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Model Name stringlengths 5 122	URL stringlengths 28 145	Crawled Text stringlengths 1 199k ⌀	text stringlengths 180 199k
Francesco/resnet18	https://huggingface.co/Francesco/resnet18	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Francesco/resnet18 ### Model URL : https://huggingface.co/Francesco/resnet18 ### Model Description : No model card New: Create and edit this model card directly on the website!
Francesco/resnet26-224-1k	https://huggingface.co/Francesco/resnet26-224-1k	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Francesco/resnet26-224-1k ### Model URL : https://huggingface.co/Francesco/resnet26-224-1k ### Model Description : No model card New: Create and edit this model card directly on the website!
Francesco/resnet26	https://huggingface.co/Francesco/resnet26	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Francesco/resnet26 ### Model URL : https://huggingface.co/Francesco/resnet26 ### Model Description : No model card New: Create and edit this model card directly on the website!
Francesco/resnet34-224-1k	https://huggingface.co/Francesco/resnet34-224-1k	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Francesco/resnet34-224-1k ### Model URL : https://huggingface.co/Francesco/resnet34-224-1k ### Model Description : No model card New: Create and edit this model card directly on the website!
Francesco/resnet34	https://huggingface.co/Francesco/resnet34	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Francesco/resnet34 ### Model URL : https://huggingface.co/Francesco/resnet34 ### Model Description : No model card New: Create and edit this model card directly on the website!
Francesco/resnet50-224-1k	https://huggingface.co/Francesco/resnet50-224-1k	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Francesco/resnet50-224-1k ### Model URL : https://huggingface.co/Francesco/resnet50-224-1k ### Model Description : No model card New: Create and edit this model card directly on the website!
Francesco/resnet50	https://huggingface.co/Francesco/resnet50	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Francesco/resnet50 ### Model URL : https://huggingface.co/Francesco/resnet50 ### Model Description : No model card New: Create and edit this model card directly on the website!
Frankie2666/twitter-roberta-base-sentiment	https://huggingface.co/Frankie2666/twitter-roberta-base-sentiment	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Frankie2666/twitter-roberta-base-sentiment ### Model URL : https://huggingface.co/Frankie2666/twitter-roberta-base-sentiment ### Model Description : No model card New: Create and edit this model card directly on the website!
FranzStrauss/ponet-base-uncased	https://huggingface.co/FranzStrauss/ponet-base-uncased	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FranzStrauss/ponet-base-uncased ### Model URL : https://huggingface.co/FranzStrauss/ponet-base-uncased ### Model Description : No model card New: Create and edit this model card directly on the website!
Fraser/ag_news	https://huggingface.co/Fraser/ag_news	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fraser/ag_news ### Model URL : https://huggingface.co/Fraser/ag_news ### Model Description : No model card New: Create and edit this model card directly on the website!
Fraser/single_latent	https://huggingface.co/Fraser/single_latent	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fraser/single_latent ### Model URL : https://huggingface.co/Fraser/single_latent ### Model Description : No model card New: Create and edit this model card directly on the website!
Fraser/to_delete	https://huggingface.co/Fraser/to_delete	Generated program synthesis datasets used to train dreamcoder. Currently just supports text & list data.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fraser/to_delete ### Model URL : https://huggingface.co/Fraser/to_delete ### Model Description : Generated program synthesis datasets used to train dreamcoder. Currently just supports text & list data.
Fraser/transformer-vae	https://huggingface.co/Fraser/transformer-vae	A PyTorch Transformer-VAE model. Uses an MMD loss to prevent posterior collapse. Will setup in the next month or so.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fraser/transformer-vae ### Model URL : https://huggingface.co/Fraser/transformer-vae ### Model Description : A PyTorch Transformer-VAE model. Uses an MMD loss to prevent posterior collapse. Will setup in the next month or so.
Fraser/wiki-vae	https://huggingface.co/Fraser/wiki-vae	A Transformer-VAE trained on all the sentences in wikipedia. Training is done on AWS SageMaker.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fraser/wiki-vae ### Model URL : https://huggingface.co/Fraser/wiki-vae ### Model Description : A Transformer-VAE trained on all the sentences in wikipedia. Training is done on AWS SageMaker.
FreakyNobleGas/DialoGPT-medium-HarryPotter	https://huggingface.co/FreakyNobleGas/DialoGPT-medium-HarryPotter	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FreakyNobleGas/DialoGPT-medium-HarryPotter ### Model URL : https://huggingface.co/FreakyNobleGas/DialoGPT-medium-HarryPotter ### Model Description : No model card New: Create and edit this model card directly on the website!
