Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:intent-classification", "task_ids:multi-class-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:af", "language:am", "language:ar", "language:az", "language:bn", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:es", "language:fa", "language:fi", "language:fr", "language:he", "language:hi", "language:hu", "language:hy", "language:id", "language:is", "language:it", "language:ja", "language:jv", "language:ka", "language:km", "language:kn", "language:ko", "language:lv", "language:ml", "language:mn", "language:ms", "language:my", "language:nb", "language:nl", "language:pl", "language:pt", "language:ro", "language:ru", "language:sl", "language:sq", "language:sv", "language:sw", "language:ta", "language:te", "language:th", "language:tl", "language:tr", "language:ur", "language:vi", "language:zh", "arxiv:2204.08582", "region:us" ]

[ "2204.08582" ]

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[ "### Dataset Summary\n\n\nMASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.", "### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for 'natural-language-understanding' (NLU) :\n\n\n* 'intent-classification'\n* 'multi-class-classification'\n* 'natural-language-understanding'", "### Languages\n\n\nThe corpora consists of parallel sentences from 51 languages :\n\n\n* 'Afrikaans - South Africa (af-ZA)'\n* 'Amharic - Ethiopia (am-ET)'\n* 'Arabic - Saudi Arabia (ar-SA)'\n* 'Azeri - Azerbaijan (az-AZ)'\n* 'Bengali - Bangladesh (bn-BD)'\n* 'Chinese - China (zh-CN)'\n* 'Chinese - Taiwan (zh-TW)'\n* 'Danish - Denmark (da-DK)'\n* 'German - Germany (de-DE)'\n* 'Greek - Greece (el-GR)'\n* 'English - United States (en-US)'\n* 'Spanish - Spain (es-ES)'\n* 'Farsi - Iran (fa-IR)'\n* 'Finnish - Finland (fi-FI)'\n* 'French - France (fr-FR)'\n* 'Hebrew - Israel (he-IL)'\n* 'Hungarian - Hungary (hu-HU)'\n* 'Armenian - Armenia (hy-AM)'\n* 'Indonesian - Indonesia (id-ID)'\n* 'Icelandic - Iceland (is-IS)'\n* 'Italian - Italy (it-IT)'\n* 'Japanese - Japan (ja-JP)'\n* 'Javanese - Indonesia (jv-ID)'\n* 'Georgian - Georgia (ka-GE)'\n* 'Khmer - Cambodia (km-KH)'\n* 'Korean - Korea (ko-KR)'\n* 'Latvian - Latvia (lv-LV)'\n* 'Mongolian - Mongolia (mn-MN)'\n* 'Malay - Malaysia (ms-MY)'\n* 'Burmese - Myanmar (my-MM)'\n* 'Norwegian - Norway (nb-NO)'\n* 'Dutch - Netherlands (nl-NL)'\n* 'Polish - Poland (pl-PL)'\n* 'Portuguese - Portugal (pt-PT)'\n* 'Romanian - Romania (ro-RO)'\n* 'Russian - Russia (ru-RU)'\n* 'Slovanian - Slovania (sl-SL)'\n* 'Albanian - Albania (sq-AL)'\n* 'Swedish - Sweden (sv-SE)'\n* 'Swahili - Kenya (sw-KE)'\n* 'Hindi - India (hi-IN)'\n* 'Kannada - India (kn-IN)'\n* 'Malayalam - India (ml-IN)'\n* 'Tamil - India (ta-IN)'\n* 'Telugu - India (te-IN)'\n* 'Thai - Thailand (th-TH)'\n* 'Tagalog - Philippines (tl-PH)'\n* 'Turkish - Turkey (tr-TR)'\n* 'Urdu - Pakistan (ur-PK)'\n* 'Vietnamese - Vietnam (vi-VN)'\n* 'Welsh - United Kingdom (cy-GB)'\n\n\nLoad the dataset with HuggingFace\n---------------------------------\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields (taken from Alexa Github)\n\n\n'id': maps to the original ID in the SLURP collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization.\n\n\n'locale': is the language and country code accoring to ISO-639-1 and ISO-3166.\n\n\n'partition': is either 'train', 'dev', or 'test', according to the original split in SLURP.\n\n\n'scenario': is the general domain, aka \"scenario\" in SLURP terminology, of an utterance\n\n\n'intent': is the specific intent of an utterance within a domain formatted as '{scenario}\\_{intent}'\n\n\n'utt': the raw utterance text without annotations\n\n\n'annot\\_utt': the text from 'utt' with slot annotations formatted as '[{label} : {entity}]'\n\n\n'worker\\_id': The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales.\n\n\n'slot\\_method': for each slot in the utterance, whether that slot was a 'translation' (i.e., same expression just in the target language), 'localization' (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or 'unchanged' (i.e., the original en-US slot value was copied over without modification).\n\n\n'judgments': Each judgment collected for the localized utterance has 6 keys. 'worker\\_id' is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Who are the source language producers?\n\n\nThe corpus has been produced and uploaded by Amazon Alexa.", "### Personal and Sensitive Information\n\n\nThe corpora is free of personal or sensitive information.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n**MASSIVE**: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan.\n\n\n**SLURP**: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena.\n\n\n**Hugging Face**: Labrak Yanis (Not affiliated with the original corpus)", "### Licensing Information\n\n\nPlease cite the following paper when using this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-Afrikaans #language-Amharic #language-Arabic #language-Azerbaijani #language-Bengali #language-Welsh #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-Armenian #language-Indonesian #language-Icelandic #language-Italian #language-Japanese #language-Javanese #language-Georgian #language-Khmer #language-Kannada #language-Korean #language-Latvian #language-Malayalam #language-Mongolian #language-Malay (macrolanguage) #language-Burmese #language-Norwegian Bokmål #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovenian #language-Albanian #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Thai #language-Tagalog #language-Turkish #language-Urdu #language-Vietnamese #language-Chinese #arxiv-2204.08582 #region-us \n", "### Dataset Summary\n\n\nMASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.", "### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for 'natural-language-understanding' (NLU) :\n\n\n* 'intent-classification'\n* 'multi-class-classification'\n* 'natural-language-understanding'", "### Languages\n\n\nThe corpora consists of parallel sentences from 51 languages :\n\n\n* 'Afrikaans - South Africa (af-ZA)'\n* 'Amharic - Ethiopia (am-ET)'\n* 'Arabic - Saudi Arabia (ar-SA)'\n* 'Azeri - Azerbaijan (az-AZ)'\n* 'Bengali - Bangladesh (bn-BD)'\n* 'Chinese - China (zh-CN)'\n* 'Chinese - Taiwan (zh-TW)'\n* 'Danish - Denmark (da-DK)'\n* 'German - Germany (de-DE)'\n* 'Greek - Greece (el-GR)'\n* 'English - United States (en-US)'\n* 'Spanish - Spain (es-ES)'\n* 'Farsi - Iran (fa-IR)'\n* 'Finnish - Finland (fi-FI)'\n* 'French - France (fr-FR)'\n* 'Hebrew - Israel (he-IL)'\n* 'Hungarian - Hungary (hu-HU)'\n* 'Armenian - Armenia (hy-AM)'\n* 'Indonesian - Indonesia (id-ID)'\n* 'Icelandic - Iceland (is-IS)'\n* 'Italian - Italy (it-IT)'\n* 'Japanese - Japan (ja-JP)'\n* 'Javanese - Indonesia (jv-ID)'\n* 'Georgian - Georgia (ka-GE)'\n* 'Khmer - Cambodia (km-KH)'\n* 'Korean - Korea (ko-KR)'\n* 'Latvian - Latvia (lv-LV)'\n* 'Mongolian - Mongolia (mn-MN)'\n* 'Malay - Malaysia (ms-MY)'\n* 'Burmese - Myanmar (my-MM)'\n* 'Norwegian - Norway (nb-NO)'\n* 'Dutch - Netherlands (nl-NL)'\n* 'Polish - Poland (pl-PL)'\n* 'Portuguese - Portugal (pt-PT)'\n* 'Romanian - Romania (ro-RO)'\n* 'Russian - Russia (ru-RU)'\n* 'Slovanian - Slovania (sl-SL)'\n* 'Albanian - Albania (sq-AL)'\n* 'Swedish - Sweden (sv-SE)'\n* 'Swahili - Kenya (sw-KE)'\n* 'Hindi - India (hi-IN)'\n* 'Kannada - India (kn-IN)'\n* 'Malayalam - India (ml-IN)'\n* 'Tamil - India (ta-IN)'\n* 'Telugu - India (te-IN)'\n* 'Thai - Thailand (th-TH)'\n* 'Tagalog - Philippines (tl-PH)'\n* 'Turkish - Turkey (tr-TR)'\n* 'Urdu - Pakistan (ur-PK)'\n* 'Vietnamese - Vietnam (vi-VN)'\n* 'Welsh - United Kingdom (cy-GB)'\n\n\nLoad the dataset with HuggingFace\n---------------------------------\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields (taken from Alexa Github)\n\n\n'id': maps to the original ID in the SLURP collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization.\n\n\n'locale': is the language and country code accoring to ISO-639-1 and ISO-3166.\n\n\n'partition': is either 'train', 'dev', or 'test', according to the original split in SLURP.\n\n\n'scenario': is the general domain, aka \"scenario\" in SLURP terminology, of an utterance\n\n\n'intent': is the specific intent of an utterance within a domain formatted as '{scenario}\\_{intent}'\n\n\n'utt': the raw utterance text without annotations\n\n\n'annot\\_utt': the text from 'utt' with slot annotations formatted as '[{label} : {entity}]'\n\n\n'worker\\_id': The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales.\n\n\n'slot\\_method': for each slot in the utterance, whether that slot was a 'translation' (i.e., same expression just in the target language), 'localization' (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or 'unchanged' (i.e., the original en-US slot value was copied over without modification).\n\n\n'judgments': Each judgment collected for the localized utterance has 6 keys. 'worker\\_id' is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Who are the source language producers?\n\n\nThe corpus has been produced and uploaded by Amazon Alexa.", "### Personal and Sensitive Information\n\n\nThe corpora is free of personal or sensitive information.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n**MASSIVE**: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan.\n\n\n**SLURP**: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena.\n\n\n**Hugging Face**: Labrak Yanis (Not affiliated with the original corpus)", "### Licensing Information\n\n\nPlease cite the following paper when using this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "arxiv:1704.05426", "region:us" ]

[ "1704.05426" ]

[]

