Datasets:

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ftopal

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huggingface-datasets

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[ "region:us" ]

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[ "# Overview\nThis dataset is a subset of the huggingface wikipedia dataset with ~70'000 rows, each about a person on wikipedia.\nEach row contains the original wikipedia texts as sentences,\nas well as a paraphrased version of each sentence. For both versions full texts with the entity the wikipedia page is about being masked.", "# features\n- id: the id in the original dataset\n- url: the link to the wikipedia page\n- title: the title of the wikipedia page\n- text: the original wikipedia text\n- sentences: text split to sentences\n- paraphrased_sentences: text split to sentences, with each sentence paraphrased (e.g. mutated a bit)\n- masked_text_original: original text with entity masked in every occurence (<mask> as token)\n- masked_entities_original: array of entities masked in masked_text_original\n- masked_text_paraphrased: paraphrased text with entity masked in every occurence\n- masked_entities_paraphrased: array of entities msked in masked_text_paraphrased\n\n---\nannotations_creators:\n- no-annotation\n- machine-generated\nlanguage:\n- en\nlanguage_creators:\n- found\nlicense:\n- afl-3.0\nmultilinguality:\n- monolingual\npretty_name: wikipedia persons paraphrased and masked\nsize_categories:\n- 10K<n<100K\nsource_datasets:\n- extended|wikipedia\ntags: []\ntask_categories:\n- fill-mask\ntask_ids:\n- slot-filling" ]

[ "TAGS\n#region-us \n", "# Overview\nThis dataset is a subset of the huggingface wikipedia dataset with ~70'000 rows, each about a person on wikipedia.\nEach row contains the original wikipedia texts as sentences,\nas well as a paraphrased version of each sentence. For both versions full texts with the entity the wikipedia page is about being masked.", "# features\n- id: the id in the original dataset\n- url: the link to the wikipedia page\n- title: the title of the wikipedia page\n- text: the original wikipedia text\n- sentences: text split to sentences\n- paraphrased_sentences: text split to sentences, with each sentence paraphrased (e.g. mutated a bit)\n- masked_text_original: original text with entity masked in every occurence (<mask> as token)\n- masked_entities_original: array of entities masked in masked_text_original\n- masked_text_paraphrased: paraphrased text with entity masked in every occurence\n- masked_entities_paraphrased: array of entities msked in masked_text_paraphrased\n\n---\nannotations_creators:\n- no-annotation\n- machine-generated\nlanguage:\n- en\nlanguage_creators:\n- found\nlicense:\n- afl-3.0\nmultilinguality:\n- monolingual\npretty_name: wikipedia persons paraphrased and masked\nsize_categories:\n- 10K<n<100K\nsource_datasets:\n- extended|wikipedia\ntags: []\ntask_categories:\n- fill-mask\ntask_ids:\n- slot-filling" ]

[ "region:us" ]

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[ "# Dataset Card for \"sarcastic-news-headlines-1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"sarcastic-news-headlines-1\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"sarcastic-news-headlines-v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"sarcastic-news-headlines-v2\"\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: Multi-class Text Classification\n* Model: lewiswatson/distilbert-base-uncased-finetuned-emotion\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 @lewtun 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: lewiswatson/distilbert-base-uncased-finetuned-emotion\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 @lewtun 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: Multi-class Text Classification\n* Model: lewtun/minilm-finetuned-emotion\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 @lewtun 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: lewtun/minilm-finetuned-emotion\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 @lewtun 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: Multi-class Text Classification\n* Model: lewtun/sagemaker-distilbert-emotion\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 @lewtun 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: lewtun/sagemaker-distilbert-emotion\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 @lewtun for evaluating this model." ]

[ "region:us" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"librispeech5k_augm_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"librispeech5k_augm_train\"\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: autoevaluate/summarization\n* Dataset: autoevaluate/xsum-sample\n* Config: autoevaluate--xsum-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: autoevaluate/summarization\n* Dataset: autoevaluate/xsum-sample\n* Config: autoevaluate--xsum-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "region:us" ]

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[ "# ObjectNet (Test set only)\n\nOriginal paper: ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# ObjectNet (Test set only)\n\nOriginal paper: ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models\n\nHomepage: URL\n\nBibtex:" ]

[ "arxiv:1703.00121", "region:us" ]

[ "1703.00121" ]

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[ "# RESISC45 (Test set only)\n\nOriginal paper: Remote Sensing Image Scene Classification: Benchmark and State of the Art\n\nHomepage (broken link): URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1703.00121 #region-us \n", "# RESISC45 (Test set only)\n\nOriginal paper: Remote Sensing Image Scene Classification: Benchmark and State of the Art\n\nHomepage (broken link): URL\n\nBibtex:" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:cs", "license:cc-by-nc-sa-4.0", "czech NER", "court decisions", "region:us" ]

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[ "cs" ]

