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jlbaker361/blender_animals

jlbaker361

--- dataset_info: features: - name: image dtype: image - name: character dtype: string - name: x dtype: string - name: y dtype: string - name: x_1 dtype: string - name: y_1 dtype: string - name: angle dtype: string splits: - name: train num_bytes: 528387.0 num_examples: 4 download_size: 522511 dataset_size: 528387.0 --- # Dataset Card for "blender_animals" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bmmm/Equilibrium-Traffic-Networks

bmmm

--- license: mit --- # Equilibrium-Traffic-Networks This document describes the datasets generated for and used in the study "A hybrid deep-learning-metaheuristic framework for bi-level network design problems" by Bahman Madadi and Gonçalo H. de Almeida Correia, published in Expert Systems with Applications. The datasets are generated and used to train and evaluate models for solving the User Equilibrium (UE) problem on three transportation networks (Sioux-Falls, Eastern-Massachusetts, and Anaheim) from the well-known "transport networks for research" repository. You can just download the datasets from [this repository](https://doi.org/10.6084/m9.figshare.27889251.v1) with the comprehensive metadata. Dataset citation: Madadi, Bahman (2024). Equilibrium-Traffic-Networks. figshare. Dataset. https://doi.org/10.6084/m9.figshare.27889251.v1 ## Metadata | Network | Nodes | Edges | OD Pairs | Train Samples | Val Samples | Test Samples | Dataset Size | Solvers | Algorithm | |-----------------------|-------|-------|----------|---------------|-------------|--------------|--------------|---------|-----------| | SiouxFalls | 24 | 76 | 576 | 18000 | 1000 | 1000 | 20,000 | Aeq, Ipp| BFW | | Eastern-Massachusetts | 74 | 258 | 5476 | 4000 | 500 | 500 | 5,000 | Aeq, Ipp| BFW | | Anaheim | 416 | 914 | 1444 | 4000 | 500 | 500 | 5,000 | Aeq, Ipp| BFW | ### Features and Data Fields | Field | Type | Description | |----------------------|--------|--------------------------------------------------| | Node Features | Array | Represent origin-destination (OD) demand matrices for travel. Each OD pair specifies travel demand between zones. | | Edge Features | Array | Include: Free-flow travel time (FFTT) and Capacity. | | Edge Labels | Array | Optimal link flows derived from solving the DUE problem. | | Number of Links | Int | Number of links in the network. | | Number of Nodes | Int | Number of nodes in the network. | | Number of OD Pairs | Int | Number of origin-destination pairs in the network. | | Train Split | Int | Number of samples in the training set. | | Validation Split | Int | Number of samples in the validation set. | | Test Split | Int | Number of samples in the test set. | | Dataset Size | Int | Total number of samples in the dataset. | | Solvers | String | Solvers used for generating the dataset. | | Algorithm | String | Algorithm used for generating the dataset. | ## Datasets The datasets are generated using the scripts `data_due_generate.py` and `data_dataset_prep.py` from the GitHub repository. Each dataset corresponds to a different transportation network and contains solved instances of the DUE problem. ### Dataset Structure Each dataset is stored as a pickle file and contains three splits: train, validation, and test. Each split is a list of DGLGraph objects with node and edge features, along with edge labels. - **Node Features**: Represent origin-destination (OD) demand matrices for travel. Each OD pair specifies travel demand between zones. Stored in the `feat` field of the DGLGraph. - **Edge Features**: Include Free-flow travel time (FFTT) and Capacity. Stored in the `feat` field of the DGLGraph. - **Edge Labels**: Optimal link flows derived from solving the DUE problem. A list of labels for each edge in the DGLGraph. ### Available Networks - SiouxFalls - Eastern-Massachusetts - Anaheim ## Usage To load a dataset, use the following code: ```python import pickle from data_dataset_prep import DUEDatasetDGL case = 'SiouxFalls' # or 'Eastern-Massachusetts', 'Anaheim' data_dir = 'DatasetsDUE' with open(f'{data_dir}/{case}/{case}.pkl', 'rb') as f: train, val, test = pickle.load(f) # Example: Accessing the first graph and its edge labels in the training set graph, edge_labels = train[0] print(graph) print(edge_labels) ``` ## Data Generation Steps 1. **Define Parameters**: Set the parameters for dataset generation in the `parameters()` function. 2. **Solve DUE Problem**: Use the `data_due_generate.py` script to solve the DUE problem for each network. 3. **Store Results**: Save the results as CSV files and clean up the data. 4. **Create DGL Dataset**: Convert the data into DGL format using the `data_dataset_prep.py` script and save it as pickle files. ## Scripts ### `data_due_generate.py` (from GitHub repository) This script generates the datasets by solving the DUE problem for each network in the benchmark networks. The parameters for dataset generation are defined in the `parameters()` function. ### `data_dataset_prep.py` (from GitHub repository) This script contains the classes and functions for preparing the datasets and converting them into DGL format. ## References - [A hybrid deep-learning-metaheuristic framework for bi-level network design problems](https://doi.org/10.1016/j.eswa.2023.122814) - [GitHub Repository: HDLMF_GIN-GA](https://github.com/bahmanmdd/HDLMF_GIN-GA) - [Dataset repository](https://doi.org/10.6084/m9.figshare.27889251.v1) ## Citation If you use these datasets in your research, please cite the following paper and dataset: Paper citation: Madadi B, de Almeida Correia GH. A hybrid deep-learning-metaheuristic framework for bi-level network design problems. Expert Systems with Applications. 2024 Jun 1;243:122814. https://doi.org/10.1016/j.eswa.2023.122814 Dataset citation: Madadi, Bahman (2024). Equilibrium-Traffic-Networks. figshare. Dataset. https://doi.org/10.6084/m9.figshare.27889251.v1 --- ### Metadata ```json { "datasets": [ { "name": "SiouxFalls", "description": "DGL dataset for the SiouxFalls transportation network with solved instances of the DUE problem.", "num_samples": 20000, "features": { "node_features": "Represent origin-destination (OD) demand matrices for travel. Each OD pair specifies travel demand between zones.", "edge_features": "Include Free-flow travel time (FFTT) and Capacity.", "edge_labels": "Optimal link flows derived from solving the DUE problem." }, "splits": ["train", "val", "test"] }, { "name": "Eastern-Massachusetts", "description": "DGL dataset for the Eastern-Massachusetts transportation network with solved instances of the DUE problem.", "num_samples": 5000, "features": { "node_features": "Represent origin-destination (OD) demand matrices for travel. Each OD pair specifies travel demand between zones.", "edge_features": "Include Free-flow travel time (FFTT) and Capacity.", "edge_labels": "Optimal link flows derived from solving the DUE problem." }, "splits": ["train", "val", "test"] }, { "name": "Anaheim", "description": "DGL dataset for the Anaheim transportation network with solved instances of the DUE problem.", "num_samples": 5000, "features": { "node_features": "Represent origin-destination (OD) demand matrices for travel. Each OD pair specifies travel demand between zones.", "edge_features": "Include Free-flow travel time (FFTT) and Capacity.", "edge_labels": "Optimal link flows derived from solving the DUE problem." }, "splits": ["train", "val", "test"] } ], "references": [ { "title": "A hybrid deep-learning-metaheuristic framework for bi-level network design problems", "doi": "10.1016/j.eswa.2023.122814", "url": "https://doi.org/10.1016/j.eswa.2023.122814", "authors": ["Bahman Madadi", "Gonçalo H. de Almeida Correia"], "journal": "Expert Systems with Applications", "year": 2024, "volume": 243, "pages": "122814" }, { "title": "GitHub Repository: HDLMF_GIN-GA", "url": "https://github.com/bahmanmdd/HDLMF_GIN-GA" } ] } ```

musashihinck/rpp_step3_qwen25-72b

musashihinck

--- dataset_info: features: - name: common_id dtype: string - name: template_id dtype: string - name: topic_id dtype: int64 - name: topic_polarity dtype: string - name: call_idx dtype: int64 - name: generation_model dtype: string - name: eval_model dtype: string - name: prompt_text dtype: string - name: response_text dtype: string - name: eval_text dtype: string - name: template_text dtype: string - name: topic_text dtype: string splits: - name: train num_bytes: 10090784708 num_examples: 3180000 download_size: 4888086079 dataset_size: 10090784708 configs: - config_name: default data_files: - split: train path: data/train-* ---

