Datasets:

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ftopal

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huggingface-datasets

like 0

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

[]

[ "hy" ]

[]

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

[ "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language:ar", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "ar" ]

[ "# Dataset Card for MASC: MASSIVE ARABIC SPEECH CORPUS", "## 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: URL\n- Repository:\n- Paper: URL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis corpus is a dataset that contains 1,000 hours of speech sampled at 16~kHz and crawled from over 700 YouTube channels. MASC is multi-regional, multi-genre, and multi-dialect dataset that is intended to advance the research and development of Arabic speech technology with the special emphasis on Arabic speech recognition", "### Supported Tasks and Leaderboards", "### Languages\n\nMulti-dialect Arabic", "## Dataset Structure", "### Data Instances", "### Data Fields\n #### masc_dev\n - speech\n - sampling_rate\n - target_text (label)", "### Data Splits\n\n #### masc_dev\n - train: 100\n - test: 40", "## 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\nNote: this is a small development set for testing.", "### Dataset Curators", "### Licensing Information\n\nCC 4.0", "### Contributions\nMohammad Al-Fetyani, Muhammad Al-Barham, Gheith Abandah, Adham Alsharkawi, Maha Dawas, August 18, 2021, \"MASC: Massive Arabic Speech Corpus\", IEEE Dataport, doi: URL" ]

[ "TAGS\n#annotations_creators-crowdsourced #language_creators-crowdsourced #language-Arabic #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for MASC: MASSIVE ARABIC SPEECH CORPUS", "## 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: URL\n- Repository:\n- Paper: URL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis corpus is a dataset that contains 1,000 hours of speech sampled at 16~kHz and crawled from over 700 YouTube channels. MASC is multi-regional, multi-genre, and multi-dialect dataset that is intended to advance the research and development of Arabic speech technology with the special emphasis on Arabic speech recognition", "### Supported Tasks and Leaderboards", "### Languages\n\nMulti-dialect Arabic", "## Dataset Structure", "### Data Instances", "### Data Fields\n #### masc_dev\n - speech\n - sampling_rate\n - target_text (label)", "### Data Splits\n\n #### masc_dev\n - train: 100\n - test: 40", "## 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\nNote: this is a small development set for testing.", "### Dataset Curators", "### Licensing Information\n\nCC 4.0", "### Contributions\nMohammad Al-Fetyani, Muhammad Al-Barham, Gheith Abandah, Adham Alsharkawi, Maha Dawas, August 18, 2021, \"MASC: Massive Arabic Speech Corpus\", IEEE Dataport, doi: URL" ]

[ "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language:ar", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "ar" ]

[ "# Dataset Card for MASC: MASSIVE ARABIC SPEECH CORPUS", "## 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: URL\n- Repository:\n- Paper: URL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis corpus is a dataset that contains 1,000 hours of speech sampled at 16~kHz and crawled from over 700 YouTube channels. MASC is multi-regional, multi-genre, and multi-dialect dataset that is intended to advance the research and development of Arabic speech technology with the special emphasis on Arabic speech recognition", "### Supported Tasks and Leaderboards", "### Languages\n\nMulti-dialect Arabic", "## Dataset Structure", "### Data Instances", "### Data Fields\n #### masc_dev\n - speech\n - sampling_rate\n - target_text (label)", "### Data Splits\n\n #### masc_dev\n - train: 100\n - test: 40", "## 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\nNote: this is a small development set for testing.", "### Dataset Curators", "### Licensing Information\n\nCC 4.0", "### Contributions\nMohammad Al-Fetyani, Muhammad Al-Barham, Gheith Abandah, Adham Alsharkawi, Maha Dawas, August 18, 2021, \"MASC: Massive Arabic Speech Corpus\", IEEE Dataport, doi: URL" ]

[ "TAGS\n#annotations_creators-crowdsourced #language_creators-crowdsourced #language-Arabic #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for MASC: MASSIVE ARABIC SPEECH CORPUS", "## 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: URL\n- Repository:\n- Paper: URL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis corpus is a dataset that contains 1,000 hours of speech sampled at 16~kHz and crawled from over 700 YouTube channels. MASC is multi-regional, multi-genre, and multi-dialect dataset that is intended to advance the research and development of Arabic speech technology with the special emphasis on Arabic speech recognition", "### Supported Tasks and Leaderboards", "### Languages\n\nMulti-dialect Arabic", "## Dataset Structure", "### Data Instances", "### Data Fields\n #### masc_dev\n - speech\n - sampling_rate\n - target_text (label)", "### Data Splits\n\n #### masc_dev\n - train: 100\n - test: 40", "## 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\nNote: this is a small development set for testing.", "### Dataset Curators", "### Licensing Information\n\nCC 4.0", "### Contributions\nMohammad Al-Fetyani, Muhammad Al-Barham, Gheith Abandah, Adham Alsharkawi, Maha Dawas, August 18, 2021, \"MASC: Massive Arabic Speech Corpus\", IEEE Dataport, doi: URL" ]

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

[]

[ "en" ]

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

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-generation", "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:unknown", "source_datasets:original", "language:en", "language:as", "language:bn", "language:gu", "language:hi", "language:kn", "language:ml", "language:mr", "language:or", "language:pa", "language:ta", "language:te", "license:cc-by-nc-4.0", "conditional-text-generation", "arxiv:2104.05596", "region:us" ]

[ "2104.05596" ]

[ "en", "as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te" ]

[ "# Dataset Card for Samanantar", "## 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: URL\n- Repository:\n- Paper: Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nSamanantar is the largest publicly available parallel corpora collection for Indic language: Assamese, Bengali,\nGujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.\n\nThe corpus has 49.6M sentence pairs between English to Indian Languages.", "### Supported Tasks and Leaderboards", "### Languages\n\nSamanantar contains parallel sentences between English ('en') and 11 Indic language:\n- Assamese ('as'),\n- Bengali ('bn'),\n- Gujarati ('gu'),\n- Hindi ('hi'),\n- Kannada ('kn'),\n- Malayalam ('ml'),\n- Marathi ('mr'),\n- Odia ('or'),\n- Punjabi ('pa'),\n- Tamil ('ta') and\n- Telugu ('te').", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'idx' (int): ID.\n- 'src' (string): Sentence in source language (English).\n- 'tgt' (string): Sentence in destination language (one of the 11 Indic languages).\n- 'data_source' (string): Source of the data.\n For created data sources, depending on the destination language, it might be one of:\n - anuvaad_catchnews\n - anuvaad_DD_National\n - anuvaad_DD_sports\n - anuvaad_drivespark\n - anuvaad_dw\n - anuvaad_financialexpress\n - anuvaad-general_corpus\n - anuvaad_goodreturns\n - anuvaad_indianexpress\n - anuvaad_mykhel\n - anuvaad_nativeplanet\n - anuvaad_newsonair\n - anuvaad_nouns_dictionary\n - anuvaad_ocr\n - anuvaad_oneindia\n - anuvaad_pib\n - anuvaad_pib_archives\n - anuvaad_prothomalo\n - anuvaad_timesofindia\n - asianetnews\n - betterindia\n - bridge\n - business_standard\n - catchnews\n - coursera\n - dd_national\n - dd_sports\n - dwnews\n - drivespark\n - fin_express\n - goodreturns\n - gu_govt\n - jagran-business\n - jagran-education\n - jagran-sports\n - ie_business\n - ie_education\n - ie_entertainment\n - ie_general\n - ie_lifestyle\n - ie_news\n - ie_sports\n - ie_tech\n - indiccorp\n - jagran-entertainment\n - jagran-lifestyle\n - jagran-news\n - jagran-tech\n - khan_academy\n - Kurzgesagt\n - marketfeed\n - mykhel\n - nativeplanet\n - nptel\n - ocr\n - oneindia\n - pa_govt\n - pmi\n - pranabmukherjee\n - sakshi\n - sentinel\n - thewire\n - toi\n - tribune\n - vsauce\n - wikipedia\n - zeebiz", "### 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\n\nCreative Commons Attribution-NonCommercial 4.0 International.", "### Contributions\n\nThanks to @albertvillanova for adding this dataset." ]

[ "TAGS\n#task_categories-text-generation #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-unknown #source_datasets-original #language-English #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Kannada #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #license-cc-by-nc-4.0 #conditional-text-generation #arxiv-2104.05596 #region-us \n", "# Dataset Card for Samanantar", "## 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: URL\n- Repository:\n- Paper: Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nSamanantar is the largest publicly available parallel corpora collection for Indic language: Assamese, Bengali,\nGujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.\n\nThe corpus has 49.6M sentence pairs between English to Indian Languages.", "### Supported Tasks and Leaderboards", "### Languages\n\nSamanantar contains parallel sentences between English ('en') and 11 Indic language:\n- Assamese ('as'),\n- Bengali ('bn'),\n- Gujarati ('gu'),\n- Hindi ('hi'),\n- Kannada ('kn'),\n- Malayalam ('ml'),\n- Marathi ('mr'),\n- Odia ('or'),\n- Punjabi ('pa'),\n- Tamil ('ta') and\n- Telugu ('te').", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'idx' (int): ID.\n- 'src' (string): Sentence in source language (English).\n- 'tgt' (string): Sentence in destination language (one of the 11 Indic languages).\n- 'data_source' (string): Source of the data.\n For created data sources, depending on the destination language, it might be one of:\n - anuvaad_catchnews\n - anuvaad_DD_National\n - anuvaad_DD_sports\n - anuvaad_drivespark\n - anuvaad_dw\n - anuvaad_financialexpress\n - anuvaad-general_corpus\n - anuvaad_goodreturns\n - anuvaad_indianexpress\n - anuvaad_mykhel\n - anuvaad_nativeplanet\n - anuvaad_newsonair\n - anuvaad_nouns_dictionary\n - anuvaad_ocr\n - anuvaad_oneindia\n - anuvaad_pib\n - anuvaad_pib_archives\n - anuvaad_prothomalo\n - anuvaad_timesofindia\n - asianetnews\n - betterindia\n - bridge\n - business_standard\n - catchnews\n - coursera\n - dd_national\n - dd_sports\n - dwnews\n - drivespark\n - fin_express\n - goodreturns\n - gu_govt\n - jagran-business\n - jagran-education\n - jagran-sports\n - ie_business\n - ie_education\n - ie_entertainment\n - ie_general\n - ie_lifestyle\n - ie_news\n - ie_sports\n - ie_tech\n - indiccorp\n - jagran-entertainment\n - jagran-lifestyle\n - jagran-news\n - jagran-tech\n - khan_academy\n - Kurzgesagt\n - marketfeed\n - mykhel\n - nativeplanet\n - nptel\n - ocr\n - oneindia\n - pa_govt\n - pmi\n - pranabmukherjee\n - sakshi\n - sentinel\n - thewire\n - toi\n - tribune\n - vsauce\n - wikipedia\n - zeebiz", "### 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\n\nCreative Commons Attribution-NonCommercial 4.0 International.", "### Contributions\n\nThanks to @albertvillanova for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:other-crystallography", "size_categories:unknown", "language:cif", "license:mit", "material-property-optimization", "material-reconstruction", "material-generation", "arxiv:2110.06197", "region:us" ]

[ "2110.06197" ]

[ "cif" ]

[ "# Dataset Card for Carbon-24", "## 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: URL\n- Paper: Crystal Diffusion Variational Autoencoder for Periodic Material Generation\n- Leaderboard:\n- Point of Contact: Tian Xie", "### Dataset Summary\n\nCarbon-24 contains 10k carbon materials, which share the same composition, but have different structures. There is 1 element and the materials have 6 - 24 atoms in the unit cells.\n\nCarbon-24 includes various carbon structures obtained via ab initio random structure searching (AIRSS) (Pickard & Needs, 2006; 2011) performed at 10 GPa.\n\nThe original dataset includes 101529 carbon structures, and we selected the 10% of the carbon structure with the lowest energy per atom to create Carbon-24. All 10153 structures are at local energy minimum after DFT relaxation. The most stable structure is diamond at 10 GPa. All remaining structures are thermodynamically unstable but may be kinetically stable.", "### 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\n\n\n\n\n\nPlease consider citing the following papers:\n\n\n\nand", "### Contributions\n\nThanks to @albertvillanova for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-other-crystallography #size_categories-unknown #language-cif #license-mit #material-property-optimization #material-reconstruction #material-generation #arxiv-2110.06197 #region-us \n", "# Dataset Card for Carbon-24", "## 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: URL\n- Paper: Crystal Diffusion Variational Autoencoder for Periodic Material Generation\n- Leaderboard:\n- Point of Contact: Tian Xie", "### Dataset Summary\n\nCarbon-24 contains 10k carbon materials, which share the same composition, but have different structures. There is 1 element and the materials have 6 - 24 atoms in the unit cells.\n\nCarbon-24 includes various carbon structures obtained via ab initio random structure searching (AIRSS) (Pickard & Needs, 2006; 2011) performed at 10 GPa.\n\nThe original dataset includes 101529 carbon structures, and we selected the 10% of the carbon structure with the lowest energy per atom to create Carbon-24. All 10153 structures are at local energy minimum after DFT relaxation. The most stable structure is diamond at 10 GPa. All remaining structures are thermodynamically unstable but may be kinetically stable.", "### 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\n\n\n\n\n\nPlease consider citing the following papers:\n\n\n\nand", "### Contributions\n\nThanks to @albertvillanova for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for pmc_open_access", "## Dataset Description", "### Dataset Summary\n\n<div class=\"course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400\">\n <p><b>Deprecated:</b> Dataset \"pmc_open_access\" is deprecated and will be deleted. Use \"<a href=\"URL instead.</p>\n</div>" ]

[ "TAGS\n#region-us \n", "# Dataset Card for pmc_open_access", "## Dataset Description", "### Dataset Summary\n\n<div class=\"course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400\">\n <p><b>Deprecated:</b> Dataset \"pmc_open_access\" is deprecated and will be deleted. Use \"<a href=\"URL instead.</p>\n</div>" ]

[ "task_categories:text-generation", "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:1M<n<10M", "source_datasets:original", "source_datasets:extended|bible_para", "source_datasets:extended|kde4", "source_datasets:extended|opus_gnome", "source_datasets:extended|open_subtitles", "source_datasets:extended|tatoeba", "language:en", "language:vi", "license:unknown", "conditional-text-generation", "region:us" ]

[]

[ "en", "vi" ]

[ "### Dataset Summary\n\n\nSAT (Style Augmented Translation) dataset contains roughly 3.3 million English-Vietnamese pairs of texts.", "### Supported Tasks and Leaderboards\n\n\n* Machine Translation", "### Languages\n\n\nThe languages in the dataset are:\n\n\n* Vietnamese ('vi')\n* English ('en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'translation':\n\t+ 'en': Parallel text in English.\n\t+ 'vi': Parallel text in Vietnamese.", "### Data Splits\n\n\nThe dataset is split in \"train\" and \"test\".\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\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nUnknown.\n\n\nUnknown.", "### Contributions\n\n\nThanks to @albertvillanova for adding this dataset." ]

[ "TAGS\n#task_categories-text-generation #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-1M<n<10M #source_datasets-original #source_datasets-extended|bible_para #source_datasets-extended|kde4 #source_datasets-extended|opus_gnome #source_datasets-extended|open_subtitles #source_datasets-extended|tatoeba #language-English #language-Vietnamese #license-unknown #conditional-text-generation #region-us \n", "### Dataset Summary\n\n\nSAT (Style Augmented Translation) dataset contains roughly 3.3 million English-Vietnamese pairs of texts.", "### Supported Tasks and Leaderboards\n\n\n* Machine Translation", "### Languages\n\n\nThe languages in the dataset are:\n\n\n* Vietnamese ('vi')\n* English ('en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'translation':\n\t+ 'en': Parallel text in English.\n\t+ 'vi': Parallel text in Vietnamese.", "### Data Splits\n\n\nThe dataset is split in \"train\" and \"test\".\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\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nUnknown.\n\n\nUnknown.", "### Contributions\n\n\nThanks to @albertvillanova for adding this dataset." ]

[ "multilinguality:translation", "source_datasets:original", "language:cv", "region:us" ]

[]

[ "cv" ]

[ "# Dataset Description", "## Chuvash-Russian parallel corpus\n\n1M parallel sentences. Manually aligned", "## Chuvash-English parallel corpus.\n\n200K parallel sentences. Automatically aligned", "## Contributions\n\nFor additional details contact @AlAntonov." ]

[ "TAGS\n#multilinguality-translation #source_datasets-original #language-Chuvash #region-us \n", "# Dataset Description", "## Chuvash-Russian parallel corpus\n\n1M parallel sentences. Manually aligned", "## Chuvash-English parallel corpus.\n\n200K parallel sentences. Automatically aligned", "## Contributions\n\nFor additional details contact @AlAntonov." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Polish #license-cc-by-nc-sa-4.0 #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-cc-by-sa-3.0 #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-cc-by-sa-4.0 #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-cc-by-sa-4.0 #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:5K", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-3.0", "paraphrase-classification", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-5K #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-cc-by-sa-3.0 #paraphrase-classification #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "size_categories:10M<n<100M", "size_categories:100M<n<1B", "size_categories:1B<n<10B", "source_datasets:original", "language:af", "language:am", "language:ar", "language:az", "language:be", "language:bg", "language:bn", "language:ca", "language:ceb", "language:co", "language:cs", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fil", "language:fr", "language:fy", "language:ga", "language:gd", "language:gl", "language:gu", "language:ha", "language:haw", "language:he", "language:hi", "language:hmn", "language:ht", "language:hu", "language:hy", "language:id", "language:ig", "language:is", "language:it", "language:iw", "language:ja", "language:jv", "language:ka", "language:kk", "language:km", "language:kn", "language:ko", "language:ku", "language:ky", "language:la", "language:lb", "language:lo", "language:lt", "language:lv", "language:mg", "language:mi", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:my", "language:ne", "language:nl", "language:no", "language:ny", "language:pa", "language:pl", "language:ps", "language:pt", "language:ro", "language:ru", "language:sd", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:so", "language:sq", "language:sr", "language:st", "language:su", "language:sv", "language:sw", "language:ta", "language:te", "language:tg", "language:th", "language:tr", "language:uk", "language:und", "language:ur", "language:uz", "language:vi", "language:xh", "language:yi", "language:yo", "language:zh", "language:zu", "license:odc-by", "arxiv:1910.10683", "region:us" ]

[ "1910.10683" ]

[ "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu" ]