[ "# Dataset Card for Czech Court Decisions NER", "## Dataset Description\nCzech Court Decisions NER is a dataset of 300 court decisions published by The Supreme Court of the Czech Republic and the Constitutional Court of the Czech Republic.\nIn the documents, 4 types of named entities are selected.", "## Dataset Features\n\nEach sample contains:\n- 'filename': file name in the original dataset\n- 'text': court decision document in plain text\n- 'entities': list of selected entities. Each entity contains:\n - 'category_id': integer identifier of the entity category\n - 'category_str': human-friendly category name in Czech (verbalizer)\n - 'start': index on which the entity starts in the source text\n - 'end': index on which the entity ends in the source text\n - 'content': entity content, it was created as 'text[start:end]'\n - 'entity_id': unique entity string identifier\n - 'refers_to': some entities (mostly of category 'Reference na rozhodnutí soudu') refer to a specific other entity. 'refers_to' attribute contains the 'entity_id' of the referred entity\n\nThe 'entity_id' field was checked to be globally unique (across data samples and dataset splits.)", "## Entity categories\n\nThe list of the recognized entities ('category_id', 'category_str' pairs):", "## Dataset Source\n\nThe dataset is a preprocessed adaptation of existing Czech Court Decisions Dataset project info, link to data. This adaptation contains (almost) same data, but converted to a convenient format and with stripped leaked xml-like tags in the texts.\nThe category names (verbalizers) were added by a Czech native speaker.\n\n\nCite authors of the original dataset:" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Czech #license-cc-by-nc-sa-4.0 #czech NER #court decisions #region-us \n", "# Dataset Card for Czech Court Decisions NER", "## Dataset Description\nCzech Court Decisions NER is a dataset of 300 court decisions published by The Supreme Court of the Czech Republic and the Constitutional Court of the Czech Republic.\nIn the documents, 4 types of named entities are selected.", "## Dataset Features\n\nEach sample contains:\n- 'filename': file name in the original dataset\n- 'text': court decision document in plain text\n- 'entities': list of selected entities. Each entity contains:\n - 'category_id': integer identifier of the entity category\n - 'category_str': human-friendly category name in Czech (verbalizer)\n - 'start': index on which the entity starts in the source text\n - 'end': index on which the entity ends in the source text\n - 'content': entity content, it was created as 'text[start:end]'\n - 'entity_id': unique entity string identifier\n - 'refers_to': some entities (mostly of category 'Reference na rozhodnutí soudu') refer to a specific other entity. 'refers_to' attribute contains the 'entity_id' of the referred entity\n\nThe 'entity_id' field was checked to be globally unique (across data samples and dataset splits.)", "## Entity categories\n\nThe list of the recognized entities ('category_id', 'category_str' pairs):", "## Dataset Source\n\nThe dataset is a preprocessed adaptation of existing Czech Court Decisions Dataset project info, link to data. This adaptation contains (almost) same data, but converted to a convenient format and with stripped leaked xml-like tags in the texts.\nThe category names (verbalizers) were added by a Czech native speaker.\n\n\nCite authors of the original dataset:" ]

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# SUN397 (Test set only)\n\nOriginal paper: SUN Database: Exploring a Large Collection of Scene Categories\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#region-us \n", "# SUN397 (Test set only)\n\nOriginal paper: SUN Database: Exploring a Large Collection of Scene Categories\n\nHomepage: URL\n\nBibtex:" ]

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

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[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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 #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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:" ]

[ "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: Palak/albert-base-v2_squad\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 @18st13 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: Palak/albert-base-v2_squad\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 @18st13 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: Question Answering\n* Model: Palak/albert-base-v2_squad\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 @18st13 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: Palak/albert-base-v2_squad\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 @18st13 for evaluating this model." ]

[ "region:us" ]

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[ "# Dataset Card for \"stack-filtered-pii-1M-java\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"stack-filtered-pii-1M-java\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# python datasets" ]

[ "TAGS\n#region-us \n", "# python datasets" ]

[ "region:us" ]

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

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

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

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

[ "region:us" ]

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[ "# Dataset Card for \"tokenized-recipe-nlg-gpt2-ingredients-to-recipe-end\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"tokenized-recipe-nlg-gpt2-ingredients-to-recipe-end\"\n\nMore Information needed" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-exact-dedup-only\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-exact-dedup-only\"\n\nMore Information needed" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "language:en", "language:code", "region:us" ]

[]

[ "en", "code" ]

[ "## Dataset Description\n\nA small subset in each dataset of 'pile-v2'(~1000 samples) of [pile-v2]() dataset, each has 1,000 random samples from the original dataset. The dataset has 255MB of text (code and english).", "## Languages\nThe dataset contains technical text on programming languages and natural language with the following subsets,\n- Bible \n- TED2020\n- PileOfLaw\n- StackExchange\n- GithubIssues\n- Opensubtitles\n- USPTO\n- S2ORC\n- DevDocs\n- CodePileReddit2022\n- USENET\n- GNOME\n- ASFPublicMail\n- PileV2Reddit2020\n- CodePilePosts\n- Discourse\n- Tanzil\n- arXiv\n- UbuntuIRC\n- PubMed\n- CodePileReddit2020\n- CodePileReddit2021\n- GlobalVoices\n- FreeLaw_Options\n- PileV2Posts", "## Dataset Structure", "### How to use it\nYou can either load the whole dataset like above, or load a specific subset such as arxiv by specifying the folder directory:" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-English #language-code #region-us \n", "## Dataset Description\n\nA small subset in each dataset of 'pile-v2'(~1000 samples) of [pile-v2]() dataset, each has 1,000 random samples from the original dataset. The dataset has 255MB of text (code and english).", "## Languages\nThe dataset contains technical text on programming languages and natural language with the following subsets,\n- Bible \n- TED2020\n- PileOfLaw\n- StackExchange\n- GithubIssues\n- Opensubtitles\n- USPTO\n- S2ORC\n- DevDocs\n- CodePileReddit2022\n- USENET\n- GNOME\n- ASFPublicMail\n- PileV2Reddit2020\n- CodePilePosts\n- Discourse\n- Tanzil\n- arXiv\n- UbuntuIRC\n- PubMed\n- CodePileReddit2020\n- CodePileReddit2021\n- GlobalVoices\n- FreeLaw_Options\n- PileV2Posts", "## Dataset Structure", "### How to use it\nYou can either load the whole dataset like above, or load a specific subset such as arxiv by specifying the folder directory:" ]

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_ids:speaker-identification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

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[ "en" ]

