Datasets:

---

ftopal

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huggingface-datasets-processed

like 0

[ "task_categories:text2text-generation", "task_categories:translation", "annotations_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:unknown", "source_datasets:extended|flores", "language:ace", "language:acm", "language:acq", "language:aeb", "language:afr", "language:ajp", "language:aka", "language:als", "language:amh", "language:apc", "language:arb", "language:ars", "language:ary", "language:arz", "language:asm", "language:ast", "language:awa", "language:ayr", "language:azb", "language:azj", "language:bak", "language:bam", "language:ban", "language:bel", "language:bem", "language:ben", "language:bho", "language:bjn", "language:bod", "language:bos", "language:bug", "language:bul", "language:cat", "language:ceb", "language:ces", "language:cjk", "language:ckb", "language:crh", "language:cym", "language:dan", "language:deu", "language:dik", "language:dyu", "language:dzo", "language:ell", "language:eng", "language:epo", "language:est", "language:eus", "language:ewe", "language:fao", "language:fij", "language:fin", "language:fon", "language:fra", "language:fur", "language:fuv", "language:gaz", "language:gla", "language:gle", "language:glg", "language:grn", "language:guj", "language:hat", "language:hau", "language:heb", "language:hin", "language:hne", "language:hrv", "language:hun", "language:hye", "language:ibo", "language:ilo", "language:ind", "language:isl", "language:ita", "language:jav", "language:jpn", "language:kab", "language:kac", "language:kam", "language:kan", "language:kas", "language:kat", "language:kaz", "language:kbp", "language:kea", "language:khk", "language:khm", "language:kik", "language:kin", "language:kir", "language:kmb", "language:kmr", "language:knc", "language:kon", "language:kor", "language:lao", "language:lij", "language:lim", "language:lin", "language:lit", "language:lmo", "language:ltg", "language:ltz", "language:lua", "language:lug", "language:luo", "language:lus", "language:lvs", "language:mag", "language:mai", "language:mal", "language:mar", "language:min", "language:mkd", "language:mlt", "language:mni", "language:mos", "language:mri", "language:mya", "language:nld", "language:nno", "language:nob", "language:npi", "language:nso", "language:nus", "language:nya", "language:oci", "language:ory", "language:pag", "language:pan", "language:pap", "language:pbt", "language:pes", "language:plt", "language:pol", "language:por", "language:prs", "language:quy", "language:ron", "language:run", "language:rus", "language:sag", "language:san", "language:sat", "language:scn", "language:shn", "language:sin", "language:slk", "language:slv", "language:smo", "language:sna", "language:snd", "language:som", "language:sot", "language:spa", "language:srd", "language:srp", "language:ssw", "language:sun", "language:swe", "language:swh", "language:szl", "language:tam", "language:taq", "language:tat", "language:tel", "language:tgk", "language:tgl", "language:tha", "language:tir", "language:tpi", "language:tsn", "language:tso", "language:tuk", "language:tum", "language:tur", "language:twi", "language:tzm", "language:uig", "language:ukr", "language:umb", "language:urd", "language:uzn", "language:vec", "language:vie", "language:war", "language:wol", "language:xho", "language:ydd", "language:yor", "language:yue", "language:zho", "language:zsm", "language:zul", "license:cc-by-sa-4.0", "conditional-text-generation", "arxiv:2207.04672", "region:us" ]

[ "2207.04672" ]

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[ "### Dataset Summary\n\n\nFLORES is a benchmark dataset for machine translation between English and low-resource languages.\n\n\n\n> \n> The creation of FLORES200 doubles the existing language coverage of FLORES-101.\n> Given the nature of the new languages, which have less standardization and require\n> more specialized professional translations, the verification process became more complex.\n> This required modifications to the translation workflow. FLORES-200 has several languages\n> which were not translated from English. Specifically, several languages were translated\n> from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also\n> includes two script alternatives for four languages. FLORES-200 consists of translations\n> from 842 distinct web articles, totaling 3001 sentences. These sentences are divided\n> into three splits: dev, devtest, and test (hidden). On average, sentences are approximately\n> 21 words long.\n> \n> \n> \n\n\nDisclaimer: \\*The Flores200 dataset is hosted by the Facebook and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.", "### Supported Tasks and Leaderboards", "#### Multilingual Machine Translation\n\n\nRefer to the Dynabench leaderboard) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.", "### Languages\n\n\nThe dataset contains parallel sentences for 200 languages, as mentioned in the original Github page for the project. Languages are identified with the ISO 639-3 code (e.g. 'eng', 'fra', 'rus') plus an additional code describing the script (e.g., \"eng\\_Latn\", \"ukr\\_Cyrl\"). See the webpage for code descriptions.\nUse the configuration 'all' to access the full set of parallel sentences for all the available languages in a single command.\nUse a hyphenated pairing to get two langauges in one datapoint (e.g., \"eng\\_Latn-ukr\\_Cyrl\" will provide sentences in the format below).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from the 'dev' split for the Ukrainian language ('ukr\\_Cyrl' config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.\n\n\nWhen using a hyphenated pairing or using the 'all' function, data will be presented as follows:\n\n\nThe text is provided as-in the original dataset, without further preprocessing or tokenization.", "### Data Fields\n\n\n* 'id': Row number for the data entry, starting at 1.\n* 'sentence': The full sentence in the specific language (may have \\_lang for pairings)\n* 'URL': The URL for the English article from which the sentence was extracted.\n* 'domain': The domain of the sentence.\n* 'topic': The topic of the sentence.\n* 'has\\_image': Whether the original article contains an image.\n* 'has\\_hyperlink': Whether the sentence contains a hyperlink.", "### Data Splits", "### Dataset Creation\n\n\nPlease refer to the original article No Language Left Behind: Scaling Human-Centered Machine Translation for additional information on dataset creation.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nSee paper for details.", "### Licensing Information\n\n\nLicensed with Creative Commons Attribution Share Alike 4.0. License available here.\n\n\nPlease cite the authors if you use these corpora in your work:" ]

[ "TAGS\n#task_categories-text2text-generation #task_categories-translation #annotations_creators-found #language_creators-expert-generated #multilinguality-multilingual #multilinguality-translation #size_categories-unknown #source_datasets-extended|flores #language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Standard Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-sa-4.0 #conditional-text-generation #arxiv-2207.04672 #region-us \n", "### Dataset Summary\n\n\nFLORES is a benchmark dataset for machine translation between English and low-resource languages.\n\n\n\n> \n> The creation of FLORES200 doubles the existing language coverage of FLORES-101.\n> Given the nature of the new languages, which have less standardization and require\n> more specialized professional translations, the verification process became more complex.\n> This required modifications to the translation workflow. FLORES-200 has several languages\n> which were not translated from English. Specifically, several languages were translated\n> from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also\n> includes two script alternatives for four languages. FLORES-200 consists of translations\n> from 842 distinct web articles, totaling 3001 sentences. These sentences are divided\n> into three splits: dev, devtest, and test (hidden). On average, sentences are approximately\n> 21 words long.\n> \n> \n> \n\n\nDisclaimer: \\*The Flores200 dataset is hosted by the Facebook and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.", "### Supported Tasks and Leaderboards", "#### Multilingual Machine Translation\n\n\nRefer to the Dynabench leaderboard) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.", "### Languages\n\n\nThe dataset contains parallel sentences for 200 languages, as mentioned in the original Github page for the project. Languages are identified with the ISO 639-3 code (e.g. 'eng', 'fra', 'rus') plus an additional code describing the script (e.g., \"eng\\_Latn\", \"ukr\\_Cyrl\"). See the webpage for code descriptions.\nUse the configuration 'all' to access the full set of parallel sentences for all the available languages in a single command.\nUse a hyphenated pairing to get two langauges in one datapoint (e.g., \"eng\\_Latn-ukr\\_Cyrl\" will provide sentences in the format below).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from the 'dev' split for the Ukrainian language ('ukr\\_Cyrl' config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.\n\n\nWhen using a hyphenated pairing or using the 'all' function, data will be presented as follows:\n\n\nThe text is provided as-in the original dataset, without further preprocessing or tokenization.", "### Data Fields\n\n\n* 'id': Row number for the data entry, starting at 1.\n* 'sentence': The full sentence in the specific language (may have \\_lang for pairings)\n* 'URL': The URL for the English article from which the sentence was extracted.\n* 'domain': The domain of the sentence.\n* 'topic': The topic of the sentence.\n* 'has\\_image': Whether the original article contains an image.\n* 'has\\_hyperlink': Whether the sentence contains a hyperlink.", "### Data Splits", "### Dataset Creation\n\n\nPlease refer to the original article No Language Left Behind: Scaling Human-Centered Machine Translation for additional information on dataset creation.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nSee paper for details.", "### Licensing Information\n\n\nLicensed with Creative Commons Attribution Share Alike 4.0. License available here.\n\n\nPlease cite the authors if you use these corpora in your work:" ]

[ 1311, 239, 10, 57, 179, 105, 122, 5, 42, 11, 35 ]

[ "passage: ", "passage: TAGS\n#task_categories-text2text-generation #task_categories-translation #annotations_creators-found #language_creators-expert-generated #multilinguality-multilingual #multilinguality-translation #size_categories-unknown #source_datasets-extended|flores #language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Standard Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-sa-4.0 #conditional-text-generation #arxiv-2207.04672 #region-us \n### Dataset Summary\n\n\nFLORES is a benchmark dataset for machine translation between English and low-resource languages.\n\n\n\n> \n> The creation of FLORES200 doubles the existing language coverage of FLORES-101.\n> Given the nature of the new languages, which have less standardization and require\n> more specialized professional translations, the verification process became more complex.\n> This required modifications to the translation workflow. FLORES-200 has several languages\n> which were not translated from English. Specifically, several languages were translated\n> from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also\n> includes two script alternatives for four languages. FLORES-200 consists of translations\n> from 842 distinct web articles, totaling 3001 sentences. These sentences are divided\n> into three splits: dev, devtest, and test (hidden). On average, sentences are approximately\n> 21 words long.\n> \n> \n> \n\n\nDisclaimer: \\*The Flores200 dataset is hosted by the Facebook and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.### Supported Tasks and Leaderboards#### Multilingual Machine Translation\n\n\nRefer to the Dynabench leaderboard) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.### Languages\n\n\nThe dataset contains parallel sentences for 200 languages, as mentioned in the original Github page for the project. Languages are identified with the ISO 639-3 code (e.g. 'eng', 'fra', 'rus') plus an additional code describing the script (e.g., \"eng\\_Latn\", \"ukr\\_Cyrl\"). See the webpage for code descriptions.\nUse the configuration 'all' to access the full set of parallel sentences for all the available languages in a single command.\nUse a hyphenated pairing to get two langauges in one datapoint (e.g., \"eng\\_Latn-ukr\\_Cyrl\" will provide sentences in the format below).\n\n\nDataset Structure\n-----------------" ]

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

[]

[]

[]

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

[ 11 ]

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

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

[]

[]

[ "# Dataset Summary\nThe Great Books dataset is a set of texts based on the St. John's College Great Books Program. It includes 83 works from authors on the Program." ]

[ "TAGS\n#license-unlicense #region-us \n", "# Dataset Summary\nThe Great Books dataset is a set of texts based on the St. John's College Great Books Program. It includes 83 works from authors on the Program." ]

[ 13, 40 ]

[ "passage: TAGS\n#license-unlicense #region-us \n# Dataset Summary\nThe Great Books dataset is a set of texts based on the St. John's College Great Books Program. It includes 83 works from authors on the Program." ]

[ "language:en", "language:fr", "language:ja", "language:pt", "language:ru", "language:zh", "license:cc-by-4.0", "arxiv:2211.01786", "arxiv:2106.12066", "region:us" ]

[ "2211.01786", "2106.12066" ]

[ "en", "fr", "ja", "pt", "ru", "zh" ]

[ "## XWinograd\n\nMultilingual winograd schema challenge as used in Crosslingual Generalization through Multitask Finetuning.", "### Languages & Samples\n\n- \"en\": 2325\n- \"fr\": 83\n- \"jp\": 959\n- \"pt\": 263 \n- \"ru\": 315\n- \"zh\": 504", "### Dataset creation\n\nThe Winograd schema challenges in this dataset combine winograd schemas from the XWinograd dataset introduced in Tikhonov et al and as it only contains 16 Chinese schemas, we add 488 Chinese schemas from 'clue/cluewsc2020'.\n\nIf you only want the original xwinograd chinese schemas only, do:\n\n'load_dataset(\"Muennighoff/xwinograd\", \"zh\")[\"test\"][0][:16]'", "## Additional Information", "### License\n\nLike the original English winograd schema challenge, this dataset is licensed under CC BY 4.0. I.e. you can use it for commercial purposes etc. :)", "### Contributions\n\nThanks to Jordan Clive, @yongzx & @khalidalt for support on adding Chinese." ]

[ "TAGS\n#language-English #language-French #language-Japanese #language-Portuguese #language-Russian #language-Chinese #license-cc-by-4.0 #arxiv-2211.01786 #arxiv-2106.12066 #region-us \n", "## XWinograd\n\nMultilingual winograd schema challenge as used in Crosslingual Generalization through Multitask Finetuning.", "### Languages & Samples\n\n- \"en\": 2325\n- \"fr\": 83\n- \"jp\": 959\n- \"pt\": 263 \n- \"ru\": 315\n- \"zh\": 504", "### Dataset creation\n\nThe Winograd schema challenges in this dataset combine winograd schemas from the XWinograd dataset introduced in Tikhonov et al and as it only contains 16 Chinese schemas, we add 488 Chinese schemas from 'clue/cluewsc2020'.\n\nIf you only want the original xwinograd chinese schemas only, do:\n\n'load_dataset(\"Muennighoff/xwinograd\", \"zh\")[\"test\"][0][:16]'", "## Additional Information", "### License\n\nLike the original English winograd schema challenge, this dataset is licensed under CC BY 4.0. I.e. you can use it for commercial purposes etc. :)", "### Contributions\n\nThanks to Jordan Clive, @yongzx & @khalidalt for support on adding Chinese." ]

[ 64, 29, 47, 114, 5, 39, 26 ]

[ "passage: TAGS\n#language-English #language-French #language-Japanese #language-Portuguese #language-Russian #language-Chinese #license-cc-by-4.0 #arxiv-2211.01786 #arxiv-2106.12066 #region-us \n## XWinograd\n\nMultilingual winograd schema challenge as used in Crosslingual Generalization through Multitask Finetuning.### Languages & Samples\n\n- \"en\": 2325\n- \"fr\": 83\n- \"jp\": 959\n- \"pt\": 263 \n- \"ru\": 315\n- \"zh\": 504### Dataset creation\n\nThe Winograd schema challenges in this dataset combine winograd schemas from the XWinograd dataset introduced in Tikhonov et al and as it only contains 16 Chinese schemas, we add 488 Chinese schemas from 'clue/cluewsc2020'.\n\nIf you only want the original xwinograd chinese schemas only, do:\n\n'load_dataset(\"Muennighoff/xwinograd\", \"zh\")[\"test\"][0][:16]'## Additional Information### License\n\nLike the original English winograd schema challenge, this dataset is licensed under CC BY 4.0. I.e. you can use it for commercial purposes etc. :)### Contributions\n\nThanks to Jordan Clive, @yongzx & @khalidalt for support on adding Chinese." ]

[ "task_categories:image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "size_categories:1K<n<10K", "source_datasets:original", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Dataset Card for OpenFire", "## 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: URL\n- Point of Contact: Pyronear <URL", "### Dataset Summary\n\nOpenFire is an image classification dataset for wildfire detection, collected\nfrom web searches.", "### Supported Tasks and Leaderboards\n\n- 'image-classification': The dataset can be used to train a model for Image Classification.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nA data point comprises an image URL and its binary label.", "### Data Fields\n\n- 'image_url': the download URL of the image.\n- 'is_wildfire': a boolean value specifying whether there is an ongoing wildfire on the image.", "### Data Splits\n\nThe data is split into training and validation sets. The training set contains 7143 images and the validation set 792 images.", "## Dataset Creation", "### Curation Rationale\n\nThe curators state that the current wildfire classification datasets typically contain close-up shots of wildfires, with limited variations of weather conditions, luminosity and backrgounds,\nmaking it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping\nwith sun flares, foggy / cloudy weather conditions and small scale.", "### Source Data", "#### Initial Data Collection and Normalization\n\nOpenFire was collected using images publicly indexed by the search engine DuckDuckGo using multiple relevant queries. The images were then manually cleaned to remove errors.", "### Annotations", "#### Annotation process\n\nEach web search query was designed to yield a single label (with wildfire or without), and additional human verification was used to remove errors.", "#### Who are the annotators?\n\nFrançois-Guillaume Fernandez", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nFrançois-Guillaume Fernandez", "### Licensing Information\n\nApache License 2.0." ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-1K<n<10K #source_datasets-original #license-apache-2.0 #region-us \n", "# Dataset Card for OpenFire", "## 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: URL\n- Point of Contact: Pyronear <URL", "### Dataset Summary\n\nOpenFire is an image classification dataset for wildfire detection, collected\nfrom web searches.", "### Supported Tasks and Leaderboards\n\n- 'image-classification': The dataset can be used to train a model for Image Classification.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nA data point comprises an image URL and its binary label.", "### Data Fields\n\n- 'image_url': the download URL of the image.\n- 'is_wildfire': a boolean value specifying whether there is an ongoing wildfire on the image.", "### Data Splits\n\nThe data is split into training and validation sets. The training set contains 7143 images and the validation set 792 images.", "## Dataset Creation", "### Curation Rationale\n\nThe curators state that the current wildfire classification datasets typically contain close-up shots of wildfires, with limited variations of weather conditions, luminosity and backrgounds,\nmaking it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping\nwith sun flares, foggy / cloudy weather conditions and small scale.", "### Source Data", "#### Initial Data Collection and Normalization\n\nOpenFire was collected using images publicly indexed by the search engine DuckDuckGo using multiple relevant queries. The images were then manually cleaned to remove errors.", "### Annotations", "#### Annotation process\n\nEach web search query was designed to yield a single label (with wildfire or without), and additional human verification was used to remove errors.", "#### Who are the annotators?\n\nFrançois-Guillaume Fernandez", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nFrançois-Guillaume Fernandez", "### Licensing Information\n\nApache License 2.0." ]

[ 69, 8, 125, 24, 29, 33, 5, 6, 20, 46, 34, 5, 102, 4, 50, 5, 37, 15, 8, 8, 7, 8, 7, 5, 12, 11 ]

[ "passage: TAGS\n#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-1K<n<10K #source_datasets-original #license-apache-2.0 #region-us \n# Dataset Card for OpenFire## 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: URL\n- Point of Contact: Pyronear <URL### Dataset Summary\n\nOpenFire is an image classification dataset for wildfire detection, collected\nfrom web searches.### Supported Tasks and Leaderboards\n\n- 'image-classification': The dataset can be used to train a model for Image Classification.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image URL and its binary label.### Data Fields\n\n- 'image_url': the download URL of the image.\n- 'is_wildfire': a boolean value specifying whether there is an ongoing wildfire on the image.### Data Splits\n\nThe data is split into training and validation sets. The training set contains 7143 images and the validation set 792 images.## Dataset Creation### Curation Rationale\n\nThe curators state that the current wildfire classification datasets typically contain close-up shots of wildfires, with limited variations of weather conditions, luminosity and backrgounds,\nmaking it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping\nwith sun flares, foggy / cloudy weather conditions and small scale." ]

[ "task_categories:text-classification", "task_categories:fill-mask", "task_ids:multi-label-classification", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:\n\n\n* The corpus covers the period 1710\u00131920, divided into three 70-year sub-periods.\n* The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.\n* The corpus never contains more than three texts by the same author.\n* The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period.", "### Supported Tasks and Leaderboards\n\n\n* 'named-entity-recognition': Since this dataset is tagged, it can be used for performing NER\n* 'text-classification': Each text comes with the date of the text and can be used to perform stylistic classification of texts", "### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA 'plain' sample looks as follows:\n\n\nA 'pos' sample looks as follows:", "### Data Fields\n\n\nThere are three configs in this dataset- 'plain', 'class' and 'pos'. 'plain' is a simple text dataset whereas 'pos' and 'class' are both annotated datasets containing pos tagging. A 'plain' data point has the following fields:\n\n\nA typical 'pos'/'class' data point has the following fields:", "### Data Splits\n\n\nTrain: 333\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of\nBritish English from 1710\u00131920, grouped into three 70-year periods (De Smet 2005; Diller et\nal. 2011). The history, versions and specifics of corpus composition can be followed up by\nreferring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)\nplain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence\nper line).\nVersion CLMET3.1 is the result of making CLMET available in a CQP format for use in\nCWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While\nthere is no change to the selection of texts, CLMET3.1 includes additions and changes in\nlinguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization\nand retagging, (b) fixing of some systematic issues that come with historical data, and (c)\nenhancing annotation by adding lemmas and simplified part-of-speech class tags", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe initial data is from OCR of texts in English from 1710-1920", "#### Who are the source language producers?\n\n\nThe text was produced by the authors of the original work and then OCRd", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nThis dataset does not contain any personal information as these are historic texts. Some content might be sensitive\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nDealing with historical data, tagging remains problematic in all areas, and should be treated\nwith caution (especially with noun recognition) and/or combined with more coarse-grained\nclass queries. Also bear in mind that the lemmas for unknown items are in lower\ncase, while proper names that the tagger did recognize are not necessarily all lower case. In\naddition, lemmatization may not be consistent, e.g. in the area of -ize/ise spellings; these were\nnot homogenized to preserve as much of the original orthography as possible.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�", "### Licensing Information\n\n\nCreative Commons Attribution Non Commercial Share Alike 4.0 International" ]