[ "### Dataset Summary\n\n\nA colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: \"URL\".\n\n\nThis is the processed version of Google's C4 dataset\n\n\nWe prepared five variants of the data: 'en', 'en.noclean', 'en.noblocklist', 'realnewslike', and 'multilingual' (mC4).\n\n\nFor reference, these are the sizes of the variants:\n\n\n* 'en': 305GB\n* 'en.noclean': 2.3TB\n* 'en.noblocklist': 380GB\n* 'realnewslike': 15GB\n* 'multilingual' (mC4): 9.7TB (108 subsets, one per language)\n\n\nThe 'en.noblocklist' variant is exactly the same as the 'en' variant, except we turned off the so-called \"badwords filter\", which removes all documents that contain words from the lists at URL", "#### How do I download this?", "##### Using Datasets\n\n\nSince this dataset is big, it is encouraged to load it in streaming mode using 'streaming=True', for example:\n\n\nYou can also load and mix multiple languages:", "##### Using Dask", "##### Using Git\n\n\nThis will download 13TB to your local drive. If you want to be more precise with what you are downloading, follow these commands instead:\n\n\nThe 'git clone' command in this variant will download a bunch of stub files that Git LFS uses, so you can see all the filenames that exist that way. You can then convert the stubs into their real files with 'git lfs pull --include \"...\"'. For example, if you wanted all the Dutch documents from the multilingual set, you would run", "### Supported Tasks and Leaderboards\n\n\nC4 and mC4 are mainly intended to pretrain language models and word representations.", "### Languages\n\n\nThe 'en', 'en.noclean', 'en.noblocklist' and 'realnewslike' variants are in English.\n\n\nThe other 108 languages are available and are reported in the table below.\n\n\nNote that the languages that end with \"-Latn\" are simply romanized variants, i.e. written using the Latin script.\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example form the 'en' config is:", "### Data Fields\n\n\nThe data have several fields:\n\n\n* 'url': url of the source as a string\n* 'text': text content as a string\n* 'timestamp': timestamp as a string", "### Data Splits\n\n\nSizes for the variants in english:\n\n\n\nA train and validation split are also provided for the other languages, but lengths are still to be added.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe C4 and mC4 datasets are collections text sourced from the public Common Crawl web scrape. It includes heuristics to extract only natural language (as opposed to boilerplate and other gibberish) in addition to extensive deduplication. You can find the code that has been used to build this dataset in URL by Tensorflow Datasets.\n\n\nC4 dataset was explicitly designed to be English only: any page that was not given a probability of at least 99% of being English by langdetect was discarded.\n\n\nTo build mC4, the authors used CLD3 to identify over 100 languages.", "### Licensing Information\n\n\nWe are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.", "### Acknowledgements\n\n\nBig ups to the good folks at Common Crawl whose data made this possible (consider donating!), to Google for creating the code that curates and filters the data, and to Huggingface, who had no issue with hosting these 3TB of data for public download!" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #size_categories-10M<n<100M #size_categories-100M<n<1B #size_categories-1B<n<10B #source_datasets-original #language-Afrikaans #language-Amharic #language-Arabic #language-Azerbaijani #language-Belarusian #language-Bulgarian #language-Bengali #language-Catalan #language-Cebuano #language-Corsican #language-Czech #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-Filipino #language-French #language-Western Frisian #language-Irish #language-Scottish Gaelic #language-Galician #language-Gujarati #language-Hausa #language-Hawaiian #language-Hebrew #language-Hindi #language-Hmong #language-Haitian #language-Hungarian #language-Armenian #language-Indonesian #language-Igbo #language-Icelandic #language-Italian #language-iw #language-Japanese #language-Javanese #language-Georgian #language-Kazakh #language-Khmer #language-Kannada #language-Korean #language-Kurdish #language-Kirghiz #language-Latin #language-Luxembourgish #language-Lao #language-Lithuanian #language-Latvian #language-Malagasy #language-Maori #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Burmese #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian #language-Nyanja #language-Panjabi #language-Polish #language-Pushto #language-Portuguese #language-Romanian #language-Russian #language-Sindhi #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Southern Sotho #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkish #language-Ukrainian #language-Undetermined #language-Urdu #language-Uzbek #language-Vietnamese #language-Xhosa #language-Yiddish #language-Yoruba #language-Chinese #language-Zulu #license-odc-by #arxiv-1910.10683 #region-us \n", "### Dataset Summary\n\n\nA colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: \"URL\".\n\n\nThis is the processed version of Google's C4 dataset\n\n\nWe prepared five variants of the data: 'en', 'en.noclean', 'en.noblocklist', 'realnewslike', and 'multilingual' (mC4).\n\n\nFor reference, these are the sizes of the variants:\n\n\n* 'en': 305GB\n* 'en.noclean': 2.3TB\n* 'en.noblocklist': 380GB\n* 'realnewslike': 15GB\n* 'multilingual' (mC4): 9.7TB (108 subsets, one per language)\n\n\nThe 'en.noblocklist' variant is exactly the same as the 'en' variant, except we turned off the so-called \"badwords filter\", which removes all documents that contain words from the lists at URL", "#### How do I download this?", "##### Using Datasets\n\n\nSince this dataset is big, it is encouraged to load it in streaming mode using 'streaming=True', for example:\n\n\nYou can also load and mix multiple languages:", "##### Using Dask", "##### Using Git\n\n\nThis will download 13TB to your local drive. If you want to be more precise with what you are downloading, follow these commands instead:\n\n\nThe 'git clone' command in this variant will download a bunch of stub files that Git LFS uses, so you can see all the filenames that exist that way. You can then convert the stubs into their real files with 'git lfs pull --include \"...\"'. For example, if you wanted all the Dutch documents from the multilingual set, you would run", "### Supported Tasks and Leaderboards\n\n\nC4 and mC4 are mainly intended to pretrain language models and word representations.", "### Languages\n\n\nThe 'en', 'en.noclean', 'en.noblocklist' and 'realnewslike' variants are in English.\n\n\nThe other 108 languages are available and are reported in the table below.\n\n\nNote that the languages that end with \"-Latn\" are simply romanized variants, i.e. written using the Latin script.\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example form the 'en' config is:", "### Data Fields\n\n\nThe data have several fields:\n\n\n* 'url': url of the source as a string\n* 'text': text content as a string\n* 'timestamp': timestamp as a string", "### Data Splits\n\n\nSizes for the variants in english:\n\n\n\nA train and validation split are also provided for the other languages, but lengths are still to be added.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe C4 and mC4 datasets are collections text sourced from the public Common Crawl web scrape. It includes heuristics to extract only natural language (as opposed to boilerplate and other gibberish) in addition to extensive deduplication. You can find the code that has been used to build this dataset in URL by Tensorflow Datasets.\n\n\nC4 dataset was explicitly designed to be English only: any page that was not given a probability of at least 99% of being English by langdetect was discarded.\n\n\nTo build mC4, the authors used CLD3 to identify over 100 languages.", "### Licensing Information\n\n\nWe are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.", "### Acknowledgements\n\n\nBig ups to the good folks at Common Crawl whose data made this possible (consider donating!), to Google for creating the code that curates and filters the data, and to Huggingface, who had no issue with hosting these 3TB of data for public download!" ]

[ "task_categories:token-classification", "task_ids:coreference-resolution", "annotations_creators:domain experts", "multilinguality:monolingual", "language:en", "license:apache-2.0", "cross-document-coreference-resolution", "structure-prediction", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nSciCo consists of clusters of mentions in context and a hierarchy over them.\nThe corpus is drawn from computer science papers, and the concept mentions are methods and tasks from across CS.\nScientific concepts pose significant challenges: they often take diverse forms (e.g., class-conditional image\nsynthesis and categorical image generation) or are ambiguous (e.g., network architecture in AI vs.\nsystems research).\nTo build SciCo, we develop a new candidate generation\napproach built on three resources: a low-coverage KB (URL a noisy hypernym extractor, and curated\ncandidates.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in English.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'flatten\\_tokens': a single list of all tokens in the topic\n* 'flatten\\_mentions': array of mentions, each mention is represented by [start, end, cluster\\_id]\n* 'tokens': array of paragraphs\n* 'doc\\_ids': doc\\_id of each paragraph in 'tokens'\n* 'metadata': metadata of each doc\\_id\n* 'sentences': sentences boundaries for each paragraph in 'tokens' [start, end]\n* 'mentions': array of mentions, each mention is represented by [paragraph\\_id, start, end, cluster\\_id]\n* 'relations': array of binary relations between cluster\\_ids [parent, child]\n* 'id': id of the topic\n* 'hard\\_10' and 'hard\\_20' (only in the test set): flag for 10% or 20% hardest topics based on Levenshtein similarity.\n* 'source': source of this topic PapersWithCode (pwc), hypernym or curated.", "### Data Splits\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\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\nThis dataset was initially created by Arie Cattan, Sophie Johnson, Daniel Weld, Ido Dagan, Iz Beltagy, Doug Downey and Tom Hope, while Arie was intern at Allen Institute of Artificial Intelligence.", "### Licensing Information\n\n\nThis dataset is distributed under Apache License 2.0.", "### Contributions\n\n\nThanks to @ariecattan for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #task_ids-coreference-resolution #annotations_creators-domain experts #multilinguality-monolingual #language-English #license-apache-2.0 #cross-document-coreference-resolution #structure-prediction #region-us \n", "### Dataset Summary\n\n\nSciCo consists of clusters of mentions in context and a hierarchy over them.\nThe corpus is drawn from computer science papers, and the concept mentions are methods and tasks from across CS.\nScientific concepts pose significant challenges: they often take diverse forms (e.g., class-conditional image\nsynthesis and categorical image generation) or are ambiguous (e.g., network architecture in AI vs.\nsystems research).\nTo build SciCo, we develop a new candidate generation\napproach built on three resources: a low-coverage KB (URL a noisy hypernym extractor, and curated\ncandidates.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in English.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'flatten\\_tokens': a single list of all tokens in the topic\n* 'flatten\\_mentions': array of mentions, each mention is represented by [start, end, cluster\\_id]\n* 'tokens': array of paragraphs\n* 'doc\\_ids': doc\\_id of each paragraph in 'tokens'\n* 'metadata': metadata of each doc\\_id\n* 'sentences': sentences boundaries for each paragraph in 'tokens' [start, end]\n* 'mentions': array of mentions, each mention is represented by [paragraph\\_id, start, end, cluster\\_id]\n* 'relations': array of binary relations between cluster\\_ids [parent, child]\n* 'id': id of the topic\n* 'hard\\_10' and 'hard\\_20' (only in the test set): flag for 10% or 20% hardest topics based on Levenshtein similarity.\n* 'source': source of this topic PapersWithCode (pwc), hypernym or curated.", "### Data Splits\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\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\nThis dataset was initially created by Arie Cattan, Sophie Johnson, Daniel Weld, Ido Dagan, Iz Beltagy, Doug Downey and Tom Hope, while Arie was intern at Allen Institute of Artificial Intelligence.", "### Licensing Information\n\n\nThis dataset is distributed under Apache License 2.0.", "### Contributions\n\n\nThanks to @ariecattan for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "### Dataset Curators\n\n\nalper bayram", "### Languages\n\n\n[TR]" ]

[ "TAGS\n#region-us \n", "### Dataset Curators\n\n\nalper bayram", "### Languages\n\n\n[TR]" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:crowdsourced", "size_categories:unknown", "language:tr", "region:us" ]

[]

[ "tr" ]

[ "# References \n- alper bayram" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-crowdsourced #size_categories-unknown #language-Turkish #region-us \n", "# References \n- alper bayram" ]

[ "region:us" ]

[]

[]

[ "# negatif 54%", "# pozitif 46%" ]

[ "TAGS\n#region-us \n", "# negatif 54%", "# pozitif 46%" ]

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

[]

[ "en" ]

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

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

[ "task_categories:text-classification", "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#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:" ]

[ "task_categories:text-classification", "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#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:" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "arxiv:2309.09826", "doi:10.57967/hf/1182", "region:us" ]

[ "2309.09826" ]

[ "en" ]

[ "# Dataset Card for Smart Contracts", "## 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 - flattened\n - flattened_plain_text\n - inflated\n - inflated_plain_text\n - parsed\n - Data Fields\n - flattened\n - flattened_plain_text\n - inflated\n - inflated_plain_text\n - parsed\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\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: URL\n- Repository: URL\n- Paper: \n- Leaderboard:\n- Point of Contact: André Storhaug", "### Dataset Summary\n\nThis is a dataset of verified Smart Contracts from URL that are deployed to the Ethereum blockchain. A set of about 100,000 to 200,000 contracts are provided, containing both Solidity and Vyper code.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "#### flattened", "#### flattened_extended\n\nSame fields as 'flattened' but with the following additional fields:", "#### flattened_plain_text", "#### inflated", "#### inflated_plain_text", "#### parsed", "### Data Fields", "#### flattened\n\n- 'contract_name' ('string'): containing the smart contract name.\n- 'contract_address' ('string'): containing the Ethereum address for the smart contract.\n- 'language' ('string'): containing the language of the smart contract.\n- 'source_code ' ('string'): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.\n- 'abi' ('string'): containing the Application Binary Interface (ABI) of the smart contract.\n- 'compiler_version' ('string'): containing the compiler version used to compile the smart contract.\n- 'optimization_used' ('boolean'): indicating if the smart contract used optimization.\n- 'runs' ('number'): containing the number of optimization steps used.\n- 'constructor_arguments' ('string'): containing the constructor arguments of the smart contract.\n- 'evm_version' ('string'): containing the EVM version used to compile the smart contract.\n- 'library' ('string'): containing the 'name:address' of libraries used separated by ';'.\n- 'license_type' ('string'): containing the license type of the smart contract.\n- 'proxy' ('boolean'): indicating if the smart contract is a proxy.\n- 'implementation' ('string'): containing the implementation of the smart contract if it is a proxy.\n- 'swarm_source' ('string'): containing the swarm source of the smart contract.", "#### flattened_extended\n\nSame fields as 'flattened' but with the following additional fields:\n- 'tx_count' ('number'): containing the number of transactions made to the smart contract.\n- 'balance' ('string'): containing the ether balancce of the smart contract.", "#### flattened_plain_text\n\n- 'text' ('string'): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.\n- 'language' ('string'): containing the language of the smart contract.", "#### inflated\n\nSame fields as 'flattened' but with the following additional fields:\n- 'file_path' ('string'): containing the original path to the file.", "#### inflated_plain_text\n\n- 'text' ('string'): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.\n- 'language' ('string'): containing the language of the smart contract.", "#### parsed\n\n- 'contract_name' ('string'): containing the smart contract name.\n- 'file_path' ('string'): containing the original path to the file.\n- 'contract_address' ('string'): containing the Ethereum address for the smart contract.\n- 'language' ('string'): containing the language of the smart contract.\n- 'class_name' ('string'): containing the name of the \"class\" (contract).\n- 'class_code' ('string'): containing the source code of the \"class\" (contract).\n- 'class_documentation' ('string'): containing the documentation (code comment) of the \"class\" (contract).\n- 'class_documentation_type' ('string'): containing the documentation type of the \"class\" (contract). Can be one of: 'NatSpecMultiLine', 'NatSpecSingleLine', 'LineComment' or 'Comment'.\n- 'func_name' ('string'): containing the name of the function definition.\n- 'func_code' ('string'): containing the source code of the function.\n- 'func_documentation' ('string'): containing the documentation (code comment) of the contract definition (or \"class\").\n- 'func_documentation_type' ('string'): containing the documentation type of the function. Can be one of: 'NatSpecMultiLine', 'NatSpecSingleLine', 'LineComment' or 'Comment'.\n- 'compiler_version' ('string'): containing the compiler version used to compile the smart contract.\n- 'license_type' ('string'): containing the license type of the smart contract.\n- 'swarm_source' ('string'): containing the swarm source of the smart contract.", "### 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 @andstor for adding this dataset." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #arxiv-2309.09826 #doi-10.57967/hf/1182 #region-us \n", "# Dataset Card for Smart Contracts", "## 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 - flattened\n - flattened_plain_text\n - inflated\n - inflated_plain_text\n - parsed\n - Data Fields\n - flattened\n - flattened_plain_text\n - inflated\n - inflated_plain_text\n - parsed\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\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: URL\n- Repository: URL\n- Paper: \n- Leaderboard:\n- Point of Contact: André Storhaug", "### Dataset Summary\n\nThis is a dataset of verified Smart Contracts from URL that are deployed to the Ethereum blockchain. A set of about 100,000 to 200,000 contracts are provided, containing both Solidity and Vyper code.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "#### flattened", "#### flattened_extended\n\nSame fields as 'flattened' but with the following additional fields:", "#### flattened_plain_text", "#### inflated", "#### inflated_plain_text", "#### parsed", "### Data Fields", "#### flattened\n\n- 'contract_name' ('string'): containing the smart contract name.\n- 'contract_address' ('string'): containing the Ethereum address for the smart contract.\n- 'language' ('string'): containing the language of the smart contract.\n- 'source_code ' ('string'): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.\n- 'abi' ('string'): containing the Application Binary Interface (ABI) of the smart contract.\n- 'compiler_version' ('string'): containing the compiler version used to compile the smart contract.\n- 'optimization_used' ('boolean'): indicating if the smart contract used optimization.\n- 'runs' ('number'): containing the number of optimization steps used.\n- 'constructor_arguments' ('string'): containing the constructor arguments of the smart contract.\n- 'evm_version' ('string'): containing the EVM version used to compile the smart contract.\n- 'library' ('string'): containing the 'name:address' of libraries used separated by ';'.\n- 'license_type' ('string'): containing the license type of the smart contract.\n- 'proxy' ('boolean'): indicating if the smart contract is a proxy.\n- 'implementation' ('string'): containing the implementation of the smart contract if it is a proxy.\n- 'swarm_source' ('string'): containing the swarm source of the smart contract.", "#### flattened_extended\n\nSame fields as 'flattened' but with the following additional fields:\n- 'tx_count' ('number'): containing the number of transactions made to the smart contract.\n- 'balance' ('string'): containing the ether balancce of the smart contract.", "#### flattened_plain_text\n\n- 'text' ('string'): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.\n- 'language' ('string'): containing the language of the smart contract.", "#### inflated\n\nSame fields as 'flattened' but with the following additional fields:\n- 'file_path' ('string'): containing the original path to the file.", "#### inflated_plain_text\n\n- 'text' ('string'): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.\n- 'language' ('string'): containing the language of the smart contract.", "#### parsed\n\n- 'contract_name' ('string'): containing the smart contract name.\n- 'file_path' ('string'): containing the original path to the file.\n- 'contract_address' ('string'): containing the Ethereum address for the smart contract.\n- 'language' ('string'): containing the language of the smart contract.\n- 'class_name' ('string'): containing the name of the \"class\" (contract).\n- 'class_code' ('string'): containing the source code of the \"class\" (contract).\n- 'class_documentation' ('string'): containing the documentation (code comment) of the \"class\" (contract).\n- 'class_documentation_type' ('string'): containing the documentation type of the \"class\" (contract). Can be one of: 'NatSpecMultiLine', 'NatSpecSingleLine', 'LineComment' or 'Comment'.\n- 'func_name' ('string'): containing the name of the function definition.\n- 'func_code' ('string'): containing the source code of the function.\n- 'func_documentation' ('string'): containing the documentation (code comment) of the contract definition (or \"class\").\n- 'func_documentation_type' ('string'): containing the documentation type of the function. Can be one of: 'NatSpecMultiLine', 'NatSpecSingleLine', 'LineComment' or 'Comment'.\n- 'compiler_version' ('string'): containing the compiler version used to compile the smart contract.\n- 'license_type' ('string'): containing the license type of the smart contract.\n- 'swarm_source' ('string'): containing the swarm source of the smart contract.", "### 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 @andstor for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Medical Histories Ru-ru\n\nMedical Histories from Russian medical textbooks.\nA text dataset with medical histories. \nAll dates were masked into <DATE>." ]

[ "TAGS\n#region-us \n", "# Medical Histories Ru-ru\n\nMedical Histories from Russian medical textbooks.\nA text dataset with medical histories. \nAll dates were masked into <DATE>." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-retrieval", "task_categories:text-classification", "task_ids:document-retrieval", "task_ids:topic-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:fr", "license:cc-by-nc-sa-4.0", "legal", "arxiv:2108.11792", "region:us" ]

[ "2108.11792" ]

[ "fr" ]

