Datasets:

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huggingface-datasets

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[ "region:us" ]

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

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

[ "region:us" ]

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

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

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[ "## Problem and Opportunity\nIn the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. \n\nFor the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups:\n* are exclusive, and do not overlap\n* are adjacent, fully covering their corresponding state and potentially county\n* have roughly the same size in area, population and voter presence\n\nAnalytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure.\n\nThe dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates." ]

[ "TAGS\n#license-mit #region-us \n", "## Problem and Opportunity\nIn the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. \n\nFor the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups:\n* are exclusive, and do not overlap\n* are adjacent, fully covering their corresponding state and potentially county\n* have roughly the same size in area, population and voter presence\n\nAnalytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure.\n\nThe dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates." ]

[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:expert", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "license:cc-by-4.0", "arxiv:2108.05921", "region:us" ]

[ "2108.05921" ]

[]

[ "# Dataset Card for HatemojiCheck", "## 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", "## Content Warning\nThis datasets contains examples of hateful language.", "## Dataset Description and Details\n- Repository: URL\n- Paper: URL\n- Point of Contact: URL@URL", "### Dataset Summary\nHatemojiCheck is a test suite of 3,930 test cases covering seven functionalities of emoji-based hate and six identities.\nHatemojiCheck contains the text for each test case and its gold-standard label from majority agreement of three annotators. We provide labels by target of hate.\nHatemojiCheck can be used to evaluate the robustness of hate speech classifiers to constructions of emoji-based hate.", "### Supported Tasks\n\nHate Speech Detection", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n3,930 test cases", "### Data Fields\n\ncase_id: The unique ID of the test case (assigned to each of the 3,930 cases generated)\n\ntempl_id: The unique ID of the template (original=.0, identity perturbation=.1, polarity perturbation=.2, emoji perturbation = .3) from which the test case was generated\n\ntest_grp_id: The ID of the set of templates (original, identity perturbation, polarity perturbation, no emoji perturbation) from which the test case was generated.\n\ntext: The text of the test case.\n\ntarget: Where applicable, the protected group targeted or referenced by the test case. We cover six protected groups in the test suite: women, trans people, gay people, black people, disabled people and Muslims.\n\nfunctionality: The shorthand for the functionality tested by the test case.\n\nset: Whether the test case is an original statement, a identity perturbation, a polarity perturbation or a no emoji perturbation.\n\nlabel_gold: The gold standard label (hateful/non-hateful) of the test case. All test cases within a given functionality have the same gold standard label.\n\nunrealistic_flags: The number of annotators (/3) who flagged the test case as unrealistic.\n\nincluded_in_test_suite: Indicator for whether test case is included in final HatemojiCheck test suite. All 3,930 test cases are included.", "### Data Splits\n\nAll of HatemojiCheck is designated for testing models so only test is provided.", "## Dataset Creation", "### Curation Rationale\n\nThe purpose of HatemojiCheck is to evaluate the performance of black-box models against varied constructions of emoji-based hate. To construct HatemojiCheck, we hand-crafted 3,930 short form English-language texts using a template-based method for group identities and slurs. Each test case exemplifies one functionality and is associated with a binary gold standard label _hateful_ versus _not hateful_. All 3,930 cases were labeled by a trained team of three annotators, who could also flag examples that were unrealistic. Any test cases with multiple disagreements or flags were replaced with alternative templates and re-issued for annotation to improve the quality of examples in the final set of test cases.", "### Source Data", "#### Initial Data Collection and Normalization\n\nBased on the literature, we define a list of potentially hateful emoji and words, and use Twitter's Streaming API to search for the Cartesian products of emoji--emoji and emoji--word pairs over a two week period. To identify different forms of emoji-based hate, we apply a grounded theory approach on a sample of 3,295 tweets, splitting out distinctive categories, and recursively selecting sub-categories until all key parts of the data are captured and the framework is 'saturated'.", "#### Who are the source language producers?\n\nAll test cases were hand-crafted by the lead author, who is a native English-speaking researcher at a UK university with extensive subject matter expertise in online harms. The test cases are in English. This choice was motivated by the researchers' and annotators' expertise, and to maximize HatemojiCheck's applicability to previous hate speech detection studies, which are predominantly conducted on English-language data. We discuss the limitations of restricting HatemojiCheck to one language and suggest that future work should prioritize expanding the test suite to other languages.", "### Annotations", "#### Annotation process\n\nTo validate the gold-standard labels assigned to each test case, we recruited three annotators with prior experience on hate speech projects. Annotators were given extensive guidelines, test tasks and training sessions, which included examining real-world examples of emoji-based hate from Twitter. We followed guidance for protecting annotator well-being. There were two iterative rounds of annotation. In the first round, each annotator labeled all 3,930 test cases as hateful or non-hateful, and had the option to flag unrealistic entries. Test cases with any disagreement or unrealistic flags were reviewed by the study authors (n=289). One-on-one interviews were conducted with annotators to identify dataset issues versus annotator error. From 289 test cases, 119 were identified as ambiguous or unrealistic, replaced with alternatives and re-issued to annotators for labeling. No further issues were raised. We measured inter-annotator agreement using Randolph's Kappa, obtaining a value of 0.85 for the final set of test cases, which indicates \"almost perfect agreement\".", "#### Who are the annotators?\n\nWe recruited a team of three annotators who worked for two weeks in May 2021 and were paid £16. All annotators were female and between 30--39 years old. One had an undergraduate degree, one a taught graduate degree and one a post-graduate research degree. There were three nationalities: Argentinian, British and Iraqi, two ethnicities: White and Arab, and three religious affiliations: Catholic, Muslim and None. One annotator was a native English speaker and the others were non-native but fluent. All annotators used emoji and social media more than once per day. All annotators had seen others targeted by abuse online, and one had been targeted personally.", "### Personal and Sensitive Information\n\nHatemojiCheck contains synthetic statements so has no personal information. It does however contains harmful examples of emoji-based hate which could be disturbing or damaging to view.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nHatemojiCheck contains challenging emoji examples on which commercial solutions and state-of-the-art transformer models have been proven to fail. Malicious actors could take inspiration for bypassing current detection systems on internet platforms, or in principal train a generative hate speech model. However, it also helps to evaluate model's weaknesses to emoji-based hate, so can be used to mitigate the harm to victims before a model is deployed.", "### Discussion of Biases\n\nHatemojiCheck only contains test cases against 6 identities: woman, trans people, gay people, disabled people, Black people and Muslims. It thus is biased towards evaluating hate directed at these targets. Additionally, HatemojiCheck was motivated by an empirical study of English-language tweets. The usage of emoji varies significantly across culture, country and demographic so there may be biases towards Western, English-language use of emoji.", "### Other Known Limitations\n\nWhile inspired by real-world instances of emoji-based hate, HatemojiCheck contains synthetic, hand-crafted test cases. These test cases are designed to be a \"minimum performance standard\" against which to hold models accountable. However, because the test cases are designed to have one \"clear, gold-standard label\" they may be easier to predict than more nuanced, complex and real-world instances of emoji-based hate.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by the lead author (Hannah Rose Kirk), then validated by the other authors and three annotators.", "### Licensing Information\n\nCreative Commons Attribution 4.0 International Public License. For full detail see: URL\n\n\n\nIf you use this dataset, please cite our paper: Kirk, H. R., Vidgen, B., Röttger, P., Thrush, T., & Hale, S. A. (2021). Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate. arXiv preprint arXiv:2108.05921.", "### Contributions\n\nThanks to @HannahKirk for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #license-cc-by-4.0 #arxiv-2108.05921 #region-us \n", "# Dataset Card for HatemojiCheck", "## 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", "## Content Warning\nThis datasets contains examples of hateful language.", "## Dataset Description and Details\n- Repository: URL\n- Paper: URL\n- Point of Contact: URL@URL", "### Dataset Summary\nHatemojiCheck is a test suite of 3,930 test cases covering seven functionalities of emoji-based hate and six identities.\nHatemojiCheck contains the text for each test case and its gold-standard label from majority agreement of three annotators. We provide labels by target of hate.\nHatemojiCheck can be used to evaluate the robustness of hate speech classifiers to constructions of emoji-based hate.", "### Supported Tasks\n\nHate Speech Detection", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n3,930 test cases", "### Data Fields\n\ncase_id: The unique ID of the test case (assigned to each of the 3,930 cases generated)\n\ntempl_id: The unique ID of the template (original=.0, identity perturbation=.1, polarity perturbation=.2, emoji perturbation = .3) from which the test case was generated\n\ntest_grp_id: The ID of the set of templates (original, identity perturbation, polarity perturbation, no emoji perturbation) from which the test case was generated.\n\ntext: The text of the test case.\n\ntarget: Where applicable, the protected group targeted or referenced by the test case. We cover six protected groups in the test suite: women, trans people, gay people, black people, disabled people and Muslims.\n\nfunctionality: The shorthand for the functionality tested by the test case.\n\nset: Whether the test case is an original statement, a identity perturbation, a polarity perturbation or a no emoji perturbation.\n\nlabel_gold: The gold standard label (hateful/non-hateful) of the test case. All test cases within a given functionality have the same gold standard label.\n\nunrealistic_flags: The number of annotators (/3) who flagged the test case as unrealistic.\n\nincluded_in_test_suite: Indicator for whether test case is included in final HatemojiCheck test suite. All 3,930 test cases are included.", "### Data Splits\n\nAll of HatemojiCheck is designated for testing models so only test is provided.", "## Dataset Creation", "### Curation Rationale\n\nThe purpose of HatemojiCheck is to evaluate the performance of black-box models against varied constructions of emoji-based hate. To construct HatemojiCheck, we hand-crafted 3,930 short form English-language texts using a template-based method for group identities and slurs. Each test case exemplifies one functionality and is associated with a binary gold standard label _hateful_ versus _not hateful_. All 3,930 cases were labeled by a trained team of three annotators, who could also flag examples that were unrealistic. Any test cases with multiple disagreements or flags were replaced with alternative templates and re-issued for annotation to improve the quality of examples in the final set of test cases.", "### Source Data", "#### Initial Data Collection and Normalization\n\nBased on the literature, we define a list of potentially hateful emoji and words, and use Twitter's Streaming API to search for the Cartesian products of emoji--emoji and emoji--word pairs over a two week period. To identify different forms of emoji-based hate, we apply a grounded theory approach on a sample of 3,295 tweets, splitting out distinctive categories, and recursively selecting sub-categories until all key parts of the data are captured and the framework is 'saturated'.", "#### Who are the source language producers?\n\nAll test cases were hand-crafted by the lead author, who is a native English-speaking researcher at a UK university with extensive subject matter expertise in online harms. The test cases are in English. This choice was motivated by the researchers' and annotators' expertise, and to maximize HatemojiCheck's applicability to previous hate speech detection studies, which are predominantly conducted on English-language data. We discuss the limitations of restricting HatemojiCheck to one language and suggest that future work should prioritize expanding the test suite to other languages.", "### Annotations", "#### Annotation process\n\nTo validate the gold-standard labels assigned to each test case, we recruited three annotators with prior experience on hate speech projects. Annotators were given extensive guidelines, test tasks and training sessions, which included examining real-world examples of emoji-based hate from Twitter. We followed guidance for protecting annotator well-being. There were two iterative rounds of annotation. In the first round, each annotator labeled all 3,930 test cases as hateful or non-hateful, and had the option to flag unrealistic entries. Test cases with any disagreement or unrealistic flags were reviewed by the study authors (n=289). One-on-one interviews were conducted with annotators to identify dataset issues versus annotator error. From 289 test cases, 119 were identified as ambiguous or unrealistic, replaced with alternatives and re-issued to annotators for labeling. No further issues were raised. We measured inter-annotator agreement using Randolph's Kappa, obtaining a value of 0.85 for the final set of test cases, which indicates \"almost perfect agreement\".", "#### Who are the annotators?\n\nWe recruited a team of three annotators who worked for two weeks in May 2021 and were paid £16. All annotators were female and between 30--39 years old. One had an undergraduate degree, one a taught graduate degree and one a post-graduate research degree. There were three nationalities: Argentinian, British and Iraqi, two ethnicities: White and Arab, and three religious affiliations: Catholic, Muslim and None. One annotator was a native English speaker and the others were non-native but fluent. All annotators used emoji and social media more than once per day. All annotators had seen others targeted by abuse online, and one had been targeted personally.", "### Personal and Sensitive Information\n\nHatemojiCheck contains synthetic statements so has no personal information. It does however contains harmful examples of emoji-based hate which could be disturbing or damaging to view.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nHatemojiCheck contains challenging emoji examples on which commercial solutions and state-of-the-art transformer models have been proven to fail. Malicious actors could take inspiration for bypassing current detection systems on internet platforms, or in principal train a generative hate speech model. However, it also helps to evaluate model's weaknesses to emoji-based hate, so can be used to mitigate the harm to victims before a model is deployed.", "### Discussion of Biases\n\nHatemojiCheck only contains test cases against 6 identities: woman, trans people, gay people, disabled people, Black people and Muslims. It thus is biased towards evaluating hate directed at these targets. Additionally, HatemojiCheck was motivated by an empirical study of English-language tweets. The usage of emoji varies significantly across culture, country and demographic so there may be biases towards Western, English-language use of emoji.", "### Other Known Limitations\n\nWhile inspired by real-world instances of emoji-based hate, HatemojiCheck contains synthetic, hand-crafted test cases. These test cases are designed to be a \"minimum performance standard\" against which to hold models accountable. However, because the test cases are designed to have one \"clear, gold-standard label\" they may be easier to predict than more nuanced, complex and real-world instances of emoji-based hate.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by the lead author (Hannah Rose Kirk), then validated by the other authors and three annotators.", "### Licensing Information\n\nCreative Commons Attribution 4.0 International Public License. For full detail see: URL\n\n\n\nIf you use this dataset, please cite our paper: Kirk, H. R., Vidgen, B., Röttger, P., Thrush, T., & Hale, S. A. (2021). Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate. arXiv preprint arXiv:2108.05921.", "### Contributions\n\nThanks to @HannahKirk for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:expert", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "license:cc-by-4.0", "arxiv:2108.05921", "arxiv:2012.15761", "region:us" ]

[ "2108.05921", "2012.15761" ]

[]

[ "# Dataset Card for HatemojiBuild", "## 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", "## Content Warning\nThis datasets contains examples of hateful language.", "## Dataset Description and Details\n\n- Repository: URL\n- Paper: URL\n- Point of Contact: URL@URL", "### Dataset Summary\nHatemojiBuild can be used to train, develop and test models on emoji-based hate with challenging adversarial examples and perturbations.\nHatemojiBuild is a dataset of 5,912 adversarially-generated examples created on Dynabench using a human-and-model-in-the-loop approach. We collect data in three consecutive rounds. Our work follows on from Vidgen et al (2021) _Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection_ (URL who collect four rounds of textual adversarial examples. The R1-R4 data is available at URL The entries in HatemojiBuild are labeled by round (R5-7). The text of each entry is given with its gold-standard label from majority agreement of three annotators. Each original entry is associated with a perturbation so each row of the dataset. matches these two cases. We also provide granular labels of type and target for hateful entries.", "### Supported Tasks\n\nHate Speech Detection", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n5,912 adversarially-generated instances", "### Data Fields\n\nentry_id: The unique ID of the entry (assigned to each of the 5,912 cases generated).\n\ntext: The text of the entry.\n\ntype: The type of hate assigned to hateful entries.\n\ntarget: The target of hate assigned to hateful entries.\n\nURL: The round where the entry was generated.\n\nURL: The round and whether the entry came from an original statement (a) or a perturbation (b).\n\nset: Whether the entry is an original or perturbation.\n\nsplit: The randomly-assigned train/dev/test split using in our work (80:10:10).\n\nlabel_gold: The gold standard label (hateful/non-hateful) of the test case.\n\nmatched_text: The text of the paired perturbation. Each original entry has one perturbation.\n\nmatched_id: The unique entry ID of the paired perturbation.", "### Data Splits\n\nTrain, Validation and Test.", "## Dataset Creation", "### Curation Rationale\n\nThe genre of texts is hateful and non-hateful statements using emoji constructions. The purpose of HatemojiBuild is address the model weaknesses to emoji-baaed hate, to \"build\" better models. 50% of the 5,912 test cases are hateful. 50% of the entries in the dataset are original content and 50% are perturbations.", "### Source Data", "#### Initial Data Collection and Normalization\n\nWe use an online interface designed for dynamic dataset generation and model benchmarking (Dynabench) to collect synthetic adversarial examples in three successive rounds, running between 24th May--11th June. Each round contains approximately 2,000 entries, where each original entry inputed to the interface is paired with an offline perturbation. Data was synthetically-generated by a team of trained annotators, i.e., not sampled from social media.", "#### Who are the source language producers?\n\nThe language producers are also the annotators.", "### Annotations", "#### Annotation process\n\nWe implemented three successive rounds of data generation and model re-training to create the HatemojiBuild dataset. \nIn each round we tasked a team of 10 trained annotators with entering content the model-in-the-loop would misclassify. We refer to this model as the target model. Annotators were instructed to generate linguistically diverse entries while ensuring each entry was (1) realistic, (2) clearly hateful or non-hateful and (3) contained at least one emoji. Each entry was first given a binary label of hateful or non-hateful, and hateful content was assigned secondary labels for the type and target of hate. Each entry was validated by two additional annotators, and an expert resolved disagreements. After validation, annotators created a perturbation for each entry that flips the label. To maximize similarity between originals and perturbations, annotators could either make an emoji substitution while fixing the text or fix the emoji and minimally change the surrounding text. Each perturbation received two additional annotations, and disagreements were resolved by the expert. This weekly cadence of annotator tasks was repeated in three consecutive weeks.", "#### Who are the annotators?\n\nTen annotators were recruited to work for three weeks, and paid £16/hour. An expert annotator was recruited for quality control purposes and paid £20/hour. In total, there were 11 annotators. All annotators received a training session prior to data collection and had previous experience working on hate speech projects. A daily 'stand-up' meeting was held every morning to communicate feedback and update guidelines as rounds progressed. Annotators were able to contact the research team at any point using a messaging platform. Of 11 annotators, 8 were between 18--29 years old and 3 between 30--39 years old. The completed education level was high school for 3 annotators, undergraduate degree for 1 annotators, taught graduate degree for 4 annotators and post-graduate research degree for 3 annotators. 6 annotators were female, and 5 were male. Annotators came from a variety of nationalities, with 7 British, as well as Jordanian, Irish, Polish and Spanish. 7 annotators identified as ethnically White and the remaining annotators came from various ethnicities including Turkish, Middle Eastern, and Mixed White and South Asian. 4 annotators were Muslim, and others identified as Atheist or as having no religious affiliation. 9 annotators were native English speakers and 2 were non-native but fluent. The majority of annotators (9) used emoji and social media more than once per day. 10 annotators had seen others targeted by abuse online, and 7 had been personally targeted.", "### Personal and Sensitive Information\n\nHatemojiBuild contains synthetic statements so has no personal information. It does however contains harmful examples of emoji-based hate which could be disturbing or damaging to view.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nHatemojiBuild contains challenging emoji examples which have \"tricked\" state-of-the-art transformers models. Malicious actors could take inspiration for bypassing current detection systems on internet platforms, or in principal train a generative hate speech model. However, it also helps to build model robustness to emoji-based hate, so can be used to mitigate the harm to victims before a model is deployed.", "### Discussion of Biases\n\nAnnotators were given substantial freedom in the targets of hate resulting in 54 unique targets, and 126 unique intersections of these. The entries from R5-R7 contain 1,082 unique emoji out of 3,521 defined in the Unicode Standard as of September 2020. This diversity helped to mitigate biases in classification towards certain targets but biases likely remain, especially since HatemojiBuild was designed for English-language use of emoji.", "### Other Known Limitations\n\nWhile annotators were trained on real-world examples of emoji-based hate from Twitter, the entries in HatemojiBuild are synthetically-generated so may deviate from real-world instances of emoji-based hate.", "## Additional Information", "### Dataset Curators\n\nThe dataset was curated by the lead author (Hannah Rose Kirk), using the Dynabench platform.", "### Licensing Information\n\nCreative Commons Attribution 4.0 International Public License. For full detail see: URL\n\n\n\nIf you use this dataset, please cite our paper: Kirk, H. R., Vidgen, B., Röttger, P., Thrush, T., & Hale, S. A. (2021). Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate. arXiv preprint arXiv:2108.05921.", "### Contributions\n\nThanks to @HannahKirk for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #license-cc-by-4.0 #arxiv-2108.05921 #arxiv-2012.15761 #region-us \n", "# Dataset Card for HatemojiBuild", "## 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", "## Content Warning\nThis datasets contains examples of hateful language.", "## Dataset Description and Details\n\n- Repository: URL\n- Paper: URL\n- Point of Contact: URL@URL", "### Dataset Summary\nHatemojiBuild can be used to train, develop and test models on emoji-based hate with challenging adversarial examples and perturbations.\nHatemojiBuild is a dataset of 5,912 adversarially-generated examples created on Dynabench using a human-and-model-in-the-loop approach. We collect data in three consecutive rounds. Our work follows on from Vidgen et al (2021) _Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection_ (URL who collect four rounds of textual adversarial examples. The R1-R4 data is available at URL The entries in HatemojiBuild are labeled by round (R5-7). The text of each entry is given with its gold-standard label from majority agreement of three annotators. Each original entry is associated with a perturbation so each row of the dataset. matches these two cases. We also provide granular labels of type and target for hateful entries.", "### Supported Tasks\n\nHate Speech Detection", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n5,912 adversarially-generated instances", "### Data Fields\n\nentry_id: The unique ID of the entry (assigned to each of the 5,912 cases generated).\n\ntext: The text of the entry.\n\ntype: The type of hate assigned to hateful entries.\n\ntarget: The target of hate assigned to hateful entries.\n\nURL: The round where the entry was generated.\n\nURL: The round and whether the entry came from an original statement (a) or a perturbation (b).\n\nset: Whether the entry is an original or perturbation.\n\nsplit: The randomly-assigned train/dev/test split using in our work (80:10:10).\n\nlabel_gold: The gold standard label (hateful/non-hateful) of the test case.\n\nmatched_text: The text of the paired perturbation. Each original entry has one perturbation.\n\nmatched_id: The unique entry ID of the paired perturbation.", "### Data Splits\n\nTrain, Validation and Test.", "## Dataset Creation", "### Curation Rationale\n\nThe genre of texts is hateful and non-hateful statements using emoji constructions. The purpose of HatemojiBuild is address the model weaknesses to emoji-baaed hate, to \"build\" better models. 50% of the 5,912 test cases are hateful. 50% of the entries in the dataset are original content and 50% are perturbations.", "### Source Data", "#### Initial Data Collection and Normalization\n\nWe use an online interface designed for dynamic dataset generation and model benchmarking (Dynabench) to collect synthetic adversarial examples in three successive rounds, running between 24th May--11th June. Each round contains approximately 2,000 entries, where each original entry inputed to the interface is paired with an offline perturbation. Data was synthetically-generated by a team of trained annotators, i.e., not sampled from social media.", "#### Who are the source language producers?\n\nThe language producers are also the annotators.", "### Annotations", "#### Annotation process\n\nWe implemented three successive rounds of data generation and model re-training to create the HatemojiBuild dataset. \nIn each round we tasked a team of 10 trained annotators with entering content the model-in-the-loop would misclassify. We refer to this model as the target model. Annotators were instructed to generate linguistically diverse entries while ensuring each entry was (1) realistic, (2) clearly hateful or non-hateful and (3) contained at least one emoji. Each entry was first given a binary label of hateful or non-hateful, and hateful content was assigned secondary labels for the type and target of hate. Each entry was validated by two additional annotators, and an expert resolved disagreements. After validation, annotators created a perturbation for each entry that flips the label. To maximize similarity between originals and perturbations, annotators could either make an emoji substitution while fixing the text or fix the emoji and minimally change the surrounding text. Each perturbation received two additional annotations, and disagreements were resolved by the expert. This weekly cadence of annotator tasks was repeated in three consecutive weeks.", "#### Who are the annotators?\n\nTen annotators were recruited to work for three weeks, and paid £16/hour. An expert annotator was recruited for quality control purposes and paid £20/hour. In total, there were 11 annotators. All annotators received a training session prior to data collection and had previous experience working on hate speech projects. A daily 'stand-up' meeting was held every morning to communicate feedback and update guidelines as rounds progressed. Annotators were able to contact the research team at any point using a messaging platform. Of 11 annotators, 8 were between 18--29 years old and 3 between 30--39 years old. The completed education level was high school for 3 annotators, undergraduate degree for 1 annotators, taught graduate degree for 4 annotators and post-graduate research degree for 3 annotators. 6 annotators were female, and 5 were male. Annotators came from a variety of nationalities, with 7 British, as well as Jordanian, Irish, Polish and Spanish. 7 annotators identified as ethnically White and the remaining annotators came from various ethnicities including Turkish, Middle Eastern, and Mixed White and South Asian. 4 annotators were Muslim, and others identified as Atheist or as having no religious affiliation. 9 annotators were native English speakers and 2 were non-native but fluent. The majority of annotators (9) used emoji and social media more than once per day. 10 annotators had seen others targeted by abuse online, and 7 had been personally targeted.", "### Personal and Sensitive Information\n\nHatemojiBuild contains synthetic statements so has no personal information. It does however contains harmful examples of emoji-based hate which could be disturbing or damaging to view.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nHatemojiBuild contains challenging emoji examples which have \"tricked\" state-of-the-art transformers models. Malicious actors could take inspiration for bypassing current detection systems on internet platforms, or in principal train a generative hate speech model. However, it also helps to build model robustness to emoji-based hate, so can be used to mitigate the harm to victims before a model is deployed.", "### Discussion of Biases\n\nAnnotators were given substantial freedom in the targets of hate resulting in 54 unique targets, and 126 unique intersections of these. The entries from R5-R7 contain 1,082 unique emoji out of 3,521 defined in the Unicode Standard as of September 2020. This diversity helped to mitigate biases in classification towards certain targets but biases likely remain, especially since HatemojiBuild was designed for English-language use of emoji.", "### Other Known Limitations\n\nWhile annotators were trained on real-world examples of emoji-based hate from Twitter, the entries in HatemojiBuild are synthetically-generated so may deviate from real-world instances of emoji-based hate.", "## Additional Information", "### Dataset Curators\n\nThe dataset was curated by the lead author (Hannah Rose Kirk), using the Dynabench platform.", "### Licensing Information\n\nCreative Commons Attribution 4.0 International Public License. For full detail see: URL\n\n\n\nIf you use this dataset, please cite our paper: Kirk, H. R., Vidgen, B., Röttger, P., Thrush, T., & Hale, S. A. (2021). Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate. arXiv preprint arXiv:2108.05921.", "### Contributions\n\nThanks to @HannahKirk for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# hotpotQA-Extended (Annotated)\n\nA version of HotpotQA-Extended with extra annotations about what part of the input contains the answer." ]