[ "TAGS\n#task_categories-text-classification #task_categories-fill-mask #task_ids-multi-label-classification #task_ids-masked-language-modeling #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nThe Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:\n\n\n* The corpus covers the period 1710\u00131920, divided into three 70-year sub-periods.\n* The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.\n* The corpus never contains more than three texts by the same author.\n* The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period.", "### Supported Tasks and Leaderboards\n\n\n* 'named-entity-recognition': Since this dataset is tagged, it can be used for performing NER\n* 'text-classification': Each text comes with the date of the text and can be used to perform stylistic classification of texts", "### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA 'plain' sample looks as follows:\n\n\nA 'pos' sample looks as follows:", "### Data Fields\n\n\nThere are three configs in this dataset- 'plain', 'class' and 'pos'. 'plain' is a simple text dataset whereas 'pos' and 'class' are both annotated datasets containing pos tagging. A 'plain' data point has the following fields:\n\n\nA typical 'pos'/'class' data point has the following fields:", "### Data Splits\n\n\nTrain: 333\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of\nBritish English from 1710\u00131920, grouped into three 70-year periods (De Smet 2005; Diller et\nal. 2011). The history, versions and specifics of corpus composition can be followed up by\nreferring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)\nplain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence\nper line).\nVersion CLMET3.1 is the result of making CLMET available in a CQP format for use in\nCWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While\nthere is no change to the selection of texts, CLMET3.1 includes additions and changes in\nlinguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization\nand retagging, (b) fixing of some systematic issues that come with historical data, and (c)\nenhancing annotation by adding lemmas and simplified part-of-speech class tags", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe initial data is from OCR of texts in English from 1710-1920", "#### Who are the source language producers?\n\n\nThe text was produced by the authors of the original work and then OCRd", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nThis dataset does not contain any personal information as these are historic texts. Some content might be sensitive\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nDealing with historical data, tagging remains problematic in all areas, and should be treated\nwith caution (especially with noun recognition) and/or combined with more coarse-grained\nclass queries. Also bear in mind that the lemmas for unknown items are in lower\ncase, while proper names that the tagger did recognize are not necessarily all lower case. In\naddition, lemmatization may not be consistent, e.g. in the area of -ize/ise spellings; these were\nnot homogenized to preserve as much of the original orthography as possible.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�", "### Licensing Information\n\n\nCreative Commons Attribution Non Commercial Share Alike 4.0 International" ]

[ 128, 238, 69, 31, 27, 92, 15, 261, 4, 27, 27, 5, 5, 9, 39, 7, 8, 141, 47, 18 ]

[ "passage: TAGS\n#task_categories-text-classification #task_categories-fill-mask #task_ids-multi-label-classification #task_ids-masked-language-modeling #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nThe Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:\n\n\n* The corpus covers the period 1710\u00131920, divided into three 70-year sub-periods.\n* The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.\n* The corpus never contains more than three texts by the same author.\n* The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period.### Supported Tasks and Leaderboards\n\n\n* 'named-entity-recognition': Since this dataset is tagged, it can be used for performing NER\n* 'text-classification': Each text comes with the date of the text and can be used to perform stylistic classification of texts### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA 'plain' sample looks as follows:\n\n\nA 'pos' sample looks as follows:", "passage: ### Data Fields\n\n\nThere are three configs in this dataset- 'plain', 'class' and 'pos'. 'plain' is a simple text dataset whereas 'pos' and 'class' are both annotated datasets containing pos tagging. A 'plain' data point has the following fields:\n\n\nA typical 'pos'/'class' data point has the following fields:### Data Splits\n\n\nTrain: 333\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nThe Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of\nBritish English from 1710\u00131920, grouped into three 70-year periods (De Smet 2005; Diller et\nal. 2011). The history, versions and specifics of corpus composition can be followed up by\nreferring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)\nplain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence\nper line).\nVersion CLMET3.1 is the result of making CLMET available in a CQP format for use in\nCWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While\nthere is no change to the selection of texts, CLMET3.1 includes additions and changes in\nlinguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization\nand retagging, (b) fixing of some systematic issues that come with historical data, and (c)\nenhancing annotation by adding lemmas and simplified part-of-speech class tags### Source Data#### Initial Data Collection and Normalization\n\n\nThe initial data is from OCR of texts in English from 1710-1920#### Who are the source language producers?\n\n\nThe text was produced by the authors of the original work and then OCRd### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nThis dataset does not contain any personal information as these are historic texts. Some content might be sensitive\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases" ]

[ "task_categories:image-to-text", "size_categories:100B<n<1T", "language:en", "license:other", "arxiv:1908.07836", "region:us" ]

[ "1908.07836" ]

[ "en" ]

[ "# PubLayNet\n\nPubLayNet is a large dataset of document images, of which the layout is annotated with both bounding boxes and polygonal segmentations. The source of the documents is PubMed Central Open Access Subset (commercial use collection). The annotations are automatically generated by matching the PDF format and the XML format of the articles in the PubMed Central Open Access Subset. More details are available in our paper \"PubLayNet: largest dataset ever for document layout analysis.\".\n\nThe public dataset is in URL format which doesn't fit nicely with huggingface streaming. Modifications have been made to optimise the delivery of the dataset for the hugginface datset api. The original files can be found here. \n\nLicence: Community Data License Agreement – Permissive – Version 1.0 License\n\nAuthor: IBM\n\nGitHub: URL\n\n@article{ zhong2019publaynet,\n title = { PubLayNet: largest dataset ever for document layout analysis },\n author = { Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno },\n journal = { arXiv preprint arXiv:1908.07836},\n year. = { 2019 }\n}" ]

[ "TAGS\n#task_categories-image-to-text #size_categories-100B<n<1T #language-English #license-other #arxiv-1908.07836 #region-us \n", "# PubLayNet\n\nPubLayNet is a large dataset of document images, of which the layout is annotated with both bounding boxes and polygonal segmentations. The source of the documents is PubMed Central Open Access Subset (commercial use collection). The annotations are automatically generated by matching the PDF format and the XML format of the articles in the PubMed Central Open Access Subset. More details are available in our paper \"PubLayNet: largest dataset ever for document layout analysis.\".\n\nThe public dataset is in URL format which doesn't fit nicely with huggingface streaming. Modifications have been made to optimise the delivery of the dataset for the hugginface datset api. The original files can be found here. \n\nLicence: Community Data License Agreement – Permissive – Version 1.0 License\n\nAuthor: IBM\n\nGitHub: URL\n\n@article{ zhong2019publaynet,\n title = { PubLayNet: largest dataset ever for document layout analysis },\n author = { Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno },\n journal = { arXiv preprint arXiv:1908.07836},\n year. = { 2019 }\n}" ]

[ 47, 262 ]

[ "passage: TAGS\n#task_categories-image-to-text #size_categories-100B<n<1T #language-English #license-other #arxiv-1908.07836 #region-us \n# PubLayNet\n\nPubLayNet is a large dataset of document images, of which the layout is annotated with both bounding boxes and polygonal segmentations. The source of the documents is PubMed Central Open Access Subset (commercial use collection). The annotations are automatically generated by matching the PDF format and the XML format of the articles in the PubMed Central Open Access Subset. More details are available in our paper \"PubLayNet: largest dataset ever for document layout analysis.\".\n\nThe public dataset is in URL format which doesn't fit nicely with huggingface streaming. Modifications have been made to optimise the delivery of the dataset for the hugginface datset api. The original files can be found here. \n\nLicence: Community Data License Agreement – Permissive – Version 1.0 License\n\nAuthor: IBM\n\nGitHub: URL\n\n@article{ zhong2019publaynet,\n title = { PubLayNet: largest dataset ever for document layout analysis },\n author = { Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno },\n journal = { arXiv preprint arXiv:1908.07836},\n year. = { 2019 }\n}" ]

[ "task_categories:image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "size_categories:1K<n<10K", "source_datasets:extended", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nA smaller subset of 10 easily classified classes from Imagenet, and a little more French.\nThis dataset was created by Jeremy Howard, and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The dataset can be used to train a model for Image Classification.", "### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA data point comprises an image URL and its classification label.", "### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'label': the expected class label of the image.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\ncf. URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nImagenette is a subset of ImageNet. Information about data collection of the source data can be found here.", "### Annotations", "#### Annotation process\n\n\ncf. URL", "#### Who are the annotators?\n\n\ncf. URL", "### Personal and Sensitive Information\n\n\ncf. URL\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\ncf. URL", "### Discussion of Biases\n\n\ncf. URL", "### Other Known Limitations\n\n\ncf. URL\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\ncf. URL\nand Jeremy Howard", "### Licensing Information\n\n\nApache License 2.0.", "### Contributions\n\n\nThis dataset was created by Jeremy Howard and published on Github. It was then only integrated into HuggingFace Datasets by @frgfm." ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-1K<n<10K #source_datasets-extended #language-English #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nA smaller subset of 10 easily classified classes from Imagenet, and a little more French.\nThis dataset was created by Jeremy Howard, and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The dataset can be used to train a model for Image Classification.", "### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA data point comprises an image URL and its classification label.", "### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'label': the expected class label of the image.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\ncf. URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nImagenette is a subset of ImageNet. Information about data collection of the source data can be found here.", "### Annotations", "#### Annotation process\n\n\ncf. URL", "#### Who are the annotators?\n\n\ncf. URL", "### Personal and Sensitive Information\n\n\ncf. URL\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\ncf. URL", "### Discussion of Biases\n\n\ncf. URL", "### Other Known Limitations\n\n\ncf. URL\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\ncf. URL\nand Jeremy Howard", "### Licensing Information\n\n\nApache License 2.0.", "### Contributions\n\n\nThis dataset was created by Jeremy Howard and published on Github. It was then only integrated into HuggingFace Datasets by @frgfm." ]

[ 75, 75, 33, 23, 20, 37, 11, 11, 4, 33, 5, 9, 13, 22, 11, 12, 18, 13, 11, 41 ]

[ "passage: TAGS\n#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-1K<n<10K #source_datasets-extended #language-English #license-apache-2.0 #region-us \n### Dataset Summary\n\n\nA smaller subset of 10 easily classified classes from Imagenet, and a little more French.\nThis dataset was created by Jeremy Howard, and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The dataset can be used to train a model for Image Classification.### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA data point comprises an image URL and its classification label.### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'label': the expected class label of the image.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\ncf. URL### Source Data#### Initial Data Collection and Normalization\n\n\nImagenette is a subset of ImageNet. Information about data collection of the source data can be found here.### Annotations#### Annotation process\n\n\ncf. URL#### Who are the annotators?\n\n\ncf. URL### Personal and Sensitive Information\n\n\ncf. URL\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\ncf. URL### Discussion of Biases\n\n\ncf. URL### Other Known Limitations\n\n\ncf. URL\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\ncf. URL\nand Jeremy Howard### Licensing Information\n\n\nApache License 2.0.### Contributions\n\n\nThis dataset was created by Jeremy Howard and published on Github. It was then only integrated into HuggingFace Datasets by @frgfm." ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:1k<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBroad Twitter Corpus NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'LOC', 'ORG', 'PER'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.", "### Data Splits" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1k<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nBroad Twitter Corpus NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'LOC', 'ORG', 'PER'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.", "### Data Splits" ]

[ 60, 48, 18, 17, 5 ]

[ "passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1k<10K #language-English #license-other #region-us \n### Dataset Summary\n\n\nBroad Twitter Corpus NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'LOC', 'ORG', 'PER'\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'train' looks as follows.### Label ID\n\n\nThe label2id dictionary can be found at here.### Data Splits" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:1k<10K", "language:en", "license:other", "arxiv:2201.07281", "region:us" ]

[ "2201.07281" ]

[ "en" ]

[ "### Dataset Summary\n\n\nTweeBank NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'LOC', 'MISC', 'PER', 'ORG'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.", "### Data Splits" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1k<10K #language-English #license-other #arxiv-2201.07281 #region-us \n", "### Dataset Summary\n\n\nTweeBank NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'LOC', 'MISC', 'PER', 'ORG'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.", "### Data Splits" ]

[ 69, 51, 18, 17, 5 ]

[ "passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1k<10K #language-English #license-other #arxiv-2201.07281 #region-us \n### Dataset Summary\n\n\nTweeBank NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'LOC', 'MISC', 'PER', 'ORG'\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'train' looks as follows.### Label ID\n\n\nThe label2id dictionary can be found at here.### Data Splits" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:1k<10K", "language:en", "license:other", "arxiv:2210.03797", "region:us" ]

[ "2210.03797" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is the official repository of TweetNER7 (\"Named Entity Recognition in Twitter:\nA Dataset and Analysis on Short-Term Temporal Shifts, AACL main conference 2022\"), an NER dataset on Twitter with 7 entity labels. Each instance of TweetNER7 comes with a timestamp which distributes from September 2019 to August 2021.\nThe tweet collection used in TweetNER7 is same as what used in TweetTopic.\nThe dataset is integrated in TweetNLP too.\n\n\n* Entity Types: 'corperation', 'creative\\_work', 'event', 'group', 'location', 'product', 'person'", "### Preprocessing\n\n\nWe pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token '{{URL}}' and non-verified usernames into '{{USERNAME}}'.\nFor verified usernames, we replace its display name (or account name) with symbols '{@}'.\nFor example, a tweet\n\n\nis transformed into the following text.\n\n\nA simple function to format tweet follows below.\n\n\nWe ask annotators to ignore those special tokens but label the verified users' mentions.", "### Data Split\n\n\n\nFor the temporal-shift setting, model should be trained on 'train\\_2020' with 'validation\\_2020' and evaluate on 'test\\_2021'.\nIn general, model would be trained on 'train\\_all', the most representative training set with 'validation\\_2021' and evaluate on 'test\\_2021'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.\n\n\nModels\n------\n\n\nSee full evaluation metrics here.", "### Main Models\n\n\n\nModel description follows below.\n\n\n* Model with suffix '-all': Model fine-tuned on 'train\\_all' and validated on 'validation\\_2021'.\n* Model with suffix '-continuous': Model fine-tuned on 'train\\_2021' continuously after fine-tuning on 'train\\_2020' and validated on 'validation\\_2021'.\n* Model with suffix '-2021': Model fine-tuned only on 'train\\_2021' and validated on 'validation\\_2021'.\n* Model with suffix '-2020': Model fine-tuned only on 'train\\_2021' and validated on 'validation\\_2020'.", "### Sub Models (used in ablation study)\n\n\n* Model fine-tuned only on 'train\\_random' and validated on 'validation\\_2020'.\n\n\n\n* Model fine-tuned on the self-labeled dataset on 'extra\\_{2020,2021}' and validated on 'validation\\_2020'.\n\n\n\nModel description follows below.\n\n\n* Model with suffix '-self2020': Fine-tuning on the self-annotated data of 'extra\\_2020' split of tweetner7.\n* Model with suffix '-self2021': Fine-tuning on the self-annotated data of 'extra\\_2021' split of tweetner7.\n* Model with suffix '-2020-self2020-all': Fine-tuning on the self-annotated data of 'extra\\_2020' split of tweetner7. Combined training dataset of 'extra\\_2020' and 'train\\_2020'.\n* Model with suffix '-2020-self2021-all': Fine-tuning on the self-annotated data of 'extra\\_2021' split of tweetner7. Combined training dataset of 'extra\\_2021' and 'train\\_2020'.\n* Model with suffix '-2020-self2020-continuous': Fine-tuning on the self-annotated data of 'extra\\_2020' split of tweetner7. Fine-tuning on 'train\\_2020' and continuing fine-tuning on 'extra\\_2020'.\n* Model with suffix '-2020-self2021-continuous': Fine-tuning on the self-annotated data of 'extra\\_2021' split of tweetner7. Fine-tuning on 'train\\_2020' and continuing fine-tuning on 'extra\\_2020'.", "### Reproduce Experimental Result\n\n\nTo reproduce the experimental result on our AACL paper, please see the repository\nURL" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1k<10K #language-English #license-other #arxiv-2210.03797 #region-us \n", "### Dataset Summary\n\n\nThis is the official repository of TweetNER7 (\"Named Entity Recognition in Twitter:\nA Dataset and Analysis on Short-Term Temporal Shifts, AACL main conference 2022\"), an NER dataset on Twitter with 7 entity labels. Each instance of TweetNER7 comes with a timestamp which distributes from September 2019 to August 2021.\nThe tweet collection used in TweetNER7 is same as what used in TweetTopic.\nThe dataset is integrated in TweetNLP too.\n\n\n* Entity Types: 'corperation', 'creative\\_work', 'event', 'group', 'location', 'product', 'person'", "### Preprocessing\n\n\nWe pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token '{{URL}}' and non-verified usernames into '{{USERNAME}}'.\nFor verified usernames, we replace its display name (or account name) with symbols '{@}'.\nFor example, a tweet\n\n\nis transformed into the following text.\n\n\nA simple function to format tweet follows below.\n\n\nWe ask annotators to ignore those special tokens but label the verified users' mentions.", "### Data Split\n\n\n\nFor the temporal-shift setting, model should be trained on 'train\\_2020' with 'validation\\_2020' and evaluate on 'test\\_2021'.\nIn general, model would be trained on 'train\\_all', the most representative training set with 'validation\\_2021' and evaluate on 'test\\_2021'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.\n\n\nModels\n------\n\n\nSee full evaluation metrics here.", "### Main Models\n\n\n\nModel description follows below.\n\n\n* Model with suffix '-all': Model fine-tuned on 'train\\_all' and validated on 'validation\\_2021'.\n* Model with suffix '-continuous': Model fine-tuned on 'train\\_2021' continuously after fine-tuning on 'train\\_2020' and validated on 'validation\\_2021'.\n* Model with suffix '-2021': Model fine-tuned only on 'train\\_2021' and validated on 'validation\\_2021'.\n* Model with suffix '-2020': Model fine-tuned only on 'train\\_2021' and validated on 'validation\\_2020'.", "### Sub Models (used in ablation study)\n\n\n* Model fine-tuned only on 'train\\_random' and validated on 'validation\\_2020'.\n\n\n\n* Model fine-tuned on the self-labeled dataset on 'extra\\_{2020,2021}' and validated on 'validation\\_2020'.\n\n\n\nModel description follows below.\n\n\n* Model with suffix '-self2020': Fine-tuning on the self-annotated data of 'extra\\_2020' split of tweetner7.\n* Model with suffix '-self2021': Fine-tuning on the self-annotated data of 'extra\\_2021' split of tweetner7.\n* Model with suffix '-2020-self2020-all': Fine-tuning on the self-annotated data of 'extra\\_2020' split of tweetner7. Combined training dataset of 'extra\\_2020' and 'train\\_2020'.\n* Model with suffix '-2020-self2021-all': Fine-tuning on the self-annotated data of 'extra\\_2021' split of tweetner7. Combined training dataset of 'extra\\_2021' and 'train\\_2020'.\n* Model with suffix '-2020-self2020-continuous': Fine-tuning on the self-annotated data of 'extra\\_2020' split of tweetner7. Fine-tuning on 'train\\_2020' and continuing fine-tuning on 'extra\\_2020'.\n* Model with suffix '-2020-self2021-continuous': Fine-tuning on the self-annotated data of 'extra\\_2021' split of tweetner7. Fine-tuning on 'train\\_2020' and continuing fine-tuning on 'extra\\_2020'.", "### Reproduce Experimental Result\n\n\nTo reproduce the experimental result on our AACL paper, please see the repository\nURL" ]

[ 69, 156, 124, 95, 18, 28, 172, 411, 26 ]

[ "passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1k<10K #language-English #license-other #arxiv-2210.03797 #region-us \n### Dataset Summary\n\n\nThis is the official repository of TweetNER7 (\"Named Entity Recognition in Twitter:\nA Dataset and Analysis on Short-Term Temporal Shifts, AACL main conference 2022\"), an NER dataset on Twitter with 7 entity labels. Each instance of TweetNER7 comes with a timestamp which distributes from September 2019 to August 2021.\nThe tweet collection used in TweetNER7 is same as what used in TweetTopic.\nThe dataset is integrated in TweetNLP too.\n\n\n* Entity Types: 'corperation', 'creative\\_work', 'event', 'group', 'location', 'product', 'person'### Preprocessing\n\n\nWe pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token '{{URL}}' and non-verified usernames into '{{USERNAME}}'.\nFor verified usernames, we replace its display name (or account name) with symbols '{@}'.\nFor example, a tweet\n\n\nis transformed into the following text.\n\n\nA simple function to format tweet follows below.\n\n\nWe ask annotators to ignore those special tokens but label the verified users' mentions.### Data Split\n\n\n\nFor the temporal-shift setting, model should be trained on 'train\\_2020' with 'validation\\_2020' and evaluate on 'test\\_2021'.\nIn general, model would be trained on 'train\\_all', the most representative training set with 'validation\\_2021' and evaluate on 'test\\_2021'.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'train' looks as follows.### Label ID\n\n\nThe label2id dictionary can be found at here.\n\n\nModels\n------\n\n\nSee full evaluation metrics here." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "task_categories:summarization", "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-MS^2", "source_datasets:extended|other-Cochrane", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical evidence from different clinical studies are summarized in literature reviews. Reviews provide the highest quality of evidence for clinical care, but are expensive to produce manually. (Semi-)automation via NLP may facilitate faster evidence synthesis without sacrificing rigor. The MSLR shared task uses two datasets to assess the current state of multidocument summarization for this task, and to encourage the development of modeling contributions, scaffolding tasks, methods for model interpretability, and improved automated evaluation methods in this domain.", "### Supported Tasks and Leaderboards\n\n\nThis dataset is used for the MSLR2022 Shared Task. For information on the shared task leaderboard, please refer here.", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\nMore information on dataset structure here.", "### Data Instances\n\n\n**MS^2**\n\n\n**Cochrane**", "### Data Fields\n\n\n**MS^2**\n\n\n* '\"review\\_id\"': The PubMed ID of the review.\n* '\"pmid\"': The PubMed IDs of the included studies.\n* '\"title\"': The titles of the included studies.\n* '\"abstract\"': The abstracts of the included studies.\n* '\"target\"': The conclusions, taken from the abstract of the review, that serve as the summarization target.\n* '\"background\"': A description of the reviews objective.\n\n\n**Cochrane**\n\n\n* '\"review\\_id\"': The PubMed ID of the review.\n* '\"pmid\"': The PubMed IDs of the included studies.\n* '\"title\"': The titles of the included studies.\n* '\"abstract\"': The abstracts of the included studies.\n* '\"target\"': The conclusions, taken from the abstract of the review, that serve as the summarization target.", "### Data Splits\n\n\nEach dataset is split into training, validation and test partitions\n\n\n**MS^2**\n\n\n\n**Cochrane**\n\n\n\nDataset Creation\n----------------\n\n\nPlease refer to the following papers for details about dataset curation:\n\n\nMSˆ2: A Dataset for Multi-Document Summarization of Medical Studies\n\n\nGenerating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization", "### 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\nLicensing information can be found here.\n\n\nDeYoung, Jay, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl and Lucy Lu Wang. \"MS2: A Dataset for Multi-Document Summarization of Medical Studies.\" EMNLP (2021).\n\n\nByron C. Wallace, Sayantani Saha, Frank Soboczenski, and Iain James Marshall. (2020). \"Generating (factual?) narrative summaries of RCTs: Experiments with neural multi-document summarization.\" AMIA Annual Symposium." ]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-MS^2 #source_datasets-extended|other-Cochrane #language-English #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nThe Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical evidence from different clinical studies are summarized in literature reviews. Reviews provide the highest quality of evidence for clinical care, but are expensive to produce manually. (Semi-)automation via NLP may facilitate faster evidence synthesis without sacrificing rigor. The MSLR shared task uses two datasets to assess the current state of multidocument summarization for this task, and to encourage the development of modeling contributions, scaffolding tasks, methods for model interpretability, and improved automated evaluation methods in this domain.", "### Supported Tasks and Leaderboards\n\n\nThis dataset is used for the MSLR2022 Shared Task. For information on the shared task leaderboard, please refer here.", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\nMore information on dataset structure here.", "### Data Instances\n\n\n**MS^2**\n\n\n**Cochrane**", "### Data Fields\n\n\n**MS^2**\n\n\n* '\"review\\_id\"': The PubMed ID of the review.\n* '\"pmid\"': The PubMed IDs of the included studies.\n* '\"title\"': The titles of the included studies.\n* '\"abstract\"': The abstracts of the included studies.\n* '\"target\"': The conclusions, taken from the abstract of the review, that serve as the summarization target.\n* '\"background\"': A description of the reviews objective.\n\n\n**Cochrane**\n\n\n* '\"review\\_id\"': The PubMed ID of the review.\n* '\"pmid\"': The PubMed IDs of the included studies.\n* '\"title\"': The titles of the included studies.\n* '\"abstract\"': The abstracts of the included studies.\n* '\"target\"': The conclusions, taken from the abstract of the review, that serve as the summarization target.", "### Data Splits\n\n\nEach dataset is split into training, validation and test partitions\n\n\n**MS^2**\n\n\n\n**Cochrane**\n\n\n\nDataset Creation\n----------------\n\n\nPlease refer to the following papers for details about dataset curation:\n\n\nMSˆ2: A Dataset for Multi-Document Summarization of Medical Studies\n\n\nGenerating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization", "### 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\nLicensing information can be found here.\n\n\nDeYoung, Jay, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl and Lucy Lu Wang. \"MS2: A Dataset for Multi-Document Summarization of Medical Studies.\" EMNLP (2021).\n\n\nByron C. Wallace, Sayantani Saha, Frank Soboczenski, and Iain James Marshall. (2020). \"Generating (factual?) narrative summaries of RCTs: Experiments with neural multi-document summarization.\" AMIA Annual Symposium." ]

