcon_180b-* - config_name: format_124 data_files: - split: meta_llama_llama_3_8b path: format_124/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_124/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_124/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_124/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_124/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_124/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_124/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_124/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_124/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_124/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_124/google_gemma_7b-* - split: google_gemma_7b_it path: format_124/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_124/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_124/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_124/tiiuae_falcon_180b-* - config_name: format_128 data_files: - split: meta_llama_llama_3_8b path: format_128/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_128/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_128/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_128/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_128/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_128/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_128/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_128/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_128/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_128/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_128/google_gemma_7b-* - split: google_gemma_7b_it path: format_128/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_128/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_128/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_128/tiiuae_falcon_180b-* - config_name: format_132 data_files: - split: meta_llama_llama_3_8b path: format_132/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_132/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_132/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_132/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_132/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_132/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_132/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_132/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_132/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_132/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_132/google_gemma_7b-* - split: google_gemma_7b_it path: format_132/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_132/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_132/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_132/tiiuae_falcon_180b-* - config_name: format_133 data_files: - split: meta_llama_llama_3_8b path: format_133/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_133/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_133/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_133/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_133/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_133/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_133/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_133/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_133/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_133/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_133/google_gemma_7b-* - split: google_gemma_7b_it path: format_133/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_133/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_133/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_133/tiiuae_falcon_180b-* - config_name: format_138 data_files: - split: meta_llama_llama_3_8b path: format_138/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_138/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_138/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_138/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_138/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_138/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_138/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_138/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_138/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_138/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_138/google_gemma_7b-* - split: google_gemma_7b_it path: format_138/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_138/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_138/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_138/tiiuae_falcon_180b-* - config_name: format_140 data_files: - split: meta_llama_llama_3_8b path: format_140/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_140/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_140/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_140/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_140/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_140/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_140/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_140/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_140/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_140/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_140/google_gemma_7b-* - split: google_gemma_7b_it path: format_140/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_140/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_140/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_140/tiiuae_falcon_180b-* - config_name: format_141 data_files: - split: meta_llama_llama_3_8b path: format_141/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_141/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_141/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_141/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_141/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_141/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_141/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_141/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_141/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_141/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_141/google_gemma_7b-* - split: google_gemma_7b_it path: format_141/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_141/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_141/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_141/tiiuae_falcon_180b-* - config_name: format_144 data_files: - split: meta_llama_llama_3_8b path: format_144/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_144/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_144/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_144/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_144/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_144/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_144/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_144/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_144/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_144/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_144/google_gemma_7b-* - split: google_gemma_7b_it path: format_144/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_144/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_144/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_144/tiiuae_falcon_180b-* - config_name: format_147 data_files: - split: meta_llama_llama_3_8b path: format_147/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_147/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_147/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_147/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_147/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_147/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_147/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_147/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_147/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_147/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_147/google_gemma_7b-* - split: google_gemma_7b_it path: format_147/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_147/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_147/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_147/tiiuae_falcon_180b-* - config_name: format_148 data_files: - split: meta_llama_llama_3_8b path: format_148/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_148/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_148/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_148/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_148/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_148/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_148/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_148/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_148/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_148/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_148/google_gemma_7b-* - split: google_gemma_7b_it path: format_148/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_148/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_148/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_148/tiiuae_falcon_180b-* - config_name: format_149 data_files: - split: meta_llama_llama_3_8b path: format_149/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_149/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_149/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_149/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_149/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_149/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_149/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_149/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_149/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_149/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_149/google_gemma_7b-* - split: google_gemma_7b_it path: format_149/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_149/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_149/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_149/tiiuae_falcon_180b-* - config_name: format_154 data_files: - split: meta_llama_llama_3_8b path: format_154/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_154/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_154/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_154/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_154/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_154/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_154/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_154/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_154/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_154/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_154/google_gemma_7b-* - split: google_gemma_7b_it path: format_154/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_154/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_154/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_154/tiiuae_falcon_180b-* - config_name: format_155 data_files: - split: meta_llama_llama_3_8b path: format_155/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_155/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_155/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_155/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_155/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_155/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_155/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_155/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_155/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_155/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_155/google_gemma_7b-* - split: google_gemma_7b_it path: format_155/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_155/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_155/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_155/tiiuae_falcon_180b-* - config_name: format_158 data_files: - split: meta_llama_llama_3_8b path: format_158/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_158/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_158/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_158/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_158/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_158/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_158/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_158/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_158/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_158/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_158/google_gemma_7b-* - split: google_gemma_7b_it path: format_158/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_158/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_158/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_158/tiiuae_falcon_180b-* - config_name: format_16 data_files: - split: meta_llama_llama_3_8b path: format_16/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_16/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_16/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_16/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_16/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_16/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_16/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_16/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_16/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_16/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_16/google_gemma_7b-* - split: google_gemma_7b_it path: format_16/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_16/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_16/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_16/tiiuae_falcon_180b-* - config_name: format_161 data_files: - split: meta_llama_llama_3_8b path: format_161/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_161/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_161/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_161/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_161/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_161/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_161/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_161/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_161/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_161/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_161/google_gemma_7b-* - split: google_gemma_7b_it path: format_161/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_161/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_161/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_161/tiiuae_falcon_180b-* - config_name: format_162 data_files: - split: meta_llama_llama_3_8b path: format_162/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_162/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_162/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_162/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_162/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_162/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_162/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_162/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_162/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_162/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_162/google_gemma_7b-* - split: google_gemma_7b_it path: format_162/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_162/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_162/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_162/tiiuae_falcon_180b-* - config_name: format_163 data_files: - split: meta_llama_llama_3_8b path: format_163/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_163/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_163/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_163/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_163/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_163/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_163/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_163/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_163/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_163/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_163/google_gemma_7b-* - split: google_gemma_7b_it path: format_163/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_163/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_163/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_163/tiiuae_falcon_180b-* - config_name: format_166 data_files: - split: meta_llama_llama_3_8b path: format_166/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_166/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_166/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_166/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_166/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_166/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_166/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_166/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_166/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_166/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_166/google_gemma_7b-* - split: google_gemma_7b_it path: format_166/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_166/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_166/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_166/tiiuae_falcon_180b-* - config_name: format_169 data_files: - split: meta_llama_llama_3_8b path: format_169/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_169/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_169/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_169/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_169/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_169/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_169/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_169/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_169/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_169/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_169/google_gemma_7b-* - split: google_gemma_7b_it path: format_169/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_169/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_169/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_169/tiiuae_falcon_180b-* - config_name: format_170 data_files: - split: meta_llama_llama_3_8b path: format_170/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_170/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_170/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_170/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_170/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_170/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_170/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_170/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_170/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_170/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_170/google_gemma_7b-* - split: google_gemma_7b_it path: format_170/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_170/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_170/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_170/tiiuae_falcon_180b-* - config_name: format_171 data_files: - split: meta_llama_llama_3_8b path: format_171/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_171/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_171/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_171/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_171/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_171/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_171/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_171/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_171/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_171/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_171/google_gemma_7b-* - split: google_gemma_7b_it path: format_171/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_171/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_171/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_171/tiiuae_falcon_180b-* - config_name: format_181 data_files: - split: meta_llama_llama_3_8b path: format_181/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_181/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_181/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_181/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_181/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_181/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_181/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_181/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_181/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_181/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_181/google_gemma_7b-* - split: google_gemma_7b_it path: format_181/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_181/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_181/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_181/tiiuae_falcon_180b-* - config_name: format_182 data_files: - split: meta_llama_llama_3_8b path: format_182/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_182/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_182/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_182/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_182/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_182/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_182/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_182/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_182/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_182/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_182/google_gemma_7b-* - split: google_gemma_7b_it path: format_182/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_182/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_182/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_182/tiiuae_falcon_180b-* - config_name: format_183 data_files: - split: meta_llama_llama_3_8b path: format_183/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_183/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_183/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_183/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_183/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_183/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_183/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_183/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_183/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_183/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_183/google_gemma_7b-* - split: google_gemma_7b_it path: format_183/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_183/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_183/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_183/tiiuae_falcon_180b-* - config_name: format_19 data_files: - split: meta_llama_llama_3_8b path: format_19/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_19/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_19/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_19/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_19/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_19/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_19/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_19/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_19/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_19/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_19/google_gemma_7b-* - split: google_gemma_7b_it path: format_19/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_19/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_19/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_19/tiiuae_falcon_180b-* - config_name: format_190 data_files: - split: meta_llama_llama_3_8b path: format_190/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_190/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_190/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_190/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_190/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_190/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_190/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_190/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_190/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_190/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_190/google_gemma_7b-* - split: google_gemma_7b_it path: format_190/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_190/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_190/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_190/tiiuae_falcon_180b-* - config_name: format_197 data_files: - split: meta_llama_llama_3_8b path: format_197/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_197/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_197/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_197/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_197/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_197/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_197/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_197/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_197/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_197/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_197/google_gemma_7b-* - split: google_gemma_7b_it path: format_197/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_197/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_197/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_197/tiiuae_falcon_180b-* - config_name: format_20 data_files: - split: meta_llama_llama_3_8b path: format_20/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_20/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_20/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_20/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_20/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_20/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_20/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_20/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_20/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_20/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_20/google_gemma_7b-* - split: google_gemma_7b_it path: format_20/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_20/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_20/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_20/tiiuae_falcon_180b-* - config_name: format_200 data_files: - split: meta_llama_llama_3_8b path: format_200/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_200/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_200/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_200/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_200/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_200/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_200/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_200/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_200/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_200/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_200/google_gemma_7b-* - split: google_gemma_7b_it path: format_200/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_200/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_200/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_200/tiiuae_falcon_180b-* - config_name: format_204 data_files: - split: meta_llama_llama_3_8b path: format_204/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_204/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_204/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_204/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_204/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_204/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_204/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_204/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_204/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_204/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_204/google_gemma_7b-* - split: google_gemma_7b_it path: format_204/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_204/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_204/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_204/tiiuae_falcon_180b-* - config_name: format_207 data_files: - split: meta_llama_llama_3_8b path: format_207/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_207/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_207/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_207/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_207/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_207/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_207/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_207/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_207/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_207/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_207/google_gemma_7b-* - split: google_gemma_7b_it path: format_207/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_207/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_207/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_207/tiiuae_falcon_180b-* - config_name: format_214 data_files: - split: meta_llama_llama_3_8b path: format_214/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_214/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_214/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_214/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_214/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_214/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_214/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_214/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_214/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_214/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_214/google_gemma_7b-* - split: google_gemma_7b_it path: format_214/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_214/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_214/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_214/tiiuae_falcon_180b-* - config_name: format_215 data_files: - split: meta_llama_llama_3_8b path: format_215/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_215/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_215/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_215/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_215/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_215/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_215/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_215/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_215/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_215/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_215/google_gemma_7b-* - split: google_gemma_7b_it path: format_215/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_215/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_215/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_215/tiiuae_falcon_180b-* - config_name: format_222 data_files: - split: meta_llama_llama_3_8b path: format_222/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_222/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_222/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_222/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_222/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_222/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_222/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_222/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_222/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_222/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_222/google_gemma_7b-* - split: google_gemma_7b_it path: format_222/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_222/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_222/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_222/tiiuae_falcon_180b-* - config_name: format_226 data_files: - split: meta_llama_llama_3_8b path: format_226/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_226/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_226/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_226/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_226/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_226/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_226/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_226/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_226/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_226/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_226/google_gemma_7b-* - split: google_gemma_7b_it path: format_226/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_226/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_226/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_226/tiiuae_falcon_180b-* - config_name: format_227 data_files: - split: meta_llama_llama_3_8b path: format_227/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_227/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_227/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_227/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_227/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_227/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_227/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_227/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_227/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_227/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_227/google_gemma_7b-* - split: google_gemma_7b_it path: format_227/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_227/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_227/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_227/tiiuae_falcon_180b-* - config_name: format_229 data_files: - split: meta_llama_llama_3_8b path: format_229/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_229/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_229/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_229/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_229/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_229/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_229/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_229/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_229/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_229/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_229/google_gemma_7b-* - split: google_gemma_7b_it path: format_229/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_229/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_229/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_229/tiiuae_falcon_180b-* - config_name: format_230 data_files: - split: meta_llama_llama_3_8b path: format_230/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_230/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_230/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_230/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_230/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_230/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_230/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_230/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_230/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_230/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_230/google_gemma_7b-* - split: google_gemma_7b_it path: format_230/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_230/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_230/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_230/tiiuae_falcon_180b-* - config_name: format_241 data_files: - split: meta_llama_llama_3_8b path: format_241/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_241/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_241/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_241/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_241/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_241/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_241/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_241/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_241/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_241/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_241/google_gemma_7b-* - split: google_gemma_7b_it path: format_241/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_241/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_241/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_241/tiiuae_falcon_180b-* - config_name: format_243 data_files: - split: meta_llama_llama_3_8b path: format_243/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_243/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_243/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_243/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_243/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_243/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_243/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_243/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_243/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_243/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_243/google_gemma_7b-* - split: google_gemma_7b_it path: format_243/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_243/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_243/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_243/tiiuae_falcon_180b-* - config_name: format_244 data_files: - split: meta_llama_llama_3_8b path: format_244/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_244/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_244/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_244/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_244/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_244/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_244/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_244/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_244/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_244/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_244/google_gemma_7b-* - split: google_gemma_7b_it path: format_244/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_244/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_244/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_244/tiiuae_falcon_180b-* - config_name: format_248 data_files: - split: meta_llama_llama_3_8b path: format_248/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_248/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_248/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_248/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_248/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_248/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_248/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_248/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_248/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_248/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_248/google_gemma_7b-* - split: google_gemma_7b_it path: format_248/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_248/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_248/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_248/tiiuae_falcon_180b-* - config_name: format_249 data_files: - split: meta_llama_llama_3_8b path: format_249/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_249/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_249/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_249/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_249/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_249/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_249/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_249/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_249/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_249/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_249/google_gemma_7b-* - split: google_gemma_7b_it path: format_249/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_249/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_249/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_249/tiiuae_falcon_180b-* - config_name: format_250 data_files: - split: meta_llama_llama_3_8b path: format_250/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_250/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_250/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_250/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_250/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_250/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_250/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_250/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_250/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_250/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_250/google_gemma_7b-* - split: google_gemma_7b_it path: format_250/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_250/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_250/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_250/tiiuae_falcon_180b-* - config_name: format_252 data_files: - split: meta_llama_llama_3_8b path: format_252/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_252/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_252/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_252/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_252/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_252/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_252/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_252/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_252/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_252/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_252/google_gemma_7b-* - split: google_gemma_7b_it path: format_252/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_252/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_252/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_252/tiiuae_falcon_180b-* - config_name: format_258 data_files: - split: meta_llama_llama_3_8b path: format_258/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_258/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_258/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_258/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_258/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_258/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_258/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_258/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_258/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_258/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_258/google_gemma_7b-* - split: google_gemma_7b_it path: format_258/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_258/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_258/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_258/tiiuae_falcon_180b-* - config_name: format_260 data_files: - split: meta_llama_llama_3_8b path: format_260/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_260/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_260/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_260/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_260/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_260/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_260/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_260/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_260/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_260/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_260/google_gemma_7b-* - split: google_gemma_7b_it path: format_260/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_260/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_260/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_260/tiiuae_falcon_180b-* - config_name: format_261 data_files: - split: meta_llama_llama_3_8b path: format_261/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_261/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_261/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_261/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_261/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_261/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_261/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_261/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_261/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_261/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_261/google_gemma_7b-* - split: google_gemma_7b_it path: format_261/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_261/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_261/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_261/tiiuae_falcon_180b-* - config_name: format_266 data_files: - split: meta_llama_llama_3_8b path: format_266/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_266/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_266/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_266/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_266/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_266/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_266/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_266/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_266/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_266/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_266/google_gemma_7b-* - split: google_gemma_7b_it path: format_266/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_266/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_266/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_266/tiiuae_falcon_180b-* - config_name: format_267 data_files: - split: meta_llama_llama_3_8b path: format_267/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_267/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_267/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_267/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_267/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_267/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_267/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_267/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_267/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_267/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_267/google_gemma_7b-* - split: google_gemma_7b_it path: format_267/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_267/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_267/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_267/tiiuae_falcon_180b-* - config_name: format_268 data_files: - split: meta_llama_llama_3_8b path: format_268/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_268/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_268/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_268/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_268/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_268/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_268/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_268/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_268/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_268/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_268/google_gemma_7b-* - split: google_gemma_7b_it path: format_268/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_268/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_268/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_268/tiiuae_falcon_180b-* - config_name: format_272 data_files: - split: meta_llama_llama_3_8b path: format_272/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_272/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_272/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_272/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_272/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_272/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_272/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_272/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_272/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_272/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_272/google_gemma_7b-* - split: google_gemma_7b_it path: format_272/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_272/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_272/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_272/tiiuae_falcon_180b-* - config_name: format_276 data_files: - split: meta_llama_llama_3_8b path: format_276/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_276/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_276/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_276/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_276/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_276/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_276/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_276/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_276/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_276/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_276/google_gemma_7b-* - split: google_gemma_7b_it path: format_276/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_276/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_276/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_276/tiiuae_falcon_180b-* - config_name: format_278 data_files: - split: meta_llama_llama_3_8b path: format_278/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_278/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_278/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_278/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_278/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_278/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_278/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_278/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_278/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_278/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_278/google_gemma_7b-* - split: google_gemma_7b_it path: format_278/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_278/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_278/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_278/tiiuae_falcon_180b-* - config_name: format_280 data_files: - split: meta_llama_llama_3_8b path: format_280/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_280/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_280/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_280/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_280/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_280/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_280/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_280/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_280/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_280/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_280/google_gemma_7b-* - split: google_gemma_7b_it path: format_280/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_280/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_280/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_280/tiiuae_falcon_180b-* - config_name: format_282 data_files: - split: meta_llama_llama_3_8b path: format_282/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_282/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_282/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_282/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_282/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_282/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_282/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_282/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_282/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_282/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_282/google_gemma_7b-* - split: google_gemma_7b_it path: format_282/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_282/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_282/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_282/tiiuae_falcon_180b-* - config_name: format_286 data_files: - split: meta_llama_llama_3_8b path: format_286/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_286/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_286/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_286/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_286/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_286/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_286/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_286/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_286/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_286/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_286/google_gemma_7b-* - split: google_gemma_7b_it path: format_286/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_286/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_286/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_286/tiiuae_falcon_180b-* - config_name: format_290 data_files: - split: meta_llama_llama_3_8b path: format_290/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_290/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_290/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_290/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_290/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_290/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_290/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_290/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_290/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_290/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_290/google_gemma_7b-* - split: google_gemma_7b_it path: format_290/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_290/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_290/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_290/tiiuae_falcon_180b-* - config_name: format_294 data_files: - split: meta_llama_llama_3_8b path: format_294/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_294/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_294/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_294/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_294/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_294/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_294/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_294/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_294/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_294/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_294/google_gemma_7b-* - split: google_gemma_7b_it path: format_294/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_294/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_294/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_294/tiiuae_falcon_180b-* - config_name: format_296 data_files: - split: meta_llama_llama_3_8b path: format_296/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_296/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_296/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_296/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_296/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_296/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_296/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_296/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_296/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_296/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_296/google_gemma_7b-* - split: google_gemma_7b_it path: format_296/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_296/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_296/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_296/tiiuae_falcon_180b-* - config_name: format_298 data_files: - split: meta_llama_llama_3_8b path: format_298/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_298/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_298/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_298/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_298/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_298/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_298/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_298/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_298/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_298/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_298/google_gemma_7b-* - split: google_gemma_7b_it path: format_298/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_298/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_298/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_298/tiiuae_falcon_180b-* - config_name: format_300 data_files: - split: meta_llama_llama_3_8b path: format_300/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_300/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_300/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_300/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_300/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_300/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_300/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_300/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_300/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_300/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_300/google_gemma_7b-* - split: google_gemma_7b_it path: format_300/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_300/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_300/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_300/tiiuae_falcon_180b-* - config_name: format_301 data_files: - split: meta_llama_llama_3_8b path: format_301/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_301/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_301/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_301/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_301/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_301/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_301/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_301/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_301/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_301/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_301/google_gemma_7b-* - split: google_gemma_7b_it path: format_301/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_301/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_301/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_301/tiiuae_falcon_180b-* - config_name: format_31 data_files: - split: meta_llama_llama_3_8b path: format_31/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_31/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_31/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_31/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_31/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_31/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_31/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_31/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_31/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_31/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_31/google_gemma_7b-* - split: google_gemma_7b_it path: format_31/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_31/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_31/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_31/tiiuae_falcon_180b-* - config_name: format_32 data_files: - split: meta_llama_llama_3_8b path: format_32/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_32/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_32/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_32/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_32/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_32/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_32/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_32/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_32/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_32/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_32/google_gemma_7b-* - split: google_gemma_7b_it path: format_32/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_32/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_32/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_32/tiiuae_falcon_180b-* - config_name: format_35 data_files: - split: meta_llama_llama_3_8b path: format_35/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_35/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_35/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_35/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_35/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_35/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_35/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_35/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_35/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_35/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_35/google_gemma_7b-* - split: google_gemma_7b_it path: format_35/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_35/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_35/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_35/tiiuae_falcon_180b-* - config_name: format_37 data_files: - split: meta_llama_llama_3_8b path: format_37/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_37/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_37/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_37/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_37/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_37/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_37/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_37/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_37/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_37/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_37/google_gemma_7b-* - split: google_gemma_7b_it path: format_37/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_37/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_37/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_37/tiiuae_falcon_180b-* - config_name: format_41 data_files: - split: meta_llama_llama_3_8b path: format_41/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_41/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_41/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_41/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_41/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_41/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_41/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_41/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_41/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_41/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_41/google_gemma_7b-* - split: google_gemma_7b_it path: format_41/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_41/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_41/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_41/tiiuae_falcon_180b-* - config_name: format_42 data_files: - split: meta_llama_llama_3_8b path: format_42/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_42/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_42/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_42/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_42/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_42/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_42/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_42/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_42/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_42/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_42/google_gemma_7b-* - split: google_gemma_7b_it path: format_42/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_42/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_42/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_42/tiiuae_falcon_180b-* - config_name: format_45 data_files: - split: meta_llama_llama_3_8b path: format_45/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_45/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_45/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_45/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_45/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_45/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_45/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_45/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_45/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_45/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_45/google_gemma_7b-* - split: google_gemma_7b_it path: format_45/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_45/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_45/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_45/tiiuae_falcon_180b-* - config_name: format_46 data_files: - split: meta_llama_llama_3_8b path: format_46/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_46/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_46/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_46/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_46/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_46/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_46/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_46/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_46/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_46/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_46/google_gemma_7b-* - split: google_gemma_7b_it path: format_46/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_46/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_46/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_46/tiiuae_falcon_180b-* - config_name: format_47 data_files: - split: meta_llama_llama_3_8b path: format_47/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_47/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_47/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_47/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_47/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_47/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_47/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_47/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_47/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_47/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_47/google_gemma_7b-* - split: google_gemma_7b_it path: format_47/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_47/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_47/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_47/tiiuae_falcon_180b-* - config_name: format_48 data_files: - split: meta_llama_llama_3_8b path: format_48/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_48/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_48/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_48/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_48/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_48/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_48/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_48/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_48/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_48/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_48/google_gemma_7b-* - split: google_gemma_7b_it path: format_48/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_48/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_48/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_48/tiiuae_falcon_180b-* - config_name: format_50 data_files: - split: meta_llama_llama_3_8b path: format_50/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_50/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_50/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_50/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_50/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_50/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_50/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_50/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_50/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_50/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_50/google_gemma_7b-* - split: google_gemma_7b_it path: format_50/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_50/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_50/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_50/tiiuae_falcon_180b-* - config_name: format_51 data_files: - split: meta_llama_llama_3_8b path: format_51/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_51/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_51/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_51/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_51/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_51/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_51/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_51/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_51/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_51/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_51/google_gemma_7b-* - split: google_gemma_7b_it path: format_51/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_51/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_51/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_51/tiiuae_falcon_180b-* - config_name: format_55 data_files: - split: meta_llama_llama_3_8b path: format_55/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_55/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_55/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_55/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_55/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_55/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_55/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_55/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_55/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_55/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_55/google_gemma_7b-* - split: google_gemma_7b_it path: format_55/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_55/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_55/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_55/tiiuae_falcon_180b-* - config_name: format_59 data_files: - split: meta_llama_llama_3_8b path: format_59/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_59/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_59/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_59/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_59/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_59/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_59/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_59/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_59/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_59/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_59/google_gemma_7b-* - split: google_gemma_7b_it path: format_59/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_59/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_59/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_59/tiiuae_falcon_180b-* - config_name: format_63 data_files: - split: meta_llama_llama_3_8b path: format_63/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_63/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_63/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_63/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_63/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_63/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_63/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_63/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_63/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_63/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_63/google_gemma_7b-* - split: google_gemma_7b_it path: format_63/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_63/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_63/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_63/tiiuae_falcon_180b-* - config_name: format_66 data_files: - split: meta_llama_llama_3_8b path: format_66/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_66/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_66/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_66/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_66/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_66/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_66/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_66/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_66/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_66/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_66/google_gemma_7b-* - split: google_gemma_7b_it path: format_66/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_66/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_66/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_66/tiiuae_falcon_180b-* - config_name: format_7 data_files: - split: meta_llama_llama_3_8b path: format_7/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_7/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_7/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_7/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_7/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_7/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_7/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_7/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_7/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_7/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_7/google_gemma_7b-* - split: google_gemma_7b_it path: format_7/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_7/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_7/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_7/tiiuae_falcon_180b-* - config_name: format_71 data_files: - split: meta_llama_llama_3_8b path: format_71/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_71/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_71/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_71/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_71/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_71/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_71/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_71/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_71/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_71/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_71/google_gemma_7b-* - split: google_gemma_7b_it path: format_71/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_71/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_71/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_71/tiiuae_falcon_180b-* - config_name: format_72 data_files: - split: meta_llama_llama_3_8b path: format_72/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_72/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_72/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_72/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_72/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_72/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_72/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_72/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_72/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_72/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_72/google_gemma_7b-* - split: google_gemma_7b_it path: format_72/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_72/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_72/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_72/tiiuae_falcon_180b-* - config_name: format_75 data_files: - split: meta_llama_llama_3_8b path: format_75/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_75/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_75/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_75/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_75/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_75/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_75/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_75/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_75/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_75/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_75/google_gemma_7b-* - split: google_gemma_7b_it path: format_75/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_75/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_75/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_75/tiiuae_falcon_180b-* - config_name: format_76 data_files: - split: meta_llama_llama_3_8b path: format_76/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_76/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_76/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_76/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_76/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_76/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_76/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_76/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_76/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_76/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_76/google_gemma_7b-* - split: google_gemma_7b_it path: format_76/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_76/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_76/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_76/tiiuae_falcon_180b-* - config_name: format_8 data_files: - split: meta_llama_llama_3_8b path: format_8/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_8/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_8/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_8/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_8/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_8/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_8/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_8/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_8/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_8/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_8/google_gemma_7b-* - split: google_gemma_7b_it path: format_8/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_8/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_8/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_8/tiiuae_falcon_180b-* - config_name: format_87 data_files: - split: meta_llama_llama_3_8b path: format_87/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_87/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_87/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_87/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_87/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_87/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_87/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_87/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_87/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_87/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_87/google_gemma_7b-* - split: google_gemma_7b_it path: format_87/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_87/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_87/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_87/tiiuae_falcon_180b-* - config_name: format_94 data_files: - split: meta_llama_llama_3_8b path: format_94/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_94/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_94/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_94/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_94/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_94/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_94/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_94/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_94/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_94/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_94/google_gemma_7b-* - split: google_gemma_7b_it path: format_94/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_94/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_94/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_94/tiiuae_falcon_180b-* - config_name: format_95 data_files: - split: meta_llama_llama_3_8b path: format_95/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_95/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_95/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_95/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_95/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_95/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_95/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_95/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_95/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_95/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_95/google_gemma_7b-* - split: google_gemma_7b_it path: format_95/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_95/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_95/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_95/tiiuae_falcon_180b-* - config_name: format_96 data_files: - split: meta_llama_llama_3_8b path: format_96/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_96/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_96/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_96/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_96/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_96/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_96/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_96/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_96/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_96/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_96/google_gemma_7b-* - split: google_gemma_7b_it path: format_96/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_96/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_96/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_96/tiiuae_falcon_180b-* - config_name: format_97 data_files: - split: meta_llama_llama_3_8b path: format_97/meta_llama_llama_3_8b-* - split: meta_llama_llama_3_8b_instruct path: format_97/meta_llama_llama_3_8b_instruct-* - split: meta_llama_llama_3_70b_instruct path: format_97/meta_llama_llama_3_70b_instruct-* - split: codellama_codellama_34b_instruct path: format_97/codellama_codellama_34b_instruct-* - split: google_flan_t5_xl path: format_97/google_flan_t5_xl-* - split: google_flan_t5_xxl path: format_97/google_flan_t5_xxl-* - split: google_flan_ul2 path: format_97/google_flan_ul2-* - split: ibm_mistralai_merlinite_7b path: format_97/ibm_mistralai_merlinite_7b-* - split: mistralai_mixtral_8x7b_instruct_v01 path: format_97/mistralai_mixtral_8x7b_instruct_v01-* - split: mistralai_mistral_7b_instruct_v0_2 path: format_97/mistralai_mistral_7b_instruct_v0_2-* - split: google_gemma_7b path: format_97/google_gemma_7b-* - split: google_gemma_7b_it path: format_97/google_gemma_7b_it-* - split: tiiuae_falcon_40b path: format_97/tiiuae_falcon_40b-* - split: mistralai_mistral_7b_v0_1 path: format_97/mistralai_mistral_7b_v0_1-* - split: tiiuae_falcon_180b path: format_97/tiiuae_falcon_180b-* --- # MMLU Multi-Prompt Evaluation Data ## Overview This dataset contains the results of a comprehensive evaluation of various Large Language Models (LLMs) using multiple prompt templates on the Massive Multitask Language Understanding (MMLU) benchmark. The data is introduced in [Maia Polo, Felipe, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, and Mikhail Yurochkin. "Efficient multi-prompt evaluation of LLMs." arXiv preprint arXiv:2405.17202 (2024).](https://arxiv.org/abs/2405.17202) ## Dataset Details The [MMLU](https://huggingface.co/datasets/cais/mmlu) benchmark comprises 57 diverse subjects and approximately 14,000 examples. It is a multiple-choice question-answering benchmark that tests the performance of LLMs across a wide range of topics. The data includes evaluation for 15 different SOTA LLMs and 100 different prompt templates. The data from a specific prompt template (format), can be downloaded using ```python from datasets import load_dataset j=0 data = load_dataset('PromptEval/tinyMMLU', f'format_{j}') ``` If you are only interested in the correctness scores, please check this lighter version of this dataset [here](https://huggingface.co/datasets/PromptEval/PromptEval_MMLU_correctness). ## Citing @article{polo2024efficient, title={Efficient multi-prompt evaluation of LLMs}, author={Polo, Felipe Maia and Xu, Ronald and Weber, Lucas and Silva, M{\'\i}rian and Bhardwaj, Onkar and Choshen, Leshem and de Oliveira, Allysson Flavio Melo and Sun, Yuekai and Yurochkin, Mikhail}, journal={arXiv preprint arXiv:2405.17202}, year={2024} } @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} }

lmms-lab/MMBench

lmms-lab

--- dataset_info: - config_name: cc features: - name: index dtype: int64 - name: question dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string splits: - name: test num_bytes: 51822980.0 num_examples: 2040 download_size: 51151713 dataset_size: 51822980.0 - config_name: cn features: - name: index dtype: int64 - name: question dtype: string - name: hint dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string - name: L2-category dtype: string - name: comment dtype: string - name: split dtype: string splits: - name: dev num_bytes: 102697367.875 num_examples: 4329 - name: test num_bytes: 148085952.75 num_examples: 6666 download_size: 238008307 dataset_size: 250783320.625 - config_name: en features: - name: index dtype: int64 - name: question dtype: string - name: hint dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string - name: L2-category dtype: string - name: comment dtype: string - name: split dtype: string splits: - name: dev num_bytes: 102785426.875 num_examples: 4329 - name: test num_bytes: 148216865.75 num_examples: 6666 download_size: 238044917 dataset_size: 251002292.625 configs: - config_name: cc data_files: - split: test path: cc/test-* - config_name: cn data_files: - split: dev path: cn/dev-* - split: test path: cn/test-* - config_name: en data_files: - split: dev path: en/dev-* - split: test path: en/test-* ---

knkarthick/dialogsum

knkarthick

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization - text2text-generation - text-generation task_ids: [] pretty_name: DIALOGSum Corpus tags: - dialogue-summary - one-liner-summary - meeting-title - email-subject --- # Dataset Card for DIALOGSum Corpus ## Dataset Description ### Links - **Homepage:** https://aclanthology.org/2021.findings-acl.449 - **Repository:** https://github.com/cylnlp/dialogsum - **Paper:** https://aclanthology.org/2021.findings-acl.449 - **Point of Contact:** https://huggingface.co/knkarthick ### Dataset Summary DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics. ### Languages English ## Dataset Structure ### Data Instances DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues (+1000 tests) split into train, test and validation. The first instance in the training set: {'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up} ### Data Fields - dialogue: text of dialogue. - summary: human written summary of the dialogue. - topic: human written topic/one liner of the dialogue. - id: unique file id of an example. ### Data Splits - train: 12460 - val: 500 - test: 1500 - holdout: 100 [Only 3 features: id, dialogue, topic] ## Dataset Creation ### Curation Rationale In paper: We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers. Compared with previous datasets, dialogues from DialogSum have distinct characteristics: Under rich real-life scenarios, including more diverse task-oriented scenarios; Have clear communication patterns and intents, which is valuable to serve as summarization sources; Have a reasonable length, which comforts the purpose of automatic summarization. We ask annotators to summarize each dialogue based on the following criteria: Convey the most salient information; Be brief; Preserve important named entities within the conversation; Be written from an observer perspective; Be written in formal language. ### Who are the source language producers? linguists ### Who are the annotators? language experts ## Licensing Information CC BY-NC-SA 4.0 ## Citation Information ``` @inproceedings{chen-etal-2021-dialogsum, title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset", author = "Chen, Yulong and Liu, Yang and Chen, Liang and Zhang, Yue", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.449", doi = "10.18653/v1/2021.findings-acl.449", pages = "5062--5074", ``` ## Contributions Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.

visual-layer/oxford-iiit-pet-vl-enriched

visual-layer

--- language: - en task_categories: - image-classification - object-detection - visual-question-answering - text-to-image - image-to-text configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image_id dtype: string - name: image dtype: image - name: label_cat_dog dtype: string - name: label_breed dtype: string - name: caption_enriched dtype: string - name: label_bbox_enriched list: - name: bbox sequence: int64 - name: label dtype: string - name: issues list: - name: confidence dtype: float64 - name: duplicate_group_id dtype: string - name: issue_type dtype: string splits: - name: train num_bytes: 148789851.0 num_examples: 3680 - name: test num_bytes: 133008511.375 num_examples: 3669 download_size: 281276926 dataset_size: 281798362.375 --- <div style="text-align: center;"> <a href="https://app.visual-layer.com/dataset/3972b3fc-1809-11ef-bb76-064432e0d220/data?p=1&page=1&utm_source=hugging_face" style="display: inline-block; padding: 10px 20px; background-color: rgba(128, 0, 128, 0.5); color: white; text-decoration: none; border-radius: 5px; font-family: Arial, sans-serif; font-size: 16px;"> Visualize on Visual Layer </a> </div> <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6195f404c07573b03c61702c/_RZTBZ6zNGz8f7g0sxow4.mp4"></video> # Oxford-IIIT-Pets-VL-Enriched An enriched version of the Oxford IIIT Pets Dataset with image caption, bounding boxes, and label issues! With this additional information, the Oxford IIIT Pet dataset can be extended to various tasks such as image retrieval or visual question answering. The label issues help to curate a cleaner and leaner dataset. ## Description The dataset consists of 6 columns: + `image_id`: Unique identifier for each image. `image_id` is the original filename of the image from the [Oxford IIIT Pet dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/). + `image`: Image data in the form of PIL Image. + `label_cat_dog`: Label for the image, whether it is a cat or a dog. Provided by the authors of the original dataset. + `label_breed`: Label for the breed of the cat or dog in the image. Consists of 37 pet breeds of cats and dogs. Provided by the authors of the original dataset. + `label_bbox_enriched`: Enriched labels for the image. Consists of bounding box coordinates, confidence score, and label for the bounding box. Generated by in-house and customized YOLOv8 model. + `caption_enriched`: Enriched captions for the image. Generated by BLIP2 captioning model. + `issues`: Quality issues found such as duplicate, mislabeled, dark, blurry, bright, and outlier image. ## Usage This dataset can be used with the Hugging Face Datasets library.: ```python import datasets ds = datasets.load_dataset("visual-layer/oxford-iiit-pet-vl-enriched") ``` More in this [notebook](usage.ipynb). ## Interactive Visualization Visual Layer provides a platform to interactively visualize the dataset. Check it out [here](https://app.visual-layer.com/dataset/3972b3fc-1809-11ef-bb76-064432e0d220/data?p=1&page=1&utm_source=hugging_face). No sign-up required. <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6195f404c07573b03c61702c/_RZTBZ6zNGz8f7g0sxow4.mp4"></video> <div style="text-align: center;"> <a href="https://app.visual-layer.com/dataset/3972b3fc-1809-11ef-bb76-064432e0d220/data?p=1&page=1&utm_source=hugging_face" style="display: inline-block; padding: 10px 20px; background-color: rgba(128, 0, 128, 0.5); color: white; text-decoration: none; border-radius: 5px; font-family: Arial, sans-serif; font-size: 16px;"> Visualize on Visual Layer </a> </div> ## License & Disclaimer We provide no warranty on the dataset, and the user takes full responsibility for the usage of the dataset. By using the dataset, you agree to the terms of the Oxford IIIT Pets dataset license. ## About Visual Layer <div style="text-align: center; margin-top:50px;"> <a href="https://visual-layer.com/" style="padding:10px; display: inline-block;"> <img alt="site" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/web.png" width="50"></a> <a href="https://medium.com/visual-layer" style="padding:10px; display: inline-block;"> <img alt="blog" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/forum.png" width="50"></a> <a href="https://github.com/visual-layer/fastdup" style="padding:10px; display: inline-block;"> <img alt="github" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/github.png" width="50"></a> <a href="https://discord.com/invite/Dqw458EG/" style="padding:10px; display: inline-block;"> <img alt="slack" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/discord.png" width="50"></a> <a href="https://www.linkedin.com/company/visual-layer/" style="padding:10px; display: inline-block;"> <img alt="linkedin" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/linkedin.png" width="50"></a> <a href="https://www.youtube.com/@visual-layer" style="padding:10px; display: inline-block;"> <img alt="youtube" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/youtube.png" width="50"></a> <a href="https://twitter.com/visual_layer" style="padding:10px; display: inline-block;"> <img alt="twitter" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/x.png" width="50"></a> </div> <div style="text-align: center;"> <img style="width:200px; display: block; margin: 0 auto;" alt="logo" src="https://d2iycffepdu1yp.cloudfront.net/design-assets/VL_horizontal_logo.png"> <div style="margin-top:20px;">Copyright © 2024 Visual Layer. All rights reserved.</div> </div>

lithium0003/findtextCenterNet_dataset

lithium0003

--- license: mit ---

lmms-lab/GQA

lmms-lab

--- license: mit dataset_info: - config_name: challenge_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: challenge num_bytes: 261636425.25 num_examples: 1590 download_size: 261271928 dataset_size: 261636425.25 - config_name: challenge_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: challenge num_bytes: 50797705 num_examples: 713449 download_size: 19869828 dataset_size: 50797705 - config_name: challenge_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: challenge num_bytes: 261636425.25 num_examples: 1590 download_size: 261333538 dataset_size: 261636425.25 - config_name: challenge_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: challenge num_bytes: 3523973 num_examples: 50726 download_size: 1787024 dataset_size: 3523973 - config_name: submission_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: submission num_bytes: 2314978438.875 num_examples: 15545 download_size: 2309217874 dataset_size: 2314978438.875 - config_name: submission_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: submission num_bytes: 298875520 num_examples: 4237524 download_size: 121458425 dataset_size: 298875520 - config_name: test_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: test num_bytes: 492571840.875 num_examples: 2993 download_size: 491611526 dataset_size: 492571840.875 - config_name: test_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: test num_bytes: 95588974 num_examples: 1340048 download_size: 39561711 dataset_size: 95588974 - config_name: test_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: test num_bytes: 491210370.625 num_examples: 2987 download_size: 490293506 dataset_size: 491210370.625 - config_name: test_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: isBalanced dtype: bool splits: - name: test num_bytes: 6622775 num_examples: 95336 download_size: 3401070 dataset_size: 6622775 - config_name: testdev_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: testdev num_bytes: 65779269.0 num_examples: 398 download_size: 65670255 dataset_size: 65779269.0 - config_name: testdev_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: testdev num_bytes: 86970760 num_examples: 172174 download_size: 23385535 dataset_size: 86970760 - config_name: testdev_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: testdev num_bytes: 65779269.0 num_examples: 398 download_size: 65647745 dataset_size: 65779269.0 - config_name: testdev_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: testdev num_bytes: 6113469 num_examples: 12578 download_size: 2090335 dataset_size: 6113469 - config_name: train_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: train num_bytes: 10509758457.0 num_examples: 74256 download_size: 10480239090 dataset_size: 10509758457.0 - config_name: train_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: train num_bytes: 6891129609 num_examples: 14305356 download_size: 1874173198 dataset_size: 6891129609 - config_name: train_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: train num_bytes: 10200292415.5 num_examples: 72140 download_size: 10171627271 dataset_size: 10200292415.5 - config_name: train_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: train num_bytes: 460429581 num_examples: 943000 download_size: 183979778 dataset_size: 460429581 - config_name: val_all_images features: - name: id dtype: string - name: image dtype: image splits: - name: val num_bytes: 1494990904.5 num_examples: 10564 download_size: 1490744689 dataset_size: 1494990904.5 - config_name: val_all_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: val num_bytes: 967338322 num_examples: 2011853 download_size: 266476025 dataset_size: 967338322 - config_name: val_balanced_images features: - name: id dtype: string - name: image dtype: image splits: - name: val num_bytes: 1447074448.75 num_examples: 10234 download_size: 1443033919 dataset_size: 1447074448.75 - config_name: val_balanced_instructions features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: val num_bytes: 64498952 num_examples: 132062 download_size: 25794272 dataset_size: 64498952 configs: - config_name: challenge_all_images data_files: - split: challenge path: challenge_all_images/challenge-* - config_name: challenge_all_instructions data_files: - split: challenge path: challenge_all_instructions/challenge-* - config_name: challenge_balanced_images data_files: - split: challenge path: challenge_balanced_images/challenge-* - config_name: challenge_balanced_instructions data_files: - split: challenge path: challenge_balanced_instructions/challenge-* - config_name: submission_all_images data_files: - split: submission path: submission_all_images/submission-* - config_name: submission_all_instructions data_files: - split: submission path: submission_all_instructions/submission-* - config_name: test_all_images data_files: - split: test path: test_all_images/test-* - config_name: test_all_instructions data_files: - split: test path: test_all_instructions/test-* - config_name: test_balanced_images data_files: - split: test path: test_balanced_images/test-* - config_name: test_balanced_instructions data_files: - split: test path: test_balanced_instructions/test-* - config_name: testdev_all_images data_files: - split: testdev path: testdev_all_images/testdev-* - config_name: testdev_all_instructions data_files: - split: testdev path: testdev_all_instructions/testdev-* - config_name: testdev_balanced_images data_files: - split: testdev path: testdev_balanced_images/testdev-* - config_name: testdev_balanced_instructions data_files: - split: testdev path: testdev_balanced_instructions/testdev-* - config_name: train_all_images data_files: - split: train path: train_all_images/train-* - config_name: train_all_instructions data_files: - split: train path: train_all_instructions/train-* - config_name: train_balanced_images data_files: - split: train path: train_balanced_images/train-* - config_name: train_balanced_instructions data_files: - split: train path: train_balanced_instructions/train-* - config_name: val_all_images data_files: - split: val path: val_all_images/val-* - config_name: val_all_instructions data_files: - split: val path: val_all_instructions/val-* - config_name: val_balanced_images data_files: - split: val path: val_balanced_images/val-* - config_name: val_balanced_instructions data_files: - split: val path: val_balanced_instructions/val-* --- <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [GQA](hhttps://cs.stanford.edu/people/dorarad/gqa/about.html). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @inproceedings{hudson2019gqa, title={Gqa: A new dataset for real-world visual reasoning and compositional question answering}, author={Hudson, Drew A and Manning, Christopher D}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, pages={6700--6709}, year={2019} } ```

fixie-ai/peoples_speech

fixie-ai

--- dataset_info: - config_name: clean features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: duration_ms dtype: int32 - name: text dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 2511523987.692 num_examples: 18622 - name: test num_bytes: 4259695510.794 num_examples: 34898 - name: train num_bytes: 401646320552.671 num_examples: 1501271 download_size: 398922548670 dataset_size: 408417540051 - config_name: dirty_sa features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: duration_ms dtype: int32 - name: text dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 144432442623.054 num_examples: 548014 - name: validation num_bytes: 2511524241.692 num_examples: 18622 - name: test num_bytes: 4259695588.794 num_examples: 34898 download_size: 149491764186 dataset_size: 151203662453.53998 configs: - config_name: clean data_files: - split: validation path: clean/validation-* - split: test path: clean/test-* - split: train path: data/train-* - config_name: dirty_sa data_files: - split: train path: dirty_sa/train-* - split: validation path: dirty_sa/validation-* - split: test path: dirty_sa/test-* ---

