Datasets:

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ftopal

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huggingface-datasets

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[ "license:unknown", "region:us" ]

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[ "TAGS\n#license-unknown #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"celeb-identities\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"celeb-identities\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"BRAD\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"BRAD\"\n\nMore Information needed" ]

[ "license:cc-by-sa-4.0", "region:us" ]

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[ "# Dataset Card for OpenSLR Nepali Large ASR Cleaned", "## Table of Contents\n- Dataset Card for OpenSLR Nepali Large ASR Cleaned\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - How to use?\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits", "## Dataset Description\n\n- Homepage: Original OpenSLR Large Nepali ASR Dataset link\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: Sagar Sapkota", "### Dataset Summary\n\nThis data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.\nThe data set has been manually quality-checked, but there might still be errors.\n\nThe audio files are sampled at a rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection.\n\nFor your reference, following was the function applied on each of the original openslr utterances.", "### How to use?\n\nThere are two configurations for the data: one to download the original data and the other to download the preprocessed data as described above.\n1. First, to download the original dataset with HuggingFace's Dataset API:\n\n\n2. To download the preprocessed dataset:", "### Supported Tasks and Leaderboards\n\n- 'automatic-speech-recognition': The dataset can be used to train a model for Automatic Speech Recognition.", "### Languages\n\nNepali", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- utterance_id: a string identifying the utterances\n- speaker_id: obfuscated unique id of the speaker whose utterances is in the current instance\n- utterance:\n - path: path to the utterance .flac file\n - array: numpy array of the utterance\n - sampling_rate: sample rate of the utterance\n- transcription: Nepali text which spoken in the utterance\n- num_frames: length of waveform array", "### Data Splits\n\nThe dataset is not split. The consumer should split it as per their requirements." ]

[ "TAGS\n#license-cc-by-sa-4.0 #region-us \n", "# Dataset Card for OpenSLR Nepali Large ASR Cleaned", "## Table of Contents\n- Dataset Card for OpenSLR Nepali Large ASR Cleaned\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - How to use?\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits", "## Dataset Description\n\n- Homepage: Original OpenSLR Large Nepali ASR Dataset link\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: Sagar Sapkota", "### Dataset Summary\n\nThis data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.\nThe data set has been manually quality-checked, but there might still be errors.\n\nThe audio files are sampled at a rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection.\n\nFor your reference, following was the function applied on each of the original openslr utterances.", "### How to use?\n\nThere are two configurations for the data: one to download the original data and the other to download the preprocessed data as described above.\n1. First, to download the original dataset with HuggingFace's Dataset API:\n\n\n2. To download the preprocessed dataset:", "### Supported Tasks and Leaderboards\n\n- 'automatic-speech-recognition': The dataset can be used to train a model for Automatic Speech Recognition.", "### Languages\n\nNepali", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- utterance_id: a string identifying the utterances\n- speaker_id: obfuscated unique id of the speaker whose utterances is in the current instance\n- utterance:\n - path: path to the utterance .flac file\n - array: numpy array of the utterance\n - sampling_rate: sample rate of the utterance\n- transcription: Nepali text which spoken in the utterance\n- num_frames: length of waveform array", "### Data Splits\n\nThe dataset is not split. The consumer should split it as per their requirements." ]

[ "license:unknown", "region:us" ]

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[ "TAGS\n#license-unknown #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"OSACT4_hatespeech\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OSACT4_hatespeech\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"Sentiment_Lexicons\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Sentiment_Lexicons\"\n\nMore Information needed" ]

[ "license:mit", "region:us" ]

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[ "## Córpus de Complexidade Textual para Estágios Escolares do Sistema Educacional Brasileiro\n\nO córpus inclui trechos de: livros-textos cuja lista completa é apresentada abaixo, notícias da Seção Para Seu Filho Ler (PSFL) do jornal Zero Hora que apresenta algumas notícias sobre o mesmo córpus do jornal do Zero Hora, mas escritas para crianças de 8 a 11 anos de idade , Exames do SAEB , Livros Digitais do Wikilivros em Português, Exames do Enem dos anos 2015, 2016 e 2017. Todo o material em português foi disponibilizado para avaliar a tarefa de complexidade textual (readability).\n\nLista completa dos Livros Didáticos e suas fontes originais\n\nEsse corpus faz parte dos recursos de meu doutorado na área de Natural Language Processing, sendo realizado no Núcleo Interinstitucional de Linguística Computacional da USP de São Carlos. Esse trabalho foi orientado pela Profa. Sandra Maria Aluísio.\n\nURL\n\n@inproceedings{mgazzola19,\n title={Predição da Complexidade Textual de Recursos Educacionais Abertos em Português},\n author={Murilo Gazzola, Sidney Evaldo Leal, Sandra Maria Aluisio},\n booktitle={Proceedings of the Brazilian Symposium in Information and Human Language Technology},\n year={2019}\n}" ]

[ "TAGS\n#license-mit #region-us \n", "## Córpus de Complexidade Textual para Estágios Escolares do Sistema Educacional Brasileiro\n\nO córpus inclui trechos de: livros-textos cuja lista completa é apresentada abaixo, notícias da Seção Para Seu Filho Ler (PSFL) do jornal Zero Hora que apresenta algumas notícias sobre o mesmo córpus do jornal do Zero Hora, mas escritas para crianças de 8 a 11 anos de idade , Exames do SAEB , Livros Digitais do Wikilivros em Português, Exames do Enem dos anos 2015, 2016 e 2017. Todo o material em português foi disponibilizado para avaliar a tarefa de complexidade textual (readability).\n\nLista completa dos Livros Didáticos e suas fontes originais\n\nEsse corpus faz parte dos recursos de meu doutorado na área de Natural Language Processing, sendo realizado no Núcleo Interinstitucional de Linguística Computacional da USP de São Carlos. Esse trabalho foi orientado pela Profa. Sandra Maria Aluísio.\n\nURL\n\n@inproceedings{mgazzola19,\n title={Predição da Complexidade Textual de Recursos Educacionais Abertos em Português},\n author={Murilo Gazzola, Sidney Evaldo Leal, Sandra Maria Aluisio},\n booktitle={Proceedings of the Brazilian Symposium in Information and Human Language Technology},\n year={2019}\n}" ]

[ "region:us" ]

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[ "# Dataset Card for \"Commonsense_Validation\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Commonsense_Validation\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"arastance\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"arastance\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"TUNIZI\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"TUNIZI\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"AQAD\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"AQAD\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"MArSum\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"MArSum\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"arabicReviews-ds-mini\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"arabicReviews-ds-mini\"\n\nMore Information needed" ]

[ "task_categories:other", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:pt", "region:us" ]

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[ "pt" ]

[ "# Dataset Card for Dicionário Português\nIt is a list of portuguese words with its inflections\nHow to use it:" ]

[ "TAGS\n#task_categories-other #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Portuguese #region-us \n", "# Dataset Card for Dicionário Português\nIt is a list of portuguese words with its inflections\nHow to use it:" ]

[ "region:us" ]

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[ "# Dataset Card for \"arab-ds-mini\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"arab-ds-mini\"\n\nMore Information needed" ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Jets for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Jets for evaluating this model." ]

[ "license:cc-by-4.0", "region:us" ]