[ "### Dataset Summary\n\n\nLibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.", "### Supported Tasks and Leaderboards\n\n\n* 'automatic-speech-recognition', 'audio-speaker-identification': The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at URL The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at URL ranks the latest models from research and academia.", "### Languages\n\n\nThe audio is in English. There are two configurations: 'clean' and 'other'.\nThe speakers in the corpus were ranked according to the WER of the transcripts of a model trained on\na different dataset, and were divided roughly in the middle,\nwith the lower-WER speakers designated as \"clean\" and the higher WER speakers designated as \"other\".\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises the path to the audio file, usually called 'file' and its transcription, called 'text'. Some additional information about the speaker and the passage which contains the transcription is provided.", "### Data Fields\n\n\n* file: A path to the downloaded audio file in .flac format.\n* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* text: the transcription of the audio file.\n* id: unique id of the data sample.\n* speaker\\_id: unique id of the speaker. The same speaker id can be found for multiple data samples.\n* chapter\\_id: id of the audiobook chapter which includes the transcription.", "### Data Splits\n\n\nThe size of the corpus makes it impractical, or at least inconvenient\nfor some users, to distribute it as a single large archive. Thus the\ntraining portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.\nA simple automatic\nprocedure was used to select the audio in the first two sets to be, on\naverage, of higher recording quality and with accents closer to US\nEnglish. An acoustic model was trained on WSJ’s si-84 data subset\nand was used to recognize the audio in the corpus, using a bigram\nLM estimated on the text of the respective books. We computed the\nWord Error Rate (WER) of this automatic transcript relative to our\nreference transcripts obtained from the book texts.\nThe speakers in the corpus were ranked according to the WER of\nthe WSJ model’s transcripts, and were divided roughly in the middle,\nwith the lower-WER speakers designated as \"clean\" and the higher-WER speakers designated as \"other\".\n\n\nFor \"clean\", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360\nrespectively accounting for 100h and 360h of the training data.\nFor \"other\", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.\n\n\n\nDataset Creation\n----------------", "### 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\n\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.", "### Licensing Information\n\n\nCC BY 4.0", "### Contributions\n\n\nThanks to @patrickvonplaten for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-audio-classification #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nLibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.", "### Supported Tasks and Leaderboards\n\n\n* 'automatic-speech-recognition', 'audio-speaker-identification': The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at URL The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at URL ranks the latest models from research and academia.", "### Languages\n\n\nThe audio is in English. There are two configurations: 'clean' and 'other'.\nThe speakers in the corpus were ranked according to the WER of the transcripts of a model trained on\na different dataset, and were divided roughly in the middle,\nwith the lower-WER speakers designated as \"clean\" and the higher WER speakers designated as \"other\".\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises the path to the audio file, usually called 'file' and its transcription, called 'text'. Some additional information about the speaker and the passage which contains the transcription is provided.", "### Data Fields\n\n\n* file: A path to the downloaded audio file in .flac format.\n* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* text: the transcription of the audio file.\n* id: unique id of the data sample.\n* speaker\\_id: unique id of the speaker. The same speaker id can be found for multiple data samples.\n* chapter\\_id: id of the audiobook chapter which includes the transcription.", "### Data Splits\n\n\nThe size of the corpus makes it impractical, or at least inconvenient\nfor some users, to distribute it as a single large archive. Thus the\ntraining portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.\nA simple automatic\nprocedure was used to select the audio in the first two sets to be, on\naverage, of higher recording quality and with accents closer to US\nEnglish. An acoustic model was trained on WSJ’s si-84 data subset\nand was used to recognize the audio in the corpus, using a bigram\nLM estimated on the text of the respective books. We computed the\nWord Error Rate (WER) of this automatic transcript relative to our\nreference transcripts obtained from the book texts.\nThe speakers in the corpus were ranked according to the WER of\nthe WSJ model’s transcripts, and were divided roughly in the middle,\nwith the lower-WER speakers designated as \"clean\" and the higher-WER speakers designated as \"other\".\n\n\nFor \"clean\", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360\nrespectively accounting for 100h and 360h of the training data.\nFor \"other\", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.\n\n\n\nDataset Creation\n----------------", "### 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\n\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.", "### Licensing Information\n\n\nCC BY 4.0", "### Contributions\n\n\nThanks to @patrickvonplaten for adding this 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: facebook/bart-large-cnn\n* Dataset: gigaword\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @geiright 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: facebook/bart-large-cnn\n* Dataset: gigaword\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @geiright for evaluating this model." ]

[ "task_categories:image-classification", "task_categories:object-detection", "task_categories:other", "task_ids:multi-class-image-classification", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "video", "action detection", "spatial-temporal action localization", "arxiv:2105.07404", "region:us" ]

[ "2105.07404" ]

[ "en" ]