HuggingFaceH4/ultrachat_200k

HuggingFaceH4

--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: | train_sft | test_sft | train_gen | test_gen | |:-------:|:-----------:|:-----:| :-----:| | 207865 | 23110 | 256032 | 28304 | The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

allenai/sciq

allenai

--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-nc-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa paperswithcode_id: sciq pretty_name: SciQ dataset_info: features: - name: question dtype: string - name: distractor3 dtype: string - name: distractor1 dtype: string - name: distractor2 dtype: string - name: correct_answer dtype: string - name: support dtype: string splits: - name: train num_bytes: 6546183 num_examples: 11679 - name: validation num_bytes: 554120 num_examples: 1000 - name: test num_bytes: 563927 num_examples: 1000 download_size: 4674410 dataset_size: 7664230 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "sciq" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/sciq](https://allenai.org/data/sciq) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.82 MB - **Size of the generated dataset:** 7.68 MB - **Total amount of disk used:** 10.50 MB ### Dataset Summary The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 2.82 MB - **Size of the generated dataset:** 7.68 MB - **Total amount of disk used:** 10.50 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "correct_answer": "coriolis effect", "distractor1": "muon effect", "distractor2": "centrifugal effect", "distractor3": "tropical effect", "question": "What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere?", "support": "\"Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to..." } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `distractor3`: a `string` feature. - `distractor1`: a `string` feature. - `distractor2`: a `string` feature. - `correct_answer`: a `string` feature. - `support`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|11679| 1000|1000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under the [Creative Commons Attribution-NonCommercial 3.0 Unported License](http://creativecommons.org/licenses/by-nc/3.0/). ### Citation Information ``` @inproceedings{SciQ, title={Crowdsourcing Multiple Choice Science Questions}, author={Johannes Welbl, Nelson F. Liu, Matt Gardner}, year={2017}, journal={arXiv:1707.06209v1} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

databricks/databricks-dolly-15k

databricks

--- license: cc-by-sa-3.0 task_categories: - question-answering - summarization language: - en size_categories: - 10K<n<100K --- # Summary `databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: English Version: 1.0 **Owner: Databricks, Inc.** # Dataset Overview `databricks-dolly-15k` is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category. Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly. For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the `context` field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. `[42]`) which we recommend users remove for downstream applications. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories. Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets. # Dataset ## Purpose of Collection As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - **Human-generated data**: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories. - **Wikipedia**: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages. ## Annotator Guidelines To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor. The annotation guidelines for each of the categories are as follows: - **Creative Writing**: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better. - **Closed QA**: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form. - **Open QA**: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation. - **Summarization**: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form. - **Information Extraction**: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form. - **Classification**: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better. - **Brainstorming**: Think up lots of examples in response to a question asking to brainstorm ideas. ## Personal or Sensitive Data This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language American English # Known Limitations - Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia - Some annotators may not be native English speakers - Annotator demographics and subject matter may reflect the makeup of Databricks employees # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # License/Attribution **Copyright (2023) Databricks, Inc.** This dataset was developed at Databricks (https://www.databricks.com) and its use is subject to the CC BY-SA 3.0 license. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license: Wikipedia (various pages) - https://www.wikipedia.org/ Copyright © Wikipedia editors and contributors.

mlfoundations/MINT-1T-PDF-CC-2023-23

mlfoundations

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-23`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c).  ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

distil-whisper/librispeech_long

distil-whisper

--- dataset_info: config_name: clean features: - name: audio dtype: audio splits: - name: validation num_bytes: 1998609.0 num_examples: 1 download_size: 1984721 dataset_size: 1998609.0 configs: - config_name: clean data_files: - split: validation path: clean/validation-* --- # Dataset Card for "librispeech_long" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mteb/sts12-sts

mteb

--- language: - en ---

drengskapur/midi-classical-music

drengskapur

--- pretty_name: MIDI Classical Music dataset_name: midi-classical-music tags: - music - classical - midi - dataset - composers - music-analysis - music-generation license: mit language: - en size_categories: - 1K<n<10K --- # MIDI Classical Music This dataset contains a comprehensive collection of MIDI files representing classical music compositions from various renowned composers. The collection includes works from composers such as Bach, Beethoven, Chopin, Mozart, and many others. The dataset is organized into directories by composer, with each directory containing MIDI files of their compositions. The dataset is ideal for music analysis, machine learning models for music generation, and other music-related research and applications.

math-ai/AutoMathText

math-ai

--- license: cc-by-sa-4.0 task_categories: - text-generation - question-answering language: - en pretty_name: AutoMathText size_categories: - 10B<n<100B configs: - config_name: web-0.50-to-1.00 data_files: - split: train path: - data/web/0.95-1.00.jsonl - data/web/0.90-0.95.jsonl - data/web/0.85-0.90.jsonl - data/web/0.80-0.85.jsonl - data/web/0.75-0.80.jsonl - data/web/0.70-0.75.jsonl - data/web/0.65-0.70.jsonl - data/web/0.60-0.65.jsonl - data/web/0.55-0.60.jsonl - data/web/0.50-0.55.jsonl default: true - config_name: web-0.60-to-1.00 data_files: - split: train path: - data/web/0.95-1.00.jsonl - data/web/0.90-0.95.jsonl - data/web/0.85-0.90.jsonl - data/web/0.80-0.85.jsonl - data/web/0.75-0.80.jsonl - data/web/0.70-0.75.jsonl - data/web/0.65-0.70.jsonl - data/web/0.60-0.65.jsonl - config_name: web-0.70-to-1.00 data_files: - split: train path: - data/web/0.95-1.00.jsonl - data/web/0.90-0.95.jsonl - data/web/0.85-0.90.jsonl - data/web/0.80-0.85.jsonl - data/web/0.75-0.80.jsonl - data/web/0.70-0.75.jsonl - config_name: web-0.80-to-1.00 data_files: - split: train path: - data/web/0.95-1.00.jsonl - data/web/0.90-0.95.jsonl - data/web/0.85-0.90.jsonl - data/web/0.80-0.85.jsonl - config_name: web-full data_files: data/web/*.jsonl - config_name: arxiv-0.50-to-1.00 data_files: - split: train path: - data/arxiv/0.90-1.00/*.jsonl - data/arxiv/0.80-0.90/*.jsonl - data/arxiv/0.70-0.80/*.jsonl - data/arxiv/0.60-0.70/*.jsonl - data/arxiv/0.50-0.60/*.jsonl - config_name: arxiv-0.60-to-1.00 data_files: - split: train path: - data/arxiv/0.90-1.00/*.jsonl - data/arxiv/0.80-0.90/*.jsonl - data/arxiv/0.70-0.80/*.jsonl - data/arxiv/0.60-0.70/*.jsonl - config_name: arxiv-0.70-to-1.00 data_files: - split: train path: - data/arxiv/0.90-1.00/*.jsonl - data/arxiv/0.80-0.90/*.jsonl - data/arxiv/0.70-0.80/*.jsonl - config_name: arxiv-0.80-to-1.00 data_files: - split: train path: - data/arxiv/0.90-1.00/*.jsonl - data/arxiv/0.80-0.90/*.jsonl - config_name: arxiv-full data_files: - split: train path: - data/arxiv/0.90-1.00/*.jsonl - 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data/code/maple/0.95-1.00.jsonl - data/code/maple/0.90-0.95.jsonl - data/code/maple/0.85-0.90.jsonl - data/code/maple/0.80-0.85.jsonl - data/code/maple/0.75-0.80.jsonl - data/code/maple/0.70-0.75.jsonl - data/code/maple/0.65-0.70.jsonl - data/code/maple/0.60-0.65.jsonl - data/code/maple/0.55-0.60.jsonl - data/code/maple/0.50-0.55.jsonl - data/code/python/0.95-1.00.jsonl - data/code/python/0.90-0.95.jsonl - data/code/python/0.85-0.90.jsonl - data/code/python/0.80-0.85.jsonl - data/code/python/0.75-0.80.jsonl - data/code/python/0.70-0.75.jsonl - data/code/python/0.65-0.70.jsonl - data/code/python/0.60-0.65.jsonl - data/code/python/0.55-0.60.jsonl - data/code/python/0.50-0.55.jsonl - data/code/r/0.95-1.00.jsonl - data/code/r/0.90-0.95.jsonl - data/code/r/0.85-0.90.jsonl - data/code/r/0.80-0.85.jsonl - data/code/r/0.75-0.80.jsonl - data/code/r/0.70-0.75.jsonl - data/code/r/0.65-0.70.jsonl - data/code/r/0.60-0.65.jsonl - data/code/r/0.55-0.60.jsonl - data/code/r/0.50-0.55.jsonl - data/code/tex/0.95-1.00.jsonl - data/code/tex/0.90-0.95.jsonl - data/code/tex/0.85-0.90.jsonl - data/code/tex/0.80-0.85.jsonl - data/code/tex/0.75-0.80.jsonl - data/code/tex/0.70-0.75.jsonl - data/code/tex/0.65-0.70.jsonl - data/code/tex/0.60-0.65.jsonl - data/code/tex/0.55-0.60.jsonl - data/code/tex/0.50-0.55.jsonl - config_name: code-python-0.50-to-1.00 data_files: - split: train path: - data/code/python/0.95-1.00.jsonl - data/code/python/0.90-0.95.jsonl - data/code/python/0.85-0.90.jsonl - data/code/python/0.80-0.85.jsonl - data/code/python/0.75-0.80.jsonl - data/code/python/0.70-0.75.jsonl - data/code/python/0.65-0.70.jsonl - data/code/python/0.60-0.65.jsonl - data/code/python/0.55-0.60.jsonl - data/code/python/0.50-0.55.jsonl - config_name: code-python-0.60-to-1.00 data_files: - split: train path: - data/code/python/0.95-1.00.jsonl - data/code/python/0.90-0.95.jsonl - data/code/python/0.85-0.90.jsonl - data/code/python/0.80-0.85.jsonl - data/code/python/0.75-0.80.jsonl - data/code/python/0.70-0.75.jsonl - data/code/python/0.65-0.70.jsonl - data/code/python/0.60-0.65.jsonl - config_name: code-python-0.70-to-1.00 data_files: - split: train path: - data/code/python/0.95-1.00.jsonl - data/code/python/0.90-0.95.jsonl - data/code/python/0.85-0.90.jsonl - data/code/python/0.80-0.85.jsonl - data/code/python/0.75-0.80.jsonl - data/code/python/0.70-0.75.jsonl - config_name: code-python-0.80-to-1.00 data_files: - split: train path: - data/code/python/0.95-1.00.jsonl - data/code/python/0.90-0.95.jsonl - data/code/python/0.85-0.90.jsonl - data/code/python/0.80-0.85.jsonl - config_name: code-jupyter-notebook-0.50-to-1.00 data_files: - split: train path: - data/code/jupyter-notebook/0.95-1.00.jsonl - data/code/jupyter-notebook/0.90-0.95.jsonl - data/code/jupyter-notebook/0.85-0.90.jsonl - data/code/jupyter-notebook/0.80-0.85.jsonl - data/code/jupyter-notebook/0.75-0.80.jsonl - data/code/jupyter-notebook/0.70-0.75.jsonl - data/code/jupyter-notebook/0.65-0.70.jsonl - data/code/jupyter-notebook/0.60-0.65.jsonl - data/code/jupyter-notebook/0.55-0.60.jsonl - data/code/jupyter-notebook/0.50-0.55.jsonl - config_name: code-jupyter-notebook-0.60-to-1.00 data_files: - split: train path: - data/code/jupyter-notebook/0.95-1.00.jsonl - data/code/jupyter-notebook/0.90-0.95.jsonl - data/code/jupyter-notebook/0.85-0.90.jsonl - data/code/jupyter-notebook/0.80-0.85.jsonl - data/code/jupyter-notebook/0.75-0.80.jsonl - data/code/jupyter-notebook/0.70-0.75.jsonl - data/code/jupyter-notebook/0.65-0.70.jsonl - data/code/jupyter-notebook/0.60-0.65.jsonl - config_name: code-jupyter-notebook-0.70-to-1.00 data_files: - split: train path: - data/code/jupyter-notebook/0.95-1.00.jsonl - data/code/jupyter-notebook/0.90-0.95.jsonl - data/code/jupyter-notebook/0.85-0.90.jsonl - data/code/jupyter-notebook/0.80-0.85.jsonl - data/code/jupyter-notebook/0.75-0.80.jsonl - data/code/jupyter-notebook/0.70-0.75.jsonl - config_name: code-jupyter-notebook-0.80-to-1.00 data_files: - split: train path: - data/code/jupyter-notebook/0.95-1.00.jsonl - data/code/jupyter-notebook/0.90-0.95.jsonl - data/code/jupyter-notebook/0.85-0.90.jsonl - data/code/jupyter-notebook/0.80-0.85.jsonl - config_name: code-full data_files: - split: train path: - data/code/*/*.jsonl tags: - mathematical-reasoning - reasoning - finetuning - pretraining - llm --- # AutoMathText **AutoMathText** is an extensive and carefully curated dataset encompassing around **200 GB** of mathematical texts. It's a compilation sourced from a diverse range of platforms including various websites, arXiv, and GitHub (OpenWebMath, RedPajama, Algebraic Stack). This rich repository has been **autonomously selected (labeled) by the state-of-the-art open-source language model**, Qwen-72B. Each piece of content in the dataset is assigned **a score `lm_q1q2_score` within the range of [0, 1]**, reflecting its relevance, quality and educational value in the context of mathematical intelligence. GitHub homepage: https://github.com/yifanzhang-pro/AutoMathText ArXiv paper: https://arxiv.org/abs/2402.07625 ## Objective The primary aim of the **AutoMathText** dataset is to provide a comprehensive and reliable resource for a wide array of users - from academic researchers and educators to AI practitioners and mathematics enthusiasts. This dataset is particularly geared towards: - Facilitating advanced research in **the intersection of mathematics and artificial intelligence**. - Serving as an educational tool for **learning and teaching complex mathematical concepts**. - Providing **a foundation for developing and training AI models** specialized in processing and understanding **mathematical content**. ## Configs ```YAML configs: - config_name: web-0.50-to-1.00 data_files: - split: train path: - data/web/0.95-1.00.jsonl - data/web/0.90-0.95.jsonl - ... - data/web/0.50-0.55.jsonl default: true - config_name: web-0.60-to-1.00 - config_name: web-0.70-to-1.00 - config_name: web-0.80-to-1.00 - config_name: web-full data_files: data/web/*.jsonl - config_name: arxiv-0.50-to-1.00 data_files: - split: train path: - data/arxiv/0.90-1.00/*.jsonl - ... - data/arxiv/0.50-0.60/*.jsonl - config_name: arxiv-0.60-to-1.00 - config_name: arxiv-0.70-to-1.00 - config_name: arxiv-0.80-to-1.00 - config_name: arxiv-full data_files: data/arxiv/*/*.jsonl - config_name: code-0.50-to-1.00 data_files: - split: train path: - data/code/*/0.95-1.00.jsonl - ... - data/code/*/0.50-0.55.jsonl - config_name: code-python-0.50-to-1.00 - split: train path: - data/code/python/0.95-1.00.jsonl - ... - data/code/python/0.50-0.55.jsonl - config_name: code-python-0.60-to-1.00 - config_name: code-python-0.70-to-1.00 - config_name: code-python-0.80-to-1.00 - config_name: code-jupyter-notebook-0.50-to-1.00 - split: train path: - data/code/jupyter-notebook/0.95-1.00.jsonl - ... - data/code/jupyter-notebook/0.50-0.55.jsonl - config_name: code-jupyter-notebook-0.60-to-1.00 - config_name: code-jupyter-notebook-0.70-to-1.00 - config_name: code-jupyter-notebook-0.80-to-1.00 - config_name: code-full data_files: data/code/*/*.jsonl ``` How to load data: ```python from datasets import load_dataset ds = load_dataset("math-ai/AutoMathText", "web-0.50-to-1.00") # or any valid config_name ``` ## Features - **Volume**: Approximately 200 GB of text data (in natural language and programming language). - **Content**: A diverse collection of mathematical texts, including but not limited to research papers, educational articles, and code documentation. - **Labeling**: Every text is **scored** by Qwen-72B, a sophisticated language model, ensuring a high standard of relevance and accuracy. - **Scope**: Covers a wide spectrum of mathematical topics, making it suitable for various applications in advanced research and education. ## References - OpenWebMath [[link]](https://huggingface.co/datasets/open-web-math/open-web-math) - RedPajama [[link]](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) - Algebraick Stack [[link]](https://huggingface.co/datasets/EleutherAI/proof-pile-2) (a subset of Proof-Pile-2) ## Citation We appreciate your use of **AutoMathText** in your work. If you find this repository helpful, please consider citing it and star this repo. Feel free to contact zhangyif21@mails.tsinghua.edu.cn or open an issue if you have any questions (GitHub homepage: https://github.com/yifanzhang-pro/AutoMathText). ```bibtex @article{zhang2024automathtext, title={Autonomous Data Selection with Language Models for Mathematical Texts}, author={Zhang, Yifan and Luo, Yifan and Yuan, Yang and Yao, Andrew Chi-Chih}, journal={arXiv preprint arXiv:2402.07625}, year={2024}, } ```

lcolonn/patfig

lcolonn

--- language: - en license: cc-by-nc-4.0 size_categories: - 10K<n<100K task_categories: - image-to-text - visual-question-answering - image-classification pretty_name: PatFig configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: image_name dtype: string - name: pub_number dtype: string - name: title dtype: string - name: figs_norm sequence: string - name: short_description sequence: string - name: long_description sequence: string - name: short_description_token_count dtype: int64 - name: long_description_token_count dtype: int64 - name: draft_class dtype: string - name: cpc_class dtype: string - name: relevant_terms list: - name: element_identifier dtype: string - name: terms sequence: string - name: associated_claims dtype: string - name: compound dtype: bool - name: references sequence: string splits: - name: train num_bytes: 1998632864.066 num_examples: 17386 - name: test num_bytes: 118291788.0 num_examples: 998 download_size: 1735361199 dataset_size: 2116924652.066 --- # PatFig Dataset <div align="center"> <img src="https://cdn-lfs-us-1.huggingface.co/repos/25/0c/250cb7eb9b83b2bd76ad6440700971baf0ec2981fdcb94b7fad768f2eb59fecc/1e79b8cf6cbe22d424c95e4816fc763e388d80cba4228908d707100f1f41182a?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27patfig_funny_logo.png%3B+filename%3D%22patfig_funny_logo.png%22%3B&response-content-type=image%2Fpng&Expires=1710484025&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDQ4NDAyNX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI1LzBjLzI1MGNiN2ViOWI4M2IyYmQ3NmFkNjQ0MDcwMDk3MWJhZjBlYzI5ODFmZGNiOTRiN2ZhZDc2OGYyZWI1OWZlY2MvMWU3OWI4Y2Y2Y2JlMjJkNDI0Yzk1ZTQ4MTZmYzc2M2UzODhkODBjYmE0MjI4OTA4ZDcwNzEwMGYxZjQxMTgyYT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=ghxVaJztNO%7EJsQTPLCGf5VjyqxZQBbQEwpEFUlg9jp0pwu6YySncyKudoNVEJkRJlXpIi6pTw0ezQ4VpMHa4BrirgO8JfmxzVJQ5h1wgq9KtRwpdYt0ba%7EH73KNCSS1KvNM50prZ5fKBon3s9yvL1wgkOgIMN2i7NPaR1MCzps8sMyhbWFpwVpvuoV91QS20OGwGsWmYW3IQ3cEsCczadefAI%7EehWswamDxG3UZ%7ErYrMqXprFlpiB1fTas7SPeKpDT4V5YdTtEHUZRQ14Lb0QEogydoNmgj4afvqrqwV-6dnL3Z7iaJxDrdxYTIk6VfPIHIH1%7E7dyLjRwG8gdhvhCQ__&Key-Pair-Id=KCD77M1F0VK2B" width="40%" alt="PatFig Dataset Logo"> </div> ## Table of Contents - [Introduction](#introduction) - [Dataset Description](#dataset-description) - [Overview](#overview) - [Structure](#structure) - [Categories](#categories) - [Usage](#usage) - [Challenges and Considerations](#challenges-and-considerations) - [License and Usage Guidelines](#license-and-usage-guidelines) ## Introduction The PatFig Dataset is a curated collection of over 18,000 patent images from more than 7,000 European patent applications, spanning the year 2020. It aims to provide a comprehensive resource for research and applications in image captioning, abstract reasoning, patent analysis, and automated documentprocessing. The overarching goal of this dataset is to advance the research in visually situated language understanding towards more hollistic consumption of the visual and textual data. ## Dataset Description ### Overview This dataset includes patent figures accompanied by short and long captions, reference numerals, corresponding terms, and a minimal set of claims, offering a detailed insight into the depicted inventions. ### Structure - **Image Files**: Technical drawings, block diagrams, flowcharts, plots, and grayscale photographs. - **Captions**: Each figure is accompanied by a short and long caption describing its content and context. - **Reference Numerals and Terms**: Key components in the figures are linked to their descriptions through reference numerals. - **Minimal Set of Claims**: Claims sentences summarizing the interactions among elements within each figure. - **Metadata**: Includes image names, publication numbers, titles, figure identifiers, and more. The detailed descriptions of the fields are available in the Dataset Documentation. ### Categories The dataset is categorized according to the International Patent Classification (IPC) system, ensuring a diverse representation of technological domains. ## Usage The PatFig Dataset is intended for use in patent image analysis, document image processing, visual question answering tasks, and image captioning in technical contexts. Users are encouraged to explore innovative applications in related fields. <p align="center"> <span style="display: inline-block; margin-right: 20px;"><img src="https://cdn-lfs-us-1.huggingface.co/repos/25/0c/250cb7eb9b83b2bd76ad6440700971baf0ec2981fdcb94b7fad768f2eb59fecc/3c626eeb8727520da886493356c116cc5165a0104fa7a3445bce92cb4117591c?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27PatFig_example.png%3B+filename%3D%22PatFig_example.png%22%3B&response-content-type=image%2Fpng&Expires=1710484079&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDQ4NDA3OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI1LzBjLzI1MGNiN2ViOWI4M2IyYmQ3NmFkNjQ0MDcwMDk3MWJhZjBlYzI5ODFmZGNiOTRiN2ZhZDc2OGYyZWI1OWZlY2MvM2M2MjZlZWI4NzI3NTIwZGE4ODY0OTMzNTZjMTE2Y2M1MTY1YTAxMDRmYTdhMzQ0NWJjZTkyY2I0MTE3NTkxYz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=ehYXk1BEjurpR-Rpn6QmYrSJmuX96AjF6c6BzNXYvSLrwkE9olnZfkstWhhR7QJ6Y%7Ef4--82aUUb0wXJdnmCBCmMrJ2JzipYMpZ92XRrIeJ41Kd2YGHr1QU-IWIXE-2eYJRXrq2GdNo3tf3dqJLKzG7FWyoxqthQt2EGpneAyMURw81LGqay1N9pvfnoB751BPEUsiRz-iDI8G8HkNkJ%7EViE7HKU5rTCV2pPfHjKKQ6pLXUW%7EIwpvkXLj02xhGD-aoo24TYZ5NdZJC1lYj56ynqyABhnPhhFqzVsD%7Eqmdi9wmw2gKa--HZU5q3bmZtm9lsifOQ4mLkJ8x4vl2TWefA__&Key-Pair-Id=KCD77M1F0VK2B" alt="PatFig Image Captioning Version" width="286"/></span> <span style="display: inline-block; margin-left: 20px;"><img src="https://cdn-lfs-us-1.huggingface.co/repos/25/0c/250cb7eb9b83b2bd76ad6440700971baf0ec2981fdcb94b7fad768f2eb59fecc/532251dbff11e080a91b60d91956c49420a70381143cd8c43ea80fb94608d7f9?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27PatFigVQA_example.png%3B+filename%3D%22PatFigVQA_example.png%22%3B&response-content-type=image%2Fpng&Expires=1710484116&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDQ4NDExNn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzI1LzBjLzI1MGNiN2ViOWI4M2IyYmQ3NmFkNjQ0MDcwMDk3MWJhZjBlYzI5ODFmZGNiOTRiN2ZhZDc2OGYyZWI1OWZlY2MvNTMyMjUxZGJmZjExZTA4MGE5MWI2MGQ5MTk1NmM0OTQyMGE3MDM4MTE0M2NkOGM0M2VhODBmYjk0NjA4ZDdmOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=DR9Zobe00j2rUf0QDFD2yxYy96TwLq0Jgl9CdGT4MrbjmtDMUbFQ0W3xOTNiCBxQ3fEZJ0ykFjmE-tNo8UjOCneWKqyj1AoqcYRDozji9HL0flVplSRceMLmnAzgMyKBSiXJNXNhFv2iEz1007qiyQiMidOpQoyPyZXXqYDzQLHQdKfhYlKUFEs-w9ZYT0vJwKDOlBIAc7pfPuPmoMnPP5sJ4etayoU2bY43WdUaL6cqoEuPS14LaCNIpwl8z2-BhZMp6YZstLoQgiktWQYun7izeLIZavqFRPRJ46GndJ0mdVXS5c%7E7QrT4BFucDQZ%7EV-skGr5CaInhfQnH99ep5w__&Key-Pair-Id=KCD77M1F0VK2B" alt="PatFig VQA Version" width="300""/></span> </p> ## Challenges and Considerations Users should be aware of challenges such as interpreting compound figures. PatFig was built automatically using high-performance machine-learning and deep-learning methods. Therefore, the data might contain noise, which was mentioned in the corresponding paper. ## License and Usage Guidelines The dataset is released under a Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) License. It is intended for non-commercial use, and users must adhere to the license terms. ## Cite as ``` @inproceedings{aubakirova2023patfig, title={PatFig: Generating Short and Long Captions for Patent Figures}, author={Aubakirova, Dana and Gerdes, Kim and Liu, Lufei}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={2843--2849}, year={2023} } ```

Zyphra/Zyda

Zyphra

--- dataset_info: config_name: default splits: - name: train num_examples: 1594197267 license: odc-by pretty_name: Zyda task_categories: - text-generation language: - en size_categories: - n>1T configs: - config_name: default data_files: - split: train path: data/*/*/* - config_name: zyda_no_starcoder data_files: - split: train path: data/zyda_no_starcoder/*/* - config_name: zyda_arxiv_only data_files: - split: train path: data/zyda_no_starcoder/zyda_arxiv/* - config_name: zyda_c4-en_only data_files: - split: train path: data/zyda_no_starcoder/c4_en/* - config_name: zyda_peS2o_only data_files: - split: train path: data/zyda_no_starcoder/zyda_peS2o/* - config_name: zyda_pile-uncopyrighted_only data_files: - split: train path: data/zyda_no_starcoder/zyda_pile-uncopyrighted/* - config_name: zyda_refinedweb_only data_files: - split: train path: data/zyda_no_starcoder/zyda_refinedweb/* - config_name: zyda_slimpajama_only data_files: - split: train path: data/zyda_no_starcoder/zyda_slimpajama/* - config_name: zyda_starcoder_only data_files: - split: train path: data/zyda_starcoder/*/* --- # Zyda <!-- Provide a quick summary of the dataset. --> Zyda is a 1.3T language modeling dataset created by collecting open and high quality datasets and combining them and performing a uniform filtering and deduplication step. We find that Zyda performs extremely well in ablations and is at least comparable and potentially better to the best openly available datasets available, due to our meticulous post-processing pipeline. We think the best use of Zyda is either as a standalone dataset for language model training up to the 1T scale, or in combination with Fineweb or Dolma for multi-trillion token training. An early version of Zyda was used as the primary dataset for phase 1 pretraining of [Zamba](https://arxiv.org/abs/2405.16712), a model which performs strongly on a per-token basis, testifying to the strength of Zyda as a pretraining dataset. Models trained on Zyda significantly outperform identical models of the Pythia suite trained on the [Pile](https://arxiv.org/abs/2101.00027) for 300B tokens. Zyda also outperforms Dolma, RefinedWeb, and Fineweb on 1.4B models trained on 50B tokens of each dataset. According to our evaluations, Zyda is the most performant per-token open dataset available in its non-starcoder variant on language tasks. The Zyda starcoder variant ties with fineweb. <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/VdrCqypZtTpjEs7bH1k9s.png" width="650" alt="Zyda performance across steps."> </center> These results are aggregate scores of classic language modeling evaluations (PIQA, WinoGrande, OpenBookQA, ARC-Easy, ARC-Challenge) across time for a 1.4B model trained on 50B tokens of each dataset. ## How to download Full dataset: ``` import datasets ds = datasets.load_dataset("Zyphra/Zyda", split="train") ``` Full dataset without StarCoder: ``` import datasets ds = datasets.load_dataset("Zyphra/Zyda", name="zyda_no_starcoder", split="train") ``` For downloading individual components put their name in the name arg of `load_dataset()`: - zyda_arxiv_only - zyda_c4-en_only - zyda_peS2o_only - zyda_pile-uncopyrighted_only - zyda_refinedweb_only - zyda_slimpajama_only - zyda_starcoder_only ## Breakdown by component | Component | Download size (parquet, GBs) | Documents (millions) | gpt-neox tokens (billions) | | --- | --- | --- | --- | | zyda_refinedweb_only | 1,712.4 | 920.5 | 564.8 | | zyda_c4-en_only | 366.7 | 254.5 | 117.5 | | zyda_slimpajama_only | 594.7 | 142.3 | 242.3 | | zyda_pile-uncopyrighted_only | 189.4 | 64.9 | 82.9 | | zyda_peS2o_only | 133.7 | 35.7 | 53.4 | | zyda_arxiv_only | 8.3 | 0.3 | 4.7 | | zyda_starcoder_only | 299.5 | 176.1 | 231.3 | | Total | 3,304.7 | 1,594.2 | 1,296.7 | ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** Zyphra - **Language(s) (NLP):** Primarily English - **License:** Open Data Commons License ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> Dataset fields: - `text`: contains actual text for training - `source`: component the text is coming from - `filtering_features`: precomputed values of different features that were used for filtering (converted to json string) - `source_other`: metadata from the source dataset (converted to json string) ### Source Data Zyda was drawn from seven component open datasets which are well-regarded in the community. These are: Pile Uncopyrighted: https://huggingface.co/datasets/monology/pile-uncopyrighted C4-en: https://huggingface.co/datasets/allenai/c4 peS2o: https://huggingface.co/datasets/allenai/peS2o RefinedWeb: https://huggingface.co/datasets/tiiuae/falcon-refinedweb SlimPajama: https://huggingface.co/datasets/cerebras/SlimPajama-627B arxiv_s2orc_parsed: https://huggingface.co/datasets/ArtifactAI/arxiv_s2orc_parsed StarCoder: https://huggingface.co/datasets/bigcode/starcoderdata <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/eCJWG3ZoA4fVk8bZZBHaG.png" width="650" alt="Composition of Zyda"> </center> <!--  --> <!--  --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> Zyda was created using a two stage post-processing pipeline consisting of *filtering* and *deduplication*. For the filtering stage, we utilized a set of hand-crafted and tuned filters derived from a number of sources such as C4, RedPajama, and Gopher, in addition to our own filters. For the deduplication stage, we used minhash approximate deduplication. We deduplicated on 13-grams and used a minhash signature size of 128 and filtered out documents above a Jaccard similarity of 0.4. For full details on our data processing, see the [Zyda technical report](https://arxiv.org/abs/2406.01981) and our [dataset processing code](https://github.com/Zyphra/Zyda_processing). #### Personal and Sensitive Information As a language modelling dataset, it likely contains PII which has not been filtered out of the component datasets and which may have been missed by our own filters. ## Bias, Risks, and Limitations As a dataset comprised of open web scrapes, it is likely that it contains biased and toxic content. ## Licensing Information We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this dataset, you are also bound by any license agreements and terms of use of the original data sources. ## Citation If you use our dataset to train a model, please cite us at: ``` @misc{tokpanov2024zyda, title={Zyda: A 1.3T Dataset for Open Language Modeling}, author={Yury Tokpanov and Beren Millidge and Paolo Glorioso and Jonathan Pilault and Adam Ibrahim and James Whittington and Quentin Anthony}, year={2024}, eprint={2406.01981}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

fixie-ai/covost2

fixie-ai

--- dataset_info: - config_name: ar_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 61607709.192 num_examples: 2283 - name: validation num_bytes: 56223234.024 num_examples: 1758 - name: test num_bytes: 54650910.41 num_examples: 1695 download_size: 160468333 dataset_size: 172481853.626 - config_name: ca_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 4397026262.322 num_examples: 95854 - name: validation num_bytes: 544108371.96 num_examples: 12730 - name: test num_bytes: 604755238.63 num_examples: 12730 download_size: 4957773433 dataset_size: 5545889872.912 - config_name: cy_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 51478765.087 num_examples: 1241 - name: validation num_bytes: 26992697.0 num_examples: 690 - name: test num_bytes: 28772216.0 num_examples: 690 download_size: 102604972 dataset_size: 107243678.087 - config_name: de_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 5680326209.222 num_examples: 127834 - name: validation num_bytes: 631442490.202 num_examples: 13511 - name: test num_bytes: 637042944.685 num_examples: 13511 download_size: 6490850158 dataset_size: 6948811644.108999 - config_name: en_ar features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14105902817.18 num_examples: 289430 - name: validation num_bytes: 718527564.808 num_examples: 15531 - name: test num_bytes: 729114452.301 num_examples: 15531 download_size: 13815709729 dataset_size: 15553544834.289001 - config_name: en_ca features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14099092976.18 num_examples: 289430 - name: validation num_bytes: 718171719.808 num_examples: 15531 - name: test num_bytes: 728790610.301 num_examples: 15531 download_size: 13814365593 dataset_size: 15546055306.289001 - config_name: en_cy features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098487703.18 num_examples: 289430 - name: validation num_bytes: 718141953.808 num_examples: 15531 - name: test num_bytes: 728793811.301 num_examples: 15531 download_size: 13813953593 dataset_size: 15545423468.289001 - config_name: en_de features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14099886814.18 num_examples: 289430 - name: validation num_bytes: 718219105.808 num_examples: 15531 - name: test num_bytes: 728857067.301 num_examples: 15531 download_size: 13815103686 dataset_size: 15546962987.289001 - config_name: en_et features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14096877545.18 num_examples: 289430 - name: validation num_bytes: 718057559.808 num_examples: 15531 - name: test num_bytes: 728710692.301 num_examples: 15531 download_size: 13813410823 dataset_size: 15543645797.289001 - config_name: en_fa features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14108661241.18 num_examples: 289430 - name: validation num_bytes: 718670909.808 num_examples: 15531 - name: test num_bytes: 729271000.301 num_examples: 15531 download_size: 13816798013 dataset_size: 15556603151.289001 - config_name: en_id features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098627451.18 num_examples: 289430 - name: validation num_bytes: 718144327.808 num_examples: 15531 - name: test num_bytes: 728802322.301 num_examples: 15531 download_size: 13813201260 dataset_size: 15545574101.289001 - config_name: en_ja features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14103911774.18 num_examples: 289430 - name: validation num_bytes: 718409304.808 num_examples: 15531 - name: test num_bytes: 729050991.301 num_examples: 15531 download_size: 13815875328 dataset_size: 15551372070.289001 - config_name: en_lv features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098703097.18 num_examples: 289430 - name: validation num_bytes: 718152571.808 num_examples: 15531 - name: test num_bytes: 728792572.301 num_examples: 15531 download_size: 13814849886 dataset_size: 15545648241.289001 - config_name: en_mn features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14113120657.18 num_examples: 289430 - name: validation num_bytes: 718940418.808 num_examples: 15531 - name: test num_bytes: 729461016.301 num_examples: 15531 download_size: 13819427515 dataset_size: 15561522092.289001 - config_name: en_sl features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14097158381.18 num_examples: 289430 - name: validation num_bytes: 718085673.808 num_examples: 15531 - name: test num_bytes: 728705188.301 num_examples: 15531 download_size: 13813603812 dataset_size: 15543949243.289001 - config_name: en_sv-SE features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14097728051.18 num_examples: 289430 - name: validation num_bytes: 718093292.808 num_examples: 15531 - name: test num_bytes: 728747422.301 num_examples: 15531 download_size: 13813332908 dataset_size: 15544568766.289001 - config_name: en_ta features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14135489205.18 num_examples: 289430 - name: validation num_bytes: 720191394.808 num_examples: 15531 - name: test num_bytes: 730578783.301 num_examples: 15531 download_size: 13825121271 dataset_size: 15586259383.289001 - config_name: en_tr features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098644786.18 num_examples: 289430 - name: validation num_bytes: 718161996.808 num_examples: 15531 - name: test num_bytes: 728786654.301 num_examples: 15531 download_size: 13814279798 dataset_size: 15545593437.289001 - config_name: en_zh-CN features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14095661460.18 num_examples: 289430 - name: validation num_bytes: 717982705.808 num_examples: 15531 - name: test num_bytes: 728655191.301 num_examples: 15531 download_size: 13812699892 dataset_size: 15542299357.289001 - config_name: es_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 630615357.241 num_examples: 13221 - name: test num_bytes: 666447063.067 num_examples: 13221 - name: train num_bytes: 3769457359.8 num_examples: 79015 download_size: 4531969416 dataset_size: 5066519780.108 - config_name: et_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 97124727.544 num_examples: 1782 - name: validation num_bytes: 80290798.168 num_examples: 1576 - name: test num_bytes: 81970364.51 num_examples: 1571 download_size: 257604448 dataset_size: 259385890.222 - config_name: fa_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 1872724297.149 num_examples: 53949 - 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name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 66152116.328 num_examples: 2337 - name: validation num_bytes: 32655276.0 num_examples: 1125 - name: test num_bytes: 50997551.638 num_examples: 1629 download_size: 137700207 dataset_size: 149804943.96600002 - config_name: mn_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 87891433.547 num_examples: 2067 - name: validation num_bytes: 77519039.943 num_examples: 1761 - name: test num_bytes: 83667460.167 num_examples: 1759 download_size: 242638800 dataset_size: 249077933.657 - config_name: nl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 216102081.4 num_examples: 7108 - name: validation num_bytes: 55386349.319 num_examples: 1699 - name: test num_bytes: 60219179.711 num_examples: 1699 download_size: 320267264 dataset_size: 331707610.43 - config_name: pt_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 274723273.528 num_examples: 9158 - name: validation num_bytes: 118345891.704 num_examples: 3318 - name: test num_bytes: 166247624.001 num_examples: 4023 download_size: 540891735 dataset_size: 559316789.233 - config_name: ru_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 557219472.672 num_examples: 12112 - name: validation num_bytes: 290218427.6 num_examples: 6110 - name: test num_bytes: 312622838.0 num_examples: 6300 download_size: 1112848246 dataset_size: 1160060738.272 - config_name: sl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 55992153.0 num_examples: 1843 - name: validation num_bytes: 15074155.0 num_examples: 509 - name: test num_bytes: 10209711.0 num_examples: 360 download_size: 83863293 dataset_size: 81276019.0 - config_name: sv-SE_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 48298330.64 num_examples: 2160 - name: validation num_bytes: 32544646.416 num_examples: 1349 - name: test num_bytes: 46894324.615 num_examples: 1595 download_size: 121860373 dataset_size: 127737301.671 - config_name: ta_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 47757197.616 num_examples: 1358 - name: validation num_bytes: 13670695.0 num_examples: 384 - name: test num_bytes: 29891516.0 num_examples: 786 download_size: 87791516 dataset_size: 91319408.616 - config_name: tr_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 119299427.798 num_examples: 3966 - name: validation num_bytes: 52552534.232 num_examples: 1624 - name: test num_bytes: 59106253.862 num_examples: 1629 download_size: 224018260 dataset_size: 230958215.89200002 - config_name: zh-CN_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 231018998.33 num_examples: 4843 - name: test num_bytes: 243850956.45 num_examples: 4898 - name: train num_bytes: 341425113.6 num_examples: 7085 download_size: 766660661 dataset_size: 816295068.38 configs: - config_name: ar_en data_files: - split: train path: ar_en/train-* - split: validation path: ar_en/validation-* - split: test path: ar_en/test-* - config_name: ca_en data_files: - split: train path: ca_en/train-* - split: validation path: ca_en/validation-* - split: test path: ca_en/test-* - config_name: cy_en data_files: - split: train path: cy_en/train-* - split: validation path: cy_en/validation-* - split: test path: cy_en/test-* - config_name: de_en data_files: - split: train path: de_en/train-* - split: validation path: de_en/validation-* - split: test path: de_en/test-* - config_name: en_ar data_files: - split: train path: en_ar/train-* - split: validation path: en_ar/validation-* - split: test path: en_ar/test-* - config_name: en_ca data_files: - split: train path: en_ca/train-* - split: validation path: en_ca/validation-* - split: test path: en_ca/test-* - config_name: en_cy data_files: - split: train path: en_cy/train-* - split: validation path: en_cy/validation-* - split: test path: en_cy/test-* - config_name: en_de data_files: - split: train path: en_de/train-* - split: validation path: en_de/validation-* - split: test path: en_de/test-* - config_name: en_et data_files: - split: train path: en_et/train-* - split: validation path: en_et/validation-* - split: test path: en_et/test-* - config_name: en_fa data_files: - split: train path: en_fa/train-* - split: validation path: en_fa/validation-* - split: test path: en_fa/test-* - config_name: en_id data_files: - split: train path: en_id/train-* - split: validation path: en_id/validation-* - split: test path: en_id/test-* - config_name: en_ja data_files: - split: train path: en_ja/train-* - split: validation path: en_ja/validation-* - split: test path: en_ja/test-* - config_name: en_lv data_files: - split: train path: en_lv/train-* - split: validation path: en_lv/validation-* - split: test path: en_lv/test-* - config_name: en_mn data_files: - split: train path: en_mn/train-* - split: validation path: en_mn/validation-* - split: test path: en_mn/test-* - config_name: en_sl data_files: - split: train path: en_sl/train-* - split: validation path: en_sl/validation-* - split: test path: en_sl/test-* - config_name: en_sv-SE data_files: - split: train path: en_sv-SE/train-* - split: validation path: en_sv-SE/validation-* - split: test path: en_sv-SE/test-* - config_name: en_ta data_files: - split: train path: en_ta/train-* - split: validation path: en_ta/validation-* - split: test path: en_ta/test-* - config_name: en_tr data_files: - split: train path: en_tr/train-* - split: validation path: en_tr/validation-* - split: test path: en_tr/test-* - config_name: en_zh-CN data_files: - split: train path: en_zh-CN/train-* - split: validation path: en_zh-CN/validation-* - split: test path: en_zh-CN/test-* - config_name: es_en data_files: - split: validation path: es_en/validation-* - split: test path: es_en/test-* - split: train path: es_en/train-* - config_name: et_en data_files: - split: train path: et_en/train-* - split: validation path: et_en/validation-* - split: test path: et_en/test-* - config_name: fa_en data_files: - split: train path: fa_en/train-* - split: validation path: fa_en/validation-* - split: test path: fa_en/test-* - config_name: fr_en data_files: - split: validation path: fr_en/validation-* - split: test path: fr_en/test-* - split: train path: fr_en/train-* - config_name: id_en data_files: - split: train path: id_en/train-* - split: validation path: id_en/validation-* - split: test path: id_en/test-* - config_name: it_en data_files: - split: train path: it_en/train-* - split: validation path: it_en/validation-* - split: test path: it_en/test-* - config_name: ja_en data_files: - split: train path: ja_en/train-* - split: validation path: ja_en/validation-* - split: test path: ja_en/test-* - config_name: lv_en data_files: - split: train path: lv_en/train-* - split: validation path: lv_en/validation-* - split: test path: lv_en/test-* - config_name: mn_en data_files: - split: train path: mn_en/train-* - split: validation path: mn_en/validation-* - split: test path: mn_en/test-* - config_name: nl_en data_files: - split: train path: nl_en/train-* - split: validation path: nl_en/validation-* - split: test path: nl_en/test-* - config_name: pt_en data_files: - split: train path: pt_en/train-* - split: validation path: pt_en/validation-* - split: test path: pt_en/test-* - config_name: ru_en data_files: - split: train path: ru_en/train-* - split: validation path: ru_en/validation-* - split: test path: ru_en/test-* - config_name: sl_en data_files: - split: train path: sl_en/train-* - split: validation path: sl_en/validation-* - split: test path: sl_en/test-* - config_name: sv-SE_en data_files: - split: train path: sv-SE_en/train-* - split: validation path: sv-SE_en/validation-* - split: test path: sv-SE_en/test-* - config_name: ta_en data_files: - split: train path: ta_en/train-* - split: validation path: ta_en/validation-* - split: test path: ta_en/test-* - config_name: tr_en data_files: - split: train path: tr_en/train-* - split: validation path: tr_en/validation-* - split: test path: tr_en/test-* - config_name: zh-CN_en data_files: - split: validation path: zh-CN_en/validation-* - split: test path: zh-CN_en/test-* - split: train path: zh-CN_en/train-* --- This is a partial copy of [CoVoST2](https://huggingface.co/datasets/facebook/covost2) dataset. The main difference is that the audio data is included in the dataset, which makes usage easier and allows browsing the samples using HF Dataset Viewer. The limitation of this method is that all audio samples of the `EN_XX` subsets are duplicated, as such the size of the dataset is larger. As such, not all the data is included: Only the `validation` and `test` subsets are available. From the `XX_EN` subsets, only `fr`, `es`, and `zh-CN` are included.