[ 117, 149, 38, 20, 16, 224, 95, 7, 4, 10, 10, 5, 5, 9, 18, 7, 8, 14, 6, 131 ]

[ "passage: TAGS\n#task_categories-summarization #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-MS^2 #source_datasets-extended|other-Cochrane #language-English #license-apache-2.0 #region-us \n### Dataset Summary\n\n\nThe Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical evidence from different clinical studies are summarized in literature reviews. Reviews provide the highest quality of evidence for clinical care, but are expensive to produce manually. (Semi-)automation via NLP may facilitate faster evidence synthesis without sacrificing rigor. The MSLR shared task uses two datasets to assess the current state of multidocument summarization for this task, and to encourage the development of modeling contributions, scaffolding tasks, methods for model interpretability, and improved automated evaluation methods in this domain.### Supported Tasks and Leaderboards\n\n\nThis dataset is used for the MSLR2022 Shared Task. For information on the shared task leaderboard, please refer here.### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------\n\n\nMore information on dataset structure here.### Data Instances\n\n\n**MS^2**\n\n\n**Cochrane**" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "source_datasets:wikipedia", "language:de", "license:cc-by-sa-3.0", "license:gfdl", "region:us" ]

[]

[ "de" ]

[ "# Dataset Card for yaakov/wikipedia-de-splits", "## Dataset Description\nThe only goal of this dataset is to have random German Wikipedia articles at\nvarious dataset sizes: Small datasets for fast development and large datasets for statistically relevant measurements.\n\nFor this purpose, I loaded the 2665357 articles in the 'test' set of the pre-processed German Wikipedia dump from 2022-03-01, randomly permuted the articles and created splits of sizes '2n': '1, 2, 4, 8, ...'. The split names are strings. The split ''all'' contains all 2665357 available articles.", "## Dataset creation\nThis dataset has been created with the following script:\n\n !apt install git-lfs\n !pip install -q transformers datasets\n \n from huggingface_hub import notebook_login\n notebook_login()\n \n from datasets import load_dataset\n wikipedia_de = load_dataset(\"wikipedia\", \"URL\")['train']\n \n shuffled = wikipedia_de.shuffle(seed=42)\n \n from datasets import DatasetDict\n res = DatasetDict()\n \n k, n = 0, 1\n while n <= shuffled.num_rows:\n res[str(k)] = URL(range(n))\n k += 1; n *= 2\n res['all'] = shuffled\n \n res.push_to_hub('yaakov/wikipedia-de-splits')" ]

[ "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-crowdsourced #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-wikipedia #language-German #license-cc-by-sa-3.0 #license-gfdl #region-us \n", "# Dataset Card for yaakov/wikipedia-de-splits", "## Dataset Description\nThe only goal of this dataset is to have random German Wikipedia articles at\nvarious dataset sizes: Small datasets for fast development and large datasets for statistically relevant measurements.\n\nFor this purpose, I loaded the 2665357 articles in the 'test' set of the pre-processed German Wikipedia dump from 2022-03-01, randomly permuted the articles and created splits of sizes '2n': '1, 2, 4, 8, ...'. The split names are strings. The split ''all'' contains all 2665357 available articles.", "## Dataset creation\nThis dataset has been created with the following script:\n\n !apt install git-lfs\n !pip install -q transformers datasets\n \n from huggingface_hub import notebook_login\n notebook_login()\n \n from datasets import load_dataset\n wikipedia_de = load_dataset(\"wikipedia\", \"URL\")['train']\n \n shuffled = wikipedia_de.shuffle(seed=42)\n \n from datasets import DatasetDict\n res = DatasetDict()\n \n k, n = 0, 1\n while n <= shuffled.num_rows:\n res[str(k)] = URL(range(n))\n k += 1; n *= 2\n res['all'] = shuffled\n \n res.push_to_hub('yaakov/wikipedia-de-splits')" ]

[ 163, 16, 129, 190 ]

[ "passage: 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-crowdsourced #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-wikipedia #language-German #license-cc-by-sa-3.0 #license-gfdl #region-us \n# Dataset Card for yaakov/wikipedia-de-splits## Dataset Description\nThe only goal of this dataset is to have random German Wikipedia articles at\nvarious dataset sizes: Small datasets for fast development and large datasets for statistically relevant measurements.\n\nFor this purpose, I loaded the 2665357 articles in the 'test' set of the pre-processed German Wikipedia dump from 2022-03-01, randomly permuted the articles and created splits of sizes '2n': '1, 2, 4, 8, ...'. The split names are strings. The split ''all'' contains all 2665357 available articles.## Dataset creation\nThis dataset has been created with the following script:\n\n !apt install git-lfs\n !pip install -q transformers datasets\n \n from huggingface_hub import notebook_login\n notebook_login()\n \n from datasets import load_dataset\n wikipedia_de = load_dataset(\"wikipedia\", \"URL\")['train']\n \n shuffled = wikipedia_de.shuffle(seed=42)\n \n from datasets import DatasetDict\n res = DatasetDict()\n \n k, n = 0, 1\n while n <= shuffled.num_rows:\n res[str(k)] = URL(range(n))\n k += 1; n *= 2\n res['all'] = shuffled\n \n res.push_to_hub('yaakov/wikipedia-de-splits')" ]

[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ 35, 98, 18, 5, 17 ]

[ "passage: TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n### Dataset Summary\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'train' looks as follows.### Data Splits### Number of Positive/Negative Word-pairs in each Split" ]

[ "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n* original analogy questions\n\n\n\n* extra analogy questions\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'test' looks as follows.\n\n\nThe 'stem' is the query word pair, 'choice' has word pair candidates,\nand 'answer' indicates the index of correct candidate which starts from '0'.\nAll data is lowercased except Google dataset.", "### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use." ]

[ "TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n* original analogy questions\n\n\n\n* extra analogy questions\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'test' looks as follows.\n\n\nThe 'stem' is the query word pair, 'choice' has word pair candidates,\nand 'answer' indicates the index of correct candidate which starts from '0'.\nAll data is lowercased except Google dataset.", "### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use." ]

[ 33, 42, 70, 45 ]

[ "passage: TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n* original analogy questions\n\n\n\n* extra analogy questions\n\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'test' looks as follows.\n\n\nThe 'stem' is the query word pair, 'choice' has word pair candidates,\nand 'answer' indicates the index of correct candidate which starts from '0'.\nAll data is lowercased except Google dataset.### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use." ]

[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-4.0", "code-generation", "arxiv:2108.07732", "region:us" ]

[ "2108.07732" ]

[ "en" ]

[ "# Dataset Card for Mostly Basic Python Problems (mbpp)", "## Table of Contents\n- Dataset Card for Mostly Basic Python Problems (mbpp))\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 - 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- Repository: URL\n- Paper: Program Synthesis with Large Language Models", "### Dataset Summary\nThe benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. \n\nReleased here as part of Program Synthesis with Large Language Models, Austin et. al., 2021.", "### Supported Tasks and Leaderboards\nThis dataset is used to evaluate code generations.", "### Languages\nEnglish - Python code", "## Dataset Structure", "### Data Instances", "#### mbpp - full", "#### mbpp - sanitized", "### Data Fields\n\n- 'source_file': unknown\n- 'text'/'prompt': description of programming task\n- 'code': solution for programming task\n- 'test_setup_code'/'test_imports': necessary code imports to execute tests\n- 'test_list': list of tests to verify solution\n- 'challenge_test_list': list of more challenging test to further probe solution", "### Data Splits\nThere are two version of the dataset (full and sanitized) which only one split each (test).", "## Dataset Creation\nSee section 2.1 of original paper.", "### Curation Rationale\nIn order to evaluate code generation functions a set of simple programming tasks as well as solutions is necessary which this dataset provides.", "### Source Data", "#### Initial Data Collection and Normalization\nThe dataset was manually created from scratch.", "#### Who are the source language producers?\nThe dataset was created with an internal crowdsourcing effort at Google.", "### Annotations", "#### Annotation process\nThe full dataset was created first and a subset then underwent a second round to improve the task descriptions.", "#### Who are the annotators?\nThe dataset was created with an internal crowdsourcing effort at Google.", "### Personal and Sensitive Information\nNone.", "## Considerations for Using the Data\nMake sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful.", "### Social Impact of Dataset\nWith this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.", "### Discussion of Biases", "### Other Known Limitations\nSince the task descriptions might not be expressive enough to solve the task. The 'sanitized' split aims at addressing this issue by having a second round of annotators improve the dataset.", "## Additional Information", "### Dataset Curators\nGoogle Research", "### Licensing Information\nCC-BY-4.0", "### Contributions\nThanks to @lvwerra for adding this dataset." ]

[ "TAGS\n#task_categories-text2text-generation #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-4.0 #code-generation #arxiv-2108.07732 #region-us \n", "# Dataset Card for Mostly Basic Python Problems (mbpp)", "## Table of Contents\n- Dataset Card for Mostly Basic Python Problems (mbpp))\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 - 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- Repository: URL\n- Paper: Program Synthesis with Large Language Models", "### Dataset Summary\nThe benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. \n\nReleased here as part of Program Synthesis with Large Language Models, Austin et. al., 2021.", "### Supported Tasks and Leaderboards\nThis dataset is used to evaluate code generations.", "### Languages\nEnglish - Python code", "## Dataset Structure", "### Data Instances", "#### mbpp - full", "#### mbpp - sanitized", "### Data Fields\n\n- 'source_file': unknown\n- 'text'/'prompt': description of programming task\n- 'code': solution for programming task\n- 'test_setup_code'/'test_imports': necessary code imports to execute tests\n- 'test_list': list of tests to verify solution\n- 'challenge_test_list': list of more challenging test to further probe solution", "### Data Splits\nThere are two version of the dataset (full and sanitized) which only one split each (test).", "## Dataset Creation\nSee section 2.1 of original paper.", "### Curation Rationale\nIn order to evaluate code generation functions a set of simple programming tasks as well as solutions is necessary which this dataset provides.", "### Source Data", "#### Initial Data Collection and Normalization\nThe dataset was manually created from scratch.", "#### Who are the source language producers?\nThe dataset was created with an internal crowdsourcing effort at Google.", "### Annotations", "#### Annotation process\nThe full dataset was created first and a subset then underwent a second round to improve the task descriptions.", "#### Who are the annotators?\nThe dataset was created with an internal crowdsourcing effort at Google.", "### Personal and Sensitive Information\nNone.", "## Considerations for Using the Data\nMake sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful.", "### Social Impact of Dataset\nWith this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.", "### Discussion of Biases", "### Other Known Limitations\nSince the task descriptions might not be expressive enough to solve the task. The 'sanitized' split aims at addressing this issue by having a second round of annotators improve the dataset.", "## Additional Information", "### Dataset Curators\nGoogle Research", "### Licensing Information\nCC-BY-4.0", "### Contributions\nThanks to @lvwerra for adding this dataset." ]

[ 120, 15, 171, 22, 114, 22, 8, 6, 6, 6, 7, 101, 27, 12, 36, 4, 20, 24, 5, 29, 23, 11, 39, 34, 8, 50, 5, 8, 11, 17 ]

[ "passage: TAGS\n#task_categories-text2text-generation #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-4.0 #code-generation #arxiv-2108.07732 #region-us \n# Dataset Card for Mostly Basic Python Problems (mbpp)## Table of Contents\n- Dataset Card for Mostly Basic Python Problems (mbpp))\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 - 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- Repository: URL\n- Paper: Program Synthesis with Large Language Models### Dataset Summary\nThe benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. \n\nReleased here as part of Program Synthesis with Large Language Models, Austin et. al., 2021.### Supported Tasks and Leaderboards\nThis dataset is used to evaluate code generations.### Languages\nEnglish - Python code## Dataset Structure### Data Instances#### mbpp - full#### mbpp - sanitized" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

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

[]

[]

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

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

[ 13, 110, 17 ]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ 13, 105, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "task_categories:text-classification", "task_ids:multi-label-classification", "task_ids:multi-class-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for lampeter_corpus", "## 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: Josef Schmied, Claudia Claridge, Rainer Siemund", "### Dataset Summary\n\nThe Lampeter Corpus of Early Modern English Tracts is a collection of texts on various subject matter published between 1640 and 1740, \u0013 a time that was marked by the rise of mass publication, the development of public discourse in many areas of everyday life and last but not least, the standardisation of British English. Each text belongs to one of the following genres: Law, Economy, Religion, Politics, Science, Miscellaneous", "### Supported Tasks and Leaderboards\n\n- 'text-classification': This dataset comes with dates and genre classifications for each text which can be used to finetune a model for text classification.", "### Languages\n\nThe text in the dataset is British English. The associated BCP-47 code is 'en-GB'", "## Dataset Structure", "### Data Instances\n\nA typical data point contains an id, a text, the head of the text (which can be missing on some occasions) and the title. The two features which can be used for classification are 'date', which is the year of publication and 'genre' which classifies the text into one of six broad areas.", "### Data Fields\n\nThe dataset contains the following fields: \n\n- 'id': Unique identifier(\"string\"),\n- 'text': ext in the document(\"string\"),\n- 'date': Date of publication(\"date64\"),\n- 'genre': Broad classification(\"string\"),\n- 'head': Often same as title. Can be missing(\"string\"),\n- 'title': Title of document(\"string\")", "### Data Splits\n\nTrain: 120", "## Dataset Creation", "### Curation Rationale\n\nThe period covered by the Lampeter Corpus, 1640 to 1740, marks a crucial period in English history and the elaboration of English as a multi-purpose language. The texts selected for the corpus reflect the standardisation process of English and historical developments between the outbreak of the Civil War and the beginning of the Industrial Revolution. To meet the needs of linguists and historians alike, the Lampeter project has attempted to create a balanced corpus rather than a randomly chosen archive or collection. A balanced corpus, then, is characterised by several transparent sampling criteria.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe original data is selected according to the following criteria:\n- Complete texts only, including dedications, prefaces, postscripts, etc.\n- Texts are of varying length, ranging from c. 3,000 to c. 20,000 words.\n- Each author appears only once to avoid idiosyncratic language use.\n- Major literary figures of the time were excluded since their writing style can be studied in other, existing collections.\n- Generally, only first editions of the texts; later editions only if changes were made by the original authors, thus ensuring the authenticity of the language.", "#### Who are the source language producers?\n\nAuthors of texts between 1640-1740", "### Annotations", "#### Annotation process\n\nN/A", "#### Who are the annotators?\n\nN/A", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nN/A", "### Discussion of Biases\n\nThe social biases of the time in terms of race, sex, gender, etc. might be encountered in this dataset", "### Other Known Limitations\n\nNone", "## Additional Information", "### Dataset Curators\n\nJosef Schmied, Claudia Claridge, Rainer Siemund", "### Licensing Information\n\nCreative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)\n\n\n\nUniversity of Oxford, The Lampeter Corpus of Early Modern English Tracts, Oxford Text Archive, URL" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #task_ids-multi-class-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-3.0 #region-us \n", "# Dataset Card for lampeter_corpus", "## 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: Josef Schmied, Claudia Claridge, Rainer Siemund", "### Dataset Summary\n\nThe Lampeter Corpus of Early Modern English Tracts is a collection of texts on various subject matter published between 1640 and 1740, \u0013 a time that was marked by the rise of mass publication, the development of public discourse in many areas of everyday life and last but not least, the standardisation of British English. Each text belongs to one of the following genres: Law, Economy, Religion, Politics, Science, Miscellaneous", "### Supported Tasks and Leaderboards\n\n- 'text-classification': This dataset comes with dates and genre classifications for each text which can be used to finetune a model for text classification.", "### Languages\n\nThe text in the dataset is British English. The associated BCP-47 code is 'en-GB'", "## Dataset Structure", "### Data Instances\n\nA typical data point contains an id, a text, the head of the text (which can be missing on some occasions) and the title. The two features which can be used for classification are 'date', which is the year of publication and 'genre' which classifies the text into one of six broad areas.", "### Data Fields\n\nThe dataset contains the following fields: \n\n- 'id': Unique identifier(\"string\"),\n- 'text': ext in the document(\"string\"),\n- 'date': Date of publication(\"date64\"),\n- 'genre': Broad classification(\"string\"),\n- 'head': Often same as title. Can be missing(\"string\"),\n- 'title': Title of document(\"string\")", "### Data Splits\n\nTrain: 120", "## Dataset Creation", "### Curation Rationale\n\nThe period covered by the Lampeter Corpus, 1640 to 1740, marks a crucial period in English history and the elaboration of English as a multi-purpose language. The texts selected for the corpus reflect the standardisation process of English and historical developments between the outbreak of the Civil War and the beginning of the Industrial Revolution. To meet the needs of linguists and historians alike, the Lampeter project has attempted to create a balanced corpus rather than a randomly chosen archive or collection. A balanced corpus, then, is characterised by several transparent sampling criteria.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe original data is selected according to the following criteria:\n- Complete texts only, including dedications, prefaces, postscripts, etc.\n- Texts are of varying length, ranging from c. 3,000 to c. 20,000 words.\n- Each author appears only once to avoid idiosyncratic language use.\n- Major literary figures of the time were excluded since their writing style can be studied in other, existing collections.\n- Generally, only first editions of the texts; later editions only if changes were made by the original authors, thus ensuring the authenticity of the language.", "#### Who are the source language producers?\n\nAuthors of texts between 1640-1740", "### Annotations", "#### Annotation process\n\nN/A", "#### Who are the annotators?\n\nN/A", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nN/A", "### Discussion of Biases\n\nThe social biases of the time in terms of race, sex, gender, etc. might be encountered in this dataset", "### Other Known Limitations\n\nNone", "## Additional Information", "### Dataset Curators\n\nJosef Schmied, Claudia Claridge, Rainer Siemund", "### Licensing Information\n\nCreative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)\n\n\n\nUniversity of Oxford, The Lampeter Corpus of Early Modern English Tracts, Oxford Text Archive, URL" ]

[ 103, 10, 112, 38, 102, 45, 26, 6, 76, 100, 8, 5, 135, 4, 147, 19, 5, 8, 12, 11, 8, 10, 35, 9, 5, 19, 42 ]

[ "passage: TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #task_ids-multi-class-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-3.0 #region-us \n# Dataset Card for lampeter_corpus## 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: Josef Schmied, Claudia Claridge, Rainer Siemund### Dataset Summary\n\nThe Lampeter Corpus of Early Modern English Tracts is a collection of texts on various subject matter published between 1640 and 1740, \u0013 a time that was marked by the rise of mass publication, the development of public discourse in many areas of everyday life and last but not least, the standardisation of British English. Each text belongs to one of the following genres: Law, Economy, Religion, Politics, Science, Miscellaneous### Supported Tasks and Leaderboards\n\n- 'text-classification': This dataset comes with dates and genre classifications for each text which can be used to finetune a model for text classification.### Languages\n\nThe text in the dataset is British English. The associated BCP-47 code is 'en-GB'## Dataset Structure", "passage: ### Data Instances\n\nA typical data point contains an id, a text, the head of the text (which can be missing on some occasions) and the title. The two features which can be used for classification are 'date', which is the year of publication and 'genre' which classifies the text into one of six broad areas.### Data Fields\n\nThe dataset contains the following fields: \n\n- 'id': Unique identifier(\"string\"),\n- 'text': ext in the document(\"string\"),\n- 'date': Date of publication(\"date64\"),\n- 'genre': Broad classification(\"string\"),\n- 'head': Often same as title. Can be missing(\"string\"),\n- 'title': Title of document(\"string\")### Data Splits\n\nTrain: 120## Dataset Creation### Curation Rationale\n\nThe period covered by the Lampeter Corpus, 1640 to 1740, marks a crucial period in English history and the elaboration of English as a multi-purpose language. The texts selected for the corpus reflect the standardisation process of English and historical developments between the outbreak of the Civil War and the beginning of the Industrial Revolution. To meet the needs of linguists and historians alike, the Lampeter project has attempted to create a balanced corpus rather than a randomly chosen archive or collection. A balanced corpus, then, is characterised by several transparent sampling criteria.### Source Data#### Initial Data Collection and Normalization\n\nThe original data is selected according to the following criteria:\n- Complete texts only, including dedications, prefaces, postscripts, etc.\n- Texts are of varying length, ranging from c. 3,000 to c. 20,000 words.\n- Each author appears only once to avoid idiosyncratic language use.\n- Major literary figures of the time were excluded since their writing style can be studied in other, existing collections.\n- Generally, only first editions of the texts; later editions only if changes were made by the original authors, thus ensuring the authenticity of the language.#### Who are the source language producers?\n\nAuthors of texts between 1640-1740### Annotations#### Annotation process\n\nN/A#### Who are the annotators?\n\nN/A### Personal and Sensitive Information\n\nN/A## Considerations for Using the Data### Social Impact of Dataset\n\nN/A### Discussion of Biases\n\nThe social biases of the time in terms of race, sex, gender, etc. might be encountered in this dataset" ]

[ "annotations_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:unknown", "source_datasets:extended|flores", "language:en", "language:ru", "language:ayr", "language:bho", "language:dyu", "language:fur", "language:wol", "license:cc-by-sa-4.0", "arxiv:2207.04672", "region:us" ]

[ "2207.04672" ]

[ "en", "ru", "ayr", "bho", "dyu", "fur", "wol" ]