Westcott/dior_text_to_image

Westcott

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2037786974.0 num_examples: 5862 - name: validation num_bytes: 1998869913.0 num_examples: 5863 download_size: 4000336494 dataset_size: 4036656887.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

dgambettaphd/D_gen6_run0_llama2-7b_wiki_doc1000_real64_synt64

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 579858 num_examples: 1000 download_size: 352374 dataset_size: 579858 configs: - config_name: default data_files: - split: train path: data/train-* ---

Shahradmz/ca_constitution_2

Shahradmz

--- license: mit dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 10469526 num_examples: 28673 - name: test num_bytes: 1847119 num_examples: 5061 download_size: 6567019 dataset_size: 12316645 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

dgambettaphd/D_gen7_run0_llama2-7b_wiki_doc1000_real64_synt64

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 579807 num_examples: 1000 download_size: 352435 dataset_size: 579807 configs: - config_name: default data_files: - split: train path: data/train-* ---

RyanYr/self-reflect_mini8Bit-om2-sft-t2_mg_t0

RyanYr

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: response@0 sequence: string splits: - name: train num_bytes: 35175150 num_examples: 19473 download_size: 16878778 dataset_size: 35175150 configs: - config_name: default data_files: - split: train path: data/train-* ---

Rudra-ai/ai-responses-helpful-70b-update-dpo

Rudra-ai

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 2572055 num_examples: 1000 download_size: 1259394 dataset_size: 2572055 configs: - config_name: default data_files: - split: train path: data/train-* ---

infinite-dataset-hub/PregnancyBotConvo

infinite-dataset-hub

--- license: mit tags: - infinite-dataset-hub - synthetic --- # PregnancyBotConvo tags: ConversationalAI, Pregnancy, Healthcare _Note: This is an AI-generated dataset so its content may be inaccurate or false_ **Dataset Description:** The 'PregnancyBotConvo' dataset is a curated collection of simulated conversations between pregnant individuals and a conversational AI designed to provide guidance and support throughout the pregnancy journey. Each row in the dataset represents a conversation snippet with associated metadata to assist in training conversational models to handle queries related to pregnancy. **CSV Content Preview:** ``` index,conversation,label 1,"User: I'm 12 weeks pregnant and I'm feeling very tired lately. Bot: It's quite common to feel more fatigued as your pregnancy progresses. Ensuring you're getting enough rest and staying hydrated can help. How have you been sleeping?",Supportive_Response 2,"User: My doctor mentioned I might need to start iron supplements. Bot: Iron is crucial during pregnancy to support your baby's growth and prevent anemia. Please discuss with your healthcare provider to get a prescription tailored for you.",Educational_Response 3,"User: I'm worried about morning sickness. Bot: Morning sickness typically subsides as the pregnancy advances. Eating small, frequent meals and avoiding certain smells can help manage the symptoms. Remember, it's normal, and you're not alone.",Reassuring_Response 4,"User: Can I continue exercising during my third trimester? Bot: Gentle exercise like walking or swimming is beneficial, but it's best to consult with your healthcare provider for personalized advice on exercise during the third trimester.",Advisory_Response 5,"User: How can I make sure I'm gaining the right amount of weight during pregnancy? Bot: Weight gain varies for each pregnancy. It's essential to follow your doctor's advice on nutrition and monitoring your weight to ensure it's within the recommended range. Shall we schedule a check-up?",Proactive_Response ``` This dataset serves as a basis for training AI models to provide empathetic, informative, and supportive interactions for pregnant individuals seeking advice or information on their pregnancy. **Source of the data:** The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'complete pregnancy chatbot': - **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=complete+pregnancy+chatbot&dataset=PregnancyBotConvo&tags=ConversationalAI,+Pregnancy,+Healthcare - **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct - **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub

heyyjudes/lmsys-en-prompt-only-dedup

heyyjudes

--- dataset_info: features: - name: user_prompt dtype: string splits: - name: train num_bytes: 170450055.3380735 num_examples: 422952 download_size: 99351483 dataset_size: 170450055.3380735 configs: - config_name: default data_files: - split: train path: data/train-* ---

dgambettaphd/D_gen9_run0_llama2-7b_wiki_doc1000_real64_synt64

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 579795 num_examples: 1000 download_size: 352338 dataset_size: 579795 configs: - config_name: default data_files: - split: train path: data/train-* ---

Evgy23/Alice_dataset

Evgy23

--- dataset_info: features: - name: Original Text dtype: string - name: Lemmatized Text sequence: string - name: "POS Annotation\t" sequence: sequence: string - name: ner sequence: sequence: string splits: - name: train num_bytes: 683 num_examples: 1 download_size: 3549 dataset_size: 683 configs: - config_name: default data_files: - split: train path: data/train-* ---

mmtg/ui_design_image_text

mmtg

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 34427370.0 num_examples: 1000 download_size: 28749395 dataset_size: 34427370.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_train_chunk_9

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 5959798 num_examples: 1032 download_size: 424528 dataset_size: 5959798 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_train_chunk_4

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 5601360 num_examples: 947 download_size: 410078 dataset_size: 5601360 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_val_chunk_25

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 257977 num_examples: 46 download_size: 30543 dataset_size: 257977 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_val_chunk_20

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 284735 num_examples: 40 download_size: 24644 dataset_size: 284735 configs: - config_name: default data_files: - split: train path: data/train-* ---

dgambettaphd/D_gen10_run0_llama2-7b_wiki_doc1000_real64_synt64

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 579816 num_examples: 1000 download_size: 352322 dataset_size: 579816 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_val_chunk_9

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 191175 num_examples: 35 download_size: 25297 dataset_size: 191175 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_val_chunk_4

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 316391 num_examples: 55 download_size: 26700 dataset_size: 316391 configs: - config_name: default data_files: - split: train path: data/train-* ---

RickBrannan/categorize_bib_lang_grammar

RickBrannan

--- license: cc-by-sa-4.0 task_categories: - text-classification language: - en pretty_name: Categorize Sentences with Biblical language grammar --- # Dataset Card for categorize_bib_lang_grammar ## Dataset Details 2,700+ sentences categorized as `0` (`NOT-GRAMMAR`) or `1` (`GRAMMAR`). For use with something like DistilBERT. Categorization is human curated. * **License:** CC-BY-SA 4.0 ### Dataset Sources Most of the sentences (~2,000) in this training data come from: * **unfoldingWord Greek Grammar**: https://git.door43.org/unfoldingWord/en_ugg, licensed CC-BY-SA 4.0 * **unfoldingWord Hebrew Grammar**: https://git.door43.org/unfoldingWord/en_uhg, licensed CC-BY-SA 4.0 A smaller portion of the sentences (~800) come from: * **SIL Open Translators Notes**: https://opentn.bible, licensed CC-BY-SA 4.0 ## Uses The original intent of the data is to locate sentences in resources like the SIL Open Translators Notes that use grammatical terminology.

