Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "task_categories:question-answering", "task_ids:extractive-qa", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-4.0", "arxiv:2210.03992", "region:us" ]

[ "2210.03992" ]

[ "en" ]

[ "# Dataset Card for \"lmqg/qa_squadshifts_synthetic\"", "## Dataset Description\n- Repository: URL\n- Paper: URL\n- Point of Contact: Asahi Ushio", "### Dataset Summary\nThis is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa_squadshifts', made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.\nThe test split is the original validation set of 'lmqg/qa_squadshifts', where the model should be evaluate on.", "### Supported Tasks and Leaderboards\n\n* 'question-answering'", "### Languages\nEnglish (en)", "## Dataset Structure", "### Data Fields\nThe data fields are the same among all splits.", "#### plain_text\n\n- 'id': a 'string' feature of id\n- 'title': a 'string' feature of title of the paragraph \n- 'context': a 'string' feature of paragraph \n- 'question': a 'string' feature of question\n- 'answers': a 'json' feature of answers", "### Data Splits\nTBA" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us \n", "# Dataset Card for \"lmqg/qa_squadshifts_synthetic\"", "## Dataset Description\n- Repository: URL\n- Paper: URL\n- Point of Contact: Asahi Ushio", "### Dataset Summary\nThis is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa_squadshifts', made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.\nThe test split is the original validation set of 'lmqg/qa_squadshifts', where the model should be evaluate on.", "### Supported Tasks and Leaderboards\n\n* 'question-answering'", "### Languages\nEnglish (en)", "## Dataset Structure", "### Data Fields\nThe data fields are the same among all splits.", "#### plain_text\n\n- 'id': a 'string' feature of id\n- 'title': a 'string' feature of title of the paragraph \n- 'context': a 'string' feature of paragraph \n- 'question': a 'string' feature of question\n- 'answers': a 'json' feature of answers", "### Data Splits\nTBA" ]

[ "task_categories:translation", "task_ids:language-modeling", "annotations_creators:expert-generated", "language_creators:other", "size_categories:1K<n<10K", "source_datasets:original", "language:kor", "language:jpn", "license:mit", "region:us" ]

[]

[ "kor", "jpn" ]

[ "# Dataset Card for \"Kor_Jpn_Translation_Dataset\"", "### Dataset Summary\n\nAI-Hub에서 제공하는 한국어-일본어 번역 말뭉치 데이터(URL)를 사용하기 쉽게 정제했습니다.\n\n- 제공처 : AI-Hub(URL\n- 제목 : 한국어-일본어 문화 분야 이중 말뭉치\n- 구축분야 : 문화재/향토/K-Food, K-POP(한류)/대중문화_공연 콘텐츠, IT/컴퓨터/모바일, 금융/증시, 사회/노동/복지, 교육, 특허/기술, 자동차\n- 구축량 : 150만 문장쌍\n- 응용분야 : 언어모델, 자동번역\n- 언어 : 원시어-한국어, 목적어-일본어", "### Supported Tasks and Leaderboards\n\n- Translation", "### Languages\n\n- Kor\n- Jpan", "## Dataset Structure\n\n features:\n - name: KOR\n dtype: string\n - name: JPN\n dtype: string\n splits:\n - name: train\n num_bytes: 294787449\n num_examples: 840000\n - name: val\n num_bytes: 88406929\n num_examples: 252000\n - name: test\n num_bytes: 37964427\n num_examples: 108000\n download_size: 289307354\n dataset_size: 421158805", "### Data Splits\n\n splits:\n - name: train\n num_bytes: 294787449\n num_examples: 840000\n - name: val\n num_bytes: 88406929\n num_examples: 252000\n - name: test\n num_bytes: 37964427\n num_examples: 108000", "### Contributions\nMore Information needed" ]

[ "TAGS\n#task_categories-translation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-other #size_categories-1K<n<10K #source_datasets-original #language-Korean #language-Japanese #license-mit #region-us \n", "# Dataset Card for \"Kor_Jpn_Translation_Dataset\"", "### Dataset Summary\n\nAI-Hub에서 제공하는 한국어-일본어 번역 말뭉치 데이터(URL)를 사용하기 쉽게 정제했습니다.\n\n- 제공처 : AI-Hub(URL\n- 제목 : 한국어-일본어 문화 분야 이중 말뭉치\n- 구축분야 : 문화재/향토/K-Food, K-POP(한류)/대중문화_공연 콘텐츠, IT/컴퓨터/모바일, 금융/증시, 사회/노동/복지, 교육, 특허/기술, 자동차\n- 구축량 : 150만 문장쌍\n- 응용분야 : 언어모델, 자동번역\n- 언어 : 원시어-한국어, 목적어-일본어", "### Supported Tasks and Leaderboards\n\n- Translation", "### Languages\n\n- Kor\n- Jpan", "## Dataset Structure\n\n features:\n - name: KOR\n dtype: string\n - name: JPN\n dtype: string\n splits:\n - name: train\n num_bytes: 294787449\n num_examples: 840000\n - name: val\n num_bytes: 88406929\n num_examples: 252000\n - name: test\n num_bytes: 37964427\n num_examples: 108000\n download_size: 289307354\n dataset_size: 421158805", "### Data Splits\n\n splits:\n - name: train\n num_bytes: 294787449\n num_examples: 840000\n - name: val\n num_bytes: 88406929\n num_examples: 252000\n - name: test\n num_bytes: 37964427\n num_examples: 108000", "### Contributions\nMore Information needed" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "multilinguality:multilingual", "size_categories:10M<n<100M", "language:da", "language:sv", "language:no", "language:is", "license:cc-by-4.0", "region:us" ]

[]

[ "da", "sv", "no", "is" ]

[ "### Dataset Summary\n\n\nScandiReddit is a filtered and post-processed corpus consisting of comments from Reddit.\n\n\nAll Reddit comments from December 2005 up until October 2022 were downloaded through PushShift, after which these were filtered based on the FastText language detection model. Any comment which was classified as Danish ('da'), Norwegian ('no'), Swedish ('sv') or Icelandic ('is') with a confidence score above 70% was kept.\n\n\nThe resulting comments were then deduplicated, removing roughly 438,000 comments. 5,000 comments written by Reddit bots were removed, and roughly 189,000 comments belonging to inappropriate subreddits (explicit and drug-related) were also removed.\n\n\nLastly, we remove roughly 40,000 near-duplicate comments from the resulting corpus, where near-duplicate here means that the comments have more than 80% of their word 5-grams in common.", "### Supported Tasks and Leaderboards\n\n\nTraining language models is the intended task for this dataset. No leaderboard is active at this point.", "### Languages\n\n\nThe dataset is available in Danish ('da'), Swedish ('sv'), Norwegian ('no') and Icelandic ('is').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 2341 MB\n* Size of the generated dataset: 3594 MB\n* Total amount of disk used: 5935 MB\n\n\nAn example from the dataset looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'doc': a 'string' feature.\n* 'subreddit': a 'string' feature.\n* 'language': a 'string' feature.\n* 'language\\_confidence': a 'float64' feature.", "### Language Distribution", "### Top-50 Subreddit Distribution\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe Scandinavian languages do not have many open source social media datasets.", "### Source Data\n\n\nThe raw Reddit data was collected through PushShift.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute curated this dataset.", "### Licensing Information\n\n\nThe dataset is licensed under the CC BY 4.0\nlicense." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #multilinguality-multilingual #size_categories-10M<n<100M #language-Danish #language-Swedish #language-Norwegian #language-Icelandic #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nScandiReddit is a filtered and post-processed corpus consisting of comments from Reddit.\n\n\nAll Reddit comments from December 2005 up until October 2022 were downloaded through PushShift, after which these were filtered based on the FastText language detection model. Any comment which was classified as Danish ('da'), Norwegian ('no'), Swedish ('sv') or Icelandic ('is') with a confidence score above 70% was kept.\n\n\nThe resulting comments were then deduplicated, removing roughly 438,000 comments. 5,000 comments written by Reddit bots were removed, and roughly 189,000 comments belonging to inappropriate subreddits (explicit and drug-related) were also removed.\n\n\nLastly, we remove roughly 40,000 near-duplicate comments from the resulting corpus, where near-duplicate here means that the comments have more than 80% of their word 5-grams in common.", "### Supported Tasks and Leaderboards\n\n\nTraining language models is the intended task for this dataset. No leaderboard is active at this point.", "### Languages\n\n\nThe dataset is available in Danish ('da'), Swedish ('sv'), Norwegian ('no') and Icelandic ('is').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 2341 MB\n* Size of the generated dataset: 3594 MB\n* Total amount of disk used: 5935 MB\n\n\nAn example from the dataset looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'doc': a 'string' feature.\n* 'subreddit': a 'string' feature.\n* 'language': a 'string' feature.\n* 'language\\_confidence': a 'float64' feature.", "### Language Distribution", "### Top-50 Subreddit Distribution\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe Scandinavian languages do not have many open source social media datasets.", "### Source Data\n\n\nThe raw Reddit data was collected through PushShift.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute curated this dataset.", "### Licensing Information\n\n\nThe dataset is licensed under the CC BY 4.0\nlicense." ]

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

[]

[]

[ "# About Dataset\nPhilosophers Quotes from URL \n\n* Arthur Schopenhauer 400+ quotes\n* Friedrich Nietzsche 200+ quotes\n* Immanuel Kant 300+ quotes\n* Aristotle 350+ quotes\n* Plato 70+ quotes\n* Sigmund Freud 400+ quotes\n* Hegel 120+ quotes\n* Jean Paul Sartre 320+ quotes\n* Spinoza 120+ quotes", "### COLLECTION METHODOLOGY\n Python Web Scraping with Selenium" ]

[ "TAGS\n#license-afl-3.0 #region-us \n", "# About Dataset\nPhilosophers Quotes from URL \n\n* Arthur Schopenhauer 400+ quotes\n* Friedrich Nietzsche 200+ quotes\n* Immanuel Kant 300+ quotes\n* Aristotle 350+ quotes\n* Plato 70+ quotes\n* Sigmund Freud 400+ quotes\n* Hegel 120+ quotes\n* Jean Paul Sartre 320+ quotes\n* Spinoza 120+ quotes", "### COLLECTION METHODOLOGY\n Python Web Scraping with Selenium" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"github-issues\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"slue-voxceleb\"\n\nMore Information needed" ]

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

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:cs", "license:lgpl-3.0", "czech QA", "wikipedia QA", "region:us" ]

[]

[ "cs" ]