[ "### Dataset Summary\n\n\nSpatio-temporal action localization is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. MultiSports is a multi-person dataset of spatio-temporal localized sports actions. Please refer to this paper for more details. Please refer to this repository for evaluation.", "### Supported Tasks and Leaderboards\n\n\n* 'Spatial-temporal action localization'\n\n\nDetails about evaluation can be found in the GitHub Repository. Previous challenge results can be found in this page and this CodaLab challenge.", "### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nDemo is available on dataset homepage.\n\n\nThe dataset contains and . The GT pkl file is a dictionary with the following structure:\n\n\nHere a is a with rows and 5 columns .", "### Data Fields\n\n\nRaw frames are organized according to their sport category. The pickle file of GT contains the following fields.\n\n\n* labels: list of labels\n* train\\_videos: a list with one split element containing the list of training videos\n* test\\_videos: a list with one split element containing the list of validation videos\n* nframes: dictionary that gives the number of frames for each video\n* resolution: dictionary that output a tuple of the resolution for each video\n* gttubes: dictionary that contains the gt tubes for each video. Gt tubes are dictionary that associates from each index of label, a list of tubes. A is a with rows and 5 columns .\n\n\nPlease note that the label index starts from 0 and the frame index starts from 1. For the label index , the label name is .\n\n\n\n\n Click here to see the full list of MultiSports class labels mapping:", "### Data Splits\n\n\n\n*GT for test split is not provided. Please wait for the new competition to start. Information will be updated in dataset homepage.*\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nSpatio-temporal action detection is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\n\n> \n> After choosing the four sports, we search for their competition videos by querying the name of sports like volleyball and the name of competition levels like Olympics and World Cup on YouTube, and then down- load videos from top search results. For each video, we only select high-resolution, e.g. 720P or 1080P, competition records and then manually cut them into clips of minutes, with less shot changes in each clip and to be more suitable for action detection.\n> \n> \n>", "#### Who are the source language producers?\n\n\nThe annotators of action categories and temporal boundaries are professional athletes of the corresponding sports. Please refer to the paper for more information.", "### Annotations", "#### Annotation process\n\n\n1. (FIRST STAGE) A team of professional athletes generate records of the action la- bel, the starting and ending frame, and the person box in the starting frame, which can ensure the efficiency, accu- racy and consistency of our annotation results.\n2. At least one annotator with domain knowledge double-check the annotations, correct wrong or inaccurate ones and also add missing annotations\n3. (SECOND STAGE) With the help of FCOT tracking algorithm, a team of crowd-sourced annotators adjust bounding boxes of tracking results at each frame for each record.\n4. Double-check each instance by playing it in 5fps and manually correct the inaccurate bounding boxes.", "#### Who are the annotators?\n\n\nFor the first stage, annotators are professional athletes. For the second stage, annotators are common volunteers.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nAuthors of this paper\n\n\n* Yixuan Li\n* Lei Chen\n* Runyu He\n* Zhenzhi Wang\n* Gangshan Wu\n* Limin Wang", "### Licensing Information\n\n\n<a rel=\"license\" href=\"URL alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.URL /> \nThis work is licensed under a <a rel=\"license\" href=\"URL Commons Attribution-NonCommercial 4.0 International License.\n\n\nIf you find this dataset useful, please cite as", "### Contributions\n\n\nThanks to @Judie1999 for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_categories-object-detection #task_categories-other #task_ids-multi-class-image-classification #annotations_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #source_datasets-original #language-English #license-cc-by-nc-4.0 #video #action detection #spatial-temporal action localization #arxiv-2105.07404 #region-us \n", "### Dataset Summary\n\n\nSpatio-temporal action localization is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. MultiSports is a multi-person dataset of spatio-temporal localized sports actions. Please refer to this paper for more details. Please refer to this repository for evaluation.", "### Supported Tasks and Leaderboards\n\n\n* 'Spatial-temporal action localization'\n\n\nDetails about evaluation can be found in the GitHub Repository. Previous challenge results can be found in this page and this CodaLab challenge.", "### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nDemo is available on dataset homepage.\n\n\nThe dataset contains and . The GT pkl file is a dictionary with the following structure:\n\n\nHere a is a with rows and 5 columns .", "### Data Fields\n\n\nRaw frames are organized according to their sport category. The pickle file of GT contains the following fields.\n\n\n* labels: list of labels\n* train\\_videos: a list with one split element containing the list of training videos\n* test\\_videos: a list with one split element containing the list of validation videos\n* nframes: dictionary that gives the number of frames for each video\n* resolution: dictionary that output a tuple of the resolution for each video\n* gttubes: dictionary that contains the gt tubes for each video. Gt tubes are dictionary that associates from each index of label, a list of tubes. A is a with rows and 5 columns .\n\n\nPlease note that the label index starts from 0 and the frame index starts from 1. For the label index , the label name is .\n\n\n\n\n Click here to see the full list of MultiSports class labels mapping:", "### Data Splits\n\n\n\n*GT for test split is not provided. Please wait for the new competition to start. Information will be updated in dataset homepage.*\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nSpatio-temporal action detection is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\n\n> \n> After choosing the four sports, we search for their competition videos by querying the name of sports like volleyball and the name of competition levels like Olympics and World Cup on YouTube, and then down- load videos from top search results. For each video, we only select high-resolution, e.g. 720P or 1080P, competition records and then manually cut them into clips of minutes, with less shot changes in each clip and to be more suitable for action detection.\n> \n> \n>", "#### Who are the source language producers?\n\n\nThe annotators of action categories and temporal boundaries are professional athletes of the corresponding sports. Please refer to the paper for more information.", "### Annotations", "#### Annotation process\n\n\n1. (FIRST STAGE) A team of professional athletes generate records of the action la- bel, the starting and ending frame, and the person box in the starting frame, which can ensure the efficiency, accu- racy and consistency of our annotation results.\n2. At least one annotator with domain knowledge double-check the annotations, correct wrong or inaccurate ones and also add missing annotations\n3. (SECOND STAGE) With the help of FCOT tracking algorithm, a team of crowd-sourced annotators adjust bounding boxes of tracking results at each frame for each record.\n4. Double-check each instance by playing it in 5fps and manually correct the inaccurate bounding boxes.", "#### Who are the annotators?\n\n\nFor the first stage, annotators are professional athletes. For the second stage, annotators are common volunteers.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nAuthors of this paper\n\n\n* Yixuan Li\n* Lei Chen\n* Runyu He\n* Zhenzhi Wang\n* Gangshan Wu\n* Limin Wang", "### Licensing Information\n\n\n<a rel=\"license\" href=\"URL alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.URL /> \nThis work is licensed under a <a rel=\"license\" href=\"URL Commons Attribution-NonCommercial 4.0 International License.\n\n\nIf you find this dataset useful, please cite as", "### Contributions\n\n\nThanks to @Judie1999 for adding this dataset." ]