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[ "# Dataset Card for uneune_image1", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n今まで私が描いたイラスト100枚のデータセットです。\n512×512にトリミングしてあります。\nさっくりとstableDiffusionでの学習用に使えるデータセットが欲しかったので作りました。\n\nThis is a data set of 100 illustrations I have drawn so far.\nCropped to 512x512.\nI wanted a dataset that can be used for learning with stableDiffusion, so I made it." ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "# Dataset Card for uneune_image1", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n今まで私が描いたイラスト100枚のデータセットです。\n512×512にトリミングしてあります。\nさっくりとstableDiffusionでの学習用に使えるデータセットが欲しかったので作りました。\n\nThis is a data set of 100 illustrations I have drawn so far.\nCropped to 512x512.\nI wanted a dataset that can be used for learning with stableDiffusion, so I made it." ]

[ "region:us" ]

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[ "TAGS\n#region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"turknews-mini\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"turknews-mini\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"balloon\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"balloon\"\n\nMore Information needed" ]

[ "license:openrail", "region:us" ]

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[ "TAGS\n#license-openrail #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"celebrity_in_movie_demo\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"celebrity_in_movie_demo\"\n\nMore Information needed" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "license:cc0-1.0", "poetry", "stylistics", "poems", "gutenberg", "region:us" ]

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[ "en" ]

[ "# Allison Parrish's Gutenberg Poetry Corpus\n\nThis corpus was originally published under the CC0 license by Allison Parrish. Please visit Allison's fantastic accompanying GitHub repository for usage inspiration as well as more information on how the data was mined, how to create your own version of the corpus, and examples of projects using it.\n\nThis dataset contains 3,085,117 lines of poetry from hundreds of Project Gutenberg books. Each line has a corresponding 'gutenberg_id' (1191 unique values) from project Gutenberg.\n\n\nA row of data looks like this:" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #language-English #license-cc0-1.0 #poetry #stylistics #poems #gutenberg #region-us \n", "# Allison Parrish's Gutenberg Poetry Corpus\n\nThis corpus was originally published under the CC0 license by Allison Parrish. Please visit Allison's fantastic accompanying GitHub repository for usage inspiration as well as more information on how the data was mined, how to create your own version of the corpus, and examples of projects using it.\n\nThis dataset contains 3,085,117 lines of poetry from hundreds of Project Gutenberg books. Each line has a corresponding 'gutenberg_id' (1191 unique values) from project Gutenberg.\n\n\nA row of data looks like this:" ]

[ "task_categories:image-classification", "task_categories:image-segmentation", "annotations_creators:Stavros Niafas", "license:apache-2.0", "region:us" ]

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[ "## Dataset Summary\n\nDepth-of-Field(DoF) dataset is comprised of 1200 annotated images, binary annotated with(0) and without(1) bokeh effect, shallow or deep depth of field. It is a forked data set from the Unsplash 25K data set.", "## Dataset Description\n\n- Repository: URL\n- Paper: \n\n\n\nNote that each DoF dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset." ]

[ "TAGS\n#task_categories-image-classification #task_categories-image-segmentation #annotations_creators-Stavros Niafas #license-apache-2.0 #region-us \n", "## Dataset Summary\n\nDepth-of-Field(DoF) dataset is comprised of 1200 annotated images, binary annotated with(0) and without(1) bokeh effect, shallow or deep depth of field. It is a forked data set from the Unsplash 25K data set.", "## Dataset Description\n\n- Repository: URL\n- Paper: \n\n\n\nNote that each DoF dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset." ]

[ "region:us" ]

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[ "# Dataset Card for \"clothes_sample\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"clothes_sample\"\n\nMore Information needed" ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: gpt2-xl\n* Dataset: inverse-scaling/NeQA\n* Config: inverse-scaling--NeQA\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @rololbot for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: gpt2-xl\n* Dataset: inverse-scaling/NeQA\n* Config: inverse-scaling--NeQA\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @rololbot for evaluating this model." ]

[ "region:us" ]

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[ "# Dataset Card for \"Iraqi_Dialect\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Iraqi_Dialect\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"Sudanese_Dialect_Tweet\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Sudanese_Dialect_Tweet\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"Sudanese_Dialect_Tweet_Tele\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Sudanese_Dialect_Tweet_Tele\"\n\nMore Information needed" ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-base-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-base-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-large-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-large-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "region:us" ]

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[ "# Dataset Card for \"cheques_sample_data\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cheques_sample_data\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"Satirical_Fake_News\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Satirical_Fake_News\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"NArabizi\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"NArabizi\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"ArSAS\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ArSAS\"\n\nMore Information needed" ]

[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "language:en", "pretraining", "language modelling", "common crawl", "web", "region:us" ]

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[ "en" ]

[ "# Dataset Card for OLM September/October 2022 Common Crawl\n\nCleaned and deduplicated pretraining dataset, created with the OLM repo here from 16% of the September/October 2022 Common Crawl snapshot.\n\nNote: 'last_modified_timestamp' was parsed from whatever a website returned in it's 'Last-Modified' header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with 'last_modified_timestamp'." ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #language-English #pretraining #language modelling #common crawl #web #region-us \n", "# Dataset Card for OLM September/October 2022 Common Crawl\n\nCleaned and deduplicated pretraining dataset, created with the OLM repo here from 16% of the September/October 2022 Common Crawl snapshot.\n\nNote: 'last_modified_timestamp' was parsed from whatever a website returned in it's 'Last-Modified' header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with 'last_modified_timestamp'." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-base-lener-br-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-base-lener-br-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: inverse-scaling/NeQA\n* Config: inverse-scaling--NeQA\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @CG80499 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-1b1\n* Dataset: inverse-scaling/NeQA\n* Config: inverse-scaling--NeQA\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @CG80499 for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-base-lener-br-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-base-lener-br-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-base-lener_br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-base-lener_br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-large-lener_br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-large-lener_br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-base-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-base-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-large-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-large-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-large-lener_br\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-large-lener_br\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-base-lener_br\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/bertimbau-base-lener_br\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-base-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-base-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-large-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: Luciano/xlm-roberta-large-finetuned-lener-br\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-base-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-base-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-large-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-large-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-base-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-base-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-large-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-large-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-base-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-base-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-large-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: pierreguillou/ner-bert-large-cased-pt-lenerbr\n* Dataset: lener_br\n* Config: lener_br\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Luciano for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"punctuation-nilc-t5\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"punctuation-nilc-t5\"\n\nMore Information needed" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "source_datasets:original", "source_datasets:extended|librispeech_asr", "source_datasets:extended|common_voice", "language:en", "license:cc-by-4.0", "license:apache-2.0", "license:cc0-1.0", "license:cc-by-nc-3.0", "license:other", "asr", "benchmark", "speech", "esc", "region:us" ]

[]

[ "en" ]