[ "# Dataset Card for [Kaggle MNLI]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL \n- Repository: chrishuber/roberta-retrained-mlni \n- Paper: Inference Detection in NLP Using the MultiNLI and SNLI Datasets\n- Leaderboard: 8\n- Point of Contact: chrish@URL", "### Dataset Summary\n\n[These are the datasets posted to Kaggle for an inference detection NLP competition. Moving them here to use with Pytorch.]", "### Supported Tasks and Leaderboards\n\nProvides train and validation data for sentence pairs with inference labels.\n[URL\n[URL", "### Languages\n\n[JSON, Python]", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\n[Reposted from URL and URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n[Please see the article at URL which discusses the creation of the MNLI dataset.]", "#### Who are the source language producers?\n\n[Please see the article at URL which discusses the creation of the MNLI dataset.]", "### Annotations", "#### Annotation process\n\n[Crowdsourcing using MechanicalTurk.]", "#### Who are the annotators?\n\n[MechanicalTurk users.]", "### Personal and Sensitive Information\n\n[None.]", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\n[Kaggle]", "### Licensing Information\n\n\n\n\n\n[URL\n[URL", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#arxiv-1704.05426 #region-us \n", "# Dataset Card for [Kaggle MNLI]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL \n- Repository: chrishuber/roberta-retrained-mlni \n- Paper: Inference Detection in NLP Using the MultiNLI and SNLI Datasets\n- Leaderboard: 8\n- Point of Contact: chrish@URL", "### Dataset Summary\n\n[These are the datasets posted to Kaggle for an inference detection NLP competition. Moving them here to use with Pytorch.]", "### Supported Tasks and Leaderboards\n\nProvides train and validation data for sentence pairs with inference labels.\n[URL\n[URL", "### Languages\n\n[JSON, Python]", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\n[Reposted from URL and URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n[Please see the article at URL which discusses the creation of the MNLI dataset.]", "#### Who are the source language producers?\n\n[Please see the article at URL which discusses the creation of the MNLI dataset.]", "### Annotations", "#### Annotation process\n\n[Crowdsourcing using MechanicalTurk.]", "#### Who are the annotators?\n\n[MechanicalTurk users.]", "### Personal and Sensitive Information\n\n[None.]", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\n[Kaggle]", "### Licensing Information\n\n\n\n\n\n[URL\n[URL", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:original", "language:sn", "language:en", "license:cc-by-3.0", "region:us" ]

[]

[ "sn", "en" ]

[ "## Dataset Description\n\n- Homepage: None\n- Repository: None\n- Paper: None\n- Leaderboard: \n- Point of Contact:" ]

[ "TAGS\n#language_creators-found #multilinguality-translation #size_categories-1K<n<10K #source_datasets-original #language-Shona #language-English #license-cc-by-3.0 #region-us \n", "## Dataset Description\n\n- Homepage: None\n- Repository: None\n- Paper: None\n- Leaderboard: \n- Point of Contact:" ]

[ "task_categories:conversational", "task_categories:text-generation", "task_ids:dialogue-modeling", "annotations_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100k", "language:en", "license:mit", "faithful-dialogue-modeling", "trustworthy-dialogue-modeling", "arxiv:2204.10757", "region:us" ]

[ "2204.10757" ]

[ "en" ]

[ "## Dataset Summary\nFaithDial is a faithful knowledge-grounded dialogue benchmark, composed of 50,761 turns spanning 5649 conversations. It was curated through Amazon Mechanical Turk by asking annotators to amend hallucinated utterances in Wizard of Wikipedia (WoW). In our dialogue setting, we simulate interactions between two speakers: an information seeker and a bot wizard. The seeker has a large degree of freedom as opposed to the wizard bot which is more restricted on what it can communicate. In fact, it must abide by the following rules: \n- First, it should be truthful by providing information that is attributable to the source knowledge *K*. \n- Second, it should provide information conversationally, i.e., use naturalistic phrasing of *K*, support follow-on discussion with questions, and prompt user's opinions. \n- Third, it should acknowledge its ignorance of the answer in those cases where *K* does not include it while still moving the conversation forward using *K*.", "## Dataset Description\n\n- Homepage: FaithDial\n- Repository: GitHub\n- Point of Contact: Nouha Dziri", "## Language\nEnglish", "## Data Instance\n\nAn example of 'train' looks as follows: \n\nIf the 'original_response' is empty, it means that the response is faithful to the source and we consider it as a FaithDial response. Faithful responses in WoW are also edited slightly if they are found to have some grammatical issues or typos.", "## Data Fields\n\n - 'history': 'List[string]'. The dialogue history.\n - 'knowledge': 'string'. The source knowkedge on which the bot wizard should ground its response.\n - 'speaker': 'string'. The current speaker.\n - 'original response': 'string'. The WoW original response before editing it.\n - 'response': 'string'. The new Wizard response.\n - 'BEGIN': 'List[string]'. The BEGIN labels for the Wizard response.\n - 'VRM': 'List[string]'. The VRM labels for the wizard response.", "## Data Splits\n\n- 'Train': 36809 turns\n- 'Valid': 6851 turns\n- 'Test': 7101 turns\n\n'Valid' includes both the 'seen' and the 'unseen' data splits from WoW. The same applies to 'Test'. We also include those splits for FaithDial valid and test data.", "## Annotations\nFollowing the guidelines for ethical crowdsourcing outlined in Sheehan. 2018, \nwe hire Amazon Mechanical Turk (AMT) workers to edit utterances in WoW dialogues that were found to exhibit unfaithful responses. To ensure clarity in the task definition, we provided detailed examples for our terminology. Moreover, we performed several staging rounds over the course of several months.", "# Who are the annotators?\n\nTo be eligible for the task, workers have to be located in the United States and Canada and have to answer successfully 20 questions as part of a qualification test. Before launching the main annotation task, we perform a small pilot round (60 HITS) to check the performance of the workers. We email workers who commit errors, providing them with examples on how to fix their mistakes in future HITS.", "## Personal and Sensitive Information\nSeeker utterances in FaithDial may contain personal and sensitive information.", "## Social Impact of Dataset\n In recent years, the conversational AI market has seen\na proliferation of a variety of applications—which are powered by large pre-trained LMs—that span\nacross a broad range of domains, such as customer\nsupport, education, e-commerce, health, entertainment, etc. Ensuring that\nthese systems are trustworthy is key to deploy systems safely at a large scale in real-world application, especially in high-stake domain. FaithDial holds promise to encourage faithfulness in information-seeking dialogue and make virtual assistants both safer and more reliable.", "## Licensing Information\n\nMIT" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #task_ids-dialogue-modeling #annotations_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100k #language-English #license-mit #faithful-dialogue-modeling #trustworthy-dialogue-modeling #arxiv-2204.10757 #region-us \n", "## Dataset Summary\nFaithDial is a faithful knowledge-grounded dialogue benchmark, composed of 50,761 turns spanning 5649 conversations. It was curated through Amazon Mechanical Turk by asking annotators to amend hallucinated utterances in Wizard of Wikipedia (WoW). In our dialogue setting, we simulate interactions between two speakers: an information seeker and a bot wizard. The seeker has a large degree of freedom as opposed to the wizard bot which is more restricted on what it can communicate. In fact, it must abide by the following rules: \n- First, it should be truthful by providing information that is attributable to the source knowledge *K*. \n- Second, it should provide information conversationally, i.e., use naturalistic phrasing of *K*, support follow-on discussion with questions, and prompt user's opinions. \n- Third, it should acknowledge its ignorance of the answer in those cases where *K* does not include it while still moving the conversation forward using *K*.", "## Dataset Description\n\n- Homepage: FaithDial\n- Repository: GitHub\n- Point of Contact: Nouha Dziri", "## Language\nEnglish", "## Data Instance\n\nAn example of 'train' looks as follows: \n\nIf the 'original_response' is empty, it means that the response is faithful to the source and we consider it as a FaithDial response. Faithful responses in WoW are also edited slightly if they are found to have some grammatical issues or typos.", "## Data Fields\n\n - 'history': 'List[string]'. The dialogue history.\n - 'knowledge': 'string'. The source knowkedge on which the bot wizard should ground its response.\n - 'speaker': 'string'. The current speaker.\n - 'original response': 'string'. The WoW original response before editing it.\n - 'response': 'string'. The new Wizard response.\n - 'BEGIN': 'List[string]'. The BEGIN labels for the Wizard response.\n - 'VRM': 'List[string]'. The VRM labels for the wizard response.", "## Data Splits\n\n- 'Train': 36809 turns\n- 'Valid': 6851 turns\n- 'Test': 7101 turns\n\n'Valid' includes both the 'seen' and the 'unseen' data splits from WoW. The same applies to 'Test'. We also include those splits for FaithDial valid and test data.", "## Annotations\nFollowing the guidelines for ethical crowdsourcing outlined in Sheehan. 2018, \nwe hire Amazon Mechanical Turk (AMT) workers to edit utterances in WoW dialogues that were found to exhibit unfaithful responses. To ensure clarity in the task definition, we provided detailed examples for our terminology. Moreover, we performed several staging rounds over the course of several months.", "# Who are the annotators?\n\nTo be eligible for the task, workers have to be located in the United States and Canada and have to answer successfully 20 questions as part of a qualification test. Before launching the main annotation task, we perform a small pilot round (60 HITS) to check the performance of the workers. We email workers who commit errors, providing them with examples on how to fix their mistakes in future HITS.", "## Personal and Sensitive Information\nSeeker utterances in FaithDial may contain personal and sensitive information.", "## Social Impact of Dataset\n In recent years, the conversational AI market has seen\na proliferation of a variety of applications—which are powered by large pre-trained LMs—that span\nacross a broad range of domains, such as customer\nsupport, education, e-commerce, health, entertainment, etc. Ensuring that\nthese systems are trustworthy is key to deploy systems safely at a large scale in real-world application, especially in high-stake domain. FaithDial holds promise to encourage faithfulness in information-seeking dialogue and make virtual assistants both safer and more reliable.", "## Licensing Information\n\nMIT" ]

[ "task_categories:audio-classification", "annotations_creators:unknown", "language_creators:unknown", "size_categories:n<1K", "source_datasets:unknown", "license:unknown", "audio-slot-filling", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-audio-classification #annotations_creators-unknown #language_creators-unknown #size_categories-n<1K #source_datasets-unknown #license-unknown #audio-slot-filling #region-us \n" ]

[ "task_categories:summarization", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-3.0", "legal", "region:us" ]

[]

[ "en" ]

[ "## Dataset Description\n- Homepage: None\n- Repository: [URL\n- Paper: None\n- Leaderboard: \n- Point of Contact:" ]

[ "TAGS\n#task_categories-summarization #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-3.0 #legal #region-us \n", "## Dataset Description\n- Homepage: None\n- Repository: [URL\n- Paper: None\n- Leaderboard: \n- Point of Contact:" ]

[ "region:us" ]

[]

[]