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_train_chunk_30

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 5746968 num_examples: 934 download_size: 399415 dataset_size: 5746968 configs: - config_name: default data_files: - split: train path: data/train-* ---

Finnish-NLP/ds_nemotron_klm_lt_filt_dpo_rejected_orig_col_pairRM_com_les_10_hhrlhf_added

Finnish-NLP

--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string - name: response_rejected dtype: string splits: - name: train num_bytes: 23181248.76174307 num_examples: 10086 download_size: 11669693 dataset_size: 23181248.76174307 configs: - config_name: default data_files: - split: train path: data/train-* ---

Gramacho/complete_pira_test_corpus2_ptbr_llama3_alpaca_46

Gramacho

--- dataset_info: features: - name: instruction dtype: string - name: id dtype: string - name: context dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 143257 num_examples: 46 download_size: 78830 dataset_size: 143257 configs: - config_name: default data_files: - split: train path: data/train-* ---

fracapuano/moss-pen

fracapuano

--- task_categories: - robotics tags: - LeRobot - moss - tutorial - eval --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_val_chunk_26

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 272952 num_examples: 49 download_size: 42683 dataset_size: 272952 configs: - config_name: default data_files: - split: train path: data/train-* ---

violetxi/NUMINA-V2-Clean-Blocks-6800_7000-0_41

violetxi

--- dataset_info: features: - name: problem dtype: string - name: source dtype: string - name: is_correct dtype: bool - name: target_answer dtype: string - name: solution dtype: string - name: solution_steps dtype: string - name: attempts dtype: string - name: model_answer dtype: string splits: - name: train num_bytes: 825110849 num_examples: 122816 download_size: 83109097 dataset_size: 825110849 configs: - config_name: default data_files: - split: train path: data/train-* ---

preetam8/accent_correction_dataset_en_speaker_7

preetam8

--- dataset_info: features: - name: sentence dtype: string - name: speaker dtype: string - name: waveform sequence: float16 splits: - name: train num_bytes: 2065771314 num_examples: 3000 download_size: 1939098830 dataset_size: 2065771314 configs: - config_name: default data_files: - split: train path: data/train-* ---

capemox/ecthr-b-preprocessed

capemox

--- dataset_info: features: - name: text dtype: string - name: labels sequence: int64 splits: - name: train num_bytes: 88986061 num_examples: 9000 - name: test num_bytes: 11802744 num_examples: 1000 - name: validation num_bytes: 10909180 num_examples: 1000 download_size: 51535941 dataset_size: 111697985 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_train_chunk_11

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 5957724 num_examples: 1043 download_size: 434109 dataset_size: 5957724 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_val_chunk_11

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 336231 num_examples: 53 download_size: 34048 dataset_size: 336231 configs: - config_name: default data_files: - split: train path: data/train-* ---

JimmyHuang790624/lele_example

JimmyHuang790624

--- license: apache-2.0 ---

depression-detect/english-merged

depression-detect

--- license: mit --- # Dataset Overview This dataset is composed of two separate smaller datasets obtained from Kaggle: 1. **`dataset.csv`** [Link to Kaggle](https://www.kaggle.com/datasets/mdforiduzzamanzihad/preprocessed-depression-dataset) 2. **`depression_dataset_reddit_cleaned.csv`** [Link to Kaggle](https://www.kaggle.com/datasets/infamouscoder/depression-reddit-cleaned) Each dataset contains preprocessed text data related to depression, useful for tasks like sentiment analysis, mental health studies, or NLP modeling.

InsultedByMathematics/rebel-ultrafeedback-test-evaluation-update-401

InsultedByMathematics

--- dataset_info: features: - name: response_0 dtype: string - name: response_1 dtype: string - name: response_2 dtype: string - name: response_3 dtype: string - name: response_4 dtype: string - name: prompt_id dtype: string - name: prompt dtype: string - name: llama_prompt dtype: string - name: llama_prompt_tokens sequence: int64 - name: response_0_reward dtype: float64 - name: response_1_reward dtype: float64 - name: response_2_reward dtype: float64 - name: response_3_reward dtype: float64 - name: response_4_reward dtype: float64 - name: chosen dtype: string - name: chosen_reward dtype: float64 - name: llama_chosen dtype: string - name: llama_chosen_tokens sequence: int64 - name: reject dtype: string - name: reject_reward dtype: float64 - name: llama_reject dtype: string - name: llama_reject_tokens sequence: int64 - name: chosen_logprob dtype: float64 - name: reject_logprob dtype: float64 - name: finetuned_response_0 dtype: string - name: finetuned_response_1 dtype: string - name: finetuned_response_2 dtype: string - name: finetuned_response_3 dtype: string - name: finetuned_response_4 dtype: string splits: - name: test_prefs num_bytes: 105679213 num_examples: 1801 download_size: 27898623 dataset_size: 105679213 configs: - config_name: default data_files: - split: test_prefs path: data/test_prefs-* ---

yuvraj17/temp2

yuvraj17

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 10319402.839161336 num_examples: 6231 download_size: 8302252 dataset_size: 10319402.839161336 configs: - config_name: default data_files: - split: train path: data/train-* ---

pruhtopia/rsc-5k-1

pruhtopia

--- dataset_info: features: - name: image_id dtype: string - name: description dtype: string - name: image dtype: binary splits: - name: train num_bytes: 199381852 num_examples: 5718 download_size: 198959477 dataset_size: 199381852 configs: - config_name: default data_files: - split: train path: data/train-* ---

Suchinthana/Test_Cricket_Data

Suchinthana

--- dataset_info: features: - name: BATTING_Tests_Mat dtype: float64 - name: BATTING_Tests_Runs dtype: float64 - name: BATTING_First-class_Mat dtype: float64 - name: BATTING_List A_Mat dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 144200 num_examples: 3605 download_size: 61522 dataset_size: 144200 configs: - config_name: default data_files: - split: train path: data/train-* license: cc0-1.0 language: - en size_categories: - 1K<n<10K --- Source dataset: [ESPN Cricket Players Data](https://www.kaggle.com/datasets/raghav333/espn-cricket-players-data).

yuvraj17/temp4

yuvraj17

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 2299394 num_examples: 829 download_size: 1071987 dataset_size: 2299394 configs: - config_name: default data_files: - split: train path: data/train-* ---

ADHIZ/image_vicky

ADHIZ

--- dataset_info: features: - name: file_name dtype: string - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 1678972.0 num_examples: 2 download_size: 1681322 dataset_size: 1678972.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

rbaradaran/RAG_multihop_data

rbaradaran

--- dataset_info: features: - name: q_id dtype: string - name: category dtype: string - name: question dtype: string - name: response dtype: string - name: documents list: - name: ds_id dtype: string - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 96376567 num_examples: 6857 download_size: 37427259 dataset_size: 96376567 configs: - config_name: default data_files: - split: train path: data/train-* ---

rbaradaran/RAG_all_data

rbaradaran

--- dataset_info: features: - name: q_id dtype: string - name: category dtype: string - name: question dtype: string - name: response dtype: string - name: documents list: - name: ds_id dtype: string - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 134252200 num_examples: 13604 - name: test num_bytes: 10037639 num_examples: 1025 download_size: 61235276 dataset_size: 144289839 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Rudra-ai/ai-responses-gsm8k-70b-update-dpo