[ "## ESC benchmark diagnostic dataset", "## Dataset Summary\n\nAs a part of ESC benchmark, we provide a small, 8h diagnostic dataset of in-domain validation data with newly annotated transcriptions. The audio data is sampled from each of the ESC validation sets, giving a range of different domains and speaking styles. The transcriptions are annotated according to a consistent style guide with two formats: normalised and un-normalised. The dataset is structured in the same way as the ESC dataset, by grouping audio-transcription samples according to the dataset from which they were taken. We encourage participants to use this dataset when evaluating their systems to quickly assess performance on a range of different speech recognition conditions.\n\nAll eight datasets in ESC can be downloaded and prepared in just a single line of code through the Hugging Face Datasets library:\n\n\n\nDatasets provided as splits, so to have clean diagnostic subset of AMI:\n\n\n\nSplits are: '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"common_voice.clean\"', '\"common_voice.other\"'. \n\nThe datasets are full prepared, such that the audio and transcription files can be used directly in training/evaluation scripts.", "## Dataset Information\n\nA data point can be accessed by indexing the dataset object loaded through 'load_dataset':\n\n\n\nA typical data point comprises the path to the audio file and its transcription. Also included is information of the dataset from which the sample derives and a unique identifier name:", "### Data Fields\n\n- 'audio': a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n\n- 'ortho_transcript': the orthographic transcription of the audio file.\n- \n- 'norm_transcript': the normalized transcription of the audio file.\n\n- 'id': unique id of the data sample.", "### Data Preparation", "#### Audio\nThe audio for all ESC datasets is segmented into sample lengths suitable for training ASR systems. The Hugging Face datasets library decodes audio files on the fly, reading the segments and converting them to a Python arrays. Consequently, no further preparation of the audio is required to be used in training/evaluation scripts.\n\nNote that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, i.e. 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.", "#### Transcriptions\nThe transcriptions corresponding to each audio file are provided in their 'error corrected' format. No transcription pre-processing is applied to the text, only necessary 'error correction' steps such as removing junk tokens (_&lt;unk>_) or converting symbolic punctuation to spelled out form (_&lt;comma>_ to _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts.\n\nTranscriptions are provided for training and validation splits. The transcriptions are not provided for the test splits. The ESC benchmark requires you to generate predictions for the test sets and upload them to URL for scoring.", "### Access\nAll eight of the datasets in ESC are accessible and licensing is freely available. Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages:\n* Common Voice: URL\n* GigaSpeech: URL\n* SPGISpeech: URL", "### Diagnostic Dataset\nESC contains a small, 8h diagnostic dataset of in-domain validation data with newly annotated transcriptions. The audio data is sampled from each of the ESC validation sets, giving a range of different domains and speaking styles. The transcriptions are annotated according to a consistent style guide with two formats: normalised and un-normalised. The dataset is structured in the same way as the ESC dataset, by grouping audio-transcription samples according to the dataset from which they were taken. We encourage participants to use this dataset when evaluating their systems to quickly assess performance on a range of different speech recognition conditions. For more information, visit: esc-bench/esc-diagnostic-dataset.", "## LibriSpeech\n\nThe LibriSpeech corpus is a standard large-scale corpus for assessing ASR systems. It consists of approximately 1,000 hours of narrated audiobooks from the LibriVox project. It is licensed under CC-BY-4.0.\n\nExample Usage:\n\n\n\nTrain/validation splits:\n- 'train' (combination of 'URL.100', 'URL.360' and 'URL.500')\n- 'URL'\n- 'URL'\n\nTest splits:\n- 'URL'\n- 'URL'\n\nAlso available are subsets of the train split, which can be accessed by setting the 'subconfig' argument:\n\n\n- 'clean.100': 100 hours of training data from the 'clean' subset\n- 'clean.360': 360 hours of training data from the 'clean' subset\n- 'other.500': 500 hours of training data from the 'other' subset", "## Common Voice\nCommon Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The English subset of contains approximately 1,400 hours of audio data from speakers of various nationalities, accents and different recording conditions. It is licensed under CC0-1.0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'", "## VoxPopuli\nVoxPopuli s a large-scale multilingual speech corpus consisting of political data sourced from 2009-2020 European Parliament event recordings. The English subset contains approximately 550 hours of speech largely from non-native English speakers. It is licensed under CC0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'", "## TED-LIUM\nTED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'", "## GigaSpeech\nGigaSpeech is a multi-domain English speech recognition corpus created from audiobooks, podcasts and YouTube. We provide the large train set (2,500 hours) and the standard validation and test splits. It is licensed under apache-2.0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train' ('l' subset of training data (2,500 h))\n- 'validation'\n\nTest splits:\n- 'test'\n\nAlso available are subsets of the train split, which can be accessed by setting the 'subconfig' argument:\n\n- 'xs': extra-small subset of training data (10 h)\n- 's': small subset of training data (250 h)\n- 'm': medium subset of training data (1,000 h)\n- 'xl': extra-large subset of training data (10,000 h)", "## SPGISpeech\nSPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement.\n\nLoading the dataset requires authorization.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train' ('l' subset of training data (~5,000 h))\n- 'validation'\n\nTest splits:\n- 'test'\n\nAlso available are subsets of the train split, which can be accessed by setting the 'subconfig' argument:\n\n- 's': small subset of training data (~200 h)\n- 'm': medium subset of training data (~1,000 h)", "## Earnings-22\nEarnings-22 is a 119-hour corpus of English-language earnings calls collected from global companies, with speakers of many different nationalities and accents. It is licensed under CC-BY-SA-4.0. \n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'", "## AMI\nThe AMI Meeting Corpus consists of 100 hours of meeting recordings from multiple recording devices synced to a common timeline. It is licensed under CC-BY-4.0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-original #source_datasets-extended|librispeech_asr #source_datasets-extended|common_voice #language-English #license-cc-by-4.0 #license-apache-2.0 #license-cc0-1.0 #license-cc-by-nc-3.0 #license-other #asr #benchmark #speech #esc #region-us \n", "## ESC benchmark diagnostic dataset", "## Dataset Summary\n\nAs a part of ESC benchmark, we provide a small, 8h diagnostic dataset of in-domain validation data with newly annotated transcriptions. The audio data is sampled from each of the ESC validation sets, giving a range of different domains and speaking styles. The transcriptions are annotated according to a consistent style guide with two formats: normalised and un-normalised. The dataset is structured in the same way as the ESC dataset, by grouping audio-transcription samples according to the dataset from which they were taken. We encourage participants to use this dataset when evaluating their systems to quickly assess performance on a range of different speech recognition conditions.\n\nAll eight datasets in ESC can be downloaded and prepared in just a single line of code through the Hugging Face Datasets library:\n\n\n\nDatasets provided as splits, so to have clean diagnostic subset of AMI:\n\n\n\nSplits are: '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"URL\"', '\"common_voice.clean\"', '\"common_voice.other\"'. \n\nThe datasets are full prepared, such that the audio and transcription files can be used directly in training/evaluation scripts.", "## Dataset Information\n\nA data point can be accessed by indexing the dataset object loaded through 'load_dataset':\n\n\n\nA typical data point comprises the path to the audio file and its transcription. Also included is information of the dataset from which the sample derives and a unique identifier name:", "### Data Fields\n\n- 'audio': a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n\n- 'ortho_transcript': the orthographic transcription of the audio file.\n- \n- 'norm_transcript': the normalized transcription of the audio file.\n\n- 'id': unique id of the data sample.", "### Data Preparation", "#### Audio\nThe audio for all ESC datasets is segmented into sample lengths suitable for training ASR systems. The Hugging Face datasets library decodes audio files on the fly, reading the segments and converting them to a Python arrays. Consequently, no further preparation of the audio is required to be used in training/evaluation scripts.\n\nNote that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, i.e. 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.", "#### Transcriptions\nThe transcriptions corresponding to each audio file are provided in their 'error corrected' format. No transcription pre-processing is applied to the text, only necessary 'error correction' steps such as removing junk tokens (_&lt;unk>_) or converting symbolic punctuation to spelled out form (_&lt;comma>_ to _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts.\n\nTranscriptions are provided for training and validation splits. The transcriptions are not provided for the test splits. The ESC benchmark requires you to generate predictions for the test sets and upload them to URL for scoring.", "### Access\nAll eight of the datasets in ESC are accessible and licensing is freely available. Three of the ESC datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages:\n* Common Voice: URL\n* GigaSpeech: URL\n* SPGISpeech: URL", "### Diagnostic Dataset\nESC contains a small, 8h diagnostic dataset of in-domain validation data with newly annotated transcriptions. The audio data is sampled from each of the ESC validation sets, giving a range of different domains and speaking styles. The transcriptions are annotated according to a consistent style guide with two formats: normalised and un-normalised. The dataset is structured in the same way as the ESC dataset, by grouping audio-transcription samples according to the dataset from which they were taken. We encourage participants to use this dataset when evaluating their systems to quickly assess performance on a range of different speech recognition conditions. For more information, visit: esc-bench/esc-diagnostic-dataset.", "## LibriSpeech\n\nThe LibriSpeech corpus is a standard large-scale corpus for assessing ASR systems. It consists of approximately 1,000 hours of narrated audiobooks from the LibriVox project. It is licensed under CC-BY-4.0.\n\nExample Usage:\n\n\n\nTrain/validation splits:\n- 'train' (combination of 'URL.100', 'URL.360' and 'URL.500')\n- 'URL'\n- 'URL'\n\nTest splits:\n- 'URL'\n- 'URL'\n\nAlso available are subsets of the train split, which can be accessed by setting the 'subconfig' argument:\n\n\n- 'clean.100': 100 hours of training data from the 'clean' subset\n- 'clean.360': 360 hours of training data from the 'clean' subset\n- 'other.500': 500 hours of training data from the 'other' subset", "## Common Voice\nCommon Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The English subset of contains approximately 1,400 hours of audio data from speakers of various nationalities, accents and different recording conditions. It is licensed under CC0-1.0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'", "## VoxPopuli\nVoxPopuli s a large-scale multilingual speech corpus consisting of political data sourced from 2009-2020 European Parliament event recordings. The English subset contains approximately 550 hours of speech largely from non-native English speakers. It is licensed under CC0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'", "## TED-LIUM\nTED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'", "## GigaSpeech\nGigaSpeech is a multi-domain English speech recognition corpus created from audiobooks, podcasts and YouTube. We provide the large train set (2,500 hours) and the standard validation and test splits. It is licensed under apache-2.0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train' ('l' subset of training data (2,500 h))\n- 'validation'\n\nTest splits:\n- 'test'\n\nAlso available are subsets of the train split, which can be accessed by setting the 'subconfig' argument:\n\n- 'xs': extra-small subset of training data (10 h)\n- 's': small subset of training data (250 h)\n- 'm': medium subset of training data (1,000 h)\n- 'xl': extra-large subset of training data (10,000 h)", "## SPGISpeech\nSPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement.\n\nLoading the dataset requires authorization.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train' ('l' subset of training data (~5,000 h))\n- 'validation'\n\nTest splits:\n- 'test'\n\nAlso available are subsets of the train split, which can be accessed by setting the 'subconfig' argument:\n\n- 's': small subset of training data (~200 h)\n- 'm': medium subset of training data (~1,000 h)", "## Earnings-22\nEarnings-22 is a 119-hour corpus of English-language earnings calls collected from global companies, with speakers of many different nationalities and accents. It is licensed under CC-BY-SA-4.0. \n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'", "## AMI\nThe AMI Meeting Corpus consists of 100 hours of meeting recordings from multiple recording devices synced to a common timeline. It is licensed under CC-BY-4.0.\n\nExample usage:\n\n\n\nTraining/validation splits:\n- 'train'\n- 'validation'\n\nTest splits:\n- 'test'" ]