[ "### Dataset Summary\n\n\nNLLB Multi Domain is a set of professionally-translated sentences in News, Unscripted informal speech, and Health domains. It is designed to enable assessment of out-of-domain performance and to study domain adaptation for machine translation. Each domain has approximately 3000 sentences.", "### Supported Tasks and Leaderboards", "#### Multilingual Machine Translation\n\n\nRefer to the Dynabench leaderboard) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.", "### Languages\n\n\n\nUse a hyphenated pairing to get two langauges in one datapoint (e.g., \"eng\\_Latn-rus\\_Cyrl\" will provide sentences in the format below).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nSee Dataset Viewer.\n\n\nThe text is provided as-in the original dataset, without further preprocessing or tokenization.", "### Data Fields\n\n\n* 'id': Row number for the data entry, starting at 1.\n* 'sentence': The full sentence in the specific language (may have \\_lang for pairings)\n* 'domain': The domain of the sentence.", "### Dataset Creation\n\n\nPlease refer to the original article No Language Left Behind: Scaling Human-Centered Machine Translation for additional information on dataset creation.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nSee paper for details.", "### Licensing Information\n\n\nLicensed with Creative Commons Attribution Share Alike 4.0. License available here.\n\n\nPlease cite the authors if you use these corpora in your work:\n\n\nPlease also cite prior work that this dataset builds on:" ]

[ "TAGS\n#annotations_creators-found #language_creators-expert-generated #multilinguality-multilingual #multilinguality-translation #size_categories-unknown #source_datasets-extended|flores #language-English #language-Russian #language-Central Aymara #language-Bhojpuri #language-Dyula #language-Friulian #language-Wolof #license-cc-by-sa-4.0 #arxiv-2207.04672 #region-us \n", "### Dataset Summary\n\n\nNLLB Multi Domain is a set of professionally-translated sentences in News, Unscripted informal speech, and Health domains. It is designed to enable assessment of out-of-domain performance and to study domain adaptation for machine translation. Each domain has approximately 3000 sentences.", "### Supported Tasks and Leaderboards", "#### Multilingual Machine Translation\n\n\nRefer to the Dynabench leaderboard) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.", "### Languages\n\n\n\nUse a hyphenated pairing to get two langauges in one datapoint (e.g., \"eng\\_Latn-rus\\_Cyrl\" will provide sentences in the format below).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nSee Dataset Viewer.\n\n\nThe text is provided as-in the original dataset, without further preprocessing or tokenization.", "### Data Fields\n\n\n* 'id': Row number for the data entry, starting at 1.\n* 'sentence': The full sentence in the specific language (may have \\_lang for pairings)\n* 'domain': The domain of the sentence.", "### Dataset Creation\n\n\nPlease refer to the original article No Language Left Behind: Scaling Human-Centered Machine Translation for additional information on dataset creation.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nSee paper for details.", "### Licensing Information\n\n\nLicensed with Creative Commons Attribution Share Alike 4.0. License available here.\n\n\nPlease cite the authors if you use these corpora in your work:\n\n\nPlease also cite prior work that this dataset builds on:" ]

[ 123, 70, 10, 57, 58, 34, 56, 42, 11, 48 ]

[ "passage: TAGS\n#annotations_creators-found #language_creators-expert-generated #multilinguality-multilingual #multilinguality-translation #size_categories-unknown #source_datasets-extended|flores #language-English #language-Russian #language-Central Aymara #language-Bhojpuri #language-Dyula #language-Friulian #language-Wolof #license-cc-by-sa-4.0 #arxiv-2207.04672 #region-us \n### Dataset Summary\n\n\nNLLB Multi Domain is a set of professionally-translated sentences in News, Unscripted informal speech, and Health domains. It is designed to enable assessment of out-of-domain performance and to study domain adaptation for machine translation. Each domain has approximately 3000 sentences.### Supported Tasks and Leaderboards#### Multilingual Machine Translation\n\n\nRefer to the Dynabench leaderboard) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.### Languages\n\n\n\nUse a hyphenated pairing to get two langauges in one datapoint (e.g., \"eng\\_Latn-rus\\_Cyrl\" will provide sentences in the format below).\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nSee Dataset Viewer.\n\n\nThe text is provided as-in the original dataset, without further preprocessing or tokenization.### Data Fields\n\n\n* 'id': Row number for the data entry, starting at 1.\n* 'sentence': The full sentence in the specific language (may have \\_lang for pairings)\n* 'domain': The domain of the sentence.### Dataset Creation\n\n\nPlease refer to the original article No Language Left Behind: Scaling Human-Centered Machine Translation for additional information on dataset creation.\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\nSee paper for details." ]

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

[]

[]

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

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

[ 13, 89, 16 ]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ 13, 102, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

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

[]

[]

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

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

[ 13, 91, 15 ]

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

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

[]

[ "en" ]

[ "# Dataset Card for dummy_tags" ]

[ "TAGS\n#language-English #test #dummy #region-us \n", "# Dataset Card for dummy_tags" ]

[ 15, 9 ]

[ "passage: TAGS\n#language-English #test #dummy #region-us \n# Dataset Card for dummy_tags" ]

[ "license:afl-3.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-afl-3.0 #region-us \n" ]

[ 14 ]

[ "passage: TAGS\n#license-afl-3.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "code", "region:us" ]

[]

[ "en" ]

[ "# Dataset description\nThis dataset consists of sequences of Python code followed by a a docstring explaining its function. It was constructed by concatenating code and text pairs \nfrom this dataset that were originally code and markdown cells in Jupyter Notebooks.\n\nThe content of each example the following:\n'", "# How to use it\n'\n'" ]

[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #code #region-us \n", "# Dataset description\nThis dataset consists of sequences of Python code followed by a a docstring explaining its function. It was constructed by concatenating code and text pairs \nfrom this dataset that were originally code and markdown cells in Jupyter Notebooks.\n\nThe content of each example the following:\n'", "# How to use it\n'\n'" ]

[ 43, 71, 7 ]

[ "passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #code #region-us \n# Dataset description\nThis dataset consists of sequences of Python code followed by a a docstring explaining its function. It was constructed by concatenating code and text pairs \nfrom this dataset that were originally code and markdown cells in Jupyter Notebooks.\n\nThe content of each example the following:\n'# How to use it\n'\n'" ]

[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2203.07814", "arxiv:2105.12655", "region:us" ]

[ "2203.07814", "2105.12655" ]

[ "en" ]

[ "### Dataset Summary\n\n\nCodeContests is a competitive programming dataset for machine-learning. This\ndataset was used when training AlphaCode.\n\n\nIt consists of programming problems, from a variety of sources:\n\n\nSite: Aizu, URL: URL, Source: CodeNet\nSite: AtCoder, URL: URL, Source: CodeNet\nSite: CodeChef, URL: URL, Source: description2code\nSite: Codeforces, URL: URL, Source: description2code and Codeforces\nSite: HackerEarth, URL: URL, Source: description2code\n\n\nProblems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.", "### Supported Tasks and Leaderboards\n\n\n* 'translation' - the competitive programming code generation problem can be viewed as a sequence-to-sequence translation task: given a problem description 𝑋 in natural language, produce a corresponding solution 𝑌 in a programming language. The metric used for evaluation is \"percentage of problems solved using 𝑛 submissions from 𝑘 samples per problem\", denoted as 𝑛@𝑘. More information on the evaluation of AlphaCode can be found in Section 2.2. and Appendix A.3. of the paper. The leaderboard for this task is available here.", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA data point corresponds to a singular contest problem:", "### Data Fields\n\n\n* 'name': The name of the contest. Note that names could agree between different sources.\n* 'description': A natural language description of a programming problem.\n* 'public\\_tests': Public tests are those that are available before submitting a solution, typically as part of the description itself. Represented as a paired 'input' and 'output' that can be used to test potential solutions. They are therefore acceptable inputs to a model.\n* 'private\\_tests': Private tests are not visible before submitting a solution, so should not be made available as inputs to a model.\n* 'generated\\_tests': Generated tests are automatically generated by modifying inputs from public and private tests and validating using known correct solutions.\n* 'source': The original source of the problem, with possible values including 'UNKNOWN\\_SOURCE' (0),'CODECHEF' (1), 'CODEFORCES' (2), 'HACKEREARTH' (3), 'CODEJAM' (4), 'ATCODER' (5) and 'AIZU' (6).\n* 'difficulty': A representation of the difficulty of the problem with possible values including 'UNKNOWN\\_DIFFICULTY' (0), 'EASY' (1), 'MEDIUM' (2), 'HARD' (3), 'HARDER' (4), 'HARDEST' (5), 'EXTERNAL' (6), 'A' (7), 'B' (8), 'C' (9), 'D' (10), 'E' (11), 'F' (12), 'G' (13), 'H' (14), 'I' (15), 'J' (16), 'K' (17), 'L' (18), 'M' (19), 'N' (20), 'O' (21), 'P' (22), 'Q' (23), 'R' (24), 'S' (25), 'T' (26), 'U' (27) and 'V' (28). Note that different sources use different, non-comparable gradings. For Codeforces problems, 'cf\\_rating' is a more reliable measure of difficulty when available.\n* 'solutions': Correct solutions to the problem. Contrast with 'incorrect\\_solutions' below.\n* 'incorrect\\_solutions': Incorrect solutions.\n* 'cf\\_contest\\_id': The Contest ID. Note that Contest ID is not monotonic with respect to time.\n* 'cf\\_index': Problem index, e.g. '\"A\"' or '\"B\"' or '\"C\"'.\n* 'cf\\_points': Points for the problem, e.g. '1000.0'\n* 'cf\\_rating': Problem rating (difficulty), e.g. '1100'\n* 'cf\\_tags': Problem tags, e.g. '['greedy', 'math']'\n* 'is\\_description\\_translated': Whether the problem was translated to English.\n* 'untranslated\\_description': The untranslated description is only available for translated problems.\n* 'time\\_limit': The time limit constraint to use when executing solutions. Represented as a dictionary with two keys, 'seconds' and 'nanos'. This field is None if not defined.\n* 'memory\\_limit\\_bytes': The memory limit constraint to use when executing solutions.\n* 'input\\_file': Most problems use stdin for IO. Some problems expect specific files to be used instead.\n* 'output\\_file': Most problems use stdout for IO. Some problems expect specific files to be used instead.\n\n\nAll tests are represented as a paired 'input' and 'output' that can be used to test potential solutions and all solutions comprise a 'language', with possible values including 'UNKNOWN\\_LANGUAGE' (0), 'PYTHON' (1) (solutions written in PYTHON2), 'CPP' (2), 'PYTHON3' (3) and 'JAVA' (4), and a 'solution' string written in that 'language'. The fields preceded with 'cf\\_' denote extra meta-data for Codeforces problems.", "### Data Splits\n\n\nThe data is split into training, validation and test set. The training set contains 13328 samples, the validation set 117 samples and the test set 165 samples.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset was created for fine-tuning AlphaCode models:\n\n\n\n> \n> Models pre-trained on GitHub can generate good code and solve simple programming problems, but\n> as shown in Appendix B.3 they can solve very few competitive programming problems. Fine-tuning\n> the model on a dedicated competitive programming dataset is critical for performance.\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe information on the data collection and normalization procedures can found in Section 3.2. and Appendinx B.2. of the paper.", "#### Who are the source language producers?\n\n\nThe problems are scraped from the following platforms: Aizu, AtCoder, CodeChef, Codeforces and HackerEarch. Additionally, some data from the existing public competitive programming dataset Description2Code (Caballero et al., 2016) and CodeNet ((Puri et al., 2021) is mixed into the training set.", "### Annotations", "#### Annotation process\n\n\nThe solutions are scapred alongside the problem descriptions.", "#### Who are the annotators?\n\n\nSame as the source data creators.", "### 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\nYujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu and Oriol Vinyals.", "### Licensing Information\n\n\nThis dataset is made available under the terms of the CC BY\n4.0 license (Creative Commons Attribution 4.0 International license).\n\n\nAdditional acknowledged contributions:\n\n\n* Codeforces materials are sourced from URL.\n* Description2Code materials are sourced from:\nDescription2Code Dataset,\nlicensed under the\nMIT open source license, copyright\nnot specified.\n* CodeNet materials are sourced from:\nProject\\_CodeNet, licensed under\nApache 2.0, copyright not\nspecified.", "### Contributions\n\n\nThanks to @mariosasko for adding this dataset." ]

[ "TAGS\n#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2203.07814 #arxiv-2105.12655 #region-us \n", "### Dataset Summary\n\n\nCodeContests is a competitive programming dataset for machine-learning. This\ndataset was used when training AlphaCode.\n\n\nIt consists of programming problems, from a variety of sources:\n\n\nSite: Aizu, URL: URL, Source: CodeNet\nSite: AtCoder, URL: URL, Source: CodeNet\nSite: CodeChef, URL: URL, Source: description2code\nSite: Codeforces, URL: URL, Source: description2code and Codeforces\nSite: HackerEarth, URL: URL, Source: description2code\n\n\nProblems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.", "### Supported Tasks and Leaderboards\n\n\n* 'translation' - the competitive programming code generation problem can be viewed as a sequence-to-sequence translation task: given a problem description 𝑋 in natural language, produce a corresponding solution 𝑌 in a programming language. The metric used for evaluation is \"percentage of problems solved using 𝑛 submissions from 𝑘 samples per problem\", denoted as 𝑛@𝑘. More information on the evaluation of AlphaCode can be found in Section 2.2. and Appendix A.3. of the paper. The leaderboard for this task is available here.", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA data point corresponds to a singular contest problem:", "### Data Fields\n\n\n* 'name': The name of the contest. Note that names could agree between different sources.\n* 'description': A natural language description of a programming problem.\n* 'public\\_tests': Public tests are those that are available before submitting a solution, typically as part of the description itself. Represented as a paired 'input' and 'output' that can be used to test potential solutions. They are therefore acceptable inputs to a model.\n* 'private\\_tests': Private tests are not visible before submitting a solution, so should not be made available as inputs to a model.\n* 'generated\\_tests': Generated tests are automatically generated by modifying inputs from public and private tests and validating using known correct solutions.\n* 'source': The original source of the problem, with possible values including 'UNKNOWN\\_SOURCE' (0),'CODECHEF' (1), 'CODEFORCES' (2), 'HACKEREARTH' (3), 'CODEJAM' (4), 'ATCODER' (5) and 'AIZU' (6).\n* 'difficulty': A representation of the difficulty of the problem with possible values including 'UNKNOWN\\_DIFFICULTY' (0), 'EASY' (1), 'MEDIUM' (2), 'HARD' (3), 'HARDER' (4), 'HARDEST' (5), 'EXTERNAL' (6), 'A' (7), 'B' (8), 'C' (9), 'D' (10), 'E' (11), 'F' (12), 'G' (13), 'H' (14), 'I' (15), 'J' (16), 'K' (17), 'L' (18), 'M' (19), 'N' (20), 'O' (21), 'P' (22), 'Q' (23), 'R' (24), 'S' (25), 'T' (26), 'U' (27) and 'V' (28). Note that different sources use different, non-comparable gradings. For Codeforces problems, 'cf\\_rating' is a more reliable measure of difficulty when available.\n* 'solutions': Correct solutions to the problem. Contrast with 'incorrect\\_solutions' below.\n* 'incorrect\\_solutions': Incorrect solutions.\n* 'cf\\_contest\\_id': The Contest ID. Note that Contest ID is not monotonic with respect to time.\n* 'cf\\_index': Problem index, e.g. '\"A\"' or '\"B\"' or '\"C\"'.\n* 'cf\\_points': Points for the problem, e.g. '1000.0'\n* 'cf\\_rating': Problem rating (difficulty), e.g. '1100'\n* 'cf\\_tags': Problem tags, e.g. '['greedy', 'math']'\n* 'is\\_description\\_translated': Whether the problem was translated to English.\n* 'untranslated\\_description': The untranslated description is only available for translated problems.\n* 'time\\_limit': The time limit constraint to use when executing solutions. Represented as a dictionary with two keys, 'seconds' and 'nanos'. This field is None if not defined.\n* 'memory\\_limit\\_bytes': The memory limit constraint to use when executing solutions.\n* 'input\\_file': Most problems use stdin for IO. Some problems expect specific files to be used instead.\n* 'output\\_file': Most problems use stdout for IO. Some problems expect specific files to be used instead.\n\n\nAll tests are represented as a paired 'input' and 'output' that can be used to test potential solutions and all solutions comprise a 'language', with possible values including 'UNKNOWN\\_LANGUAGE' (0), 'PYTHON' (1) (solutions written in PYTHON2), 'CPP' (2), 'PYTHON3' (3) and 'JAVA' (4), and a 'solution' string written in that 'language'. The fields preceded with 'cf\\_' denote extra meta-data for Codeforces problems.", "### Data Splits\n\n\nThe data is split into training, validation and test set. The training set contains 13328 samples, the validation set 117 samples and the test set 165 samples.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset was created for fine-tuning AlphaCode models:\n\n\n\n> \n> Models pre-trained on GitHub can generate good code and solve simple programming problems, but\n> as shown in Appendix B.3 they can solve very few competitive programming problems. Fine-tuning\n> the model on a dedicated competitive programming dataset is critical for performance.\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe information on the data collection and normalization procedures can found in Section 3.2. and Appendinx B.2. of the paper.", "#### Who are the source language producers?\n\n\nThe problems are scraped from the following platforms: Aizu, AtCoder, CodeChef, Codeforces and HackerEarch. Additionally, some data from the existing public competitive programming dataset Description2Code (Caballero et al., 2016) and CodeNet ((Puri et al., 2021) is mixed into the training set.", "### Annotations", "#### Annotation process\n\n\nThe solutions are scapred alongside the problem descriptions.", "#### Who are the annotators?\n\n\nSame as the source data creators.", "### 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\nYujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu and Oriol Vinyals.", "### Licensing Information\n\n\nThis dataset is made available under the terms of the CC BY\n4.0 license (Creative Commons Attribution 4.0 International license).\n\n\nAdditional acknowledged contributions:\n\n\n* Codeforces materials are sourced from URL.\n* Description2Code materials are sourced from:\nDescription2Code Dataset,\nlicensed under the\nMIT open source license, copyright\nnot specified.\n* CodeNet materials are sourced from:\nProject\\_CodeNet, licensed under\nApache 2.0, copyright not\nspecified.", "### Contributions\n\n\nThanks to @mariosasko for adding this dataset." ]

[ 92, 155, 131, 13, 17, 959, 49, 88, 4, 39, 88, 5, 18, 17, 18, 7, 8, 14, 143, 105, 17 ]

[ "passage: TAGS\n#task_categories-translation #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2203.07814 #arxiv-2105.12655 #region-us \n### Dataset Summary\n\n\nCodeContests is a competitive programming dataset for machine-learning. This\ndataset was used when training AlphaCode.\n\n\nIt consists of programming problems, from a variety of sources:\n\n\nSite: Aizu, URL: URL, Source: CodeNet\nSite: AtCoder, URL: URL, Source: CodeNet\nSite: CodeChef, URL: URL, Source: description2code\nSite: Codeforces, URL: URL, Source: description2code and Codeforces\nSite: HackerEarth, URL: URL, Source: description2code\n\n\nProblems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.### Supported Tasks and Leaderboards\n\n\n* 'translation' - the competitive programming code generation problem can be viewed as a sequence-to-sequence translation task: given a problem description 𝑋 in natural language, produce a corresponding solution 𝑌 in a programming language. The metric used for evaluation is \"percentage of problems solved using 𝑛 submissions from 𝑘 samples per problem\", denoted as 𝑛@𝑘. More information on the evaluation of AlphaCode can be found in Section 2.2. and Appendix A.3. of the paper. The leaderboard for this task is available here.### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA data point corresponds to a singular contest problem:", "passage: ### Data Fields\n\n\n* 'name': The name of the contest. Note that names could agree between different sources.\n* 'description': A natural language description of a programming problem.\n* 'public\\_tests': Public tests are those that are available before submitting a solution, typically as part of the description itself. Represented as a paired 'input' and 'output' that can be used to test potential solutions. They are therefore acceptable inputs to a model.\n* 'private\\_tests': Private tests are not visible before submitting a solution, so should not be made available as inputs to a model.\n* 'generated\\_tests': Generated tests are automatically generated by modifying inputs from public and private tests and validating using known correct solutions.\n* 'source': The original source of the problem, with possible values including 'UNKNOWN\\_SOURCE' (0),'CODECHEF' (1), 'CODEFORCES' (2), 'HACKEREARTH' (3), 'CODEJAM' (4), 'ATCODER' (5) and 'AIZU' (6).\n* 'difficulty': A representation of the difficulty of the problem with possible values including 'UNKNOWN\\_DIFFICULTY' (0), 'EASY' (1), 'MEDIUM' (2), 'HARD' (3), 'HARDER' (4), 'HARDEST' (5), 'EXTERNAL' (6), 'A' (7), 'B' (8), 'C' (9), 'D' (10), 'E' (11), 'F' (12), 'G' (13), 'H' (14), 'I' (15), 'J' (16), 'K' (17), 'L' (18), 'M' (19), 'N' (20), 'O' (21), 'P' (22), 'Q' (23), 'R' (24), 'S' (25), 'T' (26), 'U' (27) and 'V' (28). Note that different sources use different, non-comparable gradings. For Codeforces problems, 'cf\\_rating' is a more reliable measure of difficulty when available.\n* 'solutions': Correct solutions to the problem. Contrast with 'incorrect\\_solutions' below.\n* 'incorrect\\_solutions': Incorrect solutions.\n* 'cf\\_contest\\_id': The Contest ID. Note that Contest ID is not monotonic with respect to time.\n* 'cf\\_index': Problem index, e.g. '\"A\"' or '\"B\"' or '\"C\"'.\n* 'cf\\_points': Points for the problem, e.g. '1000.0'\n* 'cf\\_rating': Problem rating (difficulty), e.g. '1100'\n* 'cf\\_tags': Problem tags, e.g. '['greedy', 'math']'\n* 'is\\_description\\_translated': Whether the problem was translated to English.\n* 'untranslated\\_description': The untranslated description is only available for translated problems.\n* 'time\\_limit': The time limit constraint to use when executing solutions. Represented as a dictionary with two keys, 'seconds' and 'nanos'. This field is None if not defined.\n* 'memory\\_limit\\_bytes': The memory limit constraint to use when executing solutions.\n* 'input\\_file': Most problems use stdin for IO. Some problems expect specific files to be used instead.\n* 'output\\_file': Most problems use stdout for IO. Some problems expect specific files to be used instead.\n\n\nAll tests are represented as a paired 'input' and 'output' that can be used to test potential solutions and all solutions comprise a 'language', with possible values including 'UNKNOWN\\_LANGUAGE' (0), 'PYTHON' (1) (solutions written in PYTHON2), 'CPP' (2), 'PYTHON3' (3) and 'JAVA' (4), and a 'solution' string written in that 'language'. The fields preceded with 'cf\\_' denote extra meta-data for Codeforces problems.### Data Splits\n\n\nThe data is split into training, validation and test set. The training set contains 13328 samples, the validation set 117 samples and the test set 165 samples.\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nThis dataset was created for fine-tuning AlphaCode models:\n\n\n\n> \n> Models pre-trained on GitHub can generate good code and solve simple programming problems, but\n> as shown in Appendix B.3 they can solve very few competitive programming problems. Fine-tuning\n> the model on a dedicated competitive programming dataset is critical for performance.\n> \n> \n>### Source Data#### Initial Data Collection and Normalization\n\n\nThe information on the data collection and normalization procedures can found in Section 3.2. and Appendinx B.2. of the paper.#### Who are the source language producers?\n\n\nThe problems are scraped from the following platforms: Aizu, AtCoder, CodeChef, Codeforces and HackerEarch. Additionally, some data from the existing public competitive programming dataset Description2Code (Caballero et al., 2016) and CodeNet ((Puri et al., 2021) is mixed into the training set.### Annotations#### Annotation process\n\n\nThe solutions are scapred alongside the problem descriptions.#### Who are the annotators?\n\n\nSame as the source data creators.### 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\nYujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu and Oriol Vinyals." ]