Rudra-ai

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 3352021 num_examples: 2000 download_size: 1417949 dataset_size: 3352021 configs: - config_name: default data_files: - split: train path: data/train-* ---

yurifacanha/DPOquotes

yurifacanha

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 8917431 num_examples: 1300 download_size: 4519360 dataset_size: 8917431 configs: - config_name: default data_files: - split: train path: data/train-* ---

herteltm/UTQA_v1

herteltm

--- license: cc-by-4.0 tags: - physchem - benchmark - LLM --- # Datensatz Karte für UTQA (An Undergraduate Thermodynamics Q&A Benchmark) <!-- Provide a quick summary of the dataset. --> UTQA ist ein Multiple-Choice-Fragen-und-Antworten-Datensatz mit einfachen, aber herausfordernden Fragen zur Thermodynamik, die von Physikochemikern erstellt und validiert wurden. Wir bitten Sie **keine Beispiele aus diesem Datensatz in Klartext oder Bildern online zu veröffentlichen**, um das Risiko einer Integration in Trainingskorpora für Basismodelle zu minimieren. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> Wir präsentieren UTQA, einen herausfordernden Datensatz mit 41 Multiple-Choice-Fragen, die von Fachexperten der Physikalischen Chemie erstellt wurden. Wir gewährleisten, dass die Fragen qualitativ hochwertig und anspruchsvoll sind: Nicht-Expertinnen erreichten nur eine Genauigkeit von 34 %, obwohl sie durchschnittlich über ?? Minuten mit uneingeschränktem Zugang zum Internet aufbrachten. Die Fragen sind auch für modernste KI-Systeme schwierig: Unser stärkstes Basismodell auf GPT-4o-Basis erreichte ?? % Genauigkeit. Wenn wir zukünftige KI-Systeme dazu nutzen möchten, uns bei der Beantwortung sehr schwieriger Fragen zu helfen – beispielsweise bei der Entwicklung neuer wissenschaftlicher Erkenntnisse –, müssen wir skalierbare Aufsichtsverfahren entwickeln, die es Menschen ermöglichen, ihre Ergebnisse zu überwachen. Dies kann schwierig sein, selbst wenn die Überwacherinnen selbst erfahren und kompetent sind. Die Schwierigkeit von UTQA sowohl für erfahrene Nicht-Expertinnen als auch für fortschrittliche KI-Systeme sollte realistische Experimente für skalierbare Aufsicht ermöglichen. Wir hoffen, damit Methoden zu entwickeln, mit denen menschliche Expertinnen verlässlich wahrheitsgemäße Informationen von KI-Systemen erhalten können, die die menschlichen Fähigkeiten übertreffen. - **Curated by:** Tobias Hertel and Sophie-Luca Bien - **License:** CC BY 4.0 ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** https://github.com/ TBA - **Paper:** https://arxiv.org/abs/ TBA ## 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 Tobias Hertel: tobias.hertel@uni-wuerzburg.de --- Submit corrections to examples in UTQA via this form: TBA ---

PlanAPlanB/reddit_dataset_44

PlanAPlanB

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 Reddit Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** PlanAPlanB/reddit_dataset_44 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 0 ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Topic Modeling - Community Analysis - Content Categorization ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single Reddit post or comment with the following fields: ### Data Fields - `text` (string): The main content of the Reddit post or comment. - `label` (string): Sentiment or topic category of the content. - `dataType` (string): Indicates whether the entry is a post or a comment. - `communityName` (string): The name of the subreddit where the content was posted. - `datetime` (string): The date when the content was posted or commented. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the content. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public posts and comments on Reddit, adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in Reddit data, including demographic and content biases. This dataset reflects the content and opinions expressed on Reddit and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the nature of media sources. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public subreddits and does not include private or restricted communities. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to Reddit Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{PlanAPlanB2024datauniversereddit_dataset_44, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={PlanAPlanB}, year={2024}, url={https://huggingface.co/datasets/PlanAPlanB/reddit_dataset_44}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 1890066 - **Date Range:** 2009-10-19T00:00:00Z to 2024-11-23T00:00:00Z - **Last Updated:** 2024-11-23T15:55:14Z ### Data Distribution - Posts: 31.64% - Comments: 68.36% ### Top 10 Subreddits For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | r/pixelbuds | 1998 | 0.11% | | 2 | r/Xenosaga | 1990 | 0.11% | | 3 | r/passat | 1990 | 0.11% | | 4 | r/Unpacking | 1976 | 0.10% | | 5 | r/fastmail | 1974 | 0.10% | | 6 | r/eyelashextensions | 1964 | 0.10% | | 7 | r/freshwateraquarium | 1952 | 0.10% | | 8 | r/LoganSquare | 1946 | 0.10% | | 9 | r/breakingnews | 1924 | 0.10% | | 10 | r/RedPillWomen | 1899 | 0.10% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2024-11-23T15:33:24Z | 893558 | 893558 | | 2024-11-23T15:55:14Z | 996508 | 1890066 |

bgonzalezbustamante/toxicity-protests-ES

bgonzalezbustamante

--- license: cc-by-4.0 language: - es tags: - toxicity --- - [GitHub repository](https://github.com/training-datalab/gold-standard-toxicity). - [Dataset on Zenodo](https://zenodo.org/doi/10.5281/zenodo.12574288). - [Reference paper](https://arxiv.org/abs/2409.09741)

andrejaystevenson/dsf

andrejaystevenson

--- license: apache-2.0 ---

pingel-org/graphs

pingel-org

--- license: cdla-permissive-2.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: page_content dtype: string - name: metadata struct: - name: char_count dtype: string - name: citations dtype: string - name: court dtype: string - name: decision_date dtype: string - name: docket_number dtype: string - name: first_page dtype: int64 - name: head_matter dtype: string - name: id dtype: string - name: judges dtype: string - name: jurisdiction dtype: string - name: last_page dtype: string - name: last_updated dtype: string - name: name dtype: string - name: name_abbreviation dtype: string - name: parties dtype: string - name: provenance dtype: string - name: reporter dtype: string - name: volume dtype: string - name: word_count dtype: string splits: - name: train num_bytes: 60568000 num_examples: 21540 download_size: 29570333 dataset_size: 60568000 --- Graphs extracted from public datasets. Suitable for populating graph databases and powering GraphRAG.

alex-miller/pogona-vitticeps-gender

alex-miller

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': female '1': indeterminate '2': male splits: - name: train num_bytes: 8187973.4 num_examples: 128 - name: test num_bytes: 2043692.6 num_examples: 32 download_size: 10237708 dataset_size: 10231666.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

betteib/tn-500k

betteib

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 134814437.8295075 num_examples: 1353321 - name: test num_bytes: 33703684.170492515 num_examples: 338331 download_size: 105859065 dataset_size: 168518122.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Saibo-creator/glaiveai_func_schema

Saibo-creator

--- dataset_info: features: - name: unique_id dtype: string - name: name dtype: string - name: number_of_parameters dtype: int64 - name: number_of_required dtype: float64 - name: number_of_optional dtype: float64 - name: schema_depth dtype: int64 - name: function_schema dtype: string - name: schema_str_len dtype: int64 - name: json_schema dtype: string splits: - name: full num_bytes: 20601477 num_examples: 27365 - name: hard_2k num_bytes: 2619055 num_examples: 2000 download_size: 4714022 dataset_size: 23220532 configs: - config_name: default data_files: - split: full path: data/full-* - split: hard_2k path: data/hard_2k-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_18

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8628119 num_examples: 1414 download_size: 1438643 dataset_size: 8628119 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_12

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8778077 num_examples: 1414 download_size: 1488706 dataset_size: 8778077 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_7

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8716364 num_examples: 1414 download_size: 1459486 dataset_size: 8716364 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_3

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8155333 num_examples: 1414 download_size: 1349153 dataset_size: 8155333 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_11

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8255890 num_examples: 1414 download_size: 1374842 dataset_size: 8255890 configs: - config_name: default data_files: - split: train path: data/train-* ---

pablopertusa/ft-llama

pablopertusa

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 10984 num_examples: 15 download_size: 6431 dataset_size: 10984 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_4

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8469198 num_examples: 1414 download_size: 1417162 dataset_size: 8469198 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_5

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8778104 num_examples: 1414 download_size: 1448219 dataset_size: 8778104 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_20

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 30879 num_examples: 6 download_size: 37890 dataset_size: 30879 configs: - config_name: default data_files: - split: train path: data/train-* ---

amuvarma/luna-4days-combined-clean-wcaps

amuvarma

--- dataset_info: features: - name: text dtype: string - name: emotion dtype: string - name: audio dtype: audio - name: facodec_0 sequence: int64 - name: facodec_1 sequence: int64 - name: facodec_2 sequence: int64 - name: facodec_3 sequence: int64 - name: facodec_4 sequence: int64 - name: facodec_5 sequence: int64 - name: transcript dtype: string splits: - name: train num_bytes: 1196705702.38 num_examples: 5674 download_size: 1011230479 dataset_size: 1196705702.38 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_15