[ "TAGS\n#region-us \n", "# hotpotQA-Extended (Annotated)\n\nA version of HotpotQA-Extended with extra annotations about what part of the input contains the answer." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for squad_it", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\nConverted dataset version to be used in Huggingface.\nOriginally created by Croce et al. at 2018, the SQuAD-it The dataset contains more than 60,000 question/answer pairs in Italian derived from the original English SQuAD dataset., in Italian language. Containing 60,000+ in JSON file format.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#region-us \n", "# Dataset Card for squad_it", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\nConverted dataset version to be used in Huggingface.\nOriginally created by Croce et al. at 2018, the SQuAD-it The dataset contains more than 60,000 question/answer pairs in Italian derived from the original English SQuAD dataset., in Italian language. Containing 60,000+ in JSON file format.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "task_categories:text-generation", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:unknown", "keyphrase-generation", "keyphrase-extraction", "text-mining", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-monolingual #size_categories-100K<n<1M #language-English #license-unknown #keyphrase-generation #keyphrase-extraction #text-mining #region-us \n" ]

[ "task_categories:token-classification", "size_categories:n<1K", "language:en", "license:mit", "Business Process Management", "NLP", "ML", "DL", "arxiv:2203.04860", "region:us" ]

[ "2203.04860" ]

[ "en" ]

[ "# PET: A NEW DATASET FOR PROCESS EXTRACTION FROM TEXT", "# Dataset Card for PET", "## 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\n- Annotation Guidelines\n- Update\n- Loading data", "## Dataset Description\n\n- Homepage: URL\n- Paper: URL\n- Point of Contact: Patrizio Bellan", "### Dataset Summary\n\nAbstract. Although there is a long tradition of work in NLP on extracting entities and relations from text, to date there exists little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions. With this work we aim at filling this gap and establishing the first steps towards bridging data-driven information extraction methodologies from Natural Language Processing and the model-based formalization that is aimed from Business Process Management. For this, we develop the first corpus of business process descriptions annotated with activities, actors, activity data, gateways and their conditions. We present our new resource to benchmark the difficulty and challenges of business process extraction from text.", "### Supported Tasks and Leaderboards\n\n- Token Classification\n- Named Entity Recognition\n- Relations Extraction", "### Languages\n\nEnglish", "## Dataset Structure\n\nTest set to beanchmark *Business Process Extraction from Text* approaches.", "### Data Instances", "#### Token Classification\n\nFor each instance, there is a document name representing the name of the document of the Friedrich *et al.* dataset, an integer representing the number of the sentence, a list of tokens representing the words of the sentence instance, and a list of *ner tags* (in IOB2 format) representing the annotation of process elements of the sentence.\n\nBelow, an example of data instance.", "#### Relations Extraction\n\n\nFor each instance, there is a document name representing the name of the document of the Friedrich *et al.* dataset, a list of tokens representing the words of the document instance, a list of interger representing the words position within each sentence of the document instance, a list of *ner tags* (in IOB2 format) representing the annotation of the token, a list of sentence id representing for each token the number of the sentence, and a list of relations of the document.\n\nBelow, an example of data instance.", "### Data Fields", "#### Token Classification\n\n- *document name*: a string used to represent the name of the document.\n- *sentence-ID*: an integer (starting from 0) representing the number of the sentence within the document.\n- *tokens*: a list of string representing the words of the sentence\n- *ner-tags*: a list of string representing the annotation for each word.\n\nThe allowed ner-tags are: \n - O: An O tag indicates that a token belongs to no chunk.\n - B-Actor: This tag indicates the beginning of an *Actor* chunk.\n - I-Actor: This tag indicates that the tag is inside an *Actor* chunk.\n - B-Activity: This tag indicates the beginning of an *Activity* chunk.\n - I-Activity: This tag indicates that the tag is inside an *Activity* chunk.\n - B-Activity Data: This tag indicates the beginning of an *Activity Data* chunk.\n - I-Activity Data: This tag indicates that the tag is inside an *Activity Data* chunk.\n - B-Further Specification: This tag indicates the beginning of a *Further Specification* chunk.\n - I-Further Specification: This tag indicates that the tag is inside a *Further Specification* chunk.\n - B-XOR Gateway: This tag indicates the beginning of a *XOR Gateway* chunk.\n - I-XOR Gateway: This tag indicates that the tag is inside a *XOR Gateway* chunk.\n - B-Condition Specification: This tag indicates the beginning of a *Condition Specification* chunk.\n - I-Condition Specification: This tag indicates that the tag is inside a *Condition Specification* chunk.\n - B-AND Gateway: This tag indicates the beginning of an *AND Gateway* chunk.\n - I-AND Gateway: This tag indicates that the tag is inside an *AND Gateway* chunk.\n\nTo have a complete explanation of each process element tag please refer to the research paper and the annotation guidelines.", "### Relations Extraction\n- *document name*: a string used to represent the name of the document.\n- *tokens*: a list of string representing the words of the document\n- *tokens-IDs*: a list of interger representing the word position within a sentence.\n- *ner_tags*: a list of string representing the annotation for each word. (see ner-tags above)\n- *sentence-IDs*: a list of interger representing the sentence number for each word of the document.\n- *relations*:: a list of document relations.\n - *source-head-sentence-ID*: a list of sentence ID pointing to the sentence number of the head (first token) of the source entity.\n - *source-head-word-ID*: a list of token ID pointing to the word ID of the head (first token) of the source entity.\n - *relation-type*: a list of relation tags.\n - *target-head-sentence-ID*: a list of sentence ID pointing to the sentence number of the head (first token) of the target entity.\n - *target-head-word-ID*: a list of token ID pointing to the word ID of the head (first token) of the target entity.\n\nFor instance, a relation is defined by the instances of *source-head-sentence-ID*, *source-head-word-ID*, *relation-type*, *target-head-sentence-ID*, and *target-head-word-ID* at the same index position.\nIn the following example, the first relation of the first document is shown:\n\nthe output is:\n\nThat means:\n the entity in sentence number *1*, starting at the token position *4* has a *uses* relation with the entity in sentence number *1* starting at token position *1*", "### Data Splits\n\nThe data was not split. It contains the test set only.", "## Dataset Creation", "### Curation Rationale\n\nAlthough there is a long tradition of work in NLP on extracting entities and relations from text to date there exists little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions. With this work we aim at filling this gap and establishing the first steps towards bridging data-driven information extraction methodologies from Natural Language Processing and the model-based formalization that is aimed from Business Process Management.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset construction process has been split in five main phases:\n 1. Text pre-processing. As the first operation, we check the content of each document and we tokenized it. This initial check was necessary since some of the original texts were automatically translated into English by the authors of the dataset. The translations were never validated, indeed, several errors have been found and fixed.\n \n 2. Text Annotation. Each text has been annotated by using the guidelines. The team was composed by five annotators with high expertise in BPMN. Each document has been assigned to three experts that were in change of identifying all the elements and flows with each document. In this phase, we used the the Inception tool to support annotators.\n \n 3. Automatic annotation fixing. After the second phase, we ran an automatic procedure relying on a rule-based script to automatically fix annotations that were not compliant with the guidelines. For example, if a modal verb was erroneously included in the annotation of an Activity, the procedure removed it from the annotation. Another example is the missing of the article within an annotation related to an Actor. In this case, the script included it in the annotation. This phase allowed to remove possible annotation errors and to obtain annotations compliant with the guidelines.\n\n 4. Agreement Computation. Here, we computed, on the annotation provided by the experts, the agreement scores for each process element and for each relation between process elements pair adopting the methodology proposed in Hripcsak *et al.*. We measured the agreement in terms of the F1 measure because, besides being straightforward to calculate, it is directly interpretable. Note that chance-corrected measures like *k* approach the F1-measure as the number of cases that raters agree are negative grows. By following such a methodology, an annotation was considered in agreement among the experts if and only if they capture the same span of words and they assign the same process element tag to the annotation.\n\n 5. Reconciliation. The last phase consisted of the mitigation of disagreements within the annotations provided by the experts. The aim of this phase is to obtain a shared and agreed set of gold standard annotations on each text for both entities and relations. Such entities also enable the generation of the related full-connected process model flow that can be rendered by using, but not limited to, a BPMN diagram. During this last phase, among the 47 documents originally included into the dataset, 2 of them were discarded. These texts were not fully annotated by the annotators since they were not be able to completely understand which process elements were actually included in some specific parts of the text. For this reason, the final size of the dataset is 45 textual descriptions of the corresponding process models together with their annotations.", "#### Who are the source language producers?\n\nEnglish", "### Annotations", "#### Annotation process\nYou can read about the annotation process in the original paper URL", "#### Who are the annotators?\n\nExpert Annotators", "### Personal and Sensitive Information\n\nNo personal or sensitive information issues.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset has no social impact", "### Discussion of Biases\n\nNo bias found in the dataset", "### Other Known Limitations\n\nThe *Further specification* and *AND Gateway* elements obtained very poor performance on the baselines proposed in the paper.\nThe *AND Gateway* is the less represented process elements in this dataset.\nThe *Further Specification* process element was the most difficult element to annotate.", "## Additional Information", "### Dataset Curators\n\n- Patrizio Bellan (Fondazione Bruno Kessler, Trento, Italy and Free University of Bozen-Bolzano, Bolzano, Italy)\n- Mauro Dragoni (Fondazione Bruno Kessler, Trento, Italy)\n- Chiara Ghidini (Fondazione Bruno Kessler, Trento, Italy)\n- Han van der Aa (University of Mannheim, Mannheim, Germany)\n- Simone Ponzetto (University of Mannheim, Mannheim, Germany)", "### Licensing Information", "### Contributions\n\nThanks to Patrizio Bellan for adding this dataset.", "#### <a name=\"updates\"></a>Update\n- v1.0.0: Added token classification task\n- v1.0.1: Added extraction relation task\n- v1.1.0: Fixed minor errors, fixed performs relations\n\nVersion 1.1.0 cab be found here", "## <a name=\"annotationguidelines\"></a>Annotation Guidelines", "### Inception Schema\n\nThe inception schema can be found here", "### Annotation Guidelines\n\nThe Annotation guidelines and procedures adopted to annotate the PET dataset can be downloaded here", "### Article\n\nThe article can be downloaded here", "### Python Interface\n\nA Python interface (beta version) to interact with the dataset can be found here\n\nYou can find the BASELINES, the annotation data, and a graphical interface to visualize predictions here", "### Benchmarks\n\nA Python benchmarking procedure package to test approaches on the PET dataset ca be found here", "## <a name=\"loadingdata\"></a>Loading data", "### Token-classification task", "### Relations-extraction task" ]

[ "TAGS\n#task_categories-token-classification #size_categories-n<1K #language-English #license-mit #Business Process Management #NLP #ML #DL #arxiv-2203.04860 #region-us \n", "# PET: A NEW DATASET FOR PROCESS EXTRACTION FROM TEXT", "# Dataset Card for PET", "## 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\n- Annotation Guidelines\n- Update\n- Loading data", "## Dataset Description\n\n- Homepage: URL\n- Paper: URL\n- Point of Contact: Patrizio Bellan", "### Dataset Summary\n\nAbstract. Although there is a long tradition of work in NLP on extracting entities and relations from text, to date there exists little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions. With this work we aim at filling this gap and establishing the first steps towards bridging data-driven information extraction methodologies from Natural Language Processing and the model-based formalization that is aimed from Business Process Management. For this, we develop the first corpus of business process descriptions annotated with activities, actors, activity data, gateways and their conditions. We present our new resource to benchmark the difficulty and challenges of business process extraction from text.", "### Supported Tasks and Leaderboards\n\n- Token Classification\n- Named Entity Recognition\n- Relations Extraction", "### Languages\n\nEnglish", "## Dataset Structure\n\nTest set to beanchmark *Business Process Extraction from Text* approaches.", "### Data Instances", "#### Token Classification\n\nFor each instance, there is a document name representing the name of the document of the Friedrich *et al.* dataset, an integer representing the number of the sentence, a list of tokens representing the words of the sentence instance, and a list of *ner tags* (in IOB2 format) representing the annotation of process elements of the sentence.\n\nBelow, an example of data instance.", "#### Relations Extraction\n\n\nFor each instance, there is a document name representing the name of the document of the Friedrich *et al.* dataset, a list of tokens representing the words of the document instance, a list of interger representing the words position within each sentence of the document instance, a list of *ner tags* (in IOB2 format) representing the annotation of the token, a list of sentence id representing for each token the number of the sentence, and a list of relations of the document.\n\nBelow, an example of data instance.", "### Data Fields", "#### Token Classification\n\n- *document name*: a string used to represent the name of the document.\n- *sentence-ID*: an integer (starting from 0) representing the number of the sentence within the document.\n- *tokens*: a list of string representing the words of the sentence\n- *ner-tags*: a list of string representing the annotation for each word.\n\nThe allowed ner-tags are: \n - O: An O tag indicates that a token belongs to no chunk.\n - B-Actor: This tag indicates the beginning of an *Actor* chunk.\n - I-Actor: This tag indicates that the tag is inside an *Actor* chunk.\n - B-Activity: This tag indicates the beginning of an *Activity* chunk.\n - I-Activity: This tag indicates that the tag is inside an *Activity* chunk.\n - B-Activity Data: This tag indicates the beginning of an *Activity Data* chunk.\n - I-Activity Data: This tag indicates that the tag is inside an *Activity Data* chunk.\n - B-Further Specification: This tag indicates the beginning of a *Further Specification* chunk.\n - I-Further Specification: This tag indicates that the tag is inside a *Further Specification* chunk.\n - B-XOR Gateway: This tag indicates the beginning of a *XOR Gateway* chunk.\n - I-XOR Gateway: This tag indicates that the tag is inside a *XOR Gateway* chunk.\n - B-Condition Specification: This tag indicates the beginning of a *Condition Specification* chunk.\n - I-Condition Specification: This tag indicates that the tag is inside a *Condition Specification* chunk.\n - B-AND Gateway: This tag indicates the beginning of an *AND Gateway* chunk.\n - I-AND Gateway: This tag indicates that the tag is inside an *AND Gateway* chunk.\n\nTo have a complete explanation of each process element tag please refer to the research paper and the annotation guidelines.", "### Relations Extraction\n- *document name*: a string used to represent the name of the document.\n- *tokens*: a list of string representing the words of the document\n- *tokens-IDs*: a list of interger representing the word position within a sentence.\n- *ner_tags*: a list of string representing the annotation for each word. (see ner-tags above)\n- *sentence-IDs*: a list of interger representing the sentence number for each word of the document.\n- *relations*:: a list of document relations.\n - *source-head-sentence-ID*: a list of sentence ID pointing to the sentence number of the head (first token) of the source entity.\n - *source-head-word-ID*: a list of token ID pointing to the word ID of the head (first token) of the source entity.\n - *relation-type*: a list of relation tags.\n - *target-head-sentence-ID*: a list of sentence ID pointing to the sentence number of the head (first token) of the target entity.\n - *target-head-word-ID*: a list of token ID pointing to the word ID of the head (first token) of the target entity.\n\nFor instance, a relation is defined by the instances of *source-head-sentence-ID*, *source-head-word-ID*, *relation-type*, *target-head-sentence-ID*, and *target-head-word-ID* at the same index position.\nIn the following example, the first relation of the first document is shown:\n\nthe output is:\n\nThat means:\n the entity in sentence number *1*, starting at the token position *4* has a *uses* relation with the entity in sentence number *1* starting at token position *1*", "### Data Splits\n\nThe data was not split. It contains the test set only.", "## Dataset Creation", "### Curation Rationale\n\nAlthough there is a long tradition of work in NLP on extracting entities and relations from text to date there exists little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions. With this work we aim at filling this gap and establishing the first steps towards bridging data-driven information extraction methodologies from Natural Language Processing and the model-based formalization that is aimed from Business Process Management.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset construction process has been split in five main phases:\n 1. Text pre-processing. As the first operation, we check the content of each document and we tokenized it. This initial check was necessary since some of the original texts were automatically translated into English by the authors of the dataset. The translations were never validated, indeed, several errors have been found and fixed.\n \n 2. Text Annotation. Each text has been annotated by using the guidelines. The team was composed by five annotators with high expertise in BPMN. Each document has been assigned to three experts that were in change of identifying all the elements and flows with each document. In this phase, we used the the Inception tool to support annotators.\n \n 3. Automatic annotation fixing. After the second phase, we ran an automatic procedure relying on a rule-based script to automatically fix annotations that were not compliant with the guidelines. For example, if a modal verb was erroneously included in the annotation of an Activity, the procedure removed it from the annotation. Another example is the missing of the article within an annotation related to an Actor. In this case, the script included it in the annotation. This phase allowed to remove possible annotation errors and to obtain annotations compliant with the guidelines.\n\n 4. Agreement Computation. Here, we computed, on the annotation provided by the experts, the agreement scores for each process element and for each relation between process elements pair adopting the methodology proposed in Hripcsak *et al.*. We measured the agreement in terms of the F1 measure because, besides being straightforward to calculate, it is directly interpretable. Note that chance-corrected measures like *k* approach the F1-measure as the number of cases that raters agree are negative grows. By following such a methodology, an annotation was considered in agreement among the experts if and only if they capture the same span of words and they assign the same process element tag to the annotation.\n\n 5. Reconciliation. The last phase consisted of the mitigation of disagreements within the annotations provided by the experts. The aim of this phase is to obtain a shared and agreed set of gold standard annotations on each text for both entities and relations. Such entities also enable the generation of the related full-connected process model flow that can be rendered by using, but not limited to, a BPMN diagram. During this last phase, among the 47 documents originally included into the dataset, 2 of them were discarded. These texts were not fully annotated by the annotators since they were not be able to completely understand which process elements were actually included in some specific parts of the text. For this reason, the final size of the dataset is 45 textual descriptions of the corresponding process models together with their annotations.", "#### Who are the source language producers?\n\nEnglish", "### Annotations", "#### Annotation process\nYou can read about the annotation process in the original paper URL", "#### Who are the annotators?\n\nExpert Annotators", "### Personal and Sensitive Information\n\nNo personal or sensitive information issues.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset has no social impact", "### Discussion of Biases\n\nNo bias found in the dataset", "### Other Known Limitations\n\nThe *Further specification* and *AND Gateway* elements obtained very poor performance on the baselines proposed in the paper.\nThe *AND Gateway* is the less represented process elements in this dataset.\nThe *Further Specification* process element was the most difficult element to annotate.", "## Additional Information", "### Dataset Curators\n\n- Patrizio Bellan (Fondazione Bruno Kessler, Trento, Italy and Free University of Bozen-Bolzano, Bolzano, Italy)\n- Mauro Dragoni (Fondazione Bruno Kessler, Trento, Italy)\n- Chiara Ghidini (Fondazione Bruno Kessler, Trento, Italy)\n- Han van der Aa (University of Mannheim, Mannheim, Germany)\n- Simone Ponzetto (University of Mannheim, Mannheim, Germany)", "### Licensing Information", "### Contributions\n\nThanks to Patrizio Bellan for adding this dataset.", "#### <a name=\"updates\"></a>Update\n- v1.0.0: Added token classification task\n- v1.0.1: Added extraction relation task\n- v1.1.0: Fixed minor errors, fixed performs relations\n\nVersion 1.1.0 cab be found here", "## <a name=\"annotationguidelines\"></a>Annotation Guidelines", "### Inception Schema\n\nThe inception schema can be found here", "### Annotation Guidelines\n\nThe Annotation guidelines and procedures adopted to annotate the PET dataset can be downloaded here", "### Article\n\nThe article can be downloaded here", "### Python Interface\n\nA Python interface (beta version) to interact with the dataset can be found here\n\nYou can find the BASELINES, the annotation data, and a graphical interface to visualize predictions here", "### Benchmarks\n\nA Python benchmarking procedure package to test approaches on the PET dataset ca be found here", "## <a name=\"loadingdata\"></a>Loading data", "### Token-classification task", "### Relations-extraction task" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "task_categories:other", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "language:ru", "license:afl-3.0", "region:us" ]

[]

[ "ru" ]

[ "# Pages of Early Soviet Performance (PESP)\n\nThis dataset was created as part of the Pages of Early Soviet Performance project at Princeton and provides text and image research data from a previously scanned collection of illustrated periodicals held by Princeton University's Slavic Collections. The project was a partnership with ITMO University in Saint Petersburg. Our work focused on document segmentation and the prediction of image, text, title, and mixedtext regions in the document images. The mixedtext category refers to segments where the typeface and text layout are mixed with other visual elements such as graphics, photographs, and illustrations. This category identifies sections that present problems for OCR and also highlights the experimental use of text, images, and other elements in the documents.\n\nFor each of the ten journals of interest in Princeton's digital collections (DPUL), we started with the IIIF manifest URI. With these manifests, we downloaded each of the 24,000 document images. The URI for each of the images is included in the dataset and a full list is available in 'IIIF_URIs.json'.", "## Authors \nNatalia Ermolaev, Thomas Keenan, Katherine Reischl, Andrew Janco, Quinn Dombrowski, Antonina Puchkovskaia, Alexander Jacobson, Anastasiia Mamonova, Michael Galperin and Vladislav Tretyak", "## Journal manifests\n- Эрмитаж\n- Вестник искусств\n- Советский театр\n- Рабис\n- Даёшь\n- Персимфанс\n- Тридцать дней\n- За пролетарское искусство\n- Бригада художников\n- Зрелища", "## Model \n\nUsing URL and a custom active learning application called \"Mayakovsky\" we generated training data for a YOLOv5 model. The model was fine-tuned on the new labels and predictions were generated for all images in the collection.", "## OCR \n\nUsing the model's predictions for image, title, text and mixedtext segments, we cropped the image using the bounding boxes and ran OCR on each document segment using Tesseract, Google Vision, and ABBYY FineReader. Given that the output of these various OCR engines can be difficult to compare, the document segments give a common denominator for comparison of OCR outputs. Having three variations of the extracted text can be useful for experiments with OCR post-correction.", "## Dataset \n\nThe dataset contains an entry for each image with the following fields: \n- filename: the image name (ex. 'Советский театр_1932 No. 4_16') with journal name, year, issue, page.\n- dpul: the URL for the image's journal in Digital Princeton University Library\n- journal: the journal name\n- year: the year of the journal issue\n- issue: the issue for the image\n- URI: the IIIF URI used to fetch the image from Princeton's IIIF server \n- yolo: the raw model prediction (ex '3 0.1655 0.501396 0.311'), in Yolo's normalized xywh format (object-class x y width height). The labels are 'image'=0, 'mixedtext'=1, 'title'=2, 'textblock'=3.\n- yolo_predictions: a List with a dictionary for each of the model's predictions with fields for: \n - label: the predicted label\n - x: the x-value location of the center point of the prediction \n - y: the y-value location of the center point of the prediction\n - w: the total width of the prediction's bounding box \n - h: the total height of the prediction's bounding box\n - abbyy_text: the text extracted from the predicted document segment using ABBY FineReader. Note that due to costs, only about 800 images have this data\n - tesseract_text: the text extracted from the predicted document segment using Tesseract.\n - vision_text: the text extracted from the predicted document segment using Google Vision.\n - vision_labels: entities recognized by Google Vision in image blocks and separated by | (ex. Boating|Boat|Printmaking)", "# Useage" ]