[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe selected subset of ConceptNet used in this work, which compiled\nto fine-tune RelBERT model.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nThe selected subset of ConceptNet used in this work, which compiled\nto fine-tune RelBERT model.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ 35, 39, 18, 5, 17 ]

[ "passage: TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n### Dataset Summary\n\n\nThe selected subset of ConceptNet used in this work, which compiled\nto fine-tune RelBERT model.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'train' looks as follows.### Data Splits### Number of Positive/Negative Word-pairs in each Split" ]

[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nConceptNet5, which compiled to fine-tune RelBERT model.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nConceptNet5, which compiled to fine-tune RelBERT model.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ 35, 31, 18, 5, 17 ]

[ "passage: TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n### Dataset Summary\n\n\nConceptNet5, which compiled to fine-tune RelBERT model.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'train' looks as follows.### Data Splits### Number of Positive/Negative Word-pairs in each Split" ]

[ "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "newsbooks", "1654", "lancaster", "oxford text", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for lancaster_newsbooks", "## 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: Tony McEnery", "### Dataset Summary\n\nThis corpus consists of two collections of seventeenth-century English \"newsbooks\". Both were drawn from the Thomason Tracts collection, which is held at the British Library and available in graphical form via Early English Books Online (EEBO). The construction of these keyboarded versions were in both cases funded by the British Academy.\n\nThe FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654). This was constructed for the project \"Looking at text re-use in a corpus of seventeenth-century news reportage\", funded by the British Academy, grant reference SG-33825. \n\nThe SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook \"Mercurius Fumigosus\", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project \"Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655\", funded by the British Academy, grant reference LRG-35423. \n\nThis is version 1.0 of the corpus, released April 2007; it supercedes earlier versions circulated informally.\n\nFor more information about the corpus, see URL", "### Supported Tasks and Leaderboards\n\n'text-classification': This dataset can be used to augent existing datasets to find stylistic differences between texts from different time periods", "### Languages\n\nThe language in this dataset is English from 1654. The associated BCP-47 code is 'en:GB'", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits\n\nTrain: 303", "## Dataset Creation", "### Curation Rationale\n\nThe FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654) and was constructed for the project \"Looking at text re-use in a corpus of seventeenth-century news reportage\", funded by the British Academy, grant reference SG-33825. \nThe SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook \"Mercurius Fumigosus\", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project \"Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655\", funded by the British Academy, grant reference LRG-35423.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThis corpus was created by the Department of Linguistics and English Language, Lancaster University.", "#### Who are the source language producers?\n\nThe original data was humna-generated from existing newsbooks", "### Annotations", "#### Annotation process\n\n[N/A]", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\nNone, since this dataset is from 1654", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset provides an insight into the news and social systems from 17th century England", "### Discussion of Biases\n\nThe dataset is from the 17th century and some articles might reflect social biases of the time in terms of sexuality, gender, race, etc.", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators\n\nThis corpus was created by the Department of Linguistics and English Language, Lancaster University.\n\nProject leader: Tony McEnery\nCorpus editor: Andrew Hardie", "### Licensing Information\n\nCreative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License\n\n\n\n @misc{20.500.12024/2531,\n title = {The Lancaster Newsbooks Corpus},\n author = {Thomason, George, d. 1666},\n url = {URL\n note = {Oxford Text Archive},\n copyright = {Distributed by the University of Oxford under a Creative Commons Attribution-{NonCommercial}-{ShareAlike} 3.0 Unported License.},\n year = {2005} }" ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-3.0 #newsbooks #1654 #lancaster #oxford text #region-us \n", "# Dataset Card for lancaster_newsbooks", "## 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: Tony McEnery", "### Dataset Summary\n\nThis corpus consists of two collections of seventeenth-century English \"newsbooks\". Both were drawn from the Thomason Tracts collection, which is held at the British Library and available in graphical form via Early English Books Online (EEBO). The construction of these keyboarded versions were in both cases funded by the British Academy.\n\nThe FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654). This was constructed for the project \"Looking at text re-use in a corpus of seventeenth-century news reportage\", funded by the British Academy, grant reference SG-33825. \n\nThe SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook \"Mercurius Fumigosus\", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project \"Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655\", funded by the British Academy, grant reference LRG-35423. \n\nThis is version 1.0 of the corpus, released April 2007; it supercedes earlier versions circulated informally.\n\nFor more information about the corpus, see URL", "### Supported Tasks and Leaderboards\n\n'text-classification': This dataset can be used to augent existing datasets to find stylistic differences between texts from different time periods", "### Languages\n\nThe language in this dataset is English from 1654. The associated BCP-47 code is 'en:GB'", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits\n\nTrain: 303", "## Dataset Creation", "### Curation Rationale\n\nThe FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654) and was constructed for the project \"Looking at text re-use in a corpus of seventeenth-century news reportage\", funded by the British Academy, grant reference SG-33825. \nThe SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook \"Mercurius Fumigosus\", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project \"Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655\", funded by the British Academy, grant reference LRG-35423.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThis corpus was created by the Department of Linguistics and English Language, Lancaster University.", "#### Who are the source language producers?\n\nThe original data was humna-generated from existing newsbooks", "### Annotations", "#### Annotation process\n\n[N/A]", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\nNone, since this dataset is from 1654", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset provides an insight into the news and social systems from 17th century England", "### Discussion of Biases\n\nThe dataset is from the 17th century and some articles might reflect social biases of the time in terms of sexuality, gender, race, etc.", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators\n\nThis corpus was created by the Department of Linguistics and English Language, Lancaster University.\n\nProject leader: Tony McEnery\nCorpus editor: Andrew Hardie", "### Licensing Information\n\nCreative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License\n\n\n\n @misc{20.500.12024/2531,\n title = {The Lancaster Newsbooks Corpus},\n author = {Thomason, George, d. 1666},\n url = {URL\n note = {Oxford Text Archive},\n copyright = {Distributed by the University of Oxford under a Creative Commons Attribution-{NonCommercial}-{ShareAlike} 3.0 Unported License.},\n year = {2005} }" ]

[ 85, 11, 112, 29, 341, 44, 28, 6, 6, 5, 9, 5, 232, 4, 31, 23, 5, 10, 14, 19, 8, 24, 41, 12, 5, 40, 110 ]

[ "passage: TAGS\n#annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc-by-sa-3.0 #newsbooks #1654 #lancaster #oxford text #region-us \n# Dataset Card for lancaster_newsbooks## 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: Tony McEnery", "passage: ### Dataset Summary\n\nThis corpus consists of two collections of seventeenth-century English \"newsbooks\". Both were drawn from the Thomason Tracts collection, which is held at the British Library and available in graphical form via Early English Books Online (EEBO). The construction of these keyboarded versions were in both cases funded by the British Academy.\n\nThe FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654). This was constructed for the project \"Looking at text re-use in a corpus of seventeenth-century news reportage\", funded by the British Academy, grant reference SG-33825. \n\nThe SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook \"Mercurius Fumigosus\", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project \"Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655\", funded by the British Academy, grant reference LRG-35423. \n\nThis is version 1.0 of the corpus, released April 2007; it supercedes earlier versions circulated informally.\n\nFor more information about the corpus, see URL### Supported Tasks and Leaderboards\n\n'text-classification': This dataset can be used to augent existing datasets to find stylistic differences between texts from different time periods### Languages\n\nThe language in this dataset is English from 1654. The associated BCP-47 code is 'en:GB'## Dataset Structure### Data Instances### Data Fields### Data Splits\n\nTrain: 303## Dataset Creation### Curation Rationale\n\nThe FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654) and was constructed for the project \"Looking at text re-use in a corpus of seventeenth-century news reportage\", funded by the British Academy, grant reference SG-33825. \nThe SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook \"Mercurius Fumigosus\", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project \"Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655\", funded by the British Academy, grant reference LRG-35423.### Source Data#### Initial Data Collection and Normalization\n\nThis corpus was created by the Department of Linguistics and English Language, Lancaster University.#### Who are the source language producers?\n\nThe original data was humna-generated from existing newsbooks### Annotations#### Annotation process\n\n[N/A]#### Who are the annotators?\n\n[N/A]### Personal and Sensitive Information\n\nNone, since this dataset is from 1654## Considerations for Using the Data### Social Impact of Dataset\n\nThis dataset provides an insight into the news and social systems from 17th century England" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ 13, 96, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ 13, 99, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.", "## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ "TAGS\n#region-us \n", "## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.", "## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ 6, 140, 17 ]

[ "passage: TAGS\n#region-us \n## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ "region:us" ]

[]

[]

[ "## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.", "## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ "TAGS\n#region-us \n", "## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.", "## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ 6, 140, 17 ]

[ "passage: TAGS\n#region-us \n## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ "region:us" ]

[]

[]

[ "## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.", "## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ "TAGS\n#region-us \n", "## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.", "## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ 6, 140, 17 ]

[ "passage: TAGS\n#region-us \n## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "task_categories:image-to-text", "task_categories:text-to-image", "task_ids:image-captioning", "annotations_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# ShahNegar (A Plotted version of The Shahnameh)\n\nThis dataset is a plotted version of Ferdowsi's Shahnameh (which is a highly-regarded ancient set of Farsi poems) generated using DALL-E mini (aka craiyon). You can use this dataset using the code below:", "## 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 - 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:\n- Point of Contact: Sadra Sabouri", "### Dataset Summary\n\nThis dataset contains more than 30K images with their corresponding text from the Shahnameh. For each Shahnameh paragraph, we generated at most 9 images. Images corresponding to the same paragraphs have the same 'id' field. There was a human annotation post-process in which we removed some harmful/private generated images from the dataset. After all we reached to more than 30K, 256 * 256 images.", "### Supported Tasks and Leaderboards\n\nThe main purpose of making this dataset open source is because of its artistic value, but it can also be used for the below tasks:\n+ text-to-image\n+ image-to-text (image captioning)", "### Languages\n\nThe Shahnameh was generally written in Farsi (Persian) but the translated version we used for this dataset - satoor - was completely in English with no alignments for the corresponding Farsi poem. We are planning to add another field to dataset entries which is the corresponding Farsi poem as soon as possible.", "## Dataset Structure", "### Data Fields\n\nHere is an instance of our dataset:\n\n\n+ 'image': the image for given text.\n+ 'id': the id for the text (Not for the image).\n+ 'text': the English text for the image.", "### Data Splits\n\nThis dataset has only a split ('train' split).", "## Dataset Creation\n\nThe translated version of the Shahnameh was generally derived from the satoor website. We first extracted texts from the pdf. After that, we divided paragraphs into sentences and give each sentence to the DALL-E mini model through its online API. It generated nine images for each sentence. After a few annotations, we came up with more than 30000 images.", "### Annotations", "#### Annotation process\n\nThrough the process of image generation, we noticed a bias in the DALL-E models towards the word 'iran'. It was biased so that each sentence with this given word would have pictures from Iran's political figures which were usually totally irrelevant. The annotation process mainly focused to deal with these pictures. We removed those images which seems to be harmful to those figures and/or were irrelevant to the context.", "#### Who are the annotators?\n\nMahsa Namdar and Sadra Sabouri were the annotators of this dataset.", "### Personal and Sensitive Information\n\nSince the textual data is easily downloadable and the images were generated through an image generation model there shouldn't be any personal information in this dataset. Just in case you find something harmful or violating of one's personal information please let us know. We will take proper action as soon as possible.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset is mainly aimed to release for its artistic value. The process of generating images for the Shahnameh - which is one of the most important Farsi poem books - is our precious contribution. This dataset is not only used for this purpose but also can as a dataset in image-to-text and text-to-image tasks.", "### Discussion of Biases\n\nThe dataset's possible biases would come from the DALL-E mini biases. It's actually a good practice to check the dataset entries in order to find biases in that model. One it's worth mentioning in this work is the DALL-E mini model's bias for the word 'iran' which nearly always comes up with images from political figures of this country.", "### Other Known Limitations\n\nThere are constant debates in the literature about the limitations of machine-generated datasets. Some believe that since nowadays models are not perfect - and so do their output, it wouldn't be a good idea to use these artificially generated datasets as input to the new model. They suggest that by doing so we are actually limiting our accuracy by the model's accuracy which provided the primary dataset.", "## Additional Information", "### Dataset Curators\n\n+ Emad Fatemizadeh: The general idea for generating a graphical version of Farsi poems was firstly introduced by him.\n+ Sadra Sabouri: He looked up a translated version of the Shahnameh, extract and tokenized poems from it, and used the online DALL-E mini API to generate images from poems.\n+ Mahsa Namdar: The process of annotation as a post-process on data has been held by her.", "### Licensing Information\n\nMIT", "### Contributions\n\nThanks to @sadrasabouri for adding this dataset." ]

[ "TAGS\n#task_categories-image-to-text #task_categories-text-to-image #task_ids-image-captioning #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# ShahNegar (A Plotted version of The Shahnameh)\n\nThis dataset is a plotted version of Ferdowsi's Shahnameh (which is a highly-regarded ancient set of Farsi poems) generated using DALL-E mini (aka craiyon). You can use this dataset using the code below:", "## 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 - 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:\n- Point of Contact: Sadra Sabouri", "### Dataset Summary\n\nThis dataset contains more than 30K images with their corresponding text from the Shahnameh. For each Shahnameh paragraph, we generated at most 9 images. Images corresponding to the same paragraphs have the same 'id' field. There was a human annotation post-process in which we removed some harmful/private generated images from the dataset. After all we reached to more than 30K, 256 * 256 images.", "### Supported Tasks and Leaderboards\n\nThe main purpose of making this dataset open source is because of its artistic value, but it can also be used for the below tasks:\n+ text-to-image\n+ image-to-text (image captioning)", "### Languages\n\nThe Shahnameh was generally written in Farsi (Persian) but the translated version we used for this dataset - satoor - was completely in English with no alignments for the corresponding Farsi poem. We are planning to add another field to dataset entries which is the corresponding Farsi poem as soon as possible.", "## Dataset Structure", "### Data Fields\n\nHere is an instance of our dataset:\n\n\n+ 'image': the image for given text.\n+ 'id': the id for the text (Not for the image).\n+ 'text': the English text for the image.", "### Data Splits\n\nThis dataset has only a split ('train' split).", "## Dataset Creation\n\nThe translated version of the Shahnameh was generally derived from the satoor website. We first extracted texts from the pdf. After that, we divided paragraphs into sentences and give each sentence to the DALL-E mini model through its online API. It generated nine images for each sentence. After a few annotations, we came up with more than 30000 images.", "### Annotations", "#### Annotation process\n\nThrough the process of image generation, we noticed a bias in the DALL-E models towards the word 'iran'. It was biased so that each sentence with this given word would have pictures from Iran's political figures which were usually totally irrelevant. The annotation process mainly focused to deal with these pictures. We removed those images which seems to be harmful to those figures and/or were irrelevant to the context.", "#### Who are the annotators?\n\nMahsa Namdar and Sadra Sabouri were the annotators of this dataset.", "### Personal and Sensitive Information\n\nSince the textual data is easily downloadable and the images were generated through an image generation model there shouldn't be any personal information in this dataset. Just in case you find something harmful or violating of one's personal information please let us know. We will take proper action as soon as possible.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset is mainly aimed to release for its artistic value. The process of generating images for the Shahnameh - which is one of the most important Farsi poem books - is our precious contribution. This dataset is not only used for this purpose but also can as a dataset in image-to-text and text-to-image tasks.", "### Discussion of Biases\n\nThe dataset's possible biases would come from the DALL-E mini biases. It's actually a good practice to check the dataset entries in order to find biases in that model. One it's worth mentioning in this work is the DALL-E mini model's bias for the word 'iran' which nearly always comes up with images from political figures of this country.", "### Other Known Limitations\n\nThere are constant debates in the literature about the limitations of machine-generated datasets. Some believe that since nowadays models are not perfect - and so do their output, it wouldn't be a good idea to use these artificially generated datasets as input to the new model. They suggest that by doing so we are actually limiting our accuracy by the model's accuracy which provided the primary dataset.", "## Additional Information", "### Dataset Curators\n\n+ Emad Fatemizadeh: The general idea for generating a graphical version of Farsi poems was firstly introduced by him.\n+ Sadra Sabouri: He looked up a translated version of the Shahnameh, extract and tokenized poems from it, and used the online DALL-E mini API to generate images from poems.\n+ Mahsa Namdar: The process of annotation as a post-process on data has been held by her.", "### Licensing Information\n\nMIT", "### Contributions\n\nThanks to @sadrasabouri for adding this dataset." ]

[ 102, 74, 116, 17, 101, 56, 76, 6, 53, 19, 90, 5, 98, 29, 75, 8, 84, 98, 101, 5, 111, 7, 18 ]

[ "passage: TAGS\n#task_categories-image-to-text #task_categories-text-to-image #task_ids-image-captioning #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n# ShahNegar (A Plotted version of The Shahnameh)\n\nThis dataset is a plotted version of Ferdowsi's Shahnameh (which is a highly-regarded ancient set of Farsi poems) generated using DALL-E mini (aka craiyon). You can use this dataset using the code below:## 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 - 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:\n- Point of Contact: Sadra Sabouri### Dataset Summary\n\nThis dataset contains more than 30K images with their corresponding text from the Shahnameh. For each Shahnameh paragraph, we generated at most 9 images. Images corresponding to the same paragraphs have the same 'id' field. There was a human annotation post-process in which we removed some harmful/private generated images from the dataset. After all we reached to more than 30K, 256 * 256 images.### Supported Tasks and Leaderboards\n\nThe main purpose of making this dataset open source is because of its artistic value, but it can also be used for the below tasks:\n+ text-to-image\n+ image-to-text (image captioning)", "passage: ### Languages\n\nThe Shahnameh was generally written in Farsi (Persian) but the translated version we used for this dataset - satoor - was completely in English with no alignments for the corresponding Farsi poem. We are planning to add another field to dataset entries which is the corresponding Farsi poem as soon as possible.## Dataset Structure### Data Fields\n\nHere is an instance of our dataset:\n\n\n+ 'image': the image for given text.\n+ 'id': the id for the text (Not for the image).\n+ 'text': the English text for the image.### Data Splits\n\nThis dataset has only a split ('train' split).## Dataset Creation\n\nThe translated version of the Shahnameh was generally derived from the satoor website. We first extracted texts from the pdf. After that, we divided paragraphs into sentences and give each sentence to the DALL-E mini model through its online API. It generated nine images for each sentence. After a few annotations, we came up with more than 30000 images.### Annotations#### Annotation process\n\nThrough the process of image generation, we noticed a bias in the DALL-E models towards the word 'iran'. It was biased so that each sentence with this given word would have pictures from Iran's political figures which were usually totally irrelevant. The annotation process mainly focused to deal with these pictures. We removed those images which seems to be harmful to those figures and/or were irrelevant to the context.#### Who are the annotators?\n\nMahsa Namdar and Sadra Sabouri were the annotators of this dataset.### Personal and Sensitive Information\n\nSince the textual data is easily downloadable and the images were generated through an image generation model there shouldn't be any personal information in this dataset. Just in case you find something harmful or violating of one's personal information please let us know. We will take proper action as soon as possible.## Considerations for Using the Data### Social Impact of Dataset\n\nThis dataset is mainly aimed to release for its artistic value. The process of generating images for the Shahnameh - which is one of the most important Farsi poem books - is our precious contribution. This dataset is not only used for this purpose but also can as a dataset in image-to-text and text-to-image tasks." ]

[ "region:us" ]

[]

[]

[ "## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.", "## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ "TAGS\n#region-us \n", "## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.", "## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ 6, 140, 17 ]

[ "passage: TAGS\n#region-us \n## Donut : OCR-Free Document Understanding Transformer (ECCV 2022) -- SynthDoG datasets\n\nFor more information, please visit URL\n\n!image\n\nThe links to the SynthDoG-generated datasets are here:\n\n- 'synthdog-en': English, 0.5M.\n- 'synthdog-zh': Chinese, 0.5M.\n- 'synthdog-ja': Japanese, 0.5M.\n- 'synthdog-ko': Korean, 0.5M.\n\nTo generate synthetic datasets with our SynthDoG, please see './synthdog/URL' and our paper for details.## How to Cite\n\nIf you find this work useful to you, please cite:" ]

[ "license:afl-3.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-afl-3.0 #region-us \n" ]

[ 14 ]

[ "passage: TAGS\n#license-afl-3.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "task_categories:image-classification", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|imdb", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for 'reviews_with_drift'", "## 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", "### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.", "### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).", "### Languages\n\nText is mainly written in 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 @fjcasti1 for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n", "# Dataset Card for 'reviews_with_drift'", "## 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", "### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.", "### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).", "### Languages\n\nText is mainly written in 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 @fjcasti1 for adding this dataset." ]

[ 95, 13, 125, 4, 120, 50, 12, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 19 ]

[ "passage: TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n# Dataset Card for 'reviews_with_drift'## 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### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).### Languages\n\nText is mainly written in 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" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: ARTeLab/it5-summarization-ilpost\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @dishant16 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: ARTeLab/it5-summarization-ilpost\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @dishant16 for evaluating this model." ]

[ 13, 87, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: ARTeLab/it5-summarization-ilpost\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @dishant16 for evaluating this model." ]

[ "task_categories:audio-classification", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|vctk", "language:en", "license:odc-by", "voice-anti-spoofing", "arxiv:1911.01601", "region:us" ]