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8723947 num_examples: 1414 download_size: 1464860 dataset_size: 8723947 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_chunk_16

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string splits: - name: train num_bytes: 8639062 num_examples: 1414 download_size: 1454230 dataset_size: 8639062 configs: - config_name: default data_files: - split: train path: data/train-* ---

alihmaou/FOURSQUARE_POIS_FRANCE_20241119

alihmaou

--- dataset_info: features: - name: fsq_place_id dtype: string - name: name dtype: string - name: latitude dtype: float64 - name: longitude dtype: float64 - name: address dtype: string - name: locality dtype: string - name: region dtype: string - name: postcode dtype: string - name: admin_region dtype: string - name: post_town dtype: string - name: po_box dtype: string - name: country dtype: string - name: date_created dtype: string - name: date_refreshed dtype: string - name: date_closed dtype: string - name: tel dtype: string - name: website dtype: string - name: email dtype: string - name: facebook_id dtype: int64 - name: instagram dtype: string - name: twitter dtype: string - name: fsq_category_ids sequence: string - name: fsq_category_labels sequence: string - name: geometry dtype: binary splits: - name: train num_bytes: 1134374359 num_examples: 3060669 download_size: 760919162 dataset_size: 1134374359 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/opengpt_jaehun

mlfoundations-dev

--- dataset_info: features: - name: language dtype: string - name: quantity dtype: int64 - name: task dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: new_conversations list: - name: content dtype: string - name: role dtype: string - name: gradients sequence: float64 - name: kmeans_inertia dtype: float64 splits: - name: train num_bytes: 30723866 num_examples: 4621 download_size: 22074076 dataset_size: 30723866 configs: - config_name: default data_files: - split: train path: data/train-* ---

Lennyox/hazardous_driving_eye_gaze

Lennyox

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

Pankaj8922/Text-Comb

Pankaj8922

--- license: apache-2.0 task_categories: - text-generation language: - en --- As the Name suggests, It's a combination. Dataset combined: * tiny-textbooks by nampdn-ai * only validation set of tiny-webtext by nampdn-ai * Text-corp by Pankaj8922

violetxi/NUMINA-V2-Clean-Blocks-8000_8500-200_-1

violetxi

--- dataset_info: features: - name: problem dtype: string - name: source dtype: string - name: is_correct dtype: bool - name: target_answer dtype: string - name: solution dtype: string - name: solution_steps dtype: string - name: attempts dtype: string - name: model_answer dtype: string splits: - name: train num_bytes: 6238918541 num_examples: 731312 download_size: 627919825 dataset_size: 6238918541 configs: - config_name: default data_files: - split: train path: data/train-* ---

bizb0630/hamza_1.0

bizb0630

--- license: mit task_categories: - translation language: - uz - ru pretty_name: uzbek-russian_parallel_corpora --- # Hamza - Uzbek-Russian parallel corpora. ## Overview **Hamza** is a parallel corpus containing over 15,000 aligned sentences in Uzbek and Russian. ## Creation Created using [lingtrain](https://github.com/averkij/lingtrain-aligner). Text mined from different websites and telegram channels. ### Format The dataset is presented in TMX (Translation Memory eXchange).

data-is-better-together/dibt-prompt-translation-for-arabic

data-is-better-together

--- dataset_info: features: - name: target dtype: string - name: quality list: - name: status dtype: string - name: user_id dtype: string - name: value dtype: string - name: metadata dtype: string - name: avg_rating dtype: float64 - name: num_responses dtype: int64 - name: agreement_ratio dtype: float64 - name: raw_responses sequence: int64 - name: kind dtype: string - name: source dtype: string splits: - name: train num_bytes: 824861 num_examples: 501 download_size: 391793 dataset_size: 824861 configs: - config_name: default data_files: - split: train path: data/train-* ---

Tristepin/quakec-raw1

Tristepin

--- license: mit ---

data-is-better-together/aya_dutch_dpo_raw

data-is-better-together

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string - name: generation_model dtype: string - name: generation dtype: string - name: predicted_generation_language dtype: string - name: predicted_generation_language_score dtype: float64 - name: generations sequence: string - name: generation_models sequence: string - name: model_name dtype: string - name: ratings sequence: int64 - name: rationales sequence: string splits: - name: train num_bytes: 3530439 num_examples: 1200 download_size: 1847668 dataset_size: 3530439 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for aya_dutch_dpo_raw This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/DIBT/aya_dutch_dpo_raw/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/DIBT/aya_dutch_dpo_raw/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "annotation_type": "re-annotations", "generation": "De constructie van het fort, dat werd opgetrokken in de haven van Lahaina met behulp van massive koraalblokken die uit de omringende riffen waren verwijderd, markeerde een keerpunt in de politieke en economische ontwikkeling van het eiland Maui in de eerste helft van de negentiende eeuw.", "generation_model": "meta-llama/Meta-Llama-3-70B-Instruct", "generation_models": [ "aya", "meta-llama/Meta-Llama-3-70B-Instruct" ], "generations": [ "Een meer complexe versie van de zin is natuurlijk: \"Het fort werd gebouwd in het kadegebied bij Lahaina Harbor van koraalblokken met muren bekroond met 47 kanonnen die van schepen zijn verkregen\".", "De constructie van het fort, dat werd opgetrokken in de haven van Lahaina met behulp van massive koraalblokken die uit de omringende riffen waren verwijderd, markeerde een keerpunt in de politieke en economische ontwikkeling van het eiland Maui in de eerste helft van de negentiende eeuw." ], "instruction": "Maak een complexere zin: Het fort werd gebouwd in de haven van Lahaina met koraalblokken.", "language": "Dutch", "language_code": "nld", "model_name": "meta-llama/Meta-Llama-3-70B-Instruct", "predicted_generation_language": "nld_Latn", "predicted_generation_language_score": 0.9995737671852112, "ratings": [ 4, 5 ], "rationales": [ "The text accurately provides a more complex sentence as requested, and the added details are correct (e.g., \"muren bekroond met 47 kanonnen\"). The sentence is well-structured and easy to follow. However, the model could be more confident in its language, and some parts feel slightly redundant (e.g., \"kadegebied bij Lahaina Harbor\").", "The text provides a sophisticated and accurate sentence that not only meets the request but also adds valuable context about the significance of the fort\u0027s construction. The language is confident and precise, and the sentence is well-structured and engaging. The model demonstrates a thorough understanding of the topic and effectively conveys its knowledge without any noticeable errors or hallucinations." ], "targets": "Een meer complexe versie van de zin is natuurlijk: \"Het fort werd gebouwd in het kadegebied bij Lahaina Harbor van koraalblokken met muren bekroond met 47 kanonnen die van schepen zijn verkregen\".", "user_id": "ca908e583236b208e473e89dae5c7b7d3daf3662e2bbf6606f0702c718bb5c06" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_dutch_dpo_raw", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_dutch_dpo_raw") ``` </details>