[ "# Dataset Card for Czech Simple Question Answering Dataset 3.0\n\nThis a processed and filtered adaptation of an existing dataset. For raw and larger dataset, see 'Dataset Source' section.", "## Dataset Description\nThe data contains questions and answers based on Czech wikipeadia articles.\nEach question has an answer (or more) and a selected part of the context as the evidence.\nA majority of the answers are extractive - i.e. they are present in the context in the exact form. The remaining cases are\n\n- yes/no questions\n- answer is almost in the exact form present in the text, but the form of words was changed to suit the question (declension, ...)\n- answered in own words (should be rare, but is not)\n\nAll questions in the dataset are answerable from the context. Small minority of questions have multiple answers.\nSometimes it means that any of them is correct (e.g. either \"Pacifik\" or \"Tichý oceán\" are correct terms for Pacific Ocean)\nand sometimes it means that all of them together are a correct answer (e.g., Who was Leonardo da Vinci? [\"painter\", \"engineer\"])\n\nTotal number of examples is around:\n\n- 6,250 in train\n- 570 in validation\n- 850 in test.", "## Dataset Features\nEach example contains:\n- 'item_id': string id of the\n- 'context': \"reasonably\" big chunk (string) of wikipedia article that contains the answer\n- 'question': string\n- 'answers': list of all answers (string). mostly list of length 1\n- 'evidence_text': substring of context (typically one sentence) that is sufficient to answer the question\n- 'evidence_start': index in context, such that 'context[evidence_start:evidence_end] == evidence_text'\n- 'evidence_end': index in context\n- 'occurences':\n list of (dictionaries) occurences of the answer(s) in the evidence.\n Each answer was searched with word boundaries (\"\\b\" in regex) and case-sensitive in the evidence.\n If nothing found, try again but case-insensitive.\n If nothing found, try again but case-sensitive without word boundaries.\n If nothing found, try again but case-insensitive without word boundaries.\n This process should supress \"false positive\" occurences of the answer in the evidence.\n - 'start': index in context\n - 'end': index in context\n - 'text': the answer looked for\n- 'url': link to the wikipedia article\n- 'original_article': original parsed wikipedia article from which the context is taken\n- 'question_type': type of the question, one of: ['ABBREVIATION', 'DATETIME', 'DENOTATION', 'ENTITY', 'LOCATION', 'NUMERIC', 'ORGANIZATION', 'OTHER', 'PERSON', 'YES_NO']\n- 'answer_type': type of the answer, one of: ['ABBREVIATION', 'ADJ_PHRASE', 'CLAUSE', 'DATETIME', 'ENTITY', 'LOCATION', 'NUMERIC', 'OTHER', 'PERSON', 'VERB_PHRASE']", "## Dataset Source\n\nThe dataset is a preprocessed adaptation of existing SQAD 3.0 dataset link to data.\nThis adaptation contains (almost) same data, but converted to a convenient format.\nThe data was also filtered to remove a statistical bias where the answer was contained\nin the first sentence in the article (around 50% of all data in the original dataset, likely\ncaused by the data collection process).\n\n\nCite authors of the original dataset:" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Czech #license-lgpl-3.0 #czech QA #wikipedia QA #region-us \n", "# Dataset Card for Czech Simple Question Answering Dataset 3.0\n\nThis a processed and filtered adaptation of an existing dataset. For raw and larger dataset, see 'Dataset Source' section.", "## Dataset Description\nThe data contains questions and answers based on Czech wikipeadia articles.\nEach question has an answer (or more) and a selected part of the context as the evidence.\nA majority of the answers are extractive - i.e. they are present in the context in the exact form. The remaining cases are\n\n- yes/no questions\n- answer is almost in the exact form present in the text, but the form of words was changed to suit the question (declension, ...)\n- answered in own words (should be rare, but is not)\n\nAll questions in the dataset are answerable from the context. Small minority of questions have multiple answers.\nSometimes it means that any of them is correct (e.g. either \"Pacifik\" or \"Tichý oceán\" are correct terms for Pacific Ocean)\nand sometimes it means that all of them together are a correct answer (e.g., Who was Leonardo da Vinci? [\"painter\", \"engineer\"])\n\nTotal number of examples is around:\n\n- 6,250 in train\n- 570 in validation\n- 850 in test.", "## Dataset Features\nEach example contains:\n- 'item_id': string id of the\n- 'context': \"reasonably\" big chunk (string) of wikipedia article that contains the answer\n- 'question': string\n- 'answers': list of all answers (string). mostly list of length 1\n- 'evidence_text': substring of context (typically one sentence) that is sufficient to answer the question\n- 'evidence_start': index in context, such that 'context[evidence_start:evidence_end] == evidence_text'\n- 'evidence_end': index in context\n- 'occurences':\n list of (dictionaries) occurences of the answer(s) in the evidence.\n Each answer was searched with word boundaries (\"\\b\" in regex) and case-sensitive in the evidence.\n If nothing found, try again but case-insensitive.\n If nothing found, try again but case-sensitive without word boundaries.\n If nothing found, try again but case-insensitive without word boundaries.\n This process should supress \"false positive\" occurences of the answer in the evidence.\n - 'start': index in context\n - 'end': index in context\n - 'text': the answer looked for\n- 'url': link to the wikipedia article\n- 'original_article': original parsed wikipedia article from which the context is taken\n- 'question_type': type of the question, one of: ['ABBREVIATION', 'DATETIME', 'DENOTATION', 'ENTITY', 'LOCATION', 'NUMERIC', 'ORGANIZATION', 'OTHER', 'PERSON', 'YES_NO']\n- 'answer_type': type of the answer, one of: ['ABBREVIATION', 'ADJ_PHRASE', 'CLAUSE', 'DATETIME', 'ENTITY', 'LOCATION', 'NUMERIC', 'OTHER', 'PERSON', 'VERB_PHRASE']", "## Dataset Source\n\nThe dataset is a preprocessed adaptation of existing SQAD 3.0 dataset link to data.\nThis adaptation contains (almost) same data, but converted to a convenient format.\nThe data was also filtered to remove a statistical bias where the answer was contained\nin the first sentence in the article (around 50% of all data in the original dataset, likely\ncaused by the data collection process).\n\n\nCite authors of the original dataset:" ]

[ "region:us" ]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"yannic-kilcher-transcript\"\n\nMore Information needed" ]

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

[ "task_categories:text-classification", "task_categories:token-classification", "task_ids:intent-classification", "task_ids:natural-language-inference", "task_ids:sentiment-classification", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:coreference-resolution", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:eu", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "eu" ]