[ "### Dataset Summary\n\n\nThe Belgian Statutory Article Retrieval Dataset (BSARD) is a French native dataset for studying legal information retrieval. BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens and labeled by experienced jurists with relevant articles from the corpus.", "### Supported Tasks and Leaderboards\n\n\n* 'document-retrieval': The dataset can be used to train models for ad-hoc legal information retrieval. Such model is presented with a short user query written in natural language and asked to retrieve relevant legal information from a knowledge source (such as statutory articles).", "### Languages\n\n\nThe text in the dataset is in French, as spoken in Wallonia and Brussels-Capital region. The associated BCP-47 code is 'fr-BE'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises a question, with additional 'category', 'subcategory', and 'extra\\_description' fields that elaborate on it, and a list of 'article\\_ids' from the corpus of statutory articles that are relevant to the question.\n\n\nAn example from the BSARD test set looks as follows:", "### Data Fields\n\n\n* In \"questions\\_fr\\_train.csv\" and \"questions\\_fr\\_test.csv\":\n\n\n\t+ 'id': an *int32* feature corresponding to a unique ID number for the question.\n\t+ 'question': a *string* feature corresponding to the question.\n\t+ 'category': a *string* feature corresponding to the general topic of the question.\n\t+ 'subcategory': a *string* feature corresponding to the sub-topic of the question.\n\t+ 'extra\\_description': a *string* feature corresponding to the extra categorization tags of the question.\n\t+ 'article\\_ids': a *string* feature of comma-separated article IDs relevant to the question.\n* In \"articles\\_fr.csv\":\n\n\n\t+ 'id': an *int32* feature corresponding to a unique ID number for the article.\n\t+ 'article': a *string* feature corresponding to the full article.\n\t+ 'code': a *string* feature corresponding to the law code to which the article belongs.\n\t+ 'article\\_no': a *string* feature corresponding to the article number in the code.\n\t+ 'description': a *string* feature corresponding to the concatenated headings of the article.\n\t+ 'law\\_type': a *string* feature whose value is either *\"regional\"* or *\"national\"*.", "### Data Splits\n\n\nThis dataset is split into train/test set. Number of questions in each set is given below:\n\n\nTrain: BSARD, Test: 886\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset is intended to be used by researchers to build and evaluate models on retrieving law articles relevant to an input legal question. It should not be regarded as a reliable source of legal information at this point in time, as both the questions and articles correspond to an outdated version of the Belgian law from May 2021 (time of dataset collection). In the latter case, the user is advised to consult daily updated official legal resources (e.g., the Belgian Official Gazette).", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nBSARD was created in four stages: (i) compiling a large corpus of Belgian law articles, (ii) gathering legal questions with references to relevant law articles, (iii) refining these questions, and (iv) matching the references to the corresponding articles from the corpus.", "#### Who are the source language producers?\n\n\nSpeakers were not directly approached for inclusion in this dataset and thus could not be asked for demographic information. Questions were collected, anonimyzed, and reformulated by Droits Quotidiens. Therefore, no direct information about the speakers’ age and gender distribution, or socioeconomic status is available. However, it is expected that most, but not all, of the speakers are adults (18+ years), speak French as a native language, and live in Wallonia or Brussels-Capital region.", "### Annotations", "#### Annotation process\n\n\nEach year, Droits Quotidiens, a Belgian organization whose mission is to clarify the law for laypeople, receives and collects around 4,000 emails from Belgian citizens asking for advice on a personal legal issue. In practice, their legal clarification process consists of four steps. First, they identify the most frequently asked questions on a common legal issue. Then, they define a new anonymized \"model\" question on that issue expressed in natural language terms, i.e., as close as possible as if a layperson had asked it. Next, they search the Belgian law for articles that help answer the model question and reference them.", "#### Who are the annotators?\n\n\nA total of six Belgian jurists from Droits Quotidiens contributed to annotating the questions. All have a law degree from a Belgian university and years of experience in providing legal advice and clarifications of the law. They range in age from 30-60 years, including one man and five women, gave their ethnicity as white European, speak French as a native language, and represent upper middle class based on income levels.", "### Personal and Sensitive Information\n\n\nThe questions represent informal, asynchronous, edited, written language that does not exceed 44 words. None of them contained hateful, aggressive, or inappropriate language as they were all reviewed and reworded by Droits Quotidiens to be neutral, anonymous, and comprehensive. The legal articles represent strong, formal, written language that can contain up to 5,790 words.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nIn addition to helping advance the state-of-the-art in retrieving statutes relevant to a legal question, BSARD-based models could improve the efficiency of the legal information retrieval process in the context of legal research, therefore enabling researchers to devote themselves to more thoughtful parts of their research. Furthermore, BSARD can become a starting point of new open-source legal information search tools so that the socially weaker parties to disputes can benefit from a free professional assisting service.", "### Discussion of Biases", "### Other Known Limitations\n\n\nFirst, the corpus of articles is limited to those collected from 32 Belgian codes, which obviously does not cover the entire Belgian law as thousands of articles from decrees, directives, and ordinances are missing. During the dataset construction, all references to these uncollected articles are ignored, which causes some questions to end up with only a fraction of their initial number of relevant articles. This information loss implies that the answer contained in the remaining relevant articles might be incomplete, although it is still appropriate.\n\n\nAdditionally, it is essential to note that not all legal questions can be answered with statutes alone. For instance, the question “Can I evict my tenants if they make too much noise?” might not have a detailed answer within the statutory law that quantifies a specific noise threshold at which eviction is allowed. Instead, the landlord should probably rely more on case law and find precedents similar to their current situation (e.g., the tenant makes two parties a week until 2 am). Hence, some questions are better suited than others to the statutory article retrieval task, and the domain of the less suitable ones remains to be determined.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was created by Antoine Louis during work done at the Law & Tech lab of Maastricht University, with the help of jurists from Droits Quotidiens.", "### Licensing Information\n\n\nBSARD is licensed under the CC BY-NC-SA 4.0 license.", "### Contributions\n\n\nThanks to @antoinelouis for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_categories-text-classification #task_ids-document-retrieval #task_ids-topic-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-French #license-cc-by-nc-sa-4.0 #legal #arxiv-2108.11792 #region-us \n", "### Dataset Summary\n\n\nThe Belgian Statutory Article Retrieval Dataset (BSARD) is a French native dataset for studying legal information retrieval. BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens and labeled by experienced jurists with relevant articles from the corpus.", "### Supported Tasks and Leaderboards\n\n\n* 'document-retrieval': The dataset can be used to train models for ad-hoc legal information retrieval. Such model is presented with a short user query written in natural language and asked to retrieve relevant legal information from a knowledge source (such as statutory articles).", "### Languages\n\n\nThe text in the dataset is in French, as spoken in Wallonia and Brussels-Capital region. The associated BCP-47 code is 'fr-BE'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises a question, with additional 'category', 'subcategory', and 'extra\\_description' fields that elaborate on it, and a list of 'article\\_ids' from the corpus of statutory articles that are relevant to the question.\n\n\nAn example from the BSARD test set looks as follows:", "### Data Fields\n\n\n* In \"questions\\_fr\\_train.csv\" and \"questions\\_fr\\_test.csv\":\n\n\n\t+ 'id': an *int32* feature corresponding to a unique ID number for the question.\n\t+ 'question': a *string* feature corresponding to the question.\n\t+ 'category': a *string* feature corresponding to the general topic of the question.\n\t+ 'subcategory': a *string* feature corresponding to the sub-topic of the question.\n\t+ 'extra\\_description': a *string* feature corresponding to the extra categorization tags of the question.\n\t+ 'article\\_ids': a *string* feature of comma-separated article IDs relevant to the question.\n* In \"articles\\_fr.csv\":\n\n\n\t+ 'id': an *int32* feature corresponding to a unique ID number for the article.\n\t+ 'article': a *string* feature corresponding to the full article.\n\t+ 'code': a *string* feature corresponding to the law code to which the article belongs.\n\t+ 'article\\_no': a *string* feature corresponding to the article number in the code.\n\t+ 'description': a *string* feature corresponding to the concatenated headings of the article.\n\t+ 'law\\_type': a *string* feature whose value is either *\"regional\"* or *\"national\"*.", "### Data Splits\n\n\nThis dataset is split into train/test set. Number of questions in each set is given below:\n\n\nTrain: BSARD, Test: 886\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset is intended to be used by researchers to build and evaluate models on retrieving law articles relevant to an input legal question. It should not be regarded as a reliable source of legal information at this point in time, as both the questions and articles correspond to an outdated version of the Belgian law from May 2021 (time of dataset collection). In the latter case, the user is advised to consult daily updated official legal resources (e.g., the Belgian Official Gazette).", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nBSARD was created in four stages: (i) compiling a large corpus of Belgian law articles, (ii) gathering legal questions with references to relevant law articles, (iii) refining these questions, and (iv) matching the references to the corresponding articles from the corpus.", "#### Who are the source language producers?\n\n\nSpeakers were not directly approached for inclusion in this dataset and thus could not be asked for demographic information. Questions were collected, anonimyzed, and reformulated by Droits Quotidiens. Therefore, no direct information about the speakers’ age and gender distribution, or socioeconomic status is available. However, it is expected that most, but not all, of the speakers are adults (18+ years), speak French as a native language, and live in Wallonia or Brussels-Capital region.", "### Annotations", "#### Annotation process\n\n\nEach year, Droits Quotidiens, a Belgian organization whose mission is to clarify the law for laypeople, receives and collects around 4,000 emails from Belgian citizens asking for advice on a personal legal issue. In practice, their legal clarification process consists of four steps. First, they identify the most frequently asked questions on a common legal issue. Then, they define a new anonymized \"model\" question on that issue expressed in natural language terms, i.e., as close as possible as if a layperson had asked it. Next, they search the Belgian law for articles that help answer the model question and reference them.", "#### Who are the annotators?\n\n\nA total of six Belgian jurists from Droits Quotidiens contributed to annotating the questions. All have a law degree from a Belgian university and years of experience in providing legal advice and clarifications of the law. They range in age from 30-60 years, including one man and five women, gave their ethnicity as white European, speak French as a native language, and represent upper middle class based on income levels.", "### Personal and Sensitive Information\n\n\nThe questions represent informal, asynchronous, edited, written language that does not exceed 44 words. None of them contained hateful, aggressive, or inappropriate language as they were all reviewed and reworded by Droits Quotidiens to be neutral, anonymous, and comprehensive. The legal articles represent strong, formal, written language that can contain up to 5,790 words.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nIn addition to helping advance the state-of-the-art in retrieving statutes relevant to a legal question, BSARD-based models could improve the efficiency of the legal information retrieval process in the context of legal research, therefore enabling researchers to devote themselves to more thoughtful parts of their research. Furthermore, BSARD can become a starting point of new open-source legal information search tools so that the socially weaker parties to disputes can benefit from a free professional assisting service.", "### Discussion of Biases", "### Other Known Limitations\n\n\nFirst, the corpus of articles is limited to those collected from 32 Belgian codes, which obviously does not cover the entire Belgian law as thousands of articles from decrees, directives, and ordinances are missing. During the dataset construction, all references to these uncollected articles are ignored, which causes some questions to end up with only a fraction of their initial number of relevant articles. This information loss implies that the answer contained in the remaining relevant articles might be incomplete, although it is still appropriate.\n\n\nAdditionally, it is essential to note that not all legal questions can be answered with statutes alone. For instance, the question “Can I evict my tenants if they make too much noise?” might not have a detailed answer within the statutory law that quantifies a specific noise threshold at which eviction is allowed. Instead, the landlord should probably rely more on case law and find precedents similar to their current situation (e.g., the tenant makes two parties a week until 2 am). Hence, some questions are better suited than others to the statutory article retrieval task, and the domain of the less suitable ones remains to be determined.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was created by Antoine Louis during work done at the Law & Tech lab of Maastricht University, with the help of jurists from Droits Quotidiens.", "### Licensing Information\n\n\nBSARD is licensed under the CC BY-NC-SA 4.0 license.", "### Contributions\n\n\nThanks to @antoinelouis for adding this dataset." ]

[ "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:extended|common_voice", "language:ar", "language:br", "language:ca", "language:cnh", "language:cs", "language:cv", "language:cy", "language:de", "language:dv", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fr", "language:fy", "language:ia", "language:id", "language:it", "language:ja", "language:ka", "language:kab", "language:ky", "language:lv", "language:mn", "language:mt", "language:nl", "language:pl", "language:pt", "language:rm", "language:ro", "language:ru", "language:rw", "language:sah", "language:sl", "language:sv", "language:ta", "language:tr", "language:tt", "language:uk", "language:zh", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "ar", "br", "ca", "cnh", "cs", "cv", "cy", "de", "dv", "el", "en", "eo", "es", "et", "eu", "fa", "fr", "fy", "ia", "id", "it", "ja", "ka", "kab", "ky", "lv", "mn", "mt", "nl", "pl", "pt", "rm", "ro", "ru", "rw", "sah", "sl", "sv", "ta", "tr", "tt", "uk", "zh" ]

[ "### Dataset Summary\n\n\nThis dataset is composed of speech recordings from languages that were carefully selected from the CommonVoice database. The total duration of audio recordings is 45.1 hours (i.e., 1 hour of material for each language). The dataset has been extracted from CommonVoice to train language-id systems.", "### Supported Tasks and Leaderboards\n\n\nThe baselines for language-id are available in the SpeechBrain toolkit (see recipes/CommonLanguage):\nURL", "### Languages\n\n\nList of included language:\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises the 'path' to the audio file, and its label 'language'. Additional fields include 'age', 'client\\_id', 'gender' and 'sentence'.", "### Data Fields\n\n\n'client\\_id' ('string'): An id for which client (voice) made the recording\n\n\n'path' ('string'): The path to the audio file\n\n\n'language' ('ClassLabel'): The language of the recording (see the 'Languages' section above)\n\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n\n'age' ('string'): The age of the speaker.\n\n\n'gender' ('string'): The gender of the speaker", "### Data Splits\n\n\nThe dataset is already balanced and split into train, dev (validation) and test sets.\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 the Common Voice dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\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 the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe Mongolian and Ukrainian languages are spelled as \"Mangolian\" and \"Ukranian\" in this version of the dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @anton-l for adding this dataset." ]

[ "TAGS\n#annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|common_voice #language-Arabic #language-Breton #language-Catalan #language-Hakha Chin #language-Czech #language-Chuvash #language-Welsh #language-German #language-Dhivehi #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-French #language-Western Frisian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Italian #language-Japanese #language-Georgian #language-Kabyle #language-Kirghiz #language-Latvian #language-Mongolian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romansh #language-Romanian #language-Russian #language-Kinyarwanda #language-Yakut #language-Slovenian #language-Swedish #language-Tamil #language-Turkish #language-Tatar #language-Ukrainian #language-Chinese #license-cc-by-nc-4.0 #region-us \n", "### Dataset Summary\n\n\nThis dataset is composed of speech recordings from languages that were carefully selected from the CommonVoice database. The total duration of audio recordings is 45.1 hours (i.e., 1 hour of material for each language). The dataset has been extracted from CommonVoice to train language-id systems.", "### Supported Tasks and Leaderboards\n\n\nThe baselines for language-id are available in the SpeechBrain toolkit (see recipes/CommonLanguage):\nURL", "### Languages\n\n\nList of included language:\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises the 'path' to the audio file, and its label 'language'. Additional fields include 'age', 'client\\_id', 'gender' and 'sentence'.", "### Data Fields\n\n\n'client\\_id' ('string'): An id for which client (voice) made the recording\n\n\n'path' ('string'): The path to the audio file\n\n\n'language' ('ClassLabel'): The language of the recording (see the 'Languages' section above)\n\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n\n'age' ('string'): The age of the speaker.\n\n\n'gender' ('string'): The gender of the speaker", "### Data Splits\n\n\nThe dataset is already balanced and split into train, dev (validation) and test sets.\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 the Common Voice dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\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 the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe Mongolian and Ukrainian languages are spelled as \"Mangolian\" and \"Ukranian\" in this version of the dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @anton-l for adding this dataset." ]

[ "task_ids:keyword-spotting", "task_ids:speaker-identification", "task_ids:intent-classification", "task_ids:slot-filling", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "source_datasets:extended|librispeech_asr", "source_datasets:extended|other-librimix", "source_datasets:extended|other-speech_commands", "language:en", "license:unknown", "arxiv:2105.01051", "region:us" ]

[ "2105.01051" ]

[ "en" ]

[ "### Dataset Summary\n\n\nSUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.", "### Supported Tasks and Leaderboards\n\n\nThe SUPERB leaderboard can be found here URL and consists of the following tasks:", "#### pr\n\n\nPhoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. LibriSpeech train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER).", "#### asr\n\n\nAutomatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. LibriSpeech train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER).", "#### ks\n\n\nKeyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used Speech Commands dataset v1.0 for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC)", "##### Example of usage:\n\n\nUse these auxillary functions to:\n\n\n* load the audio file into an audio data array\n* sample from long '*silence*' audio clips\n\n\nFor other examples of handling long '*silence*' clips see the S3PRL\nor TFDS implementations.", "#### qbe\n\n\nQuery by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in QUESST 2014 challenge is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms.", "#### ic\n\n\nIntent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the Fluent Speech Commands dataset, where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC).", "#### sf\n\n\nSlot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. Audio SNIPS is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing.", "#### si\n\n\nSpeaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used VoxCeleb1 dataset is adopted, and the evaluation metric is accuracy (ACC).", "#### asv\n\n\nAutomatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER).", "#### sd\n\n\nSpeaker Diarization (SD) predicts *who is speaking when* for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. LibriMix is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER).", "##### Example of usage\n\n\nUse these auxiliary functions to:\n\n\n* load the audio file into an audio data array\n* generate the label array", "#### er\n\n\nEmotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC).", "### Languages\n\n\nThe language data in SUPERB is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### pr", "#### asr\n\n\nAn example from each split looks like:", "#### ks\n\n\nAn example from each split looks like:", "#### qbe", "#### ic", "#### sf", "#### si", "#### asv", "#### sd\n\n\nAn example from each split looks like:", "#### er", "### Data Fields", "#### pr", "#### asr\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'text' ('string'): The transcription of the audio file.\n* 'speaker\\_id' ('integer'): A unique ID of the speaker. The same speaker id can be found for multiple data samples.\n* 'chapter\\_id' ('integer'): ID of the audiobook chapter which includes the transcription.\n* 'id' ('string'): A unique ID of the data sample.", "#### ks\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'label' ('ClassLabel'): Label of the spoken command. Possible values:\n\t+ '0: \"yes\", 1: \"no\", 2: \"up\", 3: \"down\", 4: \"left\", 5: \"right\", 6: \"on\", 7: \"off\", 8: \"stop\", 9: \"go\", 10: \"*silence*\", 11: \"*unknown*\"'", "#### qbe", "#### ic\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'speaker\\_id' ('string'): ID of the speaker.\n* 'text' ('string'): Transcription of the spoken command.\n* 'action' ('ClassLabel'): Label of the command's action. Possible values:\n\t+ '0: \"activate\", 1: \"bring\", 2: \"change language\", 3: \"deactivate\", 4: \"decrease\", 5: \"increase\"'\n* 'object' ('ClassLabel'): Label of the command's object. Possible values:\n\t+ '0: \"Chinese\", 1: \"English\", 2: \"German\", 3: \"Korean\", 4: \"heat\", 5: \"juice\", 6: \"lamp\", 7: \"lights\", 8: \"music\", 9: \"newspaper\", 10: \"none\", 11: \"shoes\", 12: \"socks\", 13: \"volume\"'\n* 'location' ('ClassLabel'): Label of the command's location. Possible values:\n\t+ '0: \"bedroom\", 1: \"kitchen\", 2: \"none\", 3: \"washroom\"'", "#### sf", "#### si\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'label' ('ClassLabel'): Label (ID) of the speaker. Possible values:\n\t+ '0: \"id10001\", 1: \"id10002\", 2: \"id10003\", ..., 1250: \"id11251\"'", "#### asv", "#### sd\n\n\nThe data fields in all splits are:\n\n\n* 'record\\_id' ('string'): ID of the record.\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'start' ('integer'): Start frame of the audio.\n* 'end' ('integer'): End frame of the audio.\n* 'speakers' ('list' of 'dict'): List of speakers in the audio. Each item contains the fields:\n\t+ 'speaker\\_id' ('string'): ID of the speaker.\n\t+ 'start' ('integer'): Frame when the speaker starts speaking.\n\t+ 'end' ('integer'): Frame when the speaker stops speaking.", "#### er\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'label' ('ClassLabel'): Label of the speech emotion. Possible values:\n\t+ '0: \"neu\", 1: \"hap\", 2: \"ang\", 3: \"sad\"'", "### Data Splits", "#### pr", "#### asr", "#### ks", "#### qbe", "#### ic", "#### sf", "#### si", "#### asv", "#### sd\n\n\nThe data is split into \"train\", \"dev\" and \"test\" sets, each containing the following number of examples:", "#### er\n\n\nThe data is split into 5 sets intended for 5-fold cross-validation:\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\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @lewtun, @albertvillanova and @anton-l for adding this dataset." ]