data-is-better-together/aya_english_dpo_raw

data-is-better-together

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string - name: generation_model dtype: string - name: generation dtype: string - name: predicted_generation_language dtype: string - name: predicted_generation_language_score dtype: float64 - name: generations sequence: string - name: generation_models sequence: string - name: model_name dtype: string - name: ratings sequence: int64 - name: rationales sequence: string splits: - name: train num_bytes: 567777 num_examples: 100 download_size: 320089 dataset_size: 567777 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for aya_english_dpo_raw This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/DIBT/aya_english_dpo_raw/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/DIBT/aya_english_dpo_raw/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "annotation_type": "original-annotations", "generation": "The largest organ in the human body is the skin. It covers the entire surface of the body and has a total area of about 22 square feet (2 square meters) in adults. The skin performs many important functions, such as:\n\n* Protecting the body from external damage, such as injury, infection, and UV radiation\n* Regulating body temperature\n* Aiding in the production of vitamin D\n* Assisting in the sensation of touch and feel\n* Helping to maintain fluid balance and hydration\n\nThe skin is composed of several layers, with the outermost layer being the epidermis, followed by the dermis, and then the hypodermis. It is estimated that the skin accounts for about 15% of an adult\u0027s total body weight.\n\nIt\u0027s worth noting that while the skin is the largest organ by surface area, the liver is often considered the largest organ by mass, weighing around 3 pounds (1.4 kilograms) in adults.", "generation_model": "meta-llama/Meta-Llama-3-70B-Instruct", "generation_models": [ "aya", "meta-llama/Meta-Llama-3-70B-Instruct" ], "generations": [ "The largest organ in the human body is the skin. It is the body\u0027s largest organ by surface area and weight. The skin serves many essential functions, including protecting the body from physical damage, pathogens, and dehydration, as well as helping to regulate body temperature and producing vitamin D in response to sunlight. It\u0027s a complex organ with multiple layers, cells, and structures, each serving a unique purpose in maintaining the body\u0027s overall health and well-being.", "The largest organ in the human body is the skin. It covers the entire surface of the body and has a total area of about 22 square feet (2 square meters) in adults. The skin performs many important functions, such as:\n\n* Protecting the body from external damage, such as injury, infection, and UV radiation\n* Regulating body temperature\n* Aiding in the production of vitamin D\n* Assisting in the sensation of touch and feel\n* Helping to maintain fluid balance and hydration\n\nThe skin is composed of several layers, with the outermost layer being the epidermis, followed by the dermis, and then the hypodermis. It is estimated that the skin accounts for about 15% of an adult\u0027s total body weight.\n\nIt\u0027s worth noting that while the skin is the largest organ by surface area, the liver is often considered the largest organ by mass, weighing around 3 pounds (1.4 kilograms) in adults." ], "instruction": "What is the largest organ in the human body?", "language": "English", "language_code": "eng", "model_name": "meta-llama/Meta-Llama-3-70B-Instruct", "predicted_generation_language": "eng_Latn", "predicted_generation_language_score": 0.9590950012207031, "ratings": [ 5, 5 ], "rationales": [ "The text is accurate, informative, and comprehensive in describing the skin as the largest organ in the human body. It provides specific details about its functions, structure, and importance, aligning perfectly with the instruction.", "This text is equally excellent, providing a clear and concise answer to the question. It lists the skin\u0027s functions, describes its composition, and offers additional interesting facts, such as the comparison with the liver\u0027s mass. The text is well-structured, accurate, and confident in its information, making it an excellent response." ], "targets": "The largest organ in the human body is the skin. It is the body\u0027s largest organ by surface area and weight. The skin serves many essential functions, including protecting the body from physical damage, pathogens, and dehydration, as well as helping to regulate body temperature and producing vitamin D in response to sunlight. It\u0027s a complex organ with multiple layers, cells, and structures, each serving a unique purpose in maintaining the body\u0027s overall health and well-being.", "user_id": "29f22cf193a81e1a5c47d76af453a91b3cd19aa348995c7add1df15fe24e8801" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_english_dpo_raw", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_english_dpo_raw") ``` </details>

wisenut-nlp-team/llama_poc_total_dataset_v2

wisenut-nlp-team

--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: context_with_negative dtype: string - name: answer dtype: string - name: answer_with_origin dtype: string - name: answer_rejected dtype: string - name: input dtype: string splits: - name: train num_bytes: 7368945 num_examples: 653 download_size: 1956252 dataset_size: 7368945 configs: - config_name: default data_files: - split: train path: data/train-* ---

miracl/nomiracl-instruct

miracl

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: query_id dtype: string - name: subset dtype: string - name: language dtype: string splits: - name: train num_bytes: 154438200.3377625 num_examples: 21471 - name: test num_bytes: 17162197.6622375 num_examples: 2386 download_size: 92309140 dataset_size: 171600398 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: apache-2.0 task_categories: - text-classification - text-generation language: - ar - bn - de - en - es - fa - fi - fr - hi - id - ja - ko - ru - sw - te - th - yo - zh pretty_name: NoMIRACL Fine-tuning Dataset size_categories: - 10K<n<100K --- # Dataset Card for NoMIRACL (EMNLP 2024 Findings Track) ## Quick Overview This repository contains the fine-tuning (training & development split) of the NoMIRACL instruct dataset for fine-tuning LLMs on multilingual relevance assessment. The training dataset is a binary classification task; they need to explicitly output either `Yes, answer is present` or `I don't know`. The dataset contains training pairs from all 18 languages for both splits: relevant & non-relevant. ``` import datasets nomiracl = datasets.load_dataset('miracl/nomiracl-instruct') ``` ## What is NoMIRACL? Retrieval-augmented generation (RAG) is a powerful approach to incorporating external knowledge into large language models (LLMs) to enhance the accuracy and faithfulness of LLM-generated responses. However, evaluating query-passage relevance across diverse language families has been a challenge, leading to gaps in understanding the model's performance against errors in externally retrieved knowledge. To address this, we present NoMIRACL, a completely human-annotated dataset designed for evaluating multilingual LLM relevance across 18 diverse languages. NoMIRACL evaluates LLM relevance as a binary classification objective, containing two subsets: `non-relevant` and `relevant`. The `non-relevant` subset contains queries with all passages manually judged by an expert assessor as non-relevant, while the `relevant` subset contains queries with at least one judged relevant passage within the labeled passages. LLM relevance is measured using two key metrics: - *hallucination rate* (on the `non-relevant` subset) measuring model tendency to recognize when none of the passages provided are relevant for a given question (non-answerable). - *error rate* (on the `relevant` subset) measuring model tendency as unable to identify relevant passages when provided for a given question (answerable). ## Acknowledgement This dataset would not have been possible without all the topics being generated by native speakers of each language in conjunction with our **multilingual RAG universe** work in part 1, **MIRACL** [[TACL '23]](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438/MIRACL-A-Multilingual-Retrieval-Dataset-Covering). The queries with all non-relevant passages are used to create the `non-relevant` subset whereas queries with at least a single relevant passage (i.e., MIRACL dev and test splits) are used to create `relevant` subset. This repository contains the topics, qrels, and top-10 (maximum) annotated documents of NoMIRACL. The whole collection can be found [here](https://huggingface.co/datasets/miracl/miracl-corpus). ## Quickstart ``` import datasets nomiracl = datasets.load_dataset('miracl/nomiracl-instruct') ``` ## Dataset Description * **Website:** https://nomiracl.github.io * **Paper:** https://aclanthology.org/2024.findings-emnlp.730/ * **Repository:** https://github.com/project-miracl/nomiracl ## Dataset Structure ## Dataset Statistics For NoMIRACL dataset statistics, please refer to our EMNLP 2024 Findings publication. Paper: [https://aclanthology.org/2024.findings-emnlp.730/](https://aclanthology.org/2024.findings-emnlp.730/). ## Citation Information This work was conducted as a collaboration between the University of Waterloo and Huawei Technologies. ``` @inproceedings{thakur-etal-2024-knowing, title = "{``}Knowing When You Don{'}t Know{''}: A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation", author = "Thakur, Nandan and Bonifacio, Luiz and Zhang, Crystina and Ogundepo, Odunayo and Kamalloo, Ehsan and Alfonso-Hermelo, David and Li, Xiaoguang and Liu, Qun and Chen, Boxing and Rezagholizadeh, Mehdi and Lin, Jimmy", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.findings-emnlp.730", pages = "12508--12526", abstract = "Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. However, prior work lacks a comprehensive evaluation of different language families, making it challenging to evaluate LLM robustness against errors in external retrieved knowledge. To overcome this, we establish **NoMIRACL**, a human-annotated dataset for evaluating LLM robustness in RAG across 18 typologically diverse languages. NoMIRACL includes both a non-relevant and a relevant subset. Queries in the non-relevant subset contain passages judged as non-relevant, whereas queries in the relevant subset include at least a single judged relevant passage. We measure relevance assessment using: (i) *hallucination rate*, measuring model tendency to hallucinate when the answer is not present in passages in the non-relevant subset, and (ii) *error rate*, measuring model inaccuracy to recognize relevant passages in the relevant subset. In our work, we observe that most models struggle to balance the two capacities. Models such as LLAMA-2 and Orca-2 achieve over 88{\%} hallucination rate on the non-relevant subset. Mistral and LLAMA-3 hallucinate less but can achieve up to a 74.9{\%} error rate on the relevant subset. Overall, GPT-4 is observed to provide the best tradeoff on both subsets, highlighting future work necessary to improve LLM robustness. NoMIRACL dataset and evaluation code are available at: https://github.com/project-miracl/nomiracl.", } ```