[ "### Dataset Summary\n\n\nNatural Language Understanding (NLU) technology has improved significantly over the last few years, and multitask benchmarks such as GLUE are key to evaluate this improvement in a robust and general way. These benchmarks take into account a wide and diverse set of NLU tasks that require some form of language understanding, beyond the detection of superficial, textual clues. However, they are costly to develop and language-dependent, and therefore they are only available for a small number of languages.\n\n\nWe present BasqueGLUE, the first NLU benchmark for Basque, which has been elaborated from previously existing datasets and following similar criteria to those used for the construction of GLUE and SuperGLUE. BasqueGLUE is freely available under an open license.", "### Supported Tasks and Leaderboards\n\n\nThis benchmark comprises the following tasks:", "#### NERCid\n\n\nThis dataset contains sentences from the news domain with manually annotated named entities. The data is the merge of EIEC (a dataset of a collection of news wire articles from Euskaldunon Egunkaria newspaper, (Alegria et al. 2004)), and newly annotated data from URL. The data is annotated following the BIO annotation scheme over four categories: person, organization, location, and miscellaneous.", "#### NERCood\n\n\nThis dataset contains sentences with manually annotated named entities. The training data is the merge of EIEC (a dataset of a collection of news wire articles from Euskaldunon Egunkaria newspaper, (Alegria et al. 2004)), and newly annotated data from URL. The data is annotated following the BIO annotation scheme over four categories: person, organization, location, and miscellaneous. For validation and test sets, sentences from Wikipedia were annotated following the same annotation guidelines.", "#### FMTODeu\\_intent\n\n\nThis dataset contains utterance texts and intent annotations drawn from the manually-annotated Facebook Multilingual Task Oriented Dataset (FMTOD) (Schuster et al. 2019). Basque translated data was drawn from the datasets created for Building a Task-oriented Dialog System for languages with no training data: the Case for Basque (de Lacalle et al. 2020). The examples are annotated with one of 12 different intent classes corresponding to alarm, reminder or weather related actions.", "#### FMTODeu\\_slot\n\n\nThis dataset contains utterance texts and sequence intent argument annotations designed for slot filling tasks, drawn from the manually-annotated Facebook Multilingual Task Oriented Dataset (FMTOD) (Schuster et al. 2019). Basque translated data was drawn from the datasets created for Building a Task-oriented Dialog System for languages with no training data: the Case for Basque (de Lacalle et al. 2020). The task is a sequence labelling task similar to NERC, following BIO annotation scheme over 11 categories.", "#### BHTCv2\n\n\nThe corpus contains 12,296 news headlines (brief article descriptions) from the Basque weekly newspaper Argia. Topics are classified uniquely according to twelve thematic categories.", "#### BEC2016eu\n\n\nThe Basque Election Campaign 2016 Opinion Dataset (BEC2016eu) is a new dataset for the task of sentiment analysis, a sequence classification task, which contains tweets about the campaign for the Basque elections from 2016. The crawling was carried out during the election campaign period (2016/09/09-2016/09/23), by monitoring the main parties and their respective candidates. The tweets were manually annotated as positive, negative or neutral.", "#### VaxxStance\n\n\nThe VaxxStance (Agerri et al., 2021) dataset originally provides texts and stance annotations for social media texts around the anti-vaccine movement. Texts are given a label indicating whether they express an AGAINST, FAVOR or NEUTRAL stance towards the topic.", "#### QNLIeu\n\n\nThis task includes the QA dataset ElkarHizketak (Otegi et al. 2020), a low resource conversational Question Answering (QA) dataset for Basque created by native speaker volunteers. The dataset is built on top of Wikipedia sections about popular people and organizations, and it contains around 400 dialogues and 1600 question and answer pairs. The task was adapted into a sentence-pair binary classification task, following the design of QNLI for English (Wang et al. 2019). Each question and answer pair are given a label indicating whether the answer is entailed by the question.", "#### WiCeu\n\n\nWord in Context or WiC (Pilehvar and Camacho-Collados 2019) is a word sense disambiguation (WSD) task, designed as a particular form of sentence pair binary classification. Given two text snippets and a polyse mous word that appears in both of them (the span of the word is marked in both snippets), the task is to determine whether the word has the same sense in both sentences. This dataset is based on the EPEC-EuSemcor (Pociello et al. 2011) sense-tagged corpus.", "#### EpecKorrefBin\n\n\nEPEC-KORREF-Bin is a dataset derived from EPEC-KORREF (Soraluze et al. 2012), a corpus of Basque news documents with manually annotated mentions and coreference chains, which we have been converted into a binary classification task. In this task, the model has to predict whether two mentions from a text, which can be pronouns, nouns or noun phrases, are referring to the same entity.", "#### Leaderboard\n\n\nResults obtained for two BERT base models as a baseline for the Benchmark.\n\n\n\nThe results obtained on NERC are the average of in-domain and out-of-domain NERC.", "### Languages\n\n\nData are available in Basque (BCP-47 'eu')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### NERCid/NERCood\n\n\nAn example of 'train' looks as follows:", "#### FMTODeu\\_intent\n\n\nAn example of 'train' looks as follows:", "#### FMTODeu\\_slot\n\n\nAn example of 'train' looks as follows:", "#### BHTCv2\n\n\nAn example of 'test' looks as follows:", "#### BEC2016eu\n\n\nAn example of 'test' looks as follows:", "#### VaxxStance\n\n\nAn example of 'train' looks as follows:", "#### QNLIeu\n\n\nAn example of 'train' looks as follows:", "#### WiCeu\n\n\nAn example of 'test' looks as follows:", "#### EpecKorrefBin\n\n\nAn example of 'train' looks as follows:", "### Data Fields", "#### NERCid\n\n\n* 'tokens': a list of 'string' features\n* 'tags': a list of entity labels, with possible values including 'person' (PER), 'location' (LOC), 'organization' (ORG), 'miscellaneous' (MISC)\n* 'idx': an 'int32' feature", "#### NERCood\n\n\n* 'tokens': a list of 'string' features\n* 'tags': a list of entity labels, with possible values including 'person' (PER), 'location' (LOC), 'organization' (ORG), 'miscellaneous' (MISC)\n* 'idx': an 'int32' feature", "#### FMTODeu\\_intent\n\n\n* 'text': a 'string' feature\n* 'label': an intent label, with possible values including:\n\t+ 'alarm/cancel\\_alarm'\n\t+ 'alarm/modify\\_alarm'\n\t+ 'alarm/set\\_alarm'\n\t+ 'alarm/show\\_alarms'\n\t+ 'alarm/snooze\\_alarm'\n\t+ 'alarm/time\\_left\\_on\\_alarm'\n\t+ 'reminder/cancel\\_reminder'\n\t+ 'reminder/set\\_reminder'\n\t+ 'reminder/show\\_reminders'\n\t+ 'weather/checkSunrise'\n\t+ 'weather/checkSunset'\n\t+ 'weather/find'\n* 'idx': an 'int32' feature", "#### FMTODeu\\_slot\n\n\n* 'tokens': a list of 'string' features\n* 'tags': a list of intent labels, with possible values including:\n\t+ 'datetime'\n\t+ 'location'\n\t+ 'negation'\n\t+ 'alarm/alarm\\_modifier'\n\t+ 'alarm/recurring\\_period'\n\t+ 'reminder/noun'\n\t+ 'reminder/todo'\n\t+ 'reminder/reference'\n\t+ 'reminder/recurring\\_period'\n\t+ 'weather/attribute'\n\t+ 'weather/noun'\n* 'idx': an 'int32' feature", "#### BHTCv2\n\n\n* 'text': a 'string' feature\n* 'label': a polarity label, with possible values including 'neutral' (NEU), 'negative' (N), 'positive' (P)\n* 'idx': an 'int32' feature", "#### BEC2016eu\n\n\n* 'text': a 'string' feature\n* 'label': a topic label, with possible values including:\n\t+ 'Ekonomia'\n\t+ 'Euskal Herria'\n\t+ 'Euskara'\n\t+ 'Gizartea'\n\t+ 'Historia'\n\t+ 'Ingurumena'\n\t+ 'Iritzia'\n\t+ 'Komunikazioa'\n\t+ 'Kultura'\n\t+ 'Nazioartea'\n\t+ 'Politika'\n\t+ 'Zientzia'\n* 'idx': an 'int32' feature", "#### VaxxStance\n\n\n* 'text': a 'string' feature\n* 'label': a stance label, with possible values including 'AGAINST', 'FAVOR', 'NONE'\n* 'idx': an 'int32' feature", "#### QNLIeu\n\n\n* 'question': a 'string' feature\n* 'sentence': a 'string' feature\n* 'label': an entailment label, with possible values including 'entailment', 'not\\_entailment'\n* 'idx': an 'int32' feature", "#### WiCeu\n\n\n* 'word': a 'string' feature\n* 'sentence1': a 'string' feature\n* 'sentence2': a 'string' feature\n* 'label': a 'boolean' label indicating sense agreement, with possible values including 'true', 'false'\n* 'start1': an 'int' feature indicating character position where word occurence begins in first sentence\n* 'start2': an 'int' feature indicating character position where word occurence begins in second sentence\n* 'end1': an 'int' feature indicating character position where word occurence ends in first sentence\n* 'end2': an 'int' feature indicating character position where word occurence ends in second sentence\n* 'idx': an 'int32' feature", "#### EpecKorrefBin\n\n\n* 'text': a 'string' feature.\n* 'label': a 'boolean' coreference label, with possible values including 'true', 'false'.\n* 'span1\\_text': a 'string' feature\n* 'span2\\_text': a 'string' feature\n* 'span1\\_index': an 'int' feature indicating token index where 'span1\\_text' feature occurs in 'text'\n* 'span2\\_index': an 'int' feature indicating token index where 'span2\\_text' feature occurs in 'text'\n* 'idx': an 'int32' feature", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWe believe that BasqueGLUE is a significant contribution towards developing NLU tools in Basque, which we believe will facilitate the technological advance for the Basque language. In order to create BasqueGLUE we took as a reference the GLUE and SuperGLUE frameworks. When possible, we re-used existing datasets for Basque, adapting them to the corresponding task formats if necessary. Additionally, BasqueGLUE also includes six new datasets that have not been published before. In total, BasqueGLUE consists of nine Basque NLU tasks and covers a wide range of tasks with different difficulties across several domains. As with the original GLUE benchmark, the training data for the tasks vary in size, which allows to measure the performance of how the models transfer knowledge across tasks.", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nGorka Urbizu [1], Iñaki San Vicente [1], Xabier Saralegi [1], Rodrigo Agerri [2] and Aitor Soroa [2]\n\n\nAffiliation of the authors:\n\n\n[1] orai NLP Technologies\n\n\n[2] HiTZ Center - Ixa, University of the Basque Country UPV/EHU", "### Licensing Information\n\n\nEach dataset of the BasqueGLUE benchmark has it's own license (due to most of them being or being derived from already existing datasets). See their respective README files for details.\n\n\nHere we provide a brief summary of their licenses:\n\n\n\nFor the rest of the files of the benchmark, including the loading and evaluation scripts, the following license applies:\n\n\nCopyright (C) by Orai NLP Technologies.\nThis benchmark and evaluation scripts are licensed under the Creative Commons Attribution Share Alike 4.0\nInternational License (CC BY-SA 4.0). To view a copy of this license, visit URL", "### Contributions\n\n\nThanks to @richplant for adding this dataset to hugginface." ]