[ "TAGS\n#task_ids-keyword-spotting #task_ids-speaker-identification #task_ids-intent-classification #task_ids-slot-filling #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-unknown #source_datasets-original #source_datasets-extended|librispeech_asr #source_datasets-extended|other-librimix #source_datasets-extended|other-speech_commands #language-English #license-unknown #arxiv-2105.01051 #region-us \n", "### Dataset Summary\n\n\nSUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.", "### Supported Tasks and Leaderboards\n\n\nThe SUPERB leaderboard can be found here URL and consists of the following tasks:", "#### pr\n\n\nPhoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. LibriSpeech train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER).", "#### asr\n\n\nAutomatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. LibriSpeech train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER).", "#### ks\n\n\nKeyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used Speech Commands dataset v1.0 for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC)", "##### Example of usage:\n\n\nUse these auxillary functions to:\n\n\n* load the audio file into an audio data array\n* sample from long '*silence*' audio clips\n\n\nFor other examples of handling long '*silence*' clips see the S3PRL\nor TFDS implementations.", "#### qbe\n\n\nQuery by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in QUESST 2014 challenge is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms.", "#### ic\n\n\nIntent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the Fluent Speech Commands dataset, where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC).", "#### sf\n\n\nSlot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. Audio SNIPS is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing.", "#### si\n\n\nSpeaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used VoxCeleb1 dataset is adopted, and the evaluation metric is accuracy (ACC).", "#### asv\n\n\nAutomatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER).", "#### sd\n\n\nSpeaker Diarization (SD) predicts *who is speaking when* for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. LibriMix is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER).", "##### Example of usage\n\n\nUse these auxiliary functions to:\n\n\n* load the audio file into an audio data array\n* generate the label array", "#### er\n\n\nEmotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC).", "### Languages\n\n\nThe language data in SUPERB is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### pr", "#### asr\n\n\nAn example from each split looks like:", "#### ks\n\n\nAn example from each split looks like:", "#### qbe", "#### ic", "#### sf", "#### si", "#### asv", "#### sd\n\n\nAn example from each split looks like:", "#### er", "### Data Fields", "#### pr", "#### asr\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'text' ('string'): The transcription of the audio file.\n* 'speaker\\_id' ('integer'): A unique ID of the speaker. The same speaker id can be found for multiple data samples.\n* 'chapter\\_id' ('integer'): ID of the audiobook chapter which includes the transcription.\n* 'id' ('string'): A unique ID of the data sample.", "#### ks\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'label' ('ClassLabel'): Label of the spoken command. Possible values:\n\t+ '0: \"yes\", 1: \"no\", 2: \"up\", 3: \"down\", 4: \"left\", 5: \"right\", 6: \"on\", 7: \"off\", 8: \"stop\", 9: \"go\", 10: \"*silence*\", 11: \"*unknown*\"'", "#### qbe", "#### ic\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'speaker\\_id' ('string'): ID of the speaker.\n* 'text' ('string'): Transcription of the spoken command.\n* 'action' ('ClassLabel'): Label of the command's action. Possible values:\n\t+ '0: \"activate\", 1: \"bring\", 2: \"change language\", 3: \"deactivate\", 4: \"decrease\", 5: \"increase\"'\n* 'object' ('ClassLabel'): Label of the command's object. Possible values:\n\t+ '0: \"Chinese\", 1: \"English\", 2: \"German\", 3: \"Korean\", 4: \"heat\", 5: \"juice\", 6: \"lamp\", 7: \"lights\", 8: \"music\", 9: \"newspaper\", 10: \"none\", 11: \"shoes\", 12: \"socks\", 13: \"volume\"'\n* 'location' ('ClassLabel'): Label of the command's location. Possible values:\n\t+ '0: \"bedroom\", 1: \"kitchen\", 2: \"none\", 3: \"washroom\"'", "#### sf", "#### si\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'label' ('ClassLabel'): Label (ID) of the speaker. Possible values:\n\t+ '0: \"id10001\", 1: \"id10002\", 2: \"id10003\", ..., 1250: \"id11251\"'", "#### asv", "#### sd\n\n\nThe data fields in all splits are:\n\n\n* 'record\\_id' ('string'): ID of the record.\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'start' ('integer'): Start frame of the audio.\n* 'end' ('integer'): End frame of the audio.\n* 'speakers' ('list' of 'dict'): List of speakers in the audio. Each item contains the fields:\n\t+ 'speaker\\_id' ('string'): ID of the speaker.\n\t+ 'start' ('integer'): Frame when the speaker starts speaking.\n\t+ 'end' ('integer'): Frame when the speaker stops speaking.", "#### er\n\n\n* 'file' ('string'): Path to the WAV audio file.\n* 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.\n* 'label' ('ClassLabel'): Label of the speech emotion. Possible values:\n\t+ '0: \"neu\", 1: \"hap\", 2: \"ang\", 3: \"sad\"'", "### Data Splits", "#### pr", "#### asr", "#### ks", "#### qbe", "#### ic", "#### sf", "#### si", "#### asv", "#### sd\n\n\nThe data is split into \"train\", \"dev\" and \"test\" sets, each containing the following number of examples:", "#### er\n\n\nThe data is split into 5 sets intended for 5-fold cross-validation:\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\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @lewtun, @albertvillanova and @anton-l for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Disclaimer\nThis is a tiny subset of the SUPERB dataset, which is intended only for demo purposes!\n\nSee the full dataset here: URL" ]

[ "TAGS\n#region-us \n", "# Disclaimer\nThis is a tiny subset of the SUPERB dataset, which is intended only for demo purposes!\n\nSee the full dataset here: URL" ]

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

[]

[ "et" ]

[ "# Estonian Question Answering dataset\n\n* Dataset for extractive question answering in Estonian. It is based on Wikipedia articles, pre-filtered via PageRank. Annotation was done by one person.\n* Train set includes 776 context-question-answer triplets. There are several possible answers per question, each in a separate triplet. Number of different questions is 512.\n* Test set includes 603 samples. Each sample contains one or more golden answers. Altogether there are 892 golden ansewrs.", "### Change log\nTest set v1.1 adds some more golden answers.", "### Reference\nIf you use this dataset for research, please cite the following paper:" ]

[ "TAGS\n#language-Estonian #region-us \n", "# Estonian Question Answering dataset\n\n* Dataset for extractive question answering in Estonian. It is based on Wikipedia articles, pre-filtered via PageRank. Annotation was done by one person.\n* Train set includes 776 context-question-answer triplets. There are several possible answers per question, each in a separate triplet. Number of different questions is 512.\n* Test set includes 603 samples. Each sample contains one or more golden answers. Altogether there are 892 golden ansewrs.", "### Change log\nTest set v1.1 adds some more golden answers.", "### Reference\nIf you use this dataset for research, please cite the following paper:" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:SkelterLabsInc/JaQuAD", "language:ja", "license:cc-by-sa-3.0", "question-generation", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "ja" ]

[ "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is JaQuAD dataset compiled for question generation (QG) task. The test set of the original\ndata is not publicly released, so we randomly sampled test questions from the training set. There are no overlap in terms of the paragraph across train, test, and validation split.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nJapanese (ja)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.\n\n\nData Splits\n-----------" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-SkelterLabsInc/JaQuAD #language-Japanese #license-cc-by-sa-3.0 #question-generation #arxiv-2210.03992 #region-us \n", "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is JaQuAD dataset compiled for question generation (QG) task. The test set of the original\ndata is not publicly released, so we randomly sampled test questions from the training set. There are no overlap in terms of the paragraph across train, test, and validation split.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).", "### Languages\n\n\nJapanese (ja)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.\n\n\nData Splits\n-----------" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:squad", "language:en", "license:cc-by-4.0", "question-generation", "arxiv:2210.03992", "arxiv:1705.00106", "region:us" ]

[ "2210.03992", "1705.00106" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is SQuAD dataset for question generation (QG) task. The split\nof train/development/test set follows the \"Neural Question Generation\" work and is\ncompatible with the leader board.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).\nThis task has an active leaderboard which can be found at here.", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.\n\n\nData Splits\n-----------" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad #language-English #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #arxiv-1705.00106 #region-us \n", "### Dataset Summary\n\n\nThis is a subset of QG-Bench, a unified question generation benchmark proposed in\n\"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference\".\nThis is SQuAD dataset for question generation (QG) task. The split\nof train/development/test set follows the \"Neural Question Generation\" work and is\ncompatible with the leader board.", "### Supported Tasks and Leaderboards\n\n\n* 'question-generation': The dataset is assumed to be used to train a model for question generation.\nSuccess on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).\nThis task has an active leaderboard which can be found at here.", "### Languages\n\n\nEnglish (en)\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'paragraph': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'sentence': a 'string' feature.\n* 'paragraph\\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.\n* 'paragraph\\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.\n* 'sentence\\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.\n\n\nEach of 'paragraph\\_answer', 'paragraph\\_sentence', and 'sentence\\_answer' feature is assumed to be used to train a question generation model,\nbut with different information. The 'paragraph\\_answer' and 'sentence\\_answer' features are for answer-aware question generation and\n'paragraph\\_sentence' feature is for sentence-aware question generation.\n\n\nData Splits\n-----------" ]

[ "multilinguality:translation", "size_categories:unknown", "language:cz", "language:de", "language:it", "region:us" ]

[]

[ "cz", "de", "it" ]

[ "# MERLIN corpus\n\nProject URL: URL\n\nDataset URL: URL\n\nThe MERLIN corpus is a written learner corpus for Czech, German, and Italian that has been designed to illustrate the Common European Framework of Reference for Languages (CEFR) with authentic learner data. The corpus contains learner texts produced in standardized language certifications covering CEFR levels A1-C1. The MERLIN annotation scheme includes a wide range of language characteristics that provide researchers with concrete examples of learner performance and progress across multiple proficiency levels." ]

[ "TAGS\n#multilinguality-translation #size_categories-unknown #language-cz #language-German #language-Italian #region-us \n", "# MERLIN corpus\n\nProject URL: URL\n\nDataset URL: URL\n\nThe MERLIN corpus is a written learner corpus for Czech, German, and Italian that has been designed to illustrate the Common European Framework of Reference for Languages (CEFR) with authentic learner data. The corpus contains learner texts produced in standardized language certifications covering CEFR levels A1-C1. The MERLIN annotation scheme includes a wide range of language characteristics that provide researchers with concrete examples of learner performance and progress across multiple proficiency levels." ]

[ "multilinguality:translation", "size_categories:unknown", "language:en", "region:us" ]

[]

[ "en" ]

[ "# PIE synthetic dataset\n\nRepo: URL\n\nPaper: URL" ]

[ "TAGS\n#multilinguality-translation #size_categories-unknown #language-English #region-us \n", "# PIE synthetic dataset\n\nRepo: URL\n\nPaper: URL" ]

[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:fr", "license:cc-by-sa-4.0", "arxiv:1609.07843", "region:us" ]

[ "1609.07843" ]

[ "fr" ]

[ "### Dataset Summary\n\n\nWikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as \"quality articles\" or \"good articles\". It is designed to mirror the english benchmark from Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016.\nPointer Sentinel Mixture Models The dataset is available under the Creative Commons Attribution-ShareAlike License", "### Supported Tasks and Leaderboards\n\n\n* 'language-modeling': The dataset can be used to evaluate the generation abilites of a model. Success on this task is typically measured by achieving a *low* perplexity. The (model name currently achieves 12.9.", "### Languages\n\n\nThe dataset is in French.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe dataset consists in the agregation of paragraphs from wikipedia articles.", "### Data Fields\n\n\n* 'paragraph': This is a paragraph from the original wikipedia article.", "### Data Splits\n\n\nThe dataset is splited into a train/valid/test split.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset is created to evaluate French models with similart criteria than English.s", "### Source Data\n\n\nWikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as \"quality articles\" or \"good articles\".\nWe did not apply specific pre-treatments as transformers models might use a dedicated tokenization.s", "#### Initial Data Collection and Normalization\n\n\nWe used the Wikipedia API to collect the articles since cleaning Wikipedia articles from dumps is not a trivial task.", "### 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", "### Licensing Information\n\n\nThe dataset is available under the Creative Commons Attribution-ShareAlike License", "### Contributions\n\n\nThanks to @AntoineSimoulin for adding this dataset." ]

[ "TAGS\n#task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-French #license-cc-by-sa-4.0 #arxiv-1609.07843 #region-us \n", "### Dataset Summary\n\n\nWikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as \"quality articles\" or \"good articles\". It is designed to mirror the english benchmark from Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016.\nPointer Sentinel Mixture Models The dataset is available under the Creative Commons Attribution-ShareAlike License", "### Supported Tasks and Leaderboards\n\n\n* 'language-modeling': The dataset can be used to evaluate the generation abilites of a model. Success on this task is typically measured by achieving a *low* perplexity. The (model name currently achieves 12.9.", "### Languages\n\n\nThe dataset is in French.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe dataset consists in the agregation of paragraphs from wikipedia articles.", "### Data Fields\n\n\n* 'paragraph': This is a paragraph from the original wikipedia article.", "### Data Splits\n\n\nThe dataset is splited into a train/valid/test split.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset is created to evaluate French models with similart criteria than English.s", "### Source Data\n\n\nWikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as \"quality articles\" or \"good articles\".\nWe did not apply specific pre-treatments as transformers models might use a dedicated tokenization.s", "#### Initial Data Collection and Normalization\n\n\nWe used the Wikipedia API to collect the articles since cleaning Wikipedia articles from dumps is not a trivial task.", "### 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", "### Licensing Information\n\n\nThe dataset is available under the Creative Commons Attribution-ShareAlike License", "### Contributions\n\n\nThanks to @AntoineSimoulin for adding this dataset." ]

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

[]

[ "en" ]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Rheumatology Abstracts", "## Data Source\nThis dataset comes from PubMed, derived from my fork of the pymed package (no longer maintained). My fork can be found at URL", "## Data Structure\nThe dataset is split into train (80%) and test (20%) files (CSV). Each file contains three columns:\n- id\n- abstract (minus conclusion)\n- conclusion" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Rheumatology Abstracts", "## Data Source\nThis dataset comes from PubMed, derived from my fork of the pymed package (no longer maintained). My fork can be found at URL", "## Data Structure\nThe dataset is split into train (80%) and test (20%) files (CSV). Each file contains three columns:\n- id\n- abstract (minus conclusion)\n- conclusion" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "## Load mono corpora from OPUS\n\nOPUS provides many parallel corpora, but it has more data for a single language. This enables you to load any raw mono corpus from URL. Please check URL for the available corpora and licenses. The targeted corpus is called raw corpus on OPUS.\n\nTo use it, you need the name of the corpus, the version, and the target language code. The corpus name and version are provided in one string seperated by space (e.g. 'News-Commentary v16'). All of these can be found on URL.\n\nI didn't provide any default dataset, because this targets different datasets at once. You must provide two parameters as configurations: corpus and lang, see the example below.", "## Example:", "## Structure\nThe structure is simple.\n\n\n\"text\" can be one or more sentences, but not more than a paragraph." ]

[ "TAGS\n#region-us \n", "## Load mono corpora from OPUS\n\nOPUS provides many parallel corpora, but it has more data for a single language. This enables you to load any raw mono corpus from URL. Please check URL for the available corpora and licenses. The targeted corpus is called raw corpus on OPUS.\n\nTo use it, you need the name of the corpus, the version, and the target language code. The corpus name and version are provided in one string seperated by space (e.g. 'News-Commentary v16'). All of these can be found on URL.\n\nI didn't provide any default dataset, because this targets different datasets at once. You must provide two parameters as configurations: corpus and lang, see the example below.", "## Example:", "## Structure\nThe structure is simple.\n\n\n\"text\" can be one or more sentences, but not more than a paragraph." ]

[ "region:us" ]

[]

[]

[]

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

[ "language:en", "speaker-diarization", "speaker-identification", "automatic-speech-recognition", "named-entity-detection", "addressee-detection", "region:us" ]

[]

[ "en" ]

[ "# Bazinga! A Dataset for Multi-Party Dialogues Structuring\n\nRead paper | Watch video description\n\nThis dataset provides audio soundtracks and time-coded manual transcripts of episodes of the following TV and movies series:\n\n24, Battlestar Galactica, Breaking Bad, Buffy The Vampire Slayer, ER, Friends, Game Of Thrones, Homeland, Lost, Six Feet Under, The Big Bang Theory, The Office, The Walking Dead, Harry Potter, Star Wars, and The Lord of the Rings.\n\n<p align=\"center\">\n <img width=\"75%\" 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\" />\n</p>", "## Usage" ]

[ "TAGS\n#language-English #speaker-diarization #speaker-identification #automatic-speech-recognition #named-entity-detection #addressee-detection #region-us \n", "# Bazinga! A Dataset for Multi-Party Dialogues Structuring\n\nRead paper | Watch video description\n\nThis dataset provides audio soundtracks and time-coded manual transcripts of episodes of the following TV and movies series:\n\n24, Battlestar Galactica, Breaking Bad, Buffy The Vampire Slayer, ER, Friends, Game Of Thrones, Homeland, Lost, Six Feet Under, The Big Bang Theory, The Office, The Walking Dead, Harry Potter, Star Wars, and The Lord of the Rings.\n\n<p align=\"center\">\n <img width=\"75%\" 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\" />\n</p>", "## Usage" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "size_categories:10M<n<100M", "size_categories:100M<n<1B", "source_datasets:mc4", "source_datasets:bertin-project/mc4-sampling", "language:es", "license:odc-by", "arxiv:1910.10683", "arxiv:2207.06814", "region:us" ]

[ "1910.10683", "2207.06814" ]

[ "es" ]

[ "### Dataset Summary\n\n\nThis dataset is the result of applying perplexity sampling to the Spanish portion of mC4 using 'mc4-sampling'. Please, refer to BERTIN Project.\n\n\nYou can load the mC4 Spanish sampled like this:\n\n\nAlternatively, you can bypass the 'datasets' library and quickly download (~1.5hrs, depending on connection) a specific config in the same order used to pre-train BERTIN models in a massive (~200GB) JSON-lines files:", "### Supported Tasks and Leaderboards\n\n\nmC4-es-sampled is mainly intended for reproducibility purposes of the BERTIN Project and to pretrain language models and word representations on medium budgets.", "### Languages\n\n\nThe dataset only supports the Spanish language.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example form the 'Gaussian' config:", "### Data Fields\n\n\nThe data have several fields:\n\n\n* 'url': url of the source as a string\n* 'text': text content as a string\n* 'timestamp': timestamp as a string", "### Data Splits\n\n\nThe resulting mC4 subsets for Spanish are reported in this table:\n\n\n\nThe split 'validation' is exactly the same as the original 'mc4' dataset.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset was built from the original 'mc4' by applying perplexity-sampling via 'mc4-sampling' for Spanish.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nOriginal data by Common Crawl.", "### Licensing Information\n\n\nAllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.\n\n\nTo cite this dataset (arXiv):\n\n\nIf you use this dataset, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.\n\n\nTo cite the original 'mc4' dataset:", "### Contributions\n\n\nDataset contributed by @versae for BERTIN Project.\n\n\nThanks to @dirkgr and @lhoestq for adding the original mC4 dataset." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #size_categories-10M<n<100M #size_categories-100M<n<1B #source_datasets-mc4 #source_datasets-bertin-project/mc4-sampling #language-Spanish #license-odc-by #arxiv-1910.10683 #arxiv-2207.06814 #region-us \n", "### Dataset Summary\n\n\nThis dataset is the result of applying perplexity sampling to the Spanish portion of mC4 using 'mc4-sampling'. Please, refer to BERTIN Project.\n\n\nYou can load the mC4 Spanish sampled like this:\n\n\nAlternatively, you can bypass the 'datasets' library and quickly download (~1.5hrs, depending on connection) a specific config in the same order used to pre-train BERTIN models in a massive (~200GB) JSON-lines files:", "### Supported Tasks and Leaderboards\n\n\nmC4-es-sampled is mainly intended for reproducibility purposes of the BERTIN Project and to pretrain language models and word representations on medium budgets.", "### Languages\n\n\nThe dataset only supports the Spanish language.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example form the 'Gaussian' config:", "### Data Fields\n\n\nThe data have several fields:\n\n\n* 'url': url of the source as a string\n* 'text': text content as a string\n* 'timestamp': timestamp as a string", "### Data Splits\n\n\nThe resulting mC4 subsets for Spanish are reported in this table:\n\n\n\nThe split 'validation' is exactly the same as the original 'mc4' dataset.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset was built from the original 'mc4' by applying perplexity-sampling via 'mc4-sampling' for Spanish.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nOriginal data by Common Crawl.", "### Licensing Information\n\n\nAllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.\n\n\nTo cite this dataset (arXiv):\n\n\nIf you use this dataset, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.\n\n\nTo cite the original 'mc4' dataset:", "### Contributions\n\n\nDataset contributed by @versae for BERTIN Project.\n\n\nThanks to @dirkgr and @lhoestq for adding the original mC4 dataset." ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "size_categories:10M<n<100M", "size_categories:100M<n<1B", "size_categories:1B<n<10B", "source_datasets:original", "language:af", "language:am", "language:ar", "language:az", "language:be", "language:bg", "language:bn", "language:ca", "language:ceb", "language:co", "language:cs", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fil", "language:fr", "language:fy", "language:ga", "language:gd", "language:gl", "language:gu", "language:ha", "language:haw", "language:hi", "language:hmn", "language:ht", "language:hu", "language:hy", "language:id", "language:ig", "language:is", "language:it", "language:iw", "language:ja", "language:jv", "language:ka", "language:kk", "language:km", "language:kn", "language:ko", "language:ku", "language:ky", "language:la", "language:lb", "language:lo", "language:lt", "language:lv", "language:mg", "language:mi", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:my", "language:ne", "language:nl", "language:no", "language:ny", "language:pa", "language:pl", "language:ps", "language:pt", "language:ro", "language:ru", "language:sd", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:so", "language:sq", "language:sr", "language:st", "language:su", "language:sv", "language:sw", "language:ta", "language:te", "language:tg", "language:th", "language:tr", "language:uk", "language:und", "language:ur", "language:uz", "language:vi", "language:xh", "language:yi", "language:yo", "language:zh", "language:zu", "license:odc-by", "arxiv:1910.10683", "region:us" ]