[ "language:pt", "region:us" ]

[]

[ "pt" ]

[ "# Dataset Card for \"punctuation-nilc\"\n\nMore Information needed" ]

[ "TAGS\n#language-Portuguese #region-us \n", "# Dataset Card for \"punctuation-nilc\"\n\nMore Information needed" ]

[ "task_categories:feature-extraction", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:extended|laion/laion2B-multi", "language:ko", "license:cc-by-4.0", "region:us" ]

[]

[ "ko" ]

[ "# laion2b_multi_korean_subset_with_image", "## Dataset Description\n- Download Size 342 GB\n\nimg2dataset을 통해 다운로드에 성공한 Bingsu/laion2B-multi-korean-subset 이미지를 정리한 데이터셋입니다.\n\n이미지는 9,800,137장입니다.\n\n이미지는 짧은 쪽 길이가 256이 되도록 리사이즈 되었으며, 품질 100인 webp파일로 다운로드 되었습니다.", "## Usage", "### 1. datasets", "### 2. webdataset\n\n이 데이터셋은 webdataset으로 사용할 수 있도록 구성되어있습니다. 데이터를 다운로드하지 않고 스트리밍으로 처리한다면 1번 방법보다 훨씬 빠릅니다.\n\n!! 아래 방법은 Windows에서는 에러가 발생합니다.\n\n\n\n\n\n이 글을 작성하는 현재(22-10-18), webp이미지의 자동 디코딩을 지원하지 않고 있기 때문에(PR #215), 직접 디코딩해야 합니다.", "## Note\n\n!tar_image\n각각의 tar 파일은 위 처럼 구성되어 있습니다.\n\n다운로드에 실패한 이미지는 건너뛰어져있기 때문에 파일 이름은 완전히 연속적이지는 않습니다.\n\n각각의 json 파일은 다음처럼 되어있습니다.\n\n\n\ntxt파일은 json파일의 \"caption\"을 담고 있습니다." ]

[ "TAGS\n#task_categories-feature-extraction #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-extended|laion/laion2B-multi #language-Korean #license-cc-by-4.0 #region-us \n", "# laion2b_multi_korean_subset_with_image", "## Dataset Description\n- Download Size 342 GB\n\nimg2dataset을 통해 다운로드에 성공한 Bingsu/laion2B-multi-korean-subset 이미지를 정리한 데이터셋입니다.\n\n이미지는 9,800,137장입니다.\n\n이미지는 짧은 쪽 길이가 256이 되도록 리사이즈 되었으며, 품질 100인 webp파일로 다운로드 되었습니다.", "## Usage", "### 1. datasets", "### 2. webdataset\n\n이 데이터셋은 webdataset으로 사용할 수 있도록 구성되어있습니다. 데이터를 다운로드하지 않고 스트리밍으로 처리한다면 1번 방법보다 훨씬 빠릅니다.\n\n!! 아래 방법은 Windows에서는 에러가 발생합니다.\n\n\n\n\n\n이 글을 작성하는 현재(22-10-18), webp이미지의 자동 디코딩을 지원하지 않고 있기 때문에(PR #215), 직접 디코딩해야 합니다.", "## Note\n\n!tar_image\n각각의 tar 파일은 위 처럼 구성되어 있습니다.\n\n다운로드에 실패한 이미지는 건너뛰어져있기 때문에 파일 이름은 완전히 연속적이지는 않습니다.\n\n각각의 json 파일은 다음처럼 되어있습니다.\n\n\n\ntxt파일은 json파일의 \"caption\"을 담고 있습니다." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: csarron/bert-base-uncased-squad-v1\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: csarron/bert-base-uncased-squad-v1\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Watts\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Watts\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lsf_dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lsf_dataset\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "language:sl", "license:cc-by-nc-sa-4.0", "idiom-detection", "multiword-expression-detection", "region:us" ]

[]

[ "sl" ]