Fred/Cows	https://huggingface.co/Fred/Cows	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fred/Cows ### Model URL : https://huggingface.co/Fred/Cows ### Model Description : No model card New: Create and edit this model card directly on the website!
Frederick0291/t5-small-finetuned-billsum	https://huggingface.co/Frederick0291/t5-small-finetuned-billsum	This model is a fine-tuned version of t5-small on the billsum dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Frederick0291/t5-small-finetuned-billsum ### Model URL : https://huggingface.co/Frederick0291/t5-small-finetuned-billsum ### Model Description : This model is a fine-tuned version of t5-small on the billsum dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
Frederick0291/t5-small-finetuned-xsum-finetuned-billsum	https://huggingface.co/Frederick0291/t5-small-finetuned-xsum-finetuned-billsum	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Frederick0291/t5-small-finetuned-xsum-finetuned-billsum ### Model URL : https://huggingface.co/Frederick0291/t5-small-finetuned-xsum-finetuned-billsum ### Model Description : No model card New: Create and edit this model card directly on the website!
Frederick0291/t5-small-finetuned-xsum	https://huggingface.co/Frederick0291/t5-small-finetuned-xsum	This model is a fine-tuned version of Frederick0291/t5-small-finetuned-xsum on an unknown dataset. More information needed More information needed More information needed The following hyperparameters were used during training: This model's model-index metadata is invalid: Schema validation error. "model-index[0].results[0].metrics" is required	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Frederick0291/t5-small-finetuned-xsum ### Model URL : https://huggingface.co/Frederick0291/t5-small-finetuned-xsum ### Model Description : This model is a fine-tuned version of Frederick0291/t5-small-finetuned-xsum on an unknown dataset. More information needed More information needed More information needed The following hyperparameters were used during training: This model's model-index metadata is invalid: Schema validation error. "model-index[0].results[0].metrics" is required
Fredy/wav2vec2-base-nonnative-vietnamese-test-colab	https://huggingface.co/Fredy/wav2vec2-base-nonnative-vietnamese-test-colab	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fredy/wav2vec2-base-nonnative-vietnamese-test-colab ### Model URL : https://huggingface.co/Fredy/wav2vec2-base-nonnative-vietnamese-test-colab ### Model Description : No model card New: Create and edit this model card directly on the website!
FreeSpinsCoinMaster/dsdqfdqsfsf	https://huggingface.co/FreeSpinsCoinMaster/dsdqfdqsfsf	https://elinsborgsskolan.stockholm.se/sites/default/files/webform/ro-bux_nc-21.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/free-onlyfans-hack-2021_oq-21.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/free-v-bucks-g1_zo-21.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/free-tiktok-fans-generator_sg-21.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/spins.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/pubg.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/google.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/7frtg.pdf	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FreeSpinsCoinMaster/dsdqfdqsfsf ### Model URL : https://huggingface.co/FreeSpinsCoinMaster/dsdqfdqsfsf ### Model Description : https://elinsborgsskolan.stockholm.se/sites/default/files/webform/ro-bux_nc-21.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/free-onlyfans-hack-2021_oq-21.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/free-v-bucks-g1_zo-21.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/free-tiktok-fans-generator_sg-21.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/spins.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/pubg.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/google.pdf https://elinsborgsskolan.stockholm.se/sites/default/files/webform/7frtg.pdf
FremyCompany/xls-r-2b-nl-v2_lm-5gram-os	https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os	This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0): The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. To improve accuracy, a beam-search decoder based on pyctcdecode is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus. This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation). The model was:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FremyCompany/xls-r-2b-nl-v2_lm-5gram-os ### Model URL : https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os ### Model Description : This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0): The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. To improve accuracy, a beam-search decoder based on pyctcdecode is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus. This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation). The model was:
FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell	https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell	This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0): IMPORTANT NOTE: The hunspell typo fixer is not enabled on the website, which returns raw CTC+LM results. Hunspell reranking is only available in the eval.py decoding script. For best results, please use the code in that file while using the model locally for inference. IMPORTANT NOTE: Evaluating this model requires apt install libhunspell-dev and a pip install of hunspell in addition to pip installs of pipy-kenlm and pyctcdecode (see install_requirements.sh); in addition, the chunking lengths and strides were optimized for the model as 12s and 2s respectively (see eval.sh). QUICK REMARK: The "Robust Speech Event" set does not contain cleaned transcription text, so its WER/CER are vastly over-estimated. For instance 2014 in the dev set is left as a number but will be recognized as