[ "1910.10683" ]

[ "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu" ]

[ "# Dataset Card for mC4-sampling", "## Table of Contents\n\n- Dataset Card for mC4-sampling\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Dataset Sampling\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nThis dataset builds upon the AllenAI version of the original mC4 and adds sampling methods to perform perplexity-based filtering on the fly. Please, refer to BERTIN Project.\n\nThe original dataset is mC4, the multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: \"URL\".\n\n108 languages are available and are reported in the 'mc4' dataset.\n\nYou can load the mC4 subset of any language like this:\n\n\n\nAnd if you can even specify a list of languages:", "### Dataset Sampling\n\nThere are 3 main different ways of getting sampled versions of mc4 using this dataset.", "#### Random\n\nArguably, the simplest of methods. It keeps a document based on a probability threshold we called 'factor'. It defaults to '0.5' for random sampling:\n\n\n\nThe way to use this sampling method is by adding an extra parameter to the instantiation of the dataset:", "#### Gaussian\n\nThis sampling method tries to adjust to the underlying distribution while oversampling the central quartiles of the perplexity distribution of the documents in mC4 for a given language. Two parameters control the shape of the approximation, 'factor' (peakness of the exponential function) and 'width' (spread). Default values are selected for Spanish.\n\n\n\nIn order to use this sampling methods, information about the quartile boundaries of the underlying distribution need to be calculated beforehand and passed in to the instantiation of the dataset. Moreover, the path to a KenLM model (5-gram language model) or an object with a method '.score(text:str) -> float' need to also be passed in for the calculation of the perplexity value of a document. KenLM can be installed with pip:\n\n\n\n\n\nFacebook has created and released 5-gram Kneser-Ney models for 100 languages available to download and use within the KenLM library. To download your own Kneser-Ney language model, chose a language code from the next list:\n\n\n\nAnd run the next download command replacing 'lang' with your own language code:", "### Stepwise\n\nThe stepwise sampling method uses a simple criteria by oversampling from the central quartiles inversely proportionally their range. Only 'boundaries', 'factor' (strength of the oversampling), and 'perplexity_model' are needed:\n\n\n\nIn order to use this sampling method, a similar invocation is needed:", "### Supported Tasks and Leaderboards\n\nmC4-sampling is mainly intended to pretrain language models and word representations on a budget.", "### Languages\n\nThe dataset supports 108 languages.", "## Dataset Structure", "### Data Instances\n\nAn example form the 'en' config is:", "### Data Fields\n\nThe data have several fields:\n\n- 'url': url of the source as a string\n- 'text': text content as a string\n- 'timestamp': timestamp as a string", "### Data Splits\n\nThe same splits as in mC4 are available.", "## Additional Information", "### Licensing Information\n\nBERTIN Project is releasing this dataset under the same terms AllenAI released mC4, that is, those of the ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.\n\n\n\nTo cite this dataset:\n\n\nIf you use this dataset, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.\n\nTo cite the original 'mc4' dataset:", "### Contributions\n\nDataset contributed by @versae.\n\nThanks to @dirkgr and @lhoestq for adding the original mC4 dataset." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #size_categories-10M<n<100M #size_categories-100M<n<1B #size_categories-1B<n<10B #source_datasets-original #language-Afrikaans #language-Amharic #language-Arabic #language-Azerbaijani #language-Belarusian #language-Bulgarian #language-Bengali #language-Catalan #language-Cebuano #language-Corsican #language-Czech #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-Filipino #language-French #language-Western Frisian #language-Irish #language-Scottish Gaelic #language-Galician #language-Gujarati #language-Hausa #language-Hawaiian #language-Hindi #language-Hmong #language-Haitian #language-Hungarian #language-Armenian #language-Indonesian #language-Igbo #language-Icelandic #language-Italian #language-iw #language-Japanese #language-Javanese #language-Georgian #language-Kazakh #language-Khmer #language-Kannada #language-Korean #language-Kurdish #language-Kirghiz #language-Latin #language-Luxembourgish #language-Lao #language-Lithuanian #language-Latvian #language-Malagasy #language-Maori #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Burmese #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian #language-Nyanja #language-Panjabi #language-Polish #language-Pushto #language-Portuguese #language-Romanian #language-Russian #language-Sindhi #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Southern Sotho #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkish #language-Ukrainian #language-Undetermined #language-Urdu #language-Uzbek #language-Vietnamese #language-Xhosa #language-Yiddish #language-Yoruba #language-Chinese #language-Zulu #license-odc-by #arxiv-1910.10683 #region-us \n", "# Dataset Card for mC4-sampling", "## Table of Contents\n\n- Dataset Card for mC4-sampling\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Dataset Sampling\n - Supported Tasks and Leaderboards\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nThis dataset builds upon the AllenAI version of the original mC4 and adds sampling methods to perform perplexity-based filtering on the fly. Please, refer to BERTIN Project.\n\nThe original dataset is mC4, the multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: \"URL\".\n\n108 languages are available and are reported in the 'mc4' dataset.\n\nYou can load the mC4 subset of any language like this:\n\n\n\nAnd if you can even specify a list of languages:", "### Dataset Sampling\n\nThere are 3 main different ways of getting sampled versions of mc4 using this dataset.", "#### Random\n\nArguably, the simplest of methods. It keeps a document based on a probability threshold we called 'factor'. It defaults to '0.5' for random sampling:\n\n\n\nThe way to use this sampling method is by adding an extra parameter to the instantiation of the dataset:", "#### Gaussian\n\nThis sampling method tries to adjust to the underlying distribution while oversampling the central quartiles of the perplexity distribution of the documents in mC4 for a given language. Two parameters control the shape of the approximation, 'factor' (peakness of the exponential function) and 'width' (spread). Default values are selected for Spanish.\n\n\n\nIn order to use this sampling methods, information about the quartile boundaries of the underlying distribution need to be calculated beforehand and passed in to the instantiation of the dataset. Moreover, the path to a KenLM model (5-gram language model) or an object with a method '.score(text:str) -> float' need to also be passed in for the calculation of the perplexity value of a document. KenLM can be installed with pip:\n\n\n\n\n\nFacebook has created and released 5-gram Kneser-Ney models for 100 languages available to download and use within the KenLM library. To download your own Kneser-Ney language model, chose a language code from the next list:\n\n\n\nAnd run the next download command replacing 'lang' with your own language code:", "### Stepwise\n\nThe stepwise sampling method uses a simple criteria by oversampling from the central quartiles inversely proportionally their range. Only 'boundaries', 'factor' (strength of the oversampling), and 'perplexity_model' are needed:\n\n\n\nIn order to use this sampling method, a similar invocation is needed:", "### Supported Tasks and Leaderboards\n\nmC4-sampling is mainly intended to pretrain language models and word representations on a budget.", "### Languages\n\nThe dataset supports 108 languages.", "## Dataset Structure", "### Data Instances\n\nAn example form the 'en' config is:", "### Data Fields\n\nThe data have several fields:\n\n- 'url': url of the source as a string\n- 'text': text content as a string\n- 'timestamp': timestamp as a string", "### Data Splits\n\nThe same splits as in mC4 are available.", "## Additional Information", "### Licensing Information\n\nBERTIN Project is releasing this dataset under the same terms AllenAI released mC4, that is, those of the ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.\n\n\n\nTo cite this dataset:\n\n\nIf you use this dataset, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.\n\nTo cite the original 'mc4' dataset:", "### Contributions\n\nDataset contributed by @versae.\n\nThanks to @dirkgr and @lhoestq for adding the original mC4 dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:multi-class-classification", "task_ids:natural-language-inference", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:1610.03771", "region:us" ]

[ "1610.03771" ]

[ "en" ]

[ "# Dataset Card for [SentiHood]", "## 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- Paper: URL\n- Leaderboard: URL", "### Dataset Summary\n\nCreated as a part of the paper \"SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods\" by Saeidi et al.", "#### Abstract\n\nIn this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis that assumes a single entity per document and targeted sentiment analysis that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighborhoods are discussed by users. In this context units of text often mention several aspects of one or more neighborhoods. This is the first time that a generic social media platform in this case a QA platform, is used for fine-grained opinion mining. Text coming from QA platforms is far less constrained compared to text from review-specific platforms on which current datasets are based. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks.", "### Supported Tasks and Leaderboards", "### Languages\n\nMonolingual (only English)", "## 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 @Bhavnicksm for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #task_ids-multi-class-classification #task_ids-natural-language-inference #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1610.03771 #region-us \n", "# Dataset Card for [SentiHood]", "## 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- Paper: URL\n- Leaderboard: URL", "### Dataset Summary\n\nCreated as a part of the paper \"SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods\" by Saeidi et al.", "#### Abstract\n\nIn this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis that assumes a single entity per document and targeted sentiment analysis that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighborhoods are discussed by users. In this context units of text often mention several aspects of one or more neighborhoods. This is the first time that a generic social media platform in this case a QA platform, is used for fine-grained opinion mining. Text coming from QA platforms is far less constrained compared to text from review-specific platforms on which current datasets are based. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks.", "### Supported Tasks and Leaderboards", "### Languages\n\nMonolingual (only English)", "## 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 @Bhavnicksm for adding this dataset." ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for the Buckeye Corpus (buckeye_asr)", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\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", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThe Buckeye Corpus of conversational speech contains high-quality recordings from 40 speakers in Columbus OH conversing freely with an interviewer. The speech has been orthographically transcribed and phonetically labeled.", "### Supported Tasks and Leaderboards", "### Languages\n\nAmerican English (en-US)", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'file': filename of the audio file containing the utterance.\n- 'audio': filename of the audio file containing the utterance.\n- 'text': transcription of the utterance.\n- 'phonetic_detail': list of phonetic annotations for the utterance (start, stop and label of each phone).\n- 'word_detail': list of word annotations for the utterance (start, stop, label, broad and narrow transcriptions, syntactic class).\n- 'speaker_id': string identifying the speaker.\n- 'id': string identifying the utterance.", "### Data Splits\n\nThe data is split in training, validation and test sets with different speakers (32, 4, and 4 speakers respectively) in each set. The sets are all balanced for speaker's gender and age.", "## 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\n\nFREE for noncommercial uses.", "### Usage\n\nThe first step is to download a copy of the dataset from the official website. Once done, the dataset can be loaded directly through the 'datasets' library by running:\n\n\n\nwhere '<path_to_the_dataset>' points to the folder where the dataset is stored. An example of path to one of the audio files is then '<path_to_the_dataset>/s01/URL'." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-other #region-us \n", "# Dataset Card for the Buckeye Corpus (buckeye_asr)", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\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", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThe Buckeye Corpus of conversational speech contains high-quality recordings from 40 speakers in Columbus OH conversing freely with an interviewer. The speech has been orthographically transcribed and phonetically labeled.", "### Supported Tasks and Leaderboards", "### Languages\n\nAmerican English (en-US)", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'file': filename of the audio file containing the utterance.\n- 'audio': filename of the audio file containing the utterance.\n- 'text': transcription of the utterance.\n- 'phonetic_detail': list of phonetic annotations for the utterance (start, stop and label of each phone).\n- 'word_detail': list of word annotations for the utterance (start, stop, label, broad and narrow transcriptions, syntactic class).\n- 'speaker_id': string identifying the speaker.\n- 'id': string identifying the utterance.", "### Data Splits\n\nThe data is split in training, validation and test sets with different speakers (32, 4, and 4 speakers respectively) in each set. The sets are all balanced for speaker's gender and age.", "## 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\n\nFREE for noncommercial uses.", "### Usage\n\nThe first step is to download a copy of the dataset from the official website. Once done, the dataset can be loaded directly through the 'datasets' library by running:\n\n\n\nwhere '<path_to_the_dataset>' points to the folder where the dataset is stored. An example of path to one of the audio files is then '<path_to_the_dataset>/s01/URL'." ]

[ "task_categories:other", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:100M<n<1B", "language:en", "license:apache-2.0", "arxiv:2110.08207", "region:us" ]

[ "2110.08207" ]

[ "en" ]

[ "# Dataset Card for P3", "## 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- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: Multitask Prompted Training Enables Zero-Shot Task Generalization\n- Point of Contact: Victor Sanh", "### Dataset Summary\n\nP3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2).\n\nPrompts are collected using Promptsource, an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts of P3 is publicly available on Promptsource.\n\nTo train T0*, we used a subset of the prompts available in Promptsource (see details here). However, some of the prompts use 'URL', a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. The data available here are the materialized version of the prompted datasets used in Multitask Prompted Training Enables Zero-Shot Task Generalization which represent only a subset of the datasets for which there is at least one prompt in Promptsource.", "### Supported Tasks and Leaderboards\n\nThe tasks represented in P3 cover a diverse set of NLP tasks including multiple-choice QA, sentiment analysis or natural language inference. We detail the full list of datasets in Source Data.", "### Languages\n\nThe data in P3 are in English (BCP-47 'en').", "## Dataset Structure", "### Data Instances\n\nAn example of \"train\" looks as follows:\n\n\nIn the case of rank classification (letting the model select its the prediction the option with the highest log-likelihood), an example looks as follows:\n\n\nTo check all the prompted examples, you can use the Promptsource hosted tool and choose the 'Prompted dataset viewer' mode in the left panel.", "### Data Fields\n\nThe data fields are the same among all splits:\n- 'answer_choices': the choices (in natural language) available to the model\n- 'inputs_pretokenized': the natural language input fed to the model\n- 'targets_pretokenized': the natural language target that the model has to generate\n- 'inputs': the tokenized input with T5's tokenizer\n- 'targets': the tokenized target with T5's tokenizer\n- 'idx': identifier of the (example, answer_option_id) in the case of rank classification\n- 'weight': a weight for the example produced by seqio (always set to 1.0 in practise)\n- 'is_correct': whether the (example, answer_option_id) is the correct one", "### Data Splits\n\nThe list of data splits and their respective sizes is very long. You'll find the whole list in this file.", "## Dataset Creation", "### Curation Rationale\n\nThe Public Pool of Prompts relies on the Hugging Face Dataset library. Any public dataset in the Datasets library can be prompted. We select the datasets that have at least one subset in English and excluded datasets containing (predominantly) non-natural language examples.\n\nWe conservatively decided not to prompt datasets that contain potentially harmful content (for instance, datasets built on social media content). However, we sometimes prompt datasets that are purposefully built to measure bias and fairness of trained models, and reserve these prompted datasets (the validation or test sets) for evaluation purposes.", "### Source Data\n\nHere's the full list of the datasets present in the materialized version of P3:\n- Multiple-Choice QA\n - CommonsenseQA\n - DREAM\n - QUAIL\n - QuaRTz\n - Social IQA\n - WiQA\n - Cosmos\n - QASC\n - Quarel\n - SciQ\n - Wiki Hop\n - ARC\n - OpenBookQA\n - MultiRC\n - PIQA\n - RACE\n - HellaSwag\n - BoolQ\n- Extractive QA\n - Adversarial QA\n - Quoref\n - DuoRC\n - ROPES\n - SQuAD v2\n - ReCoRD\n- Close-book QA\n - Hotpot QA\n - Wiki QA\n - Trivia QA\n - Web Questions\n- Structure-to-text\n - Common Gen\n - Wiki Bio\n- Sentiment\n - Amazon\n - App Reviews\n - IMDB\n - Rotten Tomatoes\n - Yelp\n- Summarization\n - CNN Daily Mail\n - Gigaword\n - MultiNews\n - SamSum\n - XSum\n- Topic Classification\n - AG News\n - DBPedia\n - TREC\n- Paraphrase Identification\n - MRPC\n - PAWS\n - QQP\n- Natural Language Inference\n - ANLI\n - CB\n - RTE\n- Coreference Resolution\n - WSC\n - Winogrande\n- Word Sense disambiguation\n - WiC\n- Sentence Completion\n - COPA\n - HellaSwag\n - Story Cloze", "### Annotations\n\nThe prompts available in Promptsource are collected as part of BigScience, one-year long research workshop on large multilingual models and datasets. 36 contributors affiliated with 24 institutions in 8 countries participated to the prompt collection. Contributors are in majority machine learning researchers or machine learning engineers.\n\nThe main annotation guideline was that prompts needed to be grammatical and understandable by a native English speaker with no prior experience of the tasks. Additionally, prompts that required explicit counting or numerical indexing were removed in favor of natural language variants, e.g., instead of predicting indices of a span to extract (e.g. in extractive question answering), the model was expected to copy the span's text instead. With these minimal constraints, prompt writers were encouraged to use both formal and creative prompts and various orderings of the data. Most of the prompts correspond directly to a version of the original proposed task, although we also allowed prompts that permuted the original task (for instance, generating a document from its summary) or allowed for ambiguous output (for instance, not indicating a list of available choices).\n\nThe full annotation given to the contributors can be found here. *Note to self: the link is currently being updated with the)", "## Additional Information", "### Licensing Information\n\nThe dataset is released under Apache 2.0.", "### Contributions\n\nThanks to the contributors of promptsource for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-crowdsourced #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-100M<n<1B #language-English #license-apache-2.0 #arxiv-2110.08207 #region-us \n", "# Dataset Card for P3", "## 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- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: Multitask Prompted Training Enables Zero-Shot Task Generalization\n- Point of Contact: Victor Sanh", "### Dataset Summary\n\nP3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2).\n\nPrompts are collected using Promptsource, an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts of P3 is publicly available on Promptsource.\n\nTo train T0*, we used a subset of the prompts available in Promptsource (see details here). However, some of the prompts use 'URL', a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. The data available here are the materialized version of the prompted datasets used in Multitask Prompted Training Enables Zero-Shot Task Generalization which represent only a subset of the datasets for which there is at least one prompt in Promptsource.", "### Supported Tasks and Leaderboards\n\nThe tasks represented in P3 cover a diverse set of NLP tasks including multiple-choice QA, sentiment analysis or natural language inference. We detail the full list of datasets in Source Data.", "### Languages\n\nThe data in P3 are in English (BCP-47 'en').", "## Dataset Structure", "### Data Instances\n\nAn example of \"train\" looks as follows:\n\n\nIn the case of rank classification (letting the model select its the prediction the option with the highest log-likelihood), an example looks as follows:\n\n\nTo check all the prompted examples, you can use the Promptsource hosted tool and choose the 'Prompted dataset viewer' mode in the left panel.", "### Data Fields\n\nThe data fields are the same among all splits:\n- 'answer_choices': the choices (in natural language) available to the model\n- 'inputs_pretokenized': the natural language input fed to the model\n- 'targets_pretokenized': the natural language target that the model has to generate\n- 'inputs': the tokenized input with T5's tokenizer\n- 'targets': the tokenized target with T5's tokenizer\n- 'idx': identifier of the (example, answer_option_id) in the case of rank classification\n- 'weight': a weight for the example produced by seqio (always set to 1.0 in practise)\n- 'is_correct': whether the (example, answer_option_id) is the correct one", "### Data Splits\n\nThe list of data splits and their respective sizes is very long. You'll find the whole list in this file.", "## Dataset Creation", "### Curation Rationale\n\nThe Public Pool of Prompts relies on the Hugging Face Dataset library. Any public dataset in the Datasets library can be prompted. We select the datasets that have at least one subset in English and excluded datasets containing (predominantly) non-natural language examples.\n\nWe conservatively decided not to prompt datasets that contain potentially harmful content (for instance, datasets built on social media content). However, we sometimes prompt datasets that are purposefully built to measure bias and fairness of trained models, and reserve these prompted datasets (the validation or test sets) for evaluation purposes.", "### Source Data\n\nHere's the full list of the datasets present in the materialized version of P3:\n- Multiple-Choice QA\n - CommonsenseQA\n - DREAM\n - QUAIL\n - QuaRTz\n - Social IQA\n - WiQA\n - Cosmos\n - QASC\n - Quarel\n - SciQ\n - Wiki Hop\n - ARC\n - OpenBookQA\n - MultiRC\n - PIQA\n - RACE\n - HellaSwag\n - BoolQ\n- Extractive QA\n - Adversarial QA\n - Quoref\n - DuoRC\n - ROPES\n - SQuAD v2\n - ReCoRD\n- Close-book QA\n - Hotpot QA\n - Wiki QA\n - Trivia QA\n - Web Questions\n- Structure-to-text\n - Common Gen\n - Wiki Bio\n- Sentiment\n - Amazon\n - App Reviews\n - IMDB\n - Rotten Tomatoes\n - Yelp\n- Summarization\n - CNN Daily Mail\n - Gigaword\n - MultiNews\n - SamSum\n - XSum\n- Topic Classification\n - AG News\n - DBPedia\n - TREC\n- Paraphrase Identification\n - MRPC\n - PAWS\n - QQP\n- Natural Language Inference\n - ANLI\n - CB\n - RTE\n- Coreference Resolution\n - WSC\n - Winogrande\n- Word Sense disambiguation\n - WiC\n- Sentence Completion\n - COPA\n - HellaSwag\n - Story Cloze", "### Annotations\n\nThe prompts available in Promptsource are collected as part of BigScience, one-year long research workshop on large multilingual models and datasets. 36 contributors affiliated with 24 institutions in 8 countries participated to the prompt collection. Contributors are in majority machine learning researchers or machine learning engineers.\n\nThe main annotation guideline was that prompts needed to be grammatical and understandable by a native English speaker with no prior experience of the tasks. Additionally, prompts that required explicit counting or numerical indexing were removed in favor of natural language variants, e.g., instead of predicting indices of a span to extract (e.g. in extractive question answering), the model was expected to copy the span's text instead. With these minimal constraints, prompt writers were encouraged to use both formal and creative prompts and various orderings of the data. Most of the prompts correspond directly to a version of the original proposed task, although we also allowed prompts that permuted the original task (for instance, generating a document from its summary) or allowed for ambiguous output (for instance, not indicating a list of available choices).\n\nThe full annotation given to the contributors can be found here. *Note to self: the link is currently being updated with the)", "## Additional Information", "### Licensing Information\n\nThe dataset is released under Apache 2.0.", "### Contributions\n\nThanks to the contributors of promptsource for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# QA-Align\n\nThis dataset contains QA-Alignments --- fine-grained annotations of cross-text content overlap. \nThe task input is two sentences from two documents, roughly talking about the same event, along with their QA-SRL annotations \nwhich capture verbal predicate-argument relations in question-answer format. The output is a cross-sentence alignment between sets of QAs which denote the same information. \n\nSee the paper for details: QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions, Brook Weiss et. al., EMNLP 2021.\n\nThe script downloads the data from the original GitHub repository.", "### Format\n\nThe dataset contains the following important features:\n \n* 'abs_sent_id_1', 'abs_sent_id_2' - unique sentence ids, unique across all data sources. \n* 'text_1', 'text_2', 'prev_text_1', 'prev_text_2' - the two candidate sentences for alignments. The \"prev\" (previous) sentences are for context (shown to workers and for the model). \n* 'qas_1', 'qas_2' - the sets of QASRL QAs for each sentence. For test and dev they were created by workers, while in train, the QASRL parser generated them. \n* 'alignments' - the aligned QAs that workers have matched. This is the list of qa-alignments, where a single alignment looks like this:\n\n\nWhere the for each sentence, we save a list of the aligned QAs from that sentence. \n\nNote that this single alignment may contain multiple QAs for each sentence. While 96% of the data are one-to-one alignments, 4% contain many-to-many alignment (although most of the time it's a 2-to-1)." ]