afg1/pombe-canto-data

afg1

--- dataset_info: features: - name: triage_status dtype: large_string - name: pmid dtype: large_string - name: abstract dtype: large_string - name: citation dtype: large_string - name: token_count dtype: int32 - name: label dtype: int8 splits: - name: train num_bytes: 13736788 num_examples: 10360 - name: test num_bytes: 3422716 num_examples: 2590 download_size: 9332324 dataset_size: 17159504 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Nutanix/cpp_unit_tests_unprocessed_Phi-3-mini-128k-instruct_vs_Phi-3-small-128k-instruct_judge_gpt

Nutanix

--- dataset_info: features: - name: Code dtype: string - name: Unit Test_Phi-3-mini-128k-instruct_raw dtype: string - name: Unit Test_Phi-3-small-128k-instruct_raw dtype: string - name: Unit Test dtype: string - name: Winning Model dtype: string - name: Judgement dtype: string splits: - name: train num_bytes: 9240903 num_examples: 201 download_size: 2829507 dataset_size: 9240903 configs: - config_name: default data_files: - split: train path: data/train-* ---

BotnoiNLPteam/scdt_proofread_v1

BotnoiNLPteam

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 10769351 num_examples: 18436 - name: test num_bytes: 1300663 num_examples: 2282 - name: val num_bytes: 1307448 num_examples: 2281 download_size: 3737248 dataset_size: 13377462 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: val path: data/val-* ---

ZiyuG/SciVerse

ZiyuG

--- task_categories: - multiple-choice - question-answering - visual-question-answering language: - en size_categories: - 1K<n<10K configs: - config_name: test data_files: - split: test path: QA.parquet dataset_info: - config_name: test features: - name: id dtype: string - name: subject dtype: string - name: image dtype: string - name: vision_dominant dtype: string - name: vision_only dtype: string - name: knowledge_lite dtype: string - name: knowledge_rich dtype: string - name: knowledge_professional dtype: string - name: question_vd dtype: string - name: choiceA dtype: string - name: choiceB dtype: string - name: choiceC dtype: string - name: choiceD dtype: string - name: choiceE dtype: string - name: answer dtype: string - name: explanation dtype: string - name: question_zh dtype: string - name: explanation_zh dtype: string splits: - name: test num_examples: 1147 --- # Dataset Card for SciVerse - [Dataset Description](https://huggingface.co/datasets/ZiyuG/SciVerse/blob/main/README.md#dataset-description) - [Paper Information](https://huggingface.co/datasets/ZiyuG/SciVerse/blob/main/README.md#paper-information) - [Dataset Examples](https://huggingface.co/datasets/ZiyuG/SciVerse/blob/main/README.md#dataset-examples) - [Leaderboard](https://huggingface.co/datasets/ZiyuG/SciVerse/blob/main/README.md#leaderboard) - [Citation](https://huggingface.co/datasets/ZiyuG/SciVerse/blob/main/README.md#citation) ## Dataset Description SciVerse is a multi-modal scientific benchmark introduced to evaluate the professional scientific reasoning abilities of multi-modal large language models (MLLMs) across various disciplines. This benchmark contains **5,735** annotated multi-modal Q&A samples covering key science subjects including **physics**, **chemistry**, and **biology**. It contains six distinct subsets designed to test varying degrees of knowledge and visual-text interpretation, i.e., **Knowledge Lite, Knowledge Rich, Knowledge Professional, Vision Dominant, Text Only** and **Vision Only**. - **Knowledge Lite**: basic problems with minimal necessary contextual information. - **Knowledge Rich**: problems with scientific background information. - **Knowledge Professional**: problems with advanced, professional-level scientific information. - **Vision Dominant**: problems that prioritizes visual cues over textual content to evaluate visual comprehension. - **Text Only**: problems with only texual inforamtion. - **Vision Only**: problems with only vison information, where textual problems rendered within the images. SciVerse aims to evaluate MLLMs' scientific reasoning ability of pre-existing scientific knowledge, and their sensitivity to the content stipulated in the questions. This not only measures how effectively MLLMs can utilize their inherent scientific understanding, but also assesses their ability to integrate and reason with given scientific knowledge in real-world scenarios. Unlike existing benchmarks, which often overlook the depth and multi-modal nature of scientific understanding, SciVerse addresses the complex challenges encountered in actual scientific analysis, providing a nuanced analysis of MLLMs' strengths and limitations in both knowledge integration and practical application. ## Paper Information - Code: https://github.com/ZiyuGuo99/SciVerse - Project: https://sciverse-cuhk.github.io/ - Dataset Overview: https://sciverse-cuhk.github.io/#overview - Leaderboard: https://sciverse-cuhk.github.io/#leaderboard ## Dataset Examples ***Coming soon...*** ## Leaderboard ### Contributing to the Leaderboard 🚨 The [Leaderboard](https://sciverse-cuhk.github.io/#leaderboard) is continuously being updated. The evaluation instructions and tools will be released soon. For now, please send your results on the test set to this email: ziyuguo@link.cuhk.edu.hk ## Citation If you find **SciVerse** useful for your research and applications, please kindly cite using this BibTeX: ```latex @article{sciverse, title={SciVerse}, author={Guo, Ziyu and Zhang, Renrui and Chen, Hao and Gao, Jialin and Li, Hongsheng and Heng, Pheng-Ann}, url={https://sciverse-cuhk.github.io/}, journal={arXiv preprint}, year={2024} } ```

LLM-EDA/BuggyVerilog

LLM-EDA

--- license: apache-2.0 task_categories: - text-generation language: - en --- For usage, please take a look at https://github.com/CatIIIIIIII/VeriDebug.

nexaai2b/perry_lora_function_call_training_data

nexaai2b

--- dataset_info: features: - name: query dtype: string - name: function dtype: string splits: - name: train num_bytes: 253506.0 num_examples: 3008 download_size: 75680 dataset_size: 253506.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Madjakul/HALvest-Contrastive-Raw

Madjakul

--- dataset_info: features: - name: halid dtype: string - name: lang dtype: string - name: domain sequence: string - name: timestamp dtype: string - name: year dtype: string - name: url dtype: string - name: text dtype: string - name: size dtype: int64 - name: authorids sequence: string - name: affiliations sequence: string splits: - name: train num_bytes: 22258039817.522587 num_examples: 361863 download_size: 9390538695 dataset_size: 22258039817.522587 configs: - config_name: default data_files: - split: train path: data/train-* ---

pico-lm/pretokenized-dolma-tinsy

pico-lm

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 57564000 num_examples: 7020 download_size: 28095401 dataset_size: 57564000 configs: - config_name: default data_files: - split: train path: data/train-* --- ## The ensy-tinsy Pico Dataset A tiny little baby-version of the Pico Dataset. Meant to be used in a jupyter notebook to test things out, or quickly look at the structure of the data.