[ "TAGS\n#task_categories-other #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #source_datasets-original #language-Russian #license-afl-3.0 #region-us \n", "# Pages of Early Soviet Performance (PESP)\n\nThis dataset was created as part of the Pages of Early Soviet Performance project at Princeton and provides text and image research data from a previously scanned collection of illustrated periodicals held by Princeton University's Slavic Collections. The project was a partnership with ITMO University in Saint Petersburg. Our work focused on document segmentation and the prediction of image, text, title, and mixedtext regions in the document images. The mixedtext category refers to segments where the typeface and text layout are mixed with other visual elements such as graphics, photographs, and illustrations. This category identifies sections that present problems for OCR and also highlights the experimental use of text, images, and other elements in the documents.\n\nFor each of the ten journals of interest in Princeton's digital collections (DPUL), we started with the IIIF manifest URI. With these manifests, we downloaded each of the 24,000 document images. The URI for each of the images is included in the dataset and a full list is available in 'IIIF_URIs.json'.", "## Authors \nNatalia Ermolaev, Thomas Keenan, Katherine Reischl, Andrew Janco, Quinn Dombrowski, Antonina Puchkovskaia, Alexander Jacobson, Anastasiia Mamonova, Michael Galperin and Vladislav Tretyak", "## Journal manifests\n- Эрмитаж\n- Вестник искусств\n- Советский театр\n- Рабис\n- Даёшь\n- Персимфанс\n- Тридцать дней\n- За пролетарское искусство\n- Бригада художников\n- Зрелища", "## Model \n\nUsing URL and a custom active learning application called \"Mayakovsky\" we generated training data for a YOLOv5 model. The model was fine-tuned on the new labels and predictions were generated for all images in the collection.", "## OCR \n\nUsing the model's predictions for image, title, text and mixedtext segments, we cropped the image using the bounding boxes and ran OCR on each document segment using Tesseract, Google Vision, and ABBYY FineReader. Given that the output of these various OCR engines can be difficult to compare, the document segments give a common denominator for comparison of OCR outputs. Having three variations of the extracted text can be useful for experiments with OCR post-correction.", "## Dataset \n\nThe dataset contains an entry for each image with the following fields: \n- filename: the image name (ex. 'Советский театр_1932 No. 4_16') with journal name, year, issue, page.\n- dpul: the URL for the image's journal in Digital Princeton University Library\n- journal: the journal name\n- year: the year of the journal issue\n- issue: the issue for the image\n- URI: the IIIF URI used to fetch the image from Princeton's IIIF server \n- yolo: the raw model prediction (ex '3 0.1655 0.501396 0.311'), in Yolo's normalized xywh format (object-class x y width height). The labels are 'image'=0, 'mixedtext'=1, 'title'=2, 'textblock'=3.\n- yolo_predictions: a List with a dictionary for each of the model's predictions with fields for: \n - label: the predicted label\n - x: the x-value location of the center point of the prediction \n - y: the y-value location of the center point of the prediction\n - w: the total width of the prediction's bounding box \n - h: the total height of the prediction's bounding box\n - abbyy_text: the text extracted from the predicted document segment using ABBY FineReader. Note that due to costs, only about 800 images have this data\n - tesseract_text: the text extracted from the predicted document segment using Tesseract.\n - vision_text: the text extracted from the predicted document segment using Google Vision.\n - vision_labels: entities recognized by Google Vision in image blocks and separated by | (ex. Boating|Boat|Printmaking)", "# Useage" ]

[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Conceptual Captions", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: Conceptual Captions homepage\n- Repository: Conceptual Captions repository\n- Paper: Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning\n- Leaderboard: Conceptual Captions leaderboardhttps://URL\n- Point of Contact: Conceptual Captions e-mail", "### Dataset Summary\n\nConceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions.", "### Dataset Preprocessing\n\nThis dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:", "### Supported Tasks and Leaderboards\n\n- 'image-captioning': This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available here. Official submission output captions are scored against the reference captions from the hidden test set using this implementation of the CIDEr (primary), ROUGE-L and SPICE metrics.", "### Languages\n\nAll captions are in English.", "## Dataset Structure", "### Data Instances", "#### 'unlabeled'\n\nEach instance in this configuration represents a single image with a caption:", "#### 'labeled'\n\nEach instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores:", "### Data Fields", "#### 'unlabeled'\n\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'caption': Textual description of the image.", "#### 'labeled'\n\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'caption': Textual description of the image.\n- 'labels': A sequence of machine-generated labels obtained using the Google Cloud Vision API.\n- 'MIDs': A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry.\n- 'confidence_scores': A sequence of confidence scores denoting how likely the corresponing labels are present on the image.", "### Data Splits", "#### 'unlabeled'\n\nThe basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs.", "#### 'labeled'\n\nThe labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the 'unlabeled' config.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the homepage:\n>For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable.\n>\n>To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters.\n>\n>We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through.\n>\n>In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage.\n>\n>The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR.\n>\n>We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent.\n>\n>Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”.", "#### Who are the source language producers?\n\nNot specified.", "### Annotations", "#### Annotation process\n\nAnnotations are extracted jointly with the images using the automatic pipeline.", "#### Who are the annotators?\n\nNot specified.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nPiyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut.", "### Licensing Information\n\nThe dataset may be freely used for any purpose, although acknowledgement of\nGoogle LLC (\"Google\") as the data source would be appreciated. The dataset is\nprovided \"AS IS\" without any warranty, express or implied. Google disclaims all\nliability for any damages, direct or indirect, resulting from the use of the\ndataset.", "### Contributions\n\nThanks to @abhishekkrthakur and @mariosasko for adding this dataset." ]

[ "TAGS\n#task_categories-image-to-text #task_ids-image-captioning #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-other #region-us \n", "# Dataset Card for Conceptual Captions", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: Conceptual Captions homepage\n- Repository: Conceptual Captions repository\n- Paper: Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning\n- Leaderboard: Conceptual Captions leaderboardhttps://URL\n- Point of Contact: Conceptual Captions e-mail", "### Dataset Summary\n\nConceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions.", "### Dataset Preprocessing\n\nThis dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:", "### Supported Tasks and Leaderboards\n\n- 'image-captioning': This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available here. Official submission output captions are scored against the reference captions from the hidden test set using this implementation of the CIDEr (primary), ROUGE-L and SPICE metrics.", "### Languages\n\nAll captions are in English.", "## Dataset Structure", "### Data Instances", "#### 'unlabeled'\n\nEach instance in this configuration represents a single image with a caption:", "#### 'labeled'\n\nEach instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores:", "### Data Fields", "#### 'unlabeled'\n\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'caption': Textual description of the image.", "#### 'labeled'\n\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'caption': Textual description of the image.\n- 'labels': A sequence of machine-generated labels obtained using the Google Cloud Vision API.\n- 'MIDs': A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry.\n- 'confidence_scores': A sequence of confidence scores denoting how likely the corresponing labels are present on the image.", "### Data Splits", "#### 'unlabeled'\n\nThe basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs.", "#### 'labeled'\n\nThe labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the 'unlabeled' config.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the homepage:\n>For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable.\n>\n>To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters.\n>\n>We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through.\n>\n>In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage.\n>\n>The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR.\n>\n>We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent.\n>\n>Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”.", "#### Who are the source language producers?\n\nNot specified.", "### Annotations", "#### Annotation process\n\nAnnotations are extracted jointly with the images using the automatic pipeline.", "#### Who are the annotators?\n\nNot specified.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nPiyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut.", "### Licensing Information\n\nThe dataset may be freely used for any purpose, although acknowledgement of\nGoogle LLC (\"Google\") as the data source would be appreciated. The dataset is\nprovided \"AS IS\" without any warranty, express or implied. Google disclaims all\nliability for any damages, direct or indirect, resulting from the use of the\ndataset.", "### Contributions\n\nThanks to @abhishekkrthakur and @mariosasko for adding this dataset." ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "annotations_creators:crowd-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ko", "license:cc-by-sa-4.0", "arxiv:2202.12459", "region:us" ]

[ "2202.12459" ]

[ "ko" ]

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

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #annotations_creators-crowd-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Korean #license-cc-by-sa-4.0 #arxiv-2202.12459 #region-us \n", "### Languages\n\n\nko-KR\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

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

[]

[]

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

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

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Configuration" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for squad_it_exp", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Additional Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\nSQuAD-it-exp is a dataset derived from the SQuAD-it dataset originally created by Croce et al. to 2018.<br/>\nSQuAD-it-exp has been enriched by adding new unanswerable questions in SQuAD v2 format.<br/>\nThe dataset contains nearly 90,000 pairs of questions/answers in Italian.", "### Languages\nThe dataset is for the ITALIAN language" ]

[ "TAGS\n#region-us \n", "# Dataset Card for squad_it_exp", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Additional Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\nSQuAD-it-exp is a dataset derived from the SQuAD-it dataset originally created by Croce et al. to 2018.<br/>\nSQuAD-it-exp has been enriched by adding new unanswerable questions in SQuAD v2 format.<br/>\nThe dataset contains nearly 90,000 pairs of questions/answers in Italian.", "### Languages\nThe dataset is for the ITALIAN language" ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_fever", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Fever dataset (URL pre-processed and ready to train and evaluate.\nThe training objective is a text classification task - given a claim and evidence, predict if claim is related to evidence." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_fever #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Fever dataset (URL pre-processed and ready to train and evaluate.\nThe training objective is a text classification task - given a claim and evidence, predict if claim is related to evidence." ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_fever", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Fever dataset (URL pre-processed and, ready to train and evaluate.\nThe training objective is a text classification task - given a claim and evidence, predict if claim is related to evidence." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_fever #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Fever dataset (URL pre-processed and, ready to train and evaluate.\nThe training objective is a text classification task - given a claim and evidence, predict if claim is related to evidence." ]

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

[]

[]

[]

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

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "size_categories:100M<n<1B", "source_datasets:original", "language:en", "license:odc-by", "arxiv:1910.10683", "region:us" ]

[ "1910.10683" ]

[ "en" ]

[ "### Dataset Summary\n\n\nA colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: \"URL\".\n\n\nThis is the version prepared by AllenAI, hosted at this address: URL\n\n\nIt comes in four variants:\n\n\n* 'en': 305GB in JSON format\n* 'en.noblocklist': 380GB in JSON format\n* 'en.noclean': 2.3TB in JSON format\n* 'realnewslike': 15GB in JSON format\n\n\nThe 'en.noblocklist' variant is exactly the same as the 'en' variant, except we turned off the so-called \"badwords filter\", which removes all documents that contain words from the lists at URL", "### Supported Tasks and Leaderboards\n\n\nC4 is mainly intended to pretrain language models and word representations.", "### Languages\n\n\nThe dataset is in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example form the 'en' config is:", "### Data Fields\n\n\nThe data have several fields:\n\n\n* 'url': url of the source as a string\n* 'text': text content as a string\n* 'timestamp': timestamp as a string", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nC4 dataset is a collection of about 750GB of English-language text sourced from the public Common Crawl web scrape. It includes heuristics to extract only natural language (as opposed to boilerplate and other gibberish) in addition to extensive deduplication. You can find the code that has been used to build this dataset in URL by Tensorflow Datasets.\n\n\nThe dataset was explicitly designed to be English only: any page that was not given a probability of at least 99% of being English by langdetect was discarded.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nAllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.", "### Contributions\n\n\nThanks to @dirkgr and @lhoestq for adding this dataset." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-multilingual #size_categories-100M<n<1B #source_datasets-original #language-English #license-odc-by #arxiv-1910.10683 #region-us \n", "### Dataset Summary\n\n\nA colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: \"URL\".\n\n\nThis is the version prepared by AllenAI, hosted at this address: URL\n\n\nIt comes in four variants:\n\n\n* 'en': 305GB in JSON format\n* 'en.noblocklist': 380GB in JSON format\n* 'en.noclean': 2.3TB in JSON format\n* 'realnewslike': 15GB in JSON format\n\n\nThe 'en.noblocklist' variant is exactly the same as the 'en' variant, except we turned off the so-called \"badwords filter\", which removes all documents that contain words from the lists at URL", "### Supported Tasks and Leaderboards\n\n\nC4 is mainly intended to pretrain language models and word representations.", "### Languages\n\n\nThe dataset is in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example form the 'en' config is:", "### Data Fields\n\n\nThe data have several fields:\n\n\n* 'url': url of the source as a string\n* 'text': text content as a string\n* 'timestamp': timestamp as a string", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nC4 dataset is a collection of about 750GB of English-language text sourced from the public Common Crawl web scrape. It includes heuristics to extract only natural language (as opposed to boilerplate and other gibberish) in addition to extensive deduplication. You can find the code that has been used to build this dataset in URL by Tensorflow Datasets.\n\n\nThe dataset was explicitly designed to be English only: any page that was not given a probability of at least 99% of being English by langdetect was discarded.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nAllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.", "### Contributions\n\n\nThanks to @dirkgr and @lhoestq for adding this dataset." ]

[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:other", "arxiv:2102.08981", "region:us" ]

[ "2102.08981" ]

[ "en" ]

[ "# Dataset Card for Conceptual 12M", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: Conceptual 12M repository\n- Paper: Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts\n- Point of Contact: Conceptual Captions e-mail", "### Dataset Summary\n\nConceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training.\nIts data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M).", "### Dataset Preprocessing\n\nThis dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:", "### Supported Tasks and Leaderboards\n\n- 'image-captioning': This dataset can be used to train model for the Image Captioning task.", "### Languages\n\nAll captions are in English.", "## Dataset Structure", "### Data Instances\n\nEach instance represents a single image with a caption:", "### Data Fields\n\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'caption': Textual description of the image.", "### Data Splits\n\nThere is only training data, with a total of 12423374 rows", "## Dataset Creation", "### Curation Rationale\n\nConceptual 12M shares the same pipeline with Conceptual Captions (CC3M), but relaxes some processing steps.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper:\n> To arrive at CC12M, we keep\nthe image-text filtering intact, and relax the unimodal filters only. First, for image-based filtering, we set the maximum ratio of larger to smaller dimension to 2.5 instead of 2. \nWe still keep only JPEG images with size greater than\n400 pixels, and still exclude images that trigger pornography detectors. Second, in text-based filtering, we allow text\nbetween 3 and 256 words in the alt-text. We still discard\ncandidates with no noun or no determiner, but permit ones\nwithout prepositions. We discard the heuristics regarding\nhigh unique-word ratio covering various POS tags and word\ncapitalization. We set the maximum fraction of word repetition allowed to 0.2. Given a larger pool of text due to the\nabove relaxations, the threshold for counting a word type as\nrare is increased from 5 to 20\n\n> The main motivation for CC3M to\nperform text transformation is that a majority of candidate\ncaptions contain ultrafine-grained entities such as proper\nnames (people, venues, locations, etc.), making it extremely\ndifficult to learn as part of the image captioning task. In\ncontrast, we are not restricted by the end task of image caption generation. Our intuition is that relatively more difficult pre-training data would lead to better transferability.\nWe thus do not perform hypernimization or digit substitution. [...] The only exception to the “keep alt-texts as\nraw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy\nof the individuals in these images. For this step, we use the\nGoogle Cloud Natural Language APIs to detect all named\nentities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M\nare transformed in this fashion.", "#### Who are the source language producers?\n\nNot specified.", "### Annotations", "#### Annotation process\n\nAnnotations are extracted jointly with the images using the automatic pipeline.", "#### Who are the annotators?\n\nNot specified.", "### Personal and Sensitive Information\n\nFrom the paper:\n\n> The only exception to the “keep alt-texts as\nraw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy\nof the individuals in these images. For this step, we use the\nGoogle Cloud Natural Language APIs to detect all named\nentities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M\nare transformed in this fashion.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nSoravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut.", "### Licensing Information\n\nThe dataset may be freely used for any purpose, although acknowledgement of\nGoogle LLC (\"Google\") as the data source would be appreciated. The dataset is\nprovided \"AS IS\" without any warranty, express or implied. Google disclaims all\nliability for any damages, direct or indirect, resulting from the use of the\ndataset.", "### Contributions\n\nThanks to @thomasw21 for adding this dataset." ]

[ "TAGS\n#task_categories-image-to-text #task_ids-image-captioning #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-other #arxiv-2102.08981 #region-us \n", "# Dataset Card for Conceptual 12M", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: Conceptual 12M repository\n- Paper: Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts\n- Point of Contact: Conceptual Captions e-mail", "### Dataset Summary\n\nConceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training.\nIts data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M).", "### Dataset Preprocessing\n\nThis dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:", "### Supported Tasks and Leaderboards\n\n- 'image-captioning': This dataset can be used to train model for the Image Captioning task.", "### Languages\n\nAll captions are in English.", "## Dataset Structure", "### Data Instances\n\nEach instance represents a single image with a caption:", "### Data Fields\n\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'caption': Textual description of the image.", "### Data Splits\n\nThere is only training data, with a total of 12423374 rows", "## Dataset Creation", "### Curation Rationale\n\nConceptual 12M shares the same pipeline with Conceptual Captions (CC3M), but relaxes some processing steps.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper:\n> To arrive at CC12M, we keep\nthe image-text filtering intact, and relax the unimodal filters only. First, for image-based filtering, we set the maximum ratio of larger to smaller dimension to 2.5 instead of 2. \nWe still keep only JPEG images with size greater than\n400 pixels, and still exclude images that trigger pornography detectors. Second, in text-based filtering, we allow text\nbetween 3 and 256 words in the alt-text. We still discard\ncandidates with no noun or no determiner, but permit ones\nwithout prepositions. We discard the heuristics regarding\nhigh unique-word ratio covering various POS tags and word\ncapitalization. We set the maximum fraction of word repetition allowed to 0.2. Given a larger pool of text due to the\nabove relaxations, the threshold for counting a word type as\nrare is increased from 5 to 20\n\n> The main motivation for CC3M to\nperform text transformation is that a majority of candidate\ncaptions contain ultrafine-grained entities such as proper\nnames (people, venues, locations, etc.), making it extremely\ndifficult to learn as part of the image captioning task. In\ncontrast, we are not restricted by the end task of image caption generation. Our intuition is that relatively more difficult pre-training data would lead to better transferability.\nWe thus do not perform hypernimization or digit substitution. [...] The only exception to the “keep alt-texts as\nraw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy\nof the individuals in these images. For this step, we use the\nGoogle Cloud Natural Language APIs to detect all named\nentities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M\nare transformed in this fashion.", "#### Who are the source language producers?\n\nNot specified.", "### Annotations", "#### Annotation process\n\nAnnotations are extracted jointly with the images using the automatic pipeline.", "#### Who are the annotators?\n\nNot specified.", "### Personal and Sensitive Information\n\nFrom the paper:\n\n> The only exception to the “keep alt-texts as\nraw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy\nof the individuals in these images. For this step, we use the\nGoogle Cloud Natural Language APIs to detect all named\nentities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M\nare transformed in this fashion.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nSoravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut.", "### Licensing Information\n\nThe dataset may be freely used for any purpose, although acknowledgement of\nGoogle LLC (\"Google\") as the data source would be appreciated. The dataset is\nprovided \"AS IS\" without any warranty, express or implied. Google disclaims all\nliability for any damages, direct or indirect, resulting from the use of the\ndataset.", "### Contributions\n\nThanks to @thomasw21 for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Malay-TTS-Yasmin\n\nAll notebooks and code related at URL", "## Attributes", "### Wiki and News\n\n- 24000 sample rate, super clean.\n- narrator 'ms-MY-YasminNeural'.\n- approximate 99.4 hours.\n- Texts from Malay Wikipedia and News.\n- Sentences between 2 words and 20 words.", "### Parliament\n\n- 24000 sample rate, super clean.\n- narrator 'ms-MY-YasminNeural'.\n- approximate 142 hours.\n- Texts from Malaysia Malay Parliament.\n- Sentences between 2 words and 25 words.", "## how-to", "### Wiki and News\n\n1. Download URL and URL.\n\n2. To get wav and transcript,", "### Parliament\n\n1. Download URL and URL.\n\n2. To get wav and transcript," ]

[ "TAGS\n#region-us \n", "# Malay-TTS-Yasmin\n\nAll notebooks and code related at URL", "## Attributes", "### Wiki and News\n\n- 24000 sample rate, super clean.\n- narrator 'ms-MY-YasminNeural'.\n- approximate 99.4 hours.\n- Texts from Malay Wikipedia and News.\n- Sentences between 2 words and 20 words.", "### Parliament\n\n- 24000 sample rate, super clean.\n- narrator 'ms-MY-YasminNeural'.\n- approximate 142 hours.\n- Texts from Malaysia Malay Parliament.\n- Sentences between 2 words and 25 words.", "## how-to", "### Wiki and News\n\n1. Download URL and URL.\n\n2. To get wav and transcript,", "### Parliament\n\n1. Download URL and URL.\n\n2. To get wav and transcript," ]

[ "license:cc-by-4.0", "arxiv:1902.09914", "region:us" ]

[ "1902.09914" ]

[]

[ "### Dataset Summary\n\n\nTop Quark Tagging is a dataset of Monte Carlo simulated events produced by proton-proton collisions at the Large Hadron Collider. The top-quark signal and mixed quark-gluon background jets are produced with Pythia8 with its default tune for a center-of-mass energy of 14 TeV. Multiple interactions and pile-up are ignored. The leading 200 jet constituent four-momenta \\( (E, p\\_x, p\\_y, p\\_z) \\) are stored, with zero-padding applied to jets with fewer than 200 constituents.", "### Supported Tasks and Leaderboards\n\n\n* 'tabular-classification': The dataset can be used to train a model for tabular binary classification, which consists in predicting whether an event is produced from a top signal or quark-gluon background. Success on this task is typically measured by achieving a *high* accuracy and AUC score.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach instance in the dataset consists of the four-momenta of the leading 200 jet constituents, sorted by \\(p\\_T\\). For jets with fewer than 200 constituents, zero-padding is applied. The four-momenta of the top-quark are also provided, along with a label in the 'is\\_signal\\_new' column to indicate whether the event stems from a top-quark (1) or QCD background (0). An example instance looks as follows:", "### Data Fields\n\n\nThe fields in the dataset have the following meaning:\n\n\n* 'E\\_i': the energy of jet constituent \\(i\\).\n* 'PX\\_i': the \\(x\\) component of the jet constituent's momentum\n* 'PY\\_i': the \\(y\\) component of the jet constituent's momentum\n* 'PZ\\_i': the \\(z\\) component of the jet constituent's momentum\n* 'truthE': the energy of the top-quark\n* 'truthPX': the \\(x\\) component of the top quark's momentum\n* 'truthPY': the \\(y\\) component of the top quark's momentum\n* 'truthPZ': the \\(z\\) component of the top quark's momentum\n* 'ttv': a flag that indicates which split (train, validation, or test) that a jet belongs to. Redundant since each split is provided as a separate dataset\n* 'is\\_signal\\_new': the label for each jet. A 1 indicates a top-quark, while a 0 indicates QCD background.", "### Data Splits", "### Licensing Information\n\n\nThis dataset is released under the Creative Commons Attribution 4.0 International license.", "### Contributions\n\n\nThanks to @lewtun for adding this dataset." ]

[ "TAGS\n#license-cc-by-4.0 #arxiv-1902.09914 #region-us \n", "### Dataset Summary\n\n\nTop Quark Tagging is a dataset of Monte Carlo simulated events produced by proton-proton collisions at the Large Hadron Collider. The top-quark signal and mixed quark-gluon background jets are produced with Pythia8 with its default tune for a center-of-mass energy of 14 TeV. Multiple interactions and pile-up are ignored. The leading 200 jet constituent four-momenta \\( (E, p\\_x, p\\_y, p\\_z) \\) are stored, with zero-padding applied to jets with fewer than 200 constituents.", "### Supported Tasks and Leaderboards\n\n\n* 'tabular-classification': The dataset can be used to train a model for tabular binary classification, which consists in predicting whether an event is produced from a top signal or quark-gluon background. Success on this task is typically measured by achieving a *high* accuracy and AUC score.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach instance in the dataset consists of the four-momenta of the leading 200 jet constituents, sorted by \\(p\\_T\\). For jets with fewer than 200 constituents, zero-padding is applied. The four-momenta of the top-quark are also provided, along with a label in the 'is\\_signal\\_new' column to indicate whether the event stems from a top-quark (1) or QCD background (0). An example instance looks as follows:", "### Data Fields\n\n\nThe fields in the dataset have the following meaning:\n\n\n* 'E\\_i': the energy of jet constituent \\(i\\).\n* 'PX\\_i': the \\(x\\) component of the jet constituent's momentum\n* 'PY\\_i': the \\(y\\) component of the jet constituent's momentum\n* 'PZ\\_i': the \\(z\\) component of the jet constituent's momentum\n* 'truthE': the energy of the top-quark\n* 'truthPX': the \\(x\\) component of the top quark's momentum\n* 'truthPY': the \\(y\\) component of the top quark's momentum\n* 'truthPZ': the \\(z\\) component of the top quark's momentum\n* 'ttv': a flag that indicates which split (train, validation, or test) that a jet belongs to. Redundant since each split is provided as a separate dataset\n* 'is\\_signal\\_new': the label for each jet. A 1 indicates a top-quark, while a 0 indicates QCD background.", "### Data Splits", "### Licensing Information\n\n\nThis dataset is released under the Creative Commons Attribution 4.0 International license.", "### Contributions\n\n\nThanks to @lewtun for adding this dataset." ]