[ "1911.01601" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is a database used for the Third Automatic Speaker Verification Spoofing\nand Countermeasuers Challenge, for short, ASVspoof 2019 (URL)\norganized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor\nDelgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,\nand Andreas Nautsch in 2019.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\nLogical access (LA):\n\n\n* 'speaker\\_id': 'LA\\_', a 4-digit speaker ID\n* 'audio\\_file\\_name': name of the audio file\n* 'audio': A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* 'system\\_id': ID of the speech spoofing system (A01 - A19), or, for bonafide speech SYSTEM-ID is left blank ('-')\n* 'key': 'bonafide' for genuine speech, or, 'spoof' for spoofing speech\n\n\nPhysical access (PA):\n\n\n* 'speaker\\_id': 'PA\\_', a 4-digit speaker ID\n* 'audio\\_file\\_name': name of the audio file\n* 'audio': A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* 'environment\\_id': a triplet (S,R,D\\_s), which take one letter in the set {a,b,c} as categorical value, defined as\n* 'attack\\_id': a duple (D\\_a,Q), which take one letter in the set {A,B,C} as categorical value, defined as\n\n\n\nfor bonafide speech, 'attack\\_id' is left blank ('-')\n* 'key': 'bonafide' for genuine speech, or, 'spoof' for spoofing speech", "### 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", "### Licensing Information\n\n\nThis ASVspoof 2019 dataset is made available under the Open Data Commons Attribution License: URL" ]

[ "TAGS\n#task_categories-audio-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|vctk #language-English #license-odc-by #voice-anti-spoofing #arxiv-1911.01601 #region-us \n", "### Dataset Summary\n\n\nThis is a database used for the Third Automatic Speaker Verification Spoofing\nand Countermeasuers Challenge, for short, ASVspoof 2019 (URL)\norganized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor\nDelgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,\nand Andreas Nautsch in 2019.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\nLogical access (LA):\n\n\n* 'speaker\\_id': 'LA\\_', a 4-digit speaker ID\n* 'audio\\_file\\_name': name of the audio file\n* 'audio': A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* 'system\\_id': ID of the speech spoofing system (A01 - A19), or, for bonafide speech SYSTEM-ID is left blank ('-')\n* 'key': 'bonafide' for genuine speech, or, 'spoof' for spoofing speech\n\n\nPhysical access (PA):\n\n\n* 'speaker\\_id': 'PA\\_', a 4-digit speaker ID\n* 'audio\\_file\\_name': name of the audio file\n* 'audio': A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling\\_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n* 'environment\\_id': a triplet (S,R,D\\_s), which take one letter in the set {a,b,c} as categorical value, defined as\n* 'attack\\_id': a duple (D\\_a,Q), which take one letter in the set {A,B,C} as categorical value, defined as\n\n\n\nfor bonafide speech, 'attack\\_id' is left blank ('-')\n* 'key': 'bonafide' for genuine speech, or, 'spoof' for spoofing speech", "### 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", "### Licensing Information\n\n\nThis ASVspoof 2019 dataset is made available under the Open Data Commons Attribution License: URL" ]

[ 99, 94, 10, 12, 6, 650, 11, 7, 4, 10, 10, 5, 5, 9, 18, 7, 8, 14, 6, 25 ]

[ "passage: TAGS\n#task_categories-audio-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|vctk #language-English #license-odc-by #voice-anti-spoofing #arxiv-1911.01601 #region-us \n### Dataset Summary\n\n\nThis is a database used for the Third Automatic Speaker Verification Spoofing\nand Countermeasuers Challenge, for short, ASVspoof 2019 (URL)\norganized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor\nDelgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,\nand Andreas Nautsch in 2019.### Supported Tasks and Leaderboards### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------### Data Instances" ]

[ "task_categories:text-classification", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:heb", "license:afl-3.0", "metaphor-detection", "region:us" ]

[]

[ "heb" ]

[]

[ "TAGS\n#task_categories-text-classification #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Hebrew #license-afl-3.0 #metaphor-detection #region-us \n" ]

[ 64 ]

[ "passage: TAGS\n#task_categories-text-classification #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Hebrew #license-afl-3.0 #metaphor-detection #region-us \n" ]

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

[]

[]

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

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

[ 13, 102, 16 ]

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

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:multilingual", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pt", "language:ro", "language:sk", "language:sv", "license:cc-by-4.0", "named-entity-recognition-and-classification", "region:us" ]

[]

[ "multilingual", "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hu", "it", "lt", "lv", "mt", "nl", "pt", "ro", "sk", "sv" ]

[ "### Dataset Summary\n\n\nThe dataset consists of 12 documents (9 for Spanish due to parsing errors) taken from EUR-Lex, a multilingual corpus of court\ndecisions and legal dispositions in the 24 official languages of the European Union. The documents have been annotated\nfor named entities following the guidelines of the MAPA project which foresees two\nannotation level, a general and a more fine-grained one. The annotated corpus can be used for named entity recognition/classification.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports the task of Named Entity Recognition and Classification (NERC).", "### Languages\n\n\nThe following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pt, ro, sk, sv\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe file format is jsonl and three data splits are present (train, validation and test). Named Entity annotations are\nnon-overlapping.", "### Data Fields\n\n\nFor the annotation the documents have been split into sentences. The annotations has been done on the token level.\nThe files contain the following data fields\n\n\n* 'language': language of the sentence\n* 'type': The document type of the sentence. Currently, only EUR-LEX is supported.\n* 'file\\_name': The document file name the sentence belongs to.\n* 'sentence\\_number': The number of the sentence inside its document.\n* 'tokens': The list of tokens in the sentence.\n* 'coarse\\_grained': The coarse-grained annotations for each token\n* 'fine\\_grained': The fine-grained annotations for each token\n\n\nAs previously stated, the annotation has been conducted on a global and a more fine-grained level.\n\n\nThe tagset used for the global and the fine-grained named entities is the following:\n\n\n* Address\n\t+ Building\n\t+ City\n\t+ Country\n\t+ Place\n\t+ Postcode\n\t+ Street\n\t+ Territory\n* Amount\n\t+ Unit\n\t+ Value\n* Date\n\t+ Year\n\t+ Standard Abbreviation\n\t+ Month\n\t+ Day of the Week\n\t+ Day\n\t+ Calender Event\n* Person\n\t+ Age\n\t+ Email\n\t+ Ethnic Category\n\t+ Family Name\n\t+ Financial\n\t+ Given Name – Female\n\t+ Given Name – Male\n\t+ Health Insurance Number\n\t+ ID Document Number\n\t+ Initial Name\n\t+ Marital Status\n\t+ Medical Record Number\n\t+ Nationality\n\t+ Profession\n\t+ Role\n\t+ Social Security Number\n\t+ Title\n\t+ Url\n* Organisation\n* Time\n* Vehicle\n\t+ Build Year\n\t+ Colour\n\t+ License Plate Number\n\t+ Model\n\t+ Type\n\n\nThe final coarse grained tagset (in IOB notation) is the following:\n\n\n'['O', 'B-ORGANISATION', 'I-ORGANISATION', 'B-ADDRESS', 'I-ADDRESS', 'B-DATE', 'I-DATE', 'B-PERSON', 'I-PERSON', 'B-AMOUNT', 'I-AMOUNT', 'B-TIME', 'I-TIME']'\n\n\nThe final fine grained tagset (in IOB notation) is the following:\n\n\n'[\n'O',\n'B-BUILDING',\n'I-BUILDING',\n'B-CITY',\n'I-CITY',\n'B-COUNTRY',\n'I-COUNTRY',\n'B-PLACE',\n'I-PLACE',\n'B-TERRITORY',\n'I-TERRITORY',\n'I-UNIT',\n'B-UNIT',\n'B-VALUE',\n'I-VALUE',\n'B-YEAR',\n'I-YEAR',\n'B-STANDARD ABBREVIATION',\n'I-STANDARD ABBREVIATION',\n'B-MONTH',\n'I-MONTH',\n'B-DAY',\n'I-DAY',\n'B-AGE',\n'I-AGE',\n'B-ETHNIC CATEGORY',\n'I-ETHNIC CATEGORY',\n'B-FAMILY NAME',\n'I-FAMILY NAME',\n'B-INITIAL NAME',\n'I-INITIAL NAME',\n'B-MARITAL STATUS',\n'I-MARITAL STATUS',\n'B-PROFESSION',\n'I-PROFESSION',\n'B-ROLE',\n'I-ROLE',\n'B-NATIONALITY',\n'I-NATIONALITY',\n'B-TITLE',\n'I-TITLE',\n'B-URL',\n'I-URL',\n'B-TYPE',\n'I-TYPE',\n]'", "### Data Splits\n\n\nSplits created by Joel Niklaus.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n*„[…] to our knowledge, there exist no open resources annotated for NERC [Named Entity Recognition and Classificatio] in Spanish in the legal domain. With the\npresent contribution, we intend to fill this gap. With the release of the created resources for fine-tuning and\nevaluation of sensitive entities detection in the legal domain, we expect to encourage the development of domain-adapted\nanonymisation tools for Spanish in this field“* (de Gibert Bonet et al., 2022)", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset consists of documents taken from EUR-Lex corpus which is publicly available. No further\ninformation on the data collection process are given in de Gibert Bonet et al. (2022).", "#### Who are the source language producers?\n\n\nThe source language producers are presumably lawyers.", "### Annotations", "#### Annotation process\n\n\n*\"The annotation scheme consists of a complex two level hierarchy adapted to the legal domain, it follows the scheme\ndescribed in (Gianola et al., 2020) […] Level 1 entities refer to general categories (PERSON, DATE, TIME, ADDRESS...)\nand level 2 entities refer to more fine-grained subcategories (given name, personal name, day, year, month...). Eur-Lex,\nCPP and DE have been annotated following this annotation scheme […] The manual annotation was performed using\nINCePTION (Klie et al., 2018) by a sole annotator following the guidelines provided by the MAPA consortium.\"* (de Gibert\nBonet et al., 2022)", "#### Who are the annotators?\n\n\nOnly one annotator conducted the annotation. More information are not provdided in de Gibert Bonet et al. (2022).", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nNote that the dataset at hand presents only a small portion of a bigger corpus as described in de Gibert Bonet et al.\n(2022). At the time of writing only the annotated documents from the EUR-Lex corpus were available.\n\n\nNote that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton\nMatoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset\nconsisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the\ndataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,\ndifferences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to\nhave a look at the conversion script in order to retrace the steps for converting the\noriginal dataset into the present jsonl-format. For further information on the original dataset structure, we refer to\nthe bibliographical references and the original Github repositories and/or web pages provided in this dataset card.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe names of the original dataset curators and creators can be found in references given below, in the section *Citation\nInformation*. Additional changes were made by Joel Niklaus (Email\n; Github) and Veton Matoshi (Email\n; Github).", "### Licensing Information\n\n\nAttribution 4.0 International (CC BY 4.0)", "### Contributions\n\n\nThanks to @JoelNiklaus and @kapllan for adding this\ndataset." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-multilingual #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Portuguese #language-Romanian #language-Slovak #language-Swedish #license-cc-by-4.0 #named-entity-recognition-and-classification #region-us \n", "### Dataset Summary\n\n\nThe dataset consists of 12 documents (9 for Spanish due to parsing errors) taken from EUR-Lex, a multilingual corpus of court\ndecisions and legal dispositions in the 24 official languages of the European Union. The documents have been annotated\nfor named entities following the guidelines of the MAPA project which foresees two\nannotation level, a general and a more fine-grained one. The annotated corpus can be used for named entity recognition/classification.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports the task of Named Entity Recognition and Classification (NERC).", "### Languages\n\n\nThe following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pt, ro, sk, sv\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe file format is jsonl and three data splits are present (train, validation and test). Named Entity annotations are\nnon-overlapping.", "### Data Fields\n\n\nFor the annotation the documents have been split into sentences. The annotations has been done on the token level.\nThe files contain the following data fields\n\n\n* 'language': language of the sentence\n* 'type': The document type of the sentence. Currently, only EUR-LEX is supported.\n* 'file\\_name': The document file name the sentence belongs to.\n* 'sentence\\_number': The number of the sentence inside its document.\n* 'tokens': The list of tokens in the sentence.\n* 'coarse\\_grained': The coarse-grained annotations for each token\n* 'fine\\_grained': The fine-grained annotations for each token\n\n\nAs previously stated, the annotation has been conducted on a global and a more fine-grained level.\n\n\nThe tagset used for the global and the fine-grained named entities is the following:\n\n\n* Address\n\t+ Building\n\t+ City\n\t+ Country\n\t+ Place\n\t+ Postcode\n\t+ Street\n\t+ Territory\n* Amount\n\t+ Unit\n\t+ Value\n* Date\n\t+ Year\n\t+ Standard Abbreviation\n\t+ Month\n\t+ Day of the Week\n\t+ Day\n\t+ Calender Event\n* Person\n\t+ Age\n\t+ Email\n\t+ Ethnic Category\n\t+ Family Name\n\t+ Financial\n\t+ Given Name – Female\n\t+ Given Name – Male\n\t+ Health Insurance Number\n\t+ ID Document Number\n\t+ Initial Name\n\t+ Marital Status\n\t+ Medical Record Number\n\t+ Nationality\n\t+ Profession\n\t+ Role\n\t+ Social Security Number\n\t+ Title\n\t+ Url\n* Organisation\n* Time\n* Vehicle\n\t+ Build Year\n\t+ Colour\n\t+ License Plate Number\n\t+ Model\n\t+ Type\n\n\nThe final coarse grained tagset (in IOB notation) is the following:\n\n\n'['O', 'B-ORGANISATION', 'I-ORGANISATION', 'B-ADDRESS', 'I-ADDRESS', 'B-DATE', 'I-DATE', 'B-PERSON', 'I-PERSON', 'B-AMOUNT', 'I-AMOUNT', 'B-TIME', 'I-TIME']'\n\n\nThe final fine grained tagset (in IOB notation) is the following:\n\n\n'[\n'O',\n'B-BUILDING',\n'I-BUILDING',\n'B-CITY',\n'I-CITY',\n'B-COUNTRY',\n'I-COUNTRY',\n'B-PLACE',\n'I-PLACE',\n'B-TERRITORY',\n'I-TERRITORY',\n'I-UNIT',\n'B-UNIT',\n'B-VALUE',\n'I-VALUE',\n'B-YEAR',\n'I-YEAR',\n'B-STANDARD ABBREVIATION',\n'I-STANDARD ABBREVIATION',\n'B-MONTH',\n'I-MONTH',\n'B-DAY',\n'I-DAY',\n'B-AGE',\n'I-AGE',\n'B-ETHNIC CATEGORY',\n'I-ETHNIC CATEGORY',\n'B-FAMILY NAME',\n'I-FAMILY NAME',\n'B-INITIAL NAME',\n'I-INITIAL NAME',\n'B-MARITAL STATUS',\n'I-MARITAL STATUS',\n'B-PROFESSION',\n'I-PROFESSION',\n'B-ROLE',\n'I-ROLE',\n'B-NATIONALITY',\n'I-NATIONALITY',\n'B-TITLE',\n'I-TITLE',\n'B-URL',\n'I-URL',\n'B-TYPE',\n'I-TYPE',\n]'", "### Data Splits\n\n\nSplits created by Joel Niklaus.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n*„[…] to our knowledge, there exist no open resources annotated for NERC [Named Entity Recognition and Classificatio] in Spanish in the legal domain. With the\npresent contribution, we intend to fill this gap. With the release of the created resources for fine-tuning and\nevaluation of sensitive entities detection in the legal domain, we expect to encourage the development of domain-adapted\nanonymisation tools for Spanish in this field“* (de Gibert Bonet et al., 2022)", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset consists of documents taken from EUR-Lex corpus which is publicly available. No further\ninformation on the data collection process are given in de Gibert Bonet et al. (2022).", "#### Who are the source language producers?\n\n\nThe source language producers are presumably lawyers.", "### Annotations", "#### Annotation process\n\n\n*\"The annotation scheme consists of a complex two level hierarchy adapted to the legal domain, it follows the scheme\ndescribed in (Gianola et al., 2020) […] Level 1 entities refer to general categories (PERSON, DATE, TIME, ADDRESS...)\nand level 2 entities refer to more fine-grained subcategories (given name, personal name, day, year, month...). Eur-Lex,\nCPP and DE have been annotated following this annotation scheme […] The manual annotation was performed using\nINCePTION (Klie et al., 2018) by a sole annotator following the guidelines provided by the MAPA consortium.\"* (de Gibert\nBonet et al., 2022)", "#### Who are the annotators?\n\n\nOnly one annotator conducted the annotation. More information are not provdided in de Gibert Bonet et al. (2022).", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nNote that the dataset at hand presents only a small portion of a bigger corpus as described in de Gibert Bonet et al.\n(2022). At the time of writing only the annotated documents from the EUR-Lex corpus were available.\n\n\nNote that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton\nMatoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset\nconsisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the\ndataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,\ndifferences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to\nhave a look at the conversion script in order to retrace the steps for converting the\noriginal dataset into the present jsonl-format. For further information on the original dataset structure, we refer to\nthe bibliographical references and the original Github repositories and/or web pages provided in this dataset card.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe names of the original dataset curators and creators can be found in references given below, in the section *Citation\nInformation*. Additional changes were made by Joel Niklaus (Email\n; Github) and Veton Matoshi (Email\n; Github).", "### Licensing Information\n\n\nAttribution 4.0 International (CC BY 4.0)", "### Contributions\n\n\nThanks to @JoelNiklaus and @kapllan for adding this\ndataset." ]

[ 229, 112, 32, 65, 43, 841, 19, 114, 4, 52, 22, 5, 167, 39, 18, 7, 8, 260, 65, 14, 22 ]

[ "passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-multilingual #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Portuguese #language-Romanian #language-Slovak #language-Swedish #license-cc-by-4.0 #named-entity-recognition-and-classification #region-us \n### Dataset Summary\n\n\nThe dataset consists of 12 documents (9 for Spanish due to parsing errors) taken from EUR-Lex, a multilingual corpus of court\ndecisions and legal dispositions in the 24 official languages of the European Union. The documents have been annotated\nfor named entities following the guidelines of the MAPA project which foresees two\nannotation level, a general and a more fine-grained one. The annotated corpus can be used for named entity recognition/classification.### Supported Tasks and Leaderboards\n\n\nThe dataset supports the task of Named Entity Recognition and Classification (NERC).### Languages\n\n\nThe following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pt, ro, sk, sv\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nThe file format is jsonl and three data splits are present (train, validation and test). Named Entity annotations are\nnon-overlapping.", "passage: ### Data Fields\n\n\nFor the annotation the documents have been split into sentences. The annotations has been done on the token level.\nThe files contain the following data fields\n\n\n* 'language': language of the sentence\n* 'type': The document type of the sentence. Currently, only EUR-LEX is supported.\n* 'file\\_name': The document file name the sentence belongs to.\n* 'sentence\\_number': The number of the sentence inside its document.\n* 'tokens': The list of tokens in the sentence.\n* 'coarse\\_grained': The coarse-grained annotations for each token\n* 'fine\\_grained': The fine-grained annotations for each token\n\n\nAs previously stated, the annotation has been conducted on a global and a more fine-grained level.\n\n\nThe tagset used for the global and the fine-grained named entities is the following:\n\n\n* Address\n\t+ Building\n\t+ City\n\t+ Country\n\t+ Place\n\t+ Postcode\n\t+ Street\n\t+ Territory\n* Amount\n\t+ Unit\n\t+ Value\n* Date\n\t+ Year\n\t+ Standard Abbreviation\n\t+ Month\n\t+ Day of the Week\n\t+ Day\n\t+ Calender Event\n* Person\n\t+ Age\n\t+ Email\n\t+ Ethnic Category\n\t+ Family Name\n\t+ Financial\n\t+ Given Name – Female\n\t+ Given Name – Male\n\t+ Health Insurance Number\n\t+ ID Document Number\n\t+ Initial Name\n\t+ Marital Status\n\t+ Medical Record Number\n\t+ Nationality\n\t+ Profession\n\t+ Role\n\t+ Social Security Number\n\t+ Title\n\t+ Url\n* Organisation\n* Time\n* Vehicle\n\t+ Build Year\n\t+ Colour\n\t+ License Plate Number\n\t+ Model\n\t+ Type\n\n\nThe final coarse grained tagset (in IOB notation) is the following:\n\n\n'['O', 'B-ORGANISATION', 'I-ORGANISATION', 'B-ADDRESS', 'I-ADDRESS', 'B-DATE', 'I-DATE', 'B-PERSON', 'I-PERSON', 'B-AMOUNT', 'I-AMOUNT', 'B-TIME', 'I-TIME']'\n\n\nThe final fine grained tagset (in IOB notation) is the following:\n\n\n'[\n'O',\n'B-BUILDING',\n'I-BUILDING',\n'B-CITY',\n'I-CITY',\n'B-COUNTRY',\n'I-COUNTRY',\n'B-PLACE',\n'I-PLACE',\n'B-TERRITORY',\n'I-TERRITORY',\n'I-UNIT',\n'B-UNIT',\n'B-VALUE',\n'I-VALUE',\n'B-YEAR',\n'I-YEAR',\n'B-STANDARD ABBREVIATION',\n'I-STANDARD ABBREVIATION',\n'B-MONTH',\n'I-MONTH',\n'B-DAY',\n'I-DAY',\n'B-AGE',\n'I-AGE',\n'B-ETHNIC CATEGORY',\n'I-ETHNIC CATEGORY',\n'B-FAMILY NAME',\n'I-FAMILY NAME',\n'B-INITIAL NAME',\n'I-INITIAL NAME',\n'B-MARITAL STATUS',\n'I-MARITAL STATUS',\n'B-PROFESSION',\n'I-PROFESSION',\n'B-ROLE',\n'I-ROLE',\n'B-NATIONALITY',\n'I-NATIONALITY',\n'B-TITLE',\n'I-TITLE',\n'B-URL',\n'I-URL',\n'B-TYPE',\n'I-TYPE',\n]'### Data Splits\n\n\nSplits created by Joel Niklaus.\n\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\n*„[…] to our knowledge, there exist no open resources annotated for NERC [Named Entity Recognition and Classificatio] in Spanish in the legal domain. With the\npresent contribution, we intend to fill this gap. With the release of the created resources for fine-tuning and\nevaluation of sensitive entities detection in the legal domain, we expect to encourage the development of domain-adapted\nanonymisation tools for Spanish in this field“* (de Gibert Bonet et al., 2022)### Source Data#### Initial Data Collection and Normalization\n\n\nThe dataset consists of documents taken from EUR-Lex corpus which is publicly available. No further\ninformation on the data collection process are given in de Gibert Bonet et al. (2022).#### Who are the source language producers?\n\n\nThe source language producers are presumably lawyers.### Annotations#### Annotation process\n\n\n*\"The annotation scheme consists of a complex two level hierarchy adapted to the legal domain, it follows the scheme\ndescribed in (Gianola et al., 2020) […] Level 1 entities refer to general categories (PERSON, DATE, TIME, ADDRESS...)\nand level 2 entities refer to more fine-grained subcategories (given name, personal name, day, year, month...). Eur-Lex,\nCPP and DE have been annotated following this annotation scheme […] The manual annotation was performed using\nINCePTION (Klie et al., 2018) by a sole annotator following the guidelines provided by the MAPA consortium.\"* (de Gibert\nBonet et al., 2022)#### Who are the annotators?\n\n\nOnly one annotator conducted the annotation. More information are not provdided in de Gibert Bonet et al. (2022).### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases" ]