[ "# Dataset Card for SloIE", "### Dataset Summary\n\nSloIE is a manually labelled dataset of Slovene idiomatic expressions. It contains 29399 sentences with 75 different expressions that can occur with either a literal or an idiomatic meaning, with appropriate manual annotations for each token. The idiomatic expressions were selected from the Slovene Lexical Database. Only expressions that can occur with both a literal and an idiomatic meaning were selected. The sentences were extracted from the Gigafida corpus.\n\nFor a more detailed description of the dataset, please see the paper Škvorc et al. (2022) - see below.", "### Supported Tasks and Leaderboards\n\nIdiom detection.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the dataset:\n\n\nIn this 'sentence', the words of the expression \"barvati kaj s črnimi barvami\" are used in a literal sense, as indicated by the \"NE\" annotations inside 'is_idiom'. The \"*\" annotations indicate the words are not part of the expression.", "### Data Fields\n\n- 'sentence': raw sentence in string form - WARNING: this is at times slightly different from the words inside 'sentence_words' (e.g., \"...\" here could be \".\" in 'sentence_words'); \n- 'expression': the annotated idiomatic expression; \n- 'word_order': numbers indicating the positions of tokens that belong to the expression; \n- 'sentence_words': words in the sentence; \n- 'is_idiom': a string denoting whether each word has an idiomatic ('\"DA\"'), literal ('\"NE\"'), or ambiguous ('\"NEJASEN ZGLED\"') meaning. '\"*\"' means that the word is not part of the expression.", "## Additional Information", "### Dataset Curators\n\nTadej Škvorc, Polona Gantar, Marko Robnik-Šikonja.", "### Licensing Information\n\nCC BY-NC-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-100K<n<1M #language-Slovenian #license-cc-by-nc-sa-4.0 #idiom-detection #multiword-expression-detection #region-us \n", "# Dataset Card for SloIE", "### Dataset Summary\n\nSloIE is a manually labelled dataset of Slovene idiomatic expressions. It contains 29399 sentences with 75 different expressions that can occur with either a literal or an idiomatic meaning, with appropriate manual annotations for each token. The idiomatic expressions were selected from the Slovene Lexical Database. Only expressions that can occur with both a literal and an idiomatic meaning were selected. The sentences were extracted from the Gigafida corpus.\n\nFor a more detailed description of the dataset, please see the paper Škvorc et al. (2022) - see below.", "### Supported Tasks and Leaderboards\n\nIdiom detection.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the dataset:\n\n\nIn this 'sentence', the words of the expression \"barvati kaj s črnimi barvami\" are used in a literal sense, as indicated by the \"NE\" annotations inside 'is_idiom'. The \"*\" annotations indicate the words are not part of the expression.", "### Data Fields\n\n- 'sentence': raw sentence in string form - WARNING: this is at times slightly different from the words inside 'sentence_words' (e.g., \"...\" here could be \".\" in 'sentence_words'); \n- 'expression': the annotated idiomatic expression; \n- 'word_order': numbers indicating the positions of tokens that belong to the expression; \n- 'sentence_words': words in the sentence; \n- 'is_idiom': a string denoting whether each word has an idiomatic ('\"DA\"'), literal ('\"NE\"'), or ambiguous ('\"NEJASEN ZGLED\"') meaning. '\"*\"' means that the word is not part of the expression.", "## Additional Information", "### Dataset Curators\n\nTadej Škvorc, Polona Gantar, Marko Robnik-Šikonja.", "### Licensing Information\n\nCC BY-NC-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Sample" ]

[ "TAGS\n#region-us \n", "# Sample" ]

[ "license:other", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-other #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"error_correction_model_dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"error_correction_model_dataset\"\n\nMore Information needed" ]

[ "license:cc-by-nc-nd-4.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-cc-by-nc-nd-4.0 #region-us \n" ]

[ "license:apache-2.0", "arxiv:1906.02337", "region:us" ]

[ "1906.02337" ]

[]

[ "# MNIST-C\n\nThis repository contains the source code used to create the MNIST-C dataset, a\ncorrupted MNIST benchmark for testing out-of-distribution robustness of computer\nvision models.\n\nPlease see our full paper URL for more details.", "## Dataset\n\nThe static dataset is available for download at URL" ]

[ "TAGS\n#license-apache-2.0 #arxiv-1906.02337 #region-us \n", "# MNIST-C\n\nThis repository contains the source code used to create the MNIST-C dataset, a\ncorrupted MNIST benchmark for testing out-of-distribution robustness of computer\nvision models.\n\nPlease see our full paper URL for more details.", "## Dataset\n\nThe static dataset is available for download at URL" ]

[ "task_categories:text-classification", "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #region-us \n" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: phpthinh/ex1\n* Config: all\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @phpthinh for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: phpthinh/ex1\n* Config: all\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @phpthinh for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: phpthinh/ex2\n* Config: all\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @phpthinh for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: phpthinh/ex2\n* Config: all\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @phpthinh for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: Emanuel/bertweet-emotion-base\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nayan06 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: Emanuel/bertweet-emotion-base\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nayan06 for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: phpthinh/ex3\n* Config: all\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @phpthinh for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Zero-Shot Text Classification\n* Model: bigscience/bloom-560m\n* Dataset: phpthinh/ex3\n* Config: all\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @phpthinh for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Pokémon type captions\n\nContains official artwork and type-specific caption for Pokémon #1-898 (Bulbasaur-Calyrex). \nEach Pokémon is represented once by the default form from PokéAPI\n\nEach row contains 'image' and 'text' keys:\n- 'image' is a 475x475 PIL jpg of the Pokémon's official artwork. \n- 'text' is a label describing the Pokémon by its type(s)", "## Attributions\n\n_Images and typing information pulled from PokéAPI_\n\n_Based on the Lambda Labs Pokémon Blip Captions Dataset_" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Pokémon type captions\n\nContains official artwork and type-specific caption for Pokémon #1-898 (Bulbasaur-Calyrex). \nEach Pokémon is represented once by the default form from PokéAPI\n\nEach row contains 'image' and 'text' keys:\n- 'image' is a 475x475 PIL jpg of the Pokémon's official artwork. \n- 'text' is a label describing the Pokémon by its type(s)", "## Attributions\n\n_Images and typing information pulled from PokéAPI_\n\n_Based on the Lambda Labs Pokémon Blip Captions Dataset_" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: 0ys/mt5-small-finetuned-amazon-en-es\n* Dataset: conceptual_captions\n* Config: unlabeled\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @DonaldDaz for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: 0ys/mt5-small-finetuned-amazon-en-es\n* Dataset: conceptual_captions\n* Config: unlabeled\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @DonaldDaz for evaluating this model." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for TED descriptions\n\n\n\nMore Information needed" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for TED descriptions\n\n\n\nMore Information needed" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:de", "license:cc-by-4.0", "ner, named entity recognition, legal ner, legal texts, label classification", "arxiv:2003.13016", "doi:10.57967/hf/0046", "region:us" ]

[ "2003.13016" ]

[ "de" ]