[ "## Overview\nOriginal dataset available here.", "## Dataset curation\nOriginal 'label' column is renamed 'original_label'. The original classes are renamed as follows\n\n\n\nand encoded with the following mapping\n\n\n\nand stored in the newly created column 'label'.\n\n\nThe following splits and the corresponding columns are present in the original files\n\n\n\nNote that I only keep the common columns, which means that I drop \"annotation{1, 2, 3}\" from 'verified_test'.\nNote that there are some splits with the same instances, as found by matching on \"original_label\", \"sentence1\", \"sentence2\".", "## Code to create dataset" ]

[ "TAGS\n#region-us \n", "## Overview\nOriginal dataset available here.", "## Dataset curation\nOriginal 'label' column is renamed 'original_label'. The original classes are renamed as follows\n\n\n\nand encoded with the following mapping\n\n\n\nand stored in the newly created column 'label'.\n\n\nThe following splits and the corresponding columns are present in the original files\n\n\n\nNote that I only keep the common columns, which means that I drop \"annotation{1, 2, 3}\" from 'verified_test'.\nNote that there are some splits with the same instances, as found by matching on \"original_label\", \"sentence1\", \"sentence2\".", "## Code to create dataset" ]

[ "region:us" ]

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[ "## Overview\nOriginal dataset here.\n\nThis dataset has been proposed in Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation.", "## Dataset curation\nThis version of the dataset does not include the 'type-of-inference' \"KG\" as its label set is \n'[1, 2, 3, 4, 5]' while here we focus on NLI-related label sets, i.e. '[entailed, not-entailed]'.\nFor this reason, I named the dataset DNLI for _Diverse_ NLI, as in Liu et al 2020, instead of DNC.\n\nThis version of the dataset contains columns from the '*_data.json' and the '*_metadata.json' files available in the repo.\nIn the original repo, each data file has the following keys and values:\n\n- 'context': The context sentence for the NLI pair. The context is already tokenized.\n- 'hypothesis': The hypothesis sentence for the NLI pair. The hypothesis is already tokenized.\n- 'label': The label for the NLI pair\n- 'label-set': The set of possible labels for the specific NLI pair\n- 'binary-label': A 'True' or 'False' label. See the paper for details on how we convert the 'label' into a binary label.\n- 'split': This can be 'train', 'dev', or 'test'.\n- 'type-of-inference': A string indicating what type of inference is tested in this example.\n- 'pair-id': A unique integer id for the NLI pair. The 'pair-id' is used to find the corresponding metadata for any given NLI pair\n\nwhile each metadata file has the following columns\n\n- 'pair-id': A unique integer id for the NLI pair. \n- 'corpus': The original corpus where this example came from.\n- 'corpus-sent-id': The id of the sentence (or example) in the original dataset that we recast.\n- 'corpus-license': The license for the data from the original dataset.\n- 'creation-approach': Determines the method used to recast this example. Options are 'automatic', 'manual', or 'human-labeled'.\n- 'misc': A dictionary of other relevant information. This is an optional field.\n\nThe files are merged on the 'pair-id' key. I do not include the 'misc' column as it is not essential for NLI.\n\nNOTE: the label mapping is not the custom (i.e., 3 class) for NLI tasks. They used a binary target and I encoded them\nwith the following mapping '{\"not-entailed\": 0, \"entailed\": 1}'.\n\nNOTE: some instances are present in multiple splits (matching performed by exact matching on \"context\", \"hypothesis\", and \"label\").", "## Code to create the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\nOriginal dataset here.\n\nThis dataset has been proposed in Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation.", "## Dataset curation\nThis version of the dataset does not include the 'type-of-inference' \"KG\" as its label set is \n'[1, 2, 3, 4, 5]' while here we focus on NLI-related label sets, i.e. '[entailed, not-entailed]'.\nFor this reason, I named the dataset DNLI for _Diverse_ NLI, as in Liu et al 2020, instead of DNC.\n\nThis version of the dataset contains columns from the '*_data.json' and the '*_metadata.json' files available in the repo.\nIn the original repo, each data file has the following keys and values:\n\n- 'context': The context sentence for the NLI pair. The context is already tokenized.\n- 'hypothesis': The hypothesis sentence for the NLI pair. The hypothesis is already tokenized.\n- 'label': The label for the NLI pair\n- 'label-set': The set of possible labels for the specific NLI pair\n- 'binary-label': A 'True' or 'False' label. See the paper for details on how we convert the 'label' into a binary label.\n- 'split': This can be 'train', 'dev', or 'test'.\n- 'type-of-inference': A string indicating what type of inference is tested in this example.\n- 'pair-id': A unique integer id for the NLI pair. The 'pair-id' is used to find the corresponding metadata for any given NLI pair\n\nwhile each metadata file has the following columns\n\n- 'pair-id': A unique integer id for the NLI pair. \n- 'corpus': The original corpus where this example came from.\n- 'corpus-sent-id': The id of the sentence (or example) in the original dataset that we recast.\n- 'corpus-license': The license for the data from the original dataset.\n- 'creation-approach': Determines the method used to recast this example. Options are 'automatic', 'manual', or 'human-labeled'.\n- 'misc': A dictionary of other relevant information. This is an optional field.\n\nThe files are merged on the 'pair-id' key. I do not include the 'misc' column as it is not essential for NLI.\n\nNOTE: the label mapping is not the custom (i.e., 3 class) for NLI tasks. They used a binary target and I encoded them\nwith the following mapping '{\"not-entailed\": 0, \"entailed\": 1}'.\n\nNOTE: some instances are present in multiple splits (matching performed by exact matching on \"context\", \"hypothesis\", and \"label\").", "## Code to create the dataset" ]

[ "region:us" ]

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

[ "region:us" ]

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[ "## Overview\n\nOriginal dataset page here and dataset available here.", "## Dataset curation\nAdded new column 'label' with encoded labels with the following mapping\n\n\n\nand the columns with parse information are dropped as they are not well formatted.\n\nAlso, the name of the file from which each instance comes is added in the column 'dtype'.", "## Code to create the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\n\nOriginal dataset page here and dataset available here.", "## Dataset curation\nAdded new column 'label' with encoded labels with the following mapping\n\n\n\nand the columns with parse information are dropped as they are not well formatted.\n\nAlso, the name of the file from which each instance comes is added in the column 'dtype'.", "## Code to create the dataset" ]

[ "region:us" ]

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[]

[ "## Overview\nOriginal dataset available here.", "## Dataset curation\nNo curation.", "## Code to create the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\nOriginal dataset available here.", "## Dataset curation\nNo curation.", "## Code to create the dataset" ]

[ "region:us" ]

[]

[]

[ "## Overview\nOriginal dataset available here. Debiased dataset generated with GPT-3.", "## Dataset curation\nAll string columns are stripped. Labels are encoded with the following mapping", "## Code to create the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\nOriginal dataset available here. Debiased dataset generated with GPT-3.", "## Dataset curation\nAll string columns are stripped. Labels are encoded with the following mapping", "## Code to create the dataset" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:other", "lam", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for UK Selective Web Archive Classification Dataset", "## 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\nThe dataset comprises a manually curated selective archive produced by UKWA which includes the classification of sites into a two-tiered subject hierarchy. In partnership with the Internet Archive and JISC, UKWA had obtained access to the subset of the Internet Archive\u0019s web collection that relates to the UK. The JISC UK Web Domain Dataset (1996 - 2013) contains all of the resources from the Internet Archive that were hosted on domains ending in \u0018.uk\u0019, or that are required in order to render those UK pages. UKWA have made this manually-generated classification information available as an open dataset in Tab Separated Values (TSV) format. UKWA is particularly interested in whether high-level metadata like this can be used to train an appropriate automatic classification system so that this manually generated dataset may be used to partially automate the categorisation of the UKWA\u0019s larger archives. UKWA expects that an appropriate classifier might require more information about each site in order to produce reliable results, and a future goal is to augment this dataset with further information. Options include: for each site, making the titles of every page on that site available, and for each site, extract a set of keywords that summarise the site, via the full-text index. For more information: URL", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCreative Commons Public Domain Mark 1.0." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #lam #region-us \n", "# Dataset Card for UK Selective Web Archive Classification Dataset", "## 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\nThe dataset comprises a manually curated selective archive produced by UKWA which includes the classification of sites into a two-tiered subject hierarchy. In partnership with the Internet Archive and JISC, UKWA had obtained access to the subset of the Internet Archive\u0019s web collection that relates to the UK. The JISC UK Web Domain Dataset (1996 - 2013) contains all of the resources from the Internet Archive that were hosted on domains ending in \u0018.uk\u0019, or that are required in order to render those UK pages. UKWA have made this manually-generated classification information available as an open dataset in Tab Separated Values (TSV) format. UKWA is particularly interested in whether high-level metadata like this can be used to train an appropriate automatic classification system so that this manually generated dataset may be used to partially automate the categorisation of the UKWA\u0019s larger archives. UKWA expects that an appropriate classifier might require more information about each site in order to produce reliable results, and a future goal is to augment this dataset with further information. Options include: for each site, making the titles of every page on that site available, and for each site, extract a set of keywords that summarise the site, via the full-text index. For more information: URL", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCreative Commons Public Domain Mark 1.0." ]

[ "region:us" ]

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

[ "region:us" ]

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

[ "region:us" ]

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

[ "region:us" ]

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[ "# Python Subreddit\n\nDataset containing data scraped from the Python subreddit." ]

[ "TAGS\n#region-us \n", "# Python Subreddit\n\nDataset containing data scraped from the Python subreddit." ]

[ "region:us" ]

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[ "# Pretokenized GitHub Code Dataset", "## Dataset Description\nThis is a pretokenized version of the Python files of the GitHub Code dataset, that consists of 115M code files from GitHub in 32 programming languages. We tokenized the dataset using BPE Tokenizer trained on code, available in this repo. Having a pretokenized dataset can speed up the training loop by not having to tokenize data at each batch call. We also include 'ratio_char_token' which gives the ratio between the number of characters in a file and the number of tokens we get after tokenization, this ratio can be a good filter to detect outlier files.", "### How to use it\nTo avoid downloading the whole dataset, you can make use of the streaming API of 'datasets'. You can load and iterate through the dataset with the following two lines of code:" ]

[ "TAGS\n#region-us \n", "# Pretokenized GitHub Code Dataset", "## Dataset Description\nThis is a pretokenized version of the Python files of the GitHub Code dataset, that consists of 115M code files from GitHub in 32 programming languages. We tokenized the dataset using BPE Tokenizer trained on code, available in this repo. Having a pretokenized dataset can speed up the training loop by not having to tokenize data at each batch call. We also include 'ratio_char_token' which gives the ratio between the number of characters in a file and the number of tokens we get after tokenization, this ratio can be a good filter to detect outlier files.", "### How to use it\nTo avoid downloading the whole dataset, you can make use of the streaming API of 'datasets'. You can load and iterate through the dataset with the following two lines of code:" ]

[ "region:us" ]