[ "task_categories:token-classification", "annotations_creators:Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "abbreviation-detection", "arxiv:2204.12061", "region:us" ]

[ "2204.12061" ]

[ "en" ]

[ "### Dataset\n\n\nWe provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here.\n\n\n1. The Filtered version can be accessed via Huggingface Datasets here and a CONLL format is present here.\n2. The Unfiltered version can be accessed via Huggingface Datasets here and a CONLL format is present here.\n3. The SDU Shared Task data we use for zero-shot testing is available here.\n\n\nDataset Card for PLOD-filtered\n==============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\n\nDataset Description\n-------------------\n\n\n* Homepage:\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: Diptesh Kanojia", "### Dataset Summary\n\n\nThis PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain.", "### Supported Tasks and Leaderboards\n\n\nThis dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises an ID, a set of 'tokens' present in the text, a set of 'pos\\_tags' for the corresponding tokens obtained via Spacy NER, and a set of 'ner\\_tags' which are limited to 'AC' for 'Acronym' and 'LF' for 'long-forms'.\n\n\nAn example from the dataset:\n{'id': '1',\n'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],\n'pos\\_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],\n'ner\\_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n}", "### Data Fields\n\n\n* id: the row identifier for the dataset point.\n* tokens: The tokens contained in the text.\n* pos\\_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.\n* ner\\_tags: The tags for abbreviations and long-forms.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nExtracting the data from PLOS Journals online and then tokenization, normalization.", "#### Who are the source language producers?\n\n\nPLOS Journal\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma,\nDiptesh Kanojia, Constantin Orasan.", "### Licensing Information\n\n\nCC-BY-SA 4.0", "### Installation\n\n\nWe use the custom NER pipeline in the spaCy transformers library to train our models. This library supports training via any pre-trained language models available at the :rocket: HuggingFace repository. \n\nPlease see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy.\n\n\nOR \n\n\n\nHowever, you can also reproduce the experiments via the Python notebook we provide here which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps:\n\n\nNow, you can use the notebook to reproduce the experiments.", "### Model(s)\n\n\nOur best performing models are hosted on the HuggingFace models repository\n\n\n\nOn the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.", "### Usage\n\n\nYou can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-sa-4.0 #abbreviation-detection #arxiv-2204.12061 #region-us \n", "### Dataset\n\n\nWe provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here.\n\n\n1. The Filtered version can be accessed via Huggingface Datasets here and a CONLL format is present here.\n2. The Unfiltered version can be accessed via Huggingface Datasets here and a CONLL format is present here.\n3. The SDU Shared Task data we use for zero-shot testing is available here.\n\n\nDataset Card for PLOD-filtered\n==============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\n\nDataset Description\n-------------------\n\n\n* Homepage:\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: Diptesh Kanojia", "### Dataset Summary\n\n\nThis PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain.", "### Supported Tasks and Leaderboards\n\n\nThis dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises an ID, a set of 'tokens' present in the text, a set of 'pos\\_tags' for the corresponding tokens obtained via Spacy NER, and a set of 'ner\\_tags' which are limited to 'AC' for 'Acronym' and 'LF' for 'long-forms'.\n\n\nAn example from the dataset:\n{'id': '1',\n'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],\n'pos\\_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],\n'ner\\_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n}", "### Data Fields\n\n\n* id: the row identifier for the dataset point.\n* tokens: The tokens contained in the text.\n* pos\\_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.\n* ner\\_tags: The tags for abbreviations and long-forms.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nExtracting the data from PLOS Journals online and then tokenization, normalization.", "#### Who are the source language producers?\n\n\nPLOS Journal\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma,\nDiptesh Kanojia, Constantin Orasan.", "### Licensing Information\n\n\nCC-BY-SA 4.0", "### Installation\n\n\nWe use the custom NER pipeline in the spaCy transformers library to train our models. This library supports training via any pre-trained language models available at the :rocket: HuggingFace repository. \n\nPlease see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy.\n\n\nOR \n\n\n\nHowever, you can also reproduce the experiments via the Python notebook we provide here which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps:\n\n\nNow, you can use the notebook to reproduce the experiments.", "### Model(s)\n\n\nOur best performing models are hosted on the HuggingFace models repository\n\n\n\nOn the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.", "### Usage\n\n\nYou can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo." ]

[ "task_categories:token-classification", "annotations_creators:Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "abbreviation-detection", "arxiv:2204.12061", "region:us" ]

[ "2204.12061" ]

[ "en" ]

[ "### Dataset\n\n\nWe provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here.\n\n\n1. The Filtered version can be accessed via Huggingface Datasets here and a CONLL format is present here.\n2. The Unfiltered version can be accessed via Huggingface Datasets here and a CONLL format is present here.\n3. The SDU Shared Task data we use for zero-shot testing is available here.\n\n\nDataset Card for PLOD-unfiltered\n================================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\n\nDataset Description\n-------------------\n\n\n* Homepage:\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: Diptesh Kanojia", "### Dataset Summary\n\n\nThis PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain.", "### Supported Tasks and Leaderboards\n\n\nThis dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises an ID, a set of 'tokens' present in the text, a set of 'pos\\_tags' for the corresponding tokens obtained via Spacy NER, and a set of 'ner\\_tags' which are limited to 'AC' for 'Acronym' and 'LF' for 'long-forms'.\n\n\nAn example from the dataset:\n{'id': '1',\n'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],\n'pos\\_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],\n'ner\\_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n}", "### Data Fields\n\n\n* id: the row identifier for the dataset point.\n* tokens: The tokens contained in the text.\n* pos\\_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.\n* ner\\_tags: The tags for abbreviations and long-forms.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nExtracting the data from PLOS Journals online and then tokenization, normalization.", "#### Who are the source language producers?\n\n\nPLOS Journal\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma,\nDiptesh Kanojia, Constantin Orasan.", "### Licensing Information\n\n\nCC-BY-SA 4.0", "### Installation\n\n\nWe use the custom NER pipeline in the spaCy transformers library to train our models. This library supports training via any pre-trained language models available at the :rocket: HuggingFace repository. \n\nPlease see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy.\n\n\nOR \n\n\n\nHowever, you can also reproduce the experiments via the Python notebook we provide here which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps:\n\n\nNow, you can use the notebook to reproduce the experiments.", "### Model(s)\n\n\nOur best performing models are hosted on the HuggingFace models repository:\n\n\n\nOn the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.", "### Usage\n\n\nYou can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-sa-4.0 #abbreviation-detection #arxiv-2204.12061 #region-us \n", "### Dataset\n\n\nWe provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here.\n\n\n1. The Filtered version can be accessed via Huggingface Datasets here and a CONLL format is present here.\n2. The Unfiltered version can be accessed via Huggingface Datasets here and a CONLL format is present here.\n3. The SDU Shared Task data we use for zero-shot testing is available here.\n\n\nDataset Card for PLOD-unfiltered\n================================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\n\nDataset Description\n-------------------\n\n\n* Homepage:\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: Diptesh Kanojia", "### Dataset Summary\n\n\nThis PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain.", "### Supported Tasks and Leaderboards\n\n\nThis dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point comprises an ID, a set of 'tokens' present in the text, a set of 'pos\\_tags' for the corresponding tokens obtained via Spacy NER, and a set of 'ner\\_tags' which are limited to 'AC' for 'Acronym' and 'LF' for 'long-forms'.\n\n\nAn example from the dataset:\n{'id': '1',\n'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],\n'pos\\_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],\n'ner\\_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n}", "### Data Fields\n\n\n* id: the row identifier for the dataset point.\n* tokens: The tokens contained in the text.\n* pos\\_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.\n* ner\\_tags: The tags for abbreviations and long-forms.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nExtracting the data from PLOS Journals online and then tokenization, normalization.", "#### Who are the source language producers?\n\n\nPLOS Journal\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma,\nDiptesh Kanojia, Constantin Orasan.", "### Licensing Information\n\n\nCC-BY-SA 4.0", "### Installation\n\n\nWe use the custom NER pipeline in the spaCy transformers library to train our models. This library supports training via any pre-trained language models available at the :rocket: HuggingFace repository. \n\nPlease see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy.\n\n\nOR \n\n\n\nHowever, you can also reproduce the experiments via the Python notebook we provide here which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps:\n\n\nNow, you can use the notebook to reproduce the experiments.", "### Model(s)\n\n\nOur best performing models are hosted on the HuggingFace models repository:\n\n\n\nOn the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.", "### Usage\n\n\nYou can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo." ]

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

[]

[ "en" ]

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

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

[ "region:us" ]

[]

[]

[ "# Malay-TTS-Osman\n\nAll notebooks and code related at URL", "## Attributes", "### Wiki and News\n\n- 24000 sample rate, super clean.\n- narrator 'ms-MY-OsmanNeural'.\n- approximate 94.5 hours\n- Texts from Malay Wikipedia and News.\n- Sentences between 2 words and 20 words.", "### Parliament\n\n- 24000 sample rate, super clean.\n- narrator 'ms-MY-OsmanNeural'.\n- approximate 133.2 hours.\n- Texts from Malaysia Malay Parliament.\n- Sentences between 2 words and 25 words.", "## how-to", "### Wiki and News\n\n1. Download URL and URL.\n\n2. To get wav and transcript,", "### Parliament\n\n1. Download URL and URL.\n\n2. To get wav and transcript," ]

[ "TAGS\n#region-us \n", "# Malay-TTS-Osman\n\nAll notebooks and code related at URL", "## Attributes", "### Wiki and News\n\n- 24000 sample rate, super clean.\n- narrator 'ms-MY-OsmanNeural'.\n- approximate 94.5 hours\n- Texts from Malay Wikipedia and News.\n- Sentences between 2 words and 20 words.", "### Parliament\n\n- 24000 sample rate, super clean.\n- narrator 'ms-MY-OsmanNeural'.\n- approximate 133.2 hours.\n- Texts from Malaysia Malay Parliament.\n- Sentences between 2 words and 25 words.", "## how-to", "### Wiki and News\n\n1. Download URL and URL.\n\n2. To get wav and transcript,", "### Parliament\n\n1. Download URL and URL.\n\n2. To get wav and transcript," ]

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:as", "language:bn", "language:gu", "language:hi", "language:kn", "language:ml", "language:mr", "language:or", "language:pa", "language:ta", "language:te", "license:cc0-1.0", "arxiv:2204.08776", "region:us" ]

[ "2204.08776" ]

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

[ "### Dataset Summary\n\n\nINDICXNLI is similar to existing\nXNLI dataset in shape/form, but focusses on Indic language family. INDICXNLI include NLI\ndata for eleven major Indic languages that includes\nAssamese (‘as’), Gujarat (‘gu’), Kannada (‘kn’),\nMalayalam (‘ml’), Marathi (‘mr’), Odia (‘or’),\nPunjabi (‘pa’), Tamil (‘ta’), Telugu (‘te’), Hindi\n(‘hi’), and Bengali (‘bn’).", "### Supported Tasks and Leaderboards\n\n\nTasks: Natural Language Inference\n\n\nLeaderboards: Currently there is no Leaderboard for this dataset.", "### Languages\n\n\n* 'Assamese (as)'\n* 'Bengali (bn)'\n* 'Gujarati (gu)'\n* 'Kannada (kn)'\n* 'Hindi (hi)'\n* 'Malayalam (ml)'\n* 'Marathi (mr)'\n* 'Oriya (or)'\n* 'Punjabi (pa)'\n* 'Tamil (ta)'\n* 'Telugu (te)'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOne example from the 'hi' dataset is given below in JSON format.", "### Data Fields\n\n\n* 'premise (string)': Premise Sentence\n* 'hypothesis (string)': Hypothesis Sentence\n* 'label (integer)': Integer label '0' if hypothesis 'entails' the premise, '2' if hypothesis 'negates' the premise and '1' otherwise.", "### Data Splits\n\n\n\nDataset usage\n-------------\n\n\nCode snippet for using the dataset using datasets library.\n\n\nDataset Creation\n----------------\n\n\nMachine translation of XNLI english dataset to 11 listed Indic Languages.", "### Curation Rationale\n\n\n[More information needed]", "### Source Data\n\n\nXNLI dataset", "#### Initial Data Collection and Normalization\n\n\nDetailed in the paper", "#### Who are the source language producers?\n\n\nDetailed in the paper", "#### Human Verification Process\n\n\nDetailed in the paper\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nDetailed in the paper", "### Discussion of Biases\n\n\nDetailed in the paper", "### Other Known Limitations\n\n\nDetailed in the paper", "### Dataset Curators\n\n\nDivyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan", "### Licensing Information\n\n\nContents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Copyright of the dataset contents belongs to the original copyright holders.\n\n\nIf you use any of the datasets, models or code modules, please cite the following paper:" ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Kannada #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #license-cc0-1.0 #arxiv-2204.08776 #region-us \n", "### Dataset Summary\n\n\nINDICXNLI is similar to existing\nXNLI dataset in shape/form, but focusses on Indic language family. INDICXNLI include NLI\ndata for eleven major Indic languages that includes\nAssamese (‘as’), Gujarat (‘gu’), Kannada (‘kn’),\nMalayalam (‘ml’), Marathi (‘mr’), Odia (‘or’),\nPunjabi (‘pa’), Tamil (‘ta’), Telugu (‘te’), Hindi\n(‘hi’), and Bengali (‘bn’).", "### Supported Tasks and Leaderboards\n\n\nTasks: Natural Language Inference\n\n\nLeaderboards: Currently there is no Leaderboard for this dataset.", "### Languages\n\n\n* 'Assamese (as)'\n* 'Bengali (bn)'\n* 'Gujarati (gu)'\n* 'Kannada (kn)'\n* 'Hindi (hi)'\n* 'Malayalam (ml)'\n* 'Marathi (mr)'\n* 'Oriya (or)'\n* 'Punjabi (pa)'\n* 'Tamil (ta)'\n* 'Telugu (te)'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOne example from the 'hi' dataset is given below in JSON format.", "### Data Fields\n\n\n* 'premise (string)': Premise Sentence\n* 'hypothesis (string)': Hypothesis Sentence\n* 'label (integer)': Integer label '0' if hypothesis 'entails' the premise, '2' if hypothesis 'negates' the premise and '1' otherwise.", "### Data Splits\n\n\n\nDataset usage\n-------------\n\n\nCode snippet for using the dataset using datasets library.\n\n\nDataset Creation\n----------------\n\n\nMachine translation of XNLI english dataset to 11 listed Indic Languages.", "### Curation Rationale\n\n\n[More information needed]", "### Source Data\n\n\nXNLI dataset", "#### Initial Data Collection and Normalization\n\n\nDetailed in the paper", "#### Who are the source language producers?\n\n\nDetailed in the paper", "#### Human Verification Process\n\n\nDetailed in the paper\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nDetailed in the paper", "### Discussion of Biases\n\n\nDetailed in the paper", "### Other Known Limitations\n\n\nDetailed in the paper", "### Dataset Curators\n\n\nDivyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan", "### Licensing Information\n\n\nContents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Copyright of the dataset contents belongs to the original copyright holders.\n\n\nIf you use any of the datasets, models or code modules, please cite the following paper:" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# *_*coding: utf-8 *_*\n # author --liming--\n \n\"\"\"\n读取images.txt文件,获得每个图像的标签\n读取train_test_split.txt文件,获取每个图像的train, test标签.其中1为训练,0为测试.\n\"\"\"\n \nimport os\nimport shutil\nimport numpy as np\nimport config\nimport time\n \ntime_start = URL()", "# 文件路径\npath_images = URL + 'URL'\npath_split = URL + 'train_test_split.txt'\ntrian_save_path = URL + 'dataset/train/'\ntest_save_path = URL + 'dataset/test/'", "# 读取images.txt文件\nimages = []\nwith open(path_images,'r') as f:\n for line in f:\n URL(list(URL('\\n').split(',')))", "# 读取train_test_split.txt文件\nsplit = []\nwith open(path_split, 'r') as f_:\n for line in f_:\n URL(list(URL('\\n').split(',')))", "# 划分\nnum = len(images) # 图像的总个数\nfor k in range(num):\n file_name = images[k][0].split(' ')[1].split('/')[0]\n aaa = int(split[k][0][-1])\n if int(split[k][0][-1]) == 1: # 划分到训练集\n #判断文件夹是否存在\n if URL(trian_save_path + file_name):\n URL(URL + 'images/' + images[k][0].split(' ')[1], trian_save_path+file_name+'/'+images[k][0].split(' ')[1].split('/')[1])\n else:\n os.makedirs(trian_save_path + file_name)\n URL(URL + 'images/' + images[k][0].split(' ')[1], trian_save_path + file_name + '/' + images[k][0].split(' ')[1].split('/')[1])\n print('%s处理完毕!' % images[k][0].split(' ')[1].split('/')[1])\n else:\n #判断文件夹是否存在\n if URL(test_save_path + file_name):\n aaaa = URL + 'images/' + images[k][0].split(' ')[1]\n bbbb = test_save_path+file_name+'/'+images[k][0].split(' ')[1]\n URL(URL + 'images/' + images[k][0].split(' ')[1], test_save_path+file_name+'/'+images[k][0].split(' ')[1].split('/')[1])\n else:\n os.makedirs(test_save_path + file_name)\n URL(URL + 'images/' + images[k][0].split(' ')[1], test_save_path + file_name + '/' + images[k][0].split(' ')[1].split('/')[1])\n print('%s处理完毕!' % images[k][0].split(' ')[1].split('/')[1])\n \ntime_end = URL()\nprint('CUB200训练集和测试集划分完毕, 耗时%s!!' % (time_end - time_start))\nconfig文件", "# *_*coding: utf-8 *_*", "# author --liming--\n \npath = '/media/lm/C3F680DFF08EB695/细粒度数据集/birds/CUB200/CUB_200_2011/'\n \nROOT_TRAIN = path + 'images/train/'\nROOT_TEST = path + 'images/test/'\nBATCH_SIZE = 16\n(2) 利用Pytorch方式读取数据", "# *_*coding: utf-8 *_*", "# author --liming--\n \n\"\"\"\n用于已下载数据集的转换,便于pytorch的读取\n\"\"\"\n \nimport torch\nimport torchvision\nimport config\nfrom torchvision import datasets, transforms\n \ndata_transform = transforms.Compose([\n transforms.Resize(224),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n])\n \ndef train_data_load():\n # 训练集\n root_train = config.ROOT_TRAIN\n train_dataset = torchvision.datasets.ImageFolder(root_train,\n transform=data_transform)\n CLASS = train_dataset.class_to_idx\n print('训练数据label与文件名的关系:', CLASS)\n train_loader = URL.DataLoader(train_dataset,\n batch_size=config.BATCH_SIZE,\n shuffle=True)\n return CLASS, train_loader\n \ndef test_data_load():\n # 测试集\n root_test = config.ROOT_TEST\n test_dataset = torchvision.datasets.ImageFolder(root_test,\n transform=data_transform)\n \n CLASS = test_dataset.class_to_idx\n print('测试数据label与文件名的关系：',CLASS)\n test_loader = URL.DataLoader(test_dataset,\n batch_size=config.BATCH_SIZE,\n shuffle=True)\n return CLASS, test_loader\n \nif __name__ == '__main___':\n train_data_load()\n test_data_load()" ]

[ "TAGS\n#region-us \n", "# *_*coding: utf-8 *_*\n # author --liming--\n \n\"\"\"\n读取images.txt文件,获得每个图像的标签\n读取train_test_split.txt文件,获取每个图像的train, test标签.其中1为训练,0为测试.\n\"\"\"\n \nimport os\nimport shutil\nimport numpy as np\nimport config\nimport time\n \ntime_start = URL()", "# 文件路径\npath_images = URL + 'URL'\npath_split = URL + 'train_test_split.txt'\ntrian_save_path = URL + 'dataset/train/'\ntest_save_path = URL + 'dataset/test/'", "# 读取images.txt文件\nimages = []\nwith open(path_images,'r') as f:\n for line in f:\n URL(list(URL('\\n').split(',')))", "# 读取train_test_split.txt文件\nsplit = []\nwith open(path_split, 'r') as f_:\n for line in f_:\n URL(list(URL('\\n').split(',')))", "# 划分\nnum = len(images) # 图像的总个数\nfor k in range(num):\n file_name = images[k][0].split(' ')[1].split('/')[0]\n aaa = int(split[k][0][-1])\n if int(split[k][0][-1]) == 1: # 划分到训练集\n #判断文件夹是否存在\n if URL(trian_save_path + file_name):\n URL(URL + 'images/' + images[k][0].split(' ')[1], trian_save_path+file_name+'/'+images[k][0].split(' ')[1].split('/')[1])\n else:\n os.makedirs(trian_save_path + file_name)\n URL(URL + 'images/' + images[k][0].split(' ')[1], trian_save_path + file_name + '/' + images[k][0].split(' ')[1].split('/')[1])\n print('%s处理完毕!' % images[k][0].split(' ')[1].split('/')[1])\n else:\n #判断文件夹是否存在\n if URL(test_save_path + file_name):\n aaaa = URL + 'images/' + images[k][0].split(' ')[1]\n bbbb = test_save_path+file_name+'/'+images[k][0].split(' ')[1]\n URL(URL + 'images/' + images[k][0].split(' ')[1], test_save_path+file_name+'/'+images[k][0].split(' ')[1].split('/')[1])\n else:\n os.makedirs(test_save_path + file_name)\n URL(URL + 'images/' + images[k][0].split(' ')[1], test_save_path + file_name + '/' + images[k][0].split(' ')[1].split('/')[1])\n print('%s处理完毕!' % images[k][0].split(' ')[1].split('/')[1])\n \ntime_end = URL()\nprint('CUB200训练集和测试集划分完毕, 耗时%s!!' % (time_end - time_start))\nconfig文件", "# *_*coding: utf-8 *_*", "# author --liming--\n \npath = '/media/lm/C3F680DFF08EB695/细粒度数据集/birds/CUB200/CUB_200_2011/'\n \nROOT_TRAIN = path + 'images/train/'\nROOT_TEST = path + 'images/test/'\nBATCH_SIZE = 16\n(2) 利用Pytorch方式读取数据", "# *_*coding: utf-8 *_*", "# author --liming--\n \n\"\"\"\n用于已下载数据集的转换,便于pytorch的读取\n\"\"\"\n \nimport torch\nimport torchvision\nimport config\nfrom torchvision import datasets, transforms\n \ndata_transform = transforms.Compose([\n transforms.Resize(224),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n])\n \ndef train_data_load():\n # 训练集\n root_train = config.ROOT_TRAIN\n train_dataset = torchvision.datasets.ImageFolder(root_train,\n transform=data_transform)\n CLASS = train_dataset.class_to_idx\n print('训练数据label与文件名的关系:', CLASS)\n train_loader = URL.DataLoader(train_dataset,\n batch_size=config.BATCH_SIZE,\n shuffle=True)\n return CLASS, train_loader\n \ndef test_data_load():\n # 测试集\n root_test = config.ROOT_TEST\n test_dataset = torchvision.datasets.ImageFolder(root_test,\n transform=data_transform)\n \n CLASS = test_dataset.class_to_idx\n print('测试数据label与文件名的关系：',CLASS)\n test_loader = URL.DataLoader(test_dataset,\n batch_size=config.BATCH_SIZE,\n shuffle=True)\n return CLASS, test_loader\n \nif __name__ == '__main___':\n train_data_load()\n test_data_load()" ]

[ "task_categories:summarization", "task_categories:text-classification", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:100K<n<1M", "language:tr", "region:us" ]

[]

[ "tr" ]

[]

[ "TAGS\n#task_categories-summarization #task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #size_categories-100K<n<1M #language-Turkish #region-us \n" ]

[ "task_categories:summarization", "task_categories:text-classification", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:100K<n<1M", "language:hu", "region:us" ]