[ "task_categories:image-to-image", "size_categories:10K<n<100K", "audio", "spectrograms", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-image-to-image #size_categories-10K<n<100K #audio #spectrograms #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"librispeech5k_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"librispeech5k_train\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"uber-reviews\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nUsing Python's Beautiful Soup library and Scrappy framework, scraped date, star rating, and comment from all reviews from 2013 - 2019.", "### Languages\n\nenglish \n\n\n\nURL\nURL\nURL\nURL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us \n", "# Dataset Card for \"uber-reviews\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nUsing Python's Beautiful Soup library and Scrappy framework, scraped date, star rating, and comment from all reviews from 2013 - 2019.", "### Languages\n\nenglish \n\n\n\nURL\nURL\nURL\nURL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "task_categories:other", "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-other #multilinguality-monolingual #language-English #license-unknown #region-us \n" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"tripadvisor-hotel-reviews\"", "## Dataset Description\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: URL\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\nHotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.\nWith this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!\nCitations on a scale from 1 to 5.", "### Languages\nenglish \n\nIf you use this dataset in your research, please credit the authors.\nCitation\nAlam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.\nDOI\nLicense\nCC BY NC 4.0\nSplash banner", "### Contributions\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for \"tripadvisor-hotel-reviews\"", "## Dataset Description\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: URL\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\nHotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.\nWith this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!\nCitations on a scale from 1 to 5.", "### Languages\nenglish \n\nIf you use this dataset in your research, please credit the authors.\nCitation\nAlam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.\nDOI\nLicense\nCC BY NC 4.0\nSplash banner", "### Contributions\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:sentiment-analysis", "size_categories:1K<n<10K", "source_datasets:original", "language:es", "license:unknown", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for \"twitter-genderbias\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nThis dataset contains more than 1900 labeled Spanish tweets with the category biased or non-biased. This was made for a Hackathon to reduce gender bias on the internet.\n\n- contents: Text\n- label:\n - biased\n - non-biased", "### Languages\n\nspanish \n\n\n\nURL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #task_ids-sentiment-analysis #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-unknown #region-us \n", "# Dataset Card for \"twitter-genderbias\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nThis dataset contains more than 1900 labeled Spanish tweets with the category biased or non-biased. This was made for a Hackathon to reduce gender bias on the internet.\n\n- contents: Text\n- label:\n - biased\n - non-biased", "### Languages\n\nspanish \n\n\n\nURL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:sentiment-analysis", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"twitter-coronavirus\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nPerform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.\nThe names and usernames have been given codes to avoid any privacy concerns.\n\nColumns:\n1) Location\n2) Tweet At\n3) Original Tweet\n4) Label\n - Extremely Negative\n - Negative\n - Neutral\n - Positive\n - Extremely Positive", "### Languages\n\nenglish \n\n\n\nURL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #task_ids-sentiment-analysis #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us \n", "# Dataset Card for \"twitter-coronavirus\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nPerform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.\nThe names and usernames have been given codes to avoid any privacy concerns.\n\nColumns:\n1) Location\n2) Tweet At\n3) Original Tweet\n4) Label\n - Extremely Negative\n - Negative\n - Neutral\n - Positive\n - Extremely Positive", "### Languages\n\nenglish \n\n\n\nURL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"scalableMLDL1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"scalableMLDL1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"speech2emotion\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"speech2emotion\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "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: knkarthick/MEETING_SUMMARY\n* Dataset: bazzhangz/sumdataset\n* Config: bazzhangz--sumdataset\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @bazzhangz 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: knkarthick/MEETING_SUMMARY\n* Dataset: bazzhangz/sumdataset\n* Config: bazzhangz--sumdataset\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @bazzhangz 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: 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 @AkankshaK 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 @AkankshaK for evaluating this model." ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "arxiv:1409.0575", "region:us" ]

[ "1409.0575" ]

[]

[ "# ImageNet-1k (Test set only)\n\nOriginal paper: ImageNet Large Scale Visual Recognition Challenge\n\nHomepage: URL\n\nBibtex:" ]

[ "TAGS\n#arxiv-1409.0575 #region-us \n", "# ImageNet-1k (Test set only)\n\nOriginal paper: ImageNet Large Scale Visual Recognition Challenge\n\nHomepage: URL\n\nBibtex:" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:oc", "license:cc0-1.0", "region:us" ]

[]

[ "oc" ]

[ "# Araina Text Corpus\n\nText corpus in Aranese variety of Gascon dialect of Occitan.", "## Corpora\n\n- '_nogues': Literary texts translated by Antòni Nogués. Sourced from URL\n- '_suils': Language educational material by Jordi Suïls Subirà\n- '_conselh': Administrative proceedings from Conselh Generau d'Aran", "## Project Araina\n\nThis corpus was prepared as part of Project Araina with support from Culture Department of the Catalan autonomous government.\n\nAquest corpus s'ha elaborat en el marc del Projecte Araina amb el suport del Departament de Cultura de la Generalitat de Catalunya.\n\n<img src=\"URL width=\"400\"/>" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-Occitan (post 1500) #license-cc0-1.0 #region-us \n", "# Araina Text Corpus\n\nText corpus in Aranese variety of Gascon dialect of Occitan.", "## Corpora\n\n- '_nogues': Literary texts translated by Antòni Nogués. Sourced from URL\n- '_suils': Language educational material by Jordi Suïls Subirà\n- '_conselh': Administrative proceedings from Conselh Generau d'Aran", "## Project Araina\n\nThis corpus was prepared as part of Project Araina with support from Culture Department of the Catalan autonomous government.\n\nAquest corpus s'ha elaborat en el marc del Projecte Araina amb el suport del Departament de Cultura de la Generalitat de Catalunya.\n\n<img src=\"URL width=\"400\"/>" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"librispeech_augm_validation-tiny\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"starter2\"\n\nMore Information needed\n\nGenerated by a fine-tuned GPT3 curie model over 2020 and 2022 on 10.000 conversation starters.\n\nGeneration pipeline:\n- generate 3 conversation starters\n- fine-tuned curie classify 0-1\n- take top 1\n\nHumans have reviewed and fixed/deleted these conversation starters afterwards" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"starter2\"\n\nMore Information needed\n\nGenerated by a fine-tuned GPT3 curie model over 2020 and 2022 on 10.000 conversation starters.\n\nGeneration pipeline:\n- generate 3 conversation starters\n- fine-tuned curie classify 0-1\n- take top 1\n\nHumans have reviewed and fixed/deleted these conversation starters afterwards" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Pastel Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 6000 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Pastel Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: \n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like \n\nI trained the embedding two epochs until 6000 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"scalableMLDL2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"scalableMLDL2\"\n\nMore Information needed" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "image-to-image", "region:us" ]