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[ "## Overview\n\nOriginal dataset available here. \nThis dataset is the \"full\" JOCI dataset, which is the file named 'URL'.", "# Dataset curation\nThe following processing is applied,\n\n- 'label' column renamed to 'original_label'\n- creation of the 'label' column using the following mapping, using common practices (1, 2)\n\n\n\n- finally, converting this to the usual NLI classes, that is '{\"entailment\": 0, \"neutral\": 1, \"contradiction\": 2}'", "## Code to create dataset" ]

[ "TAGS\n#region-us \n", "## Overview\n\nOriginal dataset available here. \nThis dataset is the \"full\" JOCI dataset, which is the file named 'URL'.", "# Dataset curation\nThe following processing is applied,\n\n- 'label' column renamed to 'original_label'\n- creation of the 'label' column using the following mapping, using common practices (1, 2)\n\n\n\n- finally, converting this to the usual NLI classes, that is '{\"entailment\": 0, \"neutral\": 1, \"contradiction\": 2}'", "## Code to create dataset" ]

[ "region:us" ]

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[]

[ "## Overview\n\nProposed by\n\n\nOriginal dataset available here.", "## Dataset curation\nLabels encoded with the following mapping '{\"entailment\": 0, \"neutral\": 1, \"contradiction\": 2}'\nand made available in the 'label' column.", "## Code to create the dataset" ]

[ "TAGS\n#region-us \n", "## Overview\n\nProposed by\n\n\nOriginal dataset available here.", "## Dataset curation\nLabels encoded with the following mapping '{\"entailment\": 0, \"neutral\": 1, \"contradiction\": 2}'\nand made available in the 'label' column.", "## Code to create the dataset" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

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[ "# Dataset Card for c_corpus", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\n\nC corpus is a set of annotated data in portuguese for the recognition of named entities, being the extension of the UlyssesNER-Br corpus.", "### Supported Tasks and Leaderboards\n\nThe dataset can be used to train a model for Named Entity Recognition that aims to identify all named entities such as person names, locations, among others, in a text.", "### Languages\n\nBrazilian Portuguese", "## 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 c_corpus", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\n\nC corpus is a set of annotated data in portuguese for the recognition of named entities, being the extension of the UlyssesNER-Br corpus.", "### Supported Tasks and Leaderboards\n\nThe dataset can be used to train a model for Named Entity Recognition that aims to identify all named entities such as person names, locations, among others, in a text.", "### Languages\n\nBrazilian Portuguese", "## 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:image-segmentation", "task_ids:instance-segmentation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "size_categories:n<1K", "source_datasets:original", "license:unknown", "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\n\nList of lottiefiles uri for research purposes", "### 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-image-segmentation #task_ids-instance-segmentation #annotations_creators-crowdsourced #language_creators-crowdsourced #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:", "### Dataset Summary\n\nList of lottiefiles uri for research purposes", "### 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." ]

[ "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:" ]

[ "region:us" ]

[]

[]

[]

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

[ "language:en", "language:fr", "license:cc-by-sa-4.0", "arxiv:2010.00133", "region:us" ]

[ "2010.00133" ]

[ "en", "fr" ]

[ "# Data Statement for CrowS-Pairs-fr\n\n> How to use this document:\n> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.\n\n> For full details, the best source is the original Data Statements paper, here: URL .\n\n> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as \"URL\". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.\n\n> Only blockquoted content should be deleted; the final about statement should be left intact.\n\nData set name: Crows-Pairs-fr\n\nCitation (if available): Névéol A, Dupont Y, Bezançon J, Fort K. French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics - ACL 2022\n\nData set developer(s): Aurélie Névéol, Yoann Dupont, Julien Bezançon, Karën Fort\n\nData statement author(s): Aurélie Névéol, Yoann Dupont\n\nOthers who contributed to this document: N/A\n\nLicense: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).", "## A. CURATION RATIONALE \n\n> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.\n\nThe French part of the corpus was built by first translating the original 1,508 sentence pairs of the English corpus into French.\n\nWe then adapted the crowdsourcing method described by Nangia et al. (2020) to collect additional sentences expressing a stereotype relevant to the French socio-cultural environment. Data collection is implemented through LanguageARC (Fiumara et al., 2020), a citizen science platform supporting the development of language resources dedicated to social improvement. We created a LanguageARC project (URL to collect these additional sentences. Participants were asked to submit a statement that expressed a stereotype in French along with a selection of ten bias types: the nine bias types offered in CrowS-Pairs and the additional category _other_. We collected 210 additional sentences this way.", "## B. LANGUAGE VARIETY/VARIETIES\n\n> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., \"English as spoken in Palo Alto, California\", or \"Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin\").\n\n* BCP-47 language tags: fr-FR\n* Language variety description: French spoken by native French people from metropolitan France.", "## C. CONTRIBUTOR DEMOGRAPHIC\n> ## C. SPEAKER DEMOGRAPHIC\n\n> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include: \n\nN/A", "## D. ANNOTATOR DEMOGRAPHIC\n\n> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:\n\nParticipants to the collection project were recruited through calls for volunteers posted to social media and mailing lists in the French research community.", "## E. SPEECH SITUATION\n\nN/A", "## F. TEXT CHARACTERISTICS\n\n> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.\n\nCollected data is a collection of offensive stereotyped statements in French, they might be upsetting.\n\nAlong these stereotyped statements are paired anti-stereotyped statements.", "## G. RECORDING QUALITY\n\nN/A", "## H. OTHER\n\n> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.", "## I. PROVENANCE APPENDIX\n\nExamples were gathered using the LanguageArc site and by creating a dedicated project: URL", "## About this document\n\nA data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.\n\nData Statements are from the University of Washington. Contact: datastatements@URL. This document template is licensed as CC0.\n\nThis version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the 2020 LREC workshop on Data Statements, by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski." ]

[ "TAGS\n#language-English #language-French #license-cc-by-sa-4.0 #arxiv-2010.00133 #region-us \n", "# Data Statement for CrowS-Pairs-fr\n\n> How to use this document:\n> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.\n\n> For full details, the best source is the original Data Statements paper, here: URL .\n\n> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as \"URL\". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.\n\n> Only blockquoted content should be deleted; the final about statement should be left intact.\n\nData set name: Crows-Pairs-fr\n\nCitation (if available): Névéol A, Dupont Y, Bezançon J, Fort K. French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics - ACL 2022\n\nData set developer(s): Aurélie Névéol, Yoann Dupont, Julien Bezançon, Karën Fort\n\nData statement author(s): Aurélie Névéol, Yoann Dupont\n\nOthers who contributed to this document: N/A\n\nLicense: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).", "## A. CURATION RATIONALE \n\n> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.\n\nThe French part of the corpus was built by first translating the original 1,508 sentence pairs of the English corpus into French.\n\nWe then adapted the crowdsourcing method described by Nangia et al. (2020) to collect additional sentences expressing a stereotype relevant to the French socio-cultural environment. Data collection is implemented through LanguageARC (Fiumara et al., 2020), a citizen science platform supporting the development of language resources dedicated to social improvement. We created a LanguageARC project (URL to collect these additional sentences. Participants were asked to submit a statement that expressed a stereotype in French along with a selection of ten bias types: the nine bias types offered in CrowS-Pairs and the additional category _other_. We collected 210 additional sentences this way.", "## B. LANGUAGE VARIETY/VARIETIES\n\n> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., \"English as spoken in Palo Alto, California\", or \"Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin\").\n\n* BCP-47 language tags: fr-FR\n* Language variety description: French spoken by native French people from metropolitan France.", "## C. CONTRIBUTOR DEMOGRAPHIC\n> ## C. SPEAKER DEMOGRAPHIC\n\n> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include: \n\nN/A", "## D. ANNOTATOR DEMOGRAPHIC\n\n> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:\n\nParticipants to the collection project were recruited through calls for volunteers posted to social media and mailing lists in the French research community.", "## E. SPEECH SITUATION\n\nN/A", "## F. TEXT CHARACTERISTICS\n\n> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.\n\nCollected data is a collection of offensive stereotyped statements in French, they might be upsetting.\n\nAlong these stereotyped statements are paired anti-stereotyped statements.", "## G. RECORDING QUALITY\n\nN/A", "## H. OTHER\n\n> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.", "## I. PROVENANCE APPENDIX\n\nExamples were gathered using the LanguageArc site and by creating a dedicated project: URL", "## About this document\n\nA data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.\n\nData Statements are from the University of Washington. Contact: datastatements@URL. This document template is licensed as CC0.\n\nThis version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the 2020 LREC workshop on Data Statements, by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski." ]

[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for CrosswordQA", "## 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: URL\n- Paper: \n- Leaderboard: \n- Point of Contact: Albert Xu and Eshaan Pathak", "### Dataset Summary\n\nThe CrosswordQA dataset is a set of over 6 million clue-answer pairs scraped from the New York Times and many other crossword publishers. The dataset was created to train the Berkeley Crossword Solver's QA model. See our paper for more information. Answers are automatically segmented (e.g., BUZZLIGHTYEAR -> Buzz Lightyear), and thus may occasionally be segmented incorrectly.", "### 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#task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #language-English #license-unknown #region-us \n", "# Dataset Card for CrosswordQA", "## 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: URL\n- Paper: \n- Leaderboard: \n- Point of Contact: Albert Xu and Eshaan Pathak", "### Dataset Summary\n\nThe CrosswordQA dataset is a set of over 6 million clue-answer pairs scraped from the New York Times and many other crossword publishers. The dataset was created to train the Berkeley Crossword Solver's QA model. See our paper for more information. Answers are automatically segmented (e.g., BUZZLIGHTYEAR -> Buzz Lightyear), and thus may occasionally be segmented incorrectly.", "### 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" ]

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

[]

[]

[ "# Dataset Card for HuffPost", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:URL", "### Dataset Summary\n\nA dataset of approximately 200K news headlines from the year 2012 to 2018 collected from HuffPost.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nlicense: cc0-1.0", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# Dataset Card for HuffPost", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:URL", "### Dataset Summary\n\nA dataset of approximately 200K news headlines from the year 2012 to 2018 collected from HuffPost.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nlicense: cc0-1.0", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ru", "region:us" ]

[]

[ "ru" ]