llm-jp/llava-instruct-ja

llm-jp

--- language: - ja task_categories: - visual-question-answering size_categories: - 100K<n<1M --- ## Dataset Card for llava_instruct_ja ### Dataset details This is the Japanese version of [LLaVA-Instruct](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K), which contains 156K samples. We used `gpt-4o-mini-2024-07-18` to generate data through via Azure OpenAI API. ### License Creative Commons Attribution 4.0 License; and it should abide by [the OpenAI terms of use](https://openai.com/policies/terms-of-use)

Turbo-AI/data-train

Turbo-AI

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: relevant list: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 224299664 num_examples: 118956 download_size: 83963181 dataset_size: 224299664 configs: - config_name: default data_files: - split: train path: data/train-* ---

selmaXI/cnn_dailymail-llama2-1k

selmaXI

--- dataset_info: features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 8848392 num_examples: 1000 download_size: 5364766 dataset_size: 8848392 configs: - config_name: default data_files: - split: train path: data/train-* ---

Rudra-ai/ai-responses-dataset-math-modified-v3

Rudra-ai

--- dataset_info: features: - name: query dtype: string - name: response dtype: string - name: text dtype: string splits: - name: train num_bytes: 2382443 num_examples: 1000 download_size: 1058765 dataset_size: 2382443 configs: - config_name: default data_files: - split: train path: data/train-* ---

data-is-better-together/image_preferences_results

data-is-better-together

--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for image_preferences_results This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Using this dataset with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.Dataset.from_hub("DIBT/image_preferences_results") ``` This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation. ## Using this dataset with `datasets` To load the records of this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("DIBT/image_preferences_results") ``` This will only load the records of the dataset, but not the Argilla settings. ## Dataset Structure This dataset repo contains: * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. * A dataset configuration folder conforming to the Argilla dataset format in `.argilla`. The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. ### Fields The **fields** are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | images | images | custom | True | | ### Questions The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | preference | preference | label_selection | True | Which image do you prefer given the prompt? | ['image_1', 'image_2', 'both_good', 'both_bad'] | <!-- check length of metadata properties --> ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "_server_id": "30403740-6a5e-48d7-839e-dcea7ad0dfda", "fields": { "images": { "image_1": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_0/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg", "image_2": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_2/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg", "prompt": "8-bit intellect, pixelated wisdom, retro digital brain, vintage game insight, soft neon glow, intricate pixel art, vibrant color palette, nostalgic ambiance" } }, "id": "f5224be1-2e1b-428e-94b1-9c0f397092fa", "metadata": { "category": "Animation", "evolution": "quality", "model_1": "schnell", "model_2": "dev", "sub_category": "Pixel Art" }, "responses": { "preference": [ { "user_id": "c53e62ab-d792-4854-98f6-593b2ffb55bc", "value": "image_2" }, { "user_id": "b1ab2cdd-29b8-4cf9-b6e0-7543589d21a3", "value": "image_2" }, { "user_id": "da3e5871-920c-44da-8c44-1e94260c581e", "value": "both_good" }, { "user_id": "b31dd1ed-78b6-4d50-8f11-7ce32ba17d64", "value": "image_2" }, { "user_id": "6b984f66-86b3-421e-a32c-cd3592ee27a1", "value": "both_bad" } ] }, "status": "completed", "suggestions": {}, "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "_server_id": "30403740-6a5e-48d7-839e-dcea7ad0dfda", "category": "Animation", "evolution": "quality", "id": "f5224be1-2e1b-428e-94b1-9c0f397092fa", "images": { "image_1": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_0/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg", "image_2": "https://huggingface.co/datasets/DIBT/img_prefs_style/resolve/main/artifacts/image_generation_2/images/b172c7078a07c159f5f8da7bd1220ddd.jpeg", "prompt": "8-bit intellect, pixelated wisdom, retro digital brain, vintage game insight, soft neon glow, intricate pixel art, vibrant color palette, nostalgic ambiance" }, "model_1": "schnell", "model_2": "dev", "preference.responses": [ "image_2", "image_2", "both_good", "image_2", "both_bad" ], "preference.responses.status": [ "submitted", "submitted", "submitted", "submitted", "submitted" ], "preference.responses.users": [ "c53e62ab-d792-4854-98f6-593b2ffb55bc", "b1ab2cdd-29b8-4cf9-b6e0-7543589d21a3", "da3e5871-920c-44da-8c44-1e94260c581e", "b31dd1ed-78b6-4d50-8f11-7ce32ba17d64", "6b984f66-86b3-421e-a32c-cd3592ee27a1" ], "status": "completed", "sub_category": "Pixel Art" } ``` ### Data Splits The dataset contains a single split, which is `train`. ## 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 guidelines [More Information Needed] #### 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]

FrancophonIA/WikiCaps

FrancophonIA

--- language: - en - de - fr - ru multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/11744/ ## Description A multilingual (but non-parallel) corpus for multimodal machine translation and retrieval. The image captions were extracted from Wikimedia Commons for English, German, French, and Russian. More info on https://www.cl.uni-heidelberg.de/statnlpgroup/wikicaps/ ## Citation ``` @inproceedings{schamoni2018, author = {Schamoni, Shigehiko and Hitschler, Julian and Riezler, Stefan}, title = {A Dataset and Reranking Method for Multimodal MT of User-Generated Image Captions}, journal = {Proceedings of the 13th biennial conference of the Association for Machine Translation in the Americas}, journal-abbrev = {AMTA}, year = {2018}, city = {Boston, MA}, country = {USA}, url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/AMTA2018.1.pdf} } ```

FrancophonIA/train-o-matic

FrancophonIA

--- language: - de - en - es - fr - it - zh multilingulality: - multilingual viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/5110/ ## Description We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words ina language’s vocabulary. For more info see http://trainomatic.org/trainomatic ## Citation ``` Pasini, (2021). Train-O-Matic Large. Version 1. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/5110 ```

violetxi/NUMINA-V1-Clean-Blocks-3400_4600-526_577

violetxi

--- dataset_info: features: - name: problem dtype: string - name: is_correct dtype: bool - name: target_answer dtype: string - name: solution dtype: string - name: solution_steps dtype: string - name: attempts dtype: string - name: model_answer dtype: string splits: - name: train num_bytes: 2254797194 num_examples: 221968 download_size: 249630890 dataset_size: 2254797194 configs: - config_name: default data_files: - split: train path: data/train-* ---

emilyphamm/quanloccumon

emilyphamm

--- license: mit ---

aonmao/hcj_videos

aonmao

--- license: mit ---

kaki-paper/20241118_iitp_ti_domain_malicious_qa_dataset

kaki-paper

--- dataset_info: features: - name: template dtype: string - name: system_prompt dtype: string - name: json_data dtype: string - name: instruction_1 dtype: string - name: instruction_2 dtype: string - name: report_template dtype: string - name: output dtype: string - name: domain dtype: string - name: label dtype: string splits: - name: train num_bytes: 98024431.5 num_examples: 4500 - name: test num_bytes: 10891603.5 num_examples: 500 download_size: 66400164 dataset_size: 108916035.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

matthewdelorenzo/dpo_verilog_buggy

matthewdelorenzo

--- license: mit ---

cymen-arfor/evals-btb-whisper-large-v2-ft-ca-25awr

cymen-arfor

--- language: - cy license: cc0-1.0 tags: - speech recognition metrics: - wer - cer --- __Model__: cymen-arfor/whisper-large-v2-ft-ca-25awr __Test Set__: DewiBrynJones/banc-trawsgrifiadau-bangor-clean __Split__: test ------------------------------------------------------------------------------------------------------------------------------------ __WER: 52.721032__ __CER: 21.859754__

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_964e979c-7acc-4bd6-a1c0-3ec9a63dcfd4

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_51487f0c-5751-41d3-8bf8-46722af3818a

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_b8fa2bca-5688-4a50-bd61-dc4fae6ee848

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---