[]

[ "en" ]

[ "# Splash Art Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nI uploaded two different versions. Both embeddings create splash art images, although the splash_art2 is more consistent, splash_art generates more generic images than splash_art2. Enjoy! \n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: or depending on which version you use\n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like or \n\nI trained the embedding two epochs until 6800 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #image-to-image #region-us \n", "# Splash Art Style Embedding / Textual Inversion\n\n<img alt=\"Showcase\" src=\"URL", "## Usage\n\nI uploaded two different versions. Both embeddings create splash art images, although the splash_art2 is more consistent, splash_art generates more generic images than splash_art2. Enjoy! \n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder\n\nTo use it in a prompt: or depending on which version you use\n\nPersonally, I would recommend to use my embeddings with a strength of 0.8, like or \n\nI trained the embedding two epochs until 6800 steps.\n\nI hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: \"Nerfgun3#7508\"", "## License\n\nThis embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"image50\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"image50\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"595Gao\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"595Gao\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:de", "newspapers", "germany", "2022", "region:us" ]

[]

[ "de" ]

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

[ "TAGS\n#task_categories-text-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #language-German #newspapers #germany #2022 #region-us \n", "# Dataset Card for \"tagesschau\"\n\n\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"sheet_music_ede2110\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"sheet_music_ede2110\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lyoko-ultimate\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"news_corpus_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"news_corpus_v2\"\n\nMore Information needed" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"news-summary\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nOfficially it was supposed to be used for classification but, can you use this data set to summarize news articles?", "### Languages\n\nenglish \n\n\n\nAcknowledgements\n\nAhmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018.\nAhmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138).", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for \"news-summary\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nOfficially it was supposed to be used for classification but, can you use this data set to summarize news articles?", "### Languages\n\nenglish \n\n\n\nAcknowledgements\n\nAhmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018.\nAhmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138).", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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#region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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:" ]

[ "task_categories:text-classification", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"news-fakenews\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nCan you use this data set to make an algorithm able to determine if an article is fake news or not ?", "### Languages\n\nenglish \n\n\n\nAcknowledgements\n\nAhmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018.\nAhmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138).", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #source_datasets-original #language-English #region-us \n", "# Dataset Card for \"news-fakenews\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nCan you use this data set to make an algorithm able to determine if an article is fake news or not ?", "### Languages\n\nenglish \n\n\n\nAcknowledgements\n\nAhmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018.\nAhmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138).", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"olm-october-2022-tokenized-1024-exact-dedup-only\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-october-2022-tokenized-1024-exact-dedup-only\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-no-bigscience-filters\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-no-bigscience-filters\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Process_tested_02\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Process_tested_02\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"medical-domain\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nMedical transcription data scraped from URL\nMedical data is extremely hard to find due to HIPAA privacy regulations. This dataset offers a solution by providing medical transcription samples.\nThis dataset contains sample medical transcriptions for various medical specialties.", "### Languages\n\nenglish \n\n\n\nAcknowledgements\n\nMedical transcription data scraped from URL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #source_datasets-original #language-English #region-us \n", "# Dataset Card for \"medical-domain\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nMedical transcription data scraped from URL\nMedical data is extremely hard to find due to HIPAA privacy regulations. This dataset offers a solution by providing medical transcription samples.\nThis dataset contains sample medical transcriptions for various medical specialties.", "### Languages\n\nenglish \n\n\n\nAcknowledgements\n\nMedical transcription data scraped from URL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "region:us" ]

[]

[]

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

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

[ "language:en", "doi:10.57967/hf/0175", "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#language-English #doi-10.57967/hf/0175 #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:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Process_tested-facebook-flores\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"librispeech15k_augm_train-tiny\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"librispeech15k_augm_train-tiny\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Process_tested-Shularp-Process_tested-facebook-floresarb_Arab_to_eng_Latn\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Process_tested-Shularp-Process_tested-facebook-floresarb_Arab_to_eng_Latn\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "license:cc0-1.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-cc0-1.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"un_multi-ar-en\"\n\nMore Information needed" ]

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

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"medical-keywords\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nMedical transcription data scraped from URL\nMedical data is extremely hard to find due to HIPAA privacy regulations. This dataset offers a solution by providing medical transcription samples.\nThis dataset contains sample medical transcriptions for various medical specialties.", "### Languages\n\nenglish \n\n\n\nAcknowledgements\n\nMedical transcription data scraped from URL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #size_categories-100K<n<1M #source_datasets-original #language-English #region-us \n", "# Dataset Card for \"medical-keywords\"", "## Dataset Description\n\n- Homepage: Kaggle Challenge\n- Repository: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.", "### Dataset Summary\n\nMedical transcription data scraped from URL\nMedical data is extremely hard to find due to HIPAA privacy regulations. This dataset offers a solution by providing medical transcription samples.\nThis dataset contains sample medical transcriptions for various medical specialties.", "### Languages\n\nenglish \n\n\n\nAcknowledgements\n\nMedical transcription data scraped from URL", "### Contributions\n\nThanks to @davidberenstein1957 for adding this 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: Multi-class Text Classification\n* Model: lewtun/minilm-finetuned-emotion\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 @lewtun 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: lewtun/minilm-finetuned-emotion\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 @lewtun 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: lewtun/sagemaker-distilbert-emotion\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 @lewtun 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: lewtun/sagemaker-distilbert-emotion\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 @lewtun 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: lewiswatson/distilbert-base-uncased-finetuned-emotion\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 @lewtun 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: lewiswatson/distilbert-base-uncased-finetuned-emotion\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 @lewtun 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: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: autoevaluate/roberta-base-squad2\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Question Answering\n* Model: autoevaluate/extractive-question-answering\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: autoevaluate/extractive-question-answering\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: Question Answering\n* Model: autoevaluate/extractive-question-answering-not-evaluated\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun 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: autoevaluate/extractive-question-answering-not-evaluated\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "language:zh", "license:apache-2.0", "named entity recognition", "clue", "region:us" ]