BAAI/IndustryCorpus2

BAAI

--- license: apache-2.0 language: - en - zh size_categories: - n>1T extra_gated_prompt: "You agree to not use the dataset to conduct experiments that cause harm to human subjects." extra_gated_fields: Company/Organization: text Country: country --- Industry models play a vital role in promoting the intelligent transformation and innovative development of enterprises. High-quality industry data is the key to improving the performance of large models and realizing the implementation of industry applications. However, the data sets currently used for industry model training generally have problems such as small data volume, low quality, and lack of professionalism. In June, we released the [IndustryCorpus](https://huggingface.co/datasets/BAAI/Industry-Instruction) dataset: We have further upgraded and iterated on this dataset, and the iterative contents are as follows: - Data source: Based on the original data, we introduced more high-quality data sources, such as pile, bigcode, open-web-math and other mathematical and code data - Update the industry category system: In order to better fit the industry classification system, we combined the national economic industry classification system (20 categories) formulated by the National Bureau of Statistics and the world knowledge system to redesign the industry categories, setting up 31 industry categories, basically covering the current mainstream industries - Data semantic quality screening: We decentralized the IndustryCorpus high-quality data production plan, and used the rule filtering + model filtering solution in the IndustryCorpus2.0 open source data, which greatly improved the overall data quality; - Data quality stratification: In order to further integrate data quality at different levels, we stratify and organize the data based on the quality assessment score, dividing the data into three levels: high, middle, and low. - Data size: 1TB for Chinese and 2.2TB for English The data processing process is consistent with IndustryCorpus  ## Data Perspective ### Industry Data Distribution The disk size of each industry data after full process processing is as follows | Industry category | Data size (GB) | Industry category | Data size (GB) | | :-------------------------------------------------: | :------------: | :-----------------------------------------------: | :------------: | | Programming | 11.0 | News | 51.0 | | Biomedicine | 61.7 | Petrochemical | 40.2 | | Medical health-psychology and Chinese medicine | 271.7 | Aerospace | 38.6 | | Tourism and geography | 64.0 | Mining | 8.9 | | Law and justice | 238.5 | Finance and economics | 145.8 | | Mathematics-statistics | 156.7 | Literature and emotions | 105.5 | | Other information services_information security | 1.8 | Transportation | 40.5 | | Fire safety_food safety | 4.3 | Science and technology_scientific research | 101.6 | | Automobile | 39.3 | Water Conservancy_Ocean | 20.2 | | Accommodation-catering-hotel | 29.6 | Computer-communication | 157.8 | | Film and television entertainment | 209.4 | Subject education | 340.9 | | Real estate-construction | 105.2 | Artificial intelligence-machine learning | 7.7 | | Electric power and energy | 68.7 | Current affairs-government affairs-administration | 271.5 | | Agriculture, forestry, animal husbandry and fishery | 111.9 | Sports | 262.5 | | Games | 37.6 | Other manufacturing | 47.2 | | Others | 188.6 | | | | Total (GB) | 3276G | | | The industry data distribution chart in the summary data set is as follows  From the distribution chart, we can see that subject education, sports, current affairs, law, medical health, film and television entertainment account for most of the overall data. The data of these industries are widely available on the Internet and textbooks, and the high proportion of them is in line with expectations. It is worth mentioning that since we have supplemented the data of mathematics, we can see that the proportion of mathematics data is also high, which is inconsistent with the proportion of mathematics Internet corpus data. ### dataset repo series All our data repos have a unified naming format, f"BAAI/IndustryCorpus2_{name}", where `name` corresponds to the English name of the industry. The list of industry names is shown below ``` { "交通运输": "transportation", "医学_健康_心理_中医": "medicine_health_psychology_traditional_chinese_medicine", "数学_统计学": "mathematics_statistics", "时政_政务_行政": "current_affairs_government_administration", "消防安全_食品安全": "fire_safety_food_safety", "石油化工": "petrochemical", "计算机_通信": "computer_communication", "人工智能_机器学习": "artificial_intelligence_machine_learning", "其他信息服务_信息安全": "other_information_services_information_security", "学科教育_教育": "subject_education_education", "文学_情感": "literature_emotion", "水利_海洋": "water_resources_ocean", "游戏": "game", "科技_科学研究": "technology_scientific_research", "采矿": "mining", "住宿_餐饮_酒店": "accommodation_catering_hotel", "其他制造": "other_manufacturing", "影视_娱乐": "film_entertainment", "新闻传媒": "news_media", "汽车": "automobile", "生物医药": "biomedicine", "航空航天": "aerospace", "金融_经济": "finance_economics", "体育": "sports", "农林牧渔": "agriculture_forestry_animal_husbandry_fishery", "房地产_建筑": "real_estate_construction", "旅游_地理": "tourism_geography", "法律_司法": "law_judiciary", "电力能源": "electric_power_energy", "计算机编程_代码": "computer_programming_code", } ``` ### Data quality stratification We filter the entire data according to data quality, remove extremely low-quality data, and divide the available data into three independent groups: Low, Middle, and Hight, to facilitate data matching and combination during model training. The distribution of data of different qualities is shown below. It can be seen that the data quality distribution trends of Chinese and English are basically the same, with the largest number of middle data, followed by middle data, and the least number of low data; in addition, it can be observed that the proportion of hight data in English is higher than that in Chinese (with a larger slope), which is also in line with the current trend of distribution of different languages.  ## Industry Category Classification In order to improve the coverage of industry classification in the data set to actual industries and align with the industry catalog defined in the national standard, we refer to the national economic industry classification system and the world knowledge system formulated by the National Bureau of Statistics, merge and integrate the categories, and design the final 31 industry categories covering Chinese and English. The category table names are as follows - Data construction of industry classification model - Data construction Data source: pre-training corpus sampling and open source text classification data, of which pre-training corpus accounts for 90%. Through data sampling, the ratio of Chinese and English data is guaranteed to be 1:1 Label construction: Use the LLM model to make multiple classification judgments on the data, and select the data with consistent multiple judgments as training data Data scale: 36K The overall process of data construction is as follows:  - Model training: Parameter update: add classification head to pre-trained BERT model for text classification model training Model selection: considering model performance and inference efficiency, we selected a 0.5B scale model. Through comparative experiments, we finally selected BGE-M3 and full parameter training as our base model Training hyperparameters: full parameter training, max_length = 2048, lr = 1e-5, batch_size = 64, validation set evaluation acc: 86%  ## Data quality assessment - Why should we filter low-quality data? Below is low-quality data extracted from the data. It can be seen that this kind of data is harmful to the learning of the model. ``` {"text": "\\_\\__\n\nTranslated from *Chinese Journal of Biochemistry and Molecular Biology*, 2007, 23(2): 154--159 \\[译自:中国生物化学与分子生物学报\\]\n"} {"text": "#ifndef _IMGBMP_H_\n#define _IMGBMP_H_\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nconst uint8_t bmp[]={\n\\/\\/-- 调入了一幅图像：D:\\我的文档\\My Pictures\\12864-555.bmp --*\\/\n\\/\\/-- 宽度x高度=128x64 --\n0x00,0x06,0x0A,0xFE,0x0A,0xC6,0x00,0xE0,0x00,0xF0,0x00,0xF8,0x00,0x00,0x00,0x00,\n0x00,0x00,0xFE,0x7D,0xBB,0xC7,0xEF,0xEF,0xEF,0xEF,0xEF,0xEF,0xEF,0xC7,0xBB,0x7D,\n0xFE,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x08,\n0x0C,0xFE,0xFE,0x0C,0x08,0x20,0x60,0xFE,0xFE,0x60,0x20,0x00,0x00,0x00,0x78,0x48,\n0xFE,0x82,0xBA,0xBA,0x82,0xBA,0xBA,0x82,0xBA,0xBA,0x82,0xBA,0xBA,0x82,0xFE,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xFE,0xFF,\n0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0xFF,0xFF,0x00,0x00,0xFE,0xFF,0x03,\n0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0xFF,0xFE,0x00,0x00,0x00,0x00,0xC0,0xC0,\n0xC0,0x00,0x00,0x00,0x00,0xFE,0xFF,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,\n0xFF,0xFE,0x00,0x00,0xFE,0xFF,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0xFF,\n0xFE,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xFF,0xFF,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xFF,0xFF,0x00,0x00,0xFF,0xFF,0x0C,\n0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0xFF,0xFF,0x00,0x00,0x00,0x00,0xE1,0xE1,\n0xE1,0x00,0x00,0x00,0x00,0xFF,0xFF,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0xFF,0xFF,0x00,0x00,0xFF,0xFF,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0xFF,\n0xFF,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x0F,0x1F,\n0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x1F,0x0F,0x00,0x00,0x0F,0x1F,0x18,\n0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x1F,0x0F,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x0F,0x1F,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,\n0x1F,0x0F,0x00,0x00,0x0F,0x1F,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x1F,\n0x0F,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0xE2,0x92,0x8A,0x86,0x00,0x00,0x7C,0x82,0x82,0x82,0x7C,\n0x00,0xFE,0x00,0x82,0x92,0xAA,0xC6,0x00,0x00,0xC0,0xC0,0x00,0x7C,0x82,0x82,0x82,\n0x7C,0x00,0x00,0x02,0x02,0x02,0xFE,0x00,0x00,0xC0,0xC0,0x00,0x7C,0x82,0x82,0x82,\n0x7C,0x00,0x00,0xFE,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x24,0xA4,0x2E,0x24,0xE4,0x24,0x2E,0xA4,0x24,0x00,0x00,0x00,0xF8,0x4A,0x4C,\n0x48,0xF8,0x48,0x4C,0x4A,0xF8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xC0,0x20,0x10,0x10,\n0x10,0x10,0x20,0xC0,0x00,0x00,0xC0,0x20,0x10,0x10,0x10,0x10,0x20,0xC0,0x00,0x00,\n0x00,0x12,0x0A,0x07,0x02,0x7F,0x02,0x07,0x0A,0x12,0x00,0x00,0x00,0x0B,0x0A,0x0A,\n0x0A,0x7F,0x0A,0x0A,0x0A,0x0B,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0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\n\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif \\/\\/ _IMGBMP_H_ _SSD1306_16BIT_H_\n"} ``` - Data construction Data source: Random sampling of pre-trained corpus Label construction: Design data scoring rules, use LLM model to perform multiple rounds of scoring, and select data with a difference of less than 2 in multiple rounds of scoring Data scale: 20k scoring data, Chinese and English ratio 1:1 Data scoring prompt ``` quality_prompt = """Below is an extract from a web page. Evaluate whether the page has a high natural language value and could be useful in an naturanl language task to train a good language model using the additive 5-point scoring system described below. Points are accumulated based on the satisfaction of each criterion: - Zero score if the content contains only some meaningless content or private content, such as some random code, http url or copyright information, personally identifiable information, binary encoding of images. - Add 1 point if the extract provides some basic information, even if it includes some useless contents like advertisements and promotional material. - Add another point if the extract is written in good style, semantically fluent, and free of repetitive content and grammatical errors. - Award a third point tf the extract has relatively complete semantic content, and is written in a good and fluent style, the entire content expresses something related to the same topic, rather than a patchwork of several unrelated items. - A fourth point is awarded if the extract has obvious educational or literary value, or provides a meaningful point or content, contributes to the learning of the topic, and is written in a clear and consistent style. It may be similar to a chapter in a textbook or tutorial, providing a lot of educational content, including exercises and solutions, with little to no superfluous information. The content is coherent and focused, which is valuable for structured learning. - A fifth point is awarded if the extract has outstanding educational value or is of very high information density, provides very high value and meaningful content, does not contain useless information, and is well suited for teaching or knowledge transfer. It contains detailed reasoning, has an easy-to-follow writing style, and can provide deep and thorough insights. The extract: <{EXAMPLE}>. After examining the extract: - Briefly justify your total score, up to 50 words. - Conclude with the score using the format: "Quality score: <total points>" ... """ ``` - Model training Model selection: Similar to the classification model, we also used a 0.5b scale model and compared beg-m3 and qwen-0.5b. The final experiment showed that bge-m3 had the best overall performance Model hyperparameters: base bge-m3, full parameter training, lr=1e-5, batch_size=64, max_length = 2048 Model evaluation: On the validation set, the consistency rate of the model and GPT4 in sample quality judgment was 90%.  - Training benefits from high-quality data In order to verify whether high-quality data can bring more efficient training efficiency, we extracted high-quality data from the 50b data before screening under the same base model. It can be considered that the distribution of the two data is roughly the same, and autoregressive training is performed. As can be seen from the curve, the 14B tokens of the model trained with high-quality data can achieve the performance of the model with 50B of ordinary data. High-quality data can greatly improve training efficiency.  In addition, high-quality data can be added to the model as data in the pre-training annealing stage to further improve the model effect. To verify this conjecture, when training the industry model, we added pre-training data converted from high-quality data after screening and some instruction data to the annealing stage of the model. It can be seen that the performance of the model has been greatly improved.  Finally, high-quality pre-training predictions contain a wealth of high-value knowledge content, from which instruction data can be extracted to further improve the richness and knowledge of instruction data. This also gave rise to the [BAAI/IndustryInstruction](https://huggingface.co/datasets/BAAI/IndustryInstruction) project, which we will explain in detail there. ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc {beijing_academy_of_artificial_intelligence, author= { Xiaofeng Shi and Lulu Zhao and Hua Zhou and Donglin Hao}, title = { IndustryCorpus2}, year = 2024, url = { https://huggingface.co/datasets/BAAI/IndustryCorpus2 }, doi = { 10.57967/hf/3488 }, publisher = { Hugging Face } } ```

mteb/sickr-sts

mteb

--- language: - en ---

cardiffnlp/tweet_eval

cardiffnlp

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - extended|other-tweet-datasets task_categories: - text-classification task_ids: - intent-classification - multi-class-classification - sentiment-classification paperswithcode_id: tweeteval pretty_name: TweetEval config_names: - emoji - emotion - hate - irony - offensive - sentiment - stance_abortion - stance_atheism - stance_climate - stance_feminist - stance_hillary dataset_info: - config_name: emoji features: - name: text dtype: string - name: label dtype: class_label: names: '0': ❤ '1': 😍 '2': 😂 '3': 💕 '4': 🔥 '5': 😊 '6': 😎 '7': ✨ '8': 💙 '9': 😘 '10': 📷 '11': 🇺🇸 '12': ☀ '13': 💜 '14': 😉 '15': 💯 '16': 😁 '17': 🎄 '18': 📸 '19': 😜 splits: - name: train num_bytes: 3803167 num_examples: 45000 - name: test num_bytes: 4255901 num_examples: 50000 - name: validation num_bytes: 396079 num_examples: 5000 download_size: 5939308 dataset_size: 8455147 - config_name: emotion features: - name: text dtype: string - name: label dtype: class_label: names: '0': anger '1': joy '2': optimism '3': sadness splits: - name: train num_bytes: 338871 num_examples: 3257 - name: test num_bytes: 146645 num_examples: 1421 - name: validation num_bytes: 38273 num_examples: 374 download_size: 367016 dataset_size: 523789 - config_name: hate features: - name: text dtype: string - name: label dtype: class_label: names: '0': non-hate '1': hate splits: - name: train num_bytes: 1223650 num_examples: 9000 - name: test num_bytes: 428934 num_examples: 2970 - name: validation num_bytes: 154144 num_examples: 1000 download_size: 1196346 dataset_size: 1806728 - config_name: irony features: - name: text dtype: string - name: label dtype: class_label: names: '0': non_irony '1': irony splits: - name: train num_bytes: 259187 num_examples: 2862 - name: test num_bytes: 75897 num_examples: 784 - name: validation num_bytes: 86017 num_examples: 955 download_size: 297647 dataset_size: 421101 - config_name: offensive features: - name: text dtype: string - name: label dtype: class_label: names: '0': non-offensive '1': offensive splits: - name: train num_bytes: 1648061 num_examples: 11916 - name: test num_bytes: 135473 num_examples: 860 - name: validation num_bytes: 192417 num_examples: 1324 download_size: 1234528 dataset_size: 1975951 - config_name: sentiment features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 5425122 num_examples: 45615 - name: test num_bytes: 1279540 num_examples: 12284 - name: validation num_bytes: 239084 num_examples: 2000 download_size: 4849675 dataset_size: 6943746 - config_name: stance_abortion features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 68694 num_examples: 587 - name: test num_bytes: 33171 num_examples: 280 - name: validation num_bytes: 7657 num_examples: 66 download_size: 73517 dataset_size: 109522 - config_name: stance_atheism features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 54775 num_examples: 461 - name: test num_bytes: 25716 num_examples: 220 - name: validation num_bytes: 6320 num_examples: 52 download_size: 62265 dataset_size: 86811 - config_name: stance_climate features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 40249 num_examples: 355 - name: test num_bytes: 19925 num_examples: 169 - name: validation num_bytes: 4801 num_examples: 40 download_size: 48493 dataset_size: 64975 - config_name: stance_feminist features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 70509 num_examples: 597 - name: test num_bytes: 33305 num_examples: 285 - name: validation num_bytes: 8035 num_examples: 67 download_size: 76345 dataset_size: 111849 - config_name: stance_hillary features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 69596 num_examples: 620 - name: test num_bytes: 34487 num_examples: 295 - name: validation num_bytes: 7532 num_examples: 69 download_size: 74057 dataset_size: 111615 configs: - config_name: emoji data_files: - split: train path: emoji/train-* - split: test path: emoji/test-* - split: validation path: emoji/validation-* - config_name: emotion data_files: - split: train path: emotion/train-* - split: test path: emotion/test-* - split: validation path: emotion/validation-* - config_name: hate data_files: - split: train path: hate/train-* - split: test path: hate/test-* - split: validation path: hate/validation-* - config_name: irony data_files: - split: train path: irony/train-* - split: test path: irony/test-* - split: validation path: irony/validation-* - config_name: offensive data_files: - split: train path: offensive/train-* - split: test path: offensive/test-* - split: validation path: offensive/validation-* - config_name: sentiment data_files: - split: train path: sentiment/train-* - split: test path: sentiment/test-* - split: validation path: sentiment/validation-* - config_name: stance_abortion data_files: - split: train path: stance_abortion/train-* - split: test path: stance_abortion/test-* - split: validation path: stance_abortion/validation-* - config_name: stance_atheism data_files: - split: train path: stance_atheism/train-* - split: test path: stance_atheism/test-* - split: validation path: stance_atheism/validation-* - config_name: stance_climate data_files: - split: train path: stance_climate/train-* - split: test path: stance_climate/test-* - split: validation path: stance_climate/validation-* - config_name: stance_feminist data_files: - split: train path: stance_feminist/train-* - split: test path: stance_feminist/test-* - split: validation path: stance_feminist/validation-* - config_name: stance_hillary data_files: - split: train path: stance_hillary/train-* - split: test path: stance_hillary/test-* - split: validation path: stance_hillary/validation-* train-eval-index: - config: emotion task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: hate task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: irony task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: offensive task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: sentiment task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for tweet_eval ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** [GitHub](https://github.com/cardiffnlp/tweeteval) - **Paper:** [EMNLP Paper](https://arxiv.org/pdf/2010.12421.pdf) - **Leaderboard:** [GitHub Leaderboard](https://github.com/cardiffnlp/tweeteval) - **Point of Contact:** [Needs More Information] ### Dataset Summary TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits. ### Supported Tasks and Leaderboards - `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers. ### Languages The text in the dataset is in English, as spoken by Twitter users. ## Dataset Structure ### Data Instances An instance from `emoji` config: ``` {'label': 12, 'text': 'Sunday afternoon walking through Venice in the sun with @user ️ ️ ️ @ Abbot Kinney, Venice'} ``` An instance from `emotion` config: ``` {'label': 2, 'text': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer. #motivation #leadership #worry"} ``` An instance from `hate` config: ``` {'label': 0, 'text': '@user nice new signage. Are you not concerned by Beatlemania -style hysterical crowds crongregating on you…'} ``` An instance from `irony` config: ``` {'label': 1, 'text': 'seeing ppl walking w/ crutches makes me really excited for the next 3 weeks of my life'} ``` An instance from `offensive` config: ``` {'label': 0, 'text': '@user Bono... who cares. Soon people will understand that they gain nothing from following a phony celebrity. Become a Leader of your people instead or help and support your fellow countrymen.'} ``` An instance from `sentiment` config: ``` {'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'} ``` An instance from `stance_abortion` config: ``` {'label': 1, 'text': 'we remind ourselves that love means to be willing to give until it hurts - Mother Teresa'} ``` An instance from `stance_atheism` config: ``` {'label': 1, 'text': '@user Bless Almighty God, Almighty Holy Spirit and the Messiah. #SemST'} ``` An instance from `stance_climate` config: ``` {'label': 0, 'text': 'Why Is The Pope Upset? via @user #UnzippedTruth #PopeFrancis #SemST'} ``` An instance from `stance_feminist` config: ``` {'label': 1, 'text': "@user @user is the UK's answer to @user and @user #GamerGate #SemST"} ``` An instance from `stance_hillary` config: ``` {'label': 1, 'text': "If a man demanded staff to get him an ice tea he'd be called a sexists elitist pig.. Oink oink #Hillary #SemST"} ``` ### Data Fields For `emoji` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: ❤ `1`: 😍 `2`: 😂 `3`: 💕 `4`: 🔥 `5`: 😊 `6`: 😎 `7`: ✨ `8`: 💙 `9`: 😘 `10`: 📷 `11`: 🇺🇸 `12`: ☀ `13`: 💜 `14`: 😉 `15`: 💯 `16`: 😁 `17`: 🎄 `18`: 📸 `19`: 😜 For `emotion` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: anger `1`: joy `2`: optimism `3`: sadness For `hate` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non-hate `1`: hate For `irony` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non_irony `1`: irony For `offensive` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non-offensive `1`: offensive For `sentiment` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: negative `1`: neutral `2`: positive For `stance_abortion` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_atheism` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_climate` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_feminist` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_hillary` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor ### Data Splits | name | train | validation | test | | --------------- | ----- | ---------- | ----- | | emoji | 45000 | 5000 | 50000 | | emotion | 3257 | 374 | 1421 | | hate | 9000 | 1000 | 2970 | | irony | 2862 | 955 | 784 | | offensive | 11916 | 1324 | 860 | | sentiment | 45615 | 2000 | 12284 | | stance_abortion | 587 | 66 | 280 | | stance_atheism | 461 | 52 | 220 | | stance_climate | 355 | 40 | 169 | | stance_feminist | 597 | 67 | 285 | | stance_hillary | 620 | 69 | 295 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP. ### Licensing Information This is not a single dataset, therefore each subset has its own license (the collection itself does not have additional restrictions). All of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy) Additionally the license are: - emoji: Undefined - emotion(EmoInt): Undefined - hate (HateEval): Need permission [here](http://hatespeech.di.unito.it/hateval.html) - irony: Undefined - Offensive: Undefined - Sentiment: [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ) - Stance: Undefined ### Citation Information ``` @inproceedings{barbieri2020tweeteval, title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}}, author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo}, booktitle={Proceedings of Findings of EMNLP}, year={2020} } ``` If you use any of the TweetEval datasets, please cite their original publications: #### Emotion Recognition: ``` @inproceedings{mohammad2018semeval, title={Semeval-2018 task 1: Affect in tweets}, author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana}, booktitle={Proceedings of the 12th international workshop on semantic evaluation}, pages={1--17}, year={2018} } ``` #### Emoji Prediction: ``` @inproceedings{barbieri2018semeval, title={Semeval 2018 task 2: Multilingual emoji prediction}, author={Barbieri, Francesco and Camacho-Collados, Jose and Ronzano, Francesco and Espinosa-Anke, Luis and Ballesteros, Miguel and Basile, Valerio and Patti, Viviana and Saggion, Horacio}, booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, pages={24--33}, year={2018} } ``` #### Irony Detection: ``` @inproceedings{van2018semeval, title={Semeval-2018 task 3: Irony detection in english tweets}, author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique}, booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, pages={39--50}, year={2018} } ``` #### Hate Speech Detection: ``` @inproceedings{basile-etal-2019-semeval, title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter", author = "Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and Rangel Pardo, Francisco Manuel and Rosso, Paolo and Sanguinetti, Manuela", booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation", year = "2019", address = "Minneapolis, Minnesota, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/S19-2007", doi = "10.18653/v1/S19-2007", pages = "54--63" } ``` #### Offensive Language Identification: ``` @inproceedings{zampieri2019semeval, title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)}, author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh}, booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation}, pages={75--86}, year={2019} } ``` #### Sentiment Analysis: ``` @inproceedings{rosenthal2017semeval, title={SemEval-2017 task 4: Sentiment analysis in Twitter}, author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav}, booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)}, pages={502--518}, year={2017} } ``` #### Stance Detection: ``` @inproceedings{mohammad2016semeval, title={Semeval-2016 task 6: Detecting stance in tweets}, author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin}, booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)}, pages={31--41}, year={2016} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

nyanko7/danbooru2023

nyanko7

--- license: mit task_categories: - image-classification - image-to-image - text-to-image language: - en - ja pretty_name: danbooru2023 size_categories: - 1M<n<10M viewer: false --- <img src="https://huggingface.co/datasets/nyanko7/danbooru2023/resolve/main/cover.webp" alt="cover" width="750"/> # Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset <!-- Provide a quick summary of the dataset. --> Danbooru2023 is a large-scale anime image dataset with over 5 million images contributed and annotated in detail by an enthusiast community. Image tags cover aspects like characters, scenes, copyrights, artists, etc with an average of 30 tags per image. Danbooru is a veteran anime image board with high-quality images and extensive tag metadata. The dataset can be used to train image classification, multi-label tagging, character detection, generative models, and other computer vision tasks. - **Shared by:** Nyanko Devs - **Language(s):** English, Japanese - **License:** MIT This dataset is built on the top of [danbooru2021](https://gwern.net/danbooru2021). We expands the dataset to include images up to ID #6,857,737, adding over 1.8 million additional images and total size is now approximately 8 terabytes (8,000 GB). ## Use ## Format The goal of the dataset is to be as easy as possible to use immediately, avoiding obscure file formats, while allowing simultaneous research & seeding of the torrent, with easy updates. Images are provided in the full original form (be that JPG, PNG, GIF or otherwise) for reference/archival purposes, and bucketed into 1000 subdirectories 0000–0999 (0-padded), which is the Danbooru ID modulo 1000 (ie. all images in 0999/ have an ID ending in ‘999’); IDs can be turned into paths by dividing & padding (eg. in Bash, BUCKET=$(printf "%04d" $(( ID % 1000 )) )) and then the file is at {original,512px}/$BUCKET/$ID.$EXT. The reason for the bucketing is that a single directory would cause pathological filesystem performance, and modulo ID is a simple hash which spreads images evenly without requiring additional future directories to be made or a filesystem IO to check where the file is. The ID is not zero-padded and files end in the relevant extension, hence the file layout looks like this: ```bash $ tree / | less / ├── danbooru2023 -> /mnt/diffusionstorage/workspace/danbooru/ │ ├── metadata │ ├── readme.md │ ├── original │ │ ├── 0000 -> data-0000.tar │ │ ├── 0001 -> data-0001.tar │ │ │ ├── 10001.jpg │ │ │ ├── 210001.png │ │ │ ├── 3120001.webp │ │ │ ├── 6513001.jpg │ │ │ ├── recent │ │ ├── 0000 -> data-1000.tar │ │ ├── 0001 -> data-1001.tar │ │ │ ├── updates │ │ ├── 20240319 │ │ │ ├── dataset-0.tar │ │ │ ├── dataset-1.tar │ │ │ │ │ ├── 2024xxxx │ │ │ ├── dataset-0.tar │ │ │ ├── dataset-1.tar ``` Where `data-{1000..1999}.tar` refer to recent update files (should be updated every few months) and `updates` refer to fast patches (should be updated every few days to few weeks). Currently represented file extensions are: avi/bmp/gif/html/jpeg/jpg/mp3/mp4/mpg/pdf/png/rar/swf/webm/wmv/zip. Raw original files are treacherous. Be careful if working with the original dataset. There are many odd files: truncated, non-sRGB colorspace, wrong file extensions (eg. some PNGs have .jpg extensions like original/0146/1525146.jpg or original/0558/1422558.jpg), etc.

vera365/lexica_dataset

vera365

--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: id dtype: string - name: promptid dtype: string - name: width dtype: uint16 - name: height dtype: uint16 - name: seed dtype: uint32 - name: grid dtype: bool - name: model dtype: string - name: nsfw dtype: string - name: subject dtype: string - name: modifier10 sequence: string - name: modifier10_vector sequence: float64 splits: - name: train num_bytes: 7270597392.368 num_examples: 49173 - name: test num_bytes: 1765294302.142 num_examples: 12294 download_size: 5194348793 dataset_size: 9035891694.51 license: cc-by-4.0 task_categories: - text-to-image - image-to-text language: - en tags: - prompts - engineering - research paper pretty_name: LexicaDataset size_categories: - 10K<n<100K --- ## Dataset Description - **Repository:** [Github repository](https://github.com/verazuo/prompt-stealing-attack) - **Distribution:** [LexicaDataset on HuggingFace](https://huggingface.co/datasets/vera365/lexica_dataset) - **Paper:** [Prompt Stealing Attacks Against Text-to-Image Generation Models](https://arxiv.org/abs/2302.09923) - **Point of Contact:** [Xinyue Shen](xinyue.shen@cispa.de) ### LexicaDataset LexicaDataset is a large-scale text-to-image prompt dataset shared in [[USENIX'24] Prompt Stealing Attacks Against Text-to-Image Generation Models](https://arxiv.org/abs/2302.09923). It contains **61,467 prompt-image pairs** collected from [Lexica](https://lexica.art/). All prompts are curated by real users and images are generated by Stable Diffusion. Data collection details can be found in the paper. ### Data Splits We randomly sample 80% of a dataset as the training dataset and the rest 20% as the testing dataset. ### Load LexicaDataset You can use the Hugging Face [`Datasets`](https://huggingface.co/docs/datasets/quickstart) library to easily load prompts and images from LexicaDataset. ```python import numpy as np from datasets import load_dataset trainset = load_dataset('vera365/lexica_dataset', split='train') testset = load_dataset('vera365/lexica_dataset', split='test') ``` ### Metadata Schema `trainset` and `testset` share the same schema. | Column | Type | Description | | :------------------ | :--------- | :----------------------------------------------------------- | | `image` | `image` | The generated image | | `prompt` | `string` | The text prompt used to generate this image | | `id` | `string` | Image UUID | | `promptid` | `string` | Prompt UUID | | `width` | `uint16` | Image width | | `height` | `uint16` | Image height | | `seed` | `uint32` | Random seed used to generate this image. | | `grid` | `bool` | Whether the image is composed of multiple smaller images arranged in a grid | | `model` | `string` | Model used to generate the image | | `nsfw` | `string` | Whether the image is NSFW | | `subject` | `string` | the subject/object depicted in the image, extracted from the prompt | | `modifier10` | `sequence` | Modifiers in the prompt that appear more than 10 times in the whole dataset. We regard them as labels to train the modifier detector | | `modifier10_vector` | `sequence` | One-hot vector of `modifier10` | ## Ethics & Disclosure According to the [terms and conditions of Lexica](https://lexica.art/terms), images on the website are available under the Creative Commons Noncommercial 4.0 Attribution International License. We strictly followed Lexica’s Terms and Conditions, utilized only the official Lexica API for data retrieval, and disclosed our research to Lexica. We also responsibly disclosed our findings to related prompt marketplaces. ## License The LexicaDataset dataset is available under the [CC-BY 4.0 License](https://creativecommons.org/licenses/by/4.0/). ## Citation If you find this useful in your research, please consider citing: ```bibtex @inproceedings{SQBZ24, author = {Xinyue Shen and Yiting Qu and Michael Backes and Yang Zhang}, title = {{Prompt Stealing Attacks Against Text-to-Image Generation Models}}, booktitle = {{USENIX Security Symposium (USENIX Security)}}, publisher = {USENIX}, year = {2024} } ```

lowercaseonly/cghd

lowercaseonly

--- license: cc-by-3.0 pretty_name: A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images size_categories: - 1K<n<10K task_categories: - object-detection - image-segmentation language: - en - de --- # Public Ground-Truth Dataset for Handwritten Circuit Diagrams (GTDB-HD) This repository contains images of hand-drawn electrical circuit diagrams as well as accompanying bounding box annotation for object detection as well as segmentation ground truth files. This dataset is intended to train (e.g. neural network) models for the purpose of the extraction of electrical graphs from raster graphics. ## Structure The folder structure is made up as follows: ``` gtdh-hd │ README.md # This File │ classes.json # Classes List │ classes_color.json # Classes to Color Map │ classes_discontinuous.json # Classes Morphology Info │ classes_ports.json # Electrical Port Descriptions for Classes │ consistency.py # Dataset Statistics and Consistency Check | loader.py # Simple Dataset Loader and Storage Functions │ segmentation.py # Multiclass Segmentation Generation │ utils.py # Helper Functions │ requirements.txt # Requirements for Scripts └───drafter_D │ └───annotations # Bounding Box Annotations │ │ │ CX_DY_PZ.xml │ │ │ ... │ │ │ └───images # Raw Images │ │ │ CX_DY_PZ.jpg │ │ │ ... │ │ │ └───instances # Instance Segmentation Polygons │ │ │ CX_DY_PZ.json │ │ │ ... │ │ │ └───segmentation # Binary Segmentation Maps (Strokes vs. Background) │ │ │ CX_DY_PZ.jpg │ │ │ ... ... ``` Where: - `D` is the (globally) running number of a drafter - `X` is the (globally) running number of the circuit (12 Circuits per Drafter) - `Y` is the Local Number of the Circuit's Drawings (2 Drawings per Circuit) - `Z` is the Local Number of the Drawing's Image (4 Pictures per Drawing) ### Image Files Every image is RGB-colored and either stored as `jpg`, `jpeg` or `png` (both uppercase and lowercase suffixes exist). ### Bounding Box Annotations A complete list of class labels including a suggested mapping table to integer numbers for training and prediction purposes can be found in `classes.json`. The annotations contains **BB**s (Bounding Boxes) of **RoI**s (Regions of Interest) like electrical symbols or texts within the raw images and are stored in the [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) format. Please note: *For every Raw image in the dataset, there is an accompanying bounding box annotation file.* #### Known Labeled Issues - C25_D1_P4 cuts off a text - C27 cuts of some texts - C29_D1_P1 has one additional text - C31_D2_P4 has a text less - C33_D1_P4 has a text less - C46_D2_P2 cuts of a text ### Instance Segmentation For every binary segmentation map, there is an accompanying polygonal annotation file for instance segmentation purposes, which is stored in the [labelme](https://github.com/wkentaro/labelme) format. Note that the contained polygons are quite coarse, intended to be used in conjunction with the binary segmentation maps for connection extraction and to tell individual instances with overlapping BBs apart. ### Segmentation Maps Binary Segmentation images are available for some samples and bear the same resolution as the respective image files. They are considered to contain only black and white pixels indicating areas of drawings strokes and background respectively. ### Netlists For some images, there are also netlist files available, which are stored in the [ASC](http://ltwiki.org/LTspiceHelp/LTspiceHelp/Spice_Netlist.htm) format. ### Consistency and Statistics This repository comes with a stand-alone script to: - Obtain Statistics on - Class Distribution - BB Sizes - Check the BB Consistency - Classes with Regards to the `classes.json` - Counts between Pictures of the same Drawing - Ensure a uniform writing style of the Annotation Files (indent) The respective script is called without arguments to operate on the **entire** dataset: ``` $ python3 consistency.py ``` Note that due to a complete re-write of the annotation data, the script takes several seconds to finish. A drafter can be specified as CLI argument to restrict the evaluation (for example drafter 15): ``` $ python3 consistency.py 15 ``` ### Multi-Class (Instance) Segmentation Processing This dataset comes with a script to process both new and existing (instance) segmentation files. It is invoked as follows: ``` $ python3 segmentation.py <command> <drafter_id> <target> <source> ``` Where: - `<command>` has to be one of: - `transform` - Converts existing BB Annotations to Polygon Annotations - Default target folder: `instances` - Existing polygon files will not be overridden in the default settings, hence this command will take no effect in an completely populated dataset. - Intended to be invoked after adding new binary segmentation maps - **This step has to be performed before all other commands** - `wire` - Generates Wire Describing Polygons - Default target folder: `wires` - `keypoint` - Generates Keypoints for Component Terminals - Default target folder: `keypoints` - `create` - Generates Multi-Class segmentation Maps - Default target folder: `segmentation_multi_class` - `refine` - Refines Coarse Polygon Annotations to precisely match the annotated objects - Default target folder: `instances_refined` - For instance segmentation purposes - `pipeline` - executes `wire`,`keypoint` and `refine` stacked, with one common `source` and `target` folder - Default target folder: `instances_refined` - `assign` - Connector Point to Port Type Assignment by Geometric Transformation Matching - `<drafter_id>` **optionally** restricts the process to one of the drafters - `<target>` **optionally** specifies a divergent target folder for results to be placed in - `<source>` **optionally** specifies a divergent source folder to read from Please note that source and target forlders are **always** subfolder inside the individual drafter folders. Specifying source and target folders allow to stack the results of individual processing steps. For example, to perform the entire pipeline for drafter 20 manually, use: ``` python3 segmentation.py wire 20 instances_processed instances python3 segmentation.py keypoint 20 instances_processed instances_processed python3 segmentation.py refine 20 instances_processed instances_processed ``` ### Dataset Loader This dataset is also shipped with a set of loader and writer functions, which are internally used by the segmentation and consistency scripts and can be used for training. The dataset loader is simple, framework-agnostic and has been prepared to be callable from any location in the file system. Basic usage: ``` from loader import read_dataset db_bb = read_dataset() # Read all BB Annotations db_seg = read_dataset(segmentation=True) # Read all Polygon Annotations db_bb_val = read_dataset(drafter=12) # Read Drafter 12 BB Annotations len(db_bb) # Get The Amount of Samples db_bb[5] # Get an Arbitrary Sample db = read_images(drafter=12) # Returns a list of (Image, Annotation) pairs db = read_snippets(drafter=12) # Returns a list of (Image, Annotation) pairs ``` ## Citation If you use this dataset for scientific publications, please consider citing us as follows: ``` @inproceedings{thoma2021public, title={A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images}, author={Thoma, Felix and Bayer, Johannes and Li, Yakun and Dengel, Andreas}, booktitle={International Conference on Document Analysis and Recognition}, pages={20--27}, year={2021}, organization={Springer} } ``` ## How to Contribute If you want to contribute to the dataset as a drafter or in case of any further questions, please send an email to: <johannes.bayer@dfki.de> (corresponding author), <yakun.li@dfki.de>, <andreas.dengel@dfki.de> ## Guidelines These guidelines are used throughout the generation of the dataset. They can be used as an instruction for participants and data providers. ### Drafter Guidelines - 12 Circuits should be drawn, each of them twice (24 drawings in total) - Most important: The drawing should be as natural to the drafter as possible - Free-Hand sketches are preferred, using rulers and drawing Template stencils should be avoided unless it appears unnatural to the drafter - Different types of pens/pencils should be used for different drawings - Different kinds of (colored, structured, ruled, lined) paper should be used - One symbol set (European/American) should be used throughout one drawing (consistency) - It is recommended to use the symbol set that the drafter is most familiar with - It is **strongly** recommended to share the first one or two circuits for review by the dataset organizers before drawing the rest to avoid problems (complete redrawing in worst case) ### Image Capturing Guidelines - For each drawing, 4 images should be taken (96 images in total per drafter) - Angle should vary - Lighting should vary - Moderate (e.g. motion) blur is allowed - All circuit-related aspects of the drawing must be _human-recognicable_ - The drawing should be the main part of the image, but _naturally_ occurring objects from the environment are welcomed - The first image should be _clean_, i.e. ideal capturing conditions - Kinks and Buckling can be applied to the drawing between individual image capturing - Try to use the file name convention (`CX_DY_PZ.jpg`) as early as possible - The circuit range `X` will be given to you - `Y` should be `1` or `2` for the drawing - `Z` should be `1`,`2`,`3` or `4` for the picture ### Object Annotation Guidelines - General Placement - A **RoI** must be **completely** surrounded by its **BB** - A **BB** should be as tight as possible to the **RoI** - In case of connecting lines not completely touching the symbol, the BB should extended (only by a small margin) to enclose those gaps (epecially considering junctions) - Characters that are part of the **essential symbol definition** should be included in the BB (e.g. the `+` of a polarized capacitor should be included in its BB) - **Junction** annotations - Used for actual junction points (Connection of three or more wire segments with a small solid circle) - Used for connection of three or more sraight line wire segements where a physical connection can be inferred by context (i.e. can be distinuished from **crossover**) - Used for wire line corners - Redundant Junction Points should **not** be annotated (small solid circle in the middle of a straight line segment) - Should not be used for corners or junctions that are part of the symbol definition (e.g. Transistors) - **Crossover** Annotations - If dashed/dotted line: BB should cover the two next dots/dashes - **Text** annotations - Individual Text Lines should be annotated Individually - Text Blocks should only be annotated If Related to Circuit or Circuit's Components - Semantically meaningful chunks of information should be annotated Individually - component characteristics enclosed in a single annotation (e.g. __100Ohms__, __10%__ tolerance, __5V__ max voltage) - Component Names and Types (e.g. __C1__, __R5__, __ATTINY2313__) - Custom Component Terminal Labels (i.e. __Integrated Circuit__ Pins) - Circuit Descriptor (e.g. "Radio Amplifier") - Texts not related to the Circuit should be ignored - e.g. Brief paper, Company Logos - Drafters auxiliary markings for internal organization like "D12" - Texts on Surrounding or Background Papers - Characters which are part of the essential symbol definition should __not__ be annotated as Text dedicatedly - e.g. Schmitt Trigger __S__, , and gate __&__, motor __M__, Polarized capacitor __+__ - Only add terminal text annotation if the terminal is not part of the essential symbol definition - **Table** cells should be annotated independently - **Operation Amplifiers** - Both the triangular US symbols and the european IC-like symbols symbols for OpAmps should be labeled `operational_amplifier` - The `+` and `-` signs at the OpAmp's input terminals are considered essential and should therefore not be annotated as texts - **Complex Components** - Both the entire Component and its sub-Components and internal connections should be annotated: | Complex Component | Annotation | | ----------------- | ------------------------------------------------------ | | Optocoupler | 0. `optocoupler` as Overall Annotation | | | 1. `diode.light_emitting` | | | 2. `transistor.photo` (or `resistor.photo`) | | | 3. `optical` if LED and Photo-Sensor arrows are shared | | | Then the arrows area should be includes in all | | Relay | 0. `relay` as Overall Annotation | | (also for | 1. `inductor` | | coupled switches) | 2. `switch` | | | 3. `mechanical` for the dashed line between them | | Transformer | 0. `transformer` as Overall Annotation | | | 1. `inductor` or `inductor.coupled` (watch the dot) | | | 3. `magnetic` for the core | #### Rotation Annotations The Rotation (integer in degree) should capture the overall rotation of the symbol shape. However, the position of the terminals should also be taked into consideration. Under idealized circumstances (no perspective distorion and accurately drawn symbols according to the symbol library), these two requirements equal each other. For pathological cases however, in which shape and the set of terminals (or even individual terminals) are conflicting, the rotation should compromise between all factors. Rotation annotations are currently work in progress. They should be provided for at least the following classes: - "voltage.dc" - "resistor" - "capacitor.unpolarized" - "diode" - "transistor.bjt" #### Text Annotations - The Character Sequence in the Text Label Annotations should describe the actual Characters depicted in the respective Bounding Box as Precisely as Possible - Bounding Box Annotations of class `text` - Bear an additional `<text>` tag in which their content is given as string - The `Omega` and `Mikro` Symbols are escaped respectively - Currently Work in Progress - The utils script allows for migrating text annotations from one annotation file to another: `python3 utils.py source target` ### Segmentation Map Guidelines - Areas of __Intended__ drawing strokes (ink and pencil abrasion respectively) should be marked black, all other pixels (background) should be white - shining through the paper (from the rear side or other sheets) should be considered background ### Polygon Annotation Guidelines 0. Before starting, make sure the respective files exist for the image sample to be polygon-annotated: - BB Annotations (Pascal VOC XML File) - (Binary) Segmentation Map 1. Transform the BB annotations into raw polygons - Use: `python3 segmentation.py transform` 2. Refine the Polygons - **To Avoid Embedding Image Data into the resulting JSON**, use: `labelme --nodata` - Just make sure there are no overlaps between instances - Especially take care about overlaps with structural elements like junctions and crossovers 3. Generate Multi-Class Segmentation Maps from the refined polygons - Use: `python3 segmentation.py create` - Use the generated images for a visual inspection - After spotting problems, continue with Step 2 ### Terminal Annotation Guidelines ``` labelme --labels "connector" --config "{shift_auto_shape_color: 1}" --nodata ``` ## Licence The entire content of this repository, including all image files, annotation files as well as has sourcecode, metadata and documentation has been published under the [Creative Commons Attribution Share Alike Licence 3.0](https://creativecommons.org/licenses/by-sa/3.0/).