[ "TAGS\n#task_categories-text-classification #task_categories-token-classification #task_ids-intent-classification #task_ids-natural-language-inference #task_ids-sentiment-classification #task_ids-topic-classification #task_ids-named-entity-recognition #task_ids-coreference-resolution #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Basque #license-cc-by-nc-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nNatural Language Understanding (NLU) technology has improved significantly over the last few years, and multitask benchmarks such as GLUE are key to evaluate this improvement in a robust and general way. These benchmarks take into account a wide and diverse set of NLU tasks that require some form of language understanding, beyond the detection of superficial, textual clues. However, they are costly to develop and language-dependent, and therefore they are only available for a small number of languages.\n\n\nWe present BasqueGLUE, the first NLU benchmark for Basque, which has been elaborated from previously existing datasets and following similar criteria to those used for the construction of GLUE and SuperGLUE. BasqueGLUE is freely available under an open license.", "### Supported Tasks and Leaderboards\n\n\nThis benchmark comprises the following tasks:", "#### NERCid\n\n\nThis dataset contains sentences from the news domain with manually annotated named entities. The data is the merge of EIEC (a dataset of a collection of news wire articles from Euskaldunon Egunkaria newspaper, (Alegria et al. 2004)), and newly annotated data from URL. The data is annotated following the BIO annotation scheme over four categories: person, organization, location, and miscellaneous.", "#### NERCood\n\n\nThis dataset contains sentences with manually annotated named entities. The training data is the merge of EIEC (a dataset of a collection of news wire articles from Euskaldunon Egunkaria newspaper, (Alegria et al. 2004)), and newly annotated data from URL. The data is annotated following the BIO annotation scheme over four categories: person, organization, location, and miscellaneous. For validation and test sets, sentences from Wikipedia were annotated following the same annotation guidelines.", "#### FMTODeu\\_intent\n\n\nThis dataset contains utterance texts and intent annotations drawn from the manually-annotated Facebook Multilingual Task Oriented Dataset (FMTOD) (Schuster et al. 2019). Basque translated data was drawn from the datasets created for Building a Task-oriented Dialog System for languages with no training data: the Case for Basque (de Lacalle et al. 2020). The examples are annotated with one of 12 different intent classes corresponding to alarm, reminder or weather related actions.", "#### FMTODeu\\_slot\n\n\nThis dataset contains utterance texts and sequence intent argument annotations designed for slot filling tasks, drawn from the manually-annotated Facebook Multilingual Task Oriented Dataset (FMTOD) (Schuster et al. 2019). Basque translated data was drawn from the datasets created for Building a Task-oriented Dialog System for languages with no training data: the Case for Basque (de Lacalle et al. 2020). The task is a sequence labelling task similar to NERC, following BIO annotation scheme over 11 categories.", "#### BHTCv2\n\n\nThe corpus contains 12,296 news headlines (brief article descriptions) from the Basque weekly newspaper Argia. Topics are classified uniquely according to twelve thematic categories.", "#### BEC2016eu\n\n\nThe Basque Election Campaign 2016 Opinion Dataset (BEC2016eu) is a new dataset for the task of sentiment analysis, a sequence classification task, which contains tweets about the campaign for the Basque elections from 2016. The crawling was carried out during the election campaign period (2016/09/09-2016/09/23), by monitoring the main parties and their respective candidates. The tweets were manually annotated as positive, negative or neutral.", "#### VaxxStance\n\n\nThe VaxxStance (Agerri et al., 2021) dataset originally provides texts and stance annotations for social media texts around the anti-vaccine movement. Texts are given a label indicating whether they express an AGAINST, FAVOR or NEUTRAL stance towards the topic.", "#### QNLIeu\n\n\nThis task includes the QA dataset ElkarHizketak (Otegi et al. 2020), a low resource conversational Question Answering (QA) dataset for Basque created by native speaker volunteers. The dataset is built on top of Wikipedia sections about popular people and organizations, and it contains around 400 dialogues and 1600 question and answer pairs. The task was adapted into a sentence-pair binary classification task, following the design of QNLI for English (Wang et al. 2019). Each question and answer pair are given a label indicating whether the answer is entailed by the question.", "#### WiCeu\n\n\nWord in Context or WiC (Pilehvar and Camacho-Collados 2019) is a word sense disambiguation (WSD) task, designed as a particular form of sentence pair binary classification. Given two text snippets and a polyse mous word that appears in both of them (the span of the word is marked in both snippets), the task is to determine whether the word has the same sense in both sentences. This dataset is based on the EPEC-EuSemcor (Pociello et al. 2011) sense-tagged corpus.", "#### EpecKorrefBin\n\n\nEPEC-KORREF-Bin is a dataset derived from EPEC-KORREF (Soraluze et al. 2012), a corpus of Basque news documents with manually annotated mentions and coreference chains, which we have been converted into a binary classification task. In this task, the model has to predict whether two mentions from a text, which can be pronouns, nouns or noun phrases, are referring to the same entity.", "#### Leaderboard\n\n\nResults obtained for two BERT base models as a baseline for the Benchmark.\n\n\n\nThe results obtained on NERC are the average of in-domain and out-of-domain NERC.", "### Languages\n\n\nData are available in Basque (BCP-47 'eu')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### NERCid/NERCood\n\n\nAn example of 'train' looks as follows:", "#### FMTODeu\\_intent\n\n\nAn example of 'train' looks as follows:", "#### FMTODeu\\_slot\n\n\nAn example of 'train' looks as follows:", "#### BHTCv2\n\n\nAn example of 'test' looks as follows:", "#### BEC2016eu\n\n\nAn example of 'test' looks as follows:", "#### VaxxStance\n\n\nAn example of 'train' looks as follows:", "#### QNLIeu\n\n\nAn example of 'train' looks as follows:", "#### WiCeu\n\n\nAn example of 'test' looks as follows:", "#### EpecKorrefBin\n\n\nAn example of 'train' looks as follows:", "### Data Fields", "#### NERCid\n\n\n* 'tokens': a list of 'string' features\n* 'tags': a list of entity labels, with possible values including 'person' (PER), 'location' (LOC), 'organization' (ORG), 'miscellaneous' (MISC)\n* 'idx': an 'int32' feature", "#### NERCood\n\n\n* 'tokens': a list of 'string' features\n* 'tags': a list of entity labels, with possible values including 'person' (PER), 'location' (LOC), 'organization' (ORG), 'miscellaneous' (MISC)\n* 'idx': an 'int32' feature", "#### FMTODeu\\_intent\n\n\n* 'text': a 'string' feature\n* 'label': an intent label, with possible values including:\n\t+ 'alarm/cancel\\_alarm'\n\t+ 'alarm/modify\\_alarm'\n\t+ 'alarm/set\\_alarm'\n\t+ 'alarm/show\\_alarms'\n\t+ 'alarm/snooze\\_alarm'\n\t+ 'alarm/time\\_left\\_on\\_alarm'\n\t+ 'reminder/cancel\\_reminder'\n\t+ 'reminder/set\\_reminder'\n\t+ 'reminder/show\\_reminders'\n\t+ 'weather/checkSunrise'\n\t+ 'weather/checkSunset'\n\t+ 'weather/find'\n* 'idx': an 'int32' feature", "#### FMTODeu\\_slot\n\n\n* 'tokens': a list of 'string' features\n* 'tags': a list of intent labels, with possible values including:\n\t+ 'datetime'\n\t+ 'location'\n\t+ 'negation'\n\t+ 'alarm/alarm\\_modifier'\n\t+ 'alarm/recurring\\_period'\n\t+ 'reminder/noun'\n\t+ 'reminder/todo'\n\t+ 'reminder/reference'\n\t+ 'reminder/recurring\\_period'\n\t+ 'weather/attribute'\n\t+ 'weather/noun'\n* 'idx': an 'int32' feature", "#### BHTCv2\n\n\n* 'text': a 'string' feature\n* 'label': a polarity label, with possible values including 'neutral' (NEU), 'negative' (N), 'positive' (P)\n* 'idx': an 'int32' feature", "#### BEC2016eu\n\n\n* 'text': a 'string' feature\n* 'label': a topic label, with possible values including:\n\t+ 'Ekonomia'\n\t+ 'Euskal Herria'\n\t+ 'Euskara'\n\t+ 'Gizartea'\n\t+ 'Historia'\n\t+ 'Ingurumena'\n\t+ 'Iritzia'\n\t+ 'Komunikazioa'\n\t+ 'Kultura'\n\t+ 'Nazioartea'\n\t+ 'Politika'\n\t+ 'Zientzia'\n* 'idx': an 'int32' feature", "#### VaxxStance\n\n\n* 'text': a 'string' feature\n* 'label': a stance label, with possible values including 'AGAINST', 'FAVOR', 'NONE'\n* 'idx': an 'int32' feature", "#### QNLIeu\n\n\n* 'question': a 'string' feature\n* 'sentence': a 'string' feature\n* 'label': an entailment label, with possible values including 'entailment', 'not\\_entailment'\n* 'idx': an 'int32' feature", "#### WiCeu\n\n\n* 'word': a 'string' feature\n* 'sentence1': a 'string' feature\n* 'sentence2': a 'string' feature\n* 'label': a 'boolean' label indicating sense agreement, with possible values including 'true', 'false'\n* 'start1': an 'int' feature indicating character position where word occurence begins in first sentence\n* 'start2': an 'int' feature indicating character position where word occurence begins in second sentence\n* 'end1': an 'int' feature indicating character position where word occurence ends in first sentence\n* 'end2': an 'int' feature indicating character position where word occurence ends in second sentence\n* 'idx': an 'int32' feature", "#### EpecKorrefBin\n\n\n* 'text': a 'string' feature.\n* 'label': a 'boolean' coreference label, with possible values including 'true', 'false'.\n* 'span1\\_text': a 'string' feature\n* 'span2\\_text': a 'string' feature\n* 'span1\\_index': an 'int' feature indicating token index where 'span1\\_text' feature occurs in 'text'\n* 'span2\\_index': an 'int' feature indicating token index where 'span2\\_text' feature occurs in 'text'\n* 'idx': an 'int32' feature", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWe believe that BasqueGLUE is a significant contribution towards developing NLU tools in Basque, which we believe will facilitate the technological advance for the Basque language. In order to create BasqueGLUE we took as a reference the GLUE and SuperGLUE frameworks. When possible, we re-used existing datasets for Basque, adapting them to the corresponding task formats if necessary. Additionally, BasqueGLUE also includes six new datasets that have not been published before. In total, BasqueGLUE consists of nine Basque NLU tasks and covers a wide range of tasks with different difficulties across several domains. As with the original GLUE benchmark, the training data for the tasks vary in size, which allows to measure the performance of how the models transfer knowledge across tasks.", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nGorka Urbizu [1], Iñaki San Vicente [1], Xabier Saralegi [1], Rodrigo Agerri [2] and Aitor Soroa [2]\n\n\nAffiliation of the authors:\n\n\n[1] orai NLP Technologies\n\n\n[2] HiTZ Center - Ixa, University of the Basque Country UPV/EHU", "### Licensing Information\n\n\nEach dataset of the BasqueGLUE benchmark has it's own license (due to most of them being or being derived from already existing datasets). See their respective README files for details.\n\n\nHere we provide a brief summary of their licenses:\n\n\n\nFor the rest of the files of the benchmark, including the loading and evaluation scripts, the following license applies:\n\n\nCopyright (C) by Orai NLP Technologies.\nThis benchmark and evaluation scripts are licensed under the Creative Commons Attribution Share Alike 4.0\nInternational License (CC BY-SA 4.0). To view a copy of this license, visit URL", "### Contributions\n\n\nThanks to @richplant for adding this dataset to hugginface." ]

[ "task_categories:text-classification", "annotations_creators:other", "language_creators:other", "size_categories:1K<n<10K", "language:en", "license:gpl-3.0", "medical", "conditions", "region:us" ]

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[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n \n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits", "### Dataset Summary\n\nThis data set contains over 6,000 medical terms and their wikipedia text. It is intended to be used on a downstream task that requires medical terms and their wikipedia explanation.", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-other #language_creators-other #size_categories-1K<n<10K #language-English #license-gpl-3.0 #medical #conditions #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n \n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits", "### Dataset Summary\n\nThis data set contains over 6,000 medical terms and their wikipedia text. It is intended to be used on a downstream task that requires medical terms and their wikipedia explanation.", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"banking77_openai_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"banking77_openai_embeddings\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"broadclass_totaldataset_0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totaldataset_0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"broadclass_totalMapped0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totalMapped0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"broadclass_totaldataset_1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totaldataset_1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"broadclass_totalMapped1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totalMapped1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"broadclass_totaldataset_2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totaldataset_2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"broadclass_totalMapped2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totalMapped2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"broadclass_totaldataset_3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totaldataset_3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"broadclass_totalMapped3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totalMapped3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"broadclass_totaldataset_4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totaldataset_4\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"broadclass_totalMapped4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"broadclass_totalMapped4\"\n\nMore Information needed" ]

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

[]

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[ "# Overview\n\nThis file contains over 1.7m public tweets about Apple, Amazon, Google, Microsoft and Tesla stocks, published between 01/01/2015 and 31/12/2019." ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Overview\n\nThis file contains over 1.7m public tweets about Apple, Amazon, Google, Microsoft and Tesla stocks, published between 01/01/2015 and 31/12/2019." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:cs", "license:cc-by-nc-sa-3.0", "region:us" ]

[]

[ "cs" ]

[ "# Dataset Card for URL Product Reviews (Czech)", "## Dataset Description\n\nThe dataset contains user reviews from Czech eshop <URL>\nEach review contains text, sentiment (positive/negative/neutral), and automatically-detected language (mostly Czech, occasionaly Slovak) using lingua-py\nThe dataset has in total (train+validation+test) 30,000 reviews. The data is balanced.\n\nTrain set has 8000 positive, 8000 neutral and 8000 negative reviews.\nValidation and test set each have 1000 positive, 1000 neutral and 1000 negative reviews.", "## Dataset Features\n\nEach sample contains:\n- 'review_id': unique string identifier of the review.\n- 'rating_str': string representation of the rating - \"pozitivní\" / \"neutrální\" / \"negativní\"\n- 'rating_int': integer representation of the rating (1=positive, 0=neutral, -1=negative)\n- 'comment_language': language of the review (mostly \"cs\", occasionaly \"sk\")\n- 'comment': the string of the review", "## Dataset Source\n\nThe data is a processed adaptation of Mall CZ corpus.\nThe adaptation is label-balanced and adds automatically-detected language" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Czech #license-cc-by-nc-sa-3.0 #region-us \n", "# Dataset Card for URL Product Reviews (Czech)", "## Dataset Description\n\nThe dataset contains user reviews from Czech eshop <URL>\nEach review contains text, sentiment (positive/negative/neutral), and automatically-detected language (mostly Czech, occasionaly Slovak) using lingua-py\nThe dataset has in total (train+validation+test) 30,000 reviews. The data is balanced.\n\nTrain set has 8000 positive, 8000 neutral and 8000 negative reviews.\nValidation and test set each have 1000 positive, 1000 neutral and 1000 negative reviews.", "## Dataset Features\n\nEach sample contains:\n- 'review_id': unique string identifier of the review.\n- 'rating_str': string representation of the rating - \"pozitivní\" / \"neutrální\" / \"negativní\"\n- 'rating_int': integer representation of the rating (1=positive, 0=neutral, -1=negative)\n- 'comment_language': language of the review (mostly \"cs\", occasionaly \"sk\")\n- 'comment': the string of the review", "## Dataset Source\n\nThe data is a processed adaptation of Mall CZ corpus.\nThe adaptation is label-balanced and adds automatically-detected language" ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:cs", "license:cc-by-nc-sa-3.0", "region:us" ]

[]

[ "cs" ]