tweeduizend veertien, which counts as 3 mistakes (2014 missing, and both tweeduizend and veertien wrongly inserted). Other normalization problems in the dev set include the presence of single quotes around some words, that then end up as non-match despite being the correct word (but without quotes), and the removal of some speech words in the final transcript (ja, etc...). As a result, our real error rate on the dev set is significantly lower than reported. You can compare the predictions with the targets on the validation dev set yourself, for example using this diffing tool. WE DO SPEECH RECOGNITION: Hello reader! If you are considering using this (or another) model in production, but would benefit from a model fine-tuned specifically for your use case (using text and/or labelled speech), feel free to contact our team. This model was developped during the Robust Speech Recognition challenge event by François REMY (twitter) and Geoffroy VANDERREYDT. We would like to thank OVH for providing us with a V100S GPU. The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. To improve accuracy, a beam-search decoder based on pyctcdecode is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus. To further deal with typos, hunspell is used to propose alternative spellings for words not in the unigrams of the language model. These alternatives are then reranked based on the language model trained above, and a penalty proportional to the levenshtein edit distance between the alternative and the recognized word. This for examples enables to correct collegas into collega's or gogol into google. This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation). The model was:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell ### Model URL : https://huggingface.co/FremyCompany/xls-r-2b-nl-v2_lm-5gram-os2_hunspell ### Model Description : This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0): IMPORTANT NOTE: The hunspell typo fixer is not enabled on the website, which returns raw CTC+LM results. Hunspell reranking is only available in the eval.py decoding script. For best results, please use the code in that file while using the model locally for inference. IMPORTANT NOTE: Evaluating this model requires apt install libhunspell-dev and a pip install of hunspell in addition to pip installs of pipy-kenlm and pyctcdecode (see install_requirements.sh); in addition, the chunking lengths and strides were optimized for the model as 12s and 2s respectively (see eval.sh). QUICK REMARK: The "Robust Speech Event" set does not contain cleaned transcription text, so its WER/CER are vastly over-estimated. For instance 2014 in the dev set is left as a number but will be recognized as tweeduizend veertien, which counts as 3 mistakes (2014 missing, and both tweeduizend and veertien wrongly inserted). Other normalization problems in the dev set include the presence of single quotes around some words, that then end up as non-match despite being the correct word (but without quotes), and the removal of some speech words in the final transcript (ja, etc...). As a result, our real error rate on the dev set is significantly lower than reported. You can compare the predictions with the targets on the validation dev set yourself, for example using this diffing tool. WE DO SPEECH RECOGNITION: Hello reader! If you are considering using this (or another) model in production, but would benefit from a model fine-tuned specifically for your use case (using text and/or labelled speech), feel free to contact our team. This model was developped during the Robust Speech Recognition challenge event by François REMY (twitter) and Geoffroy VANDERREYDT. We would like to thank OVH for providing us with a V100S GPU. The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. To improve accuracy, a beam-search decoder based on pyctcdecode is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus. To further deal with typos, hunspell is used to propose alternative spellings for words not in the unigrams of the language model. These alternatives are then reranked based on the language model trained above, and a penalty proportional to the levenshtein edit distance between the alternative and the recognized word. This for examples enables to correct collegas into collega's or gogol into google. This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation). The model was:
FremyCompany/xls-r-nl-v1-cv8-lm	https://huggingface.co/FremyCompany/xls-r-nl-v1-cv8-lm	This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0): The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus. This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FremyCompany/xls-r-nl-v1-cv8-lm ### Model URL : https://huggingface.co/FremyCompany/xls-r-nl-v1-cv8-lm ### Model Description : This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0): The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus. This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).
Freych/DialoGPT-small-xinyan	https://huggingface.co/Freych/DialoGPT-small-xinyan	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Freych/DialoGPT-small-xinyan ### Model URL : https://huggingface.co/Freych/DialoGPT-small-xinyan ### Model Description : No model card New: Create and edit this model card directly on the website!
Frikallo/BATbot	https://huggingface.co/Frikallo/BATbot	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Frikallo/BATbot ### Model URL : https://huggingface.co/Frikallo/BATbot ### Model Description : No model card New: Create and edit this model card directly on the website!
FrillyMilly/DialoGPT-small-rick	https://huggingface.co/FrillyMilly/DialoGPT-small-rick	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FrillyMilly/DialoGPT-small-rick ### Model URL : https://huggingface.co/FrillyMilly/DialoGPT-small-rick ### Model Description : No model card New: Create and edit this model card directly on the website!