[ "### Dataset Summary\n\nKinopoisk movie reviews dataset (TOP250 & BOTTOM100 rank lists).\n\nIn total it contains 36,591 reviews from July 2004 to November 2012.\n\nWith following distribution along the 3-point sentiment scale:\n- Good: 27,264;\n- Bad: 4,751;\n- Neutral: 4,576.", "### Data Fields\n\nEach sample contains the following fields:\n- part: rank list top250 or bottom100;\n- movie_name;\n- review_id;\n- author: review author;\n- date: date of a review;\n- title: review title;\n- grade3: sentiment score Good, Bad or Neutral;\n- grade10: sentiment score on a 10-point scale parsed from text;\n- content: review text.", "### Python" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-10K<n<100K #language-Russian #region-us \n", "### Dataset Summary\n\nKinopoisk movie reviews dataset (TOP250 & BOTTOM100 rank lists).\n\nIn total it contains 36,591 reviews from July 2004 to November 2012.\n\nWith following distribution along the 3-point sentiment scale:\n- Good: 27,264;\n- Bad: 4,751;\n- Neutral: 4,576.", "### Data Fields\n\nEach sample contains the following fields:\n- part: rank list top250 or bottom100;\n- movie_name;\n- review_id;\n- author: review author;\n- date: date of a review;\n- title: review title;\n- grade3: sentiment score Good, Bad or Neutral;\n- grade10: sentiment score on a 10-point scale parsed from text;\n- content: review text.", "### Python" ]

[ "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:cn", "license:afl-3.0", "region:us" ]

[]

[ "cn" ]

[ "# Dataset Card for CANLI", "### Dataset Summary\nCANLI: The Chinese Causative-Passive Homonymy Disambiguation: an Adversarial Dataset for NLI and a Probing Task\n\nThe disambiguation of causative-passive homonymy (CPH) is potentially tricky for machines, as the causative and the passive\nare not distinguished by the sentences\u0019 syntactic structure. By transforming CPH disambiguation to a challenging natural\nlanguage inference (NLI) task, we present the first Chinese Adversarial NLI challenge set (CANLI). We show that the pretrained\ntransformer model RoBERTa, fine-tuned on an existing large-scale Chinese NLI benchmark dataset, performs poorly on CANLI.\nWe also employ Word Sense Disambiguation as a probing task to investigate to what extent the CPH feature is captured in\nthe model\u0019s internal representation. We find that the model\u0019s performance on CANLI does not correspond to its internal\nrepresentation of CPH, which is the crucial linguistic ability central to the CANLI dataset.", "### Languages\nChinese Mandarin\n\n\n\n @inproceedings{xu-markert-2022-chinese,\n title = \"The {C}hinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for {NLI} and a Probing Task\",\n author = \"Xu, Shanshan and Markert, Katja\",\n booktitle = \"Proceedings of the Thirteenth Language Resources and Evaluation Conference\",\n month = jun,\n year = \"2022\",\n address = \"Marseille, France\",\n publisher = \"European Language Resources Association\",\n url = \"URL\n pages = \"4316--4323\",\n}" ]

[ "TAGS\n#annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-cn #license-afl-3.0 #region-us \n", "# Dataset Card for CANLI", "### Dataset Summary\nCANLI: The Chinese Causative-Passive Homonymy Disambiguation: an Adversarial Dataset for NLI and a Probing Task\n\nThe disambiguation of causative-passive homonymy (CPH) is potentially tricky for machines, as the causative and the passive\nare not distinguished by the sentences\u0019 syntactic structure. By transforming CPH disambiguation to a challenging natural\nlanguage inference (NLI) task, we present the first Chinese Adversarial NLI challenge set (CANLI). We show that the pretrained\ntransformer model RoBERTa, fine-tuned on an existing large-scale Chinese NLI benchmark dataset, performs poorly on CANLI.\nWe also employ Word Sense Disambiguation as a probing task to investigate to what extent the CPH feature is captured in\nthe model\u0019s internal representation. We find that the model\u0019s performance on CANLI does not correspond to its internal\nrepresentation of CPH, which is the crucial linguistic ability central to the CANLI dataset.", "### Languages\nChinese Mandarin\n\n\n\n @inproceedings{xu-markert-2022-chinese,\n title = \"The {C}hinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for {NLI} and a Probing Task\",\n author = \"Xu, Shanshan and Markert, Katja\",\n booktitle = \"Proceedings of the Thirteenth Language Resources and Evaluation Conference\",\n month = jun,\n year = \"2022\",\n address = \"Marseille, France\",\n publisher = \"European Language Resources Association\",\n url = \"URL\n pages = \"4316--4323\",\n}" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\nBoth \"arxiv\" and \"pubmed\" have two features:\n\n\n* article: the body of the document, pagragraphs seperated by \"/n\".\n* abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n* section\\_names: titles of sections, seperated by \"/n\".", "### 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", "#### arxiv\n\n\n* Size of downloaded dataset files: 4295.97 MB\n* Size of the generated dataset: 7231.70 MB\n* Total amount of disk used: 11527.66 MB\n\n\nAn example of 'train' looks as follows.", "#### pubmed\n\n\n* Size of downloaded dataset files: 4295.97 MB\n* Size of the generated dataset: 2390.49 MB\n* Total amount of disk used: 6686.46 MB\n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### arxiv\n\n\n* 'article': a 'string' feature.\n* 'abstract': a 'string' feature.\n* 'section\\_names': a 'string' feature.", "#### pubmed\n\n\n* 'article': a 'string' feature.\n* 'abstract': a 'string' feature.\n* 'section\\_names': a 'string' 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", "### Contributions\n\n\nThanks to @thomwolf, @jplu, @lewtun, @patrickvonplaten for adding this dataset." ]

[ "TAGS\n#language-English #region-us \n", "### Dataset Summary\n\n\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\nBoth \"arxiv\" and \"pubmed\" have two features:\n\n\n* article: the body of the document, pagragraphs seperated by \"/n\".\n* abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n* section\\_names: titles of sections, seperated by \"/n\".", "### 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", "#### arxiv\n\n\n* Size of downloaded dataset files: 4295.97 MB\n* Size of the generated dataset: 7231.70 MB\n* Total amount of disk used: 11527.66 MB\n\n\nAn example of 'train' looks as follows.", "#### pubmed\n\n\n* Size of downloaded dataset files: 4295.97 MB\n* Size of the generated dataset: 2390.49 MB\n* Total amount of disk used: 6686.46 MB\n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### arxiv\n\n\n* 'article': a 'string' feature.\n* 'abstract': a 'string' feature.\n* 'section\\_names': a 'string' feature.", "#### pubmed\n\n\n* 'article': a 'string' feature.\n* 'abstract': a 'string' feature.\n* 'section\\_names': a 'string' 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", "### Contributions\n\n\nThanks to @thomwolf, @jplu, @lewtun, @patrickvonplaten for adding this dataset." ]

[ "annotations_creators:lexyr", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for the-reddit-nft-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\nA comprehensive dataset of Reddit's NFT discussion.", "### 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-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for the-reddit-nft-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\nA comprehensive dataset of Reddit's NFT discussion.", "### 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" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-retrieval", "task_categories:text-classification", "task_ids:fact-checking-retrieval", "task_ids:text-scoring", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "probing", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models.", "## 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", "## Dataset Description\n\n- Homepage:\nURL\n- Repository:\nURL\n- Paper:\n@inproceedings{petroni2019language,\n title={Language Models as Knowledge Bases?},\n author={F. Petroni, T. Rockt{\\\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},\n booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},\n year={2019}\n}\n\n@inproceedings{petroni2020how,\n title={How Context Affects Language Models' Factual Predictions},\n author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\\\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},\n booktitle={Automated Knowledge Base Construction},\n year={2020},\n url={URL\n}", "### Dataset Summary\n\nThis dataset provides the data for LAMA. This dataset only contains TRex\n(subset of wikidata triples).\n\nThe dataset includes some cleanup, and addition of a masked sentence\nand associated answers for the [MASK] token. The accuracy in\npredicting the [MASK] token shows how well the language model knows\nfacts and common sense information. The [MASK] tokens are only for the\n\"object\" slots.\n\nThis version also contains questions instead of templates that can be used to probe also non-masking models.\n\n\nSee the paper for more details. For more information, also see:\nURL", "### Languages\nen", "## Dataset Structure", "### Data Instances\n\n\nThe trex config has the following fields:\n\n\n''\n{'uuid': 'a37257ae-4cbb-4309-a78a-623036c96797', 'sub_label': 'Pianos Become the Teeth', 'predicate_id': 'P740', 'obj_label': 'Baltimore', 'template': '[X] was founded in [Y] .', 'type': 'N-1', 'question': 'Where was [X] founded?'}\n34039\n''", "### Data Splits\n\nThere are no data splits.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was gathered and created to probe what language models understand.", "### Source Data", "#### Initial Data Collection and Normalization\n\nSee the reaserch paper and website for more detail. The dataset was\ncreated gathered from various other datasets with cleanups for probing.", "#### Who are the source language producers?\n\nThe LAMA authors and the original authors of the various configs.", "### Annotations", "#### Annotation process\n\nHuman annotations under the original datasets (conceptnet), and various machine annotations.", "#### Who are the annotators?\n\nHuman annotations and machine annotations.", "### Personal and Sensitive Information\n\nUnkown, but likely names of famous people.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe goal for the work is to probe the understanding of language models.", "### Discussion of Biases\n\nSince the data is from human annotators, there is likely to be baises.", "### Other Known Limitations\n\nThe original documentation for the datafields are limited.", "## Additional Information", "### Dataset Curators\n\nThe authors of LAMA at Facebook and the authors of the original datasets.", "### Licensing Information\n\nThe Creative Commons Attribution-Noncommercial 4.0 International License. see URL\n\n\n\n@inproceedings{petroni2019language,\n title={Language Models as Knowledge Bases?},\n author={F. Petroni, T. Rockt{\\\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},\n booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},\n year={2019}\n}\n\n@inproceedings{petroni2020how,\n title={How Context Affects Language Models' Factual Predictions},\n author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\\\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},\n booktitle={Automated Knowledge Base Construction},\n year={2020},\n url={URL\n}" ]