[ "# Dataset Card for Czech Facebook comments", "## Dataset Description\n\nThe dataset contains user comments from Facebook. Each comment contains text, sentiment (positive/negative/neutral).\nThe dataset has in total (train+validation+test) 6,600 reviews. The data is balanced.", "## Dataset Features\n\nEach sample contains:\n- 'comment_id': unique string identifier of the comment.\n- 'sentiment_str': string representation of the rating - \"pozitivní\" / \"neutrální\" / \"negativní\"\n- 'sentiment_int': integer representation of the rating (1=positive, 0=neutral, -1=negative)\n- 'comment': the string of the comment", "## Dataset Source\n\nThe data is a processed adaptation of Facebook CZ Corpus.\nThis adaptation is label-balanced." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Czech #license-cc-by-nc-sa-3.0 #region-us \n", "# Dataset Card for Czech Facebook comments", "## Dataset Description\n\nThe dataset contains user comments from Facebook. Each comment contains text, sentiment (positive/negative/neutral).\nThe dataset has in total (train+validation+test) 6,600 reviews. The data is balanced.", "## Dataset Features\n\nEach sample contains:\n- 'comment_id': unique string identifier of the comment.\n- 'sentiment_str': string representation of the rating - \"pozitivní\" / \"neutrální\" / \"negativní\"\n- 'sentiment_int': integer representation of the rating (1=positive, 0=neutral, -1=negative)\n- 'comment': the string of the comment", "## Dataset Source\n\nThe data is a processed adaptation of Facebook CZ Corpus.\nThis adaptation is label-balanced." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totaldataset_0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totaldataset_0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totalMapped0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totalMapped0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totaldataset_1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totaldataset_1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totalMapped1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totalMapped1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totaldataset_2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totaldataset_2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totalMapped2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totalMapped2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totaldataset_3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totaldataset_3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totalMapped3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totalMapped3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totaldataset_4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totaldataset_4\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"phoneme_totalMapped4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"phoneme_totalMapped4\"\n\nMore Information needed" ]

[ "task_categories:text-retrieval", "multilinguality:multilingual", "language:en", "language:fr", "license:apache-2.0", "region:us" ]

[]

[ "en", "fr" ]

[]

[ "TAGS\n#task_categories-text-retrieval #multilinguality-multilingual #language-English #language-French #license-apache-2.0 #region-us \n" ]

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

[]

[]

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

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

[ "task_categories:multiple-choice", "task_categories:zero-shot-classification", "task_categories:question-answering", "task_ids:multiple-choice-qa", "task_ids:multiple-choice-coreference-resolution", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "gender bias", "social bias", "AI safety", "personality", "politics", "arxiv:1804.09301", "arxiv:2212.09251", "region:us" ]

[ "1804.09301", "2212.09251" ]

[ "en" ]

[ "# Model-Written Evaluation Datasets\n\nThis repository includes datasets written by language models, used in our paper on \"Discovering Language Model Behaviors with Model-Written Evaluations.\"\n\nWe intend the datasets to be useful to:\n1. Those who are interested in understanding the quality and properties of model-generated data\n2. Those who wish to use our datasets to evaluate other models for the behaviors we examined in our work (e.g., related to model persona, sycophancy, advanced AI risks, and gender bias)\n\nThe evaluations were generated to be asked to dialogue agents (e.g., a model finetuned explicitly respond to a user's utterances, or a pretrained language model prompted to behave like a dialogue agent). However, it is possible to adapt the data to test other kinds of models as well.\n\nWe describe each of our collections of datasets below:\n1. 'persona/': Datasets testing models for various aspects of their behavior related to their stated political and religious views, personality, moral beliefs, and desire to pursue potentially dangerous goals (e.g., self-preservation or power-seeking).\n2. 'sycophancy/': Datasets testing models for whether or not they repeat back a user's view to various questions (in philosophy, NLP research, and politics)\n3. 'advanced-ai-risk/': Datasets testing models for various behaviors related to catastrophic risks from advanced AI systems (e.g., ). These datasets were generated in a few-shot manner. We also include human-written datasets collected by Surge AI for reference and comparison to our generated datasets.\n4. 'winogenerated/': Our larger, model-generated version of the Winogender Dataset (Rudinger et al., 2018). We also include the names of occupation titles that we generated, to create the dataset (alongside occupation gender statistics from the Bureau of Labor Statistics)\n\nPlease see our paper for additional details on the datasets, how we generated them, human validation metrics, and other analyses of the datasets.\n\nDisclaimer: As discussed in our paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees.", "## Contact\nFor questions, please email 'ethan at anthropic dot com'", "## Bibtex Citation\n\nIf you would like to cite our work or data, you may use the following bibtex citation:" ]

[ "TAGS\n#task_categories-multiple-choice #task_categories-zero-shot-classification #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-multiple-choice-coreference-resolution #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #gender bias #social bias #AI safety #personality #politics #arxiv-1804.09301 #arxiv-2212.09251 #region-us \n", "# Model-Written Evaluation Datasets\n\nThis repository includes datasets written by language models, used in our paper on \"Discovering Language Model Behaviors with Model-Written Evaluations.\"\n\nWe intend the datasets to be useful to:\n1. Those who are interested in understanding the quality and properties of model-generated data\n2. Those who wish to use our datasets to evaluate other models for the behaviors we examined in our work (e.g., related to model persona, sycophancy, advanced AI risks, and gender bias)\n\nThe evaluations were generated to be asked to dialogue agents (e.g., a model finetuned explicitly respond to a user's utterances, or a pretrained language model prompted to behave like a dialogue agent). However, it is possible to adapt the data to test other kinds of models as well.\n\nWe describe each of our collections of datasets below:\n1. 'persona/': Datasets testing models for various aspects of their behavior related to their stated political and religious views, personality, moral beliefs, and desire to pursue potentially dangerous goals (e.g., self-preservation or power-seeking).\n2. 'sycophancy/': Datasets testing models for whether or not they repeat back a user's view to various questions (in philosophy, NLP research, and politics)\n3. 'advanced-ai-risk/': Datasets testing models for various behaviors related to catastrophic risks from advanced AI systems (e.g., ). These datasets were generated in a few-shot manner. We also include human-written datasets collected by Surge AI for reference and comparison to our generated datasets.\n4. 'winogenerated/': Our larger, model-generated version of the Winogender Dataset (Rudinger et al., 2018). We also include the names of occupation titles that we generated, to create the dataset (alongside occupation gender statistics from the Bureau of Labor Statistics)\n\nPlease see our paper for additional details on the datasets, how we generated them, human validation metrics, and other analyses of the datasets.\n\nDisclaimer: As discussed in our paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees.", "## Contact\nFor questions, please email 'ethan at anthropic dot com'", "## Bibtex Citation\n\nIf you would like to cite our work or data, you may use the following bibtex citation:" ]

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

[]

[]

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

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

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

[]

[ "code" ]

[ "## Data for the annotation project\n\n* 'short_files.json' with short code files (around 50 lines).\n* 'medium_files.json' with medium sized files (around 120 lines).\n* guidelines for the annotation." ]

[ "TAGS\n#language-code #region-us \n", "## Data for the annotation project\n\n* 'short_files.json' with short code files (around 50 lines).\n* 'medium_files.json' with medium sized files (around 120 lines).\n* guidelines for the annotation." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totaldataset_0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totaldataset_0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totalMapped0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totalMapped0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totaldataset_1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totaldataset_1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totalMapped1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totalMapped1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totaldataset_2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totaldataset_2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totalMapped2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totalMapped2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totaldataset_3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totaldataset_3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totalMapped3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totalMapped3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totaldataset_4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totaldataset_4\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"syllable_totalMapped4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"syllable_totalMapped4\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "language:de", "region:us" ]

[]

[ "de" ]

[ "# Germeval Task 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback\n\nIn the connected, modern world, customer feedback is a valuable source for insights on the quality of products or services. This feedback allows other customers to benefit from the experiences of others and enables businesses to react on requests, complaints or recommendations. However, the more people use a product or service, the more feedback is generated, which results in the major challenge of analyzing huge amounts of feedback in an efficient, but still meaningful way.\n\nThus, we propose a shared task on automatically analyzing customer reviews about “Deutsche Bahn” - the german public train operator with about two billion passengers each year.\n\nExample: \n\n> “RT @XXX: Da hört jemand in der Bahn so laut ‘700 Main Street’ durch seine Kopfhörer, dass ich mithören kann. :( :( :(“\n\nAs shown in the example, insights from reviews can be derived on different granularities. The review contains a general evaluation of the travel (The customer disliked the travel). Furthermore, the review evaluates a dedicated aspect of the train travel (“laut” → customer did not like the noise level).\n\nConsequently, we frame the task as aspect-based sentiment analysis with four sub tasks:", "## Data format", "## Links\n\n- URL\n- URL", "## How to cite" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-topic-classification #language-German #region-us \n", "# Germeval Task 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback\n\nIn the connected, modern world, customer feedback is a valuable source for insights on the quality of products or services. This feedback allows other customers to benefit from the experiences of others and enables businesses to react on requests, complaints or recommendations. However, the more people use a product or service, the more feedback is generated, which results in the major challenge of analyzing huge amounts of feedback in an efficient, but still meaningful way.\n\nThus, we propose a shared task on automatically analyzing customer reviews about “Deutsche Bahn” - the german public train operator with about two billion passengers each year.\n\nExample: \n\n> “RT @XXX: Da hört jemand in der Bahn so laut ‘700 Main Street’ durch seine Kopfhörer, dass ich mithören kann. :( :( :(“\n\nAs shown in the example, insights from reviews can be derived on different granularities. The review contains a general evaluation of the travel (The customer disliked the travel). Furthermore, the review evaluates a dedicated aspect of the train travel (“laut” → customer did not like the noise level).\n\nConsequently, we frame the task as aspect-based sentiment analysis with four sub tasks:", "## Data format", "## Links\n\n- URL\n- URL", "## How to cite" ]

[ "size_categories:1K<n<10K", "license:openrail", "not-for-all-audiences", "region:us" ]

[]

[]

[]

[ "TAGS\n#size_categories-1K<n<10K #license-openrail #not-for-all-audiences #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

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

[]

[]

[ "# NijiJourney Prompt Pairs", "#### A dataset containing txt2img prompt pairs for training on diffusion models\n\nThe final goal of this dataset is to create an OpenJourney like model but with NijiJourney images" ]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "# NijiJourney Prompt Pairs", "#### A dataset containing txt2img prompt pairs for training on diffusion models\n\nThe final goal of this dataset is to create an OpenJourney like model but with NijiJourney images" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"enwiki-20221101-sections\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"enwiki-20221101-sections\"\n\nMore Information needed" ]

[ "task_categories:multiple-choice", "region:us" ]

[]

[]

[ "# Dataset of (Du et al., 2022)", "## Abstract\n\n>Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning resources. In this paper, we fill this gap by presenting a human-annotated explainable CAusal REasoning dataset (e-CARE), which contains over 20K causal reasoning questions, together with natural language formed explanations of the causal questions. Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.", "## Notes\n\nPlease note that the original dataset has been modified so that the variable names match with those in the COPA dataset (Roemmele et al., 2011). In addition, only the training and the development sets are publicly available.", "## References\n\nDu, L., Ding, X., Xiong, K., Liu, T., & Qin, B. (2022). e-CARE: a New Dataset for Exploring Explainable Causal Reasoning. arXiv preprint arXiv:2205.05849.\n\nRoemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011." ]

[ "TAGS\n#task_categories-multiple-choice #region-us \n", "# Dataset of (Du et al., 2022)", "## Abstract\n\n>Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning resources. In this paper, we fill this gap by presenting a human-annotated explainable CAusal REasoning dataset (e-CARE), which contains over 20K causal reasoning questions, together with natural language formed explanations of the causal questions. Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.", "## Notes\n\nPlease note that the original dataset has been modified so that the variable names match with those in the COPA dataset (Roemmele et al., 2011). In addition, only the training and the development sets are publicly available.", "## References\n\nDu, L., Ding, X., Xiong, K., Liu, T., & Qin, B. (2022). e-CARE: a New Dataset for Exploring Explainable Causal Reasoning. arXiv preprint arXiv:2205.05849.\n\nRoemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011." ]