Spawning/PD12M

Spawning

--- language: - en pretty_name: "PD12M" license: "cdla-permissive-2.0" tags: - image --- # PD12M  # Summary At 12.4 million image-caption pairs, PD12M is the largest public domain image-text dataset to date, with sufficient size to train foundation models while minimizing copyright concerns. Through the Source.Plus platform, we also introduce novel, community-driven dataset governance mechanisms that reduce harm and support reproducibility over time. [Jordan Meyer](https://linkedin.com/in/jordanmeyer) [Nicholas Padgett](https://www.linkedin.com/in/nicholas-padgett-36a921a0/) [Cullen Miller](https://www.linkedin.com/in/cullen-miller-312941290/) [Laura Exline](https://www.linkedin.com/in/lauraexline/) [Paper](https://arxiv.org/abs/2410.23144) [Datasheet](https://huggingface.co/datasets/Spawning/PD12M/blob/main/Datasheet.pdf) [Project](https://source.plus/pd12m) # About PD12M was built and curated with [Source.Plus](https://source.plus) with the aim of resolving many of the data quality issues that arise in web-scraped training data: the presence of copyrighted material, low quality images and captions, violent or nsfw content, PII, decaying dataset quality via broken links, etc. PD12M consists of entirely public domain and CC0 licensed images, with automated recaptioning of image data, and quality and safety filtering. Images in PD12M are also hosted on dedicated cloud storage, separate from the original image hosts, to avoid placing an undue burden on those hosts or impacting services for regular users. This also ensures the dataset remains wholly intact over its lifetime. # Overview This dataset has two components. The first is the `metadata`, which contains the image urls, captions, image dimensions, etc. The second component are the `images`. ## Metadata The metadata is made available through a series of parquet files with the following schema: - `id`: A unique identifier for the image. - `url`: The URL of the image. - `caption`: A caption for the image. - `width`: The width of the image in pixels. - `height`: The height of the image in pixels. - `mime_type`: The MIME type of the image file. - `hash`: The MD5 hash of the image file. - `license`: The URL of the image license. - `source` : The source organization of the image. Additionally, CLIP Vit-L/14 embeddings are provided in the `embeddings` directory. ## Images The image files are all hosted in the AWS S3 bucket `pd12m`. The URLs to the images files are all maintained in the metadata files. # Tutorials [Working with the Metadata](./tutorials/metadata.md) [Downloading Images](./tutorials/images.md) [Working with the Embeddings](./tutorials/embeddings.md) # License The dataset is licensed under the [CDLA-Permissive-2.0](https://cdla.dev/permissive-2-0/). # Reporting Issues We've gone through great lengths to ensure the dataset is free from objectionable and infringing content. If you find any issues or have any concerns, please flag the item in [Source.Plus](https://source.plus/collection/pd12m-mxenifxs), where our review process will remove the infringing material, and find a suitable replacement. # Citation @misc{meyer2024publicdomain12mhighly, title={Public Domain 12M: A Highly Aesthetic Image-Text Dataset with Novel Governance Mechanisms}, author={Jordan Meyer and Nick Padgett and Cullen Miller and Laura Exline}, year={2024}, eprint={2410.23144}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2410.23144}, }

Voxel51/dacl10k

Voxel51

--- annotations_creators: [] language: en license: cc-by-4.0 size_categories: - 1K<n<10K task_categories: - image-classification - object-detection task_ids: [] pretty_name: dacl10k tags: - WACV2024 - classification - construction - defect-detection - fiftyone - image - image-classification - image-segmentation - object-detection dataset_summary: '  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 8922 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = fouh.load_from_hub("Voxel51/dacl10k") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for dacl10k dacl10k stands for damage classification 10k images and is a multi-label semantic segmentation dataset for 19 classes (13 damages and 6 objects) present on bridges. The dacl10k dataset includes images collected during concrete bridge inspections acquired from databases at authorities and engineering offices, thus, it represents real-world scenarios. Concrete bridges represent the most common building type, besides steel, steel composite, and wooden bridges. 🏆 This dataset is used in the challenge associated with the "[1st Workshop on Vision-Based Structural Inspections in Civil Engineering](https://dacl.ai/workshop.html)" at [WACV2024](https://wacv2024.thecvf.com/workshops/).  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 8922 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = fouh.load_from_hub("Voxel51/dacl10k") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description - **Curated by:** Johannes Flotzinger, Philipp J. Rösch, Thomas Braml - **Funded by:** The project was funded by the Bavarian Ministry of Economic Affairs (MoBaP research project, IUK-1911-0004// IUK639/003) - **Language(s) (NLP):** en - **License:** cc-by-4.0 ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** https://github.com/phiyodr/dacl10k-toolkit - **Paper:** https://arxiv.org/abs/2309.00460 - **Demo:** https://try.fiftyone.ai/datasets/dacl10k/samples - **Homepage:** https://dacl.ai/workshop.html ## Uses - identifying reinforced concrete defects - informing restoration works, traffic load limitations or bridge closures [More Information Needed] ## Dataset Structure The dacl10k dataset includes images collected during concrete bridge inspections acquired from databases at authorities and engineering offices, thus, it represents real-world scenarios. Concrete bridges represent the most common building type, besides steel, steel composite, and wooden bridges. dacl10k distinguishes 13 bridge defects as well as 6 bridge components that play a key role in the building assessment. Based on the assessment, actions (e.g., restoration works, traffic load limitations, and bridge closures) are determined. The inspection itself and the resulting actions often impede the traffic and thus private persons and the economy. Furthermore, an ideal timing for restoration helps achieving long-term value added and can save a lot of money. It is important to note that dacl10k includes images from bridge inspections but is not restricted to this building type. Classes of the concrete and general defect group in dacl10k can appear on any building made of concrete. Therefore, it is relevant for most of the other civil engineering structures, too. <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** ```bibtex @misc{flotzinger2023dacl10k, title={dacl10k: Benchmark for Semantic Bridge Damage Segmentation}, author={Johannes Flotzinger and Philipp J. Rösch and Thomas Braml}, year={2023}, eprint={2309.00460}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Dataset Card Authors [Jacob Marks](https://huggingface.co/jamarks)

AterMors/wikiart_recaption

AterMors

--- dataset_info: features: - name: image dtype: image - name: text sequence: string - name: index dtype: int64 splits: - name: train num_bytes: 51733249508.27 num_examples: 81355 download_size: 51732268420 dataset_size: 51733249508.27 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - image-to-text language: - en size_categories: - 10K<n<100K --- [WikiArt Dataset](https://huggingface.co/datasets/huggan/wikiart) captioned using [vikhyatk/moondream2](https://huggingface.co/vikhyatk/moondream2) model with prompt : **Generate a short, simple and only visually descriptive caption for this image.**

OpenGVLab/OmniCorpus-CC

OpenGVLab

--- language: - en license: cc-by-4.0 size_categories: - 100M<n<1B task_categories: - image-to-text - visual-question-answering dataset_info: - config_name: CC-MAIN-2013-20 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 19908676196 num_examples: 3878063 download_size: 9303464923 dataset_size: 19908676196 - config_name: CC-MAIN-2013-48 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 15282078925 num_examples: 3091537 download_size: 6965036866 dataset_size: 15282078925 - config_name: CC-MAIN-2014-10 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7227087609 num_examples: 1390034 download_size: 3259239561 dataset_size: 7227087609 - config_name: CC-MAIN-2014-15 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 10106913108 num_examples: 1968361 download_size: 4567738362 dataset_size: 10106913108 - config_name: CC-MAIN-2014-23 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7997621043 num_examples: 1455331 download_size: 3468852905 dataset_size: 7997621043 - config_name: CC-MAIN-2014-35 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 6228103779 num_examples: 1219200 download_size: 2849584613 dataset_size: 6228103779 - config_name: CC-MAIN-2014-41 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 8321822952 num_examples: 1573955 download_size: 3775989970 dataset_size: 8321822952 - config_name: CC-MAIN-2014-42 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7732679416 num_examples: 1511931 download_size: 3505766162 dataset_size: 7732679416 - config_name: CC-MAIN-2014-49 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 4473311810 num_examples: 837735 download_size: 1982728919 dataset_size: 4473311810 - config_name: CC-MAIN-2014-52 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7292722888 num_examples: 1304730 download_size: 2957626766 dataset_size: 7292722888 - config_name: CC-MAIN-2015-06 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 5775826679 num_examples: 1061940 download_size: 2462379667 dataset_size: 5775826679 - config_name: CC-MAIN-2015-11 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 6263650452 num_examples: 1129411 download_size: 2528026633 dataset_size: 6263650452 - config_name: CC-MAIN-2015-14 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 4524425019 num_examples: 885221 download_size: 1939222111 dataset_size: 4524425019 - config_name: CC-MAIN-2015-18 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 6195227565 num_examples: 1104115 download_size: 2634204322 dataset_size: 6195227565 - config_name: CC-MAIN-2015-22 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7008276790 num_examples: 1290530 download_size: 2913627974 dataset_size: 7008276790 - config_name: CC-MAIN-2015-27 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 4320140953 num_examples: 784496 download_size: 1828575226 dataset_size: 4320140953 - config_name: CC-MAIN-2015-32 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 4952806590 num_examples: 875601 download_size: 2065207099 dataset_size: 4952806590 - config_name: CC-MAIN-2015-35 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 6053257306 num_examples: 1086470 download_size: 2632032769 dataset_size: 6053257306 - config_name: CC-MAIN-2015-40 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 5206096790 num_examples: 924036 download_size: 2203603087 dataset_size: 5206096790 - config_name: CC-MAIN-2015-48 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 8343050753 num_examples: 1537468 download_size: 3489600630 dataset_size: 8343050753 - config_name: CC-MAIN-2016-07 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 9329220105 num_examples: 1738650 download_size: 4005599785 dataset_size: 9329220105 - config_name: CC-MAIN-2016-18 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 3897220786 num_examples: 747570 download_size: 1675500816 dataset_size: 3897220786 - config_name: CC-MAIN-2016-22 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 4623903344 num_examples: 857060 download_size: 2000624854 dataset_size: 4623903344 - config_name: CC-MAIN-2016-26 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 3414418701 num_examples: 627995 download_size: 1403890884 dataset_size: 3414418701 - config_name: CC-MAIN-2016-30 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7244342539 num_examples: 1183776 download_size: 2913394840 dataset_size: 7244342539 - config_name: CC-MAIN-2016-36 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 5402565529 num_examples: 915878 download_size: 2248454753 dataset_size: 5402565529 - config_name: CC-MAIN-2016-40 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 5938544915 num_examples: 1113534 download_size: 2530904625 dataset_size: 5938544915 - config_name: CC-MAIN-2016-44 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 15819536321 num_examples: 3528637 download_size: 6516546200 dataset_size: 15819536321 - config_name: CC-MAIN-2016-50 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 10822695594 num_examples: 2215939 download_size: 4439728574 dataset_size: 10822695594 - config_name: CC-MAIN-2017-04 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 11949732148 num_examples: 2441316 download_size: 5045763620 dataset_size: 11949732148 - config_name: CC-MAIN-2017-09 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - 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name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 12209904783 num_examples: 2458486 download_size: 5422393873 dataset_size: 12209904783 - config_name: CC-MAIN-2017-17 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - 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name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 43538081906 num_examples: 7102645 download_size: 21728983014 dataset_size: 43538081906 - config_name: CC-MAIN-2019-43 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 52817470138 num_examples: 8567061 download_size: 26105523209 dataset_size: 52817470138 - config_name: CC-MAIN-2019-47 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 42252827792 num_examples: 6775943 download_size: 21228532199 dataset_size: 42252827792 - config_name: CC-MAIN-2019-51 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 38926356094 num_examples: 6415558 download_size: 19510339598 dataset_size: 38926356094 - config_name: CC-MAIN-2020-05 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 48189844491 num_examples: 7921372 download_size: 24235687030 dataset_size: 48189844491 - config_name: CC-MAIN-2020-10 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - 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name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 51243682770 num_examples: 8578633 download_size: 25485035979 dataset_size: 51243682770 - config_name: CC-MAIN-2020-24 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 59424939072 num_examples: 10438139 download_size: 29827361603 dataset_size: 59424939072 - config_name: CC-MAIN-2020-29 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 66229730938 num_examples: 11475631 download_size: 33030161773 dataset_size: 66229730938 - config_name: CC-MAIN-2020-34 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 54287690582 num_examples: 9495610 download_size: 27018821467 dataset_size: 54287690582 - config_name: CC-MAIN-2020-40 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 71587907978 num_examples: 12058149 download_size: 35795677487 dataset_size: 71587907978 - config_name: CC-MAIN-2020-45 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - 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name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 58557861606 num_examples: 9539918 download_size: 29083801775 dataset_size: 58557861606 - config_name: CC-MAIN-2021-04 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 74507336015 num_examples: 12273028 download_size: 36874887518 dataset_size: 74507336015 - config_name: CC-MAIN-2021-10 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 60802783945 num_examples: 10176190 download_size: 30326513365 dataset_size: 60802783945 - config_name: CC-MAIN-2021-17 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 75061494488 num_examples: 12343366 download_size: 37345114890 dataset_size: 75061494488 - config_name: CC-MAIN-2021-21 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 70036417178 num_examples: 11584034 download_size: 34806730527 dataset_size: 70036417178 - config_name: CC-MAIN-2021-25 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 73653674063 num_examples: 12065281 download_size: 36581310312 dataset_size: 73653674063 - config_name: CC-MAIN-2021-31 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - 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name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 97602903488 num_examples: 15772898 download_size: 48711364812 dataset_size: 97602903488 - config_name: CC-MAIN-2022-21 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 127495492928 num_examples: 21745889 download_size: 63379692210 dataset_size: 127495492928 - 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name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 152577158166 num_examples: 24634059 download_size: 76529854484 dataset_size: 152577158166 - config_name: CC-MAIN-2023-06 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 174815301023 num_examples: 28962355 download_size: 87301203013 dataset_size: 174815301023 - config_name: CC-MAIN-2023-14 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 227631152876 num_examples: 37223376 download_size: 114188282465 dataset_size: 227631152876 - config_name: CC-MAIN-2023-23 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 321036722459 num_examples: 52119692 download_size: 161491274249 dataset_size: 321036722459 - config_name: CC-MAIN-2023-40 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 648032999611 num_examples: 101292016 download_size: 317965522325 dataset_size: 648032999611 - config_name: CC-MAIN-2023-50 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 744768384551 num_examples: 117073004 download_size: 365332295606 dataset_size: 744768384551 configs: - config_name: CC-MAIN-2013-20 data_files: - split: train path: CC-MAIN-2013-20/train-* - config_name: CC-MAIN-2013-48 data_files: - split: train path: CC-MAIN-2013-48/train-* - config_name: CC-MAIN-2014-10 data_files: - split: train path: CC-MAIN-2014-10/train-* - 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config_name: CC-MAIN-2020-45 data_files: - split: train path: CC-MAIN-2020-45/train-* - config_name: CC-MAIN-2020-50 data_files: - split: train path: CC-MAIN-2020-50/train-* - config_name: CC-MAIN-2021-04 data_files: - split: train path: CC-MAIN-2021-04/train-* - config_name: CC-MAIN-2021-10 data_files: - split: train path: CC-MAIN-2021-10/train-* - config_name: CC-MAIN-2021-17 data_files: - split: train path: CC-MAIN-2021-17/train-* - config_name: CC-MAIN-2021-21 data_files: - split: train path: CC-MAIN-2021-21/train-* - config_name: CC-MAIN-2021-25 data_files: - split: train path: CC-MAIN-2021-25/train-* - config_name: CC-MAIN-2021-31 data_files: - split: train path: CC-MAIN-2021-31/train-* - config_name: CC-MAIN-2021-39 data_files: - split: train path: CC-MAIN-2021-39/train-* - config_name: CC-MAIN-2021-43 data_files: - split: train path: CC-MAIN-2021-43/train-* - config_name: CC-MAIN-2021-49 data_files: - split: train path: CC-MAIN-2021-49/train-* - config_name: CC-MAIN-2022-05 data_files: - split: train path: CC-MAIN-2022-05/train-* - config_name: CC-MAIN-2022-21 data_files: - split: train path: CC-MAIN-2022-21/train-* - config_name: CC-MAIN-2022-27 data_files: - split: train path: CC-MAIN-2022-27/train-* - config_name: CC-MAIN-2022-33 data_files: - split: train path: CC-MAIN-2022-33/train-* - config_name: CC-MAIN-2022-40 data_files: - split: train path: CC-MAIN-2022-40/train-* - config_name: CC-MAIN-2022-49 data_files: - split: train path: CC-MAIN-2022-49/train-* - config_name: CC-MAIN-2023-06 data_files: - split: train path: CC-MAIN-2023-06/train-* - config_name: CC-MAIN-2023-14 data_files: - split: train path: CC-MAIN-2023-14/train-* - config_name: CC-MAIN-2023-23 data_files: - split: train path: CC-MAIN-2023-23/train-* - config_name: CC-MAIN-2023-40 data_files: - split: train path: CC-MAIN-2023-40/train-* - config_name: CC-MAIN-2023-50 data_files: - split: train path: CC-MAIN-2023-50/train-* --- ⭐️ **NOTE:** Several parquet files were marked unsafe (viruses) by official scaning of hf, while they are reported safe by ClamAV and Virustotal. We found [many false positive cases](https://discuss.huggingface.co/u/mcpotato/summary) of the hf automatic scanning in hf discussions and raise [one discussion](https://discuss.huggingface.co/t/one-parquet-file-of-my-dataset-was-marked-unsafe/113745) to ask for a re-scanning. # OmniCorpus-CC This is the repository of OmniCorpus-CC, which contains 988 million image-text interleaved documents collected from [Common Crawl](https://commoncrawl.org/). - Repository: https://github.com/OpenGVLab/OmniCorpus - Paper: https://arxiv.org/abs/2406.08418 OmniCorpus dataset is a large-scale image-text interleaved dataset, which pushes the boundaries of scale and diversity by encompassing **8.6 billion images** interleaved with **1,696 text tokens** from diverse sources, significantly surpassing previous datasets. This dataset demonstrates several advantages over its counterparts: 1. **Larger data scale:** Our dataset is 1.7 times larger in images and 12.5 times larger in texts compared to the previously largest multimodal dataset, LAION-5B, while maintaining excellent data quality. 2. **Richer data diversity:** Drawing from a broader range of data sources, our dataset is more diverse than other image-text interleaved datasets. It includes bilingual multimodal data in both Chinese and English, and encompasses text-centric and vision-centric documents extracted from common websites and video platforms. 3. **More flexible format:** The streaming data format of our dataset offers exceptional flexibility, allowing adaptation to various data structures, including pure text corpora, image-text pairs, and interleaved data formats. <img width="578" alt="image" src="https://github.com/OpenGVLab/OmniCorpus/assets/47669167/641a6427-ba50-41e6-8634-8810113fd803"> The OmniCorpus contains three sections: - **OmniCorpus-CC**: processed from dumps in Common Crawl from 2013 to Nov./Dec. 2023. - **OmniCorpus-CW**: sourced from Chinese internet resources, will be availiable in [OpenDataLab](https://opendatalab.com/) platform. - **OmniCorpus-YT**: samples Youtube video frames as images and collects subtitles as texts. Code for pre-training, evaluating, main body extracting, and filtering have been released in the official [repository](https://github.com/OpenGVLab/OmniCorpus). A pre-trained model is availiable [here](https://huggingface.co/Qingyun/OmniCorpus-InternVL). # Data Pipeline Our data pipeline consists of five key stages: main body extraction, preliminary text filtering, document deduplication, image downloading \& filtering, and detailed text filtering. Each stage efficiently reduces the dataset to retain only high-quality data. Please refer to our paper for more details about the data pipeline. <img width="723" alt="image" src="https://github.com/OpenGVLab/OmniCorpus/assets/47669167/a6de8928-58fb-4ff4-8ef9-4bd90e9ada5f"> # Usages The image-text interleaved documents are recommanded for the following usages: - Pre-training multimodal large language model (MLLM): Recent MLLMs (such as Flamingo series, EMU series, IDEFICS series, MM1, Cambrian-1, and xGen-MM) have shown that image-text interleaved data aids multimodal in-context learning and maintains the capabilities of large language models during multimodal fine-tuning. - Long text-image retrieval: We provide image-text similarities calculated with CLIP, which can convert the documents to image-text retrieval dataset with longer text. A retrieval model pre-trained on such data can retrieval images based on longer text, which can be used for multimodal RAG, converting pure text to multimodal sample, etc. - Source for futher dataset research: Our data is large-scale, which can serve as the source for researches for data curation strategies. We provide many useful attributes as metadata for each document, which can enrich the filtering strategy and reduce the cost. - ...... # Data Format Following common practices, the data is organized into Parquet file format. You might encounter errors when using `pandas.read_parquet` (because the data structure contains nested elements). We recommend using fastparquet to load the parquet files. ```Python import fastparquet df = fastparquet.ParquetFile(parquet_file_path).to_pandas() # You can also use iter_batches parquet_file = pq.ParquetFile(filepath) for batch in parquet_file.iter_batches(): df = batch.to_pandas() ``` You can convert the i-th document and convert it into a dictionary. ```Python doc_dict = df.iloc[i].to_dict() ``` The document format is as follow: ```json { 'images': [ <str: image_1_url>, None, <str: image_2_url>, None, ], 'texts': [ None, <str: text_paragraph_1_content> None, <str: text_paragraph_2_content>, ] 'metadata': [ <dict: image_1_metadata>, None, <dict: image_2_metadata>, None ], 'general_metadata': { "url": <str: document url>, "id": <str: document id>, "domain": <list[str]: domains extracted from document url>, "fluency_prob": <float: the probability of fluency>, "non_advertisement_prob": <float: the probability of non-advertisement>, "porn_prob": <float: the probability of porn content>, "politics_prob": <float: the probability of politics content>, "toxic_prob": <float: the probability of toxic content>, } } ``` Each image metadata is as follow: ```json { "img_url_sha": <str: sha code of image url>, "width": <int: image width>, "height": <int: image height>, "bytes": <int: byte number of the image file>, "d_hash": <str: d_hash code of the image, used for image deduplication>, "p_hash": <str: p_hash code of the image, used for image deduplication>, "d_hash_dup_count": <int: duplicated times detected by d_hash code>, "p_hash_dup_count": <int: duplicated times detected by p_hash code>, "aesthetic prob": <float: aesthetic probility>, "unsafe prob": <float: NSFW probility>, } ``` # License OmniCorpus is released under a [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/deed.en) license, with the primary intent of supporting research activities. # Citation ``` @article{li2024omnicorpus, title={OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text}, author={Li, Qingyun and Chen, Zhe and Wang, Weiyun and Wang, Wenhai and Ye, Shenglong and Jin, Zhenjiang and others}, journal={arXiv preprint arXiv:2406.08418}, year={2024} } ```

nvidia/OpenMathInstruct-2

nvidia

--- language: - en license: cc-by-4.0 size_categories: - 10M<n<100M task_categories: - question-answering - text-generation pretty_name: OpenMathInstruct-2 dataset_info: features: - name: problem dtype: string - name: generated_solution dtype: string - name: expected_answer dtype: string - name: problem_source dtype: string splits: - name: train_1M num_bytes: 1350383003 num_examples: 1000000 - name: train_2M num_bytes: 2760009675 num_examples: 2000000 - name: train_5M num_bytes: 6546496157 num_examples: 5000000 - name: train num_bytes: 15558412976 num_examples: 13972791 download_size: 20208929853 dataset_size: 26215301811 tags: - math - nvidia configs: - config_name: default data_files: - split: train path: data/train-* - split: train_1M path: data/train_1M-* - split: train_2M path: data/train_2M-* - split: train_5M path: data/train_5M-* --- # OpenMathInstruct-2 OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs generated using the [Llama3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) model. The training set problems of [GSM8K](https://github.com/openai/grade-school-math) and [MATH](https://github.com/hendrycks/math) are used for constructing the dataset in the following ways: - *Solution augmentation*: Generating chain-of-thought solutions for training set problems in GSM8K and MATH. - *Problem-Solution augmentation*: Generating new problems, followed by solutions for these new problems. <p> <img src="SFT Data Diagram 1.jpg" width="75%" title="Composition of OpenMathInstruct-2"> </p> OpenMathInstruct-2 dataset contains the following fields: - **problem**: Original problem from either the GSM8K or MATH training set or augmented problem from these training sets. - **generated_solution**: Synthetically generated solution. - **expected_answer**: For problems in the training set, it is the ground-truth answer provided in the datasets. **For augmented problems, it is the majority-voting answer.** - **problem_source**: Whether the problem is taken directly from GSM8K or MATH or is an augmented version derived from either dataset. <p> <img src="scaling_plot.jpg" width="40%" title="Scaling Curve"> </p> We also release the 1M, 2M, and 5M, *fair-downsampled* versions of the entire training set corresponding to points in the above scaling plot. These splits are referred to as **train_1M**, **train_2M**, and **train_5M**. To use these subsets, just specify one of these subsets as split while downloading the data: ```python from datasets import load_dataset # Download only the 1M training split dataset = load_dataset('nvidia/OpenMathInstruct-2', split='train_1M', streaming=True) ``` To download the entire training set and to convert it into the jsonl format, use the following code snippet. This might take 20-30 minutes (or more depending on your network connection) and will use ~20Gb of RAM. ```python import json from datasets import load_dataset from tqdm import tqdm dataset = load_dataset('nvidia/OpenMathInstruct-2', split='train') print("Converting dataset to jsonl format") output_file = "openmathinstruct2.jsonl" with open(output_file, 'w', encoding='utf-8') as f: for item in tqdm(dataset): f.write(json.dumps(item, ensure_ascii=False) + '\n') print(f"Conversion complete. Output saved as {output_file}") ``` Apart from the dataset, we also release the [contamination explorer](https://huggingface.co/spaces/nvidia/OpenMathInstruct-2-explorer) for looking at problems in the OpenMathInstruct-2 dataset that are similar to the [GSM8K](https://huggingface.co/datasets/openai/gsm8k), [MATH](https://github.com/hendrycks/math), [AMC 2023](https://github.com/QwenLM/Qwen2.5-Math/tree/main/evaluation/data/amc23), [AIME 2024](https://artofproblemsolving.com/wiki/index.php/2024_AIME_I), and [Omni-MATH](https://huggingface.co/datasets/KbsdJames/Omni-MATH) test set problems. See our [paper](https://arxiv.org/abs/2410.01560) to learn more details! ### Note The released dataset doesn't filter out extremely long questions. After the dataset release, we found that 564 questions (roughly 0.1%) were longer than 1024 Llama tokens. We experimented with removing these questions and didn't see a performance drop (in fact, we observed a minor bump). Dropping these questions, helps with memory as well. So we would recommend, filtering out extremely long questions. We have updated the data preparation commands in our [Github documentation](https://kipok.github.io/NeMo-Skills/openmathinstruct2/dataset/#converting-to-sft-format). ## OpenMath2 models To demonstrate the quality of this dataset, we release a series of OpenMath2 models trained on this data. | Model | GSM8K | MATH | AMC 2023 | AIME 2024 | Omni-MATH | |:---|:---:|:---:|:---:|:---:|:---:| | Llama3.1-8B-Instruct | 84.5 | 51.9 | 9/40 | 2/30 | 12.7 | | OpenMath2-Llama3.1-8B ([nemo](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B-nemo) \| [HF](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B)) | 91.7 | 67.8 | 16/40 | 3/30 | 22.0 | | + majority@256 | 94.1 | 76.1 | 23/40 | 3/30 | 24.6 | | Llama3.1-70B-Instruct | 95.8 | 67.9 | 19/40 | 6/30 | 19.0 | | OpenMath2-Llama3.1-70B ([nemo](https://huggingface.co/nvidia/OpenMath2-Llama3.1-70B-nemo) \| [HF](https://huggingface.co/nvidia/OpenMath2-Llama3.1-70B)) | 94.9 | 71.9 | 20/40 | 4/30 | 23.1 | | + majority@256 | 96.0 | 79.6 | 24/40 | 6/30 | 27.6 | The pipeline we used to produce the data and models is fully open-sourced! - [Code](https://github.com/Kipok/NeMo-Skills) - [Models](https://huggingface.co/collections/nvidia/openmath-2-66fb142317d86400783d2c7b) - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2) ## Reproducing our results We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results, including data generation. ## Citation If you find our work useful, please consider citing us! ```bibtex @article{toshniwal2024openmath2, title = {OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data}, author = {Shubham Toshniwal and Wei Du and Ivan Moshkov and Branislav Kisacanin and Alexan Ayrapetyan and Igor Gitman}, year = {2024}, journal = {arXiv preprint arXiv:2410.01560} } ```

Voxel51/PIDray

Voxel51

--- annotations_creators: [] language: en license: apache-2.0 size_categories: - 1K<n<10K task_categories: - object-detection task_ids: [] pretty_name: pidray tags: - fiftyone - image - object-detection dataset_summary: '  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 9482 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = fouh.load_from_hub("voxel51/PIDray") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for pidray PIDray is a large-scale dataset which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. The dataset contains 12 categories of prohibited items in 47, 677 X-ray images with high-quality annotated segmentation masks and bounding boxes.  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 9482 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = fouh.load_from_hub("dgural/PIDray") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description From _Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark_: Automatic security inspection using computer vision technology is a challenging task in real-world scenarios due to various factors, including intra-class variance, class imbalance, and occlusion. Most of the previous methods rarely solve the cases that the prohibited items are deliberately hidden in messy objects due to the lack of large-scale datasets, restricted their applications in real-world scenarios. Towards real-world prohibited item detection, we collect a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. With an intensive amount of effort, our dataset contains categories of prohibited items in X-ray images with high-quality annotated segmentation masks and bounding boxes. To the best of our knowledge, it is the largest prohibited items detection dataset to date. Meanwhile, we design the selective dense attention network (SDANet) to construct a strong baseline, which consists of the dense attention module and the dependency refinement module. The dense attention module formed by the spatial and channel-wise dense attentions, is designed to learn the discriminative features to boost the performance. The dependency refinement module is used to exploit the dependencies of multi-scale features. Extensive experiments conducted on the collected PIDray dataset demonstrate that the proposed method performs favorably against the state-of-the-art methods, especially for detecting the deliberately hidden items. - **Language(s) (NLP):** en - **License:** apache-2.0 The images and the corresponding annotations in PIDray Dataset can be used ONLY for academic purposes, NOT for commercial purposes. Copyright © 2021 Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences All rights reserved. ### Dataset Sources - **Repository:** https://github.com/bywang2018/security-dataset - **Paper [optional]:** https://arxiv.org/abs/2108.07020 ## Uses This used for academic research on airport security screening machines and the detection of objects being scanned. ### Out-of-Scope Use Any non-academic work is out of scope and prohibited. ## Citation @inproceedings{wang2021towards, title={Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark}, author={Wang, Boying and Zhang, Libo and Wen, Longyin and Liu, Xianglong and Wu, Yanjun}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={5412--5421}, year={2021} }

pixparse/cc3m-wds

pixparse

--- license: other license_name: conceptual-captions license_link: >- https://github.com/google-research-datasets/conceptual-captions/blob/master/LICENSE task_categories: - image-to-text size_categories: - 1M<n<10M --- # Dataset Card for Conceptual Captions (CC3M) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Conceptual Captions homepage](https://ai.google.com/research/ConceptualCaptions/) - **Repository:** [Conceptual Captions repository](https://github.com/google-research-datasets/conceptual-captions) - **Paper:** [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://www.aclweb.org/anthology/P18-1238/) - **Leaderboard:** [Conceptual Captions leaderboard](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard)https://ai.google.com/research/ConceptualCaptions/leaderboard?active_tab=leaderboard - **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com) ### Dataset Summary Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. ### Usage This instance of Conceptual Captions is in [webdataset](https://github.com/webdataset/webdataset/commits/main) .tar format. It can be used with webdataset library or upcoming releases of Hugging Face `datasets`. ...More Detail TBD ### Data Splits This dataset was downloaded using img2dataset. Images resized on download if shortest edge > 512 to shortest edge = 512. #### Train * `cc3m-train-*.tar` * Downloaded on 2021/12/22 * 576 shards, 2905954 (of 3318333) samples #### Validation * `cc3m-validation-*.tar` * Downloaded on 2023/12/13 (original validation set download in 2021 was corrupted) * 16 shards, 13443 (of 15840) samples ## Additional Information ### Dataset Curators Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ```bibtex @inproceedings{sharma2018conceptual, title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning}, author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu}, booktitle = {Proceedings of ACL}, year = {2018}, } ```

openslr/librispeech_asr

openslr

--- pretty_name: LibriSpeech annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: librispeech-1 size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - audio-classification task_ids: - speaker-identification dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.100 num_bytes: 6619683041 num_examples: 28539 - name: train.360 num_bytes: 23898214592 num_examples: 104014 - name: validation num_bytes: 359572231 num_examples: 2703 - name: test num_bytes: 367705423 num_examples: 2620 download_size: 30121377654 dataset_size: 31245175287 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.500 num_bytes: 31810256902 num_examples: 148688 - name: validation num_bytes: 337283304 num_examples: 2864 - name: test num_bytes: 352396474 num_examples: 2939 download_size: 31236565377 dataset_size: 32499936680 - config_name: all features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.clean.100 num_bytes: 6627791685 num_examples: 28539 - name: train.clean.360 num_bytes: 23927767570 num_examples: 104014 - name: train.other.500 num_bytes: 31852502880 num_examples: 148688 - name: validation.clean num_bytes: 359505691 num_examples: 2703 - name: validation.other num_bytes: 337213112 num_examples: 2864 - name: test.clean num_bytes: 368449831 num_examples: 2620 - name: test.other num_bytes: 353231518 num_examples: 2939 download_size: 61357943031 dataset_size: 63826462287 --- # Dataset Card for librispeech_asr ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [LibriSpeech ASR corpus](http://www.openslr.org/12) - **Repository:** [Needs More Information] - **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) - **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - **Point of Contact:** [Daniel Povey](mailto:dpovey@gmail.com) ### Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia. ### Languages The audio is in English. There are two configurations: `clean` and `other`. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other". ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'chapter_id': 141231, 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '1272-141231-0000', 'speaker_id': 1272, 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} ``` ### Data Fields - file: A path to the downloaded audio file in .flac format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other". For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech. | | Train.500 | Train.360 | Train.100 | Valid | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | clean | - | 104014 | 28539 | 2703 | 2620| | other | 148688 | - | - | 2864 | 2939 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, pages={5206--5210}, year={2015}, organization={IEEE} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

wis-k/instruction-following-eval

wis-k

--- license: apache-2.0 ---

datablations/oscar-dedup-expanded

datablations

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: meta struct: - name: warc_headers struct: - name: warc-record-id dtype: string - name: warc-date dtype: string - name: content-type dtype: string - name: content-length dtype: int32 - name: warc-type dtype: string - name: warc-identified-content-language dtype: string - name: warc-refers-to dtype: string - name: warc-target-uri dtype: string - name: warc-block-digest dtype: string - name: identification struct: - name: label dtype: string - name: prob dtype: float32 - name: annotations sequence: string - name: line_identifications list: - name: label dtype: string - name: prob dtype: float32 - name: perplexity_score dtype: float64 - name: text_length dtype: int64 - name: url dtype: string - name: domain dtype: string - name: dup_ratio dtype: float64 - name: pairs sequence: sequence: int64 - name: repetitions sequence: binary - name: included_in_dedup dtype: bool - name: cluster sequence: int64 - name: has_dup_25 dtype: bool splits: - name: train num_bytes: 3188540880787 num_examples: 431992659 download_size: 1732364041898 dataset_size: 3188540880787 --- Use the 25% suffix array to deduplicate the full Oscar, i.e. remove any document which has an at least 100-char span overlapping with the 25% chunk we selected in the previous bullet. This is more permissive and leaves us with 136 million documents or 31% of the original dataset. Also for reasons the explanation of which would probably involve terms like power laws, we still remove most of the most pervasive duplicates - so I'm pretty optimistic about this being useful.

hahminlew/kream-product-blip-captions

hahminlew

--- license: cc-by-nc-sa-4.0 dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1363424468 num_examples: 14904 download_size: 1328309729 dataset_size: 1363424468 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image language: - en size_categories: - 10K<n<100K images_reference: - KREAM (https://kream.co.kr/) pretty_name: KREAM Product Blip Capitions tags: - fashion - cloth - computer-vision --- ## KREAM Product Blip Captions Dataset Information  **KREAM Product Blip Captions Dataset** is a dataset card for finetuning a text-to-image generative model collected from [KREAM](https://kream.co.kr/), one of the best online-resell market in Korea. This dataset consists of 'image' and 'text' key pairs. The format of 'text' is 'category (e.g. outer), product original name (e.g. The North Face 1996 Eco Nuptse Jacket Black), blip captions (e.g. a photography of the north face black down jacket)'. You can easily construct this dataset and finetune stable diffusion from scratch using [fashion-product-generator](https://github.com/hahminlew/fashion-product-generator). ## Usage ``` from datasets import load_dataset dataset = load_dataset("hahminlew/kream-product-blip-captions", split="train") sample = dataset[0] display(sample["image"].resize((256, 256))) print(sample["text"]) ```  ``` outer, The North Face 1996 Eco Nuptse Jacket Black, a photography of the north face black down jacket ``` ## Application You can inference the finetuned Stable Diffusion XL with LoRA based on the dataset here: [hahminlew/sdxl-kream-model-lora-2.0](https://huggingface.co/hahminlew/sdxl-kream-model-lora-2.0) ## Citation If you use KREAM Product Dataset in your research or projects, please cite it as: ``` @misc{lew2023kream, author = {Lew, Hah Min}, title = {KREAM Product BLIP Captions}, year={2023}, howpublished= {\url{https://huggingface.co/datasets/hahminlew/kream-product-blip-captions/}} } ```

zoheb/sketch-scene

zoheb

--- license: cc-by-nc-sa-4.0 language: - en language_creators: - machine-generated multilinguality: - monolingual pretty_name: 'Sketch Scene Descriptions' size_categories: - n<10K source_datasets: - FS-COCO tags: [] task_categories: - text-to-image task_ids: [] --- # Dataset Card for Sketch Scene Descriptions _Dataset used to train [Sketch Scene text to image model]()_ We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description. For each row, the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided. ## Citation If you use this dataset, please cite it as: ``` @inproceedings{fscoco, title={FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context.} author={Chowdhury, Pinaki Nath and Sain, Aneeshan and Bhunia, Ayan Kumar and Xiang, Tao and Gryaditskaya, Yulia and Song, Yi-Zhe}, booktitle={ECCV}, year={2022} } ```

jinaai/fashion-captions-de

jinaai

--- license: cc-by-4.0 dataset_info: features: - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 282285477 num_examples: 10000 - name: test num_bytes: 56612023.875 num_examples: 2001 download_size: 320681179 dataset_size: 338897500.875 task_categories: - text-to-image multilinguality: - monolingual language: - de size_categories: - 1K<n<10K source_datasets: - original pretty_name: Fashion12k DE --- <br><br> <p align="center"> <img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> </p> <p align="center"> <b>The data offered by Jina AI, Finetuner team.</b> </p> ## Summary This dataset is a German-language dataset based on the [Fashion12K](https://github.com/Toloka/Fashion12K_german_queries) dataset, which originally contains both English and German text descriptions for each item. This dataset was used to to finetuner CLIP using the [Finetuner](https://finetuner.jina.ai/) tool. ## Fine-tuning Please refer to our documentation: [Multilingual Text-to-Image Search with MultilingualCLIP](https://finetuner.jina.ai/notebooks/multilingual_text_to_image/) and blog [Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models](https://jina.ai/news/improving-search-quality-non-english-queries-fine-tuned-multilingual-clip-models/) ## Instances Each data point consists of a 'text' and an 'image' field, where the 'text' field describes an item of clothing in German, and the 'image' field contains and image of that item of clothing. ## Fields - 'text': A string describing the item of clothing. - 'image': A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. `dataset[0]["image"]` should always be preferred over dataset["image"][0]. ## Splits | | train | test | |------------|-------|------| | # of items | 10000 | 2001 | ## Source Images were sampled from the [Fashion200K dataset](https://github.com/xthan/fashion-200k). ## Annotations Data was annotated using [Toloka](https://toloka.ai/). See their site for more details. ## Licensing Information This work is licensed under a Creative Commons Attribution 4.0 International License. ## Contributors Thanks to contributors from [Jina AI](https://jina.ai) and [Toloka](https://toloka.ai) for adding this dataset.