[ "TAGS\n#task_categories-text-retrieval #task_categories-text-classification #task_ids-fact-checking-retrieval #task_ids-text-scoring #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #language-English #license-cc-by-4.0 #probing #region-us \n", "# Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models.", "## 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", "## Dataset Description\n\n- Homepage:\nURL\n- Repository:\nURL\n- Paper:\n@inproceedings{petroni2019language,\n title={Language Models as Knowledge Bases?},\n author={F. Petroni, T. Rockt{\\\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},\n booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},\n year={2019}\n}\n\n@inproceedings{petroni2020how,\n title={How Context Affects Language Models' Factual Predictions},\n author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\\\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},\n booktitle={Automated Knowledge Base Construction},\n year={2020},\n url={URL\n}", "### Dataset Summary\n\nThis dataset provides the data for LAMA. This dataset only contains TRex\n(subset of wikidata triples).\n\nThe dataset includes some cleanup, and addition of a masked sentence\nand associated answers for the [MASK] token. The accuracy in\npredicting the [MASK] token shows how well the language model knows\nfacts and common sense information. The [MASK] tokens are only for the\n\"object\" slots.\n\nThis version also contains questions instead of templates that can be used to probe also non-masking models.\n\n\nSee the paper for more details. For more information, also see:\nURL", "### Languages\nen", "## Dataset Structure", "### Data Instances\n\n\nThe trex config has the following fields:\n\n\n''\n{'uuid': 'a37257ae-4cbb-4309-a78a-623036c96797', 'sub_label': 'Pianos Become the Teeth', 'predicate_id': 'P740', 'obj_label': 'Baltimore', 'template': '[X] was founded in [Y] .', 'type': 'N-1', 'question': 'Where was [X] founded?'}\n34039\n''", "### Data Splits\n\nThere are no data splits.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was gathered and created to probe what language models understand.", "### Source Data", "#### Initial Data Collection and Normalization\n\nSee the reaserch paper and website for more detail. The dataset was\ncreated gathered from various other datasets with cleanups for probing.", "#### Who are the source language producers?\n\nThe LAMA authors and the original authors of the various configs.", "### Annotations", "#### Annotation process\n\nHuman annotations under the original datasets (conceptnet), and various machine annotations.", "#### Who are the annotators?\n\nHuman annotations and machine annotations.", "### Personal and Sensitive Information\n\nUnkown, but likely names of famous people.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe goal for the work is to probe the understanding of language models.", "### Discussion of Biases\n\nSince the data is from human annotators, there is likely to be baises.", "### Other Known Limitations\n\nThe original documentation for the datafields are limited.", "## Additional Information", "### Dataset Curators\n\nThe authors of LAMA at Facebook and the authors of the original datasets.", "### Licensing Information\n\nThe Creative Commons Attribution-Noncommercial 4.0 International License. see URL\n\n\n\n@inproceedings{petroni2019language,\n title={Language Models as Knowledge Bases?},\n author={F. Petroni, T. Rockt{\\\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},\n booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},\n year={2019}\n}\n\n@inproceedings{petroni2020how,\n title={How Context Affects Language Models' Factual Predictions},\n author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\\\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},\n booktitle={Automated Knowledge Base Construction},\n year={2020},\n url={URL\n}" ]

[ "task_categories:text-classification", "task_ids:intent-classification", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2003.04807", "region:us" ]

[ "2003.04807" ]

[ "en" ]

[ "### Dataset Summary\n\n\nDataset composed of online banking queries annotated with their corresponding intents.\n\n\nBANKING77 dataset provides a very fine-grained set of intents in a banking domain.\nIt comprises 13,083 customer service queries labeled with 77 intents.\nIt focuses on fine-grained single-domain intent detection.", "### Supported Tasks and Leaderboards\n\n\nIntent classification, intent detection", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows:", "### Data Fields\n\n\n* 'text': a string feature.\n* 'label': One of classification labels (0-76) corresponding to unique intents.\n\n\nIntent names are mapped to 'label' in the following way:", "### Data Splits\n\n\nDataset statistics: Number of examples, Train: 10 003, Test: 3 080\nDataset statistics: Average character length, Train: 59.5, Test: 54.2\nDataset statistics: Number of intents, Train: 77, Test: 77\nDataset statistics: Number of domains, Train: 1, Test: 1\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nPrevious intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale *multi-domain* datasets (HWU64 and CLINC150), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered \"in the wild\". This dataset tries to fill the gap and provides a very fine-grained set of intents in a *single-domain* i.e. banking. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets.", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nThe dataset does not contain any additional annotations.", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\n[N/A]\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset it to help develop better intent detection systems.\n\n\nAny comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77.", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nPolyAI", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International", "### Contributions\n\n\nThanks to @dkajtoch for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-intent-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2003.04807 #region-us \n", "### Dataset Summary\n\n\nDataset composed of online banking queries annotated with their corresponding intents.\n\n\nBANKING77 dataset provides a very fine-grained set of intents in a banking domain.\nIt comprises 13,083 customer service queries labeled with 77 intents.\nIt focuses on fine-grained single-domain intent detection.", "### Supported Tasks and Leaderboards\n\n\nIntent classification, intent detection", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows:", "### Data Fields\n\n\n* 'text': a string feature.\n* 'label': One of classification labels (0-76) corresponding to unique intents.\n\n\nIntent names are mapped to 'label' in the following way:", "### Data Splits\n\n\nDataset statistics: Number of examples, Train: 10 003, Test: 3 080\nDataset statistics: Average character length, Train: 59.5, Test: 54.2\nDataset statistics: Number of intents, Train: 77, Test: 77\nDataset statistics: Number of domains, Train: 1, Test: 1\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nPrevious intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale *multi-domain* datasets (HWU64 and CLINC150), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered \"in the wild\". This dataset tries to fill the gap and provides a very fine-grained set of intents in a *single-domain* i.e. banking. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets.", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nThe dataset does not contain any additional annotations.", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\n[N/A]\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset it to help develop better intent detection systems.\n\n\nAny comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77.", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nPolyAI", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International", "### Contributions\n\n\nThanks to @dkajtoch for adding this dataset." ]

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

[]

[ "zh" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is zh.\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-Chinese #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is zh.\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", "task_ids:text-scoring", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-3.0", "region:us" ]

[]

[ "en" ]

[ "## Contents\n\nThe annotated data files and number of lines in each are as follows:\n\n* 4977 answers -- Annotated sentences from a random sample of posts from the Yahoo! Answers forums: URL\n* 1821 blog -- Annotated sentences from the top 100 blogs listed on URL on October 31, 2009.\n* 1701 email -- Annotated sentences from a random sample of emails from the Jeb Bush email archive: URL\n* 2775 news -- Annotated sentences from the \"breaking\", \"recent\", and \"local\" news sections of the following 20 news sites: CNN, CBS News, ABC News, Reuters, BBC News Online, New York Times, Los Angeles Times, The Guardian (U.K.), Voice of America, Boston Globe, Chicago Tribune, San Francisco Chronicle, Times Online (U.K.), URL, Xinhua, The Times of India, Seattle Post Intelligencer, Daily Mail, and Bloomberg L.P.", "## Format\n\nEach record contains the following fields:\n\n1. 'avg_score': the mean formality rating, which ranges from -3 to 3 where lower scores indicate less formal sentences\n2. 'sentence'" ]

[ "TAGS\n#task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-3.0 #region-us \n", "## Contents\n\nThe annotated data files and number of lines in each are as follows:\n\n* 4977 answers -- Annotated sentences from a random sample of posts from the Yahoo! Answers forums: URL\n* 1821 blog -- Annotated sentences from the top 100 blogs listed on URL on October 31, 2009.\n* 1701 email -- Annotated sentences from a random sample of emails from the Jeb Bush email archive: URL\n* 2775 news -- Annotated sentences from the \"breaking\", \"recent\", and \"local\" news sections of the following 20 news sites: CNN, CBS News, ABC News, Reuters, BBC News Online, New York Times, Los Angeles Times, The Guardian (U.K.), Voice of America, Boston Globe, Chicago Tribune, San Francisco Chronicle, Times Online (U.K.), URL, Xinhua, The Times of India, Seattle Post Intelligencer, Daily Mail, and Bloomberg L.P.", "## Format\n\nEach record contains the following fields:\n\n1. 'avg_score': the mean formality rating, which ranges from -3 to 3 where lower scores indicate less formal sentences\n2. 'sentence'" ]

[ "task_categories:text-classification", "language_creators:crowdsourced", "multilinguality:monolingual", "language:si", "license:mit", "region:us" ]

[]

[ "si" ]

[]

[ "TAGS\n#task_categories-text-classification #language_creators-crowdsourced #multilinguality-monolingual #language-Sinhala #license-mit #region-us \n" ]

[ "license:cc-by-sa-4.0", "arxiv:2010.00133", "region:us" ]

[ "2010.00133" ]

[]

[ "# Data Statement for SHADES\n\n> How to use this document:\n> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.\n\n> For full details, the best source is the original Data Statements paper, here: URL .\n\n> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as \"URL\". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.\n\n> Only blockquoted content should be deleted; the final about statement should be left intact.\n\nData set name: Bias-Shades\n\nCitation (if available): TODO.\n\nData set developer(s): TODO.\n\nData statement author(s): TODO.\n\nOthers who contributed to this document: N/A\n\nLicense: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).", "## A. CURATION RATIONALE \n\n> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.\n\nThis dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted.", "## B. LANGUAGE VARIETY/VARIETIES\n\n> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., \"English as spoken in Palo Alto, California\", or \"Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin\").\n\n* BCP-47 language tags: en-US, fr-FR, ....\n* Language variety description: English spoken by native American speakers, native French people from metropolitan France.", "## C. CONTRIBUTOR DEMOGRAPHIC\n> ## C. SPEAKER DEMOGRAPHIC\n\n> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include: \n\nN/A", "## D. ANNOTATOR DEMOGRAPHIC\n\n> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:\n\nParticipants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group.", "## E. SPEECH SITUATION\n\nN/A", "## F. TEXT CHARACTERISTICS\n\n> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.\n\nCollected data is a collection of offensive stereotyped statements in French, they might be upsetting.\n\nAlong these stereotyped statements are paired anti-stereotyped statements.", "## G. RECORDING QUALITY\n\nN/A", "## H. OTHER\n\n> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.", "## I. PROVENANCE APPENDIX\n\nExamples were gathered using the LanguageArc site and by creating a dedicated project: URL", "## About this document\n\nA data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.\n\nData Statements are from the University of Washington. Contact: datastatements@URL. This document template is licensed as CC0.\n\nThis version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the 2020 LREC workshop on Data Statements, by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski." ]