[ "size_categories:~ 3000 hours", "size_categories:721398 clips", "language:rw", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "rw", "en" ]

[ "# Kinyarwanda-English Commonvoice dataset\nA compilation of Kinyarwanda-english dataset to be used to train multi-lingual ASR\n\nNote: The audio dataset shall be added in the future" ]

[ "TAGS\n#size_categories-~ 3000 hours #size_categories-721398 clips #language-Kinyarwanda #language-English #license-cc-by-4.0 #region-us \n", "# Kinyarwanda-English Commonvoice dataset\nA compilation of Kinyarwanda-english dataset to be used to train multi-lingual ASR\n\nNote: The audio dataset shall be added in the future" ]

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

[]

[]

[ "# Dataset Card for Sloleks 3\n\nImportant: this is a mostly complete script for processing Sloleks 3. Certain data properties may not be exposed through the script. \nPlease see the CLARIN repository for full details on what the dataset contains, and open an issue or a pull request if you require some other information from the raw data.", "### Dataset Summary\n\nSloleks is a reference morphological lexicon of Slovene that was developed to be used in various NLP applications and language manuals. \nIt contains Slovene lemmas, their inflected or derivative word forms and the corresponding grammatical description. \nIn addition to the approx. 100,000 entries already available in Sloleks 2.0, Sloleks 3.0 contains an additional \ncca. 265,000 newly generated entries from the most frequent lemmas in Gigafida 2.0 not yet included in previous versions of Sloleks. \nFor verbs, adjectives, adverbs, and common nouns, the lemmas were checked manually by three annotators and included in Sloleks only if confirmed as legitimate by at \nleast one annotator. No manual checking was performed on proper nouns. Lemmatization rules, part-of-speech categorization and the set of feature-value pairs follow the \nMULTEXT-East morphosyntactic specifications for Slovenian.", "### Supported Tasks and Leaderboards\n\nOther (the data is a knowledge base - lexicon).", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nEntry for the verb 'absorbirati' (English: *to absorb*):", "### Data Fields\n\n- 'headword_lemma': lemma of the headword;\n- 'pos': coarse-grained part-of-speech tag (one of '{\"noun\", \"verb\", \"adjective\", \"adverb\", \"pronoun\", \"numeral\", \"preposition\", \"conjunction\", \"particle\", \"interjection\", \"abbreviation\", \"residual\"}'); \n- 'lex_unit': properties of the lexical unit corresponding to the headword ('id', 'form', 'key' and 'type');\n- 'word_forms': forms of the headword, each with its own list of possible forms and the morphosyntactic description of the form;\n- 'is_manually_checked': whether the headword was manually validated or not.", "## Additional Information", "### Dataset Curators\n\nJaka Čibej; et al. (please see URL for the full list).", "### Licensing Information\n\nCC BY-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "TAGS\n#license-cc-by-sa-4.0 #region-us \n", "# Dataset Card for Sloleks 3\n\nImportant: this is a mostly complete script for processing Sloleks 3. Certain data properties may not be exposed through the script. \nPlease see the CLARIN repository for full details on what the dataset contains, and open an issue or a pull request if you require some other information from the raw data.", "### Dataset Summary\n\nSloleks is a reference morphological lexicon of Slovene that was developed to be used in various NLP applications and language manuals. \nIt contains Slovene lemmas, their inflected or derivative word forms and the corresponding grammatical description. \nIn addition to the approx. 100,000 entries already available in Sloleks 2.0, Sloleks 3.0 contains an additional \ncca. 265,000 newly generated entries from the most frequent lemmas in Gigafida 2.0 not yet included in previous versions of Sloleks. \nFor verbs, adjectives, adverbs, and common nouns, the lemmas were checked manually by three annotators and included in Sloleks only if confirmed as legitimate by at \nleast one annotator. No manual checking was performed on proper nouns. Lemmatization rules, part-of-speech categorization and the set of feature-value pairs follow the \nMULTEXT-East morphosyntactic specifications for Slovenian.", "### Supported Tasks and Leaderboards\n\nOther (the data is a knowledge base - lexicon).", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nEntry for the verb 'absorbirati' (English: *to absorb*):", "### Data Fields\n\n- 'headword_lemma': lemma of the headword;\n- 'pos': coarse-grained part-of-speech tag (one of '{\"noun\", \"verb\", \"adjective\", \"adverb\", \"pronoun\", \"numeral\", \"preposition\", \"conjunction\", \"particle\", \"interjection\", \"abbreviation\", \"residual\"}'); \n- 'lex_unit': properties of the lexical unit corresponding to the headword ('id', 'form', 'key' and 'type');\n- 'word_forms': forms of the headword, each with its own list of possible forms and the morphosyntactic description of the form;\n- 'is_manually_checked': whether the headword was manually validated or not.", "## Additional Information", "### Dataset Curators\n\nJaka Čibej; et al. (please see URL for the full list).", "### Licensing Information\n\nCC BY-SA 4.0.", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "region:us" ]

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[ "# Dataset Card for Dmitriy007/restor_punct_Lenta2", "## Table of Contents\n- Table of Contents\n- Dataset Description\n- Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nНабор данных restor_punct_Lenta2 (версия 2.0) представляет собой набор из 800 975 блоков русскоязычных предложений, разбитых на слова, каждое слово размечено маркером для последующей классификации токенов.\n\nНабор данных очищен от символов: '...', ',', '«', '»', '\\\\', '-', '\"' \n\nВиды маркеров: L L. L! L? B B. B! N N. No\n\nПримеры значений маркеров:\n\nL -- данное слово с маленькой буквы + пробел\n\nL. -- данное слово с маленькой буквы + тчк\n\nB -- данное слово с заглавной буквы\n\nB. -- данное слово с заглавной буквы + тчк\n\nN -- Число + пробел\n\nN. -- Число + тчк\n\nNo -- Символ не определён", "### Supported Tasks and Leaderboards\n\ntoken-classification: набор данных можно использовать для обучения модели восстановления пунктуации и заглавных букв.", "### Languages\n\nТекст на русском языке", "## Dataset Structure", "### Data Instances\n\nПример из набора поездов restor_punct_Lenta2 выглядит следующим образом:\n{'words': ['фотограф-корреспондент', 'daily', 'mirror', 'рассказывает', 'случай', 'который', 'порадует', 'всех', 'друзей', 'животных'], 'labels': ['B', 'B', 'B', 'L', 'L', 'L', 'L', 'L', 'L', 'L.'], 'labels_id': [4, 4, 4, 0, 0, 0, 0, 0, 0, 1]}", "### Data Fields\n\n•\t'words': список слов, содержащая текст разбитый на отдельные слова.\n•\t'labels': строка, список маркеров\n•\t'labels_id: целое число, от 0 до 9 , обозначающее порядковый номер маркера", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nНабор данных restor_punct_Lenta2 был разработан для обучения модели восстановления пунктуации и \nаглавных букв в тексте предложения. Предполагалась, что обученная таким образом модель, будет использоваться в задачи транскрибации.", "### Source Data", "#### Initial Data Collection and Normalization\n\nДанных restor_punct_Lenta2 был основан на наборе данных Lenta2 проекта CORUS.", "#### Who are the source language producers?", "### Annotations\n\nНабор данных не содержит никаких дополнительных аннотаций.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nИмена пользователей или личная информация рецензентов не собирались вместе с обзорами, но потенциально могут быть восстановлены.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dmitriy007/restor_punct_Lenta2", "## Table of Contents\n- Table of Contents\n- Dataset Description\n- Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nНабор данных restor_punct_Lenta2 (версия 2.0) представляет собой набор из 800 975 блоков русскоязычных предложений, разбитых на слова, каждое слово размечено маркером для последующей классификации токенов.\n\nНабор данных очищен от символов: '...', ',', '«', '»', '\\\\', '-', '\"' \n\nВиды маркеров: L L. L! L? B B. B! N N. No\n\nПримеры значений маркеров:\n\nL -- данное слово с маленькой буквы + пробел\n\nL. -- данное слово с маленькой буквы + тчк\n\nB -- данное слово с заглавной буквы\n\nB. -- данное слово с заглавной буквы + тчк\n\nN -- Число + пробел\n\nN. -- Число + тчк\n\nNo -- Символ не определён", "### Supported Tasks and Leaderboards\n\ntoken-classification: набор данных можно использовать для обучения модели восстановления пунктуации и заглавных букв.", "### Languages\n\nТекст на русском языке", "## Dataset Structure", "### Data Instances\n\nПример из набора поездов restor_punct_Lenta2 выглядит следующим образом:\n{'words': ['фотограф-корреспондент', 'daily', 'mirror', 'рассказывает', 'случай', 'который', 'порадует', 'всех', 'друзей', 'животных'], 'labels': ['B', 'B', 'B', 'L', 'L', 'L', 'L', 'L', 'L', 'L.'], 'labels_id': [4, 4, 4, 0, 0, 0, 0, 0, 0, 1]}", "### Data Fields\n\n•\t'words': список слов, содержащая текст разбитый на отдельные слова.\n•\t'labels': строка, список маркеров\n•\t'labels_id: целое число, от 0 до 9 , обозначающее порядковый номер маркера", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nНабор данных restor_punct_Lenta2 был разработан для обучения модели восстановления пунктуации и \nаглавных букв в тексте предложения. Предполагалась, что обученная таким образом модель, будет использоваться в задачи транскрибации.", "### Source Data", "#### Initial Data Collection and Normalization\n\nДанных restor_punct_Lenta2 был основан на наборе данных Lenta2 проекта CORUS.", "#### Who are the source language producers?", "### Annotations\n\nНабор данных не содержит никаких дополнительных аннотаций.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nИмена пользователей или личная информация рецензентов не собирались вместе с обзорами, но потенциально могут быть восстановлены.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

[]

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[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

[]

[]

[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

[]

[]

[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

[]

[]

[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

[]

[]

[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

[]

[]

[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lego-blip-captions-512\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lego-blip-captions-512\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for NPSC Bokmål (< 15 sec. segments)", "## Dataset Description\n\n- Homepage: \n- Repository: <URL\n- Paper:\n\n\n- Point of Contact: Per Erik Solberg", "### Dataset Summary\n\nThis is the version of the Bokmål part of the Norwegian Parliamentary Speech Corpus (NPSC) used for training and testing the STORTINGET model\n in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.\nIt only contains segments of a length < 15 sec. For a full version of the NPSC, see this repository.", "### Languages\n\nNorwegian Bokmål", "## Dataset Creation", "### Source Data\nThe full version of this dataset is found in the repository of the Norwegian Language Bank", "#### Initial Data Collection and Normalization\n\nThe data was retrieved using the Spraakbanken downloader and standardized\n using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were\n extracted using this code.", "## Licensing Information\n\nCC0" ]