rezashkv/styles

rezashkv

--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - text-to-image pretty_name: styles dataset_info: features: - name: image dtype: image - name: caption dtype: string - name: style dtype: string splits: - name: train num_bytes: 14266004986.0 num_examples: 60000 download_size: 22917031463 dataset_size: 14266004986.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Styled Image Dataset Generated with FLUX.1-dev and LoRAs from the community Access the generation scripts [here](https://github.com/rezashkv/flux_style). ## Dataset Description This dataset contains **60,000 text-image-pairs**. The images are generated by adding trained **LoRA** weights to the diffusion transformer model [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The images were created using 6 different style models, with each style having its own set of **10,000 images**. Each style includes **10,000 captions** sampled from the [phiyodr/coco2017](https://huggingface.co/datasets/phiyodr/coco2017) including a trigger phrase used for LoRA generation. ## LoRA Models Used The images in each folder were generated by applying the following LoRA models to the [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) diffusion model: 1. **Aquarelle**: - LoRA Name/Path: [SebastianBodza/Flux_Aquarell_Watercolor_v2](https://huggingface.co/SebastianBodza/Flux_Aquarell_Watercolor_v2) - Description: Creates watercolor-style images, emphasizing a fluid, soft, and painterly effect. - Trigger Words: AQUACOLTOK in combination with white background 2. **Frosting Lane**: - LoRA Name/Path: [alvdansen/frosting_lane_flux](https://huggingface.co/alvdansen/frosting_lane_flux) - Description: Applies a whimsical, pastel style, often reminiscent of a dreamlike or frosted aesthetic. - Trigger Words: frstingln illustration 3. **Half Illustration**: - LoRA Name/Path: [davisbro/half_illustration](https://huggingface.co/davisbro/half_illustration) - Description: Blends elements of illustration and realism, producing images that are semi-photo-realistic and semi-graphic-illustrative. - Trigger Words: in the style of TOK 4. **PS1**: - LoRA Name/Path: [veryVANYA/ps1-style-flux](https://huggingface.co/veryVANYA/ps1-style-flux) - Description: Renders images in a style reminiscent of early PlayStation 1 graphics, with a retro and pixelated look. - Trigger Words: ps1 game screenshot 5. **Tarot**: - LoRA Name/Path: [multimodalart/flux-tarot-v1](https://huggingface.co/multimodalart/flux-tarot-v1) - Description: Generates images in a style inspired by Tarot cards, with symbolic and mystical elements. - Trigger Words: in the style of TOK a trtcrd tarot style 6. **Yarn**: - LoRA Name/Path: [linoyts/yarn_art_Flux_LoRA](https://huggingface.co/linoyts/yarn_art_Flux_LoRA) - Description: Renders images in a yarn art style. - Trigger Words: yarn art style --- ## Dataset Structure Each image in this dataset has a filename corresponding to the unique image ID from the [COCO2017](https://cocodataset.org/#home) dataset and a caption. The images are organized by style, and the dataset is divided into 6 folders, each representing a different style. The dataset is organized as follows: ``` dataset_root ├── aquarelle/ │ ├── 125971.png │ ├── 143359.png │ └── ... ├── frosting_lane/ │ ├── 125971.png │ ├── 143359.png │ └── ... ├── half_illustration/ │ ├── 125971.png │ ├── 143359.png │ └── ... ├── ps1/ │ ├── 125971.png │ ├── 143359.png │ └── ... ├── tarot/ │ ├── 125971.png │ ├── 143359.png │ └── ... └── yarn/ ├── 125971.png ├── 143359.png └── ... ``` The image IDs are consistent across all folders, meaning `125971.png` in the `tarot` folder corresponds to the same caption as `125971.png` in the `aquarelle` folder, and so on, albeit including the trigger words. --- ## Dataset Details - **Total Image-Text pairs**: 60,000 - **Images per Style**: 10,000 - **File Format**: PNG - **Resolution**: 512*512 - **Source Captions**: 10,000 captions sampled from the COCO2017 dataset. --- ## Usage To load and use this dataset, you can do so directly via the Hugging Face `datasets` library: ```python from datasets import load_dataset dataset = load_dataset("rezashkv/styles") ``` ## Example Use Cases - **Style Transfer**: Researchers can use this dataset to study how different LoRA models affect the output of generative image models and compare stylistic differences. - **Multimodal Research**: This dataset is ideal for experiments involving text-to-image generation, as it demonstrates how varying models generate diverse visual outputs from the same text input. - **Dataset Augmentation**: The 60,000 images across 6 styles can be used to augment existing image datasets with stylistic diversity for training machine learning models, especially in tasks like image classification, style transfer, and artistic generation. - **Artistic Exploration**: Artists or creators can explore how different styles interact with the same content, leveraging this dataset for creative purposes. - **Image Generation Benchmarking**: This dataset can be used for benchmarking the performance and stylistic accuracy of different image generation models. --- ## License This dataset is released under [apache-2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md). Please ensure that you follow the licensing terms of the original models and datasets used in this project: - [COCO2017](https://huggingface.co/datasets/phiyodr/coco2017) - [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) - [SebastianBodza/Flux_Aquarell_Watercolor_v2](https://huggingface.co/SebastianBodza/Flux_Aquarell_Watercolor_v2) - [alvdansen/frosting_lane_flux](https://huggingface.co/alvdansen/frosting_lane_flux) - [davisbro/half_illustration](https://huggingface.co/davisbro/half_illustration) - [veryVANYA/ps1-style-flux](https://huggingface.co/veryVANYA/ps1-style-flux) - [multimodalart/flux-tarot-v1](https://huggingface.co/multimodalart/flux-tarot-v1) - [linoyts/yarn_art_Flux_LoRA](https://huggingface.co/linoyts/yarn_art_Flux_LoRA) --- ## Citation If you use this dataset in your work, please cite our paper: ``` @article{ganjdanesh2024not, title={Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models}, author={Ganjdanesh, Alireza and Shirkavand, Reza and Gao, Shangqian and Huang, Heng}, journal={arXiv preprint arXiv:2406.12042}, year={2024} } ``` --- ## Acknowledgments We would like to thank the developers and contributors of the following models and datasets that made this project possible: - [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) - LoRA Models: - [SebastianBodza/Flux_Aquarell_Watercolor_v2](https://huggingface.co/SebastianBodza/Flux_Aquarell_Watercolor_v2) - [alvdansen/frosting_lane_flux](https://huggingface.co/alvdansen/frosting_lane_flux) - [davisbro/half_illustration](https://huggingface.co/davisbro/half_illustration) - [veryVANYA/ps1-style-flux](https://huggingface.co/veryVANYA/ps1-style-flux) - [multimodalart/flux-tarot-v1](https://huggingface.co/multimodalart/flux-tarot-v1) - [linoyts/yarn_art_Flux_LoRA](https://huggingface.co/linoyts/yarn_art_Flux_LoRA) - [COCO2017](https://huggingface.co/datasets/phiyodr/coco2017)

mlfoundations/dclm-pool-1b-1x

mlfoundations

--- license: cc-by-4.0 ---

bigdata-pw/TheSimpsons

bigdata-pw

--- license: odc-by pretty_name: The Simpsons task_categories: - image-classification - text-to-image - image-to-text tags: - tv - image --- # Dataset Card for TheSimpsons Frames from The Simpsons. ## Dataset Details ### Dataset Description Frames from each episode of The Simpsons. Entries include image and Florence-2-large captions; `caption`, `detailed_caption` and `more_detailed_caption`. Frames were extracted using `ffmepg` with "black bar removal" applied with ffmpeg's `cropdetect`. - **Curated by:** hlky - **License:** Open Data Commons Attribution License (ODC-By) v1.0 # Citation Information ``` @misc{TheSimpsons, author = {hlky}, title = {TheSimpsons}, year = {2024}, publisher = {hlky}, journal = {Hugging Face repository}, howpublished = {\url{[https://huggingface.co/datasets/bigdata-pw/TheSimpsons](https://huggingface.co/datasets/bigdata-pw/TheSimpsons)}} } ``` ## Attribution Information ``` Contains information from [TheSimpsons](https://huggingface.co/datasets/bigdata-pw/TheSimpsons) which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). ```

applied-ai-018/pretraining_v1-omega_books

applied-ai-018

--- dataset_info: config_name: CC-MAIN-2013-20 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 splits: - name: train num_bytes: 235476901236 num_examples: 51901183 download_size: 138494178972 dataset_size: 235476901236 configs: - config_name: CC-MAIN-2013-20 data_files: - split: train path: CC-MAIN-2013-20/train-* ---

cschell/xr-motion-dataset-catalogue

cschell

--- language: - en tags: - kinematic research - XR user motions - VR user motions - AR user motions - motions pretty_name: XR Motion Dataset Catalogue --- # XR Motion Dataset Catalogue ## Overview The XR Motion Dataset Catalogue, accompanying our paper "Navigating the Kinematic Maze: A Comprehensive Guide to XR Motion Dataset Standards," standardizes and simplifies access to Extended Reality (XR) motion datasets. The catalogue represents our initiative to streamline the usage of kinematic data in XR research by aligning various datasets to a consistent format and structure. ### Dataset Specifications All datasets in this catalogue have been standardized with the following specifications: - **Coordinate System:** X (Right), Y (Up), Z (Forward) - **Rotation Representation:** Quaternions - **Units of Measurement:** Centimeters for spatial data - **Time Encoding:** Milliseconds for time-related data These specifications ensure uniformity and comparability across all datasets in the catalogue. ### Conversion Scripts Repository The alignment of datasets was facilitated by a series of conversion scripts, which are available in our GitHub repository: [XR Motion Dataset Conversion Scripts](https://github.com/cschell/xr-motion-dataset-conversion-scripts). These scripts detail the process of aligning attribute names, coordinate systems, rotation representations, units of measurement, and time encoding. ### Included Datasets The catalogue includes the following datasets: 1. [LiebersBeatSaber23](https://doi.org/10.1145/3611659.3615696) 2. [Boxrr23](https://doi.org/10.25350/B5NP4V) – *edit 2024-05-04: we are still working on providing the aligned version – in the meantime you find the original version [here](https://huggingface.co/datasets/cschell/boxrr-23/)* 3. BOXRR24 – *WIP: we are currently working on the next version of the BOXRR-23 dataset, which will include significantly more user – we do our best to make it available later this year* 4. [LiebersHand22](https://doi.org/10.1080/10447318.2022.2120845) 5. [LiebersLabStudy21](https://doi.org/10.1145/3411764.3445528) 6. [MooreCrossDomain23](https://doi.org/10.1109/ISMAR59233.2023.00054) 7. <del>[RMillerBall22](https://github.com/Terascale-All-sensing-Research-Studio/VR-Biometric-Authentication)</del> *request for permissions pending* 8. [VrNet](http://arxiv.org/abs/2306.03381) 9. [WhoIsAlyx](https://doi.org/10.3389/frvir.2023.1272234) ## Installation and Usage ### Loading the Dataset with Hugging Face `datasets` Library To load a dataset from the catalogue, use the `datasets` library in Python. For example, to load the `WhoIsAlyx` dataset: ```python from datasets import load_dataset dataset = load_dataset("cschell/xr-motion-dataset-catalogue", "who_is_alyx", trust_remote_code=True) ``` ### Loading Individual Recordings with Pandas To load individual recordings, you can use `pandas`. Here's an example: ```python import pandas as pd file_url_path = "hf://datasets/cschell/xr-motion-dataset-catalogue/who_is_alyx/player_02/2022-01-07.parquet" recording = pd.read_parquet(file_url_path) ``` ## Contributing and Feedback Contributions and feedback are welcome to enhance the XR Motion Dataset Catalogue. Feel free to open a pull request or contact us directly. <!-- ## Citation If you use the XR Motion Dataset Catalogue in your research, please cite our paper: ``` @article{your_paper_identifier, title={Navigating the Kinematic Maze: A Comprehensive Guide to XR Motion Dataset Standards}, author={Your Name and Other Authors}, journal={Journal Name}, year={Year} } ``` -->

bigscience/evaluation-results

bigscience

--- pretty_name: evaluation-results size_categories: - 100M<n<1B task_categories: - other --- # BigScience BLOOM Evaluation Results This repository contains evaluation results & original predictions of BLOOM & friends. ## Usage You can load numeric results via: ```python from datasets import load_dataset ds = load_dataset("bigscience/evaluation-results", "bloom") ``` If it takes too long, it may be faster to clone the repository and load the data from disk: ```python !git clone https://huggingface.co/datasets/bigscience/evaluation-results ds = load_dataset("evaluation-results", "bloom") ``` For example generations (.jsonl files), you need to manually browse the repository. ## Structure For `bigsciencelmevalharness`, `lmevalharness` & `codeeval` evaluation_frameworks the structure is: `model_name > evaluation_framework > checkpoint_type > dataset_name > data` ## Evaluation Procedure - `bigsciencelmevalharness` files were created using the below: - https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/291 - https://github.com/bigscience-workshop/lm-evaluation-harness - `lmevalharness` files were created using the below: - https://github.com/bigscience-workshop/Megatron-DeepSpeed - https://github.com/EleutherAI/lm-evaluation-harness - `codeeval` files were created using the HumanEval code dataset with the below: - https://github.com/loubnabnl/bloom-code-evaluation