Frodnar/bee-likes	https://huggingface.co/Frodnar/bee-likes	Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Frodnar/bee-likes ### Model URL : https://huggingface.co/Frodnar/bee-likes ### Model Description : Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo.
Froggie/just-testing	https://huggingface.co/Froggie/just-testing	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Froggie/just-testing ### Model URL : https://huggingface.co/Froggie/just-testing ### Model Description : No model card New: Create and edit this model card directly on the website!
Fu10k/DialoGPT-medium-Rick	https://huggingface.co/Fu10k/DialoGPT-medium-Rick	null	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fu10k/DialoGPT-medium-Rick ### Model URL : https://huggingface.co/Fu10k/DialoGPT-medium-Rick ### Model Description :
Fujitsu/AugCode	https://huggingface.co/Fujitsu/AugCode	This is Augmented Code Model which is a fined-tune model of CodeBERT for processing of similarity between given docstring and code. This model is fined-model based on Augmented Code Corpus with ACS=4. Similar to other huggingface model, you may load the model as follows. Then you may use model to infer the similarity between a given docstring and code.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fujitsu/AugCode ### Model URL : https://huggingface.co/Fujitsu/AugCode ### Model Description : This is Augmented Code Model which is a fined-tune model of CodeBERT for processing of similarity between given docstring and code. This model is fined-model based on Augmented Code Corpus with ACS=4. Similar to other huggingface model, you may load the model as follows. Then you may use model to infer the similarity between a given docstring and code.
Fujitsu/pytorrent	https://huggingface.co/Fujitsu/pytorrent	Pretrained weights based on PyTorrent Dataset which is a curated data from a large official Python packages. We use PyTorrent dataset to train a preliminary DistilBERT-Masked Language Modeling(MLM) model from scratch. The trained model, along with the dataset, aims to help researchers to easily and efficiently work on a large dataset of Python packages using only 5 lines of codes to load the transformer-based model. We use 1M raw Python scripts of PyTorrent that includes 12,350,000 LOC to train the model. We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources. This model is trained with a Masked Language Model (MLM) objective. Preprint: https://arxiv.org/pdf/2110.01710.pdf	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Fujitsu/pytorrent ### Model URL : https://huggingface.co/Fujitsu/pytorrent ### Model Description : Pretrained weights based on PyTorrent Dataset which is a curated data from a large official Python packages. We use PyTorrent dataset to train a preliminary DistilBERT-Masked Language Modeling(MLM) model from scratch. The trained model, along with the dataset, aims to help researchers to easily and efficiently work on a large dataset of Python packages using only 5 lines of codes to load the transformer-based model. We use 1M raw Python scripts of PyTorrent that includes 12,350,000 LOC to train the model. We also train a byte-level Byte-pair encoding (BPE) tokenizer that includes 56,000 tokens, which is truncated LOC with the length of 50 to save computation resources. This model is trained with a Masked Language Model (MLM) objective. Preprint: https://arxiv.org/pdf/2110.01710.pdf
FuriouslyAsleep/markuplm-large-finetuned-qa	https://huggingface.co/FuriouslyAsleep/markuplm-large-finetuned-qa	This model is adapted from Microsoft's MarkupLM. This fine-tuned model is the result of partially following instructions in the MarkupLM git repo (with adjustments described farther below under the Fine-tuning args section.) This version not endorsed by Microsoft. Test the question answering out in the Markup QA space here --------------------------------------------------------------------------------- Fine-tuned Multimodal (text +markup language) pre-training for Document AI MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding Junlong Li, Yiheng Xu, Lei Cui, Furu Wei --------------------------------------------------------------------------------- Fine-tuning args: --per_gpu_train_batch_size 4 --warmup_ratio 0.1 --num_train_epochs 4 The number of total websites is 60 The train websites list is ['ga09'] The test websites list is [] The dev websites list is ['ga12', 'ph04', 'au08', 'ga10', 'au01', 'bo17', 'mo02', 'jo11', 'sp09', 'sp10', 'ph03', 'ph01', 'un09', 'sp14', 'jo03', 'sp07', 'un07', 'bo07', 'mo04', 'bo09', 'jo10', 'un12', 're02', 'bo01', 'ca01', 'sp15', 'au12', 'un03', 're03', 'jo13', 'ph02', 'un10', 'au09', 'au10', 'un02', 'mo07', 'sp13', 'bo08', 'sp03', 're05', 'sp06', 'ca02', 'sp02', 'sp01', 'au03', 'sp11', 'mo06', 'bo10', 'un11', 'un06', 'ga01', 'un04', 'ph05', 'au11', 'sp12', 'jo05', 'sp04', 'jo12', 'sp08'] The number of processed websites is 60 --------------------------------------------------------------------------------- Inference test here may not work. Use the transformers markuplm branch from NielsRogge transformers markuplm branch After installing from there, try the following model and tokenizer assignemnts (consider using a file for the tags dict) Go to https://github.com/uwts/ProjectRisk for sample script.