[ "license:apache-2.0", "arxiv:2207.08099", "region:us" ]

[ "2207.08099" ]

[]

[ "## advABSA\n\nAn adversarial aspect-based sentiment analysis (ABSA) benchmark, dubbed *adv*ABSA for both aspect-based sentiment classification (SC) and opinion extraction (OE).", "### *adv*ABSA (Adversarial ABSA Benchmark)\n\nIn response to the concerning robustness issue of ABSA, Arts is proposed, which contains datasets crafted only for adversarial evaluaiton on SC but not for OE. So we additionally craft datasets for adversarial evaluaiton on OE following their track. These gathered datasets form *adv*ABSA. That is, *adv*ABSA can be decomposed to two parts, where the first part is Arts-\\[domain\\]-SC reused from Arts and the second part is Arts-\\[domain\\]-OE newly produced by us.", "### *std*ABSA (Standard ABSA Benchmark)\n\nIn addition, we also provide *std*ABSA containing datasets from SemEval14 for standard evaluation, namely Sem14-\\[domain\\]-SC and Sem14-\\[domain\\]-OE. So corresponding performance drops can be measured properly.\n\nIf you find *adv*ABSA useful, please kindly star this repositary and cite our paper as follows:", "### Credits\n\nThe benchmark is mainly processed by Fang Ma." ]

[ "TAGS\n#license-apache-2.0 #arxiv-2207.08099 #region-us \n", "## advABSA\n\nAn adversarial aspect-based sentiment analysis (ABSA) benchmark, dubbed *adv*ABSA for both aspect-based sentiment classification (SC) and opinion extraction (OE).", "### *adv*ABSA (Adversarial ABSA Benchmark)\n\nIn response to the concerning robustness issue of ABSA, Arts is proposed, which contains datasets crafted only for adversarial evaluaiton on SC but not for OE. So we additionally craft datasets for adversarial evaluaiton on OE following their track. These gathered datasets form *adv*ABSA. That is, *adv*ABSA can be decomposed to two parts, where the first part is Arts-\\[domain\\]-SC reused from Arts and the second part is Arts-\\[domain\\]-OE newly produced by us.", "### *std*ABSA (Standard ABSA Benchmark)\n\nIn addition, we also provide *std*ABSA containing datasets from SemEval14 for standard evaluation, namely Sem14-\\[domain\\]-SC and Sem14-\\[domain\\]-OE. So corresponding performance drops can be measured properly.\n\nIf you find *adv*ABSA useful, please kindly star this repositary and cite our paper as follows:", "### Credits\n\nThe benchmark is mainly processed by Fang Ma." ]

[ 23, 46, 156, 108, 16 ]

[ "passage: TAGS\n#license-apache-2.0 #arxiv-2207.08099 #region-us \n## advABSA\n\nAn adversarial aspect-based sentiment analysis (ABSA) benchmark, dubbed *adv*ABSA for both aspect-based sentiment classification (SC) and opinion extraction (OE).### *adv*ABSA (Adversarial ABSA Benchmark)\n\nIn response to the concerning robustness issue of ABSA, Arts is proposed, which contains datasets crafted only for adversarial evaluaiton on SC but not for OE. So we additionally craft datasets for adversarial evaluaiton on OE following their track. These gathered datasets form *adv*ABSA. That is, *adv*ABSA can be decomposed to two parts, where the first part is Arts-\\[domain\\]-SC reused from Arts and the second part is Arts-\\[domain\\]-OE newly produced by us.### *std*ABSA (Standard ABSA Benchmark)\n\nIn addition, we also provide *std*ABSA containing datasets from SemEval14 for standard evaluation, namely Sem14-\\[domain\\]-SC and Sem14-\\[domain\\]-OE. So corresponding performance drops can be measured properly.\n\nIf you find *adv*ABSA useful, please kindly star this repositary and cite our paper as follows:### Credits\n\nThe benchmark is mainly processed by Fang Ma." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: tuner007/pegasus_summarizer\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Neez for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: tuner007/pegasus_summarizer\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Neez for evaluating this model." ]

[ 13, 86, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: tuner007/pegasus_summarizer\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Neez for evaluating this model." ]

[ "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nFive different datasets ('BLESS', 'CogALexV', 'EVALution', 'K&H+N', 'ROOT09') for lexical relation classification used in SphereRE.", "### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example looks as follows.\n\n\nThe 'stem' and 'tail' are the word pair and 'relation' is the corresponding relation label.", "### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use." ]

[ "TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nFive different datasets ('BLESS', 'CogALexV', 'EVALution', 'K&H+N', 'ROOT09') for lexical relation classification used in SphereRE.", "### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example looks as follows.\n\n\nThe 'stem' and 'tail' are the word pair and 'relation' is the corresponding relation label.", "### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use." ]

[ 33, 57, 32, 37, 45 ]

[ "passage: TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n### Dataset Summary\n\n\nFive different datasets ('BLESS', 'CogALexV', 'EVALution', 'K&H+N', 'ROOT09') for lexical relation classification used in SphereRE.### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example looks as follows.\n\n\nThe 'stem' and 'tail' are the word pair and 'relation' is the corresponding relation label.### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use." ]

[ "multilinguality:monolingual", "size_categories:1<n<1K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nRelation Mapping is a task to choose optimal combination of word pairs (see more detail in the paper).\n\n\nRelation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):\n\n\n*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example looks as follows.\n\n\n* 'source': A list of terms, which is the source of the relation mapping from.\n* 'target\\_random': A list of terms, where we want to find a mapping from 'source' to.\n* 'target': A correctly ordered 'target\\_random' that aligns with the 'source'.\n\n\nGiven 'source' and 'target\\_random', the task is to predict the correct order of 'target\\_random' so that it matches 'target'.\nIn average 7 terms are in the set, so the total number of possible order is 5040.", "### Data Splits" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-1<n<1K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nRelation Mapping is a task to choose optimal combination of word pairs (see more detail in the paper).\n\n\nRelation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):\n\n\n*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example looks as follows.\n\n\n* 'source': A list of terms, which is the source of the relation mapping from.\n* 'target\\_random': A list of terms, where we want to find a mapping from 'source' to.\n* 'target': A correctly ordered 'target\\_random' that aligns with the 'source'.\n\n\nGiven 'source' and 'target\\_random', the task is to predict the correct order of 'target\\_random' so that it matches 'target'.\nIn average 7 terms are in the set, so the total number of possible order is 5040.", "### Data Splits" ]

[ 34, 96, 154, 5 ]

[ "passage: TAGS\n#multilinguality-monolingual #size_categories-1<n<1K #language-English #license-other #region-us \n### Dataset Summary\n\n\nRelation Mapping is a task to choose optimal combination of word pairs (see more detail in the paper).\n\n\nRelation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):\n\n\n*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example looks as follows.\n\n\n* 'source': A list of terms, which is the source of the relation mapping from.\n* 'target\\_random': A list of terms, where we want to find a mapping from 'source' to.\n* 'target': A correctly ordered 'target\\_random' that aligns with the 'source'.\n\n\nGiven 'source' and 'target\\_random', the task is to predict the correct order of 'target\\_random' so that it matches 'target'.\nIn average 7 terms are in the set, so the total number of possible order is 5040.### Data Splits" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ 13, 95, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "task_categories:image-classification", "task_ids:multi-class-classification", "task_ids:multi-class-image-classification", "source_datasets:original", "license:cc", "other-image-classification", "image-classification", "region:us" ]

[]

[]

[ "### Dataset Summary\n\n\nTired of all the dogs in ImageNet (ILSVRC)? Then ecoset is here for you. 1.5m images\nfrom 565 basic level categories, chosen to be both (i) frequent in linguistic usage,\nand (ii) rated by human observers as concrete (e.g. ‘table’ is concrete, ‘romance’\nis not).\n\n\nEcoset is a typical image recognition dataset, combining images of objects with appropriate\nlabels (one label per image). Importantly, ecoset is intended to provide higher ecological\nvalidity than its counterparts, with a mislabelling error rate < 5% and filtered for NSFW content.\nFor more information on the dataset, consider reading the original publication.\n\n\nEcoset consists of a train, test, and validation subset which all are openly available to the user.", "### Supported Tasks and Leaderboards\n\n\nEcoset is a large multi-class single-label object recognition image dataset (similar to ImageNet).\n\n\nInstallation\n------------", "### Install Requirements\n\n\nIn order to work with ecoset, please make sure to install huggingface datasets:\n\n\nIf you want to work with the dataset in 'Huggingface.datasets', you might also want to make sure to install PIL ('pip install Pillow') in order to work with image input. However, downloading the dataset will work despite not having installed PIL.", "### Download Settings\n\n\nPlease set 'verification\\_mode=no\\_checks'. when downloading this dataset, else the download will result in an error, additionally you may need to\ninstall defusedxml via pip to avoid Permission Errors required by \\_generate\\_examples method:\n\n\noptionally a cache\\_dir can be specified where the zip file will be downloaded and extracted\n\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for all the configurations of the dataset. Currently, there is only one setting ('Full') available, containing all data.", "### Data Instances", "#### Full\n\n\n* Size of downloaded dataset files: 155 GB\n* Total amount of disk used: 311 GB\n\n\nDataset Creation\n----------------\n\n\nA total of 565 categories were selected based on the following: 1) their word frequency in American television and film subtitles (SUBTLEX\\_US), 2) the perceived concreteness by human observers, and 3) the availability of a minimum of 700 images. Images were sourced via the overall ImageNet database (the same resource used for ILSVRC 2012) or obtained under CC BY-NC-SA 2.0 license from Bing image search and Flickr. Thorough data cleaning procedures were put in place to remove duplicates and to assure an expected misclassification rate per category of <4%.", "### Curation Rationale\n\n\nMore information on the curation of the dataset can be found in the original publication.", "### Source Data\n\n\nThe source data is available under: URL", "### Annotations\n\n\nEach ecoset image folder is annotated with class labels according to the main object depicted in a class of images. No further annotations are added to the dataset.", "### Personal and Sensitive Information\n\n\nThe dataset was tested to exclude sensitive images using Yahoo's Open NSFW detection model, removing all image with an NSFW score above 0.8. For this dataset, only images with secured license information was used, which should prevent the inclusion of images without consent of the image's authors and subjects. Despite these measures, it is possible that the images in the dataset contain personal and sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nLarge-scale image-label datasets such as ImageNet are the backbone of modern Computer Vision. However, such large datasets often suffer from problems like mislabeling, category biases, misrepresentations, and unsafe content. Ecoset was created with the aim to reduce these biases and consequently improve the social impact of Computer Vision techniques trained on the dataset. More information on the social impact of the dataset can be found in the original publication.", "### Discussion of Biases\n\n\nDespite best efforts to provide an ecologically valid and overall less biased dataset, ecoset is still likely to contain biased data. The category selection of ecoset was based on human concreteness ratings and word frequencies in a corpus consisting of American television and film subtitles. This undoubtedly biases the category selection toward Western cultures. Image inclusion was based on the availability via Bing/Flickr search results as well as the existence of relevant ImageNet categories. Images depicting people, specifically the categories “man,” “woman,” and “child,” were not sampled according to census distributions (age, ethnicity, gender, etc.).", "### Other Known Limitations\n\n\nIn addition to points mentioned in Discussion of Biases, ecoset image and category distributions do not reflect the naturalistic, egocentric visual input typically encountered in the everyday life of infant and adults.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe corpus was put together by Johannes Mehrer, Courtney J. Spoerer, Emer C. Jones, Nikolaus Kriegeskorte, and Tim C. Kietzmann.", "### Licensing Information\n\n\nEcoset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license (cc-by-nc-sa-2.0).", "### Contributions\n\n\nThe ecoset dataloader and dataset card was created by @DiGyt on behalf of @kietzmannlab.\nFor questions and suggestions feel free to reach out." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #task_ids-multi-class-image-classification #source_datasets-original #license-cc #other-image-classification #image-classification #region-us \n", "### Dataset Summary\n\n\nTired of all the dogs in ImageNet (ILSVRC)? Then ecoset is here for you. 1.5m images\nfrom 565 basic level categories, chosen to be both (i) frequent in linguistic usage,\nand (ii) rated by human observers as concrete (e.g. ‘table’ is concrete, ‘romance’\nis not).\n\n\nEcoset is a typical image recognition dataset, combining images of objects with appropriate\nlabels (one label per image). Importantly, ecoset is intended to provide higher ecological\nvalidity than its counterparts, with a mislabelling error rate < 5% and filtered for NSFW content.\nFor more information on the dataset, consider reading the original publication.\n\n\nEcoset consists of a train, test, and validation subset which all are openly available to the user.", "### Supported Tasks and Leaderboards\n\n\nEcoset is a large multi-class single-label object recognition image dataset (similar to ImageNet).\n\n\nInstallation\n------------", "### Install Requirements\n\n\nIn order to work with ecoset, please make sure to install huggingface datasets:\n\n\nIf you want to work with the dataset in 'Huggingface.datasets', you might also want to make sure to install PIL ('pip install Pillow') in order to work with image input. However, downloading the dataset will work despite not having installed PIL.", "### Download Settings\n\n\nPlease set 'verification\\_mode=no\\_checks'. when downloading this dataset, else the download will result in an error, additionally you may need to\ninstall defusedxml via pip to avoid Permission Errors required by \\_generate\\_examples method:\n\n\noptionally a cache\\_dir can be specified where the zip file will be downloaded and extracted\n\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for all the configurations of the dataset. Currently, there is only one setting ('Full') available, containing all data.", "### Data Instances", "#### Full\n\n\n* Size of downloaded dataset files: 155 GB\n* Total amount of disk used: 311 GB\n\n\nDataset Creation\n----------------\n\n\nA total of 565 categories were selected based on the following: 1) their word frequency in American television and film subtitles (SUBTLEX\\_US), 2) the perceived concreteness by human observers, and 3) the availability of a minimum of 700 images. Images were sourced via the overall ImageNet database (the same resource used for ILSVRC 2012) or obtained under CC BY-NC-SA 2.0 license from Bing image search and Flickr. Thorough data cleaning procedures were put in place to remove duplicates and to assure an expected misclassification rate per category of <4%.", "### Curation Rationale\n\n\nMore information on the curation of the dataset can be found in the original publication.", "### Source Data\n\n\nThe source data is available under: URL", "### Annotations\n\n\nEach ecoset image folder is annotated with class labels according to the main object depicted in a class of images. No further annotations are added to the dataset.", "### Personal and Sensitive Information\n\n\nThe dataset was tested to exclude sensitive images using Yahoo's Open NSFW detection model, removing all image with an NSFW score above 0.8. For this dataset, only images with secured license information was used, which should prevent the inclusion of images without consent of the image's authors and subjects. Despite these measures, it is possible that the images in the dataset contain personal and sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nLarge-scale image-label datasets such as ImageNet are the backbone of modern Computer Vision. However, such large datasets often suffer from problems like mislabeling, category biases, misrepresentations, and unsafe content. Ecoset was created with the aim to reduce these biases and consequently improve the social impact of Computer Vision techniques trained on the dataset. More information on the social impact of the dataset can be found in the original publication.", "### Discussion of Biases\n\n\nDespite best efforts to provide an ecologically valid and overall less biased dataset, ecoset is still likely to contain biased data. The category selection of ecoset was based on human concreteness ratings and word frequencies in a corpus consisting of American television and film subtitles. This undoubtedly biases the category selection toward Western cultures. Image inclusion was based on the availability via Bing/Flickr search results as well as the existence of relevant ImageNet categories. Images depicting people, specifically the categories “man,” “woman,” and “child,” were not sampled according to census distributions (age, ethnicity, gender, etc.).", "### Other Known Limitations\n\n\nIn addition to points mentioned in Discussion of Biases, ecoset image and category distributions do not reflect the naturalistic, egocentric visual input typically encountered in the everyday life of infant and adults.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe corpus was put together by Johannes Mehrer, Courtney J. Spoerer, Emer C. Jones, Nikolaus Kriegeskorte, and Tim C. Kietzmann.", "### Licensing Information\n\n\nEcoset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license (cc-by-nc-sa-2.0).", "### Contributions\n\n\nThe ecoset dataloader and dataset card was created by @DiGyt on behalf of @kietzmannlab.\nFor questions and suggestions feel free to reach out." ]

[ 68, 188, 36, 93, 134, 6, 159, 25, 12, 44, 111, 110, 165, 58, 44, 35, 41 ]

[ "passage: TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #task_ids-multi-class-image-classification #source_datasets-original #license-cc #other-image-classification #image-classification #region-us \n### Dataset Summary\n\n\nTired of all the dogs in ImageNet (ILSVRC)? Then ecoset is here for you. 1.5m images\nfrom 565 basic level categories, chosen to be both (i) frequent in linguistic usage,\nand (ii) rated by human observers as concrete (e.g. ‘table’ is concrete, ‘romance’\nis not).\n\n\nEcoset is a typical image recognition dataset, combining images of objects with appropriate\nlabels (one label per image). Importantly, ecoset is intended to provide higher ecological\nvalidity than its counterparts, with a mislabelling error rate < 5% and filtered for NSFW content.\nFor more information on the dataset, consider reading the original publication.\n\n\nEcoset consists of a train, test, and validation subset which all are openly available to the user.### Supported Tasks and Leaderboards\n\n\nEcoset is a large multi-class single-label object recognition image dataset (similar to ImageNet).\n\n\nInstallation\n------------### Install Requirements\n\n\nIn order to work with ecoset, please make sure to install huggingface datasets:\n\n\nIf you want to work with the dataset in 'Huggingface.datasets', you might also want to make sure to install PIL ('pip install Pillow') in order to work with image input. However, downloading the dataset will work despite not having installed PIL.", "passage: ### Download Settings\n\n\nPlease set 'verification\\_mode=no\\_checks'. when downloading this dataset, else the download will result in an error, additionally you may need to\ninstall defusedxml via pip to avoid Permission Errors required by \\_generate\\_examples method:\n\n\noptionally a cache\\_dir can be specified where the zip file will be downloaded and extracted\n\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for all the configurations of the dataset. Currently, there is only one setting ('Full') available, containing all data.### Data Instances#### Full\n\n\n* Size of downloaded dataset files: 155 GB\n* Total amount of disk used: 311 GB\n\n\nDataset Creation\n----------------\n\n\nA total of 565 categories were selected based on the following: 1) their word frequency in American television and film subtitles (SUBTLEX\\_US), 2) the perceived concreteness by human observers, and 3) the availability of a minimum of 700 images. Images were sourced via the overall ImageNet database (the same resource used for ILSVRC 2012) or obtained under CC BY-NC-SA 2.0 license from Bing image search and Flickr. Thorough data cleaning procedures were put in place to remove duplicates and to assure an expected misclassification rate per category of <4%.### Curation Rationale\n\n\nMore information on the curation of the dataset can be found in the original publication.### Source Data\n\n\nThe source data is available under: URL### Annotations\n\n\nEach ecoset image folder is annotated with class labels according to the main object depicted in a class of images. No further annotations are added to the dataset.### Personal and Sensitive Information\n\n\nThe dataset was tested to exclude sensitive images using Yahoo's Open NSFW detection model, removing all image with an NSFW score above 0.8. For this dataset, only images with secured license information was used, which should prevent the inclusion of images without consent of the image's authors and subjects. Despite these measures, it is possible that the images in the dataset contain personal and sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nLarge-scale image-label datasets such as ImageNet are the backbone of modern Computer Vision. However, such large datasets often suffer from problems like mislabeling, category biases, misrepresentations, and unsafe content. Ecoset was created with the aim to reduce these biases and consequently improve the social impact of Computer Vision techniques trained on the dataset. More information on the social impact of the dataset can be found in the original publication." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: cestwc/cnn_dailymail-test50\n* Config: cestwc--cnn_dailymail-test50\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Buckeyes2019 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: cestwc/cnn_dailymail-test50\n* Config: cestwc--cnn_dailymail-test50\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Buckeyes2019 for evaluating this model." ]

[ 13, 106, 17 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: cestwc/cnn_dailymail-test50\n* Config: cestwc--cnn_dailymail-test50\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Buckeyes2019 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/pegasus-cnn_dailymail\n* Dataset: cestwc/cnn_dailymail-test50\n* Config: cestwc--cnn_dailymail-test50\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Buckeyes2019 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/pegasus-cnn_dailymail\n* Dataset: cestwc/cnn_dailymail-test50\n* Config: cestwc--cnn_dailymail-test50\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Buckeyes2019 for evaluating this model." ]

[ 13, 108, 17 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/pegasus-cnn_dailymail\n* Dataset: cestwc/cnn_dailymail-test50\n* Config: cestwc--cnn_dailymail-test50\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Buckeyes2019 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nickprock for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nickprock for evaluating this model." ]

[ 13, 99, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @nickprock for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nickprock for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nickprock for evaluating this model." ]

[ 13, 99, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @nickprock for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nickprock for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nickprock for evaluating this model." ]

[ 13, 99, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/distilbert-base-uncased-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @nickprock for evaluating this model." ]

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

[]

[]

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

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

[ 13, 88, 15 ]

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

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

[]

[]

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

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

[ 13, 91, 15 ]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/extractive-question-answering\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/extractive-question-answering\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 96, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/extractive-question-answering\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 95, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 95, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 95, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[]

[]

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

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

[ 13, 91, 15 ]

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

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

[]

[]