oscar-corpus/oscar

oscar-corpus

--- pretty_name: OSCAR annotations_creators: - no-annotation language_creators: - found language: - af - als - am - an - ar - arz - as - ast - av - az - azb - ba - bar - bcl - be - bg - bh - bn - bo - bpy - br - bs - bxr - ca - cbk - ce - ceb - ckb - cs - cv - cy - da - de - diq - dsb - dv - el - eml - en - eo - es - et - eu - fa - fi - fr - frr - fy - ga - gd - gl - gn - gom - gu - he - hi - hr - hsb - ht - hu - hy - ia - id - ie - ilo - io - is - it - ja - jbo - jv - ka - kk - km - kn - ko - krc - ku - kv - kw - ky - la - lb - lez - li - lmo - lo - lrc - lt - lv - mai - mg - mhr - min - mk - ml - mn - mr - mrj - ms - mt - mwl - my - myv - mzn - nah - nap - nds - ne - new - nl - nn - 'no' - oc - or - os - pa - pam - pl - pms - pnb - ps - pt - qu - rm - ro - ru - sa - sah - scn - sd - sh - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - tg - th - tk - tl - tr - tt - tyv - ug - uk - ur - uz - vec - vi - vo - wa - war - wuu - xal - xmf - yi - yo - yue - zh license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M - 100M<n<1B - 10K<n<100K - 10M<n<100M - 1K<n<10K - 1M<n<10M - n<1K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: oscar dataset_info: - config_name: unshuffled_deduplicated_af features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 171320914 num_examples: 130640 download_size: 65989254 dataset_size: 171320914 - config_name: unshuffled_deduplicated_als features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2915912 num_examples: 4518 download_size: 1263294 dataset_size: 2915912 - config_name: unshuffled_deduplicated_arz features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 34893248 num_examples: 79928 download_size: 10027493 dataset_size: 34893248 - config_name: unshuffled_deduplicated_an features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 842246 num_examples: 2025 download_size: 133373 dataset_size: 842246 - config_name: unshuffled_deduplicated_ast features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2150022 num_examples: 5343 download_size: 856177 dataset_size: 2150022 - config_name: unshuffled_deduplicated_ba features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 93623739 num_examples: 27050 download_size: 25983491 dataset_size: 93623739 - config_name: unshuffled_deduplicated_am features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 215618603 num_examples: 43102 download_size: 61347279 dataset_size: 215618603 - config_name: unshuffled_deduplicated_as features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 73989818 num_examples: 9212 download_size: 15513004 dataset_size: 73989818 - config_name: unshuffled_deduplicated_azb features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 20001183 num_examples: 9985 download_size: 5191704 dataset_size: 20001183 - config_name: unshuffled_deduplicated_be features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1077152244 num_examples: 307405 download_size: 306700943 dataset_size: 1077152244 - config_name: unshuffled_deduplicated_bo features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 144506264 num_examples: 15762 download_size: 22365048 dataset_size: 144506264 - config_name: unshuffled_deduplicated_bxr features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 11325 num_examples: 36 download_size: 3666 dataset_size: 11325 - config_name: unshuffled_deduplicated_ceb features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 24439249 num_examples: 26145 download_size: 7124786 dataset_size: 24439249 - config_name: unshuffled_deduplicated_az features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1526935070 num_examples: 626796 download_size: 521744076 dataset_size: 1526935070 - config_name: unshuffled_deduplicated_bcl features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 900 num_examples: 1 download_size: 594 dataset_size: 900 - config_name: unshuffled_deduplicated_cy features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 140412555 num_examples: 98225 download_size: 53629697 dataset_size: 140412555 - config_name: unshuffled_deduplicated_dsb features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 7589 num_examples: 37 download_size: 3640 dataset_size: 7589 - config_name: unshuffled_deduplicated_bn features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 6233041155 num_examples: 1114481 download_size: 1257218381 dataset_size: 6233041155 - config_name: unshuffled_deduplicated_bs features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 125977 num_examples: 702 download_size: 38669 dataset_size: 125977 - config_name: unshuffled_deduplicated_ce features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 7021674 num_examples: 2984 download_size: 1862792 dataset_size: 7021674 - config_name: unshuffled_deduplicated_cv features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 27359554 num_examples: 10130 download_size: 7461982 dataset_size: 27359554 - config_name: unshuffled_deduplicated_diq features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 161 num_examples: 1 download_size: 331 dataset_size: 161 - config_name: unshuffled_deduplicated_eml features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 24657 num_examples: 80 download_size: 10055 dataset_size: 24657 - config_name: unshuffled_deduplicated_et features: - 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config_name: unshuffled_original_es features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 298492270636 num_examples: 88199221 download_size: 106039137656 dataset_size: 298492270636 - config_name: unshuffled_original_fi features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 28571419204 num_examples: 8557453 download_size: 9970837279 dataset_size: 28571419204 - config_name: unshuffled_original_ga features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 92369035 num_examples: 83223 download_size: 29262282 dataset_size: 92369035 - config_name: unshuffled_original_gom features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2257169 num_examples: 640 download_size: 442950 dataset_size: 2257169 - config_name: unshuffled_original_hr features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 243829069 num_examples: 582219 download_size: 79417804 dataset_size: 243829069 - config_name: unshuffled_original_hy features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 3939672772 num_examples: 659430 download_size: 897364024 dataset_size: 3939672772 - config_name: unshuffled_original_ilo features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 925809 num_examples: 2638 download_size: 267451 dataset_size: 925809 - config_name: unshuffled_original_ja features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 232216718556 num_examples: 62721527 download_size: 79564645083 dataset_size: 232216718556 - config_name: unshuffled_original_kk features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2833778199 num_examples: 524591 download_size: 615067761 dataset_size: 2833778199 - config_name: unshuffled_original_krc features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2688672 num_examples: 1581 download_size: 656496 dataset_size: 2688672 - config_name: unshuffled_original_ky features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 630794622 num_examples: 146993 download_size: 152636608 dataset_size: 630794622 - config_name: unshuffled_original_li features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 31312 num_examples: 137 download_size: 11793 dataset_size: 31312 - config_name: unshuffled_original_lt features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 9445278312 num_examples: 2977757 download_size: 3439789726 dataset_size: 9445278312 - config_name: unshuffled_original_mhr features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 7553453 num_examples: 3212 download_size: 1834912 dataset_size: 7553453 - config_name: unshuffled_original_mn features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2332897881 num_examples: 395605 download_size: 472357548 dataset_size: 2332897881 - config_name: unshuffled_original_mt features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 24470330 num_examples: 26598 download_size: 7533204 dataset_size: 24470330 - config_name: unshuffled_original_mzn features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 720229 num_examples: 1055 download_size: 177817 dataset_size: 720229 - config_name: unshuffled_original_ne features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1866852959 num_examples: 299938 download_size: 355291639 dataset_size: 1866852959 - config_name: unshuffled_original_no features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 8652054976 num_examples: 5546211 download_size: 3106155643 dataset_size: 8652054976 - config_name: unshuffled_original_pa features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 801167879 num_examples: 127467 download_size: 164207256 dataset_size: 801167879 - config_name: unshuffled_original_pnb features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 12039418 num_examples: 4599 download_size: 3215579 dataset_size: 12039418 - config_name: unshuffled_original_rm features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 8027 num_examples: 41 download_size: 2691 dataset_size: 8027 - config_name: unshuffled_original_sah features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 43817239 num_examples: 22301 download_size: 9079982 dataset_size: 43817239 - config_name: unshuffled_original_si features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1469374795 num_examples: 203082 download_size: 310935021 dataset_size: 1469374795 - config_name: unshuffled_original_sq features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2440834375 num_examples: 672077 download_size: 861831806 dataset_size: 2440834375 - config_name: unshuffled_original_sw features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 14073775 num_examples: 41986 download_size: 3712739 dataset_size: 14073775 - config_name: unshuffled_original_th features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 38289228753 num_examples: 6064129 download_size: 7377469078 dataset_size: 38289228753 - config_name: unshuffled_original_tt features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 703412782 num_examples: 135923 download_size: 151056507 dataset_size: 703412782 - config_name: unshuffled_original_ur features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2802270961 num_examples: 638596 download_size: 712607161 dataset_size: 2802270961 - config_name: unshuffled_original_vo features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2118909 num_examples: 3366 download_size: 307184 dataset_size: 2118909 - config_name: unshuffled_original_xal features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 116043 num_examples: 39 download_size: 32117 dataset_size: 116043 - config_name: unshuffled_original_yue features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 3899 num_examples: 11 download_size: 647 dataset_size: 3899 - config_name: unshuffled_original_en features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2525437912097 num_examples: 455994980 download_size: 903830686146 dataset_size: 2525437912097 - config_name: unshuffled_original_eu features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 894836188 num_examples: 506883 download_size: 248190119 dataset_size: 894836188 - config_name: unshuffled_original_frr features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 4507 num_examples: 7 download_size: 527 dataset_size: 4507 - config_name: unshuffled_original_gl features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 656477422 num_examples: 544388 download_size: 235384299 dataset_size: 656477422 - config_name: unshuffled_original_he features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 21113706929 num_examples: 3808397 download_size: 5660026441 dataset_size: 21113706929 - config_name: unshuffled_original_ht features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 4083 num_examples: 13 download_size: 590 dataset_size: 4083 - config_name: unshuffled_original_id features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 32317679452 num_examples: 16236463 download_size: 10596988488 dataset_size: 32317679452 - config_name: unshuffled_original_is features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1524936467 num_examples: 625673 download_size: 533034495 dataset_size: 1524936467 - config_name: unshuffled_original_jv features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 691812 num_examples: 1445 download_size: 219246 dataset_size: 691812 - config_name: unshuffled_original_kn features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1763625096 num_examples: 350363 download_size: 342155433 dataset_size: 1763625096 - config_name: unshuffled_original_kv features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2379758 num_examples: 1549 download_size: 400725 dataset_size: 2379758 - config_name: unshuffled_original_lb features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 30595156 num_examples: 34807 download_size: 10725552 dataset_size: 30595156 - config_name: unshuffled_original_lo features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 182361509 num_examples: 52910 download_size: 33916738 dataset_size: 182361509 - config_name: unshuffled_original_mai features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 325990 num_examples: 123 download_size: 5563 dataset_size: 325990 - config_name: unshuffled_original_mk features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2202480390 num_examples: 437871 download_size: 508239918 dataset_size: 2202480390 - config_name: unshuffled_original_mrj features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1165977 num_examples: 757 download_size: 303447 dataset_size: 1165977 - config_name: unshuffled_original_my features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2021872493 num_examples: 232329 download_size: 369850157 dataset_size: 2021872493 - config_name: unshuffled_original_nap features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 17839 num_examples: 73 download_size: 5023 dataset_size: 17839 - config_name: unshuffled_original_nl features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 83230965323 num_examples: 34682142 download_size: 29352811750 dataset_size: 83230965323 - config_name: unshuffled_original_or features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 260151226 num_examples: 59463 download_size: 49834443 dataset_size: 260151226 - config_name: unshuffled_original_pl features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 117121370605 num_examples: 35440972 download_size: 42884898947 dataset_size: 117121370605 - config_name: unshuffled_original_pt features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 132635490139 num_examples: 42114520 download_size: 47257949300 dataset_size: 132635490139 - config_name: unshuffled_original_ru features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1241627166551 num_examples: 161836003 download_size: 319755378587 dataset_size: 1241627166551 - config_name: unshuffled_original_sd features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 364256869 num_examples: 44280 download_size: 90621520 dataset_size: 364256869 - config_name: unshuffled_original_sl features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2675665926 num_examples: 1746604 download_size: 956197026 dataset_size: 2675665926 - config_name: unshuffled_original_su features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 225627 num_examples: 805 download_size: 59643 dataset_size: 225627 - config_name: unshuffled_original_te features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2611548765 num_examples: 475703 download_size: 522470115 dataset_size: 2611548765 - config_name: unshuffled_original_tl features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 606295665 num_examples: 458206 download_size: 204895159 dataset_size: 606295665 - config_name: unshuffled_original_ug features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 127419368 num_examples: 22255 download_size: 27923925 dataset_size: 127419368 - config_name: unshuffled_original_vec features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 19182 num_examples: 73 download_size: 7672 dataset_size: 19182 - config_name: unshuffled_original_war features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 2682430 num_examples: 9760 download_size: 644576 dataset_size: 2682430 - config_name: unshuffled_original_yi features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 147601654 num_examples: 59364 download_size: 33337157 dataset_size: 147601654 config_names: - unshuffled_deduplicated_af - unshuffled_deduplicated_als - unshuffled_deduplicated_am - unshuffled_deduplicated_an - unshuffled_deduplicated_ar - unshuffled_deduplicated_arz - unshuffled_deduplicated_as - unshuffled_deduplicated_ast - unshuffled_deduplicated_av - unshuffled_deduplicated_az - unshuffled_deduplicated_azb - unshuffled_deduplicated_ba - unshuffled_deduplicated_bar - unshuffled_deduplicated_bcl - unshuffled_deduplicated_be - unshuffled_deduplicated_bg - unshuffled_deduplicated_bh - unshuffled_deduplicated_bn - unshuffled_deduplicated_bo - unshuffled_deduplicated_bpy - unshuffled_deduplicated_br - unshuffled_deduplicated_bs - unshuffled_deduplicated_bxr - unshuffled_deduplicated_ca - unshuffled_deduplicated_cbk - unshuffled_deduplicated_ce - unshuffled_deduplicated_ceb - unshuffled_deduplicated_ckb - unshuffled_deduplicated_cs - unshuffled_deduplicated_cv - unshuffled_deduplicated_cy - unshuffled_deduplicated_da - unshuffled_deduplicated_de - unshuffled_deduplicated_diq - unshuffled_deduplicated_dsb - unshuffled_deduplicated_dv - unshuffled_deduplicated_el - unshuffled_deduplicated_eml - unshuffled_deduplicated_en - unshuffled_deduplicated_eo - unshuffled_deduplicated_es - unshuffled_deduplicated_et - unshuffled_deduplicated_eu - unshuffled_deduplicated_fa - unshuffled_deduplicated_fi - unshuffled_deduplicated_fr - unshuffled_deduplicated_frr - unshuffled_deduplicated_fy - unshuffled_deduplicated_ga - unshuffled_deduplicated_gd - unshuffled_deduplicated_gl - unshuffled_deduplicated_gn - unshuffled_deduplicated_gom - unshuffled_deduplicated_gu - unshuffled_deduplicated_he - unshuffled_deduplicated_hi - unshuffled_deduplicated_hr - unshuffled_deduplicated_hsb - unshuffled_deduplicated_ht - unshuffled_deduplicated_hu - unshuffled_deduplicated_hy - unshuffled_deduplicated_ia - unshuffled_deduplicated_id - unshuffled_deduplicated_ie - unshuffled_deduplicated_ilo - unshuffled_deduplicated_io - unshuffled_deduplicated_is - unshuffled_deduplicated_it - unshuffled_deduplicated_ja - unshuffled_deduplicated_jbo - unshuffled_deduplicated_jv - unshuffled_deduplicated_ka - unshuffled_deduplicated_kk - unshuffled_deduplicated_km - unshuffled_deduplicated_kn - unshuffled_deduplicated_ko - unshuffled_deduplicated_krc - unshuffled_deduplicated_ku - unshuffled_deduplicated_kv - unshuffled_deduplicated_kw - unshuffled_deduplicated_ky - unshuffled_deduplicated_la - unshuffled_deduplicated_lb - unshuffled_deduplicated_lez - unshuffled_deduplicated_li - unshuffled_deduplicated_lmo - unshuffled_deduplicated_lo - unshuffled_deduplicated_lrc - unshuffled_deduplicated_lt - unshuffled_deduplicated_lv - unshuffled_deduplicated_mai - unshuffled_deduplicated_mg - unshuffled_deduplicated_mhr - unshuffled_deduplicated_min - unshuffled_deduplicated_mk - unshuffled_deduplicated_ml - unshuffled_deduplicated_mn - unshuffled_deduplicated_mr - unshuffled_deduplicated_mrj - unshuffled_deduplicated_ms - unshuffled_deduplicated_mt - unshuffled_deduplicated_mwl - unshuffled_deduplicated_my - unshuffled_deduplicated_myv - unshuffled_deduplicated_mzn - unshuffled_deduplicated_nah - unshuffled_deduplicated_nap - unshuffled_deduplicated_nds - unshuffled_deduplicated_ne - unshuffled_deduplicated_new - unshuffled_deduplicated_nl - unshuffled_deduplicated_nn - unshuffled_deduplicated_no - unshuffled_deduplicated_oc - unshuffled_deduplicated_or - unshuffled_deduplicated_os - unshuffled_deduplicated_pa - unshuffled_deduplicated_pam - unshuffled_deduplicated_pl - unshuffled_deduplicated_pms - unshuffled_deduplicated_pnb - unshuffled_deduplicated_ps - unshuffled_deduplicated_pt - unshuffled_deduplicated_qu - unshuffled_deduplicated_rm - unshuffled_deduplicated_ro - unshuffled_deduplicated_ru - unshuffled_deduplicated_sa - unshuffled_deduplicated_sah - unshuffled_deduplicated_scn - unshuffled_deduplicated_sd - unshuffled_deduplicated_sh - unshuffled_deduplicated_si - unshuffled_deduplicated_sk - unshuffled_deduplicated_sl - unshuffled_deduplicated_so - unshuffled_deduplicated_sq - unshuffled_deduplicated_sr - unshuffled_deduplicated_su - unshuffled_deduplicated_sv - unshuffled_deduplicated_sw - unshuffled_deduplicated_ta - unshuffled_deduplicated_te - unshuffled_deduplicated_tg - unshuffled_deduplicated_th - unshuffled_deduplicated_tk - unshuffled_deduplicated_tl - unshuffled_deduplicated_tr - unshuffled_deduplicated_tt - unshuffled_deduplicated_tyv - unshuffled_deduplicated_ug - unshuffled_deduplicated_uk - unshuffled_deduplicated_ur - unshuffled_deduplicated_uz - unshuffled_deduplicated_vec - unshuffled_deduplicated_vi - unshuffled_deduplicated_vo - unshuffled_deduplicated_wa - unshuffled_deduplicated_war - unshuffled_deduplicated_wuu - unshuffled_deduplicated_xal - unshuffled_deduplicated_xmf - unshuffled_deduplicated_yi - unshuffled_deduplicated_yo - unshuffled_deduplicated_yue - unshuffled_deduplicated_zh - unshuffled_original_af - unshuffled_original_als - unshuffled_original_am - unshuffled_original_an - unshuffled_original_ar - unshuffled_original_arz - unshuffled_original_as - unshuffled_original_ast - unshuffled_original_av - unshuffled_original_az - unshuffled_original_azb - unshuffled_original_ba - unshuffled_original_bar - unshuffled_original_bcl - unshuffled_original_be - unshuffled_original_bg - unshuffled_original_bh - unshuffled_original_bn - unshuffled_original_bo - unshuffled_original_bpy - unshuffled_original_br - unshuffled_original_bs - unshuffled_original_bxr - unshuffled_original_ca - unshuffled_original_cbk - unshuffled_original_ce - unshuffled_original_ceb - unshuffled_original_ckb - unshuffled_original_cs - unshuffled_original_cv - unshuffled_original_cy - unshuffled_original_da - unshuffled_original_de - unshuffled_original_diq - unshuffled_original_dsb - unshuffled_original_dv - unshuffled_original_el - unshuffled_original_eml - unshuffled_original_en - unshuffled_original_eo - unshuffled_original_es - unshuffled_original_et - unshuffled_original_eu - unshuffled_original_fa - unshuffled_original_fi - unshuffled_original_fr - unshuffled_original_frr - unshuffled_original_fy - unshuffled_original_ga - unshuffled_original_gd - unshuffled_original_gl - unshuffled_original_gn - unshuffled_original_gom - unshuffled_original_gu - unshuffled_original_he - unshuffled_original_hi - unshuffled_original_hr - unshuffled_original_hsb - unshuffled_original_ht - unshuffled_original_hu - unshuffled_original_hy - unshuffled_original_ia - unshuffled_original_id - unshuffled_original_ie - unshuffled_original_ilo - unshuffled_original_io - unshuffled_original_is - unshuffled_original_it - unshuffled_original_ja - unshuffled_original_jbo - unshuffled_original_jv - unshuffled_original_ka - unshuffled_original_kk - unshuffled_original_km - unshuffled_original_kn - unshuffled_original_ko - unshuffled_original_krc - unshuffled_original_ku - unshuffled_original_kv - unshuffled_original_kw - unshuffled_original_ky - unshuffled_original_la - unshuffled_original_lb - unshuffled_original_lez - unshuffled_original_li - unshuffled_original_lmo - unshuffled_original_lo - unshuffled_original_lrc - unshuffled_original_lt - unshuffled_original_lv - unshuffled_original_mai - unshuffled_original_mg - unshuffled_original_mhr - unshuffled_original_min - unshuffled_original_mk - unshuffled_original_ml - unshuffled_original_mn - unshuffled_original_mr - unshuffled_original_mrj - unshuffled_original_ms - unshuffled_original_mt - unshuffled_original_mwl - unshuffled_original_my - unshuffled_original_myv - unshuffled_original_mzn - unshuffled_original_nah - unshuffled_original_nap - unshuffled_original_nds - unshuffled_original_ne - unshuffled_original_new - unshuffled_original_nl - unshuffled_original_nn - unshuffled_original_no - unshuffled_original_oc - unshuffled_original_or - unshuffled_original_os - unshuffled_original_pa - unshuffled_original_pam - unshuffled_original_pl - unshuffled_original_pms - unshuffled_original_pnb - unshuffled_original_ps - unshuffled_original_pt - unshuffled_original_qu - unshuffled_original_rm - unshuffled_original_ro - unshuffled_original_ru - unshuffled_original_sa - unshuffled_original_sah - unshuffled_original_scn - unshuffled_original_sd - unshuffled_original_sh - unshuffled_original_si - unshuffled_original_sk - unshuffled_original_sl - unshuffled_original_so - unshuffled_original_sq - unshuffled_original_sr - unshuffled_original_su - unshuffled_original_sv - unshuffled_original_sw - unshuffled_original_ta - unshuffled_original_te - unshuffled_original_tg - unshuffled_original_th - unshuffled_original_tk - unshuffled_original_tl - unshuffled_original_tr - unshuffled_original_tt - unshuffled_original_tyv - unshuffled_original_ug - unshuffled_original_uk - unshuffled_original_ur - unshuffled_original_uz - unshuffled_original_vec - unshuffled_original_vi - unshuffled_original_vo - unshuffled_original_wa - unshuffled_original_war - unshuffled_original_wuu - unshuffled_original_xal - unshuffled_original_xmf - unshuffled_original_yi - unshuffled_original_yo - unshuffled_original_yue - unshuffled_original_zh --- # Dataset Card for "oscar" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://oscar-corpus.com](https://oscar-corpus.com) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary OSCAR or **O**pen **S**uper-large **C**rawled [**A**LMAnaCH](https://team.inria.fr/almanach/) co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [goclassy](https://github.com/pjox/goclassy) architecture. Data is distributed by language in both original and deduplicated form. The version here is the original OSCAR 2019 release: https://oscar-project.org/post/oscar-2019/ For more recent versions, visit the [oscar-corpus](https://huggingface.co/oscar-corpus) organization on the Hub: - OSCAR 22.01 (released in January 2022): [oscar-corpus/OSCAR-2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) - OSCAR 21.09 (released in September 2021): [oscar-corpus/OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) ### Supported Tasks and Leaderboards OSCAR is mainly inteded to pretrain language models and word represantations. ### Languages All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR. ## Dataset Structure We show detailed information for all the configurations of the dataset. ### Data Instances <details> <summary>Click to expand the Data/size information for each language (deduplicated)</summary> #### unshuffled_deduplicated_af - **Size of downloaded dataset files:** 65.99 MB - **Size of the generated dataset:** 172.30 MB - **Total amount of disk used:** 238.29 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "aanlyn markte as gevolg van ons voortgesette 'n begrip opsie handel sakeplan pdf terwyl ons steeds die gereelde ons binêre opsies handel" } ``` #### unshuffled_deduplicated_als - **Size of downloaded dataset files:** 1.26 MB - **Size of the generated dataset:** 2.96 MB - **Total amount of disk used:** 4.22 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"De Nazionalpark hät e Flächi vo 170,3 km² und isch dodemit s grösti Naturschutzgebiet vo de Schwiz. Er ligt uf em Gebiet vo de ..." } ``` #### unshuffled_deduplicated_am - **Size of downloaded dataset files:** 61.35 MB - **Size of the generated dataset:** 216.15 MB - **Total amount of disk used:** 277.50 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"አየር መንገዱ ከአዲስ አበባ ወደ ሮም ጣሊያን በማምራት ላይ በነበረበት ጊዜ ረዳት አብራሪው የጉዞውን አቅጣጫ በመቀየር ጄኔቭ አውሮፓላን ማረፊያ በማሳረፍ እጁን ለፖሊስ ሰጥቷል።\\nየኢትዮጵያ መንግስት የ..." } ``` #### unshuffled_deduplicated_an - **Size of downloaded dataset files:** 0.14 MB - **Size of the generated dataset:** 0.85 MB - **Total amount of disk used:** 0.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"واااااااأسفاه الأمم تفتخر ب 0 أمي ووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووو..." } ``` #### unshuffled_deduplicated_ar - **Size of downloaded dataset files:** 9.67 GB - **Size of the generated dataset:** 33.57 GB - **Total amount of disk used:** 43.23 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"مرحبا بك عزيز الزائر نتمنى لك أوقاتاً سعيدة معنا وأن نزداد شرفا بخدمتك ولا تنسى التسجيل معنا لتستفيد بكل جديد\\nأهلا وسهلا بك زا..." } ``` #### unshuffled_deduplicated_arz - **Size of downloaded dataset files:** 10.02 MB - **Size of the generated dataset:** 35.91 MB - **Total amount of disk used:** 45.94 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"بنى عجل : قبيلة من عجل بن لجيم بن صعب بن على بن بكر بن وائل انتقل اغلبهم الى البصرة فى العراق و اصفهان و خراسان فى ايران و اذرب..." } ``` #### unshuffled_deduplicated_as - **Size of downloaded dataset files:** 15.51 MB - **Size of the generated dataset:** 74.07 MB - **Total amount of disk used:** 89.58 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"আমি, এই সংগঠনৰ সদস্য সকলে একেলগ হৈ অসমকে ধৰি ভাৰতৰ উত্তৰ পূৰ্বাঞ্চলৰ অমূল্য কলা-সাংস্কৃতিক সম্পদৰাজি বৃহত্তৰ অষ্ট্ৰেলিয়াৰ সন্মু..." } ``` #### unshuffled_deduplicated_ast - **Size of downloaded dataset files:** 0.86 MB - **Size of the generated dataset:** 2.17 MB - **Total amount of disk used:** 3.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"The Killers llanzaron el so álbum debú, Hot Fuss, en xunu de 2004 nel Reinu Xuníu, al traviés de la discográfica Lizard King, y..." } ``` #### unshuffled_deduplicated_av - **Size of downloaded dataset files:** 0.07 MB - **Size of the generated dataset:** 0.34 MB - **Total amount of disk used:** 0.41 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Жинда малъараб ва божизе бегьулеб рагІудаса кьуризе бегьуларо гьев. Гьес насихІат гьабизе кколелъул бацІцІадаб диналъул рахъалъ..." } ``` #### unshuffled_deduplicated_az - **Size of downloaded dataset files:** 521.74 MB - **Size of the generated dataset:** 1.53 GB - **Total amount of disk used:** 2.05 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"AZTV-Artıq 7 ildir ki, Abşeron rayonu dotasiya almadan bütün xərclərini yerli daxilolmalar hesabına maliyyələşdirir.\\nDünən, 10..." } ``` #### unshuffled_deduplicated_azb - **Size of downloaded dataset files:** 5.19 MB - **Size of the generated dataset:** 20.08 MB - **Total amount of disk used:** 25.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"لعلی ١٣-جو عصرده یاشاییب یاراتمیش گؤرکملی آذربایجان شاعرلریندندیر. ١٢٢٤-جی ایلده تبریزده آنادان اولموشدور، گنج یاشلاریندا تیجار..." } ``` #### unshuffled_deduplicated_ba - **Size of downloaded dataset files:** 25.98 MB - **Size of the generated dataset:** 93.84 MB - **Total amount of disk used:** 119.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Күҙәтеү ҡуласаһы моделен хәҙер Мифтахетдин Аҡмулла исемендәге Башҡорт дәүләт педагогия университетында ла эшләргә мөмкин\\t\\nКүҙ..." } ``` #### unshuffled_deduplicated_bar - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": " vo" } ``` #### unshuffled_deduplicated_bcl - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"& ÿ ó / í 0 - ø û ù ö ú ð ï ú \\u0014 ù þ ô ö í ÷ ò \\u0014 ÷ í ù û ö í \\u0001 û ñ ç þ \\u0001 ð \\u0007 þ ò ñ ñ ò ô \\u0017 û ö ô ÷..." } ``` #### unshuffled_deduplicated_be - **Size of downloaded dataset files:** 306.70 MB - **Size of the generated dataset:** 1.08 GB - **Total amount of disk used:** 1.39 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Брэсцкія ўлады не дазволілі прафсаюзу РЭП правесці пікетаванне ў парку Воінаў-інтэрнацыяналістаў 30 мая 2018 года.\\nСітуацыю пр..." } ``` #### unshuffled_deduplicated_bg - **Size of downloaded dataset files:** 3.85 GB - **Size of the generated dataset:** 14.45 GB - **Total amount of disk used:** 18.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ЖАЛБОПОДАТЕЛЯТ директор на Дирекция „ Обжалване и данъчно-осигурителна практика“- Бургас, редовно призован, се представлява от ..." } ``` #### unshuffled_deduplicated_bh - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.04 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"सुकमा जिला भारत के छत्तीसगढ़ राज्य में एगो जिला बाटे। एकर मुख्यालय सुकमा शहर बाटे। एकर कुल रकबा 5636 वर्ग कि॰मी॰ बाटे।\"..." } ``` #### unshuffled_deduplicated_bn - **Size of downloaded dataset files:** 1.26 GB - **Size of the generated dataset:** 6.24 GB - **Total amount of disk used:** 7.50 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ভড়ং সর্বস্ব বাংলা আর্ট অ্যান্ড কালচারের হিসাব গুলিয়ে দেওয়ার ম্যাজিকের নাম ব্রাত্য রাইসু November 23, 2017\\nTagged with ডায়োজিনি..." } ``` #### unshuffled_deduplicated_bo - **Size of downloaded dataset files:** 22.37 MB - **Size of the generated dataset:** 144.65 MB - **Total amount of disk used:** 167.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"བོད་མི་འདི་དག་ནི་རང་རྒྱུད་སྒོ་རུ་ཕུད་དེ་གཞན་རྒྱུད་པང་དུ་ཉར་ནས་གསོ་སྐྱོང་བྱེད་དགོས་ཟེར་བ་དང་གཅིག་མཚུངས་རེད།\\nཚན་རིག་ནི་དང་ཐོག་རང..." } ``` #### unshuffled_deduplicated_bpy - **Size of downloaded dataset files:** 0.19 MB - **Size of the generated dataset:** 1.78 MB - **Total amount of disk used:** 1.97 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"পৌরসভা এহার আয়তন (লয়াহান) ২,৭৩০,.৬৩ বর্গ কিলোমিটার। পৌরসভা এহার মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ ইলতাই 18.63° S 48.18° W ।[১]..." } ``` #### unshuffled_deduplicated_br - **Size of downloaded dataset files:** 6.47 MB - **Size of the generated dataset:** 17.00 MB - **Total amount of disk used:** 23.47 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ar mank Magalhães(Daveoù a vank) a zo ur spesad evned, Spheniscus magellanicus an anv skiantel anezhañ.\\nGallout a reer implijo..." } ``` #### unshuffled_deduplicated_bs - **Size of downloaded dataset files:** 0.04 MB - **Size of the generated dataset:** 0.15 MB - **Total amount of disk used:** 0.18 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ž šř é ú šř šř ě šř ž é č ě ž ů ě ď éé ýš ě ě Ž č š ý ě ď é ýš ě ď ě éé ýš ě č ž ě š ý ď ě ýš é ú č ž č š ý ď ý ž é éě ď é č ýš..." } ``` #### unshuffled_deduplicated_bxr - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"2002 оной хабар буряад хэлэ бэшэгэй һалбари Үндэһэтэнэй хүмүүнлиг ухаанай дээдэ һургуули болгогдожо өөршэлэгдөө.\\nХарин мүнөө б..." } ``` #### unshuffled_deduplicated_ca - **Size of downloaded dataset files:** 1.73 GB - **Size of the generated dataset:** 4.57 GB - **Total amount of disk used:** 6.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Daniel Vendrell, conegut com Vandrell, ha sigut un dels il•lustradors contemporanis més influents, representant a la nova onada..." } ``` #### unshuffled_deduplicated_cbk - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano..." } ``` #### unshuffled_deduplicated_ce - **Size of downloaded dataset files:** 1.87 MB - **Size of the generated dataset:** 7.04 MB - **Total amount of disk used:** 8.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Шаьш анархисташ ду бохучу жигархойн дIахьедарехь дуьйцу, оьрсийн ницкъаллийн структурийн а, федералан каналан а Iалашонаш \\\"мар..." } ``` #### unshuffled_deduplicated_ceb - **Size of downloaded dataset files:** 7.12 MB - **Size of the generated dataset:** 24.83 MB - **Total amount of disk used:** 31.95 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Si Isko walay pupamilok nga nagtan-aw sa unahan, natugaw. “Naunsa ka gud diha Isko nga layo man kaayo ang imong panan-aw?” ni I..." } ``` #### unshuffled_deduplicated_ckb - **Size of downloaded dataset files:** 60.32 MB - **Size of the generated dataset:** 237.72 MB - **Total amount of disk used:** 298.05 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"رسی رۆژ - ساڵێک دوای بومەلەرزەی کرماشان میوانی بەرنامە : کاک سیاوەش حەیاتی چالاکی مەدەنی -قەسری شیرین\\nپارچە موزیک 30 / 10 / 20..." } ``` #### unshuffled_deduplicated_cs - **Size of downloaded dataset files:** 10.49 GB - **Size of the generated dataset:** 25.71 GB - **Total amount of disk used:** 36.20 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Akce anarchistů proti připravovanému novému služební řádu a nízkým mzdám 1903 – Historie českého anarchismu (1880 – 1939)\\nRost..." } ``` #### unshuffled_deduplicated_cv - **Size of downloaded dataset files:** 7.47 MB - **Size of the generated dataset:** 27.49 MB - **Total amount of disk used:** 34.95 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Шыранӑ чухне ӑнсӑртран латин кирилл саспаллисем вырӑнне латин саспаллисене ҫырсан, сайт эсир ҫырнине юсама тӑрӑшӗ.\\nКу сайтра ч..." } ``` #### unshuffled_deduplicated_cy - **Size of downloaded dataset files:** 53.63 MB - **Size of the generated dataset:** 141.22 MB - **Total amount of disk used:** 194.86 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Mae capeli Cymreig yr Andes ym Mhatagonia wedi cyhoeddi na fydd gwasanaethau yno weddill y mis, oherwydd yr eira trwm sydd wedi..." } ``` #### unshuffled_deduplicated_da - **Size of downloaded dataset files:** 3.82 GB - **Size of the generated dataset:** 10.24 GB - **Total amount of disk used:** 14.06 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Den 2.-5. februar 2016 løb det tredje kursus i uddannelsen af 4kommunesamarbejdets Local Impact Coaches, af stablen i Gentofte ..." } ``` #### unshuffled_deduplicated_de - **Size of downloaded dataset files:** 60.80 GB - **Size of the generated dataset:** 156.30 GB - **Total amount of disk used:** 217.10 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Auf dieser Seite gibt es mind. ein YouTube Video. Cookies für diese Website wurden abgelehnt. Dadurch können keine YouTube Vide..." } ``` #### unshuffled_deduplicated_diq - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Zıwanê Slawki, zıwano merdumanê Slawano. Zıwanê Slawki yew lızgeyê Zıwananê Hind u Ewropao. Keyeyê Zıwananê Slawki beno hirê letey:" } ``` #### unshuffled_deduplicated_dsb - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Pśiklaskaju južo pśed pśedstajenim... 1500 źiśi njamóžo wěcej docakaś, měsćańska hala w Chóśebuzu - wupśedana." } ``` #### unshuffled_deduplicated_dv - **Size of downloaded dataset files:** 16.84 MB - **Size of the generated dataset:** 82.19 MB - **Total amount of disk used:** 99.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ބ. އަތޮޅުގައި ހުޅުވަން ތައްޔާރުވަމުން އަންނަ ވައްކަރު ރިސޯޓުގައި ވަޒީފާ އަދާކުރަން ޝައުގުވެރިވާ ފަރާތްތަކަށް ކުރިމަތިލުމުގެ ފުރ..." } ``` #### unshuffled_deduplicated_el - **Size of downloaded dataset files:** 7.91 GB - **Size of the generated dataset:** 28.74 GB - **Total amount of disk used:** 36.65 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Νεκρός εντοπίστηκε μέσα στο σπίτι του στην οδό Ηρώδου Αττικού στον αριθμό 7 ο επικεφαλής του προξενικού τμήματος της Ρωσικής πρ..." } ``` #### unshuffled_deduplicated_eml - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"A séguit dal prucès ad rubutiśasiòṅ di abitànt dal pòpul ad Mikenes, Angoras 'l è finî dènt'r a 'n robot cun la tèsta dna rana ..." } ``` #### unshuffled_deduplicated_en - **Size of downloaded dataset files:** 496.50 GB - **Size of the generated dataset:** 1299.75 GB - **Total amount of disk used:** 1796.24 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visi..." } ``` #### unshuffled_deduplicated_eo - **Size of downloaded dataset files:** 92.86 MB - **Size of the generated dataset:** 240.12 MB - **Total amount of disk used:** 332.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ĉu ... preĝi | mediti | ricevi instigojn || kanti | muziki || informiĝi | legi | studi || prepari Diservon\\nTemas pri kolekto d..." } ``` #### unshuffled_deduplicated_es - **Size of downloaded dataset files:** 60.46 GB - **Size of the generated dataset:** 160.86 GB - **Total amount of disk used:** 221.32 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Como se librará de la celulitis en el gimnasio La piel superflua en las manos después del adelgazamiento, Los bailes fáciles pa..." } ``` #### unshuffled_deduplicated_et - **Size of downloaded dataset files:** 966.79 MB - **Size of the generated dataset:** 2.45 GB - **Total amount of disk used:** 3.41 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"MTÜ AB Video järgib oma tegevuses kodanikuühenduste eetilise tegevuse üldtunnustatud põhimõtteid, mis on lühidalt kokkuvõetud 7..." } ``` #### unshuffled_deduplicated_eu - **Size of downloaded dataset files:** 134.68 MB - **Size of the generated dataset:** 363.93 MB - **Total amount of disk used:** 498.61 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Gure jarduerek eraikuntzarekin, elkarbizitzarekin, hirigintzarekin eta ekologiarekin dute harremana, baita ideia eta konponbideak irudikatu eta garatzearekin ere, eraikuntza sektorea hobetuz, pertsonen erosotasuna eta bizi-kalitatea hobetzeko." } ``` #### unshuffled_deduplicated_fa - **Size of downloaded dataset files:** 10.46 GB - **Size of the generated dataset:** 40.06 GB - **Total amount of disk used:** 50.52 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"قـــــــــــــــــرار بود با هم کنـــــــــــــار بیایم نه اینکه از کنــــــــــــار هم رد بشیم...!!!\\nاگر روزی دلت لبریز غم بو..." } ``` #### unshuffled_deduplicated_fi - **Size of downloaded dataset files:** 5.38 GB - **Size of the generated dataset:** 13.99 GB - **Total amount of disk used:** 19.37 GB An example of 'train' looks as follows. ``` { "id": 1, "text": "Kiitos Deelle kaikesta - 1,5 viikkoa kulunut, kun Dee ei ole enää ollut omani. Reilu viikko sitten sunnuntaina vein Deen uuteen kotiinsa. Itselläni on ollut niin ristiriitaiset t..." } ``` #### unshuffled_deduplicated_fr - **Size of downloaded dataset files:** 55.46 GB - **Size of the generated dataset:** 148.28 GB - **Total amount of disk used:** 203.75 GB An example of 'train' looks as follows. ``` { "id": 0, "text": "Média de débat d'idées, de culture et de littérature. Récits, décryptages, analyses, portraits et critiques autour de la vie des idées. Magazine engagé, ouvert aux autres et au monde.. Bring up to date in french" } ``` #### unshuffled_deduplicated_frr - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Hiragana’ Practice’Sheet’1’(A -O)’ ’ Name:’________ __________________________’Section:’_______________ _’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ..." } ``` #### unshuffled_deduplicated_fy - **Size of downloaded dataset files:** 10.27 MB - **Size of the generated dataset:** 26.73 MB - **Total amount of disk used:** 37.00 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Nim in sêfte ride op Holmsjön, yn ien fan 'e lytse marren yn de omkriten, of nim se op avontueren lykas nonresidential. lâns Indalsälven wetter. Holm Sportklubb hawwe kano 's te huur, yn gearwurking mei de Baltyske Power konferinsje." } ``` #### unshuffled_deduplicated_ga - **Size of downloaded dataset files:** 22.22 MB - **Size of the generated dataset:** 63.86 MB - **Total amount of disk used:** 86.08 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Is fóram é seo chun plé a dhéanamh ar an leabhar atá roghnaithe do mhí na Samhna 2013 amháin. Ní féidir ach le baill chláraithe..." } ``` #### unshuffled_deduplicated_gd - **Size of downloaded dataset files:** 0.42 MB - **Size of the generated dataset:** 1.36 MB - **Total amount of disk used:** 1.78 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Zhou Yujun, a 'phàrtaidh Rùnaire Comataidh Sgìre Yanfeng ann Hengyang bhaile agus a Sgìre pàrtaidh agus an riaghaltas a' bhuidheann-riochdachaidh a 'tighinn a chèilidh air ar companaidh air Apr. 14, 2017." } ``` #### unshuffled_deduplicated_gl - **Size of downloaded dataset files:** 155.85 MB - **Size of the generated dataset:** 408.34 MB - **Total amount of disk used:** 564.19 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"O persoal de Inditex da provincia de Pontevedra segue a reclamar iguais condicións laborais no conxunto do país - CIG: Confeder..." } ``` #### unshuffled_deduplicated_gn - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"º ѐÆÚÓ À Ã Ð É Æ ¾ Ä ΠÀ ¼ Æ É ÄÛ = Ü Ý\\\"Þ ß†à á â ã ä å æçè ã é ê â å àë ì æê íî é á ë ï í çì àð í Ü à ñ ê é ò ä ì\"..." } ``` #### unshuffled_deduplicated_gom - **Size of downloaded dataset files:** 0.38 MB - **Size of the generated dataset:** 1.87 MB - **Total amount of disk used:** 2.24 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"दुष्ट शीळ हें कौरवांचें । रामें सविस्तर देखूनि साचें । बोलिले वचनें जें दुर्वाचे । करी तयांचें अनुस्मरण ॥२२०॥\"..." } ``` #### unshuffled_deduplicated_gu - **Size of downloaded dataset files:** 162.97 MB - **Size of the generated dataset:** 759.34 MB - **Total amount of disk used:** 922.32 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"અધિક માસ ચાલે છે. સમગ્ર ભારતમાં અને તેમાંય ખાસ કરીને પવિત્ર કે ધાર્મિક કહેવાય છે તેવા સ્થાનક પર કથાનો દોર ચાલે છે. ઉનાળાની કાળઝ..." } ``` #### unshuffled_deduplicated_he - **Size of downloaded dataset files:** 3.04 GB - **Size of the generated dataset:** 10.47 GB - **Total amount of disk used:** 13.51 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"זקוקים לרשתות נגד יתושים? מחפשים רשת מתאימה לחלון צר וקטן? רשתות נגד יתושים אקורדיון של חברת קליר-מש הן הפתרון.\\nרשתות לחלונות ..." } ``` #### unshuffled_deduplicated_hi - **Size of downloaded dataset files:** 2.01 GB - **Size of the generated dataset:** 9.57 GB - **Total amount of disk used:** 11.58 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"'आइटम गर्ल' बनकर हिट हुई थीं राखी सावंत, आज करीना-कटरीना तक फॉलो कर रही हैं ट्रेंड नक्‍सलियों का दम निकालेगा बाइक ग्रेनेड लॉन्च..." } ``` #### unshuffled_deduplicated_hr - **Size of downloaded dataset files:** 46.74 MB - **Size of the generated dataset:** 121.50 MB - **Total amount of disk used:** 168.23 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"U raspravi je sudjelovao i HSS-ov saborski zastupnik rekavši kako poljoprivrednici ne osjete mjere o kojima ministar govori jer..." } ``` #### unshuffled_deduplicated_hsb - **Size of downloaded dataset files:** 0.72 MB - **Size of the generated dataset:** 1.89 MB - **Total amount of disk used:** 2.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Budyšin (SN/BŠe). Elektronikarjo mějachu lětsa cyle hinaši zazběh do swojeho wukubłanja. Wokrjesne rjemjeslnistwo bě mjenujcy w..." } ``` #### unshuffled_deduplicated_ht - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan..." } ``` #### unshuffled_deduplicated_hu - **Size of downloaded dataset files:** 7.37 GB - **Size of the generated dataset:** 19.09 GB - **Total amount of disk used:** 26.46 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"monster - Amatőr, házi szex videók és kezdő csjaok pornó filmjei. - Free amateur, home made sex videos and online porn movies. ..." } ``` #### unshuffled_deduplicated_hy - **Size of downloaded dataset files:** 393.62 MB - **Size of the generated dataset:** 1.56 GB - **Total amount of disk used:** 1.96 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Արցախի Հանրապետության հռչակման 26-րդ տարեդարձի կապակցությամբ Շուշիի Արվեստի կենտրոնում կազմակերպվել է մոսկվաբնակ նկարիչներ՝ հայ..." } ``` #### unshuffled_deduplicated_ia - **Size of downloaded dataset files:** 0.05 MB - **Size of the generated dataset:** 0.38 MB - **Total amount of disk used:** 0.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha h..." } ``` #### unshuffled_deduplicated_id - **Size of downloaded dataset files:** 6.00 GB - **Size of the generated dataset:** 17.05 GB - **Total amount of disk used:** 23.05 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Perihal dari itu, kalau kunci hal yang demikian hilang, pemilik wajib melapor ke bengkel sah untuk dibuatkan kunci baru dengan ..." } ``` #### unshuffled_deduplicated_ie - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Plastic Yo Yo Metal Yo Yos Wooden Yo Yo Keychain Yo Yo Translucent Yo Yo Light Up Yo Yo Globe Yo Yo Stress Reliever Yo Yo Jellyfish Yo Yo Sports Ball Yo Yo Sound Yo Yo Miniature Yo Yo Promotional Yo Yo Novelty Yo Yo Video Game Yo Yo ECO Recycled Yo Yo" } ``` #### unshuffled_deduplicated_ilo - **Size of downloaded dataset files:** 0.23 MB - **Size of the generated dataset:** 0.68 MB - **Total amount of disk used:** 0.91 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Segun ken ni Ping-ay, ti yellow corn ti maysa kadagiti nadakamat a liberalized agricultural commodity iti daytoy a free trade k..." } ``` #### unshuffled_deduplicated_io - **Size of downloaded dataset files:** 0.04 MB - **Size of the generated dataset:** 0.14 MB - **Total amount of disk used:** 0.19 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Chekia esas parlamentala republiko. La chefo di stato esas la prezidanto. Til 2013 lu elektesis dal parlamento. Pos ta yaro, ol..." } ``` #### unshuffled_deduplicated_is - **Size of downloaded dataset files:** 332.87 MB - **Size of the generated dataset:** 894.28 MB - **Total amount of disk used:** 1.23 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Eyjar.net - upplýsinga- og fréttamiðill um Vestmannaeyjar - Fréttir - Nái núverandi stefna stjórnvalda fram að ganga mun það va..." } ``` #### unshuffled_deduplicated_it - **Size of downloaded dataset files:** 27.93 GB - **Size of the generated dataset:** 74.09 GB - **Total amount of disk used:** 102.03 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Jaundice - causes, treatment & pathology massaggio a osteochondrosis dellindizio di una controindicazione\\nTrattamento su un co..." } ``` #### unshuffled_deduplicated_ja - **Size of downloaded dataset files:** 40.80 GB - **Size of the generated dataset:** 113.63 GB - **Total amount of disk used:** 154.44 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"神社などへ一緒に同行して、様々な角度のショットで家族写真やお子様の写真を撮影致します！お好みに合わせて様々な写真を取ることができますので、その場でカメラマンへのリクエストも可能です！お子様の晴れ姿を、緊張していない自然な笑顔で残しませんか？\\n※七五三の..." } ``` #### unshuffled_deduplicated_jbo - **Size of downloaded dataset files:** 0.20 MB - **Size of the generated dataset:** 0.70 MB - **Total amount of disk used:** 0.91 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "ni'o 23 la cimast. cu 23moi djedi fi'o masti la cimast. noi ke'a cu cimoi masti .i 22 la cimast. cu purlamdei .ije 24 la cimast. cu bavlamdei" } ``` #### unshuffled_deduplicated_jv - **Size of downloaded dataset files:** 0.21 MB - **Size of the generated dataset:** 0.62 MB - **Total amount of disk used:** 0.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"José Mourinho (diwaca: [ʒuˈzɛ moˈɾiɲu]; lair ing Setubal, Portugal, 26 Januari 1963; umur 55 taun) iku salah siji pelatih bal k..." } ``` #### unshuffled_deduplicated_ka - **Size of downloaded dataset files:** 377.23 MB - **Size of the generated dataset:** 1.99 GB - **Total amount of disk used:** 2.36 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"წამიყვანე შენთან ერთად (ქართულად) / Возьми меня с собой (картулад) / (რუსული სერიალები ქართულად) (რუსების პორნო ონლაინში) (ruse..." } ``` #### unshuffled_deduplicated_kk - **Size of downloaded dataset files:** 389.12 MB - **Size of the generated dataset:** 1.59 GB - **Total amount of disk used:** 1.97 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Түлкібас ауданында «Латын негізді әліпби мен емле ережесі туралы насихат» жобасының тобы семинар өткізді\\nЕлорданың «Қазақстан»..." } ``` #### unshuffled_deduplicated_km - **Size of downloaded dataset files:** 114.48 MB - **Size of the generated dataset:** 610.61 MB - **Total amount of disk used:** 725.09 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ខ្សឹបដាក់ត្រចៀក៖ លោក សួស សុផានិត នាយផ្នែករដ្ឋបាលព្រៃឈើ ស្រុកភ្នំក្រវាញ់ ដែលទើបឡើងកាន់តំណែងថ្មី បើកដៃឲ្យឈ្នួញ ប្រព្រឹត្តបទល្មើស ..." } ``` #### unshuffled_deduplicated_kn - **Size of downloaded dataset files:** 215.52 MB - **Size of the generated dataset:** 1.08 GB - **Total amount of disk used:** 1.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ರಾಷ್ಟ್ರಪತಿ ಪ್ರಣಬ್ ಮುಖರ್ಜಿಯಿಂದ ಪದ್ಮ ಪ್ರಶಸ್ತಿ ಪ್ರದಾನ | President Pranab Mukherjee Confers Padma Awards | Photo Gallery on Kannada..." } ``` #### unshuffled_deduplicated_ko - **Size of downloaded dataset files:** 4.46 GB - **Size of the generated dataset:** 12.00 GB - **Total amount of disk used:** 16.47 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"CIA 프로젝트에서는 데이터베이스로 들어오는 요청을 중간에 수집(Sniffing)하고 수집한 데이터를 분석(Parsing)하여 그로 인한 결과를 판단하여 알릴 수 있는 시스템(Push Service)이 필요하다. 그리고 연구를 ..." } ``` #### unshuffled_deduplicated_krc - **Size of downloaded dataset files:** 0.62 MB - **Size of the generated dataset:** 2.41 MB - **Total amount of disk used:** 3.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Шамханланы, Бийлени къаршысына ябушуп, Батыр уланларыбызны къоллары булан «ортакъ ожакъ» къургъанбыз. Шо иш уллу зараллы иш бол..." } ``` #### unshuffled_deduplicated_ku - **Size of downloaded dataset files:** 23.34 MB - **Size of the generated dataset:** 63.09 MB - **Total amount of disk used:** 86.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Me di 114 bernameyên xwe yên berê da perçeyên ji berhemên zanyarî yên kurdzanên mezin bi wergera kurdî da ...\\nMe di 114 bernam..." } ``` #### unshuffled_deduplicated_kv - **Size of downloaded dataset files:** 0.33 MB - **Size of the generated dataset:** 1.21 MB - **Total amount of disk used:** 1.54 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Коми кытшыслӧн ыджытжык тор вӧр увтын куйлӧ, сійӧн и фаунасӧ татӧн аркмӧтӧны вӧрын олісь подаэз. Ассямаӧн лоӧ сія, мый кытшас с..." } ``` #### unshuffled_deduplicated_kw - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼Pray without ceasing🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏..." } ``` #### unshuffled_deduplicated_ky - **Size of downloaded dataset files:** 106.22 MB - **Size of the generated dataset:** 408.40 MB - **Total amount of disk used:** 514.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Turmush: Бишкек шаардык кеңешинин кезексиз отурумунда мэрге ишенбөөчүлүк көрсөтүү маселеси каралат, - депутат Т.