[ "### Dataset Summary\n\n\nA dataset of Legal Documents from German federal court decisions for Named Entity Recognition. The dataset is human-annotated with 19 fine-grained entity classes. The dataset consists of approx. 67,000 sentences and contains 54,000 annotated entities. NER tags use the 'BIO' tagging scheme.\n\n\nThe dataset includes two different versions of annotations, one with a set of 19 fine-grained semantic classes ('ner\\_tags') and another one with a set of 7 coarse-grained classes ('ner\\_coarse\\_tags'). There are 53,632 annotated entities in total, the majority of which (74.34 %) are legal entities, the others are person, location and organization (25.66 %).\n\n\n![](URL\n\n\nFor more details see URL", "### Supported Tasks and Leaderboards\n\n\n* Tasks: Named Entity Recognition\n* Leaderboards:", "### Languages\n\n\nGerman\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nDocuments in the legal domain contain multiple references to named entities, especially domain-specific named entities, i. e., jurisdictions, legal institutions, etc. Legal documents are unique and differ greatly from newspaper texts. On the one hand, the occurrence of general-domain named entities is relatively rare. On the other hand, in concrete applications, crucial domain-specific entities need to be identified in a reliable way, such as designations of legal norms and references to other legal documents (laws, ordinances, regulations, decisions, etc.). Most NER solutions operate in the general or news domain, which makes them inapplicable to the analysis of legal documents. Accordingly, there is a great need for an NER-annotated dataset consisting of legal documents, including the corresponding development of a typology of semantic concepts and uniform annotation guidelines.", "### Source Data\n\n\nCourt decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).", "#### Initial Data Collection and Normalization\n\n\nFrom the table of contents, 107 documents from each court were selected (see Table 1). The data was collected from the XML documents, i. e., it was extracted from the XML elements 'Mitwirkung, Titelzeile, Leitsatz, Tenor, Tatbestand, Entscheidungsgründe, Gründen, abweichende Meinung, and sonstiger Titel'. The metadata at the beginning of the documents (name of court, date of decision, file number, European Case Law Identifier, document type, laws) and those that belonged to previous legal proceedings was deleted. Paragraph numbers were removed.\n\n\nThe extracted data was split into sentences, tokenised using SoMaJo and manually annotated in WebAnno.", "#### Who are the source language producers?\n\n\nThe Federal Ministry of Justice and the Federal Office of Justice provide selected decisions. Court decisions were produced by humans.", "### Annotations", "#### Annotation process\n\n\nFor more details see annotation guidelines (in German).", "### Personal and Sensitive Information\n\n\nA fundamental characteristic of the published decisions is that all personal information have been anonymised for privacy reasons. This affects the classes person, location and organization.", "### Licensing Information\n\n\nCC BY-SA 4.0 license", "### Contributions" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-German #license-cc-by-4.0 #ner, named entity recognition, legal ner, legal texts, label classification #arxiv-2003.13016 #doi-10.57967/hf/0046 #region-us \n", "### Dataset Summary\n\n\nA dataset of Legal Documents from German federal court decisions for Named Entity Recognition. The dataset is human-annotated with 19 fine-grained entity classes. The dataset consists of approx. 67,000 sentences and contains 54,000 annotated entities. NER tags use the 'BIO' tagging scheme.\n\n\nThe dataset includes two different versions of annotations, one with a set of 19 fine-grained semantic classes ('ner\\_tags') and another one with a set of 7 coarse-grained classes ('ner\\_coarse\\_tags'). There are 53,632 annotated entities in total, the majority of which (74.34 %) are legal entities, the others are person, location and organization (25.66 %).\n\n\n![](URL\n\n\nFor more details see URL", "### Supported Tasks and Leaderboards\n\n\n* Tasks: Named Entity Recognition\n* Leaderboards:", "### Languages\n\n\nGerman\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nDocuments in the legal domain contain multiple references to named entities, especially domain-specific named entities, i. e., jurisdictions, legal institutions, etc. Legal documents are unique and differ greatly from newspaper texts. On the one hand, the occurrence of general-domain named entities is relatively rare. On the other hand, in concrete applications, crucial domain-specific entities need to be identified in a reliable way, such as designations of legal norms and references to other legal documents (laws, ordinances, regulations, decisions, etc.). Most NER solutions operate in the general or news domain, which makes them inapplicable to the analysis of legal documents. Accordingly, there is a great need for an NER-annotated dataset consisting of legal documents, including the corresponding development of a typology of semantic concepts and uniform annotation guidelines.", "### Source Data\n\n\nCourt decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).", "#### Initial Data Collection and Normalization\n\n\nFrom the table of contents, 107 documents from each court were selected (see Table 1). The data was collected from the XML documents, i. e., it was extracted from the XML elements 'Mitwirkung, Titelzeile, Leitsatz, Tenor, Tatbestand, Entscheidungsgründe, Gründen, abweichende Meinung, and sonstiger Titel'. The metadata at the beginning of the documents (name of court, date of decision, file number, European Case Law Identifier, document type, laws) and those that belonged to previous legal proceedings was deleted. Paragraph numbers were removed.\n\n\nThe extracted data was split into sentences, tokenised using SoMaJo and manually annotated in WebAnno.", "#### Who are the source language producers?\n\n\nThe Federal Ministry of Justice and the Federal Office of Justice provide selected decisions. Court decisions were produced by humans.", "### Annotations", "#### Annotation process\n\n\nFor more details see annotation guidelines (in German).", "### Personal and Sensitive Information\n\n\nA fundamental characteristic of the published decisions is that all personal information have been anonymised for privacy reasons. This affects the classes person, location and organization.", "### Licensing Information\n\n\nCC BY-SA 4.0 license", "### Contributions" ]

[ "license:cc-by-nc-4.0", "region:us" ]

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[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]

[ "license:cc-by-nc-4.0", "region:us" ]

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[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]

[ "license:other", "region:us" ]

[]

[]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\nGoogle翻訳APIで翻訳した日本語版SQuAD2.0", "### Supported Tasks and Leaderboards", "### Languages\nJapanese", "## Dataset Structure", "### Data Instances", "### Data Fields\n- start\n- end\n- question\n- context\n- id", "### Data Splits\n\n- train 86820\n- valid 5927", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#license-other #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\nGoogle翻訳APIで翻訳した日本語版SQuAD2.0", "### Supported Tasks and Leaderboards", "### Languages\nJapanese", "## Dataset Structure", "### Data Instances", "### Data Fields\n- start\n- end\n- question\n- context\n- id", "### Data Splits\n\n- train 86820\n- valid 5927", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "license:mit", "region:us" ]

[]

[]

[ "# event classifcaiton dataset" ]

[ "TAGS\n#license-mit #region-us \n", "# event classifcaiton dataset" ]

[ "license:cc-by-4.0", "region:us" ]

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[ "TAGS\n#license-cc-by-4.0 #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"stackoverflow-ner\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"stackoverflow-ner\"\n\nMore Information needed" ]

[ "license:cc-by-nc-4.0", "arxiv:2210.07370", "region:us" ]

[ "2210.07370" ]

[]