[]

[ "hu" ]

[]

[ "TAGS\n#task_categories-summarization #task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #size_categories-100K<n<1M #language-Hungarian #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Tweet Emotion Intensity Dataset", "## Papers: \n\n* Emotion Intensities in Tweets. Saif M. Mohammad and Felipe Bravo-Marquez. In Proceedings of the sixth joint conference on lexical and computational semantics (*Sem), August 2017, Vancouver, Canada.\n\n* WASSA-2017 Shared Task on Emotion Intensity. Saif M. Mohammad and Felipe Bravo-Marquez. In Proceedings of the EMNLP 2017 Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media (WASSA), September 2017, Copenhagen, Denmark." ]

[ "TAGS\n#region-us \n", "# Tweet Emotion Intensity Dataset", "## Papers: \n\n* Emotion Intensities in Tweets. Saif M. Mohammad and Felipe Bravo-Marquez. In Proceedings of the sixth joint conference on lexical and computational semantics (*Sem), August 2017, Vancouver, Canada.\n\n* WASSA-2017 Shared Task on Emotion Intensity. Saif M. Mohammad and Felipe Bravo-Marquez. In Proceedings of the EMNLP 2017 Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media (WASSA), September 2017, Copenhagen, Denmark." ]

[ "annotations_creators:lexyr", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for the-reddit-irl-dataset", "## 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 - Licensing Information", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: Website", "### Dataset Summary\n\nData from the humour subreddits /r/meirl and /r/me_irl, up to Apr 1 2022.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## Additional Information", "### Licensing Information\n\nCC-BY v4.0" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for the-reddit-irl-dataset", "## 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 - Licensing Information", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: Website", "### Dataset Summary\n\nData from the humour subreddits /r/meirl and /r/me_irl, up to Apr 1 2022.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## Additional Information", "### Licensing Information\n\nCC-BY v4.0" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for up to 5 configurations of the dataset.", "### Data Instances", "#### plain\\_text\n\n\n* Size of downloaded dataset files: 33.51 MB\n* Size of the generated dataset: 85.75 MB\n* Total amount of disk used: 119.27 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'int32' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\n'''", "### Contributions\n\n\nThanks to @lewtun, @albertvillanova, @patrickvonplaten, @thomwolf for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for up to 5 configurations of the dataset.", "### Data Instances", "#### plain\\_text\n\n\n* Size of downloaded dataset files: 33.51 MB\n* Size of the generated dataset: 85.75 MB\n* Total amount of disk used: 119.27 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'int32' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\n'''", "### Contributions\n\n\nThanks to @lewtun, @albertvillanova, @patrickvonplaten, @thomwolf for adding this dataset." ]

[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:apache-2.0", "conditional-text-generation", "text-editing", "arxiv:2204.03685", "region:us" ]

[ "2204.03685" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-text2text-generation #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-original #language-English #license-apache-2.0 #conditional-text-generation #text-editing #arxiv-2204.03685 #region-us \n" ]

[ "task_categories:image-classification", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nA dataset from kaggle with duplicate data removed.", "### Data Fields\n\n\nThe data instances have the following fields:\n\n\n* 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'.\n* 'labels': an 'int' classification label.", "### Class Label Mappings:", "### Data Splits" ]

[ "TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc0-1.0 #region-us \n", "### Dataset Summary\n\n\nA dataset from kaggle with duplicate data removed.", "### Data Fields\n\n\nThe data instances have the following fields:\n\n\n* 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'.\n* 'labels': an 'int' classification label.", "### Class Label Mappings:", "### Data Splits" ]

[ "task_categories:text-to-speech", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ko", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "ko" ]

[ "### KSS Dataset: Korean Single speaker Speech Dataset\n\n\nKSS Dataset is designed for the Korean text-to-speech task. It consists of audio files recorded by a professional female voice actoress and their aligned text extracted from my books. As a copyright holder, by courtesy of the publishers, I release this dataset to the public. To my best knowledge, this is the first publicly available speech dataset for Korean.", "### File Format\n\n\nEach line in 'transcript.v.1.3.txt' is delimited by '|' into six fields.\n\n\n* A. Audio file path\n* B. Original script\n* C. Expanded script\n* D. Decomposed script\n* E. Audio duration (seconds)\n* F. English translation\n\n\ne.g.,\n\n\n1/1\\_0470.wav|저는 보통 20분 정도 낮잠을 잡니다.|저는 보통 이십 분 정도 낮잠을 잡니다.|저는 보통 이십 분 정도 낮잠을 잡니다.|4.1|I usually take a nap for 20 minutes.", "### Specification\n\n\n* Audio File Type: wav\n* Total Running Time: 12+ hours\n* Sample Rate: 44,100 KHZ\n* Number of Audio Files: 12,853\n* Sources\n\t+ |1| Kyubyong Park, 500 Basic Korean Verbs, Tuttle Publishing, 2015.|\n\t+ |2| Kyubyong Park, 500 Basic Korean Adjectives 2nd Ed., Youkrak, 2015.|\n\t+ |3| Kyubyong Park, Essential Korean Vocabulary, Tuttle Publishing, 2015.|\n\t+ |4| Kyubyong Park, Tuttle Learner's Korean-English Dictionary, Tuttle Publishing, 2012.|", "### License\n\n\nNC-SA 4.0. You CANNOT use this dataset for ANY COMMERCIAL purpose. Otherwise, you can freely use this.\n\n\nIf you want to cite KSS Dataset, please refer to this:\n\n\nKyubyong Park, KSS Dataset: Korean Single speaker Speech Dataset, URL 2018", "### Reference\n\n\nCheck out this for a project using this KSS Dataset.", "### Contact\n\n\nYou can contact me at kbpark.linguist@URL.\n\n\nApril, 2018.\n\n\nKyubyong Park", "### Dataset Summary\n\n\n12,853 Korean audio files with transcription.", "### Supported Tasks and Leaderboards\n\n\ntext-to-speech", "### Languages\n\n\nkorean\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Splits" ]

[ "TAGS\n#task_categories-text-to-speech #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Korean #license-cc-by-nc-sa-4.0 #region-us \n", "### KSS Dataset: Korean Single speaker Speech Dataset\n\n\nKSS Dataset is designed for the Korean text-to-speech task. It consists of audio files recorded by a professional female voice actoress and their aligned text extracted from my books. As a copyright holder, by courtesy of the publishers, I release this dataset to the public. To my best knowledge, this is the first publicly available speech dataset for Korean.", "### File Format\n\n\nEach line in 'transcript.v.1.3.txt' is delimited by '|' into six fields.\n\n\n* A. Audio file path\n* B. Original script\n* C. Expanded script\n* D. Decomposed script\n* E. Audio duration (seconds)\n* F. English translation\n\n\ne.g.,\n\n\n1/1\\_0470.wav|저는 보통 20분 정도 낮잠을 잡니다.|저는 보통 이십 분 정도 낮잠을 잡니다.|저는 보통 이십 분 정도 낮잠을 잡니다.|4.1|I usually take a nap for 20 minutes.", "### Specification\n\n\n* Audio File Type: wav\n* Total Running Time: 12+ hours\n* Sample Rate: 44,100 KHZ\n* Number of Audio Files: 12,853\n* Sources\n\t+ |1| Kyubyong Park, 500 Basic Korean Verbs, Tuttle Publishing, 2015.|\n\t+ |2| Kyubyong Park, 500 Basic Korean Adjectives 2nd Ed., Youkrak, 2015.|\n\t+ |3| Kyubyong Park, Essential Korean Vocabulary, Tuttle Publishing, 2015.|\n\t+ |4| Kyubyong Park, Tuttle Learner's Korean-English Dictionary, Tuttle Publishing, 2012.|", "### License\n\n\nNC-SA 4.0. You CANNOT use this dataset for ANY COMMERCIAL purpose. Otherwise, you can freely use this.\n\n\nIf you want to cite KSS Dataset, please refer to this:\n\n\nKyubyong Park, KSS Dataset: Korean Single speaker Speech Dataset, URL 2018", "### Reference\n\n\nCheck out this for a project using this KSS Dataset.", "### Contact\n\n\nYou can contact me at kbpark.linguist@URL.\n\n\nApril, 2018.\n\n\nKyubyong Park", "### Dataset Summary\n\n\n12,853 Korean audio files with transcription.", "### Supported Tasks and Leaderboards\n\n\ntext-to-speech", "### Languages\n\n\nkorean\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Splits" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "language:afr", "language:amh", "language:ara", "language:asm", "language:ast", "language:azj", "language:bel", "language:ben", "language:bos", "language:cat", "language:ceb", "language:cmn", "language:ces", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:spa", "language:est", "language:fas", "language:ful", "language:fin", "language:tgl", "language:fra", "language:gle", "language:glg", "language:guj", "language:hau", "language:heb", "language:hin", "language:hrv", "language:hun", "language:hye", "language:ind", "language:ibo", "language:isl", "language:ita", "language:jpn", "language:jav", "language:kat", "language:kam", "language:kea", "language:kaz", "language:khm", "language:kan", "language:kor", "language:ckb", "language:kir", "language:ltz", "language:lug", "language:lin", "language:lao", "language:lit", "language:luo", "language:lav", "language:mri", "language:mkd", "language:mal", "language:mon", "language:mar", "language:msa", "language:mlt", "language:mya", "language:nob", "language:npi", "language:nld", "language:nso", "language:nya", "language:oci", "language:orm", "language:ory", "language:pan", "language:pol", "language:pus", "language:por", "language:ron", "language:rus", "language:bul", "language:snd", "language:slk", "language:slv", "language:sna", "language:som", "language:srp", "language:swe", "language:swh", "language:tam", "language:tel", "language:tgk", "language:tha", "language:tur", "language:ukr", "language:umb", "language:urd", "language:uzb", "language:vie", "language:wol", "language:xho", "language:yor", "language:yue", "language:zul", "license:cc-by-4.0", "speech-recognition", "arxiv:2205.12446", "arxiv:2106.03193", "region:us" ]

[ "2205.12446", "2106.03193" ]

[ "afr", "amh", "ara", "asm", "ast", "azj", "bel", "ben", "bos", "cat", "ceb", "cmn", "ces", "cym", "dan", "deu", "ell", "eng", "spa", "est", "fas", "ful", "fin", "tgl", "fra", "gle", "glg", "guj", "hau", "heb", "hin", "hrv", "hun", "hye", "ind", "ibo", "isl", "ita", "jpn", "jav", "kat", "kam", "kea", "kaz", "khm", "kan", "kor", "ckb", "kir", "ltz", "lug", "lin", "lao", "lit", "luo", "lav", "mri", "mkd", "mal", "mon", "mar", "msa", "mlt", "mya", "nob", "npi", "nld", "nso", "nya", "oci", "orm", "ory", "pan", "pol", "pus", "por", "ron", "rus", "bul", "snd", "slk", "slv", "sna", "som", "srp", "swe", "swh", "tam", "tel", "tgk", "tha", "tur", "ukr", "umb", "urd", "uzb", "vie", "wol", "xho", "yor", "yue", "zul" ]

[ "# FLEURS", "## Dataset Description\n\n- Fine-Tuning script: pytorch/speech-recognition\n- Paper: FLEURS: Few-shot Learning Evaluation of\nUniversal Representations of Speech\n- Total amount of disk used: ca. 350 GB\n\nFleurs is the speech version of the FLoRes machine translation benchmark. \nWe use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. \n\nTraining sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is\nused and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: \n\n- Western Europe: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* \n- Eastern Europe: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian*\n- Central-Asia/Middle-East/North-Africa: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek*\n- Sub-Saharan Africa: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu*\n- South-Asia: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu*\n- South-East Asia: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese*\n- CJK languages: *Cantonese and Mandarin Chinese, Japanese, Korean*", "## How to use & Supported Tasks", "### How to use\n\nThe 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function. \n\nFor example, to download the Hindi config, simply specify the corresponding language config name (i.e., \"hi_in\" for Hindi):\n\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a 'streaming=True' argument to the 'load_dataset' function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n*Bonus*: create a PyTorch dataloader directly with your own datasets (local/streamed).\n\nLocal:\n\n\n\nStreaming:\n\n\n\nTo find out more about loading and preparing audio datasets, head over to URL", "### Example scripts\n\nTrain your own CTC or Seq2Seq Automatic Speech Recognition models on FLEURS with 'transformers' - here.\n\nFine-tune your own Language Identification models on FLEURS with 'transformers' - here", "### 1. Speech Recognition (ASR)", "### 2. Language Identification\n\nLangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all.", "### 3. Retrieval\n\nRetrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English \"key\" utterance corresponding to the speech translation of \"queries\" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult.\n\n\n\nUsers can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech.", "## Dataset Structure\n\nWe show detailed information the example configurations 'af_za' of the dataset.\nAll other configurations have the same structure.", "### Data Instances\n\naf_za\n- Size of downloaded dataset files: 1.47 GB\n- Size of the generated dataset: 1 MB\n- Total amount of disk used: 1.47 GB\n\nAn example of a data instance of the config 'af_za' looks as follows:", "### Data Fields\n\nThe data fields are the same among all splits.\n- id (int): ID of audio sample\n- num_samples (int): Number of float values\n- path (str): Path to the audio file\n- audio (dict): Audio object including loaded audio array, sampling rate and path ot audio\n- raw_transcription (str): The non-normalized transcription of the audio file\n- transcription (str): Transcription of the audio file\n- gender (int): Class id of gender\n- lang_id (int): Class id of language\n- lang_group_id (int): Class id of language group", "### Data Splits\n\nEvery config only has the '\"train\"' split containing of *ca.* 1000 examples, and a '\"validation\"' and '\"test\"' split each containing of *ca.* 400 examples.", "## Dataset Creation\n\nWe collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for\ntrain, dev and test respectively.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos).", "### Discussion of Biases\n\nMost datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages.", "### Other Known Limitations\n\nThe dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding.", "## Additional Information\n\nAll datasets are licensed under the Creative Commons license (CC-BY).\n\n\n\nYou can access the FLEURS paper at URL\nPlease cite the paper when referencing the FLEURS corpus as:", "### Contributions\n\nThanks to @patrickvonplaten and @aconneau for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #language-Afrikaans #language-Amharic #language-Arabic #language-Assamese #language-Asturian #language-North Azerbaijani #language-Belarusian #language-Bengali #language-Bosnian #language-Catalan #language-Cebuano #language-Mandarin Chinese #language-Czech #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Persian #language-Fulah #language-Finnish #language-Tagalog #language-French #language-Irish #language-Galician #language-Gujarati #language-Hausa #language-Hebrew #language-Hindi #language-Croatian #language-Hungarian #language-Armenian #language-Indonesian #language-Igbo #language-Icelandic #language-Italian #language-Japanese #language-Javanese #language-Georgian #language-Kamba (Kenya) #language-Kabuverdianu #language-Kazakh #language-Khmer #language-Kannada #language-Korean #language-Central Kurdish #language-Kirghiz #language-Luxembourgish #language-Ganda #language-Lingala #language-Lao #language-Lithuanian #language-Luo (Kenya and Tanzania) #language-Latvian #language-Maori #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Burmese #language-Norwegian Bokmål #language-Nepali (individual language) #language-Dutch #language-Pedi #language-Nyanja #language-Occitan (post 1500) #language-Oromo #language-Odia #language-Panjabi #language-Polish #language-Pushto #language-Portuguese #language-Romanian #language-Russian #language-Bulgarian #language-Sindhi #language-Slovak #language-Slovenian #language-Shona #language-Somali #language-Serbian #language-Swedish #language-Swahili (individual language) #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkish #language-Ukrainian #language-Umbundu #language-Urdu #language-Uzbek #language-Vietnamese #language-Wolof #language-Xhosa #language-Yoruba #language-Yue Chinese #language-Zulu #license-cc-by-4.0 #speech-recognition #arxiv-2205.12446 #arxiv-2106.03193 #region-us \n", "# FLEURS", "## Dataset Description\n\n- Fine-Tuning script: pytorch/speech-recognition\n- Paper: FLEURS: Few-shot Learning Evaluation of\nUniversal Representations of Speech\n- Total amount of disk used: ca. 350 GB\n\nFleurs is the speech version of the FLoRes machine translation benchmark. \nWe use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. \n\nTraining sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is\nused and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: \n\n- Western Europe: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* \n- Eastern Europe: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian*\n- Central-Asia/Middle-East/North-Africa: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek*\n- Sub-Saharan Africa: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu*\n- South-Asia: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu*\n- South-East Asia: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese*\n- CJK languages: *Cantonese and Mandarin Chinese, Japanese, Korean*", "## How to use & Supported Tasks", "### How to use\n\nThe 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function. \n\nFor example, to download the Hindi config, simply specify the corresponding language config name (i.e., \"hi_in\" for Hindi):\n\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a 'streaming=True' argument to the 'load_dataset' function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n*Bonus*: create a PyTorch dataloader directly with your own datasets (local/streamed).\n\nLocal:\n\n\n\nStreaming:\n\n\n\nTo find out more about loading and preparing audio datasets, head over to URL", "### Example scripts\n\nTrain your own CTC or Seq2Seq Automatic Speech Recognition models on FLEURS with 'transformers' - here.\n\nFine-tune your own Language Identification models on FLEURS with 'transformers' - here", "### 1. Speech Recognition (ASR)", "### 2. Language Identification\n\nLangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all.", "### 3. Retrieval\n\nRetrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English \"key\" utterance corresponding to the speech translation of \"queries\" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult.\n\n\n\nUsers can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech.", "## Dataset Structure\n\nWe show detailed information the example configurations 'af_za' of the dataset.\nAll other configurations have the same structure.", "### Data Instances\n\naf_za\n- Size of downloaded dataset files: 1.47 GB\n- Size of the generated dataset: 1 MB\n- Total amount of disk used: 1.47 GB\n\nAn example of a data instance of the config 'af_za' looks as follows:", "### Data Fields\n\nThe data fields are the same among all splits.\n- id (int): ID of audio sample\n- num_samples (int): Number of float values\n- path (str): Path to the audio file\n- audio (dict): Audio object including loaded audio array, sampling rate and path ot audio\n- raw_transcription (str): The non-normalized transcription of the audio file\n- transcription (str): Transcription of the audio file\n- gender (int): Class id of gender\n- lang_id (int): Class id of language\n- lang_group_id (int): Class id of language group", "### Data Splits\n\nEvery config only has the '\"train\"' split containing of *ca.* 1000 examples, and a '\"validation\"' and '\"test\"' split each containing of *ca.* 400 examples.", "## Dataset Creation\n\nWe collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for\ntrain, dev and test respectively.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos).", "### Discussion of Biases\n\nMost datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages.", "### Other Known Limitations\n\nThe dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding.", "## Additional Information\n\nAll datasets are licensed under the Creative Commons license (CC-BY).\n\n\n\nYou can access the FLEURS paper at URL\nPlease cite the paper when referencing the FLEURS corpus as:", "### Contributions\n\nThanks to @patrickvonplaten and @aconneau for adding this dataset." ]

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

[]

[ "fro" ]

[ "# Open Medieval French\n\nSource: URL" ]

[ "TAGS\n#language-Old French (842-ca. 1400) #region-us \n", "# Open Medieval French\n\nSource: URL" ]

[ "task_categories:text-generation", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:monolingual", "size_categories:n<1K", "language:fr", "license:cc-by-4.0", "region:us" ]

[]

[ "fr" ]

[]

[ "TAGS\n#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-monolingual #size_categories-n<1K #language-French #license-cc-by-4.0 #region-us \n" ]

[ "task_categories:text-generation", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:multilingual", "size_categories:1K<n<10K", "language:fr", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "fr", "en" ]

[]

[ "TAGS\n#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-multilingual #size_categories-1K<n<10K #language-French #language-English #license-cc-by-4.0 #region-us \n" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:Geoffrey Khan", "annotations_creators:Matthew Nazari", "language:aii", "license:cc0-1.0", "region:us" ]

[]

[ "aii" ]

[ "# ‍️ _This dataset is depricated! Please see NENA Speech_ ‍️", "# Dataset Card for urmi_assyrian_voice", "## Dataset Description\n\nThe Urmi Assyrian Voice dataset is parsed from the research and fieldwork of Geoffrey Khan which is made public through the North-Eastern Neo-Aramaic Database Project. Annotation corrections as well as parsing was performed by Matthew Nazari.", "## Dataset Summary\n\nThis dataset contains labelled audio examples of the Urmi dialect of North-Eastern Neo-Aramaic. The dataset only consists of one female speaker in her late seventies. Note that you will need to normalize the utterances for machine learning tasks (clean punctuation, remove accents, etc)." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-Geoffrey Khan #annotations_creators-Matthew Nazari #language-Assyrian Neo-Aramaic #license-cc0-1.0 #region-us \n", "# ‍️ _This dataset is depricated! Please see NENA Speech_ ‍️", "# Dataset Card for urmi_assyrian_voice", "## Dataset Description\n\nThe Urmi Assyrian Voice dataset is parsed from the research and fieldwork of Geoffrey Khan which is made public through the North-Eastern Neo-Aramaic Database Project. Annotation corrections as well as parsing was performed by Matthew Nazari.", "## Dataset Summary\n\nThis dataset contains labelled audio examples of the Urmi dialect of North-Eastern Neo-Aramaic. The dataset only consists of one female speaker in her late seventies. Note that you will need to normalize the utterances for machine learning tasks (clean punctuation, remove accents, etc)." ]

[ "task_categories:summarization", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "arxiv:2010.14235", "region:us" ]

[ "2010.14235" ]

[ "en" ]

[ "### Dataset Summary\n\n\nMulti-Document, a large-scale multi-document summarization dataset created from scientific articles. Multi-Document introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in English\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n{\"id\": \"n3ByHGrxH3bvfrvF\", \"docs\": [{\"id\": \"1394519630182457344\", \"text\": \"Clover Bio's COVID-19 vaccine candidate shows immune response against SARS-CoV-2 variants in mouse model https://t.co/wNWa9GQux5\"}, {\"id\": \"1398154482463170561\", \"text\": \"The purpose of the Vaccine is not to stop you from catching COVID 19. The vaccine introduces the immune system to an inactivated form of the SARS-CoV-2 coronavirus or a small part of it. This then equips the body with the ability to fight the virus better in case you get it. https://t.co/Cz9OU6Zi7P\"}, {\"id\": \"1354844652520792071\", \"text\": \"The Moderna mRNA COVID-19 vaccine appears to be effective against the novel, rapidly spreading variants of SARS-CoV-2.\\nResearchers analysed blood samples from vaccinated people and monkeys- Both contained neutralising antibodies against the virus. \\nPT1/2\\n#COVID19vaccines #biotech https://t.co/ET1maJznot\"}, {\"id\": \"1340189698107518976\", \"text\": \"@KhandaniM Pfizer vaccine introduces viral surface protein which is constant accross SARS COV 2 variants into the body. Body builds antibodies against this protein, not any virus. These antibodies instructs macrophages & T-Cells to attack & destroy any COVID-19 v variant at infection point\"}, {\"id\": \"1374368989581778945\", \"text\": \"@DelthiaRicks \" Pfizer and BioNTech\\u2019s COVID-19 vaccine is an mRNA vaccine, which does not use the live virus but rather a small portion of the viral sequence of the SARS-CoV-2 virus to instruct the body to produce the spike protein displayed on the surface of the virus.\"\"}, {\"id\": \"1353354819315126273\", \"text\": \"Pfizer and BioNTech Publish Results of Study Showing COVID-19 Vaccine Elicits Antibodies that Neutralize Pseudovirus Bearing the SARS-CoV-2 U.K. Strain Spike Protein in Cell Culture | Pfizer https://t.co/YXcSnjLt8C\"}, {\"id\": \"1400821856362401792\", \"text\": \"Pfizer-BioNTech's covid-19 vaccine elicits lower levels of antibodies against the SARS-CoV-2\\u00a0Delta variant\\u00a0(B.1.617.2), first discovered in India, in comparison to other variants, said a research published in\\u00a0Lancet\\u00a0journal.\\n https://t.co/IaCMX81X3b\"}, {\"id\": \"1367252963190665219\", \"text\": \"New research from UNC-Chapel Hill suggests that those who have previously experienced a SARS-CoV-2 infection develop a significant antibody response to the first dose of mRNA-based COVID-19 vaccine.\\nhttps://t.co/B4vR1KUQ0w\"}, {\"id\": \"1375949502461394946\", \"text\": \"Mechanism of a COVID-19 nanoparticle vaccine candidate that elicits a broadly neutralizing antibody response to SARS-CoV-2 variants https://t.co/nc1L0uvtlI #bioRxiv\"}, {\"id\": \"1395428608349548550\", \"text\": \"JCI - Efficient maternal to neonatal transfer of antibodies against SARS-CoV-2 and BNT162b2 mRNA COVID-19 vaccine https://t.co/vIBcpPaKFZ\"}], \"summary\": \"The COVID-19 vaccine appears to be effective against the novel, rapidly spreading variants of SARS-CoV-2. Pfizer-BioNTech's COVID-19 vaccine use small portion of the viral sequence of the SARS-CoV-2 virus to equip the body with the ability to fight the virus better in case you get it.\"}", "### Data Fields\n\n\n{'id': text of paper abstract \n\n'docs': document id \n\n[\n'id': id of text \n\n'text': text data \n\n]\n'summary': summary text\n}", "### Data Splits\n\n\nThe data is split into a training, validation and test.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to [@arka0821] (URL for adding this dataset." ]