[ "TAGS\n#region-us \n", "# QA-Align\n\nThis dataset contains QA-Alignments --- fine-grained annotations of cross-text content overlap. \nThe task input is two sentences from two documents, roughly talking about the same event, along with their QA-SRL annotations \nwhich capture verbal predicate-argument relations in question-answer format. The output is a cross-sentence alignment between sets of QAs which denote the same information. \n\nSee the paper for details: QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions, Brook Weiss et. al., EMNLP 2021.\n\nThe script downloads the data from the original GitHub repository.", "### Format\n\nThe dataset contains the following important features:\n \n* 'abs_sent_id_1', 'abs_sent_id_2' - unique sentence ids, unique across all data sources. \n* 'text_1', 'text_2', 'prev_text_1', 'prev_text_2' - the two candidate sentences for alignments. The \"prev\" (previous) sentences are for context (shown to workers and for the model). \n* 'qas_1', 'qas_2' - the sets of QASRL QAs for each sentence. For test and dev they were created by workers, while in train, the QASRL parser generated them. \n* 'alignments' - the aligned QAs that workers have matched. This is the list of qa-alignments, where a single alignment looks like this:\n\n\nWhere the for each sentence, we save a list of the aligned QAs from that sentence. \n\nNote that this single alignment may contain multiple QAs for each sentence. While 96% of the data are one-to-one alignments, 4% contain many-to-many alignment (although most of the time it's a 2-to-1)." ]

[ "region:us" ]

[]

[]

[ "# QA-SRL 2020 (Gold Standard) \nThe dataset contains question-answer pairs to model verbal predicate-argument structure. \nThe questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.\nThis dataset, a.k.a \"QASRL-GS\" (Gold Standard) or \"QASRL-2020\", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as biu-nlp/qa_srl2018).\n\nSee the paper for details: Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020.\n\nCheck out our GitHub repository to find code for evaluation. \n\nThe dataset was annotated by selected workers from Amazon Mechanical Turk." ]

[ "TAGS\n#region-us \n", "# QA-SRL 2020 (Gold Standard) \nThe dataset contains question-answer pairs to model verbal predicate-argument structure. \nThe questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.\nThis dataset, a.k.a \"QASRL-GS\" (Gold Standard) or \"QASRL-2020\", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as biu-nlp/qa_srl2018).\n\nSee the paper for details: Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020.\n\nCheck out our GitHub repository to find code for evaluation. \n\nThe dataset was annotated by selected workers from Amazon Mechanical Turk." ]

[ "region:us" ]

[]

[]

[ "# QANom \n\nThis dataset contains question-answer pairs to model the predicate-argument structure of deverbal nominalizations. \nThe questions start with wh-words (Who, What, Where, What, etc.) and contain the verbal form of a nominalization from the sentence; \nthe answers are phrases in the sentence. \n\nSee the paper for details: QANom: Question-Answer driven SRL for Nominalizations (Klein et. al., COLING 2020)\n\nFor previewing the QANom data along with the verbal annotations of QASRL, check out URL \nAlso check out our GitHub repository to find code for nominalization identification, QANom annotation, evaluation, and models. \n\nThe dataset was annotated by selected workers from Amazon Mechanical Turk." ]

[ "TAGS\n#region-us \n", "# QANom \n\nThis dataset contains question-answer pairs to model the predicate-argument structure of deverbal nominalizations. \nThe questions start with wh-words (Who, What, Where, What, etc.) and contain the verbal form of a nominalization from the sentence; \nthe answers are phrases in the sentence. \n\nSee the paper for details: QANom: Question-Answer driven SRL for Nominalizations (Klein et. al., COLING 2020)\n\nFor previewing the QANom data along with the verbal annotations of QASRL, check out URL \nAlso check out our GitHub repository to find code for nominalization identification, QANom annotation, evaluation, and models. \n\nThe dataset was annotated by selected workers from Amazon Mechanical Turk." ]

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ru", "license:unknown", "region:us" ]

[]

[ "ru" ]

[ "### Dataset Summary\nThis dataset contains 190,335 Russian Q&A posts from a medical related forum.", "### Dataset Fields\n* date: date and time of the asked question, like '26 Октября 2018, 08:30'\n* categ: question category\n* theme: question topic\n* desc: question text\n* ans: question answers separated with ';\\n'\n* spec10: if present, one of 10 medical specializations" ]

[ "TAGS\n#task_categories-question-answering #task_ids-closed-domain-qa #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Russian #license-unknown #region-us \n", "### Dataset Summary\nThis dataset contains 190,335 Russian Q&A posts from a medical related forum.", "### Dataset Fields\n* date: date and time of the asked question, like '26 Октября 2018, 08:30'\n* categ: question category\n* theme: question topic\n* desc: question text\n* ans: question answers separated with ';\\n'\n* spec10: if present, one of 10 medical specializations" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "license:cc-by-4.0", "region:us" ]

[]

[ "'en-US'" ]

[ "# Dataset Card for the URL corpus", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Usage\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: URL\n- Repository: URL\n- Paper: Building an Argument Search Engine for the Web\n- Leaderboard: URL\n- Point of Contact: Webis Group", "### Dataset Summary\n\nThe URL corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, URL, Debatepedia, and URL. The arguments are extracted using heuristics that are designed for each debate portal.", "### Dataset Usage", "### Supported Tasks and Leaderboards\n\nDocument Retrieval, Argument Retrieval for Controversial Questions", "### Languages\n\nThe URL corpus is monolingual; it only includes English (mostly en-US) documents.", "## Dataset Structure", "### Data Instances", "#### Corpus", "### 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\nCreative Commons Attribution 4.0 International (CC BY 4.0)" ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-document-retrieval #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #license-cc-by-4.0 #region-us \n", "# Dataset Card for the URL corpus", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Usage\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: URL\n- Repository: URL\n- Paper: Building an Argument Search Engine for the Web\n- Leaderboard: URL\n- Point of Contact: Webis Group", "### Dataset Summary\n\nThe URL corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, URL, Debatepedia, and URL. The arguments are extracted using heuristics that are designed for each debate portal.", "### Dataset Usage", "### Supported Tasks and Leaderboards\n\nDocument Retrieval, Argument Retrieval for Controversial Questions", "### Languages\n\nThe URL corpus is monolingual; it only includes English (mostly en-US) documents.", "## Dataset Structure", "### Data Instances", "#### Corpus", "### 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\nCreative Commons Attribution 4.0 International (CC BY 4.0)" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:summarization", "task_categories:text-classification", "task_categories:text-generation", "task_ids:multi-class-classification", "task_ids:topic-classification", "size_categories:10K<n<100K", "language:fr", "conditional-text-generation", "region:us" ]

[]

[ "fr" ]

[]

[ "TAGS\n#task_categories-summarization #task_categories-text-classification #task_categories-text-generation #task_ids-multi-class-classification #task_ids-topic-classification #size_categories-10K<n<100K #language-French #conditional-text-generation #region-us \n" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "language:om", "language:am", "language:rw", "language:rn", "language:ha", "language:ig", "language:pcm", "language:so", "language:sw", "language:ti", "language:yo", "language:multilingual", "license:apache-2.0", "region:us" ]

[]

[ "om", "am", "rw", "rn", "ha", "ig", "pcm", "so", "sw", "ti", "yo", "multilingual" ]

[ "# Dataset Card for AfriBERTa's Corpus", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Loading Dataset\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Considerations for Using the Data\n - Discussion of Biases\n- Additional Information\n - Citation Information\n - Contributions", "## Dataset Description", "### Dataset Summary\nThis is the corpus on which AfriBERTa was trained on.\nThe dataset is mostly from the BBC news website, but some languages also have data from Common Crawl. \n\n- Homepage: URL\n- Models: \n - URL\n - URL\n - URL\n- Paper: URL\n- Point of Contact: URL@URL", "### Supported Tasks and Leaderboards\nThe AfriBERTa corpus was mostly intended to pre-train language models.", "### Languages", "### Loading Dataset\nAn example to load the train split of the Somali corpus:\n\n\nAn example to load the test split of the Pidgin corpus:", "## Dataset Structure", "### Data Instances\nEach data point is a line of text. \nAn example from the 'igbo' dataset:", "### Data Fields\n\nThe data fields are:\n\n- id: id of the example\n- text: content as a string", "### Data Splits\nEach language has a train and test split, with varying sizes.", "## Considerations for Using the Data", "### Discussion of Biases\nSince majority of the data is obtained from the BBC's news website, models trained on this dataset are likely going to \nbe biased towards the news domain.\n\nAlso, since some of the data is obtained from Common Crawl, care should be taken (especially for text generation models) since personal and sensitive information might be present.", "## Additional Information", "### Contributions\nThanks to Kelechi Ogueji for adding this dataset." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language-Oromo #language-Amharic #language-Kinyarwanda #language-Rundi #language-Hausa #language-Igbo #language-Nigerian Pidgin #language-Somali #language-Swahili (macrolanguage) #language-Tigrinya #language-Yoruba #language-multilingual #license-apache-2.0 #region-us \n", "# Dataset Card for AfriBERTa's Corpus", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - Loading Dataset\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Considerations for Using the Data\n - Discussion of Biases\n- Additional Information\n - Citation Information\n - Contributions", "## Dataset Description", "### Dataset Summary\nThis is the corpus on which AfriBERTa was trained on.\nThe dataset is mostly from the BBC news website, but some languages also have data from Common Crawl. \n\n- Homepage: URL\n- Models: \n - URL\n - URL\n - URL\n- Paper: URL\n- Point of Contact: URL@URL", "### Supported Tasks and Leaderboards\nThe AfriBERTa corpus was mostly intended to pre-train language models.", "### Languages", "### Loading Dataset\nAn example to load the train split of the Somali corpus:\n\n\nAn example to load the test split of the Pidgin corpus:", "## Dataset Structure", "### Data Instances\nEach data point is a line of text. \nAn example from the 'igbo' dataset:", "### Data Fields\n\nThe data fields are:\n\n- id: id of the example\n- text: content as a string", "### Data Splits\nEach language has a train and test split, with varying sizes.", "## Considerations for Using the Data", "### Discussion of Biases\nSince majority of the data is obtained from the BBC's news website, models trained on this dataset are likely going to \nbe biased towards the news domain.\n\nAlso, since some of the data is obtained from Common Crawl, care should be taken (especially for text generation models) since personal and sensitive information might be present.", "## Additional Information", "### Contributions\nThanks to Kelechi Ogueji for adding this dataset." ]

[ "task_categories:text-retrieval", "multilinguality:multilingual", "language:ar", "language:bn", "language:en", "language:fi", "language:id", "language:ja", "language:ko", "language:ru", "language:sw", "language:te", "language:th", "license:apache-2.0", "region:us" ]

[]

[ "ar", "bn", "en", "fi", "id", "ja", "ko", "ru", "sw", "te", "th" ]

[ "# Dataset Summary\nMr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations.\n\nThis dataset stores documents of Mr. TyDi. To access the queries and judgments, please refer to castorini/mr-tydi.", "# Dataset Structure\nThe only configuration here is the 'language'. As all three folds (train, dev and test) share the same corpus, there is only one fold 'train' under each language, unlike castorini/mr-tydi.\n\nAn example of document data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:" ]

[ "TAGS\n#task_categories-text-retrieval #multilinguality-multilingual #language-Arabic #language-Bengali #language-English #language-Finnish #language-Indonesian #language-Japanese #language-Korean #language-Russian #language-Swahili (macrolanguage) #language-Telugu #language-Thai #license-apache-2.0 #region-us \n", "# Dataset Summary\nMr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations.\n\nThis dataset stores documents of Mr. TyDi. To access the queries and judgments, please refer to castorini/mr-tydi.", "# Dataset Structure\nThe only configuration here is the 'language'. As all three folds (train, dev and test) share the same corpus, there is only one fold 'train' under each language, unlike castorini/mr-tydi.\n\nAn example of document data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:" ]

[ "task_categories:text-retrieval", "multilinguality:multilingual", "language:ar", "language:bn", "language:en", "language:fi", "language:id", "language:ja", "language:ko", "language:ru", "language:sw", "language:te", "language:th", "license:apache-2.0", "region:us" ]

[]

[ "ar", "bn", "en", "fi", "id", "ja", "ko", "ru", "sw", "te", "th" ]

[ "# Dataset Summary\nMr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations.\n\nThis dataset stores the queries, judgements, and example training data of Mr. TyDi. To access the corpus, please refer to castorini/mr-tydi-corpus.", "# Dataset Structure\nThe only configuration here is the 'language', \nFor each language, there are three splits: 'train', 'dev', and 'test'.\nThe negative examples from training set are sampled from the top-30 BM25 runfiles on each language.\nSpecifically, we combine the training data for all languages under the 'combined' configuration.\n\nAn example of 'train' set looks as follows:\n\n\nAn example of 'dev' and 'test' set looks as follows. We only provide the docid of positive passages here to save the space. \nAlso no candidate passages are provided at this point. \nNote that to perform the retrieval, it need to be used together with castorini/mr-tydi-corpus", "# Load Dataset\n\nAn example to load the dataset:\n\nNote that the 'combined' option has only the 'train' set." ]

[ "TAGS\n#task_categories-text-retrieval #multilinguality-multilingual #language-Arabic #language-Bengali #language-English #language-Finnish #language-Indonesian #language-Japanese #language-Korean #language-Russian #language-Swahili (macrolanguage) #language-Telugu #language-Thai #license-apache-2.0 #region-us \n", "# Dataset Summary\nMr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations.\n\nThis dataset stores the queries, judgements, and example training data of Mr. TyDi. To access the corpus, please refer to castorini/mr-tydi-corpus.", "# Dataset Structure\nThe only configuration here is the 'language', \nFor each language, there are three splits: 'train', 'dev', and 'test'.\nThe negative examples from training set are sampled from the top-30 BM25 runfiles on each language.\nSpecifically, we combine the training data for all languages under the 'combined' configuration.\n\nAn example of 'train' set looks as follows:\n\n\nAn example of 'dev' and 'test' set looks as follows. We only provide the docid of positive passages here to save the space. \nAlso no candidate passages are provided at this point. \nNote that to perform the retrieval, it need to be used together with castorini/mr-tydi-corpus", "# Load Dataset\n\nAn example to load the dataset:\n\nNote that the 'combined' option has only the 'train' set." ]

[ "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Summary\nThe repo provides queries generated for the MS MARCO V1 document corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus.\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author = \"Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin\",\n title = \"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models\",\n journal = \"arXiv:2101.05667\",\n year = 2021,\n}" ]

[ "TAGS\n#language-English #license-apache-2.0 #region-us \n", "# Dataset Summary\nThe repo provides queries generated for the MS MARCO V1 document corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus.\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author = \"Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin\",\n title = \"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models\",\n journal = \"arXiv:2101.05667\",\n year = 2021,\n}" ]

[ "region:us" ]

[]

[ "English" ]

[ "# Dataset Summary\nThe repo provides queries generated for the MS MARCO V1 document segmented corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus.\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author = \"Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin\",\n title = \"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models\",\n journal = \"arXiv:2101.05667\",\n year = 2021,\n}" ]

[ "TAGS\n#region-us \n", "# Dataset Summary\nThe repo provides queries generated for the MS MARCO V1 document segmented corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus.\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author = \"Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin\",\n title = \"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models\",\n journal = \"arXiv:2101.05667\",\n year = 2021,\n}" ]

[ "region:us" ]

[]

[ "English" ]

[ "# Dataset Summary\nThe repo provides queries generated for the MS MARCO V1 passage corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus. The queries are generated from this corpus.\n\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author={Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},\n title={The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},\n journal={arXiv:2101.05667},\n year={2021},\n}" ]

[ "TAGS\n#region-us \n", "# Dataset Summary\nThe repo provides queries generated for the MS MARCO V1 passage corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus. The queries are generated from this corpus.\n\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author={Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},\n title={The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},\n journal={arXiv:2101.05667},\n year={2021},\n}" ]

[ "region:us" ]

[]

[ "English" ]

[ "# Dataset Summary\nThe repo provides queries generated for the MS MARCO v2 document corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus.\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author = \"Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin\",\n title = \"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models\",\n journal = \"arXiv:2101.05667\",\n year = 2021,\n}" ]

[ "TAGS\n#region-us \n", "# Dataset Summary\nThe repo provides queries generated for the MS MARCO v2 document corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus.\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author = \"Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin\",\n title = \"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models\",\n journal = \"arXiv:2101.05667\",\n year = 2021,\n}" ]

[ "region:us" ]

[]

[ "English" ]