[ "# M2D2: A Massively Multi-domain Language Modeling Dataset\n\n*From the paper \"M2D2: A Massively Multi-domain Language Modeling Dataset\", (Reid et al., EMNLP 2022)*\n\nLoad the dataset as follows:", "## Domains\n - Culture_and_the_arts\n - Culture_and_the_arts__Culture_and_Humanities\n - Culture_and_the_arts__Games_and_Toys\n - Culture_and_the_arts__Mass_media\n - Culture_and_the_arts__Performing_arts\n - Culture_and_the_arts__Sports_and_Recreation\n - Culture_and_the_arts__The_arts_and_Entertainment\n - Culture_and_the_arts__Visual_arts\n - General_referece\n - General_referece__Further_research_tools_and_topics\n - General_referece__Reference_works\n - Health_and_fitness\n - Health_and_fitness__Exercise\n - Health_and_fitness__Health_science\n - Health_and_fitness__Human_medicine\n - Health_and_fitness__Nutrition\n - Health_and_fitness__Public_health\n - Health_and_fitness__Self_care\n - History_and_events\n - History_and_events__By_continent\n - History_and_events__By_period\n - History_and_events__By_region\n - Human_activites\n - Human_activites__Human_activities\n - Human_activites__Impact_of_human_activity\n - Mathematics_and_logic\n - Mathematics_and_logic__Fields_of_mathematics\n - Mathematics_and_logic__Logic\n - Mathematics_and_logic__Mathematics\n - Natural_and_physical_sciences\n - Natural_and_physical_sciences__Biology\n - Natural_and_physical_sciences__Earth_sciences\n - Natural_and_physical_sciences__Nature\n - Natural_and_physical_sciences__Physical_sciences\n - Philosophy\n - Philosophy_and_thinking\n - Philosophy_and_thinking__Philosophy\n - Philosophy_and_thinking__Thinking\n - Religion_and_belief_systems\n - Religion_and_belief_systems__Allah\n - Religion_and_belief_systems__Belief_systems\n - Religion_and_belief_systems__Major_beliefs_of_the_world\n - Society_and_social_sciences\n - Society_and_social_sciences__Social_sciences\n - Society_and_social_sciences__Society\n - Technology_and_applied_sciences\n - Technology_and_applied_sciences__Agriculture\n - Technology_and_applied_sciences__Computing\n - Technology_and_applied_sciences__Engineering\n - Technology_and_applied_sciences__Transport\n - alg-geom\n - ao-sci\n - astro-ph\n - astro-ph.CO\n - astro-ph.EP\n - astro-ph.GA\n - astro-ph.HE\n - astro-ph.IM\n - astro-ph.SR\n - astro-ph_l1\n - atom-ph\n - bayes-an\n - chao-dyn\n - chem-ph\n - cmp-lg\n - comp-gas\n - cond-mat\n - URL-nn\n - URL-hall\n - URL-sci\n - URL\n - URL-gas\n - URL\n - URL-mech\n - URL-el\n - URL-con\n - cond-mat_l1\n - cs.AI\n - cs.AR\n - cs.CC\n - cs.CE\n - cs.CG\n - cs.CL\n - cs.CR\n - cs.CV\n - cs.CY\n - cs.DB\n - cs.DC\n - cs.DL\n - cs.DM\n - cs.DS\n - cs.ET\n - cs.FL\n - cs.GL\n - cs.GR\n - cs.GT\n - cs.HC\n - cs.IR\n - cs.IT\n - cs.LG\n - cs.LO\n - cs.MA\n - cs.MM\n - cs.MS\n - cs.NA\n - cs.NE\n - cs.NI\n - cs.OH\n - cs.OS\n - cs.PF\n - cs.PL\n - cs.RO\n - cs.SC\n - cs.SD\n - cs.SE\n - cs.SI\n - cs.SY\n - cs_l1\n - dg-ga\n - econ.EM\n - econ.GN\n - econ.TH\n - econ_l1\n - eess.AS\n - eess.IV\n - eess.SP\n - eess.SY\n - eess_l1\n - eval_sets\n - funct-an\n - gr-qc\n - hep-ex\n - hep-lat\n - hep-ph\n - hep-th\n - math-ph\n - math.AC\n - math.AG\n - math.AP\n - math.AT\n - math.CA\n - math.CO\n - math.CT\n - math.CV\n - math.DG\n - math.DS\n - math.FA\n - math.GM\n - math.GN\n - math.GR\n - math.GT\n - math.HO\n - math.IT\n - math.KT\n - math.LO\n - math.MG\n - math.MP\n - math.NA\n - math.NT\n - math.OA\n - math.OC\n - math.PR\n - math.QA\n - math.RA\n - math.RT\n - math.SG\n - math.SP\n - math.ST\n - math_l1\n - mtrl-th\n - nlin.AO\n - nlin.CD\n - nlin.CG\n - nlin.PS\n - nlin.SI\n - nlin_l1\n - nucl-ex\n - nucl-th\n - patt-sol\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-clus\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-an\n - URL-ph\n - URL-dyn\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-det\n - URL-ph\n - URL\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-ph\n - physics_l1\n - plasm-ph\n - q-alg\n - q-bio\n - q-bio.BM\n - q-bio.CB\n - q-bio.GN\n - q-bio.MN\n - q-bio.NC\n - q-bio.OT\n - q-bio.PE\n - q-bio.QM\n - q-bio.SC\n - q-bio.TO\n - q-bio_l1\n - q-fin.CP\n - q-fin.EC\n - q-fin.GN\n - q-fin.MF\n - q-fin.PM\n - q-fin.PR\n - q-fin.RM\n - q-fin.ST\n - q-fin.TR\n - q-fin_l1\n - quant-ph\n - solv-int\n - stat.AP\n - stat.CO\n - stat.ME\n - stat.ML\n - stat.OT\n - stat.TH\n - stat_l1\n - supr-con\nsupr-con\n\nPlease cite this work if you found this data useful." ]