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

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

[ 13, 97, 15 ]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 95, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Auditor Sentiment", "## 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", "## Dataset Description\nAuditor review sentiment collected by News Department\n\n- Point of Contact:\nTalked to COE for Auditing, currently sue@URL", "### Dataset Summary\n\nAuditor sentiment dataset of sentences from financial news. The dataset consists of several thousand sentences from English language financial news categorized by sentiment.", "### Supported Tasks and Leaderboards\n\nSentiment Classification", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0)", "### Data Splits\n\nA train/test split was created randomly with a 75/25 split", "## Dataset Creation", "### Curation Rationale\n\nTo gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe corpus used in this paper is made out of English news reports.", "#### Who are the source language producers?\n\nThe source data was written by various auditors.", "### Annotations", "#### Annotation process\n\nThis release of the auditor reviews covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. The subset here is where inter-annotation agreement was greater than 75%.", "#### Who are the annotators?\n\nThey were pulled from the SME list, names are held by sue@URL", "### Personal and Sensitive Information\n\nThere is no personal or sensitive information in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nAll annotators were from the same institution and so interannotator agreement\nshould be understood with this taken into account.", "### Licensing Information\n\nLicense: Demo.Org Proprietary - DO NOT SHARE\n\nThis dataset is based on the financial phrasebank dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #region-us \n", "# Dataset Card for Auditor Sentiment", "## 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", "## Dataset Description\nAuditor review sentiment collected by News Department\n\n- Point of Contact:\nTalked to COE for Auditing, currently sue@URL", "### Dataset Summary\n\nAuditor sentiment dataset of sentences from financial news. The dataset consists of several thousand sentences from English language financial news categorized by sentiment.", "### Supported Tasks and Leaderboards\n\nSentiment Classification", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0)", "### Data Splits\n\nA train/test split was created randomly with a 75/25 split", "## Dataset Creation", "### Curation Rationale\n\nTo gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe corpus used in this paper is made out of English news reports.", "#### Who are the source language producers?\n\nThe source data was written by various auditors.", "### Annotations", "#### Annotation process\n\nThis release of the auditor reviews covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. The subset here is where inter-annotation agreement was greater than 75%.", "#### Who are the annotators?\n\nThey were pulled from the SME list, names are held by sue@URL", "### Personal and Sensitive Information\n\nThere is no personal or sensitive information in this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nAll annotators were from the same institution and so interannotator agreement\nshould be understood with this taken into account.", "### Licensing Information\n\nLicense: Demo.Org Proprietary - DO NOT SHARE\n\nThis dataset is based on the financial phrasebank dataset." ]

[ 93, 8, 117, 30, 37, 14, 5, 6, 6, 51, 19, 5, 48, 4, 24, 20, 5, 60, 25, 20, 8, 7, 33, 33 ]

[ "passage: TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #region-us \n# Dataset Card for Auditor Sentiment## 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## Dataset Description\nAuditor review sentiment collected by News Department\n\n- Point of Contact:\nTalked to COE for Auditing, currently sue@URL### Dataset Summary\n\nAuditor sentiment dataset of sentences from financial news. The dataset consists of several thousand sentences from English language financial news categorized by sentiment.### Supported Tasks and Leaderboards\n\nSentiment Classification### Languages\n\nEnglish## Dataset Structure### Data Instances### Data Fields\n\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0)### Data Splits\n\nA train/test split was created randomly with a 75/25 split## Dataset Creation### Curation Rationale\n\nTo gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.### Source Data#### Initial Data Collection and Normalization\n\nThe corpus used in this paper is made out of English news reports.#### Who are the source language producers?\n\nThe source data was written by various auditors.### Annotations" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for song_lyrics", "## 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: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nStructured song lyrics", "### Supported Tasks and Leaderboards\n\ntext generation", "### Languages\n\nEnglish", "## 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" ]

[ "TAGS\n#region-us \n", "# Dataset Card for song_lyrics", "## 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: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nStructured song lyrics", "### Supported Tasks and Leaderboards\n\ntext generation", "### Languages\n\nEnglish", "## 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" ]

[ 6, 10, 112, 24, 11, 12, 5, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for song_lyrics## 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: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nStructured song lyrics### Supported Tasks and Leaderboards\n\ntext generation### Languages\n\nEnglish## 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" ]

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

[]

[]

[]

[ "TAGS\n#license-apache-2.0 #region-us \n" ]

[ 14 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 12 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"WikiArt\"\n\nMore Information needed" ]

[ "task_categories:image-classification", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|imdb", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for 'reviews_with_drift'", "## 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", "### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.", "### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).", "### Languages\n\nText is mainly written in 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 @fjcasti1 for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n", "# Dataset Card for 'reviews_with_drift'", "## 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", "### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.", "### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).", "### Languages\n\nText is mainly written in 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 @fjcasti1 for adding this dataset." ]

[ 95, 13, 125, 4, 120, 50, 12, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 19 ]

[ "passage: TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n# Dataset Card for 'reviews_with_drift'## 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### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).### Languages\n\nText is mainly written in 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" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pt", "license:cc-by-4.0", "ner", "portuguese-ner", "economic-defense", "region:us" ]

[]

[ "pt" ]

[ "# Dataset Card for NERDE", "## 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- Repository: NERDE repository\n- Point of Contact: Guilherme P. Paiva", "### Dataset Summary\n\nNERDE is a dataset for Named Entity Recognition for Economic Defense. It was created in collaboration with LATITUDE/UnB Laboratory and the Administrative Council for Economic Defense (Cade)", "### Supported Tasks and Leaderboards", "### Languages\n\nThe language in the dataset is Brazilian Portuguese from legal documents. The BCP-47 code for Brazilian Portuguese is pt-BR", "## 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 @guipaiva for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Portuguese #license-cc-by-4.0 #ner #portuguese-ner #economic-defense #region-us \n", "# Dataset Card for NERDE", "## 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- Repository: NERDE repository\n- Point of Contact: Guilherme P. Paiva", "### Dataset Summary\n\nNERDE is a dataset for Named Entity Recognition for Economic Defense. It was created in collaboration with LATITUDE/UnB Laboratory and the Administrative Council for Economic Defense (Cade)", "### Supported Tasks and Leaderboards", "### Languages\n\nThe language in the dataset is Brazilian Portuguese from legal documents. The BCP-47 code for Brazilian Portuguese is pt-BR", "## 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 @guipaiva for adding this dataset." ]

[ 114, 7, 125, 26, 51, 10, 35, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 17 ]

[ "passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Portuguese #license-cc-by-4.0 #ner #portuguese-ner #economic-defense #region-us \n# Dataset Card for NERDE## 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- Repository: NERDE repository\n- Point of Contact: Guilherme P. Paiva### Dataset Summary\n\nNERDE is a dataset for Named Entity Recognition for Economic Defense. It was created in collaboration with LATITUDE/UnB Laboratory and the Administrative Council for Economic Defense (Cade)### Supported Tasks and Leaderboards### Languages\n\nThe language in the dataset is Brazilian Portuguese from legal documents. The BCP-47 code for Brazilian Portuguese is pt-BR## 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" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: distilbert-base-uncased-finetuned-sst-2-english\n* Dataset: sst2\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Neez for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: distilbert-base-uncased-finetuned-sst-2-english\n* Dataset: sst2\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Neez for evaluating this model." ]

[ 13, 99, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: distilbert-base-uncased-finetuned-sst-2-english\n* Dataset: sst2\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Neez for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: tuner007/pegasus_summarizer\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Neez for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: tuner007/pegasus_summarizer\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Neez for evaluating this model." ]

[ 13, 90, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: tuner007/pegasus_summarizer\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Neez for evaluating this model." ]

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

[]

[]

[]

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

[ 11 ]

[ "passage: TAGS\n#license-cc #region-us \n" ]

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

[]

[]

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

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

[ 13, 91, 15 ]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 90, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[]

[]

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

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

[ 13, 97, 15 ]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 95, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/extractive-question-answering\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/extractive-question-answering\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 96, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/extractive-question-answering\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/distilbert-base-cased-distilled-squad\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/distilbert-base-cased-distilled-squad\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 102, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/distilbert-base-cased-distilled-squad\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "arxiv:2112.10668", "region:us" ]

[ "2112.10668" ]

[]

[ "# Languages\nru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.", "# Data Splits\nThis dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.", "# Access English StoryCloze\nPlease request the original English StoryCloze dataset through the official channel. You can create a split of the en data following our data split scheme using the following commands:", "# Licence\nXStoryCloze is opensourced under CC BY-SA 4.0, the same license as the original English StoryCloze.\n\nIf you use XStoryCloze in your work, please cite" ]

[ "TAGS\n#arxiv-2112.10668 #region-us \n", "# Languages\nru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.", "# Data Splits\nThis dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.", "# Access English StoryCloze\nPlease request the original English StoryCloze dataset through the official channel. You can create a split of the en data following our data split scheme using the following commands:", "# Licence\nXStoryCloze is opensourced under CC BY-SA 4.0, the same license as the original English StoryCloze.\n\nIf you use XStoryCloze in your work, please cite" ]

[ 15, 33, 74, 41, 42 ]

[ "passage: TAGS\n#arxiv-2112.10668 #region-us \n# Languages\nru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.# Data Splits\nThis dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.# Access English StoryCloze\nPlease request the original English StoryCloze dataset through the official channel. You can create a split of the en data following our data split scheme using the following commands:# Licence\nXStoryCloze is opensourced under CC BY-SA 4.0, the same license as the original English StoryCloze.\n\nIf you use XStoryCloze in your work, please cite" ]

[ "task_categories:text-generation", "annotations_creators:no-annotation", "language_creators:machine-generated", "size_categories:100K<n<1M", "language:en", "language:code", "license:cc-by-nc-sa-3.0", "code", "region:us" ]

[]

[ "en", "code" ]

[ "# Dataset Card for Shadertoys-fine", "## 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 - Source Data\n- Licensing Information", "## Dataset Description\n\n- Repository: URL (private placeholder)", "### Dataset Summary\n\nfine variant of the Shadertoys dataset (still WIP), where individual functions are avaialable as Datapoints.", "### Supported Tasks and Leaderboards\n\n 'language-modeling': The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages.", "### Languages\n\n- English (names, comments)\n- Shadercode programming language", "## Dataset Structure", "### Data Instances\n\nA data point consists of the function string, it's name as well as a bit of metadata like the author and source URL. (in the future there might be a function string without comments)\n\n\nA data point in the 'return_completion' subset for the return-completion task in ShaderEval includes just two features:", "### Data Fields\n\n- 'name' funciton identifier composed of the type and the name of the function\n- 'code' the raw code (including comments) of function.\n- 'source' URL to the shader. It might be on a different renderpass\n- 'author' username of the shader author\n\n- 'body' the body of the function without the return statement (no comments)\n- 'return_statment' the return statement of the function. everything infront of the semicolon is kept and white sapces are stripped in the custome Evaluator.", "### Data Splits\n\nCurrently available (shuffled):\n - train (85.0%)\n - test (15.0%)\n\nThese splits should be indexed the same across both subsets. So if you are fine-tuning on the 'fine' subset you won't get exposed to the 'return_completion' test split. However there are many duplicates among both subsets and splits.", "## Dataset Creation\n\nData retrieved starting 2022-07-20", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll data was collected via the URL API and then by looking for keywords and counting curly brackets to figure out what is part of a function and what isn't.", "#### Who are the source language producers?\n\nURL contributers which publish shaders as 'public+API'", "## Licensing Information\n\nThe Default licnese for each Shader is CC BY-NC-SA 3.0. However, some Shaders might have a different license attached. The Dataset is currently not filtering for any licensis." ]

[ "TAGS\n#task_categories-text-generation #annotations_creators-no-annotation #language_creators-machine-generated #size_categories-100K<n<1M #language-English #language-code #license-cc-by-nc-sa-3.0 #code #region-us \n", "# Dataset Card for Shadertoys-fine", "## 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 - Source Data\n- Licensing Information", "## Dataset Description\n\n- Repository: URL (private placeholder)", "### Dataset Summary\n\nfine variant of the Shadertoys dataset (still WIP), where individual functions are avaialable as Datapoints.", "### Supported Tasks and Leaderboards\n\n 'language-modeling': The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages.", "### Languages\n\n- English (names, comments)\n- Shadercode programming language", "## Dataset Structure", "### Data Instances\n\nA data point consists of the function string, it's name as well as a bit of metadata like the author and source URL. (in the future there might be a function string without comments)\n\n\nA data point in the 'return_completion' subset for the return-completion task in ShaderEval includes just two features:", "### Data Fields\n\n- 'name' funciton identifier composed of the type and the name of the function\n- 'code' the raw code (including comments) of function.\n- 'source' URL to the shader. It might be on a different renderpass\n- 'author' username of the shader author\n\n- 'body' the body of the function without the return statement (no comments)\n- 'return_statment' the return statement of the function. everything infront of the semicolon is kept and white sapces are stripped in the custome Evaluator.", "### Data Splits\n\nCurrently available (shuffled):\n - train (85.0%)\n - test (15.0%)\n\nThese splits should be indexed the same across both subsets. So if you are fine-tuning on the 'fine' subset you won't get exposed to the 'return_completion' test split. However there are many duplicates among both subsets and splits.", "## Dataset Creation\n\nData retrieved starting 2022-07-20", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll data was collected via the URL API and then by looking for keywords and counting curly brackets to figure out what is part of a function and what isn't.", "#### Who are the source language producers?\n\nURL contributers which publish shaders as 'public+API'", "## Licensing Information\n\nThe Default licnese for each Shader is CC BY-NC-SA 3.0. However, some Shaders might have a different license attached. The Dataset is currently not filtering for any licensis." ]

[ 76, 11, 63, 16, 34, 48, 19, 6, 81, 127, 89, 13, 4, 49, 23, 51 ]

[ "passage: TAGS\n#task_categories-text-generation #annotations_creators-no-annotation #language_creators-machine-generated #size_categories-100K<n<1M #language-English #language-code #license-cc-by-nc-sa-3.0 #code #region-us \n# Dataset Card for Shadertoys-fine## 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 - Source Data\n- Licensing Information## Dataset Description\n\n- Repository: URL (private placeholder)### Dataset Summary\n\nfine variant of the Shadertoys dataset (still WIP), where individual functions are avaialable as Datapoints.### Supported Tasks and Leaderboards\n\n 'language-modeling': The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages.### Languages\n\n- English (names, comments)\n- Shadercode programming language## Dataset Structure### Data Instances\n\nA data point consists of the function string, it's name as well as a bit of metadata like the author and source URL. (in the future there might be a function string without comments)\n\n\nA data point in the 'return_completion' subset for the return-completion task in ShaderEval includes just two features:### Data Fields\n\n- 'name' funciton identifier composed of the type and the name of the function\n- 'code' the raw code (including comments) of function.\n- 'source' URL to the shader. It might be on a different renderpass\n- 'author' username of the shader author\n\n- 'body' the body of the function without the return statement (no comments)\n- 'return_statment' the return statement of the function. everything infront of the semicolon is kept and white sapces are stripped in the custome Evaluator." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ 13, 94, 15 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "language:es", "language:eu", "language:hi", "language:id", "language:zh", "language:ru", "language:my", "license:unknown", "other-story-completion", "region:us" ]

[]

[ "ar", "es", "eu", "hi", "id", "zh", "ru", "my" ]

[ "### Dataset Summary\n\n\nStory Cloze Test' is a new commonsense reasoning framework for evaluating story understanding,\nstory generation, and script learning.This test requires a system to choose the correct ending\nto a four-sentence story.", "### Data Instances\n\n\n* Size of downloaded dataset files: 2.03 MB\n* Size of the generated dataset: 2.03 MB\n* Total amount of disk used: 2.05 MB\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'input\\_sentence\\_1': The first statement in the story.\n* 'input\\_sentence\\_2': The second statement in the story.\n* 'input\\_sentence\\_3': The third statement in the story.\n* 'input\\_sentence\\_4': The forth statement in the story.\n* 'sentence\\_quiz1': first possible continuation of the story.\n* 'sentence\\_quiz2': second possible continuation of the story.\n* 'answer\\_right\\_ending': correct possible ending; either 1 or 2.\n* 'story\\_id': story id.", "### Data Splits" ]

[ "TAGS\n#annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #language-Spanish #language-Basque #language-Hindi #language-Indonesian #language-Chinese #language-Russian #language-Burmese #license-unknown #other-story-completion #region-us \n", "### Dataset Summary\n\n\nStory Cloze Test' is a new commonsense reasoning framework for evaluating story understanding,\nstory generation, and script learning.This test requires a system to choose the correct ending\nto a four-sentence story.", "### Data Instances\n\n\n* Size of downloaded dataset files: 2.03 MB\n* Size of the generated dataset: 2.03 MB\n* Total amount of disk used: 2.05 MB\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'input\\_sentence\\_1': The first statement in the story.\n* 'input\\_sentence\\_2': The second statement in the story.\n* 'input\\_sentence\\_3': The third statement in the story.\n* 'input\\_sentence\\_4': The forth statement in the story.\n* 'sentence\\_quiz1': first possible continuation of the story.\n* 'sentence\\_quiz2': second possible continuation of the story.\n* 'answer\\_right\\_ending': correct possible ending; either 1 or 2.\n* 'story\\_id': story id.", "### Data Splits" ]

[ 105, 50, 52, 167, 5 ]

[ "passage: TAGS\n#annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #language-Spanish #language-Basque #language-Hindi #language-Indonesian #language-Chinese #language-Russian #language-Burmese #license-unknown #other-story-completion #region-us \n### Dataset Summary\n\n\nStory Cloze Test' is a new commonsense reasoning framework for evaluating story understanding,\nstory generation, and script learning.This test requires a system to choose the correct ending\nto a four-sentence story.### Data Instances\n\n\n* Size of downloaded dataset files: 2.03 MB\n* Size of the generated dataset: 2.03 MB\n* Total amount of disk used: 2.05 MB\nAn example of 'train' looks as follows.### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'input\\_sentence\\_1': The first statement in the story.\n* 'input\\_sentence\\_2': The second statement in the story.\n* 'input\\_sentence\\_3': The third statement in the story.\n* 'input\\_sentence\\_4': The forth statement in the story.\n* 'sentence\\_quiz1': first possible continuation of the story.\n* 'sentence\\_quiz2': second possible continuation of the story.\n* 'answer\\_right\\_ending': correct possible ending; either 1 or 2.\n* 'story\\_id': story id.### Data Splits" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deberta-v3-xsmall-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deberta-v3-xsmall-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ 13, 100, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deberta-v3-xsmall-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @nbroad for evaluating this model." ]

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

[]

[]

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

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

[ 13, 93, 15 ]

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

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 10.0", "## 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: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 20817 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 15234 validated hours in 96 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\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.", "## Considerations for Using the Data", "### Social Impact of Dataset\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", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 10.0", "## 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: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 20817 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 15234 validated hours in 96 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## 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\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.", "## Considerations for Using the Data", "### Social Impact of Dataset\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", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ 87, 10, 120, 32, 108, 30, 4, 6, 77, 378, 145, 233, 5, 7, 4, 10, 10, 5, 5, 9, 42, 8, 41, 8, 7, 5, 6, 11 ]

[ "passage: TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n# Dataset Card for Common Voice Corpus 10.0## 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: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 20817 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 15234 validated hours in 96 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench### Languages## Dataset Structure### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "passage: ### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.## 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\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.## Considerations for Using the Data" ]

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

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[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/xdistil-l12-h384-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/xdistil-l12-h384-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ 13, 100, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/xdistil-l12-h384-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @nbroad for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-base-squad2-distilled\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yjernite for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-base-squad2-distilled\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yjernite for evaluating this model." ]

[ 13, 97, 16 ]

[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-base-squad2-distilled\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @yjernite for evaluating this model." ]

[ "task_categories:text-classification", "task_categories:text-generation", "task_ids:multi-class-classification", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-4.0", "speeches", "politics", "parliament", "British", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nA dataset containing every speech in the House of Commons from May 1979-July 2020. Quoted from the dataset homepage\n\n\n\n> \n> Please contact me if you find any errors in the dataset. The integrity of the public Hansard record is questionable at times, and while I have improved it, the data is presented \"as is\".\n> \n> \n>", "### Supported Tasks and Leaderboards\n\n\n* 'text-classification': This dataset can be used to classify various texts (transcribed from speeches) as different time periods or as different types\n* 'language-modeling': This dataset can contribute to the training or the evaluation of language models for historical texts.", "### Languages\n\n\n'en:GB'\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nTrain: 2694375\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset contains all the speeches made in the House of Commons and can be used for a number of deep learning tasks like detecting how language and societal views have changed over the >40 years. The dataset also provides language closer to the spoken language used in an elite British institution.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset is created by getting the data from URL. There is no normalization.", "#### Who are the source language producers?\n\n\n[N/A]", "### Annotations", "#### Annotation process\n\n\nNone", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nThis is public information, so there should not be any personal and sensitive information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to understand how language use and society's views have changed over time.", "### Discussion of Biases\n\n\nBecause of the long time period this dataset spans, it might contain language and opinions that are unacceptable in modern society.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset was built on top of parlparse by Evan Odell", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International License\n\n\nThanks to @shamikbose for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_categories-text-generation #task_ids-multi-class-classification #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #speeches #politics #parliament #British #region-us \n", "### Dataset Summary\n\n\nA dataset containing every speech in the House of Commons from May 1979-July 2020. Quoted from the dataset homepage\n\n\n\n> \n> Please contact me if you find any errors in the dataset. The integrity of the public Hansard record is questionable at times, and while I have improved it, the data is presented \"as is\".\n> \n> \n>", "### Supported Tasks and Leaderboards\n\n\n* 'text-classification': This dataset can be used to classify various texts (transcribed from speeches) as different time periods or as different types\n* 'language-modeling': This dataset can contribute to the training or the evaluation of language models for historical texts.", "### Languages\n\n\n'en:GB'\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nTrain: 2694375\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset contains all the speeches made in the House of Commons and can be used for a number of deep learning tasks like detecting how language and societal views have changed over the >40 years. The dataset also provides language closer to the spoken language used in an elite British institution.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset is created by getting the data from URL. There is no normalization.", "#### Who are the source language producers?\n\n\n[N/A]", "### Annotations", "#### Annotation process\n\n\nNone", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nThis is public information, so there should not be any personal and sensitive information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to understand how language use and society's views have changed over time.", "### Discussion of Biases\n\n\nBecause of the long time period this dataset spans, it might contain language and opinions that are unacceptable in modern society.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset was built on top of parlparse by Evan Odell", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International License\n\n\nThanks to @shamikbose for adding this dataset." ]

[ 142, 84, 74, 16, 6, 5, 16, 72, 4, 28, 15, 5, 7, 14, 33, 29, 35, 14, 21, 25 ]

[ "passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #task_ids-multi-class-classification #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #speeches #politics #parliament #British #region-us \n### Dataset Summary\n\n\nA dataset containing every speech in the House of Commons from May 1979-July 2020. Quoted from the dataset homepage\n\n\n\n> \n> Please contact me if you find any errors in the dataset. The integrity of the public Hansard record is questionable at times, and while I have improved it, the data is presented \"as is\".\n> \n> \n>### Supported Tasks and Leaderboards\n\n\n* 'text-classification': This dataset can be used to classify various texts (transcribed from speeches) as different time periods or as different types\n* 'language-modeling': This dataset can contribute to the training or the evaluation of language models for historical texts.### Languages\n\n\n'en:GB'\n\n\nDataset Structure\n-----------------### Data Instances### Data Fields### Data Splits\n\n\nTrain: 2694375\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nThis dataset contains all the speeches made in the House of Commons and can be used for a number of deep learning tasks like detecting how language and societal views have changed over the >40 years. The dataset also provides language closer to the spoken language used in an elite British institution.### Source Data#### Initial Data Collection and Normalization\n\n\nThe dataset is created by getting the data from URL. There is no normalization.#### Who are the source language producers?\n\n\n[N/A]### Annotations#### Annotation process\n\n\nNone#### Who are the annotators?\n\n\n[N/A]" ]