[ "# Dataset Summary\nThe repo provides queries generated for the MS MARCO v2 document segmented corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus.\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author = \"Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin\",\n title = \"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models\",\n journal = \"arXiv:2101.05667\",\n year = 2021,\n}" ]

[ "TAGS\n#region-us \n", "# Dataset Summary\nThe repo provides queries generated for the MS MARCO v2 document segmented corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus.\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author = \"Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin\",\n title = \"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models\",\n journal = \"arXiv:2101.05667\",\n year = 2021,\n}" ]

[ "region:us" ]

[]

[ "English" ]

[ "# Dataset Summary\nThe repo provides queries generated for the MS MARCO v2 passage corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus. The queries are generated from this corpus.\n\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author={Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},\n title={The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},\n journal={arXiv:2101.05667},\n year={2021},\n}" ]

[ "TAGS\n#region-us \n", "# Dataset Summary\nThe repo provides queries generated for the MS MARCO v2 passage corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.", "# Dataset Structure\n\nAll three folds (train, dev and test) share the same corpus. The queries are generated from this corpus.\n\nAn example data entry looks as follows:", "# Load Dataset\nAn example to load the dataset:\n\n\n'''\n\n@article{docTTTTTquery,\n title={From doc2query to {docTTTTTquery}},\n author={Nogueira, Rodrigo and Lin, Jimmy},\n year={2019}\n}\n\n@article{emdt5,\n author={Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},\n title={The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},\n journal={arXiv:2101.05667},\n year={2021},\n}" ]

[ "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Summary\nThe repo provides answer, title and sentence expansions for the Natural Questions corpus with gar-T5.", "# Dataset Structure\nThere are dev and test folds\n\nAn example data entry of the dev split looks as follows:\n\n\nAn example data entry of the test split looks as follows:", "# Load Dataset\nAn example to load the dataset:" ]

[ "TAGS\n#language-English #license-apache-2.0 #region-us \n", "# Dataset Summary\nThe repo provides answer, title and sentence expansions for the Natural Questions corpus with gar-T5.", "# Dataset Structure\nThere are dev and test folds\n\nAn example data entry of the dev split looks as follows:\n\n\nAn example data entry of the test split looks as follows:", "# Load Dataset\nAn example to load the dataset:" ]

[ "region:us" ]

[]

[ "English" ]

[ "# Dataset Summary\r\nThe repo provides answer,title and sentence expansions for the Trivia QA corpus with gar-T5.", "# Dataset Structure\r\nThere are dev and test folds\r\n\r\nAn example data entry of the dev split looks as follows:\r\n\r\n\r\nAn example data entry of the test split looks as follows:", "# Load Dataset\r\nAn example to load the dataset:" ]

[ "TAGS\n#region-us \n", "# Dataset Summary\r\nThe repo provides answer,title and sentence expansions for the Trivia QA corpus with gar-T5.", "# Dataset Structure\r\nThere are dev and test folds\r\n\r\nAn example data entry of the dev split looks as follows:\r\n\r\n\r\nAn example data entry of the test split looks as follows:", "# Load Dataset\r\nAn example to load the dataset:" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "size_categories:10K<n<100K", "language:en", "long context", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-topic-classification #size_categories-10K<n<100K #language-English #long context #region-us \n" ]

[ "task_categories:summarization", "task_categories:text-generation", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "conditional-text-generation", "region:us" ]

[]

[ "en" ]

[ "### Data Fields\n\n\n* 'id': paper id\n* 'article': a string containing the body of the paper\n* 'abstract': a string containing the abstract of the paper", "### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*. \n\nToken counts are white space based.\n\n\n\nCite original article\n=====================" ]

[ "TAGS\n#task_categories-summarization #task_categories-text-generation #multilinguality-monolingual #size_categories-100K<n<1M #language-English #conditional-text-generation #region-us \n", "### Data Fields\n\n\n* 'id': paper id\n* 'article': a string containing the body of the paper\n* 'abstract': a string containing the abstract of the paper", "### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*. \n\nToken counts are white space based.\n\n\n\nCite original article\n=====================" ]

[ "task_categories:summarization", "task_categories:text-generation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:apache-2.0", "conditional-text-generation", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.", "### Supported Tasks and Leaderboards\n\n\n* 'summarization': Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset can be used to train a model for abstractive and extractive summarization (Version 1.0.0 was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's ROUGE score for a given article is when compared to the highlight as written by the original article author. Zhong et al (2020) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the Papers With Code leaderboard for more models.", "### Languages\n\n\nThe BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is a string for the article, a string for the highlights, and a string for the id. See the CNN / Daily Mail dataset viewer to explore more examples.\n\n\nThe average token count for the articles and the highlights are provided below:", "### Data Fields\n\n\n* 'id': a string containing the heximal formated SHA1 hash of the url where the story was retrieved from\n* 'article': a string containing the body of the news article\n* 'highlights': a string containing the highlight of the article as written by the article author", "### Data Splits\n\n\nThe CNN/DailyMail dataset has 3 splits: *train*, *validation*, and *test*. Below are the statistics for Version 3.0.0 of the dataset.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nVersion 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015.\n\n\nThe code for the original data collection is available at <URL The articles were downloaded using archives of and on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <URL An updated version of the code that does not anonymize the data is available at <URL\n\n\nHermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.", "#### Who are the source language producers?\n\n\nThe text was written by journalists at CNN and the Daily Mail.", "### Annotations\n\n\nThe dataset does not contain any additional annotations.", "#### Annotation process\n\n\n[N/A]", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nVersion 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences.\n\n\nThis task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.", "### Discussion of Biases\n\n\nBordia and Bowman (2019) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.\n\n\nBecause the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published.", "### Other Known Limitations\n\n\nNews articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article (Kryściński et al, 2019). Chen et al (2016) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors.\n\n\nIt should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format.\n\n\nRamesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.\n\n\nThe code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <URL The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.", "### Licensing Information\n\n\nThe CNN / Daily Mail dataset version 1.0.0 is released under the Apache-2.0 License.", "### Contributions\n\n\nThanks to @thomwolf, @lewtun, @jplu, @jbragg, @patrickvonplaten and @mcmillanmajora for adding this dataset." ]

[ "TAGS\n#task_categories-summarization #task_categories-text-generation #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #conditional-text-generation #region-us \n", "### Dataset Summary\n\n\nThe CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.", "### Supported Tasks and Leaderboards\n\n\n* 'summarization': Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset can be used to train a model for abstractive and extractive summarization (Version 1.0.0 was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's ROUGE score for a given article is when compared to the highlight as written by the original article author. Zhong et al (2020) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the Papers With Code leaderboard for more models.", "### Languages\n\n\nThe BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is a string for the article, a string for the highlights, and a string for the id. See the CNN / Daily Mail dataset viewer to explore more examples.\n\n\nThe average token count for the articles and the highlights are provided below:", "### Data Fields\n\n\n* 'id': a string containing the heximal formated SHA1 hash of the url where the story was retrieved from\n* 'article': a string containing the body of the news article\n* 'highlights': a string containing the highlight of the article as written by the article author", "### Data Splits\n\n\nThe CNN/DailyMail dataset has 3 splits: *train*, *validation*, and *test*. Below are the statistics for Version 3.0.0 of the dataset.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nVersion 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015.\n\n\nThe code for the original data collection is available at <URL The articles were downloaded using archives of and on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <URL An updated version of the code that does not anonymize the data is available at <URL\n\n\nHermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.", "#### Who are the source language producers?\n\n\nThe text was written by journalists at CNN and the Daily Mail.", "### Annotations\n\n\nThe dataset does not contain any additional annotations.", "#### Annotation process\n\n\n[N/A]", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nVersion 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences.\n\n\nThis task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.", "### Discussion of Biases\n\n\nBordia and Bowman (2019) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.\n\n\nBecause the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published.", "### Other Known Limitations\n\n\nNews articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article (Kryściński et al, 2019). Chen et al (2016) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors.\n\n\nIt should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format.\n\n\nRamesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.\n\n\nThe code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <URL The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.", "### Licensing Information\n\n\nThe CNN / Daily Mail dataset version 1.0.0 is released under the Apache-2.0 License.", "### Contributions\n\n\nThanks to @thomwolf, @lewtun, @jplu, @jbragg, @patrickvonplaten and @mcmillanmajora for adding this dataset." ]

[ "task_categories:summarization", "task_categories:text-generation", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "conditional-text-generation", "arxiv:2104.02112", "region:us" ]

[ "2104.02112" ]

[ "en" ]

[ "### Data Fields\n\n\n* 'id': paper id\n* 'report': a string containing the body of the report\n* 'summary': a string containing the summary of the report", "### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*. \n\nToken counts with a RoBERTa tokenizer.\n\n\n\nCite original article\n=====================" ]

[ "TAGS\n#task_categories-summarization #task_categories-text-generation #multilinguality-monolingual #size_categories-10K<n<100K #language-English #conditional-text-generation #arxiv-2104.02112 #region-us \n", "### Data Fields\n\n\n* 'id': paper id\n* 'report': a string containing the body of the report\n* 'summary': a string containing the summary of the report", "### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*. \n\nToken counts with a RoBERTa tokenizer.\n\n\n\nCite original article\n=====================" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "size_categories:10K<n<100K", "language:en", "long context", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-topic-classification #size_categories-10K<n<100K #language-English #long context #region-us \n" ]

[ "task_categories:summarization", "task_categories:text-generation", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "conditional-text-generation", "region:us" ]

[]

[ "en" ]

[ "### Data Fields\n\n\n* 'id': paper id\n* 'article': a string containing the body of the paper\n* 'abstract': a string containing the abstract of the paper", "### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*. \n\nToken counts are white space based.\n\n\n\nCite original article\n=====================" ]

[ "TAGS\n#task_categories-summarization #task_categories-text-generation #multilinguality-monolingual #size_categories-100K<n<1M #language-English #conditional-text-generation #region-us \n", "### Data Fields\n\n\n* 'id': paper id\n* 'article': a string containing the body of the paper\n* 'abstract': a string containing the abstract of the paper", "### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*. \n\nToken counts are white space based.\n\n\n\nCite original article\n=====================" ]

[ "task_categories:translation", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "## Dataset Description\n\n- Homepage: URL", "# Dataset Summary: \nPig-latin machine and English parallel machine translation corpus. \n\nBased on The Project Gutenberg EBook of \"De Bello Gallico\" and Other Commentaries\n\nConverted to pig-latin with URL\n\nBlank lines removed.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'translation': a dictionary containing two strings paired with a key indicating the corresponding language.", "### Data Splits\n- 'train': most of the data, 13,232 samples total.\n- 'dev': 1k holdout samples, created with the datasets.train_test_split() function" ]

[ "TAGS\n#task_categories-translation #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "## Dataset Description\n\n- Homepage: URL", "# Dataset Summary: \nPig-latin machine and English parallel machine translation corpus. \n\nBased on The Project Gutenberg EBook of \"De Bello Gallico\" and Other Commentaries\n\nConverted to pig-latin with URL\n\nBlank lines removed.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'translation': a dictionary containing two strings paired with a key indicating the corresponding language.", "### Data Splits\n- 'train': most of the data, 13,232 samples total.\n- 'dev': 1k holdout samples, created with the datasets.train_test_split() function" ]

[ "language:wo", "language:fuc", "language:srr", "language:mnk", "language:snk", "region:us" ]

[]

[ "wo", "fuc", "srr", "mnk", "snk" ]

[ "## Dataset Description\n- Homepage: URL\n\n\nTEMPORARY TEST DATASET\nNot for actual use! Attempting to test out a dataset script for loading URL" ]

[ "TAGS\n#language-Wolof #language-Pulaar #language-Serer #language-Mandinka #language-Soninke #region-us \n", "## Dataset Description\n- Homepage: URL\n\n\nTEMPORARY TEST DATASET\nNot for actual use! Attempting to test out a dataset script for loading URL" ]

[ "region:us" ]

[]

[]

[]

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

[ "language:en", "language:hi", "region:us" ]

[]

[ "en", "hi" ]

[ "# IITB-English-Hindi Parallel Corpus\n\n![License: CC BY-NC 4.0](URL \n![Twitter Follow](URL\n![Twitter Follow](URL", "## About\n\nThe IIT Bombay English-Hindi corpus contains parallel corpus for English-Hindi as well as monolingual Hindi corpus collected from a variety of existing sources and corpora developed at the Center for Indian Language Technology, IIT Bombay over the years. This page describes the corpus. This corpus has been used at the Workshop on Asian Language Translation Shared Task since 2016 the Hindi-to-English and English-to-Hindi languages pairs and as a pivot language pair for the Hindi-to-Japanese and Japanese-to-Hindi language pairs.\n\nThe complete details of this corpus are available at this URL. We also provide this parallel corpus via browser download from the same URL. We also provide a monolingual Hindi corpus on the same URL.", "### Recent Updates\n* Version 3.1 - December 2021 - Added 49,400 sentence pairs to the parallel corpus.\n* Version 3.0 - August 2020 - Added ~47,000 sentence pairs to the parallel corpus.", "## Usage\nWe provide a notebook that shows how to import the IITB English-Hindi Parallel Corpus from the HuggingFace datasets repository. The notebook also shows how to segment the corpus using BPE tokenization which can be used to train an English-Hindi MT System.\nURL", "## Other\nYou can find a catalogue of other English-Hindi and other Indian language parallel corpora here: Indic NLP Catalog", "## Maintainer(s)\n\nDiptesh Kanojia<br/>\nShivam Mhasker<br/>\n\nIf you use this corpus or its derivate resources for your research, kindly cite it as follows:\n\nAnoop Kunchukuttan, Pratik Mehta, Pushpak Bhattacharyya. The IIT Bombay English-Hindi Parallel Corpus. Language Resources and Evaluation Conference. 2018.", "### BiBTeX Citation" ]

[ "TAGS\n#language-English #language-Hindi #region-us \n", "# IITB-English-Hindi Parallel Corpus\n\n![License: CC BY-NC 4.0](URL \n![Twitter Follow](URL\n![Twitter Follow](URL", "## About\n\nThe IIT Bombay English-Hindi corpus contains parallel corpus for English-Hindi as well as monolingual Hindi corpus collected from a variety of existing sources and corpora developed at the Center for Indian Language Technology, IIT Bombay over the years. This page describes the corpus. This corpus has been used at the Workshop on Asian Language Translation Shared Task since 2016 the Hindi-to-English and English-to-Hindi languages pairs and as a pivot language pair for the Hindi-to-Japanese and Japanese-to-Hindi language pairs.\n\nThe complete details of this corpus are available at this URL. We also provide this parallel corpus via browser download from the same URL. We also provide a monolingual Hindi corpus on the same URL.", "### Recent Updates\n* Version 3.1 - December 2021 - Added 49,400 sentence pairs to the parallel corpus.\n* Version 3.0 - August 2020 - Added ~47,000 sentence pairs to the parallel corpus.", "## Usage\nWe provide a notebook that shows how to import the IITB English-Hindi Parallel Corpus from the HuggingFace datasets repository. The notebook also shows how to segment the corpus using BPE tokenization which can be used to train an English-Hindi MT System.\nURL", "## Other\nYou can find a catalogue of other English-Hindi and other Indian language parallel corpora here: Indic NLP Catalog", "## Maintainer(s)\n\nDiptesh Kanojia<br/>\nShivam Mhasker<br/>\n\nIf you use this corpus or its derivate resources for your research, kindly cite it as follows:\n\nAnoop Kunchukuttan, Pratik Mehta, Pushpak Bhattacharyya. The IIT Bombay English-Hindi Parallel Corpus. Language Resources and Evaluation Conference. 2018.", "### BiBTeX Citation" ]

[ "region:us" ]

[]

[]

[ "# Point CLoud MNIST\n\nA point cloud version of the original MNIST.\n\n!sample", "## Getting Started", "## Format\n\n* 'points': '(batch, point, 3)' array of uint8.\n* 'label': '(batch, 1)' array of uint8.\n\nWhere 'point' is the number of points in the point cloud. Points have no order and were shuffled when creating the data. Each point has the structure '[x, y, v]' where:\n\n* 'x': is the x coordinate of the point in the image.\n* 'y': is the y coordinate of the point in the image.\n* 'v': is the value of the pixel at the point in the image.\n\nSamples are padded with '0's such that 'point = 351' since its the largest number of non-zero pixels per image in the original dataset. You can tell apart padding point because they are the only ones where 'v = 0'. \n\nHere is the distribution of non-zero pixels in the MNIST:\n\n!distribution" ]

[ "TAGS\n#region-us \n", "# Point CLoud MNIST\n\nA point cloud version of the original MNIST.\n\n!sample", "## Getting Started", "## Format\n\n* 'points': '(batch, point, 3)' array of uint8.\n* 'label': '(batch, 1)' array of uint8.\n\nWhere 'point' is the number of points in the point cloud. Points have no order and were shuffled when creating the data. Each point has the structure '[x, y, v]' where:\n\n* 'x': is the x coordinate of the point in the image.\n* 'y': is the y coordinate of the point in the image.\n* 'v': is the value of the pixel at the point in the image.\n\nSamples are padded with '0's such that 'point = 351' since its the largest number of non-zero pixels per image in the original dataset. You can tell apart padding point because they are the only ones where 'v = 0'. \n\nHere is the distribution of non-zero pixels in the MNIST:\n\n!distribution" ]

[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:en", "language:pt", "language:es", "license:cc-by-nc-sa-3.0", "region:us" ]

[]

[ "en", "pt", "es" ]

[ "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nThis dataset contains all text from open-access PDFs on URL. As of Dec. 5 2021, the total number of books available is 962. Some of them are not in native PDF format (e.g. scanned images) though.", "### Supported Tasks and Leaderboards\n\n- 'sequence-modeling' or 'language-modeling': The dataset can be used to train a language model.", "### Languages\n\nAs of Dec. 5 2021, there are 902 books in Portuguese, 55 in Spanish, and 5 in English.", "## Dataset Structure", "### Data Instances\n\nProvide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.", "### Data Fields\n\nAll fields are of string type except 'year'.", "### Data Splits\n\nAll records are in the default 'train' split.", "## Dataset Creation", "### Curation Rationale\n\nPart of the big science efforts to create lanague modeling datasets.", "### Source Data\n\nURL", "#### Initial Data Collection and Normalization\n\nAll PDFs are directly downloaded from the website and text is extracted with pdftotext lib.", "#### Who are the source language producers?\n\nNA", "### Annotations\n\nNo annotation is available.", "#### Annotation process\n\nNA", "#### Who are the annotators?\n\nNA", "### Personal and Sensitive Information\n\nNA", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNA", "### Discussion of Biases\n\nNA", "### Other Known Limitations\n\nNA", "## Additional Information", "### Dataset Curators\n\n@chenghao", "### Licensing Information\n\nProvide the license and link to the license webpage if available.\n\nCC BY-NC-SA 3.0", "### Contributions\n\nNA" ]

[ "TAGS\n#task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-multilingual #size_categories-n<1K #source_datasets-original #language-English #language-Portuguese #language-Spanish #license-cc-by-nc-sa-3.0 #region-us \n", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nThis dataset contains all text from open-access PDFs on URL. As of Dec. 5 2021, the total number of books available is 962. Some of them are not in native PDF format (e.g. scanned images) though.", "### Supported Tasks and Leaderboards\n\n- 'sequence-modeling' or 'language-modeling': The dataset can be used to train a language model.", "### Languages\n\nAs of Dec. 5 2021, there are 902 books in Portuguese, 55 in Spanish, and 5 in English.", "## Dataset Structure", "### Data Instances\n\nProvide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.", "### Data Fields\n\nAll fields are of string type except 'year'.", "### Data Splits\n\nAll records are in the default 'train' split.", "## Dataset Creation", "### Curation Rationale\n\nPart of the big science efforts to create lanague modeling datasets.", "### Source Data\n\nURL", "#### Initial Data Collection and Normalization\n\nAll PDFs are directly downloaded from the website and text is extracted with pdftotext lib.", "#### Who are the source language producers?\n\nNA", "### Annotations\n\nNo annotation is available.", "#### Annotation process\n\nNA", "#### Who are the annotators?\n\nNA", "### Personal and Sensitive Information\n\nNA", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNA", "### Discussion of Biases\n\nNA", "### Other Known Limitations\n\nNA", "## Additional Information", "### Dataset Curators\n\n@chenghao", "### Licensing Information\n\nProvide the license and link to the license webpage if available.\n\nCC BY-NC-SA 3.0", "### Contributions\n\nNA" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [KsponSpeech]", "## 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\n\nKsponSpeech is a large-scale spontaneous speech corpus of Korean conversations. This corpus contains 969 hrs of general open-domain dialog utterances, spoken by about 2,000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. KsponSpeech is publicly available on an open data hub site of the Korea government. (URL", "### 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 @github-username for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for [KsponSpeech]", "## 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\n\nKsponSpeech is a large-scale spontaneous speech corpus of Korean conversations. This corpus contains 969 hrs of general open-domain dialog utterances, spoken by about 2,000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. KsponSpeech is publicly available on an open data hub site of the Korea government. (URL", "### 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 @github-username for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:n<1K", "language:pl", "region:us" ]