[ "TAGS\n#license-cc-by-nc-4.0 #arxiv-2210.07370 #region-us \n", "# M2D2: A Massively Multi-domain Language Modeling Dataset\n\n*From the paper \"M2D2: A Massively Multi-domain Language Modeling Dataset\", (Reid et al., EMNLP 2022)*\n\nLoad the dataset as follows:", "## Domains\n - Culture_and_the_arts\n - Culture_and_the_arts__Culture_and_Humanities\n - Culture_and_the_arts__Games_and_Toys\n - Culture_and_the_arts__Mass_media\n - Culture_and_the_arts__Performing_arts\n - Culture_and_the_arts__Sports_and_Recreation\n - Culture_and_the_arts__The_arts_and_Entertainment\n - Culture_and_the_arts__Visual_arts\n - General_referece\n - General_referece__Further_research_tools_and_topics\n - General_referece__Reference_works\n - Health_and_fitness\n - Health_and_fitness__Exercise\n - Health_and_fitness__Health_science\n - Health_and_fitness__Human_medicine\n - Health_and_fitness__Nutrition\n - Health_and_fitness__Public_health\n - Health_and_fitness__Self_care\n - History_and_events\n - History_and_events__By_continent\n - History_and_events__By_period\n - History_and_events__By_region\n - Human_activites\n - Human_activites__Human_activities\n - Human_activites__Impact_of_human_activity\n - Mathematics_and_logic\n - Mathematics_and_logic__Fields_of_mathematics\n - Mathematics_and_logic__Logic\n - Mathematics_and_logic__Mathematics\n - Natural_and_physical_sciences\n - Natural_and_physical_sciences__Biology\n - Natural_and_physical_sciences__Earth_sciences\n - Natural_and_physical_sciences__Nature\n - Natural_and_physical_sciences__Physical_sciences\n - Philosophy\n - Philosophy_and_thinking\n - Philosophy_and_thinking__Philosophy\n - Philosophy_and_thinking__Thinking\n - Religion_and_belief_systems\n - Religion_and_belief_systems__Allah\n - Religion_and_belief_systems__Belief_systems\n - Religion_and_belief_systems__Major_beliefs_of_the_world\n - Society_and_social_sciences\n - Society_and_social_sciences__Social_sciences\n - Society_and_social_sciences__Society\n - Technology_and_applied_sciences\n - Technology_and_applied_sciences__Agriculture\n - Technology_and_applied_sciences__Computing\n - Technology_and_applied_sciences__Engineering\n - Technology_and_applied_sciences__Transport\n - alg-geom\n - ao-sci\n - astro-ph\n - astro-ph.CO\n - astro-ph.EP\n - astro-ph.GA\n - astro-ph.HE\n - astro-ph.IM\n - astro-ph.SR\n - astro-ph_l1\n - atom-ph\n - bayes-an\n - chao-dyn\n - chem-ph\n - cmp-lg\n - comp-gas\n - cond-mat\n - URL-nn\n - URL-hall\n - URL-sci\n - URL\n - URL-gas\n - URL\n - URL-mech\n - URL-el\n - URL-con\n - cond-mat_l1\n - cs.AI\n - cs.AR\n - cs.CC\n - cs.CE\n - cs.CG\n - cs.CL\n - cs.CR\n - cs.CV\n - cs.CY\n - cs.DB\n - cs.DC\n - cs.DL\n - cs.DM\n - cs.DS\n - cs.ET\n - cs.FL\n - cs.GL\n - cs.GR\n - cs.GT\n - cs.HC\n - cs.IR\n - cs.IT\n - cs.LG\n - cs.LO\n - cs.MA\n - cs.MM\n - cs.MS\n - cs.NA\n - cs.NE\n - cs.NI\n - cs.OH\n - cs.OS\n - cs.PF\n - cs.PL\n - cs.RO\n - cs.SC\n - cs.SD\n - cs.SE\n - cs.SI\n - cs.SY\n - cs_l1\n - dg-ga\n - econ.EM\n - econ.GN\n - econ.TH\n - econ_l1\n - eess.AS\n - eess.IV\n - eess.SP\n - eess.SY\n - eess_l1\n - eval_sets\n - funct-an\n - gr-qc\n - hep-ex\n - hep-lat\n - hep-ph\n - hep-th\n - math-ph\n - math.AC\n - math.AG\n - math.AP\n - math.AT\n - math.CA\n - math.CO\n - math.CT\n - math.CV\n - math.DG\n - math.DS\n - math.FA\n - math.GM\n - math.GN\n - math.GR\n - math.GT\n - math.HO\n - math.IT\n - math.KT\n - math.LO\n - math.MG\n - math.MP\n - math.NA\n - math.NT\n - math.OA\n - math.OC\n - math.PR\n - math.QA\n - math.RA\n - math.RT\n - math.SG\n - math.SP\n - math.ST\n - math_l1\n - mtrl-th\n - nlin.AO\n - nlin.CD\n - nlin.CG\n - nlin.PS\n - nlin.SI\n - nlin_l1\n - nucl-ex\n - nucl-th\n - patt-sol\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-clus\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-an\n - URL-ph\n - URL-dyn\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-det\n - URL-ph\n - URL\n - URL-ph\n - URL-ph\n - URL-ph\n - URL-ph\n - physics_l1\n - plasm-ph\n - q-alg\n - q-bio\n - q-bio.BM\n - q-bio.CB\n - q-bio.GN\n - q-bio.MN\n - q-bio.NC\n - q-bio.OT\n - q-bio.PE\n - q-bio.QM\n - q-bio.SC\n - q-bio.TO\n - q-bio_l1\n - q-fin.CP\n - q-fin.EC\n - q-fin.GN\n - q-fin.MF\n - q-fin.PM\n - q-fin.PR\n - q-fin.RM\n - q-fin.ST\n - q-fin.TR\n - q-fin_l1\n - quant-ph\n - solv-int\n - stat.AP\n - stat.CO\n - stat.ME\n - stat.ML\n - stat.OT\n - stat.TH\n - stat_l1\n - supr-con\nsupr-con\n\nPlease cite this work if you found this data useful." ]

[ "license:bigscience-openrail-m", "region:us" ]

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[ "TAGS\n#license-bigscience-openrail-m #region-us \n" ]

[ "license:bigscience-openrail-m", "region:us" ]

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[ "TAGS\n#license-bigscience-openrail-m #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"huggingface_hub-dependents\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"huggingface_hub-dependents\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"hub-docs-dependents\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"hub-docs-dependents\"\n\nMore Information needed" ]

[ "license:mit", "region:us" ]

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[]

[ "TAGS\n#license-mit #region-us \n" ]

[ "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #region-us \n" ]

[ "license:cc-by-nc-4.0", "region:us" ]

[]

[]

[ "# Dataset Card for BBBicycles", "## Dataset Summary\nBent & Broken Bicycles (BBBicycles) dataset is a benchmark set for the novel task of damaged object re-identification, which aims to identify the same object in multiple images even in the presence of breaks, deformations, and missing parts. You can find an interactive preview here.", "## Dataset Structure\nThe final dataset contains:\n\n - Total of 39,200 image\n - 2,800 unique IDs\n - 20 models\n - 140 IDs for each model\n\n<table border-collapse=\"collapse\">\n <tr>\n <td><b style=\"font-size:25px\">Information for each ID:</b></td>\n <td><b style=\"font-size:25px\">Information for each render:</b></td>\n </tr>\n <tr>\n <td>\n <ul>\n <li>Model</li>\n <li>Type</li>\n <li>Texture type</li>\n <li>Stickers</li>\n </ul>\n </td>\n <td>\n <ul>\n <li>Background</li>\n <li>Viewing Side</li>\n <li>Focal Length</li>\n <li>Presence of dirt</li>\n </ul>\n </td>\n </tr>\n</table>", "### Credits\nThe authors gratefully acknowledge the financial support of Reale Mutua Assicurazioni." ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for BBBicycles", "## Dataset Summary\nBent & Broken Bicycles (BBBicycles) dataset is a benchmark set for the novel task of damaged object re-identification, which aims to identify the same object in multiple images even in the presence of breaks, deformations, and missing parts. You can find an interactive preview here.", "## Dataset Structure\nThe final dataset contains:\n\n - Total of 39,200 image\n - 2,800 unique IDs\n - 20 models\n - 140 IDs for each model\n\n<table border-collapse=\"collapse\">\n <tr>\n <td><b style=\"font-size:25px\">Information for each ID:</b></td>\n <td><b style=\"font-size:25px\">Information for each render:</b></td>\n </tr>\n <tr>\n <td>\n <ul>\n <li>Model</li>\n <li>Type</li>\n <li>Texture type</li>\n <li>Stickers</li>\n </ul>\n </td>\n <td>\n <ul>\n <li>Background</li>\n <li>Viewing Side</li>\n <li>Focal Length</li>\n <li>Presence of dirt</li>\n </ul>\n </td>\n </tr>\n</table>", "### Credits\nThe authors gratefully acknowledge the financial support of Reale Mutua Assicurazioni." ]

[ "license:mit", "region:us" ]

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[ "TAGS\n#license-mit #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"ott-qa-20k\"\n\nMore Information needed\n\nThe data was obtained from here" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ott-qa-20k\"\n\nMore Information needed\n\nThe data was obtained from here" ]

[ "license:creativeml-openrail-m", "region:us" ]

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[ "# Arcane Diffusion Dataset\nDataset containing the 75 images used to train the Arcane Diffusion model.\n\nSettings for training:" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "# Arcane Diffusion Dataset\nDataset containing the 75 images used to train the Arcane Diffusion model.\n\nSettings for training:" ]

[ "region:us" ]

[]

[]

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[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"laion-2b-vietnamese-subset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"laion-2b-vietnamese-subset\"\n\nMore Information needed" ]

[ "autotrain", "evaluation", "region:us" ]

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[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @DongfuTingle for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @DongfuTingle for evaluating this model." ]

[ "region:us" ]

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[ "# Dataset Card for \"CelebA-faces-cropped-128\"\n\nJust a 128px version of the CelebA-faces dataset, which I've cropped to the face regions using dlib. Processing notebook: URL\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"CelebA-faces-cropped-128\"\n\nJust a 128px version of the CelebA-faces dataset, which I've cropped to the face regions using dlib. Processing notebook: URL\n\nMore Information needed" ]