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FuriouslyAsleep/markuplm-large-finetuned-qa ### Model URL : https://huggingface.co/FuriouslyAsleep/markuplm-large-finetuned-qa ### Model Description : This model is adapted from Microsoft's MarkupLM. This fine-tuned model is the result of partially following instructions in the MarkupLM git repo (with adjustments described farther below under the Fine-tuning args section.) This version not endorsed by Microsoft. Test the question answering out in the Markup QA space here --------------------------------------------------------------------------------- Fine-tuned Multimodal (text +markup language) pre-training for Document AI MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding Junlong Li, Yiheng Xu, Lei Cui, Furu Wei --------------------------------------------------------------------------------- Fine-tuning args: --per_gpu_train_batch_size 4 --warmup_ratio 0.1 --num_train_epochs 4 The number of total websites is 60 The train websites list is ['ga09'] The test websites list is [] The dev websites list is ['ga12', 'ph04', 'au08', 'ga10', 'au01', 'bo17', 'mo02', 'jo11', 'sp09', 'sp10', 'ph03', 'ph01', 'un09', 'sp14', 'jo03', 'sp07', 'un07', 'bo07', 'mo04', 'bo09', 'jo10', 'un12', 're02', 'bo01', 'ca01', 'sp15', 'au12', 'un03', 're03', 'jo13', 'ph02', 'un10', 'au09', 'au10', 'un02', 'mo07', 'sp13', 'bo08', 'sp03', 're05', 'sp06', 'ca02', 'sp02', 'sp01', 'au03', 'sp11', 'mo06', 'bo10', 'un11', 'un06', 'ga01', 'un04', 'ph05', 'au11', 'sp12', 'jo05', 'sp04', 'jo12', 'sp08'] The number of processed websites is 60 --------------------------------------------------------------------------------- Inference test here may not work. Use the transformers markuplm branch from NielsRogge transformers markuplm branch After installing from there, try the following model and tokenizer assignemnts (consider using a file for the tags dict) Go to https://github.com/uwts/ProjectRisk for sample script.
Furkan/Furkan	https://huggingface.co/Furkan/Furkan	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Furkan/Furkan ### Model URL : https://huggingface.co/Furkan/Furkan ### Model Description : No model card New: Create and edit this model card directly on the website!
FutureFanatik/DialoGPT-small-rick	https://huggingface.co/FutureFanatik/DialoGPT-small-rick	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : FutureFanatik/DialoGPT-small-rick ### Model URL : https://huggingface.co/FutureFanatik/DialoGPT-small-rick ### Model Description : No model card New: Create and edit this model card directly on the website!
GD/bert-base-uncased-gh	https://huggingface.co/GD/bert-base-uncased-gh	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : GD/bert-base-uncased-gh ### Model URL : https://huggingface.co/GD/bert-base-uncased-gh ### Model Description : No model card New: Create and edit this model card directly on the website!
GD/cq-bert-model-repo	https://huggingface.co/GD/cq-bert-model-repo	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : GD/cq-bert-model-repo ### Model URL : https://huggingface.co/GD/cq-bert-model-repo ### Model Description : No model card New: Create and edit this model card directly on the website!
GD/qqp-bert-model-repo	https://huggingface.co/GD/qqp-bert-model-repo	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : GD/qqp-bert-model-repo ### Model URL : https://huggingface.co/GD/qqp-bert-model-repo ### Model Description : No model card New: Create and edit this model card directly on the website!
GD/qqp_1_glue_cased_	https://huggingface.co/GD/qqp_1_glue_cased_	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : GD/qqp_1_glue_cased_ ### Model URL : https://huggingface.co/GD/qqp_1_glue_cased_ ### Model Description : No model card New: Create and edit this model card directly on the website!
GD/qqp_multi_glue_cased_finish_5_epochs_only_qqp_repo	https://huggingface.co/GD/qqp_multi_glue_cased_finish_5_epochs_only_qqp_repo	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : GD/qqp_multi_glue_cased_finish_5_epochs_only_qqp_repo ### Model URL : https://huggingface.co/GD/qqp_multi_glue_cased_finish_5_epochs_only_qqp_repo ### Model Description : No model card New: Create and edit this model card directly on the website!