[]

[ "pl" ]

[ "### Forced-aligned transcriptions\n\n\nWe use force-aligned transcriptions of the original texts to approximate ASR output. Files in the *.clntmstmp* format contain forced-alignment of the original text together with the audio file read out by a group of volunteers. The files may contain errors resulting from incorrect reading of the text (skipping fragments, adding words missing from the original text) and alignment errors resulting from the configuration of the alignment tool for text and audio files. The configuration targeted Polish; names from foreign languages may be poorly recognised, with the word duration equal to zero (start and end timestamps are equal). Data is given in the following format:\n\n\n(timestamp\\_start,timestamp\\_end) word\n\n\n...\n\n\n</s>\n\n\nwhere </s> is a symbol of the end of recognition.\n\n\nExample:\n\n\n(990,1200) Rosja\n\n\n(1230,1500) zaczyna\n\n\n(1590,1950) powracać\n\n\n(1980,2040) do\n\n\n(2070,2400) praktyk\n\n\n(2430,2490) z\n\n\n(2520,2760) czasów\n\n\n(2820,3090) zimnej\n\n\n(3180,3180) wojny.\n\n\n(3960,4290) Rosjanie\n\n\n(4380,4770) wznowili\n\n\n(4860,5070) bowiem\n\n\n(5100,5160) na\n\n\n(5220,5430) stałe\n\n\n(5520,5670) loty\n\n\n(5760,6030) swoich\n\n\n(6120,6600) bombowców\n\n\n(6630,7230) strategicznych\n\n\n(7350,7530) poza\n\n\n(7590,7890) granice\n\n\n(8010,8010) kraju.\n\n\n(8880,9300) Prezydent\n\n\n(9360,9810) Władimir\n\n\n(9930,10200) Putin\n\n\n(10650,10650) wyjaśnił,\n\n\n(10830,10920) iż\n\n\n(10980,11130) jest\n\n\n(11160,11190) to\n\n\n(11220,11520) odpowiedź\n\n\n(11550,11640) na\n\n\n(11670,12120) zagrożenie\n\n\n(12240,12300) ze\n\n\n(12330,12570) strony\n\n\n(12660,12870) innych\n\n\n(13140,13140) państw.\n\n\n</s>\n\n\nEvaluation procedure\n--------------------\n\n\nBaseline results will be provided in final evaluation.", "### Punctuation\n\n\nDuring the task the following punctuation marks will be evaluated:\n\n\n\nNote that semi-colon (';') is disregarded here.", "### Submission format\n\n\nThe output to be evaluated is just the text with punctuation marks added.", "### Metrics\n\n\nFinal results are evaluated in terms of precision, recall, and F1 scores for predicting each punctuation mark separately. Submissions are compared with respect to the weighted average of F1 scores for each punctuation mark.", "##### Per-document score:\n\n\n![](URL", "##### Global score per punctuation mark *p*:\n\n\n![](URL\n\n\nFinal scoring metric calculated as weighted average of global scores per\n![](URL\n\n\nWe would like to invite participants to discussion about evaluation metrics, taking into account such factors as:\n\n\n* ASR and Forced-Alignment errors,\n* inconsistencies among annotators,\n* impact of only slight displacement of punctuation,\n* assigning different weights to different types of errors.", "### Video introduction\n\n\n![Video instruction](URL \"Video instruction\")", "### Downloads\n\n\nData has been published in the following repository: URL\n\n\nTraining data is provided in train/\\*.tsv. Additional data can be downloaded from Google Drive. Below is a list of file names along with a description of what they contain.\n\n\n* poleval\\_fa.URL - archive contains forced-alignment of the original text together with the audio file\n* poleval\\_wav.URL - archive contains training audio files\n* poleval\\_wav.URL - archive contains test audio files\n* poleval\\_text.URL - archive contains additional text provided in JSON formatand CSV for which no audio files were provided (can be used for training purposes)", "### Challenge stage\n\n\nThe competition in September 2021. Now the challenge is in the after-competition stage. You can submit solutions,\nbut they will be marked with a different color.", "### License\n\n\nCreative Commons - Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)", "### References\n\n\n1. Yi, J., Tao, J., Bai, Y., Tian, Z., & Fan, C. (2020). Adversarial transfer learning for punctuation restoration. *arXiv preprint arXiv:2004.00248*.\n2. Nguyen, Thai Binh, et al. \"Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models.\" *Proc. Interspeech 2020* (2020): 4263-4267.\n3. Hlubík, Pavel, et al. \"Inserting Punctuation to ASR Output in a Real-Time Production Environment.\" *International Conference on Text, Speech, and Dialogue*. Springer, Cham, 2020.\n4. Sirts, Kairit, and Kairit Peekman. \"Evaluating Sentence Segmentation and Word Tokenization Systems on Estonian Web Texts.\" *Human Language Technologies–The Baltic Perspective: Proceedings of the Ninth International Conference Baltic HLT 2020*. Vol. 328. IOS Press, 2020.\n5. Wang, Xueyujie. \"Analysis of Sentence Boundary of the Host's Spoken Language Based on Semantic Orientation Pointwise Mutual Information Algorithm.\" *2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)*. IEEE, 2020.\n6. Sunkara, Monica, et al. \"Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech.\" *arXiv preprint arXiv:2008.00702* (2020)." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<1K #language-Polish #region-us \n", "### Forced-aligned transcriptions\n\n\nWe use force-aligned transcriptions of the original texts to approximate ASR output. Files in the *.clntmstmp* format contain forced-alignment of the original text together with the audio file read out by a group of volunteers. The files may contain errors resulting from incorrect reading of the text (skipping fragments, adding words missing from the original text) and alignment errors resulting from the configuration of the alignment tool for text and audio files. The configuration targeted Polish; names from foreign languages may be poorly recognised, with the word duration equal to zero (start and end timestamps are equal). Data is given in the following format:\n\n\n(timestamp\\_start,timestamp\\_end) word\n\n\n...\n\n\n</s>\n\n\nwhere </s> is a symbol of the end of recognition.\n\n\nExample:\n\n\n(990,1200) Rosja\n\n\n(1230,1500) zaczyna\n\n\n(1590,1950) powracać\n\n\n(1980,2040) do\n\n\n(2070,2400) praktyk\n\n\n(2430,2490) z\n\n\n(2520,2760) czasów\n\n\n(2820,3090) zimnej\n\n\n(3180,3180) wojny.\n\n\n(3960,4290) Rosjanie\n\n\n(4380,4770) wznowili\n\n\n(4860,5070) bowiem\n\n\n(5100,5160) na\n\n\n(5220,5430) stałe\n\n\n(5520,5670) loty\n\n\n(5760,6030) swoich\n\n\n(6120,6600) bombowców\n\n\n(6630,7230) strategicznych\n\n\n(7350,7530) poza\n\n\n(7590,7890) granice\n\n\n(8010,8010) kraju.\n\n\n(8880,9300) Prezydent\n\n\n(9360,9810) Władimir\n\n\n(9930,10200) Putin\n\n\n(10650,10650) wyjaśnił,\n\n\n(10830,10920) iż\n\n\n(10980,11130) jest\n\n\n(11160,11190) to\n\n\n(11220,11520) odpowiedź\n\n\n(11550,11640) na\n\n\n(11670,12120) zagrożenie\n\n\n(12240,12300) ze\n\n\n(12330,12570) strony\n\n\n(12660,12870) innych\n\n\n(13140,13140) państw.\n\n\n</s>\n\n\nEvaluation procedure\n--------------------\n\n\nBaseline results will be provided in final evaluation.", "### Punctuation\n\n\nDuring the task the following punctuation marks will be evaluated:\n\n\n\nNote that semi-colon (';') is disregarded here.", "### Submission format\n\n\nThe output to be evaluated is just the text with punctuation marks added.", "### Metrics\n\n\nFinal results are evaluated in terms of precision, recall, and F1 scores for predicting each punctuation mark separately. Submissions are compared with respect to the weighted average of F1 scores for each punctuation mark.", "##### Per-document score:\n\n\n![](URL", "##### Global score per punctuation mark *p*:\n\n\n![](URL\n\n\nFinal scoring metric calculated as weighted average of global scores per\n![](URL\n\n\nWe would like to invite participants to discussion about evaluation metrics, taking into account such factors as:\n\n\n* ASR and Forced-Alignment errors,\n* inconsistencies among annotators,\n* impact of only slight displacement of punctuation,\n* assigning different weights to different types of errors.", "### Video introduction\n\n\n![Video instruction](URL \"Video instruction\")", "### Downloads\n\n\nData has been published in the following repository: URL\n\n\nTraining data is provided in train/\\*.tsv. Additional data can be downloaded from Google Drive. Below is a list of file names along with a description of what they contain.\n\n\n* poleval\\_fa.URL - archive contains forced-alignment of the original text together with the audio file\n* poleval\\_wav.URL - archive contains training audio files\n* poleval\\_wav.URL - archive contains test audio files\n* poleval\\_text.URL - archive contains additional text provided in JSON formatand CSV for which no audio files were provided (can be used for training purposes)", "### Challenge stage\n\n\nThe competition in September 2021. Now the challenge is in the after-competition stage. You can submit solutions,\nbut they will be marked with a different color.", "### License\n\n\nCreative Commons - Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)", "### References\n\n\n1. Yi, J., Tao, J., Bai, Y., Tian, Z., & Fan, C. (2020). Adversarial transfer learning for punctuation restoration. *arXiv preprint arXiv:2004.00248*.\n2. Nguyen, Thai Binh, et al. \"Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models.\" *Proc. Interspeech 2020* (2020): 4263-4267.\n3. Hlubík, Pavel, et al. \"Inserting Punctuation to ASR Output in a Real-Time Production Environment.\" *International Conference on Text, Speech, and Dialogue*. Springer, Cham, 2020.\n4. Sirts, Kairit, and Kairit Peekman. \"Evaluating Sentence Segmentation and Word Tokenization Systems on Estonian Web Texts.\" *Human Language Technologies–The Baltic Perspective: Proceedings of the Ninth International Conference Baltic HLT 2020*. Vol. 328. IOS Press, 2020.\n5. Wang, Xueyujie. \"Analysis of Sentence Boundary of the Host's Spoken Language Based on Semantic Orientation Pointwise Mutual Information Algorithm.\" *2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)*. IEEE, 2020.\n6. Sunkara, Monica, et al. \"Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech.\" *arXiv preprint arXiv:2008.00702* (2020)." ]

[ "task_categories:token-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:mit", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-token-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-1K #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-mit #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:other", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:18K", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:cc-by-3.0", "structure-prediction", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-other #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-18K #size_categories-10K<n<100K #source_datasets-original #language-Polish #license-cc-by-3.0 #structure-prediction #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:other", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:pl", "license:gpl-3.0", "structure-prediction", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-other #task_ids-part-of-speech #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Polish #license-gpl-3.0 #structure-prediction #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:8K", "size_categories:1K<n<10K", "source_datasets:original", "language:pl", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "pl" ]

[ "### Loading", "### Evaluation" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-8K #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-cc-by-sa-4.0 #region-us \n", "### Loading", "### Evaluation" ]

[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:en", "license:other", "hate-speech-detection", "offensive-language", "arxiv:1906.02045", "region:us" ]

[ "1906.02045" ]

[ "en" ]

[ "# Offensive language dataset of Croatian comments FRENK 1.0\n\nEnglish subset of the FRENK dataset. Also available on HuggingFace dataset hub: Croatian subset, Slovenian subset.", "## Dataset Description\n\n- Homepage: URL\n\n- Repository: URL\n\n- Paper: URL\n\n- Project page URL", "## Description of the original dataset\n\nThe original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [URL Usernames in the metadata are pseudo-anonymised and removed from the comments.\n\nThe data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.\n\nFor this dataset only the English data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split).", "## Usage in 'Transformers'\n\n\nFor binary classification the following encoding is used:\n\n\n\nThe original labels are available if the dataset is loaded with the 'multiclass' option:\n\n\n\nIn this case the encoding used is:\n\n\n\nThe original labels are available if the dataset is loaded with the 'multiclass' option:\n\n\n\nIn this case the encoding used is:", "## Data structure\n\n* 'text': text\n* 'target': who is the target of the hate-speech text (\"no target\", \"commenter\", \"target\" (migrants or LGBT, depending on the topic), or \"related to\" (again, the topic))\n* 'topic': whether the text relates to lgbt or migrants hate-speech domains\n* 'label': label of the text instance, see above.", "## Data instance", "## Licensing information\n\nCLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0\n\ninformation\n\nWhen using this dataset please cite the following paper:\n\n\n\nThe original dataset can be cited as" ]

[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-other #hate-speech-detection #offensive-language #arxiv-1906.02045 #region-us \n", "# Offensive language dataset of Croatian comments FRENK 1.0\n\nEnglish subset of the FRENK dataset. Also available on HuggingFace dataset hub: Croatian subset, Slovenian subset.", "## Dataset Description\n\n- Homepage: URL\n\n- Repository: URL\n\n- Paper: URL\n\n- Project page URL", "## Description of the original dataset\n\nThe original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [URL Usernames in the metadata are pseudo-anonymised and removed from the comments.\n\nThe data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.\n\nFor this dataset only the English data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split).", "## Usage in 'Transformers'\n\n\nFor binary classification the following encoding is used:\n\n\n\nThe original labels are available if the dataset is loaded with the 'multiclass' option:\n\n\n\nIn this case the encoding used is:\n\n\n\nThe original labels are available if the dataset is loaded with the 'multiclass' option:\n\n\n\nIn this case the encoding used is:", "## Data structure\n\n* 'text': text\n* 'target': who is the target of the hate-speech text (\"no target\", \"commenter\", \"target\" (migrants or LGBT, depending on the topic), or \"related to\" (again, the topic))\n* 'topic': whether the text relates to lgbt or migrants hate-speech domains\n* 'label': label of the text instance, see above.", "## Data instance", "## Licensing information\n\nCLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0\n\ninformation\n\nWhen using this dataset please cite the following paper:\n\n\n\nThe original dataset can be cited as" ]

[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:hr", "license:other", "hate-speech-detection", "offensive-language", "arxiv:1906.02045", "region:us" ]

[ "1906.02045" ]

[ "hr" ]

[ "# Offensive language dataset of Croatian comments FRENK 1.0\n\nCroatian subset of the FRENK dataset. Also available on HuggingFace dataset hub: English subset, Slovenian subset.", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Project page URL", "## Description of the original dataset\n\n>The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [URL Usernames in the metadata are pseudo-anonymised and removed from the comments.\n>\n>The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.\n\nFor this dataset only the Croatian data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split). Test segment has been preserved in its original form.", "## Usage in 'Transformers'\n\n\n\nFor binary classification the following encoding is used:\n\n\n\nThe original labels are available if the dataset is loaded with the 'multiclass' option:\n\n\n\nIn this case the encoding used is:", "## Data structure\n\n* 'text': text\n* 'target': who is the target of the hate-speech text (\"no target\", \"commenter\", \"target\" (migrants or LGBT, depending on the topic), or \"related to\" (again, the topic))\n* 'topic': whether the text relates to lgbt or migrants hate-speech domains\n* 'label': label of the text instance, see above.", "## Data instance", "## Licensing information\n\nCLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0\n\ninformation\n\nWhen using this dataset please cite the following paper:\n\n\n\nThe original dataset can be cited as" ]

[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Croatian #license-other #hate-speech-detection #offensive-language #arxiv-1906.02045 #region-us \n", "# Offensive language dataset of Croatian comments FRENK 1.0\n\nCroatian subset of the FRENK dataset. Also available on HuggingFace dataset hub: English subset, Slovenian subset.", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Project page URL", "## Description of the original dataset\n\n>The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [URL Usernames in the metadata are pseudo-anonymised and removed from the comments.\n>\n>The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.\n\nFor this dataset only the Croatian data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split). Test segment has been preserved in its original form.", "## Usage in 'Transformers'\n\n\n\nFor binary classification the following encoding is used:\n\n\n\nThe original labels are available if the dataset is loaded with the 'multiclass' option:\n\n\n\nIn this case the encoding used is:", "## Data structure\n\n* 'text': text\n* 'target': who is the target of the hate-speech text (\"no target\", \"commenter\", \"target\" (migrants or LGBT, depending on the topic), or \"related to\" (again, the topic))\n* 'topic': whether the text relates to lgbt or migrants hate-speech domains\n* 'label': label of the text instance, see above.", "## Data instance", "## Licensing information\n\nCLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0\n\ninformation\n\nWhen using this dataset please cite the following paper:\n\n\n\nThe original dataset can be cited as" ]

[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:sl", "license:other", "hate-speech-detection", "offensive-language", "arxiv:1906.02045", "region:us" ]

[ "1906.02045" ]

[ "sl" ]

[ "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Project page URL", "## Description of the original dataset\n\n>The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [URL Usernames in the metadata are pseudo-anonymised and removed from the comments.\n>\n>The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.\n\nFor this dataset only the Croatian data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split).", "## Usage in 'Transformers'\n\n\n\nFor binary classification the following encoding is used:\n\n\n\nThe original labels are available if the dataset is loaded with the 'multiclass' option:\n\n\n\nIn this case the encoding used is:", "## Data structure\n\n* 'text': text\n* 'target': who is the target of the hate-speech text (\"no target\", \"commenter\", \"target\" (migrants or LGBT, depending on the topic), or \"related to\" (again, the topic))\n* 'topic': whether the text relates to lgbt or migrants hate-speech domains\n* 'label': label of the text instance, see above.", "## Data instance", "## Licensing information\n\nCLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0\n\ninformation\n\nWhen using this dataset please cite the following paper:\n\n\n\nThe original dataset can be cited as" ]

[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Slovenian #license-other #hate-speech-detection #offensive-language #arxiv-1906.02045 #region-us \n", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Project page URL", "## Description of the original dataset\n\n>The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [URL Usernames in the metadata are pseudo-anonymised and removed from the comments.\n>\n>The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.\n\nFor this dataset only the Croatian data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split).", "## Usage in 'Transformers'\n\n\n\nFor binary classification the following encoding is used:\n\n\n\nThe original labels are available if the dataset is loaded with the 'multiclass' option:\n\n\n\nIn this case the encoding used is:", "## Data structure\n\n* 'text': text\n* 'target': who is the target of the hate-speech text (\"no target\", \"commenter\", \"target\" (migrants or LGBT, depending on the topic), or \"related to\" (again, the topic))\n* 'topic': whether the text relates to lgbt or migrants hate-speech domains\n* 'label': label of the text instance, see above.", "## Data instance", "## Licensing information\n\nCLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0\n\ninformation\n\nWhen using this dataset please cite the following paper:\n\n\n\nThe original dataset can be cited as" ]

[ "task_categories:text-classification", "task_ids:natural-language-inference", "language:hr", "license:cc-by-sa-4.0", "causal-reasoning", "textual-entailment", "commonsense-reasoning", "arxiv:2005.00333", "arxiv:2104.09243", "region:us" ]

[ "2005.00333", "2104.09243" ]

[ "hr" ]

[]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #language-Croatian #license-cc-by-sa-4.0 #causal-reasoning #textual-entailment #commonsense-reasoning #arxiv-2005.00333 #arxiv-2104.09243 #region-us \n" ]

[ "task_categories:other", "task_ids:lemmatization", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "language:hr", "license:cc-by-sa-4.0", "structure-prediction", "normalization", "tokenization", "region:us" ]

[]

[ "hr" ]

[]

[ "TAGS\n#task_categories-other #task_ids-lemmatization #task_ids-named-entity-recognition #task_ids-part-of-speech #language-Croatian #license-cc-by-sa-4.0 #structure-prediction #normalization #tokenization #region-us \n" ]