[ "TAGS\n#license-cc-by-sa-4.0 #arxiv-2010.00133 #region-us \n", "# Data Statement for SHADES\n\n> How to use this document:\n> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.\n\n> For full details, the best source is the original Data Statements paper, here: URL .\n\n> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as \"URL\". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.\n\n> Only blockquoted content should be deleted; the final about statement should be left intact.\n\nData set name: Bias-Shades\n\nCitation (if available): TODO.\n\nData set developer(s): TODO.\n\nData statement author(s): TODO.\n\nOthers who contributed to this document: N/A\n\nLicense: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).", "## A. CURATION RATIONALE \n\n> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.\n\nThis dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted.", "## B. LANGUAGE VARIETY/VARIETIES\n\n> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., \"English as spoken in Palo Alto, California\", or \"Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin\").\n\n* BCP-47 language tags: en-US, fr-FR, ....\n* Language variety description: English spoken by native American speakers, native French people from metropolitan France.", "## C. CONTRIBUTOR DEMOGRAPHIC\n> ## C. SPEAKER DEMOGRAPHIC\n\n> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include: \n\nN/A", "## D. ANNOTATOR DEMOGRAPHIC\n\n> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:\n\nParticipants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group.", "## E. SPEECH SITUATION\n\nN/A", "## F. TEXT CHARACTERISTICS\n\n> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.\n\nCollected data is a collection of offensive stereotyped statements in French, they might be upsetting.\n\nAlong these stereotyped statements are paired anti-stereotyped statements.", "## G. RECORDING QUALITY\n\nN/A", "## H. OTHER\n\n> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.", "## I. PROVENANCE APPENDIX\n\nExamples were gathered using the LanguageArc site and by creating a dedicated project: URL", "## About this document\n\nA data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.\n\nData Statements are from the University of Washington. Contact: datastatements@URL. This document template is licensed as CC0.\n\nThis version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the 2020 LREC workshop on Data Statements, by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:intent-classification", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:af-ZA", "multilinguality:am-ET", "multilinguality:ar-SA", "multilinguality:az-AZ", "multilinguality:bn-BD", "multilinguality:ca-ES", "multilinguality:cy-GB", "multilinguality:da-DK", "multilinguality:de-DE", "multilinguality:el-GR", "multilinguality:en-US", "multilinguality:es-ES", "multilinguality:fa-IR", "multilinguality:fi-FI", "multilinguality:fr-FR", "multilinguality:he-IL", "multilinguality:hi-IN", "multilinguality:hu-HU", "multilinguality:hy-AM", "multilinguality:id-ID", "multilinguality:is-IS", "multilinguality:it-IT", "multilinguality:ja-JP", "multilinguality:jv-ID", "multilinguality:ka-GE", "multilinguality:km-KH", "multilinguality:kn-IN", "multilinguality:ko-KR", "multilinguality:lv-LV", "multilinguality:ml-IN", "multilinguality:mn-MN", "multilinguality:ms-MY", "multilinguality:my-MM", "multilinguality:nb-NO", "multilinguality:nl-NL", "multilinguality:pl-PL", "multilinguality:pt-PT", "multilinguality:ro-RO", "multilinguality:ru-RU", "multilinguality:sl-SL", "multilinguality:sq-AL", "multilinguality:sv-SE", "multilinguality:sw-KE", "multilinguality:ta-IN", "multilinguality:te-IN", "multilinguality:th-TH", "multilinguality:tl-PH", "multilinguality:tr-TR", "multilinguality:ur-PK", "multilinguality:vi-VN", "multilinguality:zh-CN", "multilinguality:zh-TW", "size_categories:100K<n<1M", "source_datasets:original", "license:cc-by-4.0", "natural-language-understanding", "arxiv:2204.08582", "region:us" ]

[ "2204.08582" ]

[]

[ "### Dataset Summary\n\n\nMASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.", "### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for 'natural-language-understanding' (NLU) :\n\n\n* 'intent-classification'\n* 'multi-class-classification'\n* 'natural-language-understanding'", "### Languages\n\n\nThe MASSIVE 1.1 corpora consists of parallel sentences from 52 languages :\n\n\n* 'Afrikaans - South Africa (af-ZA)'\n* 'Amharic - Ethiopia (am-ET)'\n* 'Arabic - Saudi Arabia (ar-SA)'\n* 'Azeri - Azerbaijan (az-AZ)'\n* 'Bengali - Bangladesh (bn-BD)'\n* 'Catalan - Spain (ca-ES)'\n* 'Chinese - China (zh-CN)'\n* 'Chinese - Taiwan (zh-TW)'\n* 'Danish - Denmark (da-DK)'\n* 'German - Germany (de-DE)'\n* 'Greek - Greece (el-GR)'\n* 'English - United States (en-US)'\n* 'Spanish - Spain (es-ES)'\n* 'Farsi - Iran (fa-IR)'\n* 'Finnish - Finland (fi-FI)'\n* 'French - France (fr-FR)'\n* 'Hebrew - Israel (he-IL)'\n* 'Hungarian - Hungary (hu-HU)'\n* 'Armenian - Armenia (hy-AM)'\n* 'Indonesian - Indonesia (id-ID)'\n* 'Icelandic - Iceland (is-IS)'\n* 'Italian - Italy (it-IT)'\n* 'Japanese - Japan (ja-JP)'\n* 'Javanese - Indonesia (jv-ID)'\n* 'Georgian - Georgia (ka-GE)'\n* 'Khmer - Cambodia (km-KH)'\n* 'Korean - Korea (ko-KR)'\n* 'Latvian - Latvia (lv-LV)'\n* 'Mongolian - Mongolia (mn-MN)'\n* 'Malay - Malaysia (ms-MY)'\n* 'Burmese - Myanmar (my-MM)'\n* 'Norwegian - Norway (nb-NO)'\n* 'Dutch - Netherlands (nl-NL)'\n* 'Polish - Poland (pl-PL)'\n* 'Portuguese - Portugal (pt-PT)'\n* 'Romanian - Romania (ro-RO)'\n* 'Russian - Russia (ru-RU)'\n* 'Slovanian - Slovania (sl-SL)'\n* 'Albanian - Albania (sq-AL)'\n* 'Swedish - Sweden (sv-SE)'\n* 'Swahili - Kenya (sw-KE)'\n* 'Hindi - India (hi-IN)'\n* 'Kannada - India (kn-IN)'\n* 'Malayalam - India (ml-IN)'\n* 'Tamil - India (ta-IN)'\n* 'Telugu - India (te-IN)'\n* 'Thai - Thailand (th-TH)'\n* 'Tagalog - Philippines (tl-PH)'\n* 'Turkish - Turkey (tr-TR)'\n* 'Urdu - Pakistan (ur-PK)'\n* 'Vietnamese - Vietnam (vi-VN)'\n* 'Welsh - United Kingdom (cy-GB)'\n\n\nLoad the dataset with HuggingFace\n---------------------------------\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n'id': maps to the original ID in the SLURP collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization.\n\n\n'locale': is the language and country code accoring to ISO-639-1 and ISO-3166.\n\n\n'partition': is either 'train', 'dev', or 'test', according to the original split in SLURP.\n\n\n'scenario': is the general domain, aka \"scenario\" in SLURP terminology, of an utterance\n\n\n'intent': is the specific intent of an utterance within a domain formatted as '{scenario}\\_{intent}'\n\n\n'utt': the raw utterance text without annotations\n\n\n'annot\\_utt': the text from 'utt' with slot annotations formatted as '[{label} : {entity}]'\n\n\n'worker\\_id': The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales.\n\n\n'slot\\_method': for each slot in the utterance, whether that slot was a 'translation' (i.e., same expression just in the target language), 'localization' (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or 'unchanged' (i.e., the original en-US slot value was copied over without modification).\n\n\n'judgments': Each judgment collected for the localized utterance has 6 keys. 'worker\\_id' is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker.", "### Data Splits", "### Personal and Sensitive Information\n\n\nThe corpora is free of personal or sensitive information.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n**MASSIVE**: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan.\n\n\n**SLURP**: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena.\n\n\n**Hugging Face Upload and Integration**: Labrak Yanis (Not affiliated with the original corpus)", "### Licensing Information\n\n\nPlease cite the following papers when using this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-intent-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-af-ZA #multilinguality-am-ET #multilinguality-ar-SA #multilinguality-az-AZ #multilinguality-bn-BD #multilinguality-ca-ES #multilinguality-cy-GB #multilinguality-da-DK #multilinguality-de-DE #multilinguality-el-GR #multilinguality-en-US #multilinguality-es-ES #multilinguality-fa-IR #multilinguality-fi-FI #multilinguality-fr-FR #multilinguality-he-IL #multilinguality-hi-IN #multilinguality-hu-HU #multilinguality-hy-AM #multilinguality-id-ID #multilinguality-is-IS #multilinguality-it-IT #multilinguality-ja-JP #multilinguality-jv-ID #multilinguality-ka-GE #multilinguality-km-KH #multilinguality-kn-IN #multilinguality-ko-KR #multilinguality-lv-LV #multilinguality-ml-IN #multilinguality-mn-MN #multilinguality-ms-MY #multilinguality-my-MM #multilinguality-nb-NO #multilinguality-nl-NL #multilinguality-pl-PL #multilinguality-pt-PT #multilinguality-ro-RO #multilinguality-ru-RU #multilinguality-sl-SL #multilinguality-sq-AL #multilinguality-sv-SE #multilinguality-sw-KE #multilinguality-ta-IN #multilinguality-te-IN #multilinguality-th-TH #multilinguality-tl-PH #multilinguality-tr-TR #multilinguality-ur-PK #multilinguality-vi-VN #multilinguality-zh-CN #multilinguality-zh-TW #size_categories-100K<n<1M #source_datasets-original #license-cc-by-4.0 #natural-language-understanding #arxiv-2204.08582 #region-us \n", "### Dataset Summary\n\n\nMASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.", "### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for 'natural-language-understanding' (NLU) :\n\n\n* 'intent-classification'\n* 'multi-class-classification'\n* 'natural-language-understanding'", "### Languages\n\n\nThe MASSIVE 1.1 corpora consists of parallel sentences from 52 languages :\n\n\n* 'Afrikaans - South Africa (af-ZA)'\n* 'Amharic - Ethiopia (am-ET)'\n* 'Arabic - Saudi Arabia (ar-SA)'\n* 'Azeri - Azerbaijan (az-AZ)'\n* 'Bengali - Bangladesh (bn-BD)'\n* 'Catalan - Spain (ca-ES)'\n* 'Chinese - China (zh-CN)'\n* 'Chinese - Taiwan (zh-TW)'\n* 'Danish - Denmark (da-DK)'\n* 'German - Germany (de-DE)'\n* 'Greek - Greece (el-GR)'\n* 'English - United States (en-US)'\n* 'Spanish - Spain (es-ES)'\n* 'Farsi - Iran (fa-IR)'\n* 'Finnish - Finland (fi-FI)'\n* 'French - France (fr-FR)'\n* 'Hebrew - Israel (he-IL)'\n* 'Hungarian - Hungary (hu-HU)'\n* 'Armenian - Armenia (hy-AM)'\n* 'Indonesian - Indonesia (id-ID)'\n* 'Icelandic - Iceland (is-IS)'\n* 'Italian - Italy (it-IT)'\n* 'Japanese - Japan (ja-JP)'\n* 'Javanese - Indonesia (jv-ID)'\n* 'Georgian - Georgia (ka-GE)'\n* 'Khmer - Cambodia (km-KH)'\n* 'Korean - Korea (ko-KR)'\n* 'Latvian - Latvia (lv-LV)'\n* 'Mongolian - Mongolia (mn-MN)'\n* 'Malay - Malaysia (ms-MY)'\n* 'Burmese - Myanmar (my-MM)'\n* 'Norwegian - Norway (nb-NO)'\n* 'Dutch - Netherlands (nl-NL)'\n* 'Polish - Poland (pl-PL)'\n* 'Portuguese - Portugal (pt-PT)'\n* 'Romanian - Romania (ro-RO)'\n* 'Russian - Russia (ru-RU)'\n* 'Slovanian - Slovania (sl-SL)'\n* 'Albanian - Albania (sq-AL)'\n* 'Swedish - Sweden (sv-SE)'\n* 'Swahili - Kenya (sw-KE)'\n* 'Hindi - India (hi-IN)'\n* 'Kannada - India (kn-IN)'\n* 'Malayalam - India (ml-IN)'\n* 'Tamil - India (ta-IN)'\n* 'Telugu - India (te-IN)'\n* 'Thai - Thailand (th-TH)'\n* 'Tagalog - Philippines (tl-PH)'\n* 'Turkish - Turkey (tr-TR)'\n* 'Urdu - Pakistan (ur-PK)'\n* 'Vietnamese - Vietnam (vi-VN)'\n* 'Welsh - United Kingdom (cy-GB)'\n\n\nLoad the dataset with HuggingFace\n---------------------------------\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n'id': maps to the original ID in the SLURP collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization.\n\n\n'locale': is the language and country code accoring to ISO-639-1 and ISO-3166.\n\n\n'partition': is either 'train', 'dev', or 'test', according to the original split in SLURP.\n\n\n'scenario': is the general domain, aka \"scenario\" in SLURP terminology, of an utterance\n\n\n'intent': is the specific intent of an utterance within a domain formatted as '{scenario}\\_{intent}'\n\n\n'utt': the raw utterance text without annotations\n\n\n'annot\\_utt': the text from 'utt' with slot annotations formatted as '[{label} : {entity}]'\n\n\n'worker\\_id': The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales.\n\n\n'slot\\_method': for each slot in the utterance, whether that slot was a 'translation' (i.e., same expression just in the target language), 'localization' (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or 'unchanged' (i.e., the original en-US slot value was copied over without modification).\n\n\n'judgments': Each judgment collected for the localized utterance has 6 keys. 'worker\\_id' is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker.", "### Data Splits", "### Personal and Sensitive Information\n\n\nThe corpora is free of personal or sensitive information.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n**MASSIVE**: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan.\n\n\n**SLURP**: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena.\n\n\n**Hugging Face Upload and Integration**: Labrak Yanis (Not affiliated with the original corpus)", "### Licensing Information\n\n\nPlease cite the following papers when using this dataset." ]