[ "TAGS\n#region-us \n", "# Dataset Card for NPSC Bokmål (< 15 sec. segments)", "## Dataset Description\n\n- Homepage: \n- Repository: <URL\n- Paper:\n\n\n- Point of Contact: Per Erik Solberg", "### Dataset Summary\n\nThis is the version of the Bokmål part of the Norwegian Parliamentary Speech Corpus (NPSC) used for training and testing the STORTINGET model\n in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.\nIt only contains segments of a length < 15 sec. For a full version of the NPSC, see this repository.", "### Languages\n\nNorwegian Bokmål", "## Dataset Creation", "### Source Data\nThe full version of this dataset is found in the repository of the Norwegian Language Bank", "#### Initial Data Collection and Normalization\n\nThe data was retrieved using the Spraakbanken downloader and standardized\n using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were\n extracted using this code.", "## Licensing Information\n\nCC0" ]

[ "region:us" ]

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[ "# Dataset Card for \"test_tags\"\n\nMore Information needed\n\nTest\n\nTest123\n\ndata" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"test_tags\"\n\nMore Information needed\n\nTest\n\nTest123\n\ndata" ]

[ "license:unknown", "arxiv:2110.15731", "region:us" ]

[ "2110.15731" ]

[]

[]

[ "TAGS\n#license-unknown #arxiv-2110.15731 #region-us \n" ]

[ "region:us" ]

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[]

[ "# Dataset Card for \"unnatural-instructions\"\n\nMore Information needed" ]

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

[ "arxiv:2212.09689", "region:us" ]

[ "2212.09689" ]

[]

[ "# Dataset Card for Unnatural Instructions (Core data)\nThis info comes from the Unnatural Instructions GitHub repo.\n\nUnnatural Instructions is a dataset of instructions automatically generated by a Large Language model.\nSee full details in the paper: \"Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor\"", "## ️ Content\nThe Unnatural Instructions core dataset of 68,478 instruction-input-output triplets.", "## Format", "### Core data\nEach example contains:\n- 'input': An input for the task described by the 'instruction'\n- 'instruction_with_input': The instruction concatenated with the 'input'\n- 'constraints': The task's output space constraints\n- 'output': The output of executing 'instruction' with the given 'input'", "## Citation\nIf you make use of Unnatural Instructions, please cite the following paper:\n\n\nMore Information needed" ]

[ "TAGS\n#arxiv-2212.09689 #region-us \n", "# Dataset Card for Unnatural Instructions (Core data)\nThis info comes from the Unnatural Instructions GitHub repo.\n\nUnnatural Instructions is a dataset of instructions automatically generated by a Large Language model.\nSee full details in the paper: \"Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor\"", "## ️ Content\nThe Unnatural Instructions core dataset of 68,478 instruction-input-output triplets.", "## Format", "### Core data\nEach example contains:\n- 'input': An input for the task described by the 'instruction'\n- 'instruction_with_input': The instruction concatenated with the 'input'\n- 'constraints': The task's output space constraints\n- 'output': The output of executing 'instruction' with the given 'input'", "## Citation\nIf you make use of Unnatural Instructions, please cite the following paper:\n\n\nMore Information needed" ]

[ "arxiv:2212.09689", "region:us" ]

[ "2212.09689" ]

[]

[ "# Dataset Card for Unnatural Instructions (Full data)\nThis info comes from the Unnatural Instructions GitHub repo.\n\nUnnatural Instructions is a dataset of instructions automatically generated by a Large Language model.\nSee full details in the paper: \"Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor\"", "## ️ Content\nIt contains the full 240,670 Unnatural Instructions (instruction-input-output triplets) examples. It was constructed by expanding the core data with automatically generated instruction paraphrases.", "## Format", "### Full data\nIt has the same structure as Core Data, but with one additional field - 'reformulations'. 'reformulations' is an array of JSON objects, each corresponds to an automatically generated paraphrase for the given instruction. Each reformulation contains the fields:\n- 'instruction': A paraphrase of the original instruction\n- 'input': An input for the task described by the 'instruction'\n- 'instruction_with_input': The paraphrased instruction concatenated with the 'input'\n- 'output': The output of executing 'instruction' with the given 'input'", "## Citation\nIf you make use of Unnatural Instructions, please cite the following paper:\n\n\nMore Information needed" ]

[ "TAGS\n#arxiv-2212.09689 #region-us \n", "# Dataset Card for Unnatural Instructions (Full data)\nThis info comes from the Unnatural Instructions GitHub repo.\n\nUnnatural Instructions is a dataset of instructions automatically generated by a Large Language model.\nSee full details in the paper: \"Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor\"", "## ️ Content\nIt contains the full 240,670 Unnatural Instructions (instruction-input-output triplets) examples. It was constructed by expanding the core data with automatically generated instruction paraphrases.", "## Format", "### Full data\nIt has the same structure as Core Data, but with one additional field - 'reformulations'. 'reformulations' is an array of JSON objects, each corresponds to an automatically generated paraphrase for the given instruction. Each reformulation contains the fields:\n- 'instruction': A paraphrase of the original instruction\n- 'input': An input for the task described by the 'instruction'\n- 'instruction_with_input': The paraphrased instruction concatenated with the 'input'\n- 'output': The output of executing 'instruction' with the given 'input'", "## Citation\nIf you make use of Unnatural Instructions, please cite the following paper:\n\n\nMore Information needed" ]

[ "region:us" ]

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[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"preprocessed_jsut_jsss_css10_fleurs_common_voice_11\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"preprocessed_jsut_jsss_css10_fleurs_common_voice_11\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wikipedia-ptbr-20221220\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wikipedia-ptbr-20221220\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for PopQA", "## Dataset Summary \nPopQA is a large-scale open-domain question answering (QA) dataset, consisting of 14k entity-centric QA pairs. Each question is created by converting a knowledge tuple retrieved from Wikidata using a template. Each question come with the original 'subject_entitiey', 'object_entity'and 'relationship_type' annotation, as well as Wikipedia monthly page views.", "## Languages\nThe dataset contains samples in English only.", "## Dataset Structure", "### Data Instances\n- Size of downloaded dataset file: 5.2 MB", "## Data Fields\n- 'id': question id\n- 'subj': subject entity name\n- 'prop': relationship type\n- 'obj': object entity name\n- 'subj_id': Wikidata ID of the subject entity\n- 'prop_id': Wikidata relationship type ID\n- 'obj_id': Wikidata ID of the object entity\n- 's_aliases': aliases of the subject entity\n- 'o_aliases': aliases of the object entity\n- 's_uri': Wikidata URI of the subject entity \n- 'o_uri': Wikidata URI of the object entity\n- 's_wiki_title': Wikipedia page title of the subject entity\n- 'o_wiki_title': Wikipedia page title of the object entity\n- 's_pop': Wikipedia monthly pageview of the subject entity\n- 'o_pop': Wikipedia monthly pageview of the object entity\n- 'question': PopQA question\n- 'possible_answers': a list of the gold answers." ]

[ "TAGS\n#region-us \n", "# Dataset Card for PopQA", "## Dataset Summary \nPopQA is a large-scale open-domain question answering (QA) dataset, consisting of 14k entity-centric QA pairs. Each question is created by converting a knowledge tuple retrieved from Wikidata using a template. Each question come with the original 'subject_entitiey', 'object_entity'and 'relationship_type' annotation, as well as Wikipedia monthly page views.", "## Languages\nThe dataset contains samples in English only.", "## Dataset Structure", "### Data Instances\n- Size of downloaded dataset file: 5.2 MB", "## Data Fields\n- 'id': question id\n- 'subj': subject entity name\n- 'prop': relationship type\n- 'obj': object entity name\n- 'subj_id': Wikidata ID of the subject entity\n- 'prop_id': Wikidata relationship type ID\n- 'obj_id': Wikidata ID of the object entity\n- 's_aliases': aliases of the subject entity\n- 'o_aliases': aliases of the object entity\n- 's_uri': Wikidata URI of the subject entity \n- 'o_uri': Wikidata URI of the object entity\n- 's_wiki_title': Wikipedia page title of the subject entity\n- 'o_wiki_title': Wikipedia page title of the object entity\n- 's_pop': Wikipedia monthly pageview of the subject entity\n- 'o_pop': Wikipedia monthly pageview of the object entity\n- 'question': PopQA question\n- 'possible_answers': a list of the gold answers." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dataset-v-1.4_CLIP_identities_random_seeds\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset-v-1.4_CLIP_identities_random_seeds\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"rick-and-morty-s06e01-blip-captions\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"rick-and-morty-s06e01-blip-captions\"\n\nMore Information needed" ]

[ "task_categories:text2text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:code", "license:mit", "code-generation", "doc retrieval", "retrieval augmented generation", "arxiv:2207.05987", "arxiv:1805.08949", "region:us" ]

[ "2207.05987", "1805.08949" ]

[ "code" ]

[ "## Dataset Description\n- Repository: URL\n- Paper: DocPrompting: Generating Code by Retrieving the Docs", "### Dataset Summary\nThis is the re-split of CoNaLa dataset. \nFor each code snippet in the dev and test set, at least one function is held out from the training set. \nThis split aims at testing a code generation model's capacity in generating *unseen* functions\nWe further make sure that examples from the same StackOverflow post (same 'question_id' before '-') are in the same split.", "### Supported Tasks and Leaderboards\nThis dataset is used to evaluate code generations.", "### Languages\nEnglish - Python code.", "## Dataset Structure", "### Data Fields\ntrain/dev/test:\n- nl: The natural language intent\n- cmd: The reference code snippet\n- question_id: 'x-y'where 'x' is the StackOverflow post ID\n- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split\n- canonical_cmd: The canonical version reference code snippet\n \n\ndocs:\n- doc_id: the id of a doc\n- doc_content: the content of the doc", "## Dataset Creation\nThe dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper" ]

[ "TAGS\n#task_categories-text2text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-code #license-mit #code-generation #doc retrieval #retrieval augmented generation #arxiv-2207.05987 #arxiv-1805.08949 #region-us \n", "## Dataset Description\n- Repository: URL\n- Paper: DocPrompting: Generating Code by Retrieving the Docs", "### Dataset Summary\nThis is the re-split of CoNaLa dataset. \nFor each code snippet in the dev and test set, at least one function is held out from the training set. \nThis split aims at testing a code generation model's capacity in generating *unseen* functions\nWe further make sure that examples from the same StackOverflow post (same 'question_id' before '-') are in the same split.", "### Supported Tasks and Leaderboards\nThis dataset is used to evaluate code generations.", "### Languages\nEnglish - Python code.", "## Dataset Structure", "### Data Fields\ntrain/dev/test:\n- nl: The natural language intent\n- cmd: The reference code snippet\n- question_id: 'x-y'where 'x' is the StackOverflow post ID\n- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split\n- canonical_cmd: The canonical version reference code snippet\n \n\ndocs:\n- doc_id: the id of a doc\n- doc_content: the content of the doc", "## Dataset Creation\nThe dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper" ]

[ "region:us" ]

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[]

[]

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

[ "region:us" ]

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[ "# Cleaned russian traffic sign images dataset\n\nDataset is generated from Russian traffic sign images dataset and detected signs in the dataset." ]

[ "TAGS\n#region-us \n", "# Cleaned russian traffic sign images dataset\n\nDataset is generated from Russian traffic sign images dataset and detected signs in the dataset." ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"diachronia-ocr-dev\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"fiszki-ocr-dev\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"fiszki-ocr\"\n\nMore Information needed" ]