[]

[ "zh" ]

[ "# CLUE-NER 命名实体识别数据集\n\n字段说明\n\n+ 'text': 文本\n\n+ 'entities': 文本中包含的实体\n\n + 'id': 实体 'id'\n\n + 'entity': 实体对应的字符串\n\n + 'start_offset': 实体开始位置\n\n + 'end_offset': 实体结束位置的下一位\n\n + 'label': 实体对应的开始位置" ]

[ "TAGS\n#language-Chinese #license-apache-2.0 #named entity recognition #clue #region-us \n", "# CLUE-NER 命名实体识别数据集\n\n字段说明\n\n+ 'text': 文本\n\n+ 'entities': 文本中包含的实体\n\n + 'id': 实体 'id'\n\n + 'entity': 实体对应的字符串\n\n + 'start_offset': 实体开始位置\n\n + 'end_offset': 实体结束位置的下一位\n\n + 'label': 实体对应的开始位置" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"arxiv-pyserini\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"small-oscar-dedup\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"small-oscar-repetitions\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"olm-october-2022-tokenized-1024-no-bigscience-filters\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-october-2022-tokenized-1024-no-bigscience-filters\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# CMeEE 中文医学命名实体识别数据集\n\n字段说明\n\n+ 'text': 文本\n\n+ 'entities': 文本中包含的实体\n\n + 'id': 实体 'id'\n\n + 'entity': 实体对应的字符串\n\n + 'start_offset': 实体开始位置\n\n + 'end_offset': 实体结束位置的下一位\n\n + 'label': 实体对应的开始位置" ]

[ "TAGS\n#region-us \n", "# CMeEE 中文医学命名实体识别数据集\n\n字段说明\n\n+ 'text': 文本\n\n+ 'entities': 文本中包含的实体\n\n + 'id': 实体 'id'\n\n + 'entity': 实体对应的字符串\n\n + 'start_offset': 实体开始位置\n\n + 'end_offset': 实体结束位置的下一位\n\n + 'label': 实体对应的开始位置" ]

[ "region:us" ]

[]

[]

[ "# 人民日报命名实体识别数据集\n\n字段说明\n\n+ 'text': 文本\n\n+ 'entities': 文本中包含的实体\n\n + 'id': 实体 'id'\n\n + 'entity': 实体对应的字符串\n\n + 'start_offset': 实体开始位置\n\n + 'end_offset': 实体结束位置的下一位\n\n + 'label': 实体对应的开始位置" ]

[ "TAGS\n#region-us \n", "# 人民日报命名实体识别数据集\n\n字段说明\n\n+ 'text': 文本\n\n+ 'entities': 文本中包含的实体\n\n + 'id': 实体 'id'\n\n + 'entity': 实体对应的字符串\n\n + 'start_offset': 实体开始位置\n\n + 'end_offset': 实体结束位置的下一位\n\n + 'label': 实体对应的开始位置" ]

[ "region:us" ]

[]

[]

[ "# DuIE 关系抽取数据集\n\n字段说明\n\n+ 'text': 文本\n\n+ 'spo_list': 文本中包含的关系三元组\n\n + 'subject': 头实体（主语）\n\n + 'subject_type': 头实体（主语）的类型\n\n + 'object': 尾实体（主语）\n\n + 'object_type': 尾实体（主语）的类型\n\n + 'predicate': 关系" ]

[ "TAGS\n#region-us \n", "# DuIE 关系抽取数据集\n\n字段说明\n\n+ 'text': 文本\n\n+ 'spo_list': 文本中包含的关系三元组\n\n + 'subject': 头实体（主语）\n\n + 'subject_type': 头实体（主语）的类型\n\n + 'object': 尾实体（主语）\n\n + 'object_type': 尾实体（主语）的类型\n\n + 'predicate': 关系" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"banking77_vectors\"\n\n\nInstall 'pip install fast-sentence-transformers'\n\n\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"banking77_vectors\"\n\n\nInstall 'pip install fast-sentence-transformers'\n\n\n\nMore Information needed" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|common_voice", "source_datasets:extended|openslr", "language:ta", "license:cc-by-4.0", "region:us" ]

[]

[ "ta" ]

[ "# 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", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## 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 @parambharat for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|common_voice #source_datasets-extended|openslr #language-Tamil #license-cc-by-4.0 #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", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## 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 @parambharat for adding this dataset." ]

[ "task_categories:token-classification", "task_ids:part-of-speech", "task_ids:lemmatization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:cc-by-4.0", "National Corpus of Polish", "Narodowy Korpus Języka Polskiego", "region:us" ]

[]

[ "pl" ]