[ "TAGS\n#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #arxiv-2010.14235 #region-us \n", "### Dataset Summary\n\n\nMulti-Document, a large-scale multi-document summarization dataset created from scientific articles. Multi-Document introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in English\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n{\"id\": \"n3ByHGrxH3bvfrvF\", \"docs\": [{\"id\": \"1394519630182457344\", \"text\": \"Clover Bio's COVID-19 vaccine candidate shows immune response against SARS-CoV-2 variants in mouse model https://t.co/wNWa9GQux5\"}, {\"id\": \"1398154482463170561\", \"text\": \"The purpose of the Vaccine is not to stop you from catching COVID 19. The vaccine introduces the immune system to an inactivated form of the SARS-CoV-2 coronavirus or a small part of it. This then equips the body with the ability to fight the virus better in case you get it. https://t.co/Cz9OU6Zi7P\"}, {\"id\": \"1354844652520792071\", \"text\": \"The Moderna mRNA COVID-19 vaccine appears to be effective against the novel, rapidly spreading variants of SARS-CoV-2.\\nResearchers analysed blood samples from vaccinated people and monkeys- Both contained neutralising antibodies against the virus. \\nPT1/2\\n#COVID19vaccines #biotech https://t.co/ET1maJznot\"}, {\"id\": \"1340189698107518976\", \"text\": \"@KhandaniM Pfizer vaccine introduces viral surface protein which is constant accross SARS COV 2 variants into the body. Body builds antibodies against this protein, not any virus. These antibodies instructs macrophages & T-Cells to attack & destroy any COVID-19 v variant at infection point\"}, {\"id\": \"1374368989581778945\", \"text\": \"@DelthiaRicks \" Pfizer and BioNTech\\u2019s COVID-19 vaccine is an mRNA vaccine, which does not use the live virus but rather a small portion of the viral sequence of the SARS-CoV-2 virus to instruct the body to produce the spike protein displayed on the surface of the virus.\"\"}, {\"id\": \"1353354819315126273\", \"text\": \"Pfizer and BioNTech Publish Results of Study Showing COVID-19 Vaccine Elicits Antibodies that Neutralize Pseudovirus Bearing the SARS-CoV-2 U.K. Strain Spike Protein in Cell Culture | Pfizer https://t.co/YXcSnjLt8C\"}, {\"id\": \"1400821856362401792\", \"text\": \"Pfizer-BioNTech's covid-19 vaccine elicits lower levels of antibodies against the SARS-CoV-2\\u00a0Delta variant\\u00a0(B.1.617.2), first discovered in India, in comparison to other variants, said a research published in\\u00a0Lancet\\u00a0journal.\\n https://t.co/IaCMX81X3b\"}, {\"id\": \"1367252963190665219\", \"text\": \"New research from UNC-Chapel Hill suggests that those who have previously experienced a SARS-CoV-2 infection develop a significant antibody response to the first dose of mRNA-based COVID-19 vaccine.\\nhttps://t.co/B4vR1KUQ0w\"}, {\"id\": \"1375949502461394946\", \"text\": \"Mechanism of a COVID-19 nanoparticle vaccine candidate that elicits a broadly neutralizing antibody response to SARS-CoV-2 variants https://t.co/nc1L0uvtlI #bioRxiv\"}, {\"id\": \"1395428608349548550\", \"text\": \"JCI - Efficient maternal to neonatal transfer of antibodies against SARS-CoV-2 and BNT162b2 mRNA COVID-19 vaccine https://t.co/vIBcpPaKFZ\"}], \"summary\": \"The COVID-19 vaccine appears to be effective against the novel, rapidly spreading variants of SARS-CoV-2. Pfizer-BioNTech's COVID-19 vaccine use small portion of the viral sequence of the SARS-CoV-2 virus to equip the body with the ability to fight the virus better in case you get it.\"}", "### Data Fields\n\n\n{'id': text of paper abstract \n\n'docs': document id \n\n[\n'id': id of text \n\n'text': text data \n\n]\n'summary': summary text\n}", "### Data Splits\n\n\nThe data is split into a training, validation and test.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to [@arka0821] (URL for adding this dataset." ]

[ "task_categories:text2text-generation", "annotations_creators:no-annotation", "language_creators:found", "source_datasets:original", "language:id", "language:ms", "region:us" ]

[]

[ "id", "ms" ]

[ "### Dataset Summary\n\n\nOriginally a Malay/Indonesian Lexicon retrieved from ipa-dict. We removed the accented letters (because Indonesian graphemes do not use accents), separated homographs, and removed backslashes in phonemes -- resulting in a word-to-phoneme dataset.", "### Languages\n\n\n* Indonesian\n* Malay\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'word': Word (grapheme) as a string.\n* 'phoneme': Phoneme (IPA) as a string.", "### Data Splits\n\n\n\nAdditional Information\n----------------------" ]

[ "TAGS\n#task_categories-text2text-generation #annotations_creators-no-annotation #language_creators-found #source_datasets-original #language-Indonesian #language-Malay (macrolanguage) #region-us \n", "### Dataset Summary\n\n\nOriginally a Malay/Indonesian Lexicon retrieved from ipa-dict. We removed the accented letters (because Indonesian graphemes do not use accents), separated homographs, and removed backslashes in phonemes -- resulting in a word-to-phoneme dataset.", "### Languages\n\n\n* Indonesian\n* Malay\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'word': Word (grapheme) as a string.\n* 'phoneme': Phoneme (IPA) as a string.", "### Data Splits\n\n\n\nAdditional Information\n----------------------" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:am", "language:ar", "language:az", "language:bn", "language:my", "language:zh", "language:en", "language:fr", "language:gu", "language:ha", "language:hi", "language:ig", "language:id", "language:ja", "language:rn", "language:ko", "language:ky", "language:mr", "language:ne", "language:om", "language:ps", "language:fa", "language:pcm", "language:pt", "language:pa", "language:ru", "language:gd", "language:sr", "language:si", "language:so", "language:es", "language:sw", "language:ta", "language:te", "language:th", "language:ti", "language:tr", "language:uk", "language:ur", "language:uz", "language:vi", "language:cy", "language:yo", "license:cc-by-nc-sa-4.0", "arxiv:2112.08804", "region:us" ]

[ "2112.08804" ]

[ "am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo" ]

[ "### Dataset Summary\n\n\nWe present CrossSum, a large-scale dataset\ncomprising 1.70 million cross-lingual article summary samples in 1500+ language-pairs\nconstituting 45 languages. We use the multilingual XL-Sum dataset and align identical\narticles written in different languages via crosslingual retrieval using a language-agnostic\nrepresentation model.", "### Supported Tasks and Leaderboards\n\n\nMore information needed", "### Languages\n\n\n* 'amharic'\n* 'arabic'\n* 'azerbaijani'\n* 'bengali'\n* 'burmese'\n* 'chinese\\_simplified'\n* 'chinese\\_traditional'\n* 'english'\n* 'french'\n* 'gujarati'\n* 'hausa'\n* 'hindi'\n* 'igbo'\n* 'indonesian'\n* 'japanese'\n* 'kirundi'\n* 'korean'\n* 'kyrgyz'\n* 'marathi'\n* 'nepali'\n* 'oromo'\n* 'pashto'\n* 'persian'\n* 'pidgin'\n* 'portuguese'\n* 'punjabi'\n* 'russian'\n* 'scottish\\_gaelic'\n* 'serbian\\_cyrillic'\n* 'serbian\\_latin'\n* 'sinhala'\n* 'somali'\n* 'spanish'\n* 'swahili'\n* 'tamil'\n* 'telugu'\n* 'thai'\n* 'tigrinya'\n* 'turkish'\n* 'ukrainian'\n* 'urdu'\n* 'uzbek'\n* 'vietnamese'\n* 'welsh'\n* 'yoruba'\n\n\nLoading the dataset\n-------------------\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOne example from the 'English' dataset is given below in JSON format.", "### Data Fields\n\n\n* 'source\\_url': A string representing the source article URL.\n* 'target\\_url': A string representing the target article URL.\n* 'summary': A string containing the article summary.\n* 'text' : A string containing the article text.", "### Data Splits\n\n\nNo. of total examples for each language pair are as follows:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nMore information needed", "### Source Data\n\n\nBBC News", "#### Initial Data Collection and Normalization\n\n\nDetailed in the paper", "#### Who are the source language producers?\n\n\nDetailed in the paper", "### Annotations\n\n\nDetailed in the paper", "#### Annotation process\n\n\nDetailed in the paper", "#### Who are the annotators?\n\n\nDetailed in the paper", "### Personal and Sensitive Information\n\n\nMore information needed\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nMore information needed", "### Discussion of Biases\n\n\nMore information needed", "### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMore information needed", "### Licensing Information\n\n\nContents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.\n\n\nIf you use any of the datasets, models or code modules, please cite the following paper:", "### Contributions\n\n\nThanks to @abhik1505040 and @Tahmid for adding this dataset." ]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Amharic #language-Arabic #language-Azerbaijani #language-Bengali #language-Burmese #language-Chinese #language-English #language-French #language-Gujarati #language-Hausa #language-Hindi #language-Igbo #language-Indonesian #language-Japanese #language-Rundi #language-Korean #language-Kirghiz #language-Marathi #language-Nepali (macrolanguage) #language-Oromo #language-Pushto #language-Persian #language-Nigerian Pidgin #language-Portuguese #language-Panjabi #language-Russian #language-Scottish Gaelic #language-Serbian #language-Sinhala #language-Somali #language-Spanish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Thai #language-Tigrinya #language-Turkish #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Welsh #language-Yoruba #license-cc-by-nc-sa-4.0 #arxiv-2112.08804 #region-us \n", "### Dataset Summary\n\n\nWe present CrossSum, a large-scale dataset\ncomprising 1.70 million cross-lingual article summary samples in 1500+ language-pairs\nconstituting 45 languages. We use the multilingual XL-Sum dataset and align identical\narticles written in different languages via crosslingual retrieval using a language-agnostic\nrepresentation model.", "### Supported Tasks and Leaderboards\n\n\nMore information needed", "### Languages\n\n\n* 'amharic'\n* 'arabic'\n* 'azerbaijani'\n* 'bengali'\n* 'burmese'\n* 'chinese\\_simplified'\n* 'chinese\\_traditional'\n* 'english'\n* 'french'\n* 'gujarati'\n* 'hausa'\n* 'hindi'\n* 'igbo'\n* 'indonesian'\n* 'japanese'\n* 'kirundi'\n* 'korean'\n* 'kyrgyz'\n* 'marathi'\n* 'nepali'\n* 'oromo'\n* 'pashto'\n* 'persian'\n* 'pidgin'\n* 'portuguese'\n* 'punjabi'\n* 'russian'\n* 'scottish\\_gaelic'\n* 'serbian\\_cyrillic'\n* 'serbian\\_latin'\n* 'sinhala'\n* 'somali'\n* 'spanish'\n* 'swahili'\n* 'tamil'\n* 'telugu'\n* 'thai'\n* 'tigrinya'\n* 'turkish'\n* 'ukrainian'\n* 'urdu'\n* 'uzbek'\n* 'vietnamese'\n* 'welsh'\n* 'yoruba'\n\n\nLoading the dataset\n-------------------\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOne example from the 'English' dataset is given below in JSON format.", "### Data Fields\n\n\n* 'source\\_url': A string representing the source article URL.\n* 'target\\_url': A string representing the target article URL.\n* 'summary': A string containing the article summary.\n* 'text' : A string containing the article text.", "### Data Splits\n\n\nNo. of total examples for each language pair are as follows:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nMore information needed", "### Source Data\n\n\nBBC News", "#### Initial Data Collection and Normalization\n\n\nDetailed in the paper", "#### Who are the source language producers?\n\n\nDetailed in the paper", "### Annotations\n\n\nDetailed in the paper", "#### Annotation process\n\n\nDetailed in the paper", "#### Who are the annotators?\n\n\nDetailed in the paper", "### Personal and Sensitive Information\n\n\nMore information needed\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nMore information needed", "### Discussion of Biases\n\n\nMore information needed", "### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMore information needed", "### Licensing Information\n\n\nContents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.\n\n\nIf you use any of the datasets, models or code modules, please cite the following paper:", "### Contributions\n\n\nThanks to @abhik1505040 and @Tahmid for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_text", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a climate related claim and evidence, predict if claim is related to evidence." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_text #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a climate related claim and evidence, predict if claim is related to evidence." ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_text", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a climate related claim and evidence, predict if evidence is related to claim." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_text #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a climate related claim and evidence, predict if evidence is related to claim." ]

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:crowdsourced", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2201.05955", "region:us" ]

[ "2201.05955" ]

[ "en" ]

[ "### Dataset Summary\n\n\nWANLI (Worker-AI Collaboration for NLI) is a collection of 108K English sentence pairs for the task of natural language inference (NLI).\nEach example is created by first identifying a \"pocket\" of examples in MultiNLI (Williams et al., 2018) that share a challenging reasoning pattern, then instructing GPT-3 to write a new example with the same pattern.\nThe set of generated examples are automatically filtered to contain those most likely to aid model training, and finally labeled and optionally revised by human annotators.\n\n\nWANLI presents unique empirical strengths compared to existing NLI datasets. Remarkably, training a model on WANLI instead of MultiNLI (which is 4 times larger) improves performance on seven out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI.", "### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for natural language inference, which determines whether a premise entails (i.e., implies the truth of) a hypothesis, both expressed in natural language. Success on this task is typically measured by achieving a high accuracy. A RoBERTa-large model currently achieves 75.40%.\n\n\nModels trained on NLI are often adapted to other downstream tasks, and NLI data can be mixed with other sources of supervision.", "### Languages\n\n\nThe dataset consists of English examples generated by GPT-3 and revised by English-speaking crowdworkers located in the United States.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nHere is an example of an NLI example in 'data/wanli/URL' or 'data/wanli/URL'.\n\n\n* 'id': unique identifier for the example\n* 'premise': a piece of text\n* 'hypothesis': a piece of text that may be true, false, or whose truth conditions may not be knowable when compared to the premise\n* 'gold': one of 'entailment', 'neutral', and 'contradiction'\n* 'genre': one of 'generated' and 'generated\\_revised', depending on whether the example was revised by annotators\n* 'pairID': id of seed MNLI example, corresponding to those in 'data/mnli/URL'\n\n\nWe also release the raw annotations for each worker, which can be found in 'data/wanli/anonymized\\_annotations.jsonl'.\n\n\n* 'WorkerId': a unique identification for each crowdworker (NOT the real worker ID from AMT)\n* 'id': id of generated example\n* 'nearest\\_neighbors': ordered ids of the group of MNLI nearest neighbors that were used as in-context examples, where the first one is seed ambiguous MNLI example. MNLI ids correspond to those in 'mnli/URL'.\n* 'premise': GPT-3 generated premise\n* 'hypothesis': GPT-3 generated hypothesis\n* 'label': the shared label of the in-context examples, which is the \"intended\" label for this generation\n* 'revised\\_premise': premise after human review\n* 'revised\\_hypothesis': hypothesis after human review\n* 'gold': annotator-assigned gold label for the (potentially revised) example\n* 'revised': whether the example was revised", "### Data Splits\n\n\nThe dataset is randomly split into a *train* and *test* set.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nA recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. On the other hand, there has been remarkable progress in open-ended text generation based on massive language models. We create WANLI to demonstrate the effectiveness an approach that leverages the best of both worlds: a language model's ability to efficiently generate diverse examples, and a human's ability to revise the examples for quality and assign a gold label.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nOur pipeline starts with an existing dataset, MultiNLI (Williams et al., 2018). We use dataset cartography from Swayamdipta et al. (2020) to automatically identify pockets of examples that demonstrate challenging reasoning patterns rela081 tive to a trained model. Using each group as a set of in-context examples, we leverage a pretrained language model to *generate new examples* likely to have the same pattern. We then automatically filter generations to keep those that are most likely to aid model learning. Finally, we validate the generated examples by subjecting them to human review, where crowdworkers assign a gold label and (optionally) revise for quality.", "#### Who are the source language producers?\n\n\nThe GPT-3 Curie model generated examples which were then revised and labeled by crowdworkers on Amazon Mechanical Turk.\nWorkers were paid $0.12 for each example that they annotate. At the end of data collection, we aggregate the earning and time spent from each crowdworker, and find that the median hourly rate was $22.72, with 85% of workers being paid over the $15/hour target.", "### Annotations", "#### Annotation process\n\n\nGiven an unlabeled example, annotators are asked to optionally revise it for quality (while preserving the intended meaning as much as possible through minimal revisions), and then assign a label. Alternatively, if an example would require a great deal of revision to fix *or* if it could be perceived as offensive, they were asked to discard it.\nDetails about instructions, guidelines, and instructional examples can be found in Appendix D of the paper.\n\n\nCrowdworkers annotate a total of 118,724 examples, with two distinct workers reviewing each example.\nFor examples that both annotators labeled without revision, annotators achieved a Cohen Kappa score of 0.60, indicating substantial agreement.", "#### Who are the annotators?\n\n\nAnnotators were required to have a HIT approval rate of 98%, a total of 10,000 approved HITs, and be located in the United States.\n\n\n300 Turkers took our qualification test, of which 69 passed. Turkers who were later found to produce extremely careless annotations were removed from the qualification list (and oftentimes, their annotations were discarded, though they were still paid for their work). The number of workers who contributed to the final dataset is 62.", "### Personal and Sensitive Information\n\n\nThe dataset does not contain any personal information about the authors or the crowdworkers.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis dataset was developed to explore the potential of worker-AI collaboration for dataset curation, train more robust NLI models, and provide more challenging evaluation of existing systems.", "### Discussion of Biases\n\n\nText generated from large pretrained language models is susceptible to perpetuating social harms and containing toxic language.\nTo partially remedy this, we ask annotators to discard any examples that may be perceived as offensive.\nNonetheless, it is possible that harmful examples (especially if they contain subtle biases) may have been missed by annotators and included in the final dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nWANLI was developed by Alisa Liu, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi from the University of Washington and AI2." ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-crowdsourced #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2201.05955 #region-us \n", "### Dataset Summary\n\n\nWANLI (Worker-AI Collaboration for NLI) is a collection of 108K English sentence pairs for the task of natural language inference (NLI).\nEach example is created by first identifying a \"pocket\" of examples in MultiNLI (Williams et al., 2018) that share a challenging reasoning pattern, then instructing GPT-3 to write a new example with the same pattern.\nThe set of generated examples are automatically filtered to contain those most likely to aid model training, and finally labeled and optionally revised by human annotators.\n\n\nWANLI presents unique empirical strengths compared to existing NLI datasets. Remarkably, training a model on WANLI instead of MultiNLI (which is 4 times larger) improves performance on seven out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI.", "### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for natural language inference, which determines whether a premise entails (i.e., implies the truth of) a hypothesis, both expressed in natural language. Success on this task is typically measured by achieving a high accuracy. A RoBERTa-large model currently achieves 75.40%.\n\n\nModels trained on NLI are often adapted to other downstream tasks, and NLI data can be mixed with other sources of supervision.", "### Languages\n\n\nThe dataset consists of English examples generated by GPT-3 and revised by English-speaking crowdworkers located in the United States.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nHere is an example of an NLI example in 'data/wanli/URL' or 'data/wanli/URL'.\n\n\n* 'id': unique identifier for the example\n* 'premise': a piece of text\n* 'hypothesis': a piece of text that may be true, false, or whose truth conditions may not be knowable when compared to the premise\n* 'gold': one of 'entailment', 'neutral', and 'contradiction'\n* 'genre': one of 'generated' and 'generated\\_revised', depending on whether the example was revised by annotators\n* 'pairID': id of seed MNLI example, corresponding to those in 'data/mnli/URL'\n\n\nWe also release the raw annotations for each worker, which can be found in 'data/wanli/anonymized\\_annotations.jsonl'.\n\n\n* 'WorkerId': a unique identification for each crowdworker (NOT the real worker ID from AMT)\n* 'id': id of generated example\n* 'nearest\\_neighbors': ordered ids of the group of MNLI nearest neighbors that were used as in-context examples, where the first one is seed ambiguous MNLI example. MNLI ids correspond to those in 'mnli/URL'.\n* 'premise': GPT-3 generated premise\n* 'hypothesis': GPT-3 generated hypothesis\n* 'label': the shared label of the in-context examples, which is the \"intended\" label for this generation\n* 'revised\\_premise': premise after human review\n* 'revised\\_hypothesis': hypothesis after human review\n* 'gold': annotator-assigned gold label for the (potentially revised) example\n* 'revised': whether the example was revised", "### Data Splits\n\n\nThe dataset is randomly split into a *train* and *test* set.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nA recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. On the other hand, there has been remarkable progress in open-ended text generation based on massive language models. We create WANLI to demonstrate the effectiveness an approach that leverages the best of both worlds: a language model's ability to efficiently generate diverse examples, and a human's ability to revise the examples for quality and assign a gold label.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nOur pipeline starts with an existing dataset, MultiNLI (Williams et al., 2018). We use dataset cartography from Swayamdipta et al. (2020) to automatically identify pockets of examples that demonstrate challenging reasoning patterns rela081 tive to a trained model. Using each group as a set of in-context examples, we leverage a pretrained language model to *generate new examples* likely to have the same pattern. We then automatically filter generations to keep those that are most likely to aid model learning. Finally, we validate the generated examples by subjecting them to human review, where crowdworkers assign a gold label and (optionally) revise for quality.", "#### Who are the source language producers?\n\n\nThe GPT-3 Curie model generated examples which were then revised and labeled by crowdworkers on Amazon Mechanical Turk.\nWorkers were paid $0.12 for each example that they annotate. At the end of data collection, we aggregate the earning and time spent from each crowdworker, and find that the median hourly rate was $22.72, with 85% of workers being paid over the $15/hour target.", "### Annotations", "#### Annotation process\n\n\nGiven an unlabeled example, annotators are asked to optionally revise it for quality (while preserving the intended meaning as much as possible through minimal revisions), and then assign a label. Alternatively, if an example would require a great deal of revision to fix *or* if it could be perceived as offensive, they were asked to discard it.\nDetails about instructions, guidelines, and instructional examples can be found in Appendix D of the paper.\n\n\nCrowdworkers annotate a total of 118,724 examples, with two distinct workers reviewing each example.\nFor examples that both annotators labeled without revision, annotators achieved a Cohen Kappa score of 0.60, indicating substantial agreement.", "#### Who are the annotators?\n\n\nAnnotators were required to have a HIT approval rate of 98%, a total of 10,000 approved HITs, and be located in the United States.\n\n\n300 Turkers took our qualification test, of which 69 passed. Turkers who were later found to produce extremely careless annotations were removed from the qualification list (and oftentimes, their annotations were discarded, though they were still paid for their work). The number of workers who contributed to the final dataset is 62.", "### Personal and Sensitive Information\n\n\nThe dataset does not contain any personal information about the authors or the crowdworkers.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis dataset was developed to explore the potential of worker-AI collaboration for dataset curation, train more robust NLI models, and provide more challenging evaluation of existing systems.", "### Discussion of Biases\n\n\nText generated from large pretrained language models is susceptible to perpetuating social harms and containing toxic language.\nTo partially remedy this, we ask annotators to discard any examples that may be perceived as offensive.\nNonetheless, it is possible that harmful examples (especially if they contain subtle biases) may have been missed by annotators and included in the final dataset.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nWANLI was developed by Alisa Liu, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi from the University of Washington and AI2." ]

[ "task_categories:text-classification", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "arxiv:2010.05700", "region:us" ]

[ "2010.05700" ]

[]

[ "# Dataset Card for Corpus of Diverse Styles", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary", "## Disclaimer\nI am not the original author of the paper that presents the Corpus of Diverse Styles. I uploaded the dataset to HuggingFace as a convenience.", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nA new benchmark dataset that contains 15M\nsentences from 11 diverse styles.\n\nTo create CDS, we obtain data from existing academic\nresearch datasets and public APIs or online collections\nlike Project Gutenberg. We choose\nstyles that are easy for human readers to identify at\na sentence level (e.g., Tweets or Biblical text). While\nprior benchmarks involve a transfer between two\nstyles, CDS has 110 potential transfer directions." ]