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

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

[]

[]

[ "# Dataset Card for Horse-30", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W.}, title = {Pretraining Boosts Out-of-Domain Robustness for Pose Estimation}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {1859-1868} \n- Leaderboard: URL\n- Point of Contact: Mackenzie Mathis", "### Dataset Summary\n\nPose estimation is an important tool for measuring behavior, and thus widely used in technology, medicine and biology. Due to innovations in both deep learning algorithms and large-scale datasets pose estimation on humans has gotten very powerful. However, typical human pose estimation benchmarks, such as MPII pose and COCO, contain many different individuals (>10K) in different contexts, but only very few example postures per individual. In real world application of pose estimation, users want to estimate the location of user-defined bodyparts by only labeling a few hundred frames on a small subset of individuals, yet want this to generalize to new individuals. Thus, one naturally asks the following question: Assume you have trained an algorithm that performs with high accuracy on a given (individual) animal for the whole repertoire of movement - how well will it generalize to different individuals that have slightly or a dramatically different appearance? Unlike in common human pose estimation benchmarks here the setting is that datasets have many (annotated) poses per individual (>200) but only few individuals (1-25). \nTo allow the field to tackle this challenge, we developed a novel benchmark, called Horse-10, comprising 30 diverse Thoroughbred horses, for which 22 body parts were labeled by an expert in 8,114 frames. Horses have various coat colors and the “in-the-wild” aspect of the collected data at various Thoroughbred yearling sales and farms added additional complexity. \n\n- Homepage: URL\n- Repository: URL\n- Paper: '{Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W.}, title = {Pretraining Boosts Out-of-Domain Robustness for Pose Estimation}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {1859-1868} '\n- Leaderboard: URL\n- Point of Contact: Mackenzie Mathis", "### Supported Tasks and Leaderboards\n\nHorse-10 task: Train on a subset of individuals (10) and evaluate on held-out “out-of-domain” horses (20).", "### Languages\n\nPython, deeplabcut, tensorflow, pytorch", "## Dataset Structure", "### Data Instances\n\nOver 8,000 expertly labeled frames across 30 individual thoroughbred horses", "### Data Splits\n\nThe ground truth training data is provided as 3 splits of 10 Horses each. The download provides you a project compatible with loading into the deeplabcut framework, but ground truth labels/training data can be easily loaded in pandas to accommodate your framework (example loader here). \n\nPlease do NOT train on all three splits simultaneously. You must train independently (as some horses can be considered out-of-domain in other splits for evaluation!). Integrity matters!\n\nThe download also includes all of Horse-30 images and annotations (thus is ~850MB)." ]

[ "TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for Horse-30", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W.}, title = {Pretraining Boosts Out-of-Domain Robustness for Pose Estimation}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {1859-1868} \n- Leaderboard: URL\n- Point of Contact: Mackenzie Mathis", "### Dataset Summary\n\nPose estimation is an important tool for measuring behavior, and thus widely used in technology, medicine and biology. Due to innovations in both deep learning algorithms and large-scale datasets pose estimation on humans has gotten very powerful. However, typical human pose estimation benchmarks, such as MPII pose and COCO, contain many different individuals (>10K) in different contexts, but only very few example postures per individual. In real world application of pose estimation, users want to estimate the location of user-defined bodyparts by only labeling a few hundred frames on a small subset of individuals, yet want this to generalize to new individuals. Thus, one naturally asks the following question: Assume you have trained an algorithm that performs with high accuracy on a given (individual) animal for the whole repertoire of movement - how well will it generalize to different individuals that have slightly or a dramatically different appearance? Unlike in common human pose estimation benchmarks here the setting is that datasets have many (annotated) poses per individual (>200) but only few individuals (1-25). \nTo allow the field to tackle this challenge, we developed a novel benchmark, called Horse-10, comprising 30 diverse Thoroughbred horses, for which 22 body parts were labeled by an expert in 8,114 frames. Horses have various coat colors and the “in-the-wild” aspect of the collected data at various Thoroughbred yearling sales and farms added additional complexity. \n\n- Homepage: URL\n- Repository: URL\n- Paper: '{Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W.}, title = {Pretraining Boosts Out-of-Domain Robustness for Pose Estimation}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {1859-1868} '\n- Leaderboard: URL\n- Point of Contact: Mackenzie Mathis", "### Supported Tasks and Leaderboards\n\nHorse-10 task: Train on a subset of individuals (10) and evaluate on held-out “out-of-domain” horses (20).", "### Languages\n\nPython, deeplabcut, tensorflow, pytorch", "## Dataset Structure", "### Data Instances\n\nOver 8,000 expertly labeled frames across 30 individual thoroughbred horses", "### Data Splits\n\nThe ground truth training data is provided as 3 splits of 10 Horses each. The download provides you a project compatible with loading into the deeplabcut framework, but ground truth labels/training data can be easily loaded in pandas to accommodate your framework (example loader here). \n\nPlease do NOT train on all three splits simultaneously. You must train independently (as some horses can be considered out-of-domain in other splits for evaluation!). Integrity matters!\n\nThe download also includes all of Horse-30 images and annotations (thus is ~850MB)." ]

[ "region:us" ]

[]

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

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

[ "region:us" ]

[]

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[ "# Dataset Card for \"rm-static\"\n\nSplit of hh-static used for training reward models after supervised fine-tuning." ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"rm-static\"\n\nSplit of hh-static used for training reward models after supervised fine-tuning." ]

[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "pretraining", "language modelling", "wikipedia", "web", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for OLM December 2022 Wikipedia\n\nPretraining dataset, created with the OLM repo here from a December 2022 Wikipedia snapshot." ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #language-English #pretraining #language modelling #wikipedia #web #region-us \n", "# Dataset Card for OLM December 2022 Wikipedia\n\nPretraining dataset, created with the OLM repo here from a December 2022 Wikipedia snapshot." ]

[ "task_categories:text2text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:code", "license:mit", "code-generation", "doc retrieval", "retrieval augmented generation", "arxiv:2207.05987", "region:us" ]

[ "2207.05987" ]

[ "code" ]

[ "## Dataset Description\n- Repository: URL\n- Paper: DocPrompting: Generating Code by Retrieving the Docs", "### Dataset Summary\nThis is the natural language to bash generation dataset we harvested from the English subset of 'tldr'\nWe split the dataset by bash commands. Every command in the dev and test set is held out from the training set.", "### Supported Tasks and Leaderboards\nThis dataset is used to evaluate code generations.", "### Languages\nEnglish - Bash", "## Dataset Structure", "### Data Fields\ntrain/dev/test:\n- nl: The natural language intent\n- cmd: The reference code snippet\n- question_id: the unique id of a question\n- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split\n- cmd_name: the bash command of this code snippet\n- tldr_cmd_name: the bash command used in tldr github repo. The 'cmd_name' and 'tldr_cmd_name' can be different due to naming difference\n- manual_exist: whether the manual exists in URL\n- matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token.\n \n\ndocs:\n- doc_id: the id of a doc\n- doc_content: the content of the doc", "## Dataset Creation\nThe dataset was curated from 'tldr'. \nThe project aims to provide frequent usage of bash commands with natural language intents. \nFor more details, please check the repo." ]

[ "TAGS\n#task_categories-text2text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-code #license-mit #code-generation #doc retrieval #retrieval augmented generation #arxiv-2207.05987 #region-us \n", "## Dataset Description\n- Repository: URL\n- Paper: DocPrompting: Generating Code by Retrieving the Docs", "### Dataset Summary\nThis is the natural language to bash generation dataset we harvested from the English subset of 'tldr'\nWe split the dataset by bash commands. Every command in the dev and test set is held out from the training set.", "### Supported Tasks and Leaderboards\nThis dataset is used to evaluate code generations.", "### Languages\nEnglish - Bash", "## Dataset Structure", "### Data Fields\ntrain/dev/test:\n- nl: The natural language intent\n- cmd: The reference code snippet\n- question_id: the unique id of a question\n- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split\n- cmd_name: the bash command of this code snippet\n- tldr_cmd_name: the bash command used in tldr github repo. The 'cmd_name' and 'tldr_cmd_name' can be different due to naming difference\n- manual_exist: whether the manual exists in URL\n- matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token.\n \n\ndocs:\n- doc_id: the id of a doc\n- doc_content: the content of the doc", "## Dataset Creation\nThe dataset was curated from 'tldr'. \nThe project aims to provide frequent usage of bash commands with natural language intents. \nFor more details, please check the repo." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "en" ]

[ "# Dataset Card for \"financial_news_sentiment\"\n\nManually validated sentiment for ~2000 Canadian news articles.\n\nThe dataset also include a column topic which contains one of the following value:\n* acquisition\n* other\n* quaterly financial release\n* appointment to new position\n* dividend\n* corporate update\n* drillings results\n* conference\n* share repurchase program\n* grant of stocks\n\nThis was generated automatically using a zero-shot classification model and was not reviewed manually." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-mit #region-us \n", "# Dataset Card for \"financial_news_sentiment\"\n\nManually validated sentiment for ~2000 Canadian news articles.\n\nThe dataset also include a column topic which contains one of the following value:\n* acquisition\n* other\n* quaterly financial release\n* appointment to new position\n* dividend\n* corporate update\n* drillings results\n* conference\n* share repurchase program\n* grant of stocks\n\nThis was generated automatically using a zero-shot classification model and was not reviewed manually." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dataset-v-1.4_CLIP_us_identities_random_seeds\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset-v-1.4_CLIP_us_identities_random_seeds\"\n\nMore Information needed" ]

[ "task_categories:summarization", "region:us" ]

[]

[]

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

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

[ "license:creativeml-openrail-m", "pytorch", "diffusers", "stable-diffusion", "text-to-image", "diffusion-models-class", "dreambooth-hackathon", "animal", "region:us" ]

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[ "# DreamBooth model for pugsly trained by lewtun on the Shirleyphd/Pug dataset.\n\nThis is a Stable Diffusion model fine-tuned the ccorgi concept taught to Stable Diffusion with DreamBooth.\nIt can be used by modifying the 'instance_prompt': a photo of pugsly dog\n\nThis model was created as part of the DreamBooth Hackathon . Visit the organisation page for instructions on how to take part!", "## Description\n\n\nThis is a Stable Diffusion model fine-tuned on 'dog' images for the animal theme.", "## Usage" ]

[ "TAGS\n#license-creativeml-openrail-m #pytorch #diffusers #stable-diffusion #text-to-image #diffusion-models-class #dreambooth-hackathon #animal #region-us \n", "# DreamBooth model for pugsly trained by lewtun on the Shirleyphd/Pug dataset.\n\nThis is a Stable Diffusion model fine-tuned the ccorgi concept taught to Stable Diffusion with DreamBooth.\nIt can be used by modifying the 'instance_prompt': a photo of pugsly dog\n\nThis model was created as part of the DreamBooth Hackathon . Visit the organisation page for instructions on how to take part!", "## Description\n\n\nThis is a Stable Diffusion model fine-tuned on 'dog' images for the animal theme.", "## Usage" ]

[ "region:us" ]

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[ "# Dataset Card for \"ev-skins-blip-lg\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ev-skins-blip-lg\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"booksum-fullbooks\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

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[ "# Dataset Card for \"stable-diffusion-prompts\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[]

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