Сагынов\\nБишкек..." } ``` #### unshuffled_deduplicated_la - **Size of downloaded dataset files:** 3.42 MB - **Size of the generated dataset:** 9.79 MB - **Total amount of disk used:** 13.22 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Hæ sunt generationes Noë: Noë vir justus atque perfectus fuit in generationibus suis; cum Deo ambulavit.\\nEcce ego adducam aqua..." } ``` #### unshuffled_deduplicated_lb - **Size of downloaded dataset files:** 8.30 MB - **Size of the generated dataset:** 21.42 MB - **Total amount of disk used:** 29.72 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Während dem Gaardefestival \\\"Ambiance Jardins\\\" vum 15. bis de 17. Mee huet den SNJ nees zesumme mam Groupe Animateur en Inform..." } ``` #### unshuffled_deduplicated_lez - **Size of downloaded dataset files:** 0.77 MB - **Size of the generated dataset:** 3.08 MB - **Total amount of disk used:** 3.84 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Ахцегь хуьр, виридалай ч1ехи лезги хуьрерикая я. Ам Урусатдин виридалай къиблепатавай хуьрерикай я. Ин хуьр...\"..." } ``` #### unshuffled_deduplicated_li - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.03 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"'t Good Goedenraad aan de Ezerbaek besjteit oet 'n kesjtièl mèt gesjlote haof en 'n park van 26 hectare. Hie in sjtoon väól beu..." } ``` #### unshuffled_deduplicated_lmo - **Size of downloaded dataset files:** 0.10 MB - **Size of the generated dataset:** 0.46 MB - **Total amount of disk used:** 0.57 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Serét (en tortonés: Sregh; en piemontés: Srèj) l'è 'n cümü italià, de la regiù del Piemónt, en Pruvìncia de Alessandria. El g'h..." } ``` #### unshuffled_deduplicated_lo - **Size of downloaded dataset files:** 23.63 MB - **Size of the generated dataset:** 119.29 MB - **Total amount of disk used:** 142.92 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ຜູ້ພິພາກສາ ປະຈຳເຂດ ສຫລ ທ່ານນຶ່ງ ຕັດສິນວ່າ ໂຄງການເກັບກຳຂໍ້ມູນ ທາງໂທລະສັບ ຂອງອົງການ ຄວາມໝັ້ນຄົງແຫ່ງຊາດ ແມ່ນຖືກຕ້ອງ ຕາມກົດໝາຍ.\\nກະ..." } ``` #### unshuffled_deduplicated_lrc - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.06 MB - **Total amount of disk used:** 0.08 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"آرلینگتون یئ گئل د شأریا ڤولاتچە ڤیرجینیا و یئ گئل د شأریا ڤولات ڤولاتچە یا یأکاگئرئتە ئمریکاە. ئی شأر دویومی کألوٙن شأر د راسا..." } ``` #### unshuffled_deduplicated_lt - **Size of downloaded dataset files:** 1.65 GB - **Size of the generated dataset:** 4.20 GB - **Total amount of disk used:** 5.86 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Čir vir vir pavasaris! Čia čia čia… dalinamės labai simpatiška video pamokėle, kurią pristato ab888art galerija.\\nBe galo papra..." } ``` #### unshuffled_deduplicated_lv - **Size of downloaded dataset files:** 710.45 MB - **Size of the generated dataset:** 1.91 GB - **Total amount of disk used:** 2.62 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Dekoratīvi sliekšņi MITSUBISHI OUTLANDER 2007, izgatavoti no ovālas formas, pulētas nerūsējošā tērauda caurules...\\ndažādas tūn..." } ``` #### unshuffled_deduplicated_mai - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"१ · २ · ३ · ४ · ५ · ६ · ७ · ८ · ९ · १० · ११ · १२ · १३ · १४ · १५ · १६ · १७ · १८ · १९ · २० · २१ · २२ · २३ · २४ · २५ · २६ · २७ · २..." } ``` #### unshuffled_deduplicated_mg - **Size of downloaded dataset files:** 4.30 MB - **Size of the generated dataset:** 13.59 MB - **Total amount of disk used:** 17.89 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Nanamboatra taratasy apetaka sy soso-kevitra ho an'ny olona te-hanatevin-daharana ity fihetsiketsehana ity i Anocrena.\\nNosorat..." } ``` #### unshuffled_deduplicated_mhr - **Size of downloaded dataset files:** 1.63 MB - **Size of the generated dataset:** 6.26 MB - **Total amount of disk used:** 7.89 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Акрет жап годым Уганда кундемым Пигмей племена- влак айлен шогеныт. мемнан эран 1 курым гыч Банту племена влакат тиде кундемышк..." } ``` #### unshuffled_deduplicated_min - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.31 MB - **Total amount of disk used:** 0.33 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ..." } ``` #### unshuffled_deduplicated_mk - **Size of downloaded dataset files:** 303.12 MB - **Size of the generated dataset:** 1.19 GB - **Total amount of disk used:** 1.49 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"„Филм плус“ е насловен првиот филмски месечник во Македонија, чиј прв број ќе биде промовиран вечер во „Менада“. Новото македон..." } ``` #### unshuffled_deduplicated_ml - **Size of downloaded dataset files:** 496.80 MB - **Size of the generated dataset:** 2.69 GB - **Total amount of disk used:** 3.18 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"സ്ത്രീ പ്രവേശനം സര്‍ക്കാര്‍ പൂര്‍ണമായും അംഗീകരിക്കുന്നുവെന്നും ശബരിമലയുടെ സുരക്ഷയില്‍ ഇടപെടുമെന്നും സര്‍ക്കാര്‍ ഹൈക്കോടതിയില്‍\\..." } ``` #### unshuffled_deduplicated_mn - **Size of downloaded dataset files:** 219.52 MB - **Size of the generated dataset:** 883.46 MB - **Total amount of disk used:** 1.10 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"МУБИС-ын багш мэргэжлийн хөрвөх сургалтыг төгссөн багшид багшлах эрх олгох тухай ~ БМДИ-ийн захирлын тушаал - Багшийн мэргэжил ..." } ``` #### unshuffled_deduplicated_mr - **Size of downloaded dataset files:** 299.68 MB - **Size of the generated dataset:** 1.49 GB - **Total amount of disk used:** 1.79 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Home / motivational marathi story / उद्योजकता (Entrepreneurship) / यांना हे जमलय, तर आपल्याला का नाही जमणार ?\\nयापैकी कोणाचीही ..." } ``` #### unshuffled_deduplicated_mrj - **Size of downloaded dataset files:** 0.29 MB - **Size of the generated dataset:** 1.10 MB - **Total amount of disk used:** 1.38 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Лӹпӹвлӓ (латинлӓ Lepidoptera ; алыкмарла лыве-влак) — капшангывлӓ йыхыш пырышы сӱмӓн нӹл шылдыран капшангывлӓ. Цилӓжӹ 180000 тӹ..." } ``` #### unshuffled_deduplicated_ms - **Size of downloaded dataset files:** 16.39 MB - **Size of the generated dataset:** 49.45 MB - **Total amount of disk used:** 65.85 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Sanad pertama daripada Zuhair bin Harb daripada ‘Affan daripada Hammad daripada Thabit daripada Anas.\\nSanad kedua daripada ‘Ab..." } ``` #### unshuffled_deduplicated_mt - **Size of downloaded dataset files:** 5.90 MB - **Size of the generated dataset:** 17.68 MB - **Total amount of disk used:** 23.58 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "tibgħat il-kawża lura lill-Qorti Ġenerali għall-annullament jew għat-tnaqqis tal-penalità imposta mill-Kummissjoni bid-deċiżjoni inizjali kif emendata bid-deċiżjoni ta’ rettifika;" } ``` #### unshuffled_deduplicated_mwl - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Deciplina social i outónoma que angloba atebidades de ouserbaçon, de análeze, de çcriçon, cumparaçon, de sistematizaçon i de sp..." } ``` #### unshuffled_deduplicated_my - **Size of downloaded dataset files:** 207.14 MB - **Size of the generated dataset:** 1.11 GB - **Total amount of disk used:** 1.32 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ျမ၀တီ - ရန္ကုန္တိုင္းေဒသႀကီး ေျမာက္ဥကၠလာပႏွင္႕ ဗဟန္းၿမိဳ႔နယ္ မေကြးတိုင္း ေဒသႀကီး ပခုကၠဴၿမိဳ႔နယ္တို႔၌ ျမန္မာ႕တပ္မေတာ္အား ေထာက္ခံ..." } ``` #### unshuffled_deduplicated_myv - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"2018 иень умарьковонь 6-це чистэ сась паро куля! Россиянь культурань Министерствась макссь невтемань конёв (прокатной удостовер..." } ``` #### unshuffled_deduplicated_mzn - **Size of downloaded dataset files:** 0.16 MB - **Size of the generated dataset:** 0.63 MB - **Total amount of disk used:** 0.79 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"قرآن یا قوران اسلام ِآسمونی کتاب هسته. مسلمونون گانّّه قرآن ره خدا، وحی جه برسنی‌یه، «محمد معجزه» هسته و ثقلین حدیث دله ونه خَو..." } ``` #### unshuffled_deduplicated_nah - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "In mācuīlpōhualxihuitl VI (inic chicuacē) in mācuīlpōhualli xiuhitl cāhuitl īhuīcpa 501 xihuitl oc 600 xihuitl." } ``` #### unshuffled_deduplicated_nap - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ò AUDIT í Ç è î ÿ å å 30 ò ÿ ÿ é, õ ñ ì ÿ, ê ã- ò à ì. å â å í ç â à à é ñ è å é ó ó ë. å å å û è å î é è à. à è à AUDIT 1-7 â ..." } ``` #### unshuffled_deduplicated_nds - **Size of downloaded dataset files:** 5.27 MB - **Size of the generated dataset:** 13.48 MB - **Total amount of disk used:** 18.76 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Dor kann sik vun nu af an de hele plattdüütsche Welt – vun Niebüll bit New York, vun Helgoland bit Honolulu – drapen. Allens, w..." } ``` #### unshuffled_deduplicated_ne - **Size of downloaded dataset files:** 240.63 MB - **Size of the generated dataset:** 1.24 GB - **Total amount of disk used:** 1.48 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"बर्दिबास नगरपालिकाको तेस्रो नगर परिषदबाट पारित आ.व.२०७३।७४ को संशोधित र २०७४।७५ को प्रस्तावित नीति, कार्यक्रम तथा बजेट\\nअार्थिक..." } ``` #### unshuffled_deduplicated_new - **Size of downloaded dataset files:** 0.83 MB - **Size of the generated dataset:** 4.26 MB - **Total amount of disk used:** 5.09 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"थ्व शहरयागु अक्षांश ३४.७००१६४ उत्तर व देशान्तर ८६.३७६४६९ पश्चिम खः (34.700164° N 86.376469° W)। थ्व थासे ७२२६७३२ वर्ग मिटर (२.७..." } ``` #### unshuffled_deduplicated_nl - **Size of downloaded dataset files:** 15.73 GB - **Size of the generated dataset:** 41.91 GB - **Total amount of disk used:** 57.65 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Op vrijdag 31 augustus wordt het nieuwe studiejaar van de masteropleiding architectuur geopend met een dagexcursie naar Venlo.\\..." } ``` #### unshuffled_deduplicated_nn - **Size of downloaded dataset files:** 23.58 MB - **Size of the generated dataset:** 58.32 MB - **Total amount of disk used:** 81.90 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Planomtale krav til innhald Bakgrunn: Spørsmål frå fleire kommunar om kva ein planomtale/planbeskrivelse bør innehalde Fylkeskommunen og fylkesmannen har i ein del saker reist motsegn på formelt grunnlag" } ``` #### unshuffled_deduplicated_no - **Size of downloaded dataset files:** 1.96 GB - **Size of the generated dataset:** 5.11 GB - **Total amount of disk used:** 7.07 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Ytterligere aktører i primærhelsetjenesten og andre NHS-virksomheter ble infisert, inkludert legekontor.Læreren vår er så attra..." } ``` #### unshuffled_deduplicated_oc - **Size of downloaded dataset files:** 1.34 MB - **Size of the generated dataset:** 4.00 MB - **Total amount of disk used:** 5.34 MB An example of 'train' looks as follows. ``` { "id": 1, "text": ".рф (rf, còdi punycode: .xn--p1ai)[1] es lo nom de domeni en rus per Russia. Foguèt activat lo 12 de mai de 2010. Lo còdi latin es .ru." } ``` #### unshuffled_deduplicated_or - **Size of downloaded dataset files:** 38.72 MB - **Size of the generated dataset:** 197.63 MB - **Total amount of disk used:** 236.36 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ଭୁବନେଶ୍ୱର, ୨୭/୧– (ଓଡ଼ିଆ ପୁଅ) ସିପିଆଇ ଜାତୀୟ ପରିଷଦର ଆହ୍ୱାନକ୍ରମେ ଗତକାଲି ଜାନୁୟାରୀ ୨୬ ସାଧାରଣତନ୍ତ୍ର ଦିବସକୁ ଦେଶ ବ୍ୟାପୀ ସମ୍ବିଧାନ ସୁରକ୍ଷା ..." } ``` #### unshuffled_deduplicated_os - **Size of downloaded dataset files:** 2.83 MB - **Size of the generated dataset:** 11.00 MB - **Total amount of disk used:** 13.83 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"1. Лæппу æмæ чызг казрæдзийы зæрдæмæ куы фæцæуынц æмæ, куы сфæнд кæнынц сæ цард баиу кæнын, уæд лæппу бар ракуры чызгæй, цæмæй ..." } ``` #### unshuffled_deduplicated_pa - **Size of downloaded dataset files:** 102.39 MB - **Size of the generated dataset:** 483.04 MB - **Total amount of disk used:** 585.42 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ਰਜਿ: ਨੰ: PB/JL-138/2018-20 ਜਿਲਦ 63, ਬਾਨੀ ਸੰਪਾਦਕ (ਸਵ:) ਡਾ: ਸਾਧੂ ਸਿੰਘ ਹਮਦਰਦ ਫ਼ੋਨ : 0181-2455961-62-63, 5032400, ਫੈਕਸ : 2455960, 2..." } ``` #### unshuffled_deduplicated_pam - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Áku pu i Anak ning Aláya at ngeni ipákit kó kékayu ngan nûng makanánu lang susúlat détinang kulit a mágkas. Lauan ya ing tarátu..." } ``` #### unshuffled_deduplicated_pl - **Size of downloaded dataset files:** 20.19 GB - **Size of the generated dataset:** 50.59 GB - **Total amount of disk used:** 70.78 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"System informatyczny - Załącznik nr 1 do zarządzenia Wójta Gminy Podegrodzie Nr 530/2013 z dnia 27 maja 2013 r\\nSystem informat..." } ``` #### unshuffled_deduplicated_pms - **Size of downloaded dataset files:** 0.71 MB - **Size of the generated dataset:** 2.00 MB - **Total amount of disk used:** 2.72 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Louvigné-du-Désert a l'é na comun-a fransèisa ant la region aministrativa dla Brëtagna, ant ël dipartiment d'Ille-et-Vilaine. A..." } ``` #### unshuffled_deduplicated_pnb - **Size of downloaded dataset files:** 2.58 MB - **Size of the generated dataset:** 9.44 MB - **Total amount of disk used:** 12.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ایہ فائل Wikimedia Commons توں اے تے دوجیاں ویونتاں تے وی ورتی جاےکدی اے۔ گل بات اس دے فائل گل بات صفہ تے تھلے دتی گئی۔\"..." } ``` #### unshuffled_deduplicated_ps - **Size of downloaded dataset files:** 71.83 MB - **Size of the generated dataset:** 254.79 MB - **Total amount of disk used:** 326.61 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Many people usually use the time period ‘business to business (B2B) advertising,’ however most of them do not know precisely wh..." } ``` #### unshuffled_deduplicated_pt - **Size of downloaded dataset files:** 26.00 GB - **Size of the generated dataset:** 68.37 GB - **Total amount of disk used:** 94.37 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Você pode estar lendo este texto no sofá, levantar pra pegar uma breja na geladeira, dar uma cagada e sentar novamente, sem int..." } ``` #### unshuffled_deduplicated_qu - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.09 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Warayu wichay (kastilla simipi: Ascensión de Guarayos) nisqaqa Buliwya mama llaqtapi, Santa Krus suyupi, huk llaqtam, Warayu pruwinsyap uma llaqtanmi." } ``` #### unshuffled_deduplicated_rm - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"practicists agrars / practicistas agraras AFP pon far ina furmaziun da basa scursanida per cuntanscher in attestat federal da q..." } ``` #### unshuffled_deduplicated_ro - **Size of downloaded dataset files:** 4.48 GB - **Size of the generated dataset:** 11.66 GB - **Total amount of disk used:** 16.14 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"“În viață, oportunitatea nu este totul. Cine atrage Lumina, cineva bun în umbră. Timpul ne creează.” maestru\\nLyn.Evans: Ce mar..." } ``` #### unshuffled_deduplicated_ru - **Size of downloaded dataset files:** 166.68 GB - **Size of the generated dataset:** 611.70 GB - **Total amount of disk used:** 778.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Доступ к данному профилю для публичного просмотра закрыт администрацией сайта - профиль находится на модерации.\\nРазработчикам ..." } ``` #### unshuffled_deduplicated_sa - **Size of downloaded dataset files:** 7.27 MB - **Size of the generated dataset:** 38.33 MB - **Total amount of disk used:** 45.60 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"अनिरुद्धनगरे क्रीडिता रामलीला सम्‍प्रति समाप्‍ता अस्ति । तस्‍य कानिचन् चित्राणि पूर्वमेव प्रकाशितानि सन्ति । द्वौ चलचित्रौ अपि ..." } ``` #### unshuffled_deduplicated_sah - **Size of downloaded dataset files:** 7.01 MB - **Size of the generated dataset:** 27.46 MB - **Total amount of disk used:** 34.49 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████..." } ``` #### unshuffled_deduplicated_scn - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "La gilusìa è nu sintimentu dulurusu ca nasci d'un disideriu di pussessu sclusivu ntê cunfrunti dâ pirsuna amata e dû timuri, dû suspettu o dâ cirtizza dâ sò nfidiltati." } ``` #### unshuffled_deduplicated_sd - **Size of downloaded dataset files:** 74.17 MB - **Size of the generated dataset:** 275.48 MB - **Total amount of disk used:** 349.66 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"هر ڪو ڄاڻي ٿو ته جڏهن توهان هڪ وڏي خريد ڪرڻ چاهيون ٿا, توهان پڄي ضروري حڪم ۾ ان جي ڪم ڪرڻ جي هٿ ۾ لاڳاپو ڪيو آهي. جي شيء آهي ته..." } ``` #### unshuffled_deduplicated_sh - **Size of downloaded dataset files:** 1.45 MB - **Size of the generated dataset:** 6.44 MB - **Total amount of disk used:** 7.87 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Opština Gornja Radgona se nalazi u sjeveroistočnoj Sloveniji i graniči s susjednom Austriji duž rijeke Mure. Sa tridesetim nase..." } ``` #### unshuffled_deduplicated_si - **Size of downloaded dataset files:** 175.62 MB - **Size of the generated dataset:** 842.57 MB - **Total amount of disk used:** 1.02 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ලාංකීය සිතිවිලි සිංහල බ්ලොග් කියවනය කොත්තු සින්ඩිය ලංකා Blogger හත්මාළුව ලංකා බ්ලොග් කියවනය මාතලන්ගේ සින්ඩිය මොබයිල්lk\\nඅවකාශය ..." } ``` #### unshuffled_deduplicated_sk - **Size of downloaded dataset files:** 1.96 GB - **Size of the generated dataset:** 4.80 GB - **Total amount of disk used:** 6.76 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Aktivity | Agentúra podporovaného zamestnávania | vzdelávanie pre klientov, vzdelávanie pre odborníkov, kurzy\\nŠpecializované k..." } ``` #### unshuffled_deduplicated_sl - **Size of downloaded dataset files:** 523.22 MB - **Size of the generated dataset:** 1.32 GB - **Total amount of disk used:** 1.85 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Če Creatures, ki je želel, da pridejo na čas, predvsem je povedlo – razlikuje od ljubosumja začel grizenja kolen (ali zadnjica)..." } ``` #### unshuffled_deduplicated_so - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт ттттттттттттттттуууууууууууу..." } ``` #### unshuffled_deduplicated_sq - **Size of downloaded dataset files:** 445.36 MB - **Size of the generated dataset:** 1.21 GB - **Total amount of disk used:** 1.66 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Çfarë do të më pëlqente tek një femër ose çfarë do të më shndërronte në një shpërthim drite? – Albert Vataj\\nTë gjithëve një zo..." } ``` #### unshuffled_deduplicated_sr - **Size of downloaded dataset files:** 665.03 MB - **Size of the generated dataset:** 2.36 GB - **Total amount of disk used:** 3.03 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Корисни савети за сваки дан. На сајту су разне категорије, као што су љепота, мода, кување и поправка властитим рукама.\\nШколск..." } ``` #### unshuffled_deduplicated_su - **Size of downloaded dataset files:** 0.05 MB - **Size of the generated dataset:** 0.16 MB - **Total amount of disk used:** 0.21 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Kartu krédit nyaéta \"duit plastik\" anu dikaluarkeun ku bank pikeun alat pambayaran di tempat-tempat nu tangtu samisal jiga di hotél, réstoran, tempat rékréasi jeung sajabana.[1]" } ``` #### unshuffled_deduplicated_sv - **Size of downloaded dataset files:** 10.19 GB - **Size of the generated dataset:** 26.33 GB - **Total amount of disk used:** 36.51 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"1783 är ett viktigt årtal i den nya tidens historia. Det året slöts en fred i Paris och därmed blev de 13 brittiska kolonierna ..." } ``` #### unshuffled_deduplicated_sw - **Size of downloaded dataset files:** 2.95 MB - **Size of the generated dataset:** 8.98 MB - **Total amount of disk used:** 11.92 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Miripuko hiyo inakuja mwanzoni mwa Wiki Takatifu kuelekea Pasaka na ikiwa ni wiki chache tu kabla ya Papa Francis kuanza ziara yake katika nchi hiyo yenye idadi kubwa kabisa ya watu katika ulimwengu wa nchi za Kiarabu." } ``` #### unshuffled_deduplicated_ta - **Size of downloaded dataset files:** 971.12 MB - **Size of the generated dataset:** 5.48 GB - **Total amount of disk used:** 6.45 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"பொழுது சாய்ந்து வெகு நேரமாகிவிட்டது. கூலி வேலைக்குப் போயிருந்த 'சித்தாள் ' பெண்கள் எல்லோரும் வீடு திரும்பி விட்டார்கள். இன்னும்..." } ``` #### unshuffled_deduplicated_te - **Size of downloaded dataset files:** 342.43 MB - **Size of the generated dataset:** 1.70 GB - **Total amount of disk used:** 2.04 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"హర్యానాలో టోల్ దగ్గర సిబ్బంది.. స్థానిక ప్రజలు కొట్టుకున్నారు. కర్నాల్ అనే గ్రామానికి సమీపంలో టోల్ గేట్ ఉంది. అయితే సాధారణంగా స..." } ``` #### unshuffled_deduplicated_tg - **Size of downloaded dataset files:** 62.90 MB - **Size of the generated dataset:** 261.68 MB - **Total amount of disk used:** 324.60 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ҳумайро гуфтааст, мухолифи низом аст, низоме, ки дар Тоҷикистон вуҷуд дорад. Ба ин маънӣ, худро мухолифи давлату ҳукумати Тоҷик..." } ``` #### unshuffled_deduplicated_th - **Size of downloaded dataset files:** 3.54 GB - **Size of the generated dataset:** 17.11 GB - **Total amount of disk used:** 20.65 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ฟันที่แลดูขาวสะอาดไม่มีเศษอาหารติดอยู่ เหงือกสีชมพู ไม่เจ็บ หรือมีเลือดออกเวลาแปรงฟันหรือขัดฟัน ไม่มีปัญหาเรื่องกลิ่นปาก ทำให้ก..." } ``` #### unshuffled_deduplicated_tk - **Size of downloaded dataset files:** 2.22 MB - **Size of the generated dataset:** 7.12 MB - **Total amount of disk used:** 9.34 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Türkmenistanyň Prezidenti agyr atletika boýunça dünýä çempionatyna taýýarlyk işleriniň barşy bilen tanyşdy\\nHalallykdan kemal t..." } ``` #### unshuffled_deduplicated_tl - **Size of downloaded dataset files:** 151.34 MB - **Size of the generated dataset:** 431.69 MB - **Total amount of disk used:** 583.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"“Gusto ko manawagan sa mga Unit Head ng Chanel 2 Salve. Kasi napapansin ko iyon mga alaga ko ang taping halos once a week lang,..." } ``` #### unshuffled_deduplicated_tr - **Size of downloaded dataset files:** 10.39 GB - **Size of the generated dataset:** 28.47 GB - **Total amount of disk used:** 38.86 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Son yıllarda görülen ay tutulmalarına göre daha etkili olacağı söylenen Kanlı veya Kırmızı Ay Tutulmasına saatler kaldı. Bu akş..." } ``` #### unshuffled_deduplicated_tt - **Size of downloaded dataset files:** 85.89 MB - **Size of the generated dataset:** 321.37 MB - **Total amount of disk used:** 407.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"\\\"Иремнең вафатына 40 көн узгач, Алмаз да безнең өйгә кереп үлде\\\". Арчада 35 яшьлек ир өстенә кондызлар ега башлаган агач төшк..." } ``` #### unshuffled_deduplicated_tyv - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Экии, хүндүлуг аалчылар болгаш тыва дылдың деткикчилери! Тыва дылдың болгаш чогаалдың ховар бир башкызынга, Менги Ооржакка, ажы..." } ``` #### unshuffled_deduplicated_ug - **Size of downloaded dataset files:** 20.53 MB - **Size of the generated dataset:** 86.44 MB - **Total amount of disk used:** 106.97 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"زاڭ-ءتۇزىم | عىلىم-تەحنيكا | ءتىل-ادەبيەت | تۇرمىس | دەنە تاربيە | ساياحات-ورتا | سۋرەتتى حابار | سىر سۇحبات | ارناۋلى تاقىرىپ ..." } ``` #### unshuffled_deduplicated_uk - **Size of downloaded dataset files:** 8.04 GB - **Size of the generated dataset:** 29.86 GB - **Total amount of disk used:** 37.90 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Про надання роз'яснення (щодо форми письмового зобов'язання громадян про зворотне ввезення/вивезення товарів), Державна митна с..." } ``` #### unshuffled_deduplicated_ur - **Size of downloaded dataset files:** 483.59 MB - **Size of the generated dataset:** 1.82 GB - **Total amount of disk used:** 2.31 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"آئیے اہم اسلامی کتب کو یونیکوڈ میں انٹرنیٹ پر پیش کرنے کے لئے مل جل کر آن لائن ٹائپنگ کریں۔ محدث ٹائپنگ پراجیکٹ کے ذریعے آپ روز..." } ``` #### unshuffled_deduplicated_uz - **Size of downloaded dataset files:** 4.30 MB - **Size of the generated dataset:** 12.00 MB - **Total amount of disk used:** 16.29 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Qurama tog'lari tizmasining Toshkentdan 154 km uzoqlikdagi Toshkent-Ush yo'li yeqasidaxushmanzara tabiat qo'ynida joylashgan maydoni 30 ga.\nBolalarni sog'lomlashtirish oromgohi Bo'stonliq tumani Oqtosh muntaqasining soy-salqin gushasida joylashgan." } ``` #### unshuffled_deduplicated_vec - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Par ogni pónto, ła derivada ła xe ła pendensa de ła reta tangente a ła curva de ła funsion f. Ła reta de cołor róso l'è senpre ..." } ``` #### unshuffled_deduplicated_vi - **Size of downloaded dataset files:** 10.71 GB - **Size of the generated dataset:** 33.60 GB - **Total amount of disk used:** 44.31 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Canh chua cá bông lau không chỉ là món ăn giải nhiệt, thanh mát ngày hè mà còn là món siêu bổ dưỡng, rất tốt cho người gầy ốm. ..." } ``` #### unshuffled_deduplicated_vo - **Size of downloaded dataset files:** 0.30 MB - **Size of the generated dataset:** 2.10 MB - **Total amount of disk used:** 2.40 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Sarniguet binon zif in ziläk: Hautes-Pyrénées, in topäd: Midi-Pyrénées, in Fransän. Sarniguet topon videtü 43°19’ 7’’ N e lunetü 0°5’ 19’’ L." } ``` #### unshuffled_deduplicated_wa - **Size of downloaded dataset files:** 0.08 MB - **Size of the generated dataset:** 0.22 MB - **Total amount of disk used:** 0.29 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Cisse pådje ci n' est co k' on djermon, dj' ô bén k' el pådje est djusse sibåtcheye, eyet co trop tene; et s' divreut ele ecråxhî ene miete." } ``` #### unshuffled_deduplicated_war - **Size of downloaded dataset files:** 0.55 MB - **Size of the generated dataset:** 2.36 MB - **Total amount of disk used:** 2.90 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "An Honce amo in usa ka baryo ngan munisipalidad ha distrito han Rožňava ha rehiyon han Košice ha nasod han Slovakia.\nAn Rumegies amo in usa ka komyun ha departamento han Nord ngan ha rehiyon han Nord-Pas-de-Calais ha nasod han Fransya." } ``` #### unshuffled_deduplicated_wuu - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.03 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"伊春元旦天气 伊春腊八天气 伊春春节天气 伊春情人节天气 伊春元宵节天气 伊春愚人节天气 伊春清明节天气 伊春劳动节天气 伊春母亲节天气 伊春端午节天气 伊春七夕节天气 伊春教师节天气 伊春中秋节天气 伊春国庆节天气 伊春重阳节天气 伊春万圣节天气 伊春..." } ``` #### unshuffled_deduplicated_xal - **Size of downloaded dataset files:** 0.03 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.15 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Арнгудин Орн гисн Европд бәәдг һазр. 2007 җилин тooһaр эн орн нутгт 3,600,523 әмтн бәәдг билә. Арнгудин Орнин хотл балһсна нерн..." } ``` #### unshuffled_deduplicated_xmf - **Size of downloaded dataset files:** 0.94 MB - **Size of the generated dataset:** 4.63 MB - **Total amount of disk used:** 5.58 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"მოჩამილი ტექსტი წჷმორინელი რე Creative Commons Attribution-ShareAlike ლიცენზიათ; შილებე გეძინელი პირობეფიშ არსებუა. კილიშკილიშა..." } ``` #### unshuffled_deduplicated_yi - **Size of downloaded dataset files:** 22.20 MB - **Size of the generated dataset:** 88.29 MB - **Total amount of disk used:** 110.49 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ממשותדיק - חבֿרה, איך אַרבעט איצט אױף אַ זשורנאַל. טאָמער איר האָט עפּעס צוצוגעבן זאָלט איר שיקן מיר אַן אָנזאָג. ס'װעט הײסן \\\"..." } ``` #### unshuffled_deduplicated_yo - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.03 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Copyright © 2018 BBC. BBC kò mọ̀ nípa àwọn ohun tí ó wà ní àwọn ojú òpó tí ó wà ní ìta. Ọwọ́ tí a fi mú ìbáṣepọ̀ ti ìta.\"..." } ``` #### unshuffled_deduplicated_yue - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 你還不爆 我累了 投降輸一半可以嗎\"..." } ``` #### unshuffled_deduplicated_zh - **Size of downloaded dataset files:** 99.98 GB - **Size of the generated dataset:** 267.88 GB - **Total amount of disk used:** 367.86 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"中国铝灰网 中国有色金属矿产网 中国黄莲网 中国水轮发电机网 中国抽油泵网 中国数控雕刻机网 中国不锈钢抛光网 中国磨具加工网 中国压铸铝网 中国耐水腻子网 中国手机摄像头网 中国粗粮网 中国车门锁网 中国钛粉网 中国轮圈网\\n天天中奖彩票图 天天中彩票..." } ``` </details> <details> <summary>Click to expand the Data/size information for each language (original)</summary> #### unshuffled_original_af - **Size of downloaded dataset files:** 85.79 MB - **Size of the generated dataset:** 254.08 MB - **Total amount of disk used:** 339.87 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "aanlyn markte as gevolg van ons voortgesette 'n begrip opsie handel sakeplan pdf terwyl ons steeds die gereelde ons binêre opsies handel" } ``` #### unshuffled_original_als - **Size of downloaded dataset files:** 1.49 MB - **Size of the generated dataset:** 5.30 MB - **Total amount of disk used:** 6.78 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"De Nazionalpark hät e Flächi vo 170,3 km² und isch dodemit s grösti Naturschutzgebiet vo de Schwiz. Er ligt uf em Gebiet vo de ..." } ``` #### unshuffled_original_am - **Size of downloaded dataset files:** 102.79 MB - **Size of the generated dataset:** 378.06 MB - **Total amount of disk used:** 480.85 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"አየር መንገዱ ከአዲስ አበባ ወደ ሮም ጣሊያን በማምራት ላይ በነበረበት ጊዜ ረዳት አብራሪው የጉዞውን አቅጣጫ በመቀየር ጄኔቭ አውሮፓላን ማረፊያ በማሳረፍ እጁን ለፖሊስ ሰጥቷል።\\nየኢትዮጵያ መንግስት የ..." } ``` #### unshuffled_original_an - **Size of downloaded dataset files:** 0.15 MB - **Size of the generated dataset:** 1.33 MB - **Total amount of disk used:** 1.48 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"واااااااأسفاه الأمم تفتخر ب 0 أمي ووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووو..." } ``` #### unshuffled_original_ar - **Size of downloaded dataset files:** 22.23 GB - **Size of the generated dataset:** 87.94 GB - **Total amount of disk used:** 110.17 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"مرحبا بك عزيز الزائر نتمنى لك أوقاتاً سعيدة معنا وأن نزداد شرفا بخدمتك ولا تنسى التسجيل معنا لتستفيد بكل جديد\\nأهلا وسهلا بك زا..." } ``` #### unshuffled_original_arz - **Size of downloaded dataset files:** 15.90 MB - **Size of the generated dataset:** 70.13 MB - **Total amount of disk used:** 86.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"بنى عجل : قبيلة من عجل بن لجيم بن صعب بن على بن بكر بن وائل انتقل اغلبهم الى البصرة فى العراق و اصفهان و خراسان فى ايران و اذرب..." } ``` #### unshuffled_original_as - **Size of downloaded dataset files:** 21.43 MB - **Size of the generated dataset:** 117.73 MB - **Total amount of disk used:** 139.17 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"আমি, এই সংগঠনৰ সদস্য সকলে একেলগ হৈ অসমকে ধৰি ভাৰতৰ উত্তৰ পূৰ্বাঞ্চলৰ অমূল্য কলা-সাংস্কৃতিক সম্পদৰাজি বৃহত্তৰ অষ্ট্ৰেলিয়াৰ সন্মু..." } ``` #### unshuffled_original_ast - **Size of downloaded dataset files:** 0.92 MB - **Size of the generated dataset:** 2.54 MB - **Total amount of disk used:** 3.46 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"The Killers llanzaron el so álbum debú, Hot Fuss, en xunu de 2004 nel Reinu Xuníu, al traviés de la discográfica Lizard King, y..." } ``` #### unshuffled_original_av - **Size of downloaded dataset files:** 0.08 MB - **Size of the generated dataset:** 0.42 MB - **Total amount of disk used:** 0.50 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Жинда малъараб ва божизе бегьулеб рагІудаса кьуризе бегьуларо гьев. Гьес насихІат гьабизе кколелъул бацІцІадаб диналъул рахъалъ..." } ``` #### unshuffled_original_az - **Size of downloaded dataset files:** 927.76 MB - **Size of the generated dataset:** 2.96 GB - **Total amount of disk used:** 3.89 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"AZTV-Artıq 7 ildir ki, Abşeron rayonu dotasiya almadan bütün xərclərini yerli daxilolmalar hesabına maliyyələşdirir.\\nDünən, 10..." } ``` #### unshuffled_original_azb - **Size of downloaded dataset files:** 6.64 MB - **Size of the generated dataset:** 28.47 MB - **Total amount of disk used:** 35.11 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"لعلی ١٣-جو عصرده یاشاییب یاراتمیش گؤرکملی آذربایجان شاعرلریندندیر. ١٢٢٤-جی ایلده تبریزده آنادان اولموشدور، گنج یاشلاریندا تیجار..." } ``` #### unshuffled_original_ba - **Size of downloaded dataset files:** 33.22 MB - **Size of the generated dataset:** 133.70 MB - **Total amount of disk used:** 166.92 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Күҙәтеү ҡуласаһы моделен хәҙер Мифтахетдин Аҡмулла исемендәге Башҡорт дәүләт педагогия университетында ла эшләргә мөмкин\\t\\nКүҙ..." } ``` #### unshuffled_original_bar - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": " vo" } ``` #### unshuffled_original_bcl - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"& ÿ ó / í 0 - ø û ù ö ú ð ï ú \\u0014 ù þ ô ö í ÷ ò \\u0014 ÷ í ù û ö í \\u0001 û ñ ç þ \\u0001 ð \\u0007 þ ò ñ ñ ò ô \\u0017 û ö ô ÷..." } ``` #### unshuffled_original_be - **Size of downloaded dataset files:** 498.29 MB - **Size of the generated dataset:** 1.88 GB - **Total amount of disk used:** 2.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Брэсцкія ўлады не дазволілі прафсаюзу РЭП правесці пікетаванне ў парку Воінаў-інтэрнацыяналістаў 30 мая 2018 года.\\nСітуацыю пр..." } ``` #### unshuffled_original_bg - **Size of downloaded dataset files:** 8.34 GB - **Size of the generated dataset:** 33.75 GB - **Total amount of disk used:** 42.09 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ЖАЛБОПОДАТЕЛЯТ директор на Дирекция „ Обжалване и данъчно-осигурителна практика“- Бургас, редовно призован, се представлява от ..." } ``` #### unshuffled_original_bh - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.13 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"सुकमा जिला भारत के छत्तीसगढ़ राज्य में एगो जिला बाटे। एकर मुख्यालय सुकमा शहर बाटे। एकर कुल रकबा 5636 वर्ग कि॰मी॰ बाटे।\"..." } ``` #### unshuffled_original_bn - **Size of downloaded dataset files:** 2.14 GB - **Size of the generated dataset:** 10.77 GB - **Total amount of disk used:** 12.91 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ভড়ং সর্বস্ব বাংলা আর্ট অ্যান্ড কালচারের হিসাব গুলিয়ে দেওয়ার ম্যাজিকের নাম ব্রাত্য রাইসু November 23, 2017\\nভড়ং সর্বস্ব বাংলা আর..." } ``` #### unshuffled_original_bo - **Size of downloaded dataset files:** 28.94 MB - **Size of the generated dataset:** 195.40 MB - **Total amount of disk used:** 224.34 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"བོད་མི་འདི་དག་ནི་རང་རྒྱུད་སྒོ་རུ་ཕུད་དེ་གཞན་རྒྱུད་པང་དུ་ཉར་ནས་གསོ་སྐྱོང་བྱེད་དགོས་ཟེར་བ་དང་གཅིག་མཚུངས་རེད།\\nཚན་རིག་ནི་དང་ཐོག་རང..." } ``` #### unshuffled_original_bpy - **Size of downloaded dataset files:** 0.34 MB - **Size of the generated dataset:** 4.35 MB - **Total amount of disk used:** 4.69 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"পৌরসভা এহার আয়তন (লয়াহান) ২,৭৩০,.৬৩ বর্গ কিলোমিটার। পৌরসভা এহার মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ ইলতাই 18.63° S 48.18° W ।[১]..." } ``` #### unshuffled_original_br - **Size of downloaded dataset files:** 9.18 MB - **Size of the generated dataset:** 30.20 MB - **Total amount of disk used:** 39.38 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ar mank Magalhães(Daveoù a vank) a zo ur spesad evned, Spheniscus magellanicus an anv skiantel anezhañ.\\nGallout a reer implijo..." } ``` #### unshuffled_original_bs - **Size of downloaded dataset files:** 0.05 MB - **Size of the generated dataset:** 0.48 MB - **Total amount of disk used:** 0.53 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ž šř é ú šř šř ě šř ž é č ě ž ů ě ď éé ýš ě ě Ž č š ý ě ď é ýš ě ď ě éé ýš ě č ž ě š ý ď ě ýš é ú č ž č š ý ď ý ž é éě ď é č ýš..." } ``` #### unshuffled_original_bxr - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"2002 оной хабар буряад хэлэ бэшэгэй һалбари Үндэһэтэнэй хүмүүнлиг ухаанай дээдэ һургуули болгогдожо өөршэлэгдөө.\\nХарин мүнөө б..." } ``` #### unshuffled_original_ca - **Size of downloaded dataset files:** 3.10 GB - **Size of the generated dataset:** 8.62 GB - **Total amount of disk used:** 11.73 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Daniel Vendrell, conegut com Vandrell, ha sigut un dels il•lustradors contemporanis més influents, representant a la nova onada..." } ``` #### unshuffled_original_cbk - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano..." } ``` #### unshuffled_original_ce - **Size of downloaded dataset files:** 2.09 MB - **Size of the generated dataset:** 8.73 MB - **Total amount of disk used:** 10.82 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Шаьш анархисташ ду бохучу жигархойн дIахьедарехь дуьйцу, оьрсийн ницкъаллийн структурийн а, федералан каналан а Iалашонаш \\\"мар..." } ``` #### unshuffled_original_ceb - **Size of downloaded dataset files:** 11.07 MB - **Size of the generated dataset:** 40.97 MB - **Total amount of disk used:** 52.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Si Isko walay pupamilok nga nagtan-aw sa unahan, natugaw. “Naunsa ka gud diha Isko nga layo man kaayo ang imong panan-aw?” ni I..." } ``` #### unshuffled_original_ckb - **Size of downloaded dataset files:** 111.88 MB - **Size of the generated dataset:** 510.97 MB - **Total amount of disk used:** 622.85 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"رسی رۆژ - ساڵێک دوای بومەلەرزەی کرماشان میوانی بەرنامە : کاک سیاوەش حەیاتی چالاکی مەدەنی -قەسری شیرین\\nپارچە موزیک 30 / 10 / 20..." } ``` #### unshuffled_original_cs - **Size of downloaded dataset files:** 21.72 GB - **Size of the generated dataset:** 57.08 GB - **Total amount of disk used:** 78.80 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Akce anarchistů proti připravovanému novému služební řádu a nízkým mzdám 1903 – Historie českého anarchismu (1880 – 1939)\\nRost..." } ``` #### unshuffled_original_cv - **Size of downloaded dataset files:** 9.40 MB - **Size of the generated dataset:** 41.05 MB - **Total amount of disk used:** 50.45 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Шыранӑ чухне ӑнсӑртран латин кирилл саспаллисем вырӑнне латин саспаллисене ҫырсан, сайт эсир ҫырнине юсама тӑрӑшӗ.\\nКу сайтра ч..." } ``` #### unshuffled_original_cy - **Size of downloaded dataset files:** 81.74 MB - **Size of the generated dataset:** 224.93 MB - **Total amount of disk used:** 306.67 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Mae capeli Cymreig yr Andes ym Mhatagonia wedi cyhoeddi na fydd gwasanaethau yno weddill y mis, oherwydd yr eira trwm sydd wedi..." } ``` #### unshuffled_original_da - **Size of downloaded dataset files:** 6.00 GB - **Size of the generated dataset:** 16.76 GB - **Total amount of disk used:** 22.76 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Den 2.-5. februar 2016 løb det tredje kursus i uddannelsen af 4kommunesamarbejdets Local Impact Coaches, af stablen i Gentofte ..." } ``` #### unshuffled_original_de - **Size of downloaded dataset files:** 119.51 GB - **Size of the generated dataset:** 331.22 GB - **Total amount of disk used:** 450.73 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Auf dieser Seite gibt es mind. ein YouTube Video. Cookies für diese Website wurden abgelehnt. Dadurch können keine YouTube Vide..." } ``` #### unshuffled_original_diq - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Zıwanê Slawki, zıwano merdumanê Slawano. Zıwanê Slawki yew lızgeyê Zıwananê Hind u Ewropao. Keyeyê Zıwananê Slawki beno hirê letey:" } ``` #### unshuffled_original_dsb - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Pśiklaskaju južo pśed pśedstajenim... 1500 źiśi njamóžo wěcej docakaś, měsćańska hala w Chóśebuzu - wupśedana." } ``` #### unshuffled_original_dv - **Size of downloaded dataset files:** 24.91 MB - **Size of the generated dataset:** 131.63 MB - **Total amount of disk used:** 156.54 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ބ. އަތޮޅުގައި ހުޅުވަން ތައްޔާރުވަމުން އަންނަ ވައްކަރު ރިސޯޓުގައި ވަޒީފާ އަދާކުރަން ޝައުގުވެރިވާ ފަރާތްތަކަށް ކުރިމަތިލުމުގެ ފުރ..." } ``` #### unshuffled_original_el - **Size of downloaded dataset files:** 17.31 GB - **Size of the generated dataset:** 66.27 GB - **Total amount of disk used:** 83.58 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Νεκρός εντοπίστηκε μέσα στο σπίτι του στην οδό Ηρώδου Αττικού στον αριθμό 7 ο επικεφαλής του προξενικού τμήματος της Ρωσικής πρ..." } ``` #### unshuffled_original_eml - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"A séguit dal prucès ad rubutiśasiòṅ di abitànt dal pòpul ad Mikenes, Angoras 'l è finî dènt'r a 'n robot cun la tèsta dna rana ..." } ``` #### unshuffled_original_en - **Size of downloaded dataset files:** 903.83 GB - **Size of the generated dataset:** 2525.44 GB - **Total amount of disk used:** 3429.27 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visi..." } ``` #### unshuffled_original_eo - **Size of downloaded dataset files:** 117.07 MB - **Size of the generated dataset:** 314.18 MB - **Total amount of disk used:** 431.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ĉu ... preĝi | mediti | ricevi instigojn || kanti | muziki || informiĝi | legi | studi || prepari Diservon\\nTemas pri kolekto d..." } ``` #### unshuffled_original_es - **Size of downloaded dataset files:** 106.04 GB - **Size of the generated dataset:** 298.49 GB - **Total amount of disk used:** 404.53 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Como se librará de la celulitis en el gimnasio La piel superflua en las manos después del adelgazamiento, Los bailes fáciles pa..." } ``` #### unshuffled_original_et - **Size of downloaded dataset files:** 1.88 GB - **Size of the generated dataset:** 5.17 GB - **Total amount of disk used:** 7.06 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"MTÜ AB Video järgib oma tegevuses kodanikuühenduste eetilise tegevuse üldtunnustatud põhimõtteid, mis on lühidalt kokkuvõetud 7..." } ``` #### unshuffled_original_eu - **Size of downloaded dataset files:** 248.19 MB - **Size of the generated dataset:** 894.83 MB - **Total amount of disk used:** 1.14 GB An example of 'train' looks as follows. ``` { "id": 0, "text": "Gure jarduerek eraikuntzarekin, elkarbizitzarekin, hirigintzarekin eta ekologiarekin dute harremana, baita ideia eta konponbideak irudikatu eta garatzearekin ere, eraikuntza sektorea hobetuz, pertsonen erosotasuna eta bizi-kalitatea hobetzeko." } ``` #### unshuffled_original_fa - **Size of downloaded dataset files:** 20.96 GB - **Size of the generated dataset:** 84.21 GB - **Total amount of disk used:** 105.17 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"قـــــــــــــــــرار بود با هم کنـــــــــــــار بیایم نه اینکه از کنــــــــــــار هم رد بشیم...!!!\\nاگر روزی دلت لبریز غم بو..." } ``` #### unshuffled_original_fi - **Size of downloaded dataset files:** 9.97 GB - **Size of the generated dataset:** 28.57 GB - **Total amount of disk used:** 38.54 GB An example of 'train' looks as follows. ``` { "id": 1, "text": "Kiitos Deelle kaikesta - 1,5 viikkoa kulunut, kun Dee ei ole enää ollut omani. Reilu viikko sitten sunnuntaina vein Deen uuteen kotiinsa. Itselläni on ollut niin ristiriitaiset t..." } ``` #### unshuffled_original_fr - **Size of downloaded dataset files:** 105.32 GB - **Size of the generated dataset:** 303.19 GB - **Total amount of disk used:** 408.51 GB An example of 'train' looks as follows. ``` { "id": 0, "text": "Média de débat d'idées, de culture et de littérature. Récits, décryptages, analyses, portraits et critiques autour de la vie des idées. Magazine engagé, ouvert aux autres et au monde.. Bring up to date in french" } ``` #### unshuffled_original_frr - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Hiragana’ Practice’Sheet’1’(A -O)’ ’ Name:’________ __________________________’Section:’_______________ _’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ..." } ``` #### unshuffled_original_fy - **Size of downloaded dataset files:** 12.40 MB - **Size of the generated dataset:** 36.24 MB - **Total amount of disk used:** 48.64 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Nim in sêfte ride op Holmsjön, yn ien fan 'e lytse marren yn de omkriten, of nim se op avontueren lykas nonresidential. lâns Indalsälven wetter. Holm Sportklubb hawwe kano 's te huur, yn gearwurking mei de Baltyske Power konferinsje." } ``` #### unshuffled_original_ga - **Size of downloaded dataset files:** 29.27 MB - **Size of the generated dataset:** 92.37 MB - **Total amount of disk used:** 121.63 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Is fóram é seo chun plé a dhéanamh ar an leabhar atá roghnaithe do mhí na Samhna 2013 amháin. Ní féidir ach le baill chláraithe..." } ``` #### unshuffled_original_gd - **Size of downloaded dataset files:** 0.52 MB - **Size of the generated dataset:** 2.02 MB - **Total amount of disk used:** 2.55 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Zhou Yujun, a 'phàrtaidh Rùnaire Comataidh Sgìre Yanfeng ann Hengyang bhaile agus a Sgìre pàrtaidh agus an riaghaltas a' bhuidheann-riochdachaidh a 'tighinn a chèilidh air ar companaidh air Apr. 14, 2017." } ``` #### unshuffled_original_gl - **Size of downloaded dataset files:** 235.38 MB - **Size of the generated dataset:** 656.48 MB - **Total amount of disk used:** 891.87 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"O persoal de Inditex da provincia de Pontevedra segue a reclamar iguais condicións laborais no conxunto do país - CIG: Confeder..." } ``` #### unshuffled_original_gn - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.04 MB - **Total amount of disk used:** 0.05 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"º ѐÆÚÓ À Ã Ð É Æ ¾ Ä ΠÀ ¼ Æ É ÄÛ = Ü Ý\\\"Þ ß†à á â ã ä å æçè ã é ê â å àë ì æê íî é á ë ï í çì àð í Ü à ñ ê é ò ä ì\"..." } ``` #### unshuffled_original_gom - **Size of downloaded dataset files:** 0.44 MB - **Size of the generated dataset:** 2.25 MB - **Total amount of disk used:** 2.71 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"दुष्ट शीळ हें कौरवांचें । रामें सविस्तर देखूनि साचें । बोलिले वचनें जें दुर्वाचे । करी तयांचें अनुस्मरण ॥२२०॥\"..." } ``` #### unshuffled_original_gu - **Size of downloaded dataset files:** 232.02 MB - **Size of the generated dataset:** 1.09 GB - **Total amount of disk used:** 1.33 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"અધિક માસ ચાલે છે. સમગ્ર ભારતમાં અને તેમાંય ખાસ કરીને પવિત્ર કે ધાર્મિક કહેવાય છે તેવા સ્થાનક પર કથાનો દોર ચાલે છે. ઉનાળાની કાળઝ..." } ``` #### unshuffled_original_he - **Size of downloaded dataset files:** 5.66 GB - **Size of the generated dataset:** 21.11 GB - **Total amount of disk used:** 26.77 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"זקוקים לרשתות נגד יתושים? מחפשים רשת מתאימה לחלון צר וקטן? רשתות נגד יתושים אקורדיון של חברת קליר-מש הן הפתרון.\\nרשתות לחלונות ..." } ``` #### unshuffled_original_hi - **Size of downloaded dataset files:** 3.66 GB - **Size of the generated dataset:** 17.93 GB - **Total amount of disk used:** 21.59 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"'आइटम गर्ल' बनकर हिट हुई थीं राखी सावंत, आज करीना-कटरीना तक फॉलो कर रही हैं ट्रेंड नक्‍सलियों का दम निकालेगा बाइक ग्रेनेड लॉन्च..." } ``` #### unshuffled_original_hr - **Size of downloaded dataset files:** 79.42 MB - **Size of the generated dataset:** 243.83 MB - **Total amount of disk used:** 323.24 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"U raspravi je sudjelovao i HSS-ov saborski zastupnik rekavši kako poljoprivrednici ne osjete mjere o kojima ministar govori jer..." } ``` #### unshuffled_original_hsb - **Size of downloaded dataset files:** 1.39 MB - **Size of the generated dataset:** 4.49 MB - **Total amount of disk used:** 5.87 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Budyšin (SN/BŠe). Elektronikarjo mějachu lětsa cyle hinaši zazběh do swojeho wukubłanja. Wokrjesne rjemjeslnistwo bě mjenujcy w..." } ``` #### unshuffled_original_ht - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan..." } ``` #### unshuffled_original_hu - **Size of downloaded dataset files:** 15.69 GB - **Size of the generated dataset:** 43.07 GB - **Total amount of disk used:** 58.77 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"monster - Amatőr, házi szex videók és kezdő csjaok pornó filmjei. - Free amateur, home made sex videos and online porn movies. ..." } ``` #### unshuffled_original_hy - **Size of downloaded dataset files:** 897.36 MB - **Size of the generated dataset:** 3.94 GB - **Total amount of disk used:** 4.84 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Արցախի Հանրապետության հռչակման 26-րդ տարեդարձի կապակցությամբ Շուշիի Արվեստի կենտրոնում կազմակերպվել է մոսկվաբնակ նկարիչներ՝ հայ..." } ``` #### unshuffled_original_ia - **Size of downloaded dataset files:** 0.08 MB - **Size of the generated dataset:** 0.69 MB - **Total amount of disk used:** 0.78 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha h..." } ``` #### unshuffled_original_id - **Size of downloaded dataset files:** 10.60 GB - **Size of the generated dataset:** 32.32 GB - **Total amount of disk used:** 42.91 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Perihal dari itu, kalau kunci hal yang demikian hilang, pemilik wajib melapor ke bengkel sah untuk dibuatkan kunci baru dengan ..." } ``` #### unshuffled_original_ie - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Plastic Yo Yo Metal Yo Yos Wooden Yo Yo Keychain Yo Yo Translucent Yo Yo Light Up Yo Yo Globe Yo Yo Stress Reliever Yo Yo Jellyfish Yo Yo Sports Ball Yo Yo Sound Yo Yo Miniature Yo Yo Promotional Yo Yo Novelty Yo Yo Video Game Yo Yo ECO Recycled Yo Yo" } ``` #### unshuffled_original_ilo - **Size of downloaded dataset files:** 0.27 MB - **Size of the generated dataset:** 0.92 MB - **Total amount of disk used:** 1.20 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Segun ken ni Ping-ay, ti yellow corn ti maysa kadagiti nadakamat a liberalized agricultural commodity iti daytoy a free trade k..." } ``` #### unshuffled_original_io - **Size of downloaded dataset files:** 0.04 MB - **Size of the generated dataset:** 0.16 MB - **Total amount of disk used:** 0.20 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Chekia esas parlamentala republiko. La chefo di stato esas la prezidanto. Til 2013 lu elektesis dal parlamento. Pos ta yaro, ol..." } ``` #### unshuffled_original_is - **Size of downloaded dataset files:** 533.03 MB - **Size of the generated dataset:** 1.52 GB - **Total amount of disk used:** 2.06 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Eyjar.net - upplýsinga- og fréttamiðill um Vestmannaeyjar - Fréttir - Nái núverandi stefna stjórnvalda fram að ganga mun það va..." } ``` #### unshuffled_original_it - **Size of downloaded dataset files:** 52.16 GB - **Size of the generated dataset:** 147.38 GB - **Total amount of disk used:** 199.54 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Jaundice - causes, treatment & pathology massaggio a osteochondrosis dellindizio di una controindicazione\\nTrattamento su un co..." } ``` #### unshuffled_original_ja - **Size of downloaded dataset files:** 79.56 GB - **Size of the generated dataset:** 232.22 GB - **Total amount of disk used:** 311.78 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"神社などへ一緒に同行して、様々な角度のショットで家族写真やお子様の写真を撮影致します！お好みに合わせて様々な写真を取ることができますので、その場でカメラマンへのリクエストも可能です！お子様の晴れ姿を、緊張していない自然な笑顔で残しませんか？\\n※七五三の..." } ``` #### unshuffled_original_jbo - **Size of downloaded dataset files:** 0.21 MB - **Size of the generated dataset:** 0.77 MB - **Total amount of disk used:** 0.98 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "ni'o 23 la cimast. cu 23moi djedi fi'o masti la cimast. noi ke'a cu cimoi masti .i 22 la cimast. cu purlamdei .ije 24 la cimast. cu bavlamdei" } ``` #### unshuffled_original_jv - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 0.69 MB - **Total amount of disk used:** 0.91 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"José Mourinho (diwaca: [ʒuˈzɛ moˈɾiɲu]; lair ing Setubal, Portugal, 26 Januari 1963; umur 55 taun) iku salah siji pelatih bal k..." } ``` #### unshuffled_original_ka - **Size of downloaded dataset files:** 680.74 MB - **Size of the generated dataset:** 3.77 GB - **Total amount of disk used:** 4.45 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"წამიყვანე შენთან ერთად (ქართულად) / Возьми меня с собой (картулад) / (რუსული სერიალები ქართულად) (რუსების პორნო ონლაინში) (ruse..." } ``` #### unshuffled_original_kk - **Size of downloaded dataset files:** 615.06 MB - **Size of the generated dataset:** 2.83 GB - **Total amount of disk used:** 3.45 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Түлкібас ауданында «Латын негізді әліпби мен емле ережесі туралы насихат» жобасының тобы семинар өткізді\\nЕлорданың «Қазақстан»..." } ``` #### unshuffled_original_km - **Size of downloaded dataset files:** 193.28 MB - **Size of the generated dataset:** 1.10 GB - **Total amount of disk used:** 1.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ខ្សឹបដាក់ត្រចៀក៖ លោក សួស សុផានិត នាយផ្នែករដ្ឋបាលព្រៃឈើ ស្រុកភ្នំក្រវាញ់ ដែលទើបឡើងកាន់តំណែងថ្មី បើកដៃឲ្យឈ្នួញ ប្រព្រឹត្តបទល្មើស ..." } ``` #### unshuffled_original_kn - **Size of downloaded dataset files:** 342.15 MB - **Size of the generated dataset:** 1.76 GB - **Total amount of disk used:** 2.11 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ರಾಷ್ಟ್ರಪತಿ ಪ್ರಣಬ್ ಮುಖರ್ಜಿಯಿಂದ ಪದ್ಮ ಪ್ರಶಸ್ತಿ ಪ್ರದಾನ | President Pranab Mukherjee Confers Padma Awards | Photo Gallery on Kannada..." } ``` #### unshuffled_original_ko - **Size of downloaded dataset files:** 8.81 GB - **Size of the generated dataset:** 25.29 GB - **Total amount of disk used:** 34.10 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"CIA 프로젝트에서는 데이터베이스로 들어오는 요청을 중간에 수집(Sniffing)하고 수집한 데이터를 분석(Parsing)하여 그로 인한 결과를 판단하여 알릴 수 있는 시스템(Push Service)이 필요하다. 그리고 연구를 ..." } ``` #### unshuffled_original_krc - **Size of downloaded dataset files:** 0.66 MB - **Size of the generated dataset:** 2.68 MB - **Total amount of disk used:** 3.34 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Шамханланы, Бийлени къаршысына ябушуп, Батыр уланларыбызны къоллары булан «ортакъ ожакъ» къургъанбыз. Шо иш уллу зараллы иш бол..." } ``` #### unshuffled_original_ku - **Size of downloaded dataset files:** 33.38 MB - **Size of the generated dataset:** 99.06 MB - **Total amount of disk used:** 132.44 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Me di 114 bernameyên xwe yên berê da perçeyên ji berhemên zanyarî yên kurdzanên mezin bi wergera kurdî da ...