[ "TAGS\n#task_categories-text-classification #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #arxiv-2010.05700 #region-us \n", "# Dataset Card for Corpus of Diverse Styles", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary", "## Disclaimer\nI am not the original author of the paper that presents the Corpus of Diverse Styles. I uploaded the dataset to HuggingFace as a convenience.", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nA new benchmark dataset that contains 15M\nsentences from 11 diverse styles.\n\nTo create CDS, we obtain data from existing academic\nresearch datasets and public APIs or online collections\nlike Project Gutenberg. We choose\nstyles that are easy for human readers to identify at\na sentence level (e.g., Tweets or Biblical text). While\nprior benchmarks involve a transfer between two\nstyles, CDS has 110 potential transfer directions." ]

[ "region:us" ]

[]

[]

[]

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

[ "language:nl", "text-classification", "sentiment-analysis", "region:us" ]

[]

[ "nl" ]

[ "## Dataset overview\nThis is a dataset that contains restaurant reviews gathered in 2019 using a webscraping tool in Python. Reviews on restaurant visits and restaurant features were collected for Dutch restaurants. \nThe dataset is formatted using the DatasetDict format and contains the following indices:\n- train, 116693 records\n- test, 14587 records\n- validation, 14587 records\n\nThe dataset holds both information of the restaurant level as well as the review level and contains the following features:\n- [restaurant_ID] > unique restaurant ID\n- [restaurant_review_ID] > unique review ID\n- [michelin_label] > indicator whether this restaurant was awarded one (or more) Michelin stars prior to 2020\n- [score_total] > restaurant level total score\n- [score_food] > restaurant level food score\n- [score_service] > restaurant level service score\n- [score_decor] > restaurant level decor score\n- [fame_reviewer] > label for how often a reviewer has posted a restaurant review\n- [reviewscore_food] > review level food score\n- [reviewscore_service] > review level service score\n- [reviewscore_ambiance] > review level ambiance score\n- [reviewscore_waiting] > review level waiting score\n- [reviewscore_value] > review level value for money score\n- [reviewscore_noise] > review level noise score\n- [review_text] > the full review that was written by the reviewer for this restaurant\n- [review_length] > total length of the review (tokens)", "## Purpose\nThe restaurant reviews submitted by visitor can be used to model the restaurant scores (food, ambiance etc) or used to model Michelin star holders. In this blog series we used the review texts to predict next Michelin star restaurants, using R." ]

[ "TAGS\n#language-Dutch #text-classification #sentiment-analysis #region-us \n", "## Dataset overview\nThis is a dataset that contains restaurant reviews gathered in 2019 using a webscraping tool in Python. Reviews on restaurant visits and restaurant features were collected for Dutch restaurants. \nThe dataset is formatted using the DatasetDict format and contains the following indices:\n- train, 116693 records\n- test, 14587 records\n- validation, 14587 records\n\nThe dataset holds both information of the restaurant level as well as the review level and contains the following features:\n- [restaurant_ID] > unique restaurant ID\n- [restaurant_review_ID] > unique review ID\n- [michelin_label] > indicator whether this restaurant was awarded one (or more) Michelin stars prior to 2020\n- [score_total] > restaurant level total score\n- [score_food] > restaurant level food score\n- [score_service] > restaurant level service score\n- [score_decor] > restaurant level decor score\n- [fame_reviewer] > label for how often a reviewer has posted a restaurant review\n- [reviewscore_food] > review level food score\n- [reviewscore_service] > review level service score\n- [reviewscore_ambiance] > review level ambiance score\n- [reviewscore_waiting] > review level waiting score\n- [reviewscore_value] > review level value for money score\n- [reviewscore_noise] > review level noise score\n- [review_text] > the full review that was written by the reviewer for this restaurant\n- [review_length] > total length of the review (tokens)", "## Purpose\nThe restaurant reviews submitted by visitor can be used to model the restaurant scores (food, ambiance etc) or used to model Michelin star holders. In this blog series we used the review texts to predict next Michelin star restaurants, using R." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [FrozenLake-v1] with slippery = False" ]

[ "TAGS\n#region-us \n", "# Dataset Card for [FrozenLake-v1] with slippery = False" ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_text", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a claim and climate related evidence, predict if claim is related to evidence." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_text #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a claim and climate related evidence, predict if claim is related to evidence." ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_text", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a claim and climate related evidence, predict if evidence is related to claim." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_text #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a claim and climate related evidence, predict if evidence is related to claim." ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_text", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a climate related evidence and claim, predict if pair is related." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_text #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a climate related evidence and claim, predict if pair is related." ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_text", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a claim and climate related evidence, predict if pair is related." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_text #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Text dataset (URL pre-processed and, ready to evaluate.\nThe evaluation objective is a text classification task - given a claim and climate related evidence, predict if pair is related." ]

[ "task_categories:image-classification", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|fashion_mnist", "language:en", "license:mit", "arxiv:1906.02337", "region:us" ]

[ "1906.02337" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-image-classification #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|fashion_mnist #language-English #license-mit #arxiv-1906.02337 #region-us \n" ]

[ "task_categories:image-to-text", "task_categories:object-detection", "task_categories:visual-question-answering", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Visual Genome", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: URL\n- Leaderboard:\n- Point of Contact: ranjaykrishna [at] gmail [dot] com", "### Dataset Summary\n\nVisual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language.\n\nFrom the paper:\n> Despite progress in perceptual tasks such as\nimage classification, computers still perform poorly on\ncognitive tasks such as image description and question\nanswering. Cognition is core to tasks that involve not\njust recognizing, but reasoning about our visual world.\nHowever, models used to tackle the rich content in images for cognitive tasks are still being trained using the\nsame datasets designed for perceptual tasks. To achieve\nsuccess at cognitive tasks, models need to understand\nthe interactions and relationships between objects in an\nimage. When asked “What vehicle is the person riding?”,\ncomputers will need to identify the objects in an image\nas well as the relationships riding(man, carriage) and\npulling(horse, carriage) to answer correctly that “the\nperson is riding a horse-drawn carriage.”\n\nVisual Genome has:\n - 108,077 image\n - 5.4 Million Region Descriptions\n - 1.7 Million Visual Question Answers\n - 3.8 Million Object Instances\n - 2.8 Million Attributes\n - 2.3 Million Relationships\n\nFrom the paper:\n> Our dataset contains over 108K images where each\nimage has an average of 35 objects, 26 attributes, and 21\npairwise relationships between objects. We canonicalize\nthe objects, attributes, relationships, and noun phrases\nin region descriptions and questions answer pairs to\nWordNet synsets.", "### Dataset Preprocessing", "### Supported Tasks and Leaderboards", "### Languages\n\nAll of annotations use English as primary language.", "## Dataset Structure", "### Data Instances\n\nWhen loading a specific configuration, users has to append a version dependent suffix:", "#### region_descriptions\n\nAn example of looks as follows.", "#### objects\n\nAn example of looks as follows.", "#### attributes\n\nAn example of looks as follows.", "#### relationships\n\nAn example of looks as follows.", "#### question_answers\n\nAn example of looks as follows.", "### Data Fields\n\nWhen loading a specific configuration, users has to append a version dependent suffix:", "#### region_descriptions\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'regions': Holds a list of 'Region' dataclasses:\n - 'region_id': Unique numeric ID of the region.\n - 'image_id': Unique numeric ID of the image.\n - 'x': x coordinate of bounding box's top left corner.\n - 'y': y coordinate of bounding box's top left corner.\n - 'width': Bounding box width.\n - 'height': Bounding box height.", "#### objects\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'objects': Holds a list of 'Object' dataclasses:\n - 'object_id': Unique numeric ID of the object.\n - 'x': x coordinate of bounding box's top left corner.\n - 'y': y coordinate of bounding box's top left corner.\n - 'w': Bounding box width.\n - 'h': Bounding box height.\n - 'names': List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at URL\n - 'synsets': List of 'WordNet synsets'.", "#### attributes\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'attributes': Holds a list of 'Object' dataclasses:\n - 'object_id': Unique numeric ID of the region.\n - 'x': x coordinate of bounding box's top left corner.\n - 'y': y coordinate of bounding box's top left corner.\n - 'w': Bounding box width.\n - 'h': Bounding box height.\n - 'names': List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at URL\n - 'synsets': List of 'WordNet synsets'.\n - 'attributes': List of attributes associated with the object.", "#### relationships\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'relationships': Holds a list of 'Relationship' dataclasses:\n - 'relationship_id': Unique numeric ID of the object.\n - 'predicate': Predicate defining relationship between a subject and an object.\n - 'synsets': List of 'WordNet synsets'.\n - 'subject': Object dataclass. See subsection on 'objects'.\n - 'object': Object dataclass. See subsection on 'objects'.", "#### question_answers\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'qas': Holds a list of 'Question-Answering' dataclasses:\n - 'qa_id': Unique numeric ID of the question-answer pair.\n - 'image_id': Unique numeric ID of the image.\n - 'question': Question.\n - 'answer': Answer.\n - 'q_objects': List of object dataclass associated with 'question' field. See subsection on 'objects'.\n - 'a_objects': List of object dataclass associated with 'answer' field. See subsection on 'objects'.", "### Data Splits\n\nAll the data is contained in training set.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\nFrom the paper:\n> We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over\n33, 000 unique workers contributed to the dataset. The\ndataset was collected over the course of 6 months after\n15 months of experimentation and iteration on the data\nrepresentation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where\neach HIT involved creating descriptions, questions and\nanswers, or region graphs. Each HIT was designed such\nthat workers manage to earn anywhere between $6-$8\nper hour if they work continuously, in line with ethical\nresearch standards on Mechanical Turk (Salehi et al.,\n2015). Visual Genome HITs achieved a 94.1% retention\nrate, meaning that 94.1% of workers who completed one\nof our tasks went ahead to do more. [...] 93.02% of workers contributed from the United States.\nThe majority of our workers were\nbetween the ages of 25 and 34 years old. Our youngest\ncontributor was 18 years and the oldest was 68 years\nold. We also had a near-balanced split of 54.15% male\nand 45.85% female workers.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nVisual Genome by Ranjay Krishna is licensed under a Creative Commons Attribution 4.0 International License.", "### Contributions\n\nDue to limitation of the dummy_data creation, we provide a 'fix_generated_dummy_data.py' script that fix the dataset in-place.\n\nThanks to @thomasw21 for adding this dataset." ]

[ "TAGS\n#task_categories-image-to-text #task_categories-object-detection #task_categories-visual-question-answering #task_ids-image-captioning #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for Visual Genome", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: URL\n- Leaderboard:\n- Point of Contact: ranjaykrishna [at] gmail [dot] com", "### Dataset Summary\n\nVisual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language.\n\nFrom the paper:\n> Despite progress in perceptual tasks such as\nimage classification, computers still perform poorly on\ncognitive tasks such as image description and question\nanswering. Cognition is core to tasks that involve not\njust recognizing, but reasoning about our visual world.\nHowever, models used to tackle the rich content in images for cognitive tasks are still being trained using the\nsame datasets designed for perceptual tasks. To achieve\nsuccess at cognitive tasks, models need to understand\nthe interactions and relationships between objects in an\nimage. When asked “What vehicle is the person riding?”,\ncomputers will need to identify the objects in an image\nas well as the relationships riding(man, carriage) and\npulling(horse, carriage) to answer correctly that “the\nperson is riding a horse-drawn carriage.”\n\nVisual Genome has:\n - 108,077 image\n - 5.4 Million Region Descriptions\n - 1.7 Million Visual Question Answers\n - 3.8 Million Object Instances\n - 2.8 Million Attributes\n - 2.3 Million Relationships\n\nFrom the paper:\n> Our dataset contains over 108K images where each\nimage has an average of 35 objects, 26 attributes, and 21\npairwise relationships between objects. We canonicalize\nthe objects, attributes, relationships, and noun phrases\nin region descriptions and questions answer pairs to\nWordNet synsets.", "### Dataset Preprocessing", "### Supported Tasks and Leaderboards", "### Languages\n\nAll of annotations use English as primary language.", "## Dataset Structure", "### Data Instances\n\nWhen loading a specific configuration, users has to append a version dependent suffix:", "#### region_descriptions\n\nAn example of looks as follows.", "#### objects\n\nAn example of looks as follows.", "#### attributes\n\nAn example of looks as follows.", "#### relationships\n\nAn example of looks as follows.", "#### question_answers\n\nAn example of looks as follows.", "### Data Fields\n\nWhen loading a specific configuration, users has to append a version dependent suffix:", "#### region_descriptions\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'regions': Holds a list of 'Region' dataclasses:\n - 'region_id': Unique numeric ID of the region.\n - 'image_id': Unique numeric ID of the image.\n - 'x': x coordinate of bounding box's top left corner.\n - 'y': y coordinate of bounding box's top left corner.\n - 'width': Bounding box width.\n - 'height': Bounding box height.", "#### objects\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'objects': Holds a list of 'Object' dataclasses:\n - 'object_id': Unique numeric ID of the object.\n - 'x': x coordinate of bounding box's top left corner.\n - 'y': y coordinate of bounding box's top left corner.\n - 'w': Bounding box width.\n - 'h': Bounding box height.\n - 'names': List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at URL\n - 'synsets': List of 'WordNet synsets'.", "#### attributes\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'attributes': Holds a list of 'Object' dataclasses:\n - 'object_id': Unique numeric ID of the region.\n - 'x': x coordinate of bounding box's top left corner.\n - 'y': y coordinate of bounding box's top left corner.\n - 'w': Bounding box width.\n - 'h': Bounding box height.\n - 'names': List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at URL\n - 'synsets': List of 'WordNet synsets'.\n - 'attributes': List of attributes associated with the object.", "#### relationships\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'relationships': Holds a list of 'Relationship' dataclasses:\n - 'relationship_id': Unique numeric ID of the object.\n - 'predicate': Predicate defining relationship between a subject and an object.\n - 'synsets': List of 'WordNet synsets'.\n - 'subject': Object dataclass. See subsection on 'objects'.\n - 'object': Object dataclass. See subsection on 'objects'.", "#### question_answers\n\n- 'image': A 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'image_id': Unique numeric ID of the image.\n- 'url': URL of source image.\n- 'width': Image width.\n- 'height': Image height.\n- 'coco_id': Id mapping to MSCOCO indexing.\n- 'flickr_id': Id mapping to Flicker indexing.\n- 'qas': Holds a list of 'Question-Answering' dataclasses:\n - 'qa_id': Unique numeric ID of the question-answer pair.\n - 'image_id': Unique numeric ID of the image.\n - 'question': Question.\n - 'answer': Answer.\n - 'q_objects': List of object dataclass associated with 'question' field. See subsection on 'objects'.\n - 'a_objects': List of object dataclass associated with 'answer' field. See subsection on 'objects'.", "### Data Splits\n\nAll the data is contained in training set.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\nFrom the paper:\n> We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over\n33, 000 unique workers contributed to the dataset. The\ndataset was collected over the course of 6 months after\n15 months of experimentation and iteration on the data\nrepresentation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where\neach HIT involved creating descriptions, questions and\nanswers, or region graphs. Each HIT was designed such\nthat workers manage to earn anywhere between $6-$8\nper hour if they work continuously, in line with ethical\nresearch standards on Mechanical Turk (Salehi et al.,\n2015). Visual Genome HITs achieved a 94.1% retention\nrate, meaning that 94.1% of workers who completed one\nof our tasks went ahead to do more. [...] 93.02% of workers contributed from the United States.\nThe majority of our workers were\nbetween the ages of 25 and 34 years old. Our youngest\ncontributor was 18 years and the oldest was 68 years\nold. We also had a near-balanced split of 54.15% male\nand 45.85% female workers.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nVisual Genome by Ranjay Krishna is licensed under a Creative Commons Attribution 4.0 International License.", "### Contributions\n\nDue to limitation of the dummy_data creation, we provide a 'fix_generated_dummy_data.py' script that fix the dataset in-place.\n\nThanks to @thomasw21 for adding this dataset." ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# PIE Dataset Card for \"conll2003\"\n\nThis is a PyTorch-IE wrapper for the\nCoNLL 2003 Huggingface dataset loading script.", "## Data Schema\n\nThe document type for this dataset is 'CoNLL2003Document' which defines the following data fields:\n\n- 'text' (str)\n- 'id' (str, optional)\n- 'metadata' (dictionary, optional)\n\nand the following annotation layers:\n\n- 'entities' (annotation type: 'LabeledSpan', target: 'text')\n\nSee here for the annotation type definitions.", "## Document Converters\n\nThe dataset provides document converters for the following target document types:\n\n- 'pytorch_ie.documents.TextDocumentWithLabeledSpans'\n\nSee here for the document type\ndefinitions." ]

[ "TAGS\n#region-us \n", "# PIE Dataset Card for \"conll2003\"\n\nThis is a PyTorch-IE wrapper for the\nCoNLL 2003 Huggingface dataset loading script.", "## Data Schema\n\nThe document type for this dataset is 'CoNLL2003Document' which defines the following data fields:\n\n- 'text' (str)\n- 'id' (str, optional)\n- 'metadata' (dictionary, optional)\n\nand the following annotation layers:\n\n- 'entities' (annotation type: 'LabeledSpan', target: 'text')\n\nSee here for the annotation type definitions.", "## Document Converters\n\nThe dataset provides document converters for the following target document types:\n\n- 'pytorch_ie.documents.TextDocumentWithLabeledSpans'\n\nSee here for the document type\ndefinitions." ]

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|iit_cdip", "language:en", "license:other", "arxiv:1502.07058", "region:us" ]

[ "1502.07058" ]

[ "en" ]

[ "### Dataset Summary\n\n\nThe RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available here.", "### Languages\n\n\nAll the classes and documents use English as their primary language.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from the training set is provided below :", "### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing a document.\n* 'label': an 'int' classification label.\n\n\n\nClass Label Mappings", "### Data Splits\n\n\n\nThe dataset was split in proportions similar to those of ImageNet.\n\n\n* 320000 images were used for training,\n* 40000 images for validation, and\n* 40000 images for testing.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFrom the paper:\n\n\n\n> \n> This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000\n> document images across 16 categories, useful for training new CNNs for document analysis.\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe same as in the IIT-CDIP collection.", "#### Who are the source language producers?\n\n\nThe same as in the IIT-CDIP collection.", "### Annotations", "#### Annotation process\n\n\nThe same as in the IIT-CDIP collection.", "#### Who are the annotators?\n\n\nThe same as in the IIT-CDIP collection.", "### 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\nThe dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis.", "### Licensing Information\n\n\nRVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.", "### Contributions\n\n\nThanks to @dnaveenr for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|iit_cdip #language-English #license-other #arxiv-1502.07058 #region-us \n", "### Dataset Summary\n\n\nThe RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available here.", "### Languages\n\n\nAll the classes and documents use English as their primary language.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from the training set is provided below :", "### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing a document.\n* 'label': an 'int' classification label.\n\n\n\nClass Label Mappings", "### Data Splits\n\n\n\nThe dataset was split in proportions similar to those of ImageNet.\n\n\n* 320000 images were used for training,\n* 40000 images for validation, and\n* 40000 images for testing.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFrom the paper:\n\n\n\n> \n> This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000\n> document images across 16 categories, useful for training new CNNs for document analysis.\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe same as in the IIT-CDIP collection.", "#### Who are the source language producers?\n\n\nThe same as in the IIT-CDIP collection.", "### Annotations", "#### Annotation process\n\n\nThe same as in the IIT-CDIP collection.", "#### Who are the annotators?\n\n\nThe same as in the IIT-CDIP collection.", "### 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\nThe dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis.", "### Licensing Information\n\n\nRVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.", "### Contributions\n\n\nThanks to @dnaveenr for adding this dataset." ]

[ "task_categories:summarization", "task_ids:news-articles-headline-generation", "annotations_creators:no-annotation", "language_creators:machine-generated", "size_categories:100K<n<1M", "source_datasets:original", "region:us" ]

[]

[]

[ "### Dataset Summary\n\n\nGoud-sum contains 158k articles and their headlines extracted from URL news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).", "### Supported Tasks and Leaderboards\n\n\nText Summarization", "### Languages\n\n\n* Moroccan Arabic (Darija)\n* Modern Standard Arabic\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe dataset consists of article-headline pairs in string format.", "### Data Fields\n\n\n* article: a string containing the body of the news article\n* headline: a string containing the article's headline\n* categories: a list of string of article categories", "### Data Splits\n\n\nGoud-sum dataset has 3 splits: *train*, *validation*, and *test*. Below are the number of instances in each split.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\nThe text was written by journalists at Goud.", "### Annotations\n\n\nThe dataset does not contain any additional annotations.", "#### Annotation process\n\n\n[N/A]", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @issam9 and @KhalilMrini for adding this dataset." ]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-headline-generation #annotations_creators-no-annotation #language_creators-machine-generated #size_categories-100K<n<1M #source_datasets-original #region-us \n", "### Dataset Summary\n\n\nGoud-sum contains 158k articles and their headlines extracted from URL news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).", "### Supported Tasks and Leaderboards\n\n\nText Summarization", "### Languages\n\n\n* Moroccan Arabic (Darija)\n* Modern Standard Arabic\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe dataset consists of article-headline pairs in string format.", "### Data Fields\n\n\n* article: a string containing the body of the news article\n* headline: a string containing the article's headline\n* categories: a list of string of article categories", "### Data Splits\n\n\nGoud-sum dataset has 3 splits: *train*, *validation*, and *test*. Below are the number of instances in each split.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\nThe text was written by journalists at Goud.", "### Annotations\n\n\nThe dataset does not contain any additional annotations.", "#### Annotation process\n\n\n[N/A]", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @issam9 and @KhalilMrini for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: SeqPlan-SportSett" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: SeqPlan-SportSett" ]

[ "region:us" ]

[]

[]

[ "## Overview\nOriginal dataset available here.", "## Dataset curation\nFilled in the empty rows of columns \"lexical semantics\", \"predicate-argument structure\", \n\"logic\", \"knowledge\" with empty string '\"\"'.\nLabels are encoded as follows", "## Code to create dataset" ]

[ "TAGS\n#region-us \n", "## Overview\nOriginal dataset available here.", "## Dataset curation\nFilled in the empty rows of columns \"lexical semantics\", \"predicate-argument structure\", \n\"logic\", \"knowledge\" with empty string '\"\"'.\nLabels are encoded as follows", "## Code to create dataset" ]

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

[]

[ "en" ]

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

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

[ "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:af", "language:ar", "language:be", "language:bg", "language:bn", "language:ca", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hu", "language:id", "language:it", "language:ja", "language:ko", "language:ml", "language:mr", "language:ms", "language:nl", "language:no", "language:pl", "language:pt", "language:ro", "language:ru", "language:si", "language:sk", "language:sl", "language:sr", "language:sv", "language:sw", "language:ta", "language:te", "language:th", "language:tr", "language:uk", "language:vi", "language:zh", "license:cc-by-4.0", "arxiv:2204.06535", "region:us" ]

[ "2204.06535" ]

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[ "### Dataset Summary\n\n\nXLEL-WD is a multilingual event linking dataset. This supplementary dataset contains a dictionary of event items from Wikidata. The descriptions for Wikidata event items are taken from the corresponding multilingual Wikipedia articles.", "### Supported Tasks and Leaderboards\n\n\nThis dictionary can be used as a part of the event linking task.", "### Languages\n\n\nThis dataset contains text from 44 languages. The language names and their ISO 639-1 codes are listed below. For details on the dataset distribution for each language, refer to the original paper.\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach instance in the 'label\\_dict.jsonl' file follows the below template,", "### Data Fields", "### Data Splits\n\n\nThis dictionary has a single split, 'dictionary'. It contains 10947 event items from Wikidata and a total of 114834 text descriptions collected from multilingual Wikipedia articles.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis datasets helps address the task of event linking. KB linking is extensively studied for entities, but its unclear if the same methodologies can be extended for linking mentions to events from KB. Event items are collected from Wikidata.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nA Wikidata item is considered a potential event if it has spatial and temporal properties. The final event set is collected after post-processing for quality control.", "#### Who are the source language producers?\n\n\nThe titles and descriptions for the events are written by Wikipedia contributors.", "### Annotations", "#### Annotation process\n\n\nThis dataset was automatically compiled from Wikidata. It was post-processed to improve data quality.", "#### Who are the annotators?\n\n\nWikidata and Wikipedia contributors.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nThis dictionary primarily contains eventive nouns from Wikidata. It does not include other event items from Wikidata such as disease outbreak (Q3241045), military offensive (Q2001676), war (Q198), etc.,\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was curated by Adithya Pratapa, Rishubh Gupta and Teruko Mitamura. The code for collecting the dataset is available at Github:xlel-wd.", "### Licensing Information\n\n\nXLEL-WD dataset is released under CC-BY-4.0 license.", "### Contributions\n\n\nThanks to @adithya7 for adding this dataset." ]