[ "region:us" ]

[]

[]

[ "# CodeParrot Dataset Cleaned and filtered (validation)", "## Dataset Description\n\nA dataset of Python files from Github. It is a more filtered version of the validation split codeparrot-clean-valid of codeparrot-clean. The additional filters aim at detecting configuration and test files, as well as outlier files that are unlikely to help the model learn code. The first three filters are applied with a probability of 0.7:\n\n- files with a mention of \"test file\" or \"configuration file\" or similar in the first 5 lines\n- files with high occurence of the keywords \"test \" or \"config\" \n- files without a mention of the keywords 'def', 'for', 'while' and 'class'\n- files that use the assignment operator less than 5 times \n- files with ratio between number of characters and number of tokens after tokenization < 1.5" ]

[ "TAGS\n#region-us \n", "# CodeParrot Dataset Cleaned and filtered (validation)", "## Dataset Description\n\nA dataset of Python files from Github. It is a more filtered version of the validation split codeparrot-clean-valid of codeparrot-clean. The additional filters aim at detecting configuration and test files, as well as outlier files that are unlikely to help the model learn code. The first three filters are applied with a probability of 0.7:\n\n- files with a mention of \"test file\" or \"configuration file\" or similar in the first 5 lines\n- files with high occurence of the keywords \"test \" or \"config\" \n- files without a mention of the keywords 'def', 'for', 'while' and 'class'\n- files that use the assignment operator less than 5 times \n- files with ratio between number of characters and number of tokens after tokenization < 1.5" ]

[ "region:us" ]

[]

[]

[ "# CodeParrot Dataset Cleaned and filtered (train)", "## Dataset Description\n\nA dataset of Python files from Github. It is a more filtered version of the train split codeparrot-clean-train of codeparrot-clean. The additional filters aim at detecting configuration and test files, as well as outlier files that are unlikely to help the model learn code. The first three filters are applied with a probability of 0.7:\n\n- files with a mention of \"test file\" or \"configuration file\" or similar in the first 5 lines\n- files with high occurence of the keywords \"test \" or \"config\" \n- files without a mention of the keywords 'def', 'for', 'while' and 'class'\n- files that use the assignment operator less than 5 times \n- files with ratio between number of characters and number of tokens after tokenization < 1.5" ]

[ "TAGS\n#region-us \n", "# CodeParrot Dataset Cleaned and filtered (train)", "## Dataset Description\n\nA dataset of Python files from Github. It is a more filtered version of the train split codeparrot-clean-train of codeparrot-clean. The additional filters aim at detecting configuration and test files, as well as outlier files that are unlikely to help the model learn code. The first three filters are applied with a probability of 0.7:\n\n- files with a mention of \"test file\" or \"configuration file\" or similar in the first 5 lines\n- files with high occurence of the keywords \"test \" or \"config\" \n- files without a mention of the keywords 'def', 'for', 'while' and 'class'\n- files that use the assignment operator less than 5 times \n- files with ratio between number of characters and number of tokens after tokenization < 1.5" ]

[ "task_categories:summarization", "task_categories:text-generation", "task_categories:text2text-generation", "annotations_creators:no-annotation", "multilinguality:monolingual", "language:es", "license:apache-2.0", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for Spanish IMDb Synopsis", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Fields\n- Dataset Creation", "## Dataset Description\n\n4969 movie synopsis from IMDb in spanish.", "### Dataset Summary\n[N/A]", "### Languages\n\nAll descriptions are in spanish, the other fields have some mix of spanish and english.", "## Dataset Structure\n[N/A]", "### Data Fields\n- 'description': IMDb description for the movie (string), should be spanish\n- 'keywords': IMDb keywords for the movie (string), mix of spanish and english\n- 'genre': The genres of the movie (string), mix of spanish and english\n- 'year': The year the movie was published (float)\n- 'name': The name of the movie (string), mix of spanish and english\n- 'director': The name of the main director in the movie, can be empty (string)", "## Dataset Creation\n\nThis kaggle dataset was used as a starting point. Then IMDb was scraped downloading the synopsis of the movies that have more than 5000 votes/reviews and those that did not have a synopsis available in Spanish were discarded." ]

[ "TAGS\n#task_categories-summarization #task_categories-text-generation #task_categories-text2text-generation #annotations_creators-no-annotation #multilinguality-monolingual #language-Spanish #license-apache-2.0 #region-us \n", "# Dataset Card for Spanish IMDb Synopsis", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Fields\n- Dataset Creation", "## Dataset Description\n\n4969 movie synopsis from IMDb in spanish.", "### Dataset Summary\n[N/A]", "### Languages\n\nAll descriptions are in spanish, the other fields have some mix of spanish and english.", "## Dataset Structure\n[N/A]", "### Data Fields\n- 'description': IMDb description for the movie (string), should be spanish\n- 'keywords': IMDb keywords for the movie (string), mix of spanish and english\n- 'genre': The genres of the movie (string), mix of spanish and english\n- 'year': The year the movie was published (float)\n- 'name': The name of the movie (string), mix of spanish and english\n- 'director': The name of the main director in the movie, can be empty (string)", "## Dataset Creation\n\nThis kaggle dataset was used as a starting point. Then IMDb was scraped downloading the synopsis of the movies that have more than 5000 votes/reviews and those that did not have a synopsis available in Spanish were discarded." ]

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

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:fact-checking", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:da", "license:cc-by-4.0", "knowledge-verification", "region:us" ]

[]

[ "da" ]

[ "# Dataset Card for DanFEVER", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: Leon Derczynski\n- Size of downloaded dataset files: 2.82 MiB\n- Size of the generated dataset: 2.80 MiB\n- Total amount of disk used: 5.62 MiB", "### Dataset Summary\n\nWe present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.", "### Supported Tasks and Leaderboards\n\nThis dataset supports the FEVER task, but in Danish.\n\n* PwC leaderboard: Fact Verification on DanFEVER", "### Languages\n\nThis dataset is in Danish; the bcp47 is 'da_DK'.", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nA dump of the Danish Wikipedia of 13 February 2020 was stored as well as the relevant articles from Den Store Danske (excerpts only, to comply with copyright laws). Two teams of two people independently sampled evidence, and created and annotated claims from these two sites.", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\nThe source language is from Wikipedia contributors editors and from dictionary contributors and editors.", "### Annotations", "#### Annotation process\n\nDetailed in this paper.", "#### Who are the annotators?\n\nThe annotators are native Danish speakers and masters students of IT; two female, two male, ages 25-35.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to enable construction of fact-checking systems in Danish. A system that succeeds at this may be able to identify questionable conclusions or inferences.", "### Discussion of Biases\n\nThe data is drawn from relatively formal topics, and so may perform poorly outside these areas.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nThe data here is licensed CC-BY 4.0. If you use this data, you MUST state its origin.\n\n\n\nRefer to this work as:\n\n> Nørregaard and Derczynski (2021). \"DanFEVER: claim verification dataset for Danish\", Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa).\n\nBibliographic reference:" ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #task_ids-natural-language-inference #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Danish #license-cc-by-4.0 #knowledge-verification #region-us \n", "# Dataset Card for DanFEVER", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: Leon Derczynski\n- Size of downloaded dataset files: 2.82 MiB\n- Size of the generated dataset: 2.80 MiB\n- Total amount of disk used: 5.62 MiB", "### Dataset Summary\n\nWe present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.", "### Supported Tasks and Leaderboards\n\nThis dataset supports the FEVER task, but in Danish.\n\n* PwC leaderboard: Fact Verification on DanFEVER", "### Languages\n\nThis dataset is in Danish; the bcp47 is 'da_DK'.", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nA dump of the Danish Wikipedia of 13 February 2020 was stored as well as the relevant articles from Den Store Danske (excerpts only, to comply with copyright laws). Two teams of two people independently sampled evidence, and created and annotated claims from these two sites.", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\nThe source language is from Wikipedia contributors editors and from dictionary contributors and editors.", "### Annotations", "#### Annotation process\n\nDetailed in this paper.", "#### Who are the annotators?\n\nThe annotators are native Danish speakers and masters students of IT; two female, two male, ages 25-35.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to enable construction of fact-checking systems in Danish. A system that succeeds at this may be able to identify questionable conclusions or inferences.", "### Discussion of Biases\n\nThe data is drawn from relatively formal topics, and so may perform poorly outside these areas.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nThe data here is licensed CC-BY 4.0. If you use this data, you MUST state its origin.\n\n\n\nRefer to this work as:\n\n> Nørregaard and Derczynski (2021). \"DanFEVER: claim verification dataset for Danish\", Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa).\n\nBibliographic reference:" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.\n\n\nSee the paper, Broad Twitter Corpus