\\nMe di 114 bernam..." } ``` #### unshuffled_original_kv - **Size of downloaded dataset files:** 0.40 MB - **Size of the generated dataset:** 2.38 MB - **Total amount of disk used:** 2.78 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Коми кытшыслӧн ыджытжык тор вӧр увтын куйлӧ, сійӧн и фаунасӧ татӧн аркмӧтӧны вӧрын олісь подаэз. Ассямаӧн лоӧ сія, мый кытшас с..." } ``` #### unshuffled_original_kw - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.04 MB - **Total amount of disk used:** 0.05 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼Pray without ceasing🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏..." } ``` #### unshuffled_original_ky - **Size of downloaded dataset files:** 152.64 MB - **Size of the generated dataset:** 630.79 MB - **Total amount of disk used:** 783.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Turmush: Бишкек шаардык кеңешинин кезексиз отурумунда мэрге ишенбөөчүлүк көрсөтүү маселеси каралат, - депутат Т.Сагынов\\nБишкек..." } ``` #### unshuffled_original_la - **Size of downloaded dataset files:** 5.46 MB - **Size of the generated dataset:** 27.80 MB - **Total amount of disk used:** 33.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Hæ sunt generationes Noë: Noë vir justus atque perfectus fuit in generationibus suis; cum Deo ambulavit.\\nEcce ego adducam aqua..." } ``` #### unshuffled_original_lb - **Size of downloaded dataset files:** 10.73 MB - **Size of the generated dataset:** 30.60 MB - **Total amount of disk used:** 41.32 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Während dem Gaardefestival \\\"Ambiance Jardins\\\" vum 15. bis de 17. Mee huet den SNJ nees zesumme mam Groupe Animateur en Inform..." } ``` #### unshuffled_original_lez - **Size of downloaded dataset files:** 0.83 MB - **Size of the generated dataset:** 3.38 MB - **Total amount of disk used:** 4.20 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Ахцегь хуьр, виридалай ч1ехи лезги хуьрерикая я. Ам Урусатдин виридалай къиблепатавай хуьрерикай я. Ин хуьр...\"..." } ``` #### unshuffled_original_li - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.03 MB - **Total amount of disk used:** 0.04 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"'t Good Goedenraad aan de Ezerbaek besjteit oet 'n kesjtièl mèt gesjlote haof en 'n park van 26 hectare. Hie in sjtoon väól beu..." } ``` #### unshuffled_original_lmo - **Size of downloaded dataset files:** 0.10 MB - **Size of the generated dataset:** 0.47 MB - **Total amount of disk used:** 0.58 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Serét (en tortonés: Sregh; en piemontés: Srèj) l'è 'n cümü italià, de la regiù del Piemónt, en Pruvìncia de Alessandria. El g'h..." } ``` #### unshuffled_original_lo - **Size of downloaded dataset files:** 33.92 MB - **Size of the generated dataset:** 182.36 MB - **Total amount of disk used:** 216.28 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ຜູ້ພິພາກສາ ປະຈຳເຂດ ສຫລ ທ່ານນຶ່ງ ຕັດສິນວ່າ ໂຄງການເກັບກຳຂໍ້ມູນ ທາງໂທລະສັບ ຂອງອົງການ ຄວາມໝັ້ນຄົງແຫ່ງຊາດ ແມ່ນຖືກຕ້ອງ ຕາມກົດໝາຍ.\\nກະ..." } ``` #### unshuffled_original_lrc - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.09 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"آرلینگتون یئ گئل د شأریا ڤولاتچە ڤیرجینیا و یئ گئل د شأریا ڤولات ڤولاتچە یا یأکاگئرئتە ئمریکاە. ئی شأر دویومی کألوٙن شأر د راسا..." } ``` #### unshuffled_original_lt - **Size of downloaded dataset files:** 3.44 GB - **Size of the generated dataset:** 9.45 GB - **Total amount of disk used:** 12.89 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Čir vir vir pavasaris! Čia čia čia… dalinamės labai simpatiška video pamokėle, kurią pristato ab888art galerija.\\nBe galo papra..." } ``` #### unshuffled_original_lv - **Size of downloaded dataset files:** 1.49 GB - **Size of the generated dataset:** 4.27 GB - **Total amount of disk used:** 5.75 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Dekoratīvi sliekšņi MITSUBISHI OUTLANDER 2007, izgatavoti no ovālas formas, pulētas nerūsējošā tērauda caurules...\\ndažādas tūn..." } ``` #### unshuffled_original_mai - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.33 MB - **Total amount of disk used:** 0.34 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"१ · २ · ३ · ४ · ५ · ६ · ७ · ८ · ९ · १० · ११ · १२ · १३ · १४ · १५ · १६ · १७ · १८ · १९ · २० · २१ · २२ · २३ · २४ · २५ · २६ · २७ · २..." } ``` #### unshuffled_original_mg - **Size of downloaded dataset files:** 6.22 MB - **Size of the generated dataset:** 21.79 MB - **Total amount of disk used:** 28.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Nanamboatra taratasy apetaka sy soso-kevitra ho an'ny olona te-hanatevin-daharana ity fihetsiketsehana ity i Anocrena.\\nNosorat..." } ``` #### unshuffled_original_mhr - **Size of downloaded dataset files:** 1.84 MB - **Size of the generated dataset:** 7.55 MB - **Total amount of disk used:** 9.38 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Акрет жап годым Уганда кундемым Пигмей племена- влак айлен шогеныт. мемнан эран 1 курым гыч Банту племена влакат тиде кундемышк..." } ``` #### unshuffled_original_min - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.63 MB - **Total amount of disk used:** 0.64 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ‏‏‎ ..." } ``` #### unshuffled_original_mk - **Size of downloaded dataset files:** 508.24 MB - **Size of the generated dataset:** 2.20 GB - **Total amount of disk used:** 2.71 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"„Филм плус“ е насловен првиот филмски месечник во Македонија, чиј прв број ќе биде промовиран вечер во „Менада“. Новото македон..." } ``` #### unshuffled_original_ml - **Size of downloaded dataset files:** 938.69 MB - **Size of the generated dataset:** 5.24 GB - **Total amount of disk used:** 6.18 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"സ്ത്രീ പ്രവേശനം സര്‍ക്കാര്‍ പൂര്‍ണമായും അംഗീകരിക്കുന്നുവെന്നും ശബരിമലയുടെ സുരക്ഷയില്‍ ഇടപെടുമെന്നും സര്‍ക്കാര്‍ ഹൈക്കോടതിയില്‍\\..." } ``` #### unshuffled_original_mn - **Size of downloaded dataset files:** 472.36 MB - **Size of the generated dataset:** 2.33 GB - **Total amount of disk used:** 2.81 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Монгол улс, Улаанбаатар хот - 14191 Энхтайваны өргөн чөлөө - 10, Багш хөгжлийн ордон, Багшийн мэргэжил дээшлүүлэх институт\\nБаг..." } ``` #### unshuffled_original_mr - **Size of downloaded dataset files:** 525.31 MB - **Size of the generated dataset:** 2.82 GB - **Total amount of disk used:** 3.34 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Home / motivational marathi story / उद्योजकता (Entrepreneurship) / यांना हे जमलय, तर आपल्याला का नाही जमणार ?\\nयापैकी कोणाचीही ..." } ``` #### unshuffled_original_mrj - **Size of downloaded dataset files:** 0.30 MB - **Size of the generated dataset:** 1.16 MB - **Total amount of disk used:** 1.47 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Лӹпӹвлӓ (латинлӓ Lepidoptera ; алыкмарла лыве-влак) — капшангывлӓ йыхыш пырышы сӱмӓн нӹл шылдыран капшангывлӓ. Цилӓжӹ 180000 тӹ..." } ``` #### unshuffled_original_ms - **Size of downloaded dataset files:** 28.46 MB - **Size of the generated dataset:** 122.33 MB - **Total amount of disk used:** 150.79 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Sanad pertama daripada Zuhair bin Harb daripada ‘Affan daripada Hammad daripada Thabit daripada Anas.\\nSanad kedua daripada ‘Ab..." } ``` #### unshuffled_original_mt - **Size of downloaded dataset files:** 7.53 MB - **Size of the generated dataset:** 24.47 MB - **Total amount of disk used:** 32.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "tibgħat il-kawża lura lill-Qorti Ġenerali għall-annullament jew għat-tnaqqis tal-penalità imposta mill-Kummissjoni bid-deċiżjoni inizjali kif emendata bid-deċiżjoni ta’ rettifika;" } ``` #### unshuffled_original_mwl - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Deciplina social i outónoma que angloba atebidades de ouserbaçon, de análeze, de çcriçon, cumparaçon, de sistematizaçon i de sp..." } ``` #### unshuffled_original_my - **Size of downloaded dataset files:** 369.85 MB - **Size of the generated dataset:** 2.02 GB - **Total amount of disk used:** 2.39 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ျမ၀တီ - ရန္ကုန္တိုင္းေဒသႀကီး ေျမာက္ဥကၠလာပႏွင္႕ ဗဟန္းၿမိဳ႔နယ္ မေကြးတိုင္း ေဒသႀကီး ပခုကၠဴၿမိဳ႔နယ္တို႔၌ ျမန္မာ႕တပ္မေတာ္အား ေထာက္ခံ..." } ``` #### unshuffled_original_myv - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"2018 иень умарьковонь 6-це чистэ сась паро куля! Россиянь культурань Министерствась макссь невтемань конёв (прокатной удостовер..." } ``` #### unshuffled_original_mzn - **Size of downloaded dataset files:** 0.18 MB - **Size of the generated dataset:** 0.72 MB - **Total amount of disk used:** 0.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"قرآن یا قوران اسلام ِآسمونی کتاب هسته. مسلمونون گانّّه قرآن ره خدا، وحی جه برسنی‌یه، «محمد معجزه» هسته و ثقلین حدیث دله ونه خَو..." } ``` #### unshuffled_original_nah - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "In mācuīlpōhualxihuitl VI (inic chicuacē) in mācuīlpōhualli xiuhitl cāhuitl īhuīcpa 501 xihuitl oc 600 xihuitl." } ``` #### unshuffled_original_nap - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.02 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ò AUDIT í Ç è î ÿ å å 30 ò ÿ ÿ é, õ ñ ì ÿ, ê ã- ò à ì. å â å í ç â à à é ñ è å é ó ó ë. å å å û è å î é è à. à è à AUDIT 1-7 â ..." } ``` #### unshuffled_original_nds - **Size of downloaded dataset files:** 6.74 MB - **Size of the generated dataset:** 18.23 MB - **Total amount of disk used:** 24.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Dor kann sik vun nu af an de hele plattdüütsche Welt – vun Niebüll bit New York, vun Helgoland bit Honolulu – drapen. Allens, w..." } ``` #### unshuffled_original_ne - **Size of downloaded dataset files:** 355.29 MB - **Size of the generated dataset:** 1.87 GB - **Total amount of disk used:** 2.22 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"बर्दिबास नगरपालिकाको तेस्रो नगर परिषदबाट पारित आ.व.२०७३।७४ को संशोधित र २०७४।७५ को प्रस्तावित नीति, कार्यक्रम तथा बजेट\\nअार्थिक..." } ``` #### unshuffled_original_new - **Size of downloaded dataset files:** 1.03 MB - **Size of the generated dataset:** 5.77 MB - **Total amount of disk used:** 6.79 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"थ्व शहरयागु अक्षांश ३४.७००१६४ उत्तर व देशान्तर ८६.३७६४६९ पश्चिम खः (34.700164° N 86.376469° W)। थ्व थासे ७२२६७३२ वर्ग मिटर (२.७..." } ``` #### unshuffled_original_nl - **Size of downloaded dataset files:** 29.35 GB - **Size of the generated dataset:** 83.23 GB - **Total amount of disk used:** 112.58 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Op vrijdag 31 augustus wordt het nieuwe studiejaar van de masteropleiding architectuur geopend met een dagexcursie naar Venlo.\\..." } ``` #### unshuffled_original_nn - **Size of downloaded dataset files:** 32.86 MB - **Size of the generated dataset:** 90.84 MB - **Total amount of disk used:** 123.70 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "Planomtale krav til innhald Bakgrunn: Spørsmål frå fleire kommunar om kva ein planomtale/planbeskrivelse bør innehalde Fylkeskommunen og fylkesmannen har i ein del saker reist motsegn på formelt grunnlag" } ``` #### unshuffled_original_no - **Size of downloaded dataset files:** 3.11 GB - **Size of the generated dataset:** 8.65 GB - **Total amount of disk used:** 11.76 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Ytterligere aktører i primærhelsetjenesten og andre NHS-virksomheter ble infisert, inkludert legekontor.Læreren vår er så attra..." } ``` #### unshuffled_original_oc - **Size of downloaded dataset files:** 1.57 MB - **Size of the generated dataset:** 6.12 MB - **Total amount of disk used:** 7.71 MB An example of 'train' looks as follows. ``` { "id": 1, "text": ".рф (rf, còdi punycode: .xn--p1ai)[1] es lo nom de domeni en rus per Russia. Foguèt activat lo 12 de mai de 2010. Lo còdi latin es .ru." } ``` #### unshuffled_original_or - **Size of downloaded dataset files:** 49.84 MB - **Size of the generated dataset:** 260.15 MB - **Total amount of disk used:** 309.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ଭୁବନେଶ୍ୱର, ୨୭/୧– (ଓଡ଼ିଆ ପୁଅ) ସିପିଆଇ ଜାତୀୟ ପରିଷଦର ଆହ୍ୱାନକ୍ରମେ ଗତକାଲି ଜାନୁୟାରୀ ୨୬ ସାଧାରଣତନ୍ତ୍ର ଦିବସକୁ ଦେଶ ବ୍ୟାପୀ ସମ୍ବିଧାନ ସୁରକ୍ଷା ..." } ``` #### unshuffled_original_os - **Size of downloaded dataset files:** 3.09 MB - **Size of the generated dataset:** 12.90 MB - **Total amount of disk used:** 15.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"1. Лæппу æмæ чызг казрæдзийы зæрдæмæ куы фæцæуынц æмæ, куы сфæнд кæнынц сæ цард баиу кæнын, уæд лæппу бар ракуры чызгæй, цæмæй ..." } ``` #### unshuffled_original_pa - **Size of downloaded dataset files:** 164.21 MB - **Size of the generated dataset:** 801.16 MB - **Total amount of disk used:** 965.37 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ਰਜਿ: ਨੰ: PB/JL-138/2018-20 ਜਿਲਦ 63, ਬਾਨੀ ਸੰਪਾਦਕ (ਸਵ:) ਡਾ: ਸਾਧੂ ਸਿੰਘ ਹਮਦਰਦ ਫ਼ੋਨ : 0181-2455961-62-63, 5032400, ਫੈਕਸ : 2455960, 2..." } ``` #### unshuffled_original_pam - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Áku pu i Anak ning Aláya at ngeni ipákit kó kékayu ngan nûng makanánu lang susúlat détinang kulit a mágkas. Lauan ya ing tarátu..." } ``` #### unshuffled_original_pl - **Size of downloaded dataset files:** 42.88 GB - **Size of the generated dataset:** 117.12 GB - **Total amount of disk used:** 160.01 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"System informatyczny - Załącznik nr 1 do zarządzenia Wójta Gminy Podegrodzie Nr 530/2013 z dnia 27 maja 2013 r\\nSystem informat..." } ``` #### unshuffled_original_pms - **Size of downloaded dataset files:** 0.75 MB - **Size of the generated dataset:** 2.15 MB - **Total amount of disk used:** 2.92 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Louvigné-du-Désert a l'é na comun-a fransèisa ant la region aministrativa dla Brëtagna, ant ël dipartiment d'Ille-et-Vilaine. A..." } ``` #### unshuffled_original_pnb - **Size of downloaded dataset files:** 3.22 MB - **Size of the generated dataset:** 12.04 MB - **Total amount of disk used:** 15.26 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ایہ فائل Wikimedia Commons توں اے تے دوجیاں ویونتاں تے وی ورتی جاےکدی اے۔ گل بات اس دے فائل گل بات صفہ تے تھلے دتی گئی۔\"..." } ``` #### unshuffled_original_ps - **Size of downloaded dataset files:** 103.66 MB - **Size of the generated dataset:** 379.51 MB - **Total amount of disk used:** 483.17 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Many people usually use the time period ‘business to business (B2B) advertising,’ however most of them do not know precisely wh..." } ``` #### unshuffled_original_pt - **Size of downloaded dataset files:** 47.26 GB - **Size of the generated dataset:** 132.64 GB - **Total amount of disk used:** 179.89 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Você pode estar lendo este texto no sofá, levantar pra pegar uma breja na geladeira, dar uma cagada e sentar novamente, sem int..." } ``` #### unshuffled_original_qu - **Size of downloaded dataset files:** 0.02 MB - **Size of the generated dataset:** 0.08 MB - **Total amount of disk used:** 0.10 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Warayu wichay (kastilla simipi: Ascensión de Guarayos) nisqaqa Buliwya mama llaqtapi, Santa Krus suyupi, huk llaqtam, Warayu pruwinsyap uma llaqtanmi." } ``` #### unshuffled_original_rm - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"practicists agrars / practicistas agraras AFP pon far ina furmaziun da basa scursanida per cuntanscher in attestat federal da q..." } ``` #### unshuffled_original_ro - **Size of downloaded dataset files:** 9.53 GB - **Size of the generated dataset:** 26.87 GB - **Total amount of disk used:** 36.40 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"“În viață, oportunitatea nu este totul. Cine atrage Lumina, cineva bun în umbră. Timpul ne creează.” maestru\\nLyn.Evans: Ce mar..." } ``` #### unshuffled_original_ru - **Size of downloaded dataset files:** 319.76 GB - **Size of the generated dataset:** 1241.63 GB - **Total amount of disk used:** 1561.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Доступ к данному профилю для публичного просмотра закрыт администрацией сайта - профиль находится на модерации.\\nРазработчикам ..." } ``` #### unshuffled_original_sa - **Size of downloaded dataset files:** 17.52 MB - **Size of the generated dataset:** 97.06 MB - **Total amount of disk used:** 114.58 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"अनिरुद्धनगरे क्रीडिता रामलीला सम्‍प्रति समाप्‍ता अस्ति । तस्‍य कानिचन् चित्राणि पूर्वमेव प्रकाशितानि सन्ति । द्वौ चलचित्रौ अपि ..." } ``` #### unshuffled_original_sah - **Size of downloaded dataset files:** 9.08 MB - **Size of the generated dataset:** 43.82 MB - **Total amount of disk used:** 52.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████..." } ``` #### unshuffled_original_scn - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` { "id": 0, "text": "La gilusìa è nu sintimentu dulurusu ca nasci d'un disideriu di pussessu sclusivu ntê cunfrunti dâ pirsuna amata e dû timuri, dû suspettu o dâ cirtizza dâ sò nfidiltati." } ``` #### unshuffled_original_sd - **Size of downloaded dataset files:** 90.62 MB - **Size of the generated dataset:** 364.25 MB - **Total amount of disk used:** 454.88 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"هر ڪو ڄاڻي ٿو ته جڏهن توهان هڪ وڏي خريد ڪرڻ چاهيون ٿا, توهان پڄي ضروري حڪم ۾ ان جي ڪم ڪرڻ جي هٿ ۾ لاڳاپو ڪيو آهي. جي شيء آهي ته..." } ``` #### unshuffled_original_sh - **Size of downloaded dataset files:** 3.46 MB - **Size of the generated dataset:** 25.84 MB - **Total amount of disk used:** 29.30 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Opština Gornja Radgona se nalazi u sjeveroistočnoj Sloveniji i graniči s susjednom Austriji duž rijeke Mure. Sa tridesetim nase..." } ``` #### unshuffled_original_si - **Size of downloaded dataset files:** 310.93 MB - **Size of the generated dataset:** 1.47 GB - **Total amount of disk used:** 1.78 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"ලාංකීය සිතිවිලි සිංහල බ්ලොග් කියවනය කොත්තු සින්ඩිය ලංකා Blogger හත්මාළුව ලංකා බ්ලොග් කියවනය මාතලන්ගේ සින්ඩිය මොබයිල්lk\\nඅවකාශය ..." } ``` #### unshuffled_original_sk - **Size of downloaded dataset files:** 3.71 GB - **Size of the generated dataset:** 9.81 GB - **Total amount of disk used:** 13.52 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Aktivity | Agentúra podporovaného zamestnávania | vzdelávanie pre klientov, vzdelávanie pre odborníkov, kurzy\\nŠpecializované k..." } ``` #### unshuffled_original_sl - **Size of downloaded dataset files:** 956.20 MB - **Size of the generated dataset:** 2.68 GB - **Total amount of disk used:** 3.63 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Če Creatures, ki je želel, da pridejo na čas, predvsem je povedlo – razlikuje od ljubosumja začel grizenja kolen (ali zadnjica)..." } ``` #### unshuffled_original_so - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.06 MB - **Total amount of disk used:** 0.06 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт ттттттттттттттттуууууууууууу..." } ``` #### unshuffled_original_sq - **Size of downloaded dataset files:** 861.84 MB - **Size of the generated dataset:** 2.44 GB - **Total amount of disk used:** 3.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Çfarë do të më pëlqente tek një femër ose çfarë do të më shndërronte në një shpërthim drite? – Albert Vataj\\nTë gjithëve një zo..." } ``` #### unshuffled_original_sr - **Size of downloaded dataset files:** 1.08 GB - **Size of the generated dataset:** 4.13 GB - **Total amount of disk used:** 5.21 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Корисни савети за сваки дан. На сајту су разне категорије, као што су љепота, мода, кување и поправка властитим рукама.\\nШколск..." } ``` #### unshuffled_original_su - **Size of downloaded dataset files:** 0.06 MB - **Size of the generated dataset:** 0.23 MB - **Total amount of disk used:** 0.28 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Kartu krédit nyaéta \"duit plastik\" anu dikaluarkeun ku bank pikeun alat pambayaran di tempat-tempat nu tangtu samisal jiga di hotél, réstoran, tempat rékréasi jeung sajabana.[1]" } ``` #### unshuffled_original_sv - **Size of downloaded dataset files:** 17.18 GB - **Size of the generated dataset:** 47.00 GB - **Total amount of disk used:** 64.18 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"1783 är ett viktigt årtal i den nya tidens historia. Det året slöts en fred i Paris och därmed blev de 13 brittiska kolonierna ..." } ``` #### unshuffled_original_sw - **Size of downloaded dataset files:** 3.71 MB - **Size of the generated dataset:** 14.07 MB - **Total amount of disk used:** 17.78 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Miripuko hiyo inakuja mwanzoni mwa Wiki Takatifu kuelekea Pasaka na ikiwa ni wiki chache tu kabla ya Papa Francis kuanza ziara yake katika nchi hiyo yenye idadi kubwa kabisa ya watu katika ulimwengu wa nchi za Kiarabu." } ``` #### unshuffled_original_ta - **Size of downloaded dataset files:** 1.74 GB - **Size of the generated dataset:** 9.93 GB - **Total amount of disk used:** 11.67 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"பொழுது சாய்ந்து வெகு நேரமாகிவிட்டது. கூலி வேலைக்குப் போயிருந்த 'சித்தாள் ' பெண்கள் எல்லோரும் வீடு திரும்பி விட்டார்கள். இன்னும்..." } ``` #### unshuffled_original_te - **Size of downloaded dataset files:** 522.47 MB - **Size of the generated dataset:** 2.61 GB - **Total amount of disk used:** 3.13 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"హర్యానాలో టోల్ దగ్గర సిబ్బంది.. స్థానిక ప్రజలు కొట్టుకున్నారు. కర్నాల్ అనే గ్రామానికి సమీపంలో టోల్ గేట్ ఉంది. అయితే సాధారణంగా స..." } ``` #### unshuffled_original_tg - **Size of downloaded dataset files:** 90.97 MB - **Size of the generated dataset:** 397.43 MB - **Total amount of disk used:** 488.41 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Ҳумайро гуфтааст, мухолифи низом аст, низоме, ки дар Тоҷикистон вуҷуд дорад. Ба ин маънӣ, худро мухолифи давлату ҳукумати Тоҷик..." } ``` #### unshuffled_original_th - **Size of downloaded dataset files:** 7.38 GB - **Size of the generated dataset:** 38.29 GB - **Total amount of disk used:** 45.67 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ฟันที่แลดูขาวสะอาดไม่มีเศษอาหารติดอยู่ เหงือกสีชมพู ไม่เจ็บ หรือมีเลือดออกเวลาแปรงฟันหรือขัดฟัน ไม่มีปัญหาเรื่องกลิ่นปาก ทำให้ก..." } ``` #### unshuffled_original_tk - **Size of downloaded dataset files:** 2.96 MB - **Size of the generated dataset:** 10.66 MB - **Total amount of disk used:** 13.62 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"Türkmenistanyň Prezidenti agyr atletika boýunça dünýä çempionatyna taýýarlyk işleriniň barşy bilen tanyşdy\\nHalallykdan kemal t..." } ``` #### unshuffled_original_tl - **Size of downloaded dataset files:** 204.89 MB - **Size of the generated dataset:** 606.30 MB - **Total amount of disk used:** 811.19 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"“Gusto ko manawagan sa mga Unit Head ng Chanel 2 Salve. Kasi napapansin ko iyon mga alaga ko ang taping halos once a week lang,..." } ``` #### unshuffled_original_tr - **Size of downloaded dataset files:** 21.96 GB - **Size of the generated dataset:** 63.58 GB - **Total amount of disk used:** 85.54 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Son yıllarda görülen ay tutulmalarına göre daha etkili olacağı söylenen Kanlı veya Kırmızı Ay Tutulmasına saatler kaldı. Bu akş..." } ``` #### unshuffled_original_tt - **Size of downloaded dataset files:** 151.06 MB - **Size of the generated dataset:** 703.42 MB - **Total amount of disk used:** 854.47 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"\\\"Иремнең вафатына 40 көн узгач, Алмаз да безнең өйгә кереп үлде\\\". Арчада 35 яшьлек ир өстенә кондызлар ега башлаган агач төшк..." } ``` #### unshuffled_original_tyv - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.01 MB - **Total amount of disk used:** 0.01 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Экии, хүндүлуг аалчылар болгаш тыва дылдың деткикчилери! Тыва дылдың болгаш чогаалдың ховар бир башкызынга, Менги Ооржакка, ажы..." } ``` #### unshuffled_original_ug - **Size of downloaded dataset files:** 27.92 MB - **Size of the generated dataset:** 127.42 MB - **Total amount of disk used:** 155.35 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"زاڭ-ءتۇزىم | عىلىم-تەحنيكا | ءتىل-ادەبيەت | تۇرمىس | دەنە تاربيە | ساياحات-ورتا | سۋرەتتى حابار | سىر سۇحبات | ارناۋلى تاقىرىپ ..." } ``` #### unshuffled_original_uk - **Size of downloaded dataset files:** 14.42 GB - **Size of the generated dataset:** 56.44 GB - **Total amount of disk used:** 70.86 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Про надання роз'яснення (щодо форми письмового зобов'язання громадян про зворотне ввезення/вивезення товарів), Державна митна с..." } ``` #### unshuffled_original_ur - **Size of downloaded dataset files:** 712.61 MB - **Size of the generated dataset:** 2.80 GB - **Total amount of disk used:** 3.51 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"آئیے اہم اسلامی کتب کو یونیکوڈ میں انٹرنیٹ پر پیش کرنے کے لئے مل جل کر آن لائن ٹائپنگ کریں۔ محدث ٹائپنگ پراجیکٹ کے ذریعے آپ روز..." } ``` #### unshuffled_original_uz - **Size of downloaded dataset files:** 5.78 MB - **Size of the generated dataset:** 21.46 MB - **Total amount of disk used:** 27.24 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Qurama tog'lari tizmasining Toshkentdan 154 km uzoqlikdagi Toshkent-Ush yo'li yeqasidaxushmanzara tabiat qo'ynida joylashgan maydoni 30 ga.\nBolalarni sog'lomlashtirish oromgohi Bo'stonliq tumani Oqtosh muntaqasining soy-salqin gushasida joylashgan." } ``` #### unshuffled_original_vec - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.02 MB - **Total amount of disk used:** 0.03 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Par ogni pónto, ła derivada ła xe ła pendensa de ła reta tangente a ła curva de ła funsion f. Ła reta de cołor róso l'è senpre ..." } ``` #### unshuffled_original_vi - **Size of downloaded dataset files:** 21.50 GB - **Size of the generated dataset:** 72.23 GB - **Total amount of disk used:** 93.73 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Canh chua cá bông lau không chỉ là món ăn giải nhiệt, thanh mát ngày hè mà còn là món siêu bổ dưỡng, rất tốt cho người gầy ốm. ..." } ``` #### unshuffled_original_vo - **Size of downloaded dataset files:** 0.30 MB - **Size of the generated dataset:** 2.12 MB - **Total amount of disk used:** 2.42 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Sarniguet binon zif in ziläk: Hautes-Pyrénées, in topäd: Midi-Pyrénées, in Fransän. Sarniguet topon videtü 43°19’ 7’’ N e lunetü 0°5’ 19’’ L." } ``` #### unshuffled_original_wa - **Size of downloaded dataset files:** 0.09 MB - **Size of the generated dataset:** 0.29 MB - **Total amount of disk used:** 0.38 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "Cisse pådje ci n' est co k' on djermon, dj' ô bén k' el pådje est djusse sibåtcheye, eyet co trop tene; et s' divreut ele ecråxhî ene miete." } ``` #### unshuffled_original_war - **Size of downloaded dataset files:** 0.64 MB - **Size of the generated dataset:** 2.68 MB - **Total amount of disk used:** 3.32 MB An example of 'train' looks as follows. ``` { "id": 1, "text": "An Honce amo in usa ka baryo ngan munisipalidad ha distrito han Rožňava ha rehiyon han Košice ha nasod han Slovakia.\nAn Rumegies amo in usa ka komyun ha departamento han Nord ngan ha rehiyon han Nord-Pas-de-Calais ha nasod han Fransya." } ``` #### unshuffled_original_wuu - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.13 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"伊春元旦天气 伊春腊八天气 伊春春节天气 伊春情人节天气 伊春元宵节天气 伊春愚人节天气 伊春清明节天气 伊春劳动节天气 伊春母亲节天气 伊春端午节天气 伊春七夕节天气 伊春教师节天气 伊春中秋节天气 伊春国庆节天气 伊春重阳节天气 伊春万圣节天气 伊春..." } ``` #### unshuffled_original_xal - **Size of downloaded dataset files:** 0.03 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.15 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Арнгудин Орн гисн Европд бәәдг һазр. 2007 җилин тooһaр эн орн нутгт 3,600,523 әмтн бәәдг билә. Арнгудин Орнин хотл балһсна нерн..." } ``` #### unshuffled_original_xmf - **Size of downloaded dataset files:** 1.05 MB - **Size of the generated dataset:** 6.12 MB - **Total amount of disk used:** 7.17 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"მოჩამილი ტექსტი წჷმორინელი რე Creative Commons Attribution-ShareAlike ლიცენზიათ; შილებე გეძინელი პირობეფიშ არსებუა. კილიშკილიშა..." } ``` #### unshuffled_original_yi - **Size of downloaded dataset files:** 33.33 MB - **Size of the generated dataset:** 147.60 MB - **Total amount of disk used:** 180.94 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"ממשותדיק - חבֿרה, איך אַרבעט איצט אױף אַ זשורנאַל. טאָמער איר האָט עפּעס צוצוגעבן זאָלט איר שיקן מיר אַן אָנזאָג. ס'װעט הײסן \\\"..." } ``` #### unshuffled_original_yo - **Size of downloaded dataset files:** 0.01 MB - **Size of the generated dataset:** 0.06 MB - **Total amount of disk used:** 0.06 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 0, "text": "\"Copyright © 2018 BBC. BBC kò mọ̀ nípa àwọn ohun tí ó wà ní àwọn ojú òpó tí ó wà ní ìta. Ọwọ́ tí a fi mú ìbáṣepọ̀ ti ìta.\"..." } ``` #### unshuffled_original_yue - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 0.00 MB - **Total amount of disk used:** 0.00 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 你還不爆 我累了 投降輸一半可以嗎\"..." } ``` #### unshuffled_original_zh - **Size of downloaded dataset files:** 206.00 GB - **Size of the generated dataset:** 545.61 GB - **Total amount of disk used:** 751.61 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "id": 1, "text": "\"中国铝灰网 中国有色金属矿产网 中国黄莲网 中国水轮发电机网 中国抽油泵网 中国数控雕刻机网 中国不锈钢抛光网 中国磨具加工网 中国压铸铝网 中国耐水腻子网 中国手机摄像头网 中国粗粮网 中国车门锁网 中国钛粉网 中国轮圈网\\n天天中奖彩票图 天天中彩票..." } ``` </details> ### Data Fields The data fields are the same among all configs. - `id`: a `int64` feature. - `text`: a `string` feature. ### Data Splits <details> <summary>Click to expand the number of samples per configuration</summary> | Language | Language code | Name original | Train original | Words original | Size original | Name deduplicated | Train deduplicated | Words deduplicated | Size deduplicated | | ----------------- | ------------- | ----------------------- | -------------- | --------------- | ------------- | --------------------------- | ------------------ | ------------------ | ----------------- | | Afrikaans | af | unshuffled_original_af | 201117 | 43,482,801 | 241M | unshuffled_deduplicated_af | 130640 | 29,533,437 | 163M | | Albanian | sq | unshuffled_original_sq | 672077 | 374,196,110 | 2.3G | unshuffled_deduplicated_sq | 461598 | 186,856,699 | 1.2G | | Alemannic | als | unshuffled_original_als | 7324 | 841,750 | 5.0M | unshuffled_deduplicated_als | 4518 | 459,001 | 2.8M | | Amharic | am | unshuffled_original_am | 83663 | 28,301,601 | 360M | unshuffled_deduplicated_am | 43102 | 16,086,628 | 206M | | Arabic | ar | unshuffled_original_ar | 16365602 | 8,117,162,828 | 82G | unshuffled_deduplicated_ar | 9006977 | 3,171,221,354 | 32G | | Aragonese | an | unshuffled_original_an | 2449 | 52,896 | 1.3M | unshuffled_deduplicated_an | 2025 | 45,669 | 801K | | Armenian | hy | unshuffled_original_hy | 659430 | 273,919,388 | 3.7G | unshuffled_deduplicated_hy | 396093 | 110,196,043 | 1.5G | | Assamese | as | unshuffled_original_as | 14985 | 6,956,663 | 113M | unshuffled_deduplicated_as | 9212 | 4,366,570 | 71M | | Asturian | ast | unshuffled_original_ast | 6999 | 381,005 | 2.4M | unshuffled_deduplicated_ast | 5343 | 325,237 | 2.0M | | Avaric | av | unshuffled_original_av | 456 | 24,720 | 409K | unshuffled_deduplicated_av | 360 | 19,478 | 324K | | Azerbaijani | az | unshuffled_original_az | 912330 | 322,641,710 | 2.8G | unshuffled_deduplicated_az | 626796 | 167,742,296 | 1.5G | | Bashkir | ba | unshuffled_original_ba | 42551 | 9,796,764 | 128M | unshuffled_deduplicated_ba | 27050 | 6,922,589 | 90M | | Basque | eu | unshuffled_original_eu | 506883 | 120,456,652 | 848M | unshuffled_deduplicated_eu | 256513 | 45,359,710 | 342M | | Bavarian | bar | unshuffled_original_bar | 4 | 399 | 503 | unshuffled_deduplicated_bar | 4 | 399 | 503 | | Belarusian | be | unshuffled_original_be | 586031 | 144,579,630 | 1.8G | unshuffled_deduplicated_be | 307405 | 83,499,037 | 1.1G | | Bengali | bn | unshuffled_original_bn | 1675515 | 623,575,733 | 11G | unshuffled_deduplicated_bn | 1114481 | 363,766,143 | 5.8G | | Bihari | bh | unshuffled_original_bh | 336 | 8,848 | 110K | unshuffled_deduplicated_bh | 82 | 2,875 | 34K | | Bishnupriya | bpy | unshuffled_original_bpy | 6046 | 198,286 | 4.1M | unshuffled_deduplicated_bpy | 1770 | 96,940 | 1.7M | | Bosnian | bs | unshuffled_original_bs | 2143 | 106,448 | 447K | unshuffled_deduplicated_bs | 702 | 20,485 | 116K | | Breton | br | unshuffled_original_br | 37085 | 5,013,241 | 29M | unshuffled_deduplicated_br | 14724 | 2,890,384 | 16M | | Bulgarian | bg | unshuffled_original_bg | 5869686 | 2,947,648,106 | 32G | unshuffled_deduplicated_bg | 3398679 | 1,268,114,977 | 14G | | Burmese | my | unshuffled_original_my | 232329 | 56,111,184 | 1.9G | unshuffled_deduplicated_my | 136639 | 30,102,173 | 1.1G | | Catalan | ca | unshuffled_original_ca | 4390754 | 1,360,212,450 | 8.0G | unshuffled_deduplicated_ca | 2458067 | 729,333,440 | 4.3G | | Cebuano | ceb | unshuffled_original_ceb | 56248 | 6,603,567 | 39M | unshuffled_deduplicated_ceb | 26145 | 3,675,024 | 24M | | Central Bikol | bcl | unshuffled_original_bcl | 1 | 312 | 885 | unshuffled_deduplicated_bcl | 1 | 312 | 885 | | Central Khmer | km | unshuffled_original_km | 159363 | 20,690,610 | 1.1G | unshuffled_deduplicated_km | 108346 | 10,082,245 | 581M | | Central Kurdish | ckb | unshuffled_original_ckb | 103639 | 48,478,334 | 487M | unshuffled_deduplicated_ckb | 68210 | 18,726,721 | 226M | | Chavacano | cbk | unshuffled_original_cbk | 1 | 130 | 520 | unshuffled_deduplicated_cbk | 1 | 130 | 520 | | Chechen | ce | unshuffled_original_ce | 4042 | 711,051 | 8.3M | unshuffled_deduplicated_ce | 2984 | 568,146 | 6.7M | | Chinese | zh | unshuffled_original_zh | 60137667 | 14,986,424,850 | 508G | unshuffled_deduplicated_zh | 41708901 | 6,350,215,113 | 249G | | Chuvash | cv | unshuffled_original_cv | 20281 | 3,041,614 | 39M | unshuffled_deduplicated_cv | 10130 | 2,054,810 | 26M | | Cornish | kw | unshuffled_original_kw | 203 | 8,329 | 44K | unshuffled_deduplicated_kw | 68 | 2,704 | 14K | | Croatian | hr | unshuffled_original_hr | 582219 | 34,232,765 | 226M | unshuffled_deduplicated_hr | 321484 | 16,727,640 | 110M | | Czech | cs | unshuffled_original_cs | 21001388 | 7,715,977,441 | 53G | unshuffled_deduplicated_cs | 12308039 | 3,540,997,509 | 24G | | Danish | da | unshuffled_original_da | 7664010 | 2,637,463,889 | 16G | unshuffled_deduplicated_da | 4771098 | 1,620,091,317 | 9.5G | | Dhivehi | dv | unshuffled_original_dv | 21018 | 7,559,472 | 126M | unshuffled_deduplicated_dv | 17024 | 4,726,660 | 79M | | Dimli | diq | unshuffled_original_diq | 1 | 19 | 146 | unshuffled_deduplicated_diq | 1 | 19 | 146 | | Dutch | nl | unshuffled_original_nl | 34682142 | 13,020,136,373 | 78G | unshuffled_deduplicated_nl | 20812149 | 6,598,786,137 | 39G | | Eastern Mari | mhr | unshuffled_original_mhr | 3212 | 565,992 | 7.2M | unshuffled_deduplicated_mhr | 2515 | 469,297 | 6.0M | | Egyptian Arabic | arz | unshuffled_original_arz | 158113 | 7,305,151 | 66M | unshuffled_deduplicated_arz | 79928 | 3,659,419 | 33M | | Emilian-Romagnol | eml | unshuffled_original_eml | 84 | 6,376 | 25K | unshuffled_deduplicated_eml | 80 | 6,121 | 24K | | English | en | unshuffled_original_en | 455994980 | 418,187,793,408 | 2.3T | unshuffled_deduplicated_en | 304230423 | 215,841,256,971 | 1.2T | | Erzya | myv | unshuffled_original_myv | 6 | 90 | 1.4K | unshuffled_deduplicated_myv | 5 | 78 | 1.2K | | Esperanto | eo | unshuffled_original_eo | 121171 | 48,486,161 | 299M | unshuffled_deduplicated_eo | 84752 | 37,324,446 | 228M | | Estonian | et | unshuffled_original_et | 2093621 | 643,163,730 | 4.8G | unshuffled_deduplicated_et | 1172041 | 309,931,463 | 2.3G | | Finnish | fi | unshuffled_original_fi | 8557453 | 3,196,666,419 | 27G | unshuffled_deduplicated_fi | 5326443 | 1,597,855,468 | 13G | | French | fr | unshuffled_original_fr | 96742378 | 46,896,036,417 | 282G | unshuffled_deduplicated_fr | 59448891 | 23,206,776,649 | 138G | | Galician | gl | unshuffled_original_gl | 544388 | 102,011,291 | 620M | unshuffled_deduplicated_gl | 284320 | 63,600,602 | 384M | | Georgian | ka | unshuffled_original_ka | 563916 | 171,950,621 | 3.6G | unshuffled_deduplicated_ka | 372158 | 91,569,739 | 1.9G | | German | de | unshuffled_original_de | 104913504 | 44,878,908,446 | 308G | unshuffled_deduplicated_de | 62398034 | 21,529,164,172 | 145G | | Goan Konkani | gom | unshuffled_original_gom | 640 | 124,277 | 2.2M | unshuffled_deduplicated_gom | 484 | 102,306 | 1.8M | | Guarani | gn | unshuffled_original_gn | 106 | 7,382 | 36K | unshuffled_deduplicated_gn | 68 | 4,680 | 24K | | Gujarati | gu | unshuffled_original_gu | 240691 | 72,045,701 | 1.1G | unshuffled_deduplicated_gu | 169834 | 50,023,432 | 722M | | Haitian | ht | unshuffled_original_ht | 13 | 1,014 | 3.9K | unshuffled_deduplicated_ht | 9 | 832 | 3.3K | | Hebrew | he | unshuffled_original_he | 3808397 | 2,067,753,528 | 20G | unshuffled_deduplicated_he | 2375030 | 1,032,018,056 | 9.8G | | Hindi | hi | unshuffled_original_hi | 3264660 | 1,372,234,782 | 17G | unshuffled_deduplicated_hi | 1909387 | 745,774,934 | 8.9G | | Hungarian | hu | unshuffled_original_hu | 11197780 | 5,163,936,345 | 40G | unshuffled_deduplicated_hu | 6582908 | 2,339,127,555 | 18G | | Icelandic | is | unshuffled_original_is | 625673 | 219,900,094 | 1.5G | unshuffled_deduplicated_is | 389515 | 129,818,331 | 846M | | Ido | io | unshuffled_original_io | 694 | 25,702 | 147K | unshuffled_deduplicated_io | 617 | 22,773 | 130K | | Iloko | ilo | unshuffled_original_ilo | 2638 | 142,942 | 874K | unshuffled_deduplicated_ilo | 1578 | 105,564 | 636K | | Indonesian | id | unshuffled_original_id | 16236463 | 4,574,692,265 | 30G | unshuffled_deduplicated_id | 9948521 | 2,394,957,629 | 16G | | Interlingua | ia | unshuffled_original_ia | 1040 | 180,231 | 662K | unshuffled_deduplicated_ia | 529 | 100,019 | 360K | | Interlingue | ie | unshuffled_original_ie | 101 | 5,352 | 24K | unshuffled_deduplicated_ie | 11 | 602 | 1.6K | | Irish | ga | unshuffled_original_ga | 83223 | 14,483,593 | 88M | unshuffled_deduplicated_ga | 46493 | 10,017,303 | 60M | | Italian | it | unshuffled_original_it | 46981781 | 22,248,707,341 | 137G | unshuffled_deduplicated_it | 28522082 | 11,250,012,896 | 69G | | Japanese | ja | unshuffled_original_ja | 62721527 | 4,962,979,182 | 216G | unshuffled_deduplicated_ja | 39496439 | 1,123,067,063 | 106G | | Javanese | jv | unshuffled_original_jv | 1445 | 104,896 | 659K | unshuffled_deduplicated_jv | 1163 | 86,654 | 583K | | Kalmyk | xal | unshuffled_original_xal | 39 | 10,277 | 113K | unshuffled_deduplicated_xal | 36 | 10,155 | 112K | | Kannada | kn | unshuffled_original_kn | 350363 | 81,186,863 | 1.7G | unshuffled_deduplicated_kn | 251064 | 49,343,462 | 1.1G | | Karachay-Balkar | krc | unshuffled_original_krc | 1581 | 185,436 | 2.6M | unshuffled_deduplicated_krc | 1377 | 166,496 | 2.3M | | Kazakh | kk | unshuffled_original_kk | 524591 | 191,126,469 | 2.7G | unshuffled_deduplicated_kk | 338073 | 108,388,743 | 1.5G | | Kirghiz | ky | unshuffled_original_ky | 146993 | 44,194,823 | 600M | unshuffled_deduplicated_ky | 86561 | 28,982,620 | 388M | | Komi | kv | unshuffled_original_kv | 1549 | 201,404 | 2.3M | unshuffled_deduplicated_kv | 924 | 95,243 | 1.2M | | Korean | ko | unshuffled_original_ko | 7345075 | 2,368,765,142 | 24G | unshuffled_deduplicated_ko | 3675420 | 1,120,375,149 | 12G | | Kurdish | ku | unshuffled_original_ku | 46535 | 15,561,003 | 94M | unshuffled_deduplicated_ku | 29054 | 9,946,440 | 60M | | Lao | lo | unshuffled_original_lo | 52910 | 4,133,311 | 174M | unshuffled_deduplicated_lo | 32652 | 2,583,342 | 114M | | Latin | la | unshuffled_original_la | 94588 | 4,122,201 | 26M | unshuffled_deduplicated_la | 18808 | 1,328,038 | 8.3M | | Latvian | lv | unshuffled_original_lv | 1593820 | 520,761,977 | 4.0G | unshuffled_deduplicated_lv | 843195 | 236,428,905 | 1.8G | | Lezghian | lez | unshuffled_original_lez | 1485 | 247,646 | 3.3M | unshuffled_deduplicated_lez | 1381 | 224,871 | 3.0M | | Limburgan | li | unshuffled_original_li | 137 | 4,730 | 29K | unshuffled_deduplicated_li | 118 | 4,283 | 27K | | Lithuanian | lt | unshuffled_original_lt | 2977757 | 1,159,661,742 | 8.8G | unshuffled_deduplicated_lt | 1737411 | 516,183,525 | 3.9G | | Lojban | jbo | unshuffled_original_jbo | 832 | 154,330 | 736K | unshuffled_deduplicated_jbo | 617 | 141,973 | 678K | | Lombard | lmo | unshuffled_original_lmo | 1401 | 75,229 | 443K | unshuffled_deduplicated_lmo | 1374 | 73,665 | 433K | | Low German | nds | unshuffled_original_nds | 18174 | 2,906,347 | 18M | unshuffled_deduplicated_nds | 8714 | 2,146,417 | 13M | | Lower Sorbian | dsb | unshuffled_original_dsb | 65 | 1,787 | 13K | unshuffled_deduplicated_dsb | 37 | 966 | 7.1K | | Luxembourgish | lb | unshuffled_original_lb | 34807 | 4,403,577 | 29M | unshuffled_deduplicated_lb | 21735 | 3,087,650 | 21M | | Macedonian | mk | unshuffled_original_mk | 437871 | 189,289,873 | 2.1G | unshuffled_deduplicated_mk | 299457 | 102,849,595 | 1.2G | | Maithili | mai | unshuffled_original_mai | 123 | 69,161 | 317K | unshuffled_deduplicated_mai | 25 | 874 | 11K | | Malagasy | mg | unshuffled_original_mg | 17957 | 3,068,360 | 21M | unshuffled_deduplicated_mg | 13343 | 1,872,044 | 13M | | Malay | ms | unshuffled_original_ms | 534016 | 16,696,882 | 111M | unshuffled_deduplicated_ms | 183443 | 6,045,753 | 42M | | Malayalam | ml | unshuffled_original_ml | 603937 | 189,534,472 | 4.9G | unshuffled_deduplicated_ml | 453904 | 95,892,551 | 2.5G | | Maltese | mt | unshuffled_original_mt | 26598 | 2,995,654 | 24M | unshuffled_deduplicated_mt | 16383 | 2,163,358 | 17M | | Marathi | mr | unshuffled_original_mr | 326804 | 162,609,404 | 2.7G | unshuffled_deduplicated_mr | 212556 | 82,130,803 | 1.4G | | Mazanderani | mzn | unshuffled_original_mzn | 1055 | 73,870 | 691K | unshuffled_deduplicated_mzn | 917 | 64,481 | 602K | | Minangkabau | min | unshuffled_original_min | 220 | 5,682 | 608K | unshuffled_deduplicated_min | 166 | 4,825 | 310K | | Mingrelian | xmf | unshuffled_original_xmf | 3783 | 299,098 | 5.8M | unshuffled_deduplicated_xmf | 2418 | 228,629 | 4.4M | | Mirandese | mwl | unshuffled_original_mwl | 8 | 171 | 1.2K | unshuffled_deduplicated_mwl | 7 | 152 | 1.1K | | Modern Greek | el | unshuffled_original_el | 10425596 | 5,479,180,137 | 62G | unshuffled_deduplicated_el | 6521169 | 2,412,419,435 | 27G | | Mongolian | mn | unshuffled_original_mn | 395605 | 181,307,167 | 2.2G | unshuffled_deduplicated_mn | 197878 | 68,362,013 | 838M | | Nahuatl languages | nah | unshuffled_original_nah | 61 | 1,234 | 12K | unshuffled_deduplicated_nah | 58 | 1,193 | 11K | | Neapolitan | nap | unshuffled_original_nap | 73 | 5,282 | 17K | unshuffled_deduplicated_nap | 55 | 4,147 | 13K | | Nepali | ne | unshuffled_original_ne | 299938 | 107,448,208 | 1.8G | unshuffled_deduplicated_ne | 219334 | 71,628,317 | 1.2G | | Newari | new | unshuffled_original_new | 4696 | 564,697 | 5.5M | unshuffled_deduplicated_new | 2126 | 288,995 | 4.1M | | Northern Frisian | frr | unshuffled_original_frr | 7 | 1,516 | 4.4K | unshuffled_deduplicated_frr | 7 | 1,516 | 4.4K | | Northern Luri | lrc | unshuffled_original_lrc | 88 | 8,022 | 76K | unshuffled_deduplicated_lrc | 72 | 6,740 | 63K | | Norwegian | no | unshuffled_original_no | 5546211 | 1,344,326,388 | 8.0G | unshuffled_deduplicated_no | 3229940 | 804,894,377 | 4.7G | | Norwegian Nynorsk | nn | unshuffled_original_nn | 185884 | 14,764,980 | 85M | unshuffled_deduplicated_nn | 109118 | 9,435,139 | 54M | | Occitan | oc | unshuffled_original_oc | 10709 | 750,301 | 5.8M | unshuffled_deduplicated_oc | 6485 | 512,678 | 3.7M | | Oriya | or | unshuffled_original_or | 59463 | 14,938,567 | 248M | unshuffled_deduplicated_or | 44230 | 11,321,740 | 188M | | Ossetian | os | unshuffled_original_os | 5213 | 1,031,268 | 13M | unshuffled_deduplicated_os | 2559 | 878,765 | 11M | | Pampanga | pam | unshuffled_original_pam | 3 | 130 | 760 | unshuffled_deduplicated_pam | 1 | 52 | 304 | | Panjabi | pa | unshuffled_original_pa | 127467 | 61,847,806 | 763M | unshuffled_deduplicated_pa | 87235 | 37,555,835 | 460M | | Persian | fa | unshuffled_original_fa | 13704702 | 9,096,554,121 | 79G | unshuffled_deduplicated_fa | 8203495 | 4,363,505,319 | 38G | | Piemontese | pms | unshuffled_original_pms | 3225 | 362,013 | 2.1M | unshuffled_deduplicated_pms | 2859 | 337,246 | 1.9M | | Polish | pl | unshuffled_original_pl | 35440972 | 15,277,255,137 | 109G | unshuffled_deduplicated_pl | 20682611 | 6,708,709,674 | 47G | | Portuguese | pt | unshuffled_original_pt | 42114520 | 20,641,903,898 | 124G | unshuffled_deduplicated_pt | 26920397 | 10,751,156,918 | 64G | | Pushto | ps | unshuffled_original_ps | 98216 | 46,559,441 | 361M | unshuffled_deduplicated_ps | 67921 | 31,347,348 | 242M | | Quechua | qu | unshuffled_original_qu | 452 | 10,186 | 78K | unshuffled_deduplicated_qu | 411 | 8,691 | 67K | | Romanian | ro | unshuffled_original_ro | 9387265 | 3,984,317,058 | 25G | unshuffled_deduplicated_ro | 5044757 | 1,741,794,069 | 11G | | Romansh | rm | unshuffled_original_rm | 41 | 1,093 | 7.4K | unshuffled_deduplicated_rm | 34 | 960 | 6.5K | | Russia Buriat | bxr | unshuffled_original_bxr | 42 | 963 | 13K | unshuffled_deduplicated_bxr | 36 | 809 | 11K | | Russian | ru | unshuffled_original_ru | 161836003 | 92,522,407,837 | 1.2T | unshuffled_deduplicated_ru | 115954598 | 46,692,691,520 | 568G | | Sanskrit | sa | unshuffled_original_sa | 14291 | 4,331,569 | 93M | unshuffled_deduplicated_sa | 7121 | 1,713,930 | 37M | | Scottish Gaelic | gd | unshuffled_original_gd | 5799 | 310,689 | 1.9M | unshuffled_deduplicated_gd | 3883 | 207,110 | 1.3M | | Serbian | sr | unshuffled_original_sr | 1013619 | 364,395,411 | 3.9G | unshuffled_deduplicated_sr | 645747 | 207,561,168 | 2.2G | | Serbo-Croatian | sh | unshuffled_original_sh | 36700 | 5,292,184 | 25M | unshuffled_deduplicated_sh | 17610 | 1,040,573 | 5.8M | | Sicilian | scn | unshuffled_original_scn | 21 | 554 | 3.3K | unshuffled_deduplicated_scn | 17 | 468 | 2.8K | | Sindhi | sd | unshuffled_original_sd | 44280 | 43,530,158 | 347M | unshuffled_deduplicated_sd | 33925 | 33,028,015 | 263M | | Sinhala | si | unshuffled_original_si | 203082 | 93,053,465 | 1.4G | unshuffled_deduplicated_si | 120684 | 50,864,857 | 802M | | Slovak | sk | unshuffled_original_sk | 5492194 | 1,322,247,763 | 9.1G | unshuffled_deduplicated_sk | 2820821 | 656,346,179 | 4.5G | | Slovenian | sl | unshuffled_original_sl | 1746604 | 387,399,700 | 2.5G | unshuffled_deduplicated_sl | 886223 | 193,926,684 | 1.3G | | Somali | so | unshuffled_original_so | 156 | 1,202 | 61K | unshuffled_deduplicated_so | 42 | 472 | 16K | | South Azerbaijani | azb | unshuffled_original_azb | 15446 | 2,175,054 | 27M | unshuffled_deduplicated_azb | 9985 | 1,528,709 | 19M | | Spanish | es | unshuffled_original_es | 88199221 | 47,545,122,279 | 278G | unshuffled_deduplicated_es | 56326016 | 25,928,290,729 | 149G | | Sundanese | su | unshuffled_original_su | 805 | 30,321 | 211K | unshuffled_deduplicated_su | 511 | 20,278 | 141K | | Swahili | sw | unshuffled_original_sw | 41986 | 2,211,927 | 13M | unshuffled_deduplicated_sw | 24803 | 1,376,963 | 8.1M | | Swedish | sv | unshuffled_original_sv | 17395625 | 7,155,994,312 | 44G | unshuffled_deduplicated_sv | 11014487 | 4,106,120,608 | 25G | | Tagalog | tl | unshuffled_original_tl | 458206 | 98,949,299 | 573M | unshuffled_deduplicated_tl | 294132 | 70,121,601 | 407M | | Tajik | tg | unshuffled_original_tg | 89002 | 31,758,142 | 379M | unshuffled_deduplicated_tg | 56259 | 21,029,893 | 249M | | Tamil | ta | unshuffled_original_ta | 1263280 | 420,537,132 | 9.3G | unshuffled_deduplicated_ta | 833101 | 226,013,330 | 5.1G | | Tatar | tt | unshuffled_original_tt | 135923 | 51,034,893 | 670M | unshuffled_deduplicated_tt | 82738 | 23,825,695 | 305M | | Telugu | te | unshuffled_original_te | 475703 | 123,711,517 | 2.5G | unshuffled_deduplicated_te | 312644 | 79,094,167 | 1.6G | | Thai | th | unshuffled_original_th | 6064129 | 951,743,087 | 36G | unshuffled_deduplicated_th | 3749826 | 368,965,202 | 16G | | Tibetan | bo | unshuffled_original_bo | 26795 | 1,483,589 | 187M | unshuffled_deduplicated_bo | 15762 | 936,556 | 138M | | Turkish | tr | unshuffled_original_tr | 18535253 | 7,577,388,700 | 60G | unshuffled_deduplicated_tr | 11596446 | 3,365,734,289 | 27G | | Turkmen | tk | unshuffled_original_tk | 6456 | 1,113,869 | 11M | unshuffled_deduplicated_tk | 4694 | 752,326 | 6.8M | | Tuvinian | tyv | unshuffled_original_tyv | 34 | 759 | 12K | unshuffled_deduplicated_tyv | 24 | 540 | 7.9K | | Uighur | ug | unshuffled_original_ug | 22255 | 8,657,141 | 122M | unshuffled_deduplicated_ug | 15503 | 5,852,225 | 83M | | Ukrainian | uk | unshuffled_original_uk | 12973467 | 4,204,381,276 | 53G | unshuffled_deduplicated_uk | 7782375 | 2,252,380,351 | 28G | | Upper Sorbian | hsb | unshuffled_original_hsb | 7959 | 545,351 | 4.2M | unshuffled_deduplicated_hsb | 3084 | 236,867 | 1.8M | | Urdu | ur | unshuffled_original_ur | 638596 | 331,817,982 | 2.7G | unshuffled_deduplicated_ur | 428674 | 218,030,228 | 1.7G | | Uzbek | uz | unshuffled_original_uz | 27537 | 2,450,256 | 21M | unshuffled_deduplicated_uz | 15074 | 1,381,644 | 12M | | Venetian | vec | unshuffled_original_vec | 73 | 3,492 | 18K | unshuffled_deduplicated_vec | 64 | 3,199 | 17K | | Vietnamese | vi | unshuffled_original_vi | 14898250 | 12,036,845,359 | 68G | unshuffled_deduplicated_vi | 9897709 | 5,577,159,843 | 32G | | Volapük | vo | unshuffled_original_vo | 3366 | 321,121 | 2.0M | unshuffled_deduplicated_vo | 3317 | 318,568 | 2.0M | | Walloon | wa | unshuffled_original_wa | 1001 | 50,720 | 273K | unshuffled_deduplicated_wa | 677 | 37,543 | 203K | | Waray | war | unshuffled_original_war | 9760 | 397,315 | 2.5M | unshuffled_deduplicated_war | 9161 | 336,311 | 2.2M | | Welsh | cy | unshuffled_original_cy | 157698 | 37,422,441 | 213M | unshuffled_deduplicated_cy | 98225 | 23,574,673 | 133M | | Western Frisian | fy | unshuffled_original_fy | 33053 | 5,691,077 | 35M | unshuffled_deduplicated_fy | 20661 | 4,223,816 | 26M | | Western Mari | mrj | unshuffled_original_mrj | 757 | 93,338 | 1.2M | unshuffled_deduplicated_mrj | 669 | 87,780 | 1.1M | | Western Panjabi | pnb | unshuffled_original_pnb | 4599 | 1,426,986 | 12M | unshuffled_deduplicated_pnb | 3463 | 1,111,112 | 9.0M | | Wu Chinese | wuu | unshuffled_original_wuu | 214 | 11,189 | 109K | unshuffled_deduplicated_wuu | 64 | 4,333 | 32K | | Yakut | sah | unshuffled_original_sah | 22301 | 2,547,623 | 42M | unshuffled_deduplicated_sah | 8555 | 1,789,174 | 26M | | Yiddish | yi | unshuffled_original_yi | 59364 | 13,834,320 | 141M | unshuffled_deduplicated_yi | 32919 | 8,212,970 | 84M | | Yoruba | yo | unshuffled_original_yo | 214 | 8,906 | 55K | unshuffled_deduplicated_yo | 49 | 3,518 | 27K | | Yue Chinese | yue | unshuffled_original_yue | 11 | 186 | 3.7K | unshuffled_deduplicated_yue | 7 | 128 | 2.2K | </details> ## Dataset Creation ### Curation Rationale OSCAR was constructed new pipeline derived from the [fastText's one](https://github.com/facebookresearch/fastText), called [_goclassy_](https://github.com/pjox/goclassy). Goclassy reuses the [fastText linear classifier](https://fasttext.cc) and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner. The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the [Go programming language](https://golang.org/) so it lets the [Go runtime](https://golang.org/src/runtime/mprof.go) handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process. Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed. ### Source Data #### Initial Data Collection and Normalization [Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies. Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics. To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **November 2018** snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header. #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process N/A #### Who are the annotators? N/A ### Personal and Sensitive Information Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models. ## Considerations for Using the Data ### Social Impact of Dataset OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures. ### Discussion of Biases OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models. ### Other Known Limitations The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571). ## Additional Information ### Dataset Curators The corpus was put together by [Pedro J. Ortiz](https://pjortiz.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/). ### Licensing Information These data are released under this licensing scheme We do not own any of the text from which these data has been extracted. We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/ To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR This work is published from: France. Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. * Clearly identify the copyrighted work claimed to be infringed. * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. We will comply to legitimate requests by removing the affected sources from the next release of the corpus. ### Citation Information ``` @inproceedings{ortiz-suarez-etal-2020-monolingual, title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages", author = "Ortiz Su{'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.156", pages = "1703--1714", abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{"u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} } ``` ### Contributions Thanks to [@pjox](https://github.com/pjox) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.