[ "TAGS\n#annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Afrikaans #language-Arabic #language-Belarusian #language-Bulgarian #language-Bengali #language-Catalan #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Persian #language-Finnish #language-French #language-Hebrew #language-Hindi #language-Hungarian #language-Indonesian #language-Italian #language-Japanese #language-Korean #language-Malayalam #language-Marathi #language-Malay (macrolanguage) #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Sinhala #language-Slovak #language-Slovenian #language-Serbian #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Thai #language-Turkish #language-Ukrainian #language-Vietnamese #language-Chinese #license-cc-by-4.0 #arxiv-2204.06535 #region-us \n", "### Dataset Summary\n\n\nXLEL-WD is a multilingual event linking dataset. This supplementary dataset contains a dictionary of event items from Wikidata. The descriptions for Wikidata event items are taken from the corresponding multilingual Wikipedia articles.", "### Supported Tasks and Leaderboards\n\n\nThis dictionary can be used as a part of the event linking task.", "### Languages\n\n\nThis dataset contains text from 44 languages. The language names and their ISO 639-1 codes are listed below. For details on the dataset distribution for each language, refer to the original paper.\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach instance in the 'label\\_dict.jsonl' file follows the below template,", "### Data Fields", "### Data Splits\n\n\nThis dictionary has a single split, 'dictionary'. It contains 10947 event items from Wikidata and a total of 114834 text descriptions collected from multilingual Wikipedia articles.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis datasets helps address the task of event linking. KB linking is extensively studied for entities, but its unclear if the same methodologies can be extended for linking mentions to events from KB. Event items are collected from Wikidata.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nA Wikidata item is considered a potential event if it has spatial and temporal properties. The final event set is collected after post-processing for quality control.", "#### Who are the source language producers?\n\n\nThe titles and descriptions for the events are written by Wikipedia contributors.", "### Annotations", "#### Annotation process\n\n\nThis dataset was automatically compiled from Wikidata. It was post-processed to improve data quality.", "#### Who are the annotators?\n\n\nWikidata and Wikipedia contributors.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nThis dictionary primarily contains eventive nouns from Wikidata. It does not include other event items from Wikidata such as disease outbreak (Q3241045), military offensive (Q2001676), war (Q198), etc.,\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was curated by Adithya Pratapa, Rishubh Gupta and Teruko Mitamura. The code for collecting the dataset is available at Github:xlel-wd.", "### Licensing Information\n\n\nXLEL-WD dataset is released under CC-BY-4.0 license.", "### Contributions\n\n\nThanks to @adithya7 for adding this dataset." ]

[ "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:af", "language:ar", "language:be", "language:bg", "language:bn", "language:ca", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hu", "language:id", "language:it", "language:ja", "language:ko", "language:ml", "language:mr", "language:ms", "language:nl", "language:no", "language:pl", "language:pt", "language:ro", "language:ru", "language:si", "language:sk", "language:sl", "language:sr", "language:sv", "language:sw", "language:ta", "language:te", "language:th", "language:tr", "language:uk", "language:vi", "language:zh", "license:cc-by-4.0", "arxiv:2204.06535", "region:us" ]

[ "2204.06535" ]

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[ "### Dataset Summary\n\n\nXLEL-WD is a multilingual event linking dataset. This dataset repo contains mention references in multilingual Wikipedia/Wikinews articles to event items from Wikidata.\n\n\nThe descriptions for Wikidata event items were collected from the corresponding Wikipedia articles. Download the event dictionary from adithya7/xlel\\_wd\\_dictionary.", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of event linking. There are two variants of the task, multilingual and crosslingual.\n\n\n* Multilingual linking: mention and the event descriptions are in the same language.\n* Crosslingual linking: the event descriptions are only available in English.", "### Languages\n\n\nThis dataset contains text from 44 languages. The language names and their ISO 639-1 codes are listed below. For details on the dataset distribution for each language, refer to the original paper.\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach instance in the 'URL', 'URL' and 'URL' files follow the below template.", "### Data Fields", "### Data Splits\n\n\nThe Wikipedia-based corpus has three splits. This is a zero-shot evaluation setup.\n\n\n\nThe Wikinews-based evaluation set has two variants, one for cross-domain evaluation and another for zero-shot evaluation.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset helps address the task of event linking. KB linking is extensively studied for entities, but its unclear if the same methodologies can be extended for linking mentions to events from KB. We use Wikidata as our KB, as it allows for linking mentions from multilingual Wikipedia and Wikinews articles.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nFirst, we utilize spatial & temporal properties from Wikidata to identify event items. Second, we identify corresponding multilingual Wikipedia pages for each Wikidata event item. Third, we pool hyperlinks from multilingual Wikipedia & Wikinews articles to these event items.", "#### Who are the source language producers?\n\n\nThe documents in XLEL-WD are written by Wikipedia and Wikinews contributors in respective languages.", "### Annotations", "#### Annotation process\n\n\nThis dataset was originally collected automatically from Wikipedia, Wikinews and Wikidata. It was post-processed to improve data quality.", "#### Who are the annotators?\n\n\nThe annotations in XLEL-WD (hyperlinks from Wikipedia/Wikinews to Wikidata) are added the original Wiki contributors.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nXLEL-WD v1.0.0 mostly caters to eventive nouns from Wikidata. It does not include any links to other event items from Wikidata such as disease outbreak (Q3241045), military offensive (Q2001676) and war (Q198).\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was curated by Adithya Pratapa, Rishubh Gupta and Teruko Mitamura. The code for collecting the dataset is available at Github:xlel-wd.", "### Licensing Information\n\n\nXLEL-WD dataset is released under CC-BY-4.0 license.", "### Contributions\n\n\nThanks to @adithya7 for adding this dataset." ]

[ "TAGS\n#annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Afrikaans #language-Arabic #language-Belarusian #language-Bulgarian #language-Bengali #language-Catalan #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Persian #language-Finnish #language-French #language-Hebrew #language-Hindi #language-Hungarian #language-Indonesian #language-Italian #language-Japanese #language-Korean #language-Malayalam #language-Marathi #language-Malay (macrolanguage) #language-Dutch #language-Norwegian #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Sinhala #language-Slovak #language-Slovenian #language-Serbian #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Thai #language-Turkish #language-Ukrainian #language-Vietnamese #language-Chinese #license-cc-by-4.0 #arxiv-2204.06535 #region-us \n", "### Dataset Summary\n\n\nXLEL-WD is a multilingual event linking dataset. This dataset repo contains mention references in multilingual Wikipedia/Wikinews articles to event items from Wikidata.\n\n\nThe descriptions for Wikidata event items were collected from the corresponding Wikipedia articles. Download the event dictionary from adithya7/xlel\\_wd\\_dictionary.", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of event linking. There are two variants of the task, multilingual and crosslingual.\n\n\n* Multilingual linking: mention and the event descriptions are in the same language.\n* Crosslingual linking: the event descriptions are only available in English.", "### Languages\n\n\nThis dataset contains text from 44 languages. The language names and their ISO 639-1 codes are listed below. For details on the dataset distribution for each language, refer to the original paper.\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach instance in the 'URL', 'URL' and 'URL' files follow the below template.", "### Data Fields", "### Data Splits\n\n\nThe Wikipedia-based corpus has three splits. This is a zero-shot evaluation setup.\n\n\n\nThe Wikinews-based evaluation set has two variants, one for cross-domain evaluation and another for zero-shot evaluation.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset helps address the task of event linking. KB linking is extensively studied for entities, but its unclear if the same methodologies can be extended for linking mentions to events from KB. We use Wikidata as our KB, as it allows for linking mentions from multilingual Wikipedia and Wikinews articles.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nFirst, we utilize spatial & temporal properties from Wikidata to identify event items. Second, we identify corresponding multilingual Wikipedia pages for each Wikidata event item. Third, we pool hyperlinks from multilingual Wikipedia & Wikinews articles to these event items.", "#### Who are the source language producers?\n\n\nThe documents in XLEL-WD are written by Wikipedia and Wikinews contributors in respective languages.", "### Annotations", "#### Annotation process\n\n\nThis dataset was originally collected automatically from Wikipedia, Wikinews and Wikidata. It was post-processed to improve data quality.", "#### Who are the annotators?\n\n\nThe annotations in XLEL-WD (hyperlinks from Wikipedia/Wikinews to Wikidata) are added the original Wiki contributors.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nXLEL-WD v1.0.0 mostly caters to eventive nouns from Wikidata. It does not include any links to other event items from Wikidata such as disease outbreak (Q3241045), military offensive (Q2001676) and war (Q198).\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was curated by Adithya Pratapa, Rishubh Gupta and Teruko Mitamura. The code for collecting the dataset is available at Github:xlel-wd.", "### Licensing Information\n\n\nXLEL-WD dataset is released under CC-BY-4.0 license.", "### Contributions\n\n\nThanks to @adithya7 for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [FrozenLake-v1] with slippery = True" ]

[ "TAGS\n#region-us \n", "# Dataset Card for [FrozenLake-v1] with slippery = True" ]

[ "region:us" ]

[]

[]

[ "## Overview\nOriginal dataset here.\n\nBelow the original description reported for convenience.\n\n\nAdapted from URL We took 'P1, ..., Pn' as premise and H as hypothesis. Labels have been mapped as follows '{'yes': \"entailment\", 'no': 'contradiction', 'undef': \"neutral\", 'unknown': \"neutral\"}'. And we randomly split 80/20 for train/dev.", "## Dataset curation\nOne hypothesis in the dev set and three hypotheses in the train set are empty and have been\nfilled in with the empty string '\"\"'. Labels are encoded with custom NLI mapping, that is", "## Code to create the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\nOriginal dataset here.\n\nBelow the original description reported for convenience.\n\n\nAdapted from URL We took 'P1, ..., Pn' as premise and H as hypothesis. Labels have been mapped as follows '{'yes': \"entailment\", 'no': 'contradiction', 'undef': \"neutral\", 'unknown': \"neutral\"}'. And we randomly split 80/20 for train/dev.", "## Dataset curation\nOne hypothesis in the dev set and three hypotheses in the train set are empty and have been\nfilled in with the empty string '\"\"'. Labels are encoded with custom NLI mapping, that is", "## Code to create the dataset" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Tatoeba", "## 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- Homepage: URL\n- Repository: None\n- Paper: URL\n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\nTatoeba is a collection of sentences and translations.\nTo load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.\nYou can find the valid pairs in Homepage section of Dataset Description: URL\nE.g.\n'dataset = load_dataset(\"tatoeba\", lang1=\"en\", lang2=\"he\")'\nThe default date is v2021-07-22, but you can also change the date with\n'dataset = load_dataset(\"tatoeba\", lang1=\"en\", lang2=\"he\", date=\"v2020-11-09\")'", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n@loretoparisi" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Tatoeba", "## 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- Homepage: URL\n- Repository: None\n- Paper: URL\n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\nTatoeba is a collection of sentences and translations.\nTo load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.\nYou can find the valid pairs in Homepage section of Dataset Description: URL\nE.g.\n'dataset = load_dataset(\"tatoeba\", lang1=\"en\", lang2=\"he\")'\nThe default date is v2021-07-22, but you can also change the date with\n'dataset = load_dataset(\"tatoeba\", lang1=\"en\", lang2=\"he\", date=\"v2020-11-09\")'", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n@loretoparisi" ]

[ "region:us" ]

[]

[]

[ "## Overview\nOriginal dataset is available on the HuggingFace Hub here.", "## Dataset curation\nThis is the same as the 'snli_format' split of the SciTail dataset available on the HuggingFace Hub (i.e., same data, same splits, etc).\nThe only differences are the following:\n\n- selecting only the columns '[\"sentence1\", \"sentence2\", \"gold_label\", \"label\"]'\n- renaming columns with the following mapping '{\"sentence1\": \"premise\", \"sentence2\": \"hypothesis\"}'\n- creating a new column \"label\" from \"gold_label\" with the following mapping '{\"entailment\": \"entailment\", \"neutral\": \"not_entailment\"}'\n- encoding labels with the following mapping '{\"not_entailment\": 0, \"entailment\": 1}'\n\nNote that there are 10 overlapping instances (as found by merging on columns \"label\", \"premise\", and \"hypothesis\") between\n'train' and 'test' splits.", "## Code to create the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\nOriginal dataset is available on the HuggingFace Hub here.", "## Dataset curation\nThis is the same as the 'snli_format' split of the SciTail dataset available on the HuggingFace Hub (i.e., same data, same splits, etc).\nThe only differences are the following:\n\n- selecting only the columns '[\"sentence1\", \"sentence2\", \"gold_label\", \"label\"]'\n- renaming columns with the following mapping '{\"sentence1\": \"premise\", \"sentence2\": \"hypothesis\"}'\n- creating a new column \"label\" from \"gold_label\" with the following mapping '{\"entailment\": \"entailment\", \"neutral\": \"not_entailment\"}'\n- encoding labels with the following mapping '{\"not_entailment\": 0, \"entailment\": 1}'\n\nNote that there are 10 overlapping instances (as found by merging on columns \"label\", \"premise\", and \"hypothesis\") between\n'train' and 'test' splits.", "## Code to create the dataset" ]

[ "task_categories:text-generation", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:multilingual", "size_categories:n<1K", "language:fr", "license:cc-by-4.0", "region:us" ]

[]

[ "fr" ]

[]

[ "TAGS\n#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-multilingual #size_categories-n<1K #language-French #license-cc-by-4.0 #region-us \n" ]

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

[]

[ "ar" ]

[ "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

[ "TAGS\n#language-Arabic #license-cc-by-4.0 #region-us \n", "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

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

[]

[ "en" ]

[ "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

[ "TAGS\n#language-English #license-cc-by-4.0 #region-us \n", "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

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

[]

[ "es" ]

[ "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

[ "TAGS\n#language-Spanish #license-cc-by-4.0 #region-us \n", "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

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

[]

[ "fr" ]

[ "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

[ "TAGS\n#language-French #license-cc-by-4.0 #region-us \n", "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

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

[]

[ "zh" ]

[ "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

[ "TAGS\n#language-Chinese #license-cc-by-4.0 #region-us \n", "# uncorpus\n\n- Dataset uid: 'uncorpus'", "### Description", "### Homepage", "### Licensing", "### Speaker Locations", "### Sizes\n\n- 2.8023 % of total\n- 10.7390 % of ar\n- 5.7970 % of fr\n- 9.7477 % of es\n- 2.0417 % of en\n- 1.2540 % of zh", "### BigScience processing steps", "#### Filters applied to: ar\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_300", "#### Filters applied to: fr\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: es\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: en\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024", "#### Filters applied to: zh\n\n- dedup_document\n- filter_remove_empty_docs\n- filter_small_docs_bytes_1024" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:hi", "license:cc-by-sa-4.0", "arxiv:2204.13743", "region:us" ]

[ "2204.13743" ]

[ "hi" ]

[ "### Dataset Summary\n\n\nThis dataset was created for the fundamental NLP task of Named Entity Recognition for the Hindi language at CFILT Lab, IIT Bombay. We gathered the dataset from various government information webpages and manually annotated these sentences as a part of our data collection strategy.\n\n\nNote: The dataset contains sentences from ILCI and other sources. ILCI dataset requires license from Indian Language Consortium due to which we do not distribute the ILCI portion of the data. Please send us a mail with proof of ILCI data acquisition to obtain the full dataset.", "### Supported Tasks and Leaderboards\n\n\nNamed Entity Recognition", "### Languages\n\n\nHindi\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n{'id': '0', 'tokens': ['प्राचीन', 'समय', 'में', 'उड़ीसा', 'को', 'कलिंग', 'के', 'नाम', 'से', 'जाना', 'जाता', 'था', '।'], 'ner\\_tags': [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0]}", "### Data Fields\n\n\n* 'id': The ID value of the data point.\n* 'tokens': Raw tokens in the dataset.\n* 'ner\\_tags': the NER tags for this dataset.", "### Data Splits\n\n\n\nAbout\n-----\n\n\nThis repository contains the Hindi Named Entity Recognition dataset (HiNER) published at the Langauge Resources and Evaluation conference (LREC) in 2022. A pre-print via arXiv is available here.", "### Recent Updates\n\n\n* Version 0.0.5: HiNER initial release\n\n\nUsage\n-----\n\n\nYou should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:\n\n\nTo use the original dataset with all the tags, please use: \n\n\n\nTo use the collapsed dataset with only PER, LOC, and ORG tags, please use: \n\n\n\nHowever, the CoNLL format dataset files can also be found on this Git repository under the data folder.\n\n\nModel(s)\n--------\n\n\nOur best performing models are hosted on the HuggingFace models repository:\n\n\n1. HiNER-Collapsed-XLM-R\n2. HiNER-Original-XLM-R\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nHiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi. This dataset was built for the task of Named Entity Recognition. The dataset was introduced to introduce new resources to the Hindi language that was under-served for Natural Language Processing.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nHiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi", "#### Who are the source language producers?\n\n\nVarious Government of India webpages", "### Annotations", "#### Annotation process\n\n\nThis dataset was manually annotated by a single annotator of a long span of time.", "#### Who are the annotators?\n\n\nPallab Bhattacharjee", "### Personal and Sensitive Information\n\n\nWe ensured that there was no sensitive information present in the dataset. All the data points are curated from publicly available information.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to provide a large Hindi Named Entity Recognition dataset. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.", "### Discussion of Biases\n\n\nAny biases contained in the data released by the Indian government are bound to be present in our data.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nPallab Bhattacharjee", "### Licensing Information\n\n\nCC-BY-SA 4.0" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Hindi #license-cc-by-sa-4.0 #arxiv-2204.13743 #region-us \n", "### Dataset Summary\n\n\nThis dataset was created for the fundamental NLP task of Named Entity Recognition for the Hindi language at CFILT Lab, IIT Bombay. We gathered the dataset from various government information webpages and manually annotated these sentences as a part of our data collection strategy.\n\n\nNote: The dataset contains sentences from ILCI and other sources. ILCI dataset requires license from Indian Language Consortium due to which we do not distribute the ILCI portion of the data. Please send us a mail with proof of ILCI data acquisition to obtain the full dataset.", "### Supported Tasks and Leaderboards\n\n\nNamed Entity Recognition", "### Languages\n\n\nHindi\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n{'id': '0', 'tokens': ['प्राचीन', 'समय', 'में', 'उड़ीसा', 'को', 'कलिंग', 'के', 'नाम', 'से', 'जाना', 'जाता', 'था', '।'], 'ner\\_tags': [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0]}", "### Data Fields\n\n\n* 'id': The ID value of the data point.\n* 'tokens': Raw tokens in the dataset.\n* 'ner\\_tags': the NER tags for this dataset.", "### Data Splits\n\n\n\nAbout\n-----\n\n\nThis repository contains the Hindi Named Entity Recognition dataset (HiNER) published at the Langauge Resources and Evaluation conference (LREC) in 2022. A pre-print via arXiv is available here.", "### Recent Updates\n\n\n* Version 0.0.5: HiNER initial release\n\n\nUsage\n-----\n\n\nYou should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:\n\n\nTo use the original dataset with all the tags, please use: \n\n\n\nTo use the collapsed dataset with only PER, LOC, and ORG tags, please use: \n\n\n\nHowever, the CoNLL format dataset files can also be found on this Git repository under the data folder.\n\n\nModel(s)\n--------\n\n\nOur best performing models are hosted on the HuggingFace models repository:\n\n\n1. HiNER-Collapsed-XLM-R\n2. HiNER-Original-XLM-R\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nHiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi. This dataset was built for the task of Named Entity Recognition. The dataset was introduced to introduce new resources to the Hindi language that was under-served for Natural Language Processing.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nHiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi", "#### Who are the source language producers?\n\n\nVarious Government of India webpages", "### Annotations", "#### Annotation process\n\n\nThis dataset was manually annotated by a single annotator of a long span of time.", "#### Who are the annotators?\n\n\nPallab Bhattacharjee", "### Personal and Sensitive Information\n\n\nWe ensured that there was no sensitive information present in the dataset. All the data points are curated from publicly available information.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to provide a large Hindi Named Entity Recognition dataset. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.", "### Discussion of Biases\n\n\nAny biases contained in the data released by the Indian government are bound to be present in our data.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nPallab Bhattacharjee", "### Licensing Information\n\n\nCC-BY-SA 4.0" ]

[ "task_categories:text-generation", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-monolingual #size_categories-n<1K #language-English #license-cc-by-4.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "## Overview\n\nOriginal dataset here.", "## Dataset curation\nSame data and splits as the original. The following columns have been added:\n\n- 'premise': concatenation of 'premise1', 'premise2', 'premise3', and 'premise4'\n- 'label': encoded 'gold_label' with the following mapping '{\"entailment\": 0, \"neutral\": 1, \"contradiction\": 2}'", "## Code to create the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\n\nOriginal dataset here.", "## Dataset curation\nSame data and splits as the original. The following columns have been added:\n\n- 'premise': concatenation of 'premise1', 'premise2', 'premise3', and 'premise4'\n- 'label': encoded 'gold_label' with the following mapping '{\"entailment\": 0, \"neutral\": 1, \"contradiction\": 2}'", "## Code to create the dataset" ]

[ "region:us" ]

[]

[]

[ "## Overview\n\nOriginal data available here.", "## Dataset curation\n\n'premise' and 'hypothesis' columns have been cleaned following common practices (1, 2), that is\n\n- remove HTML tags '<b>', '<u>', '</b>', '</u>'\n- normalize repeated white spaces\n- strip\n\n'mean_human_score' has been transformed into class labels following common practices (1, 2), that is\n\n- for test set: 'mean_human_score <= 3 -> \"not-entailed\"' and 'mean_human_score >= 4 -> \"entailed\"' (anything between 3 and 4 has been removed)\n- for all other splits: 'mean_human_score < 3.5 -> \"not-entailed\"' else '\"entailed\"'\n\nmore details below.", "## Code to generate the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\n\nOriginal data available here.", "## Dataset curation\n\n'premise' and 'hypothesis' columns have been cleaned following common practices (1, 2), that is\n\n- remove HTML tags '<b>', '<u>', '</b>', '</u>'\n- normalize repeated white spaces\n- strip\n\n'mean_human_score' has been transformed into class labels following common practices (1, 2), that is\n\n- for test set: 'mean_human_score <= 3 -> \"not-entailed\"' and 'mean_human_score >= 4 -> \"entailed\"' (anything between 3 and 4 has been removed)\n- for all other splits: 'mean_human_score < 3.5 -> \"not-entailed\"' else '\"entailed\"'\n\nmore details below.", "## Code to generate the dataset" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "license:unknown", "region:us" ]

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[ "en-US" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact: Andrés Pitta: URL@URL", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #license-unknown #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact: Andrés Pitta: URL@URL", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "region:us" ]

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[ "## Overview\n\nThis dataset has been introduced by \"Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework\", Aaron Steven White, Pushpendre Rastogi, Kevin Duh, Benjamin Van Durme. IJCNLP, 2017. Original data available here.", "## Dataset curation\nThe following processing is applied\n\n- 'hypothesis_grammatical' and 'judgement_valid' columns are filled with '\"\"' when empty\n- all columns are stripped\n- the 'entailed' column is renamed 'label'\n- 'label' column is encoded with the following mapping '{\"not-entailed\": 0, \"entailed\": 1}'\n- columns 'rating' and 'good_word' are dropped from 'fnplus' dataset", "## Code to generate the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\n\nThis dataset has been introduced by \"Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework\", Aaron Steven White, Pushpendre Rastogi, Kevin Duh, Benjamin Van Durme. IJCNLP, 2017. Original data available here.", "## Dataset curation\nThe following processing is applied\n\n- 'hypothesis_grammatical' and 'judgement_valid' columns are filled with '\"\"' when empty\n- all columns are stripped\n- the 'entailed' column is renamed 'label'\n- 'label' column is encoded with the following mapping '{\"not-entailed\": 0, \"entailed\": 1}'\n- columns 'rating' and 'good_word' are dropped from 'fnplus' dataset", "## Code to generate the dataset" ]

[ "region:us" ]

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[]

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

[ "region:us" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for dei_article_sentiment", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nDiversity Equity and Inclusion related article title, content, url, sentiment and basis. Basis is a term I use to describe the underline topic related to diveristy I have four at the moment 1 = Gender, 2 = Race, 3 = Disability and 4 = Other.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nID\nTitle\nContent\nBasis \nURL\nSentiment", "### Data Splits\n\ntrain\nvalidate", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#region-us \n", "# Dataset Card for dei_article_sentiment", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nDiversity Equity and Inclusion related article title, content, url, sentiment and basis. Basis is a term I use to describe the underline topic related to diveristy I have four at the moment 1 = Gender, 2 = Race, 3 = Disability and 4 = Other.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nID\nTitle\nContent\nBasis \nURL\nSentiment", "### Data Splits\n\ntrain\nvalidate", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "region:us" ]

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[ "# Dataset Card for [IU Ontology Trahsed]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for [IU Ontology Trahsed]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "region:us" ]

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[]

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

[ "region:us" ]

[]

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