[ "### Dataset Summary\n\n\nThis is the official dataset for NKJP1M – the 1-million token balanced subcorpus of the National Corpus of Polish (Narodowy Korpus Języka Polskiego)\n\n\nBesides the text (divided into paragraphs/samples and sentences) the set contains lemmas and morpho-syntactic tags for all tokens in the corpus.\n\n\nThis release, known as NKJP1M-SGJP, corresponds to the version 1.2 of the corpus with later corrections and improvements. In particular the morpho-syntactic annotation has been aligned with the present version of Morfeusz2 SGJP morphological analyser (as of 2022.12.04).", "### Supported Tasks and Leaderboards\n\n\nThe main use of this resource lays in training models for lemmatisation and part of speech tagging of Polish.", "### Languages\n\n\nPolish (monolingual)\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'nkjp\\_text', 'nkjp\\_par', 'nkjp\\_sent' (strings): XML identifiers of the present text (document), paragraph and sentence in NKJP. (These allow to map the data point back to the source corpus and to identify paragraphs/samples.)\n* 'tokens' (sequence of strings): tokens of the text defined as in NKJP.\n* 'lemmas' (sequence of strings): lemmas corresponding to the tokens.\n* 'tags' (sequence of labels): morpho-syntactic tags according to Morfeusz2 tagset (1019 distinct tags).\n* 'poses' (sequence of labels): flexemic class (detailed part of speech, 40 classes) – the first element of the corresponding tag.\n* 'cposes' (sequence of labels): coarse part of speech (13 classes): all verbal and deverbal flexemic classes get mapped to a 'V', nominal – 'N', adjectival – 'A', “strange” (abbreviations, alien elements, symbols, emojis…) – 'X', rest as in 'poses'.\n* 'nps' (sequence of booleans): 'True' means that the corresponding token is not preceded by a space in the source text.\n* 'nkjp\\_ids' (sequence of strings): XML identifiers of particular tokens in NKJP (probably an overkill).", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe National Corpus of Polish (NKJP) was envisioned as the reference corpus of contemporary Polish.\n\n\nThe manually annotated subcorpus (NKJP1M) was thought of as the training data for various NLP tasks.", "### Source Data\n\n\nNKJP is balanced with respect to Polish readership. The detailed rationale is described in Chapter 3 of the NKJP book (roughly: 50% press, 30% books, 10% speech, 10% other). The corpus contains texts from the years 1945–2010 (with 80% of the text in the range 1990–2010). Only original Polish texts were gathered (no translations from other languages). The composition of NKJP1M follows this schema (see Chapter 5).", "### Annotations\n\n\nThe rules of morphosyntactic annotation used for NKJP are discussed in Chapter 6 of the NKJP book. Presently (2020), the corpus uses a common tagset with the morphological analyzer Morfeusz 2.", "#### Annotation process\n\n\nThe texts were processed with Morfeusz and then the resulting annotations were manually disambiguated and validated/corrected. Each text sample was independently processed by two annotators. In case of annotation conflicts an adjudicator stepped in.", "### Licensing Information\n\n\n!Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.\n\n\nInfo on the source corpus: link\n\n\nCurrent annotation scheme: link" ]

[ "TAGS\n#task_categories-token-classification #task_ids-part-of-speech #task_ids-lemmatization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Polish #license-cc-by-4.0 #National Corpus of Polish #Narodowy Korpus Języka Polskiego #region-us \n", "### Dataset Summary\n\n\nThis is the official dataset for NKJP1M – the 1-million token balanced subcorpus of the National Corpus of Polish (Narodowy Korpus Języka Polskiego)\n\n\nBesides the text (divided into paragraphs/samples and sentences) the set contains lemmas and morpho-syntactic tags for all tokens in the corpus.\n\n\nThis release, known as NKJP1M-SGJP, corresponds to the version 1.2 of the corpus with later corrections and improvements. In particular the morpho-syntactic annotation has been aligned with the present version of Morfeusz2 SGJP morphological analyser (as of 2022.12.04).", "### Supported Tasks and Leaderboards\n\n\nThe main use of this resource lays in training models for lemmatisation and part of speech tagging of Polish.", "### Languages\n\n\nPolish (monolingual)\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'nkjp\\_text', 'nkjp\\_par', 'nkjp\\_sent' (strings): XML identifiers of the present text (document), paragraph and sentence in NKJP. (These allow to map the data point back to the source corpus and to identify paragraphs/samples.)\n* 'tokens' (sequence of strings): tokens of the text defined as in NKJP.\n* 'lemmas' (sequence of strings): lemmas corresponding to the tokens.\n* 'tags' (sequence of labels): morpho-syntactic tags according to Morfeusz2 tagset (1019 distinct tags).\n* 'poses' (sequence of labels): flexemic class (detailed part of speech, 40 classes) – the first element of the corresponding tag.\n* 'cposes' (sequence of labels): coarse part of speech (13 classes): all verbal and deverbal flexemic classes get mapped to a 'V', nominal – 'N', adjectival – 'A', “strange” (abbreviations, alien elements, symbols, emojis…) – 'X', rest as in 'poses'.\n* 'nps' (sequence of booleans): 'True' means that the corresponding token is not preceded by a space in the source text.\n* 'nkjp\\_ids' (sequence of strings): XML identifiers of particular tokens in NKJP (probably an overkill).", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe National Corpus of Polish (NKJP) was envisioned as the reference corpus of contemporary Polish.\n\n\nThe manually annotated subcorpus (NKJP1M) was thought of as the training data for various NLP tasks.", "### Source Data\n\n\nNKJP is balanced with respect to Polish readership. The detailed rationale is described in Chapter 3 of the NKJP book (roughly: 50% press, 30% books, 10% speech, 10% other). The corpus contains texts from the years 1945–2010 (with 80% of the text in the range 1990–2010). Only original Polish texts were gathered (no translations from other languages). The composition of NKJP1M follows this schema (see Chapter 5).", "### Annotations\n\n\nThe rules of morphosyntactic annotation used for NKJP are discussed in Chapter 6 of the NKJP book. Presently (2020), the corpus uses a common tagset with the morphological analyzer Morfeusz 2.", "#### Annotation process\n\n\nThe texts were processed with Morfeusz and then the resulting annotations were manually disambiguated and validated/corrected. Each text sample was independently processed by two annotators. In case of annotation conflicts an adjudicator stepped in.", "### Licensing Information\n\n\n!Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.\n\n\nInfo on the source corpus: link\n\n\nCurrent annotation scheme: link" ]

[ "region:us" ]

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[ "# Dataset Card for \"fa-paraphrase\"\n\nThis dataset contains over 1.1 million rows. Each row contains a pair of Farsi sentences which are a paraphrase of each other. The datasets used to create this dataset can be found here:\n\n* tapaco\n* kaggle\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"fa-paraphrase\"\n\nThis dataset contains over 1.1 million rows. Each row contains a pair of Farsi sentences which are a paraphrase of each other. The datasets used to create this dataset can be found here:\n\n* tapaco\n* kaggle\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"olm-bookcorpus-tokenized-1024\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"olm-bookcorpus-tokenized-1024\"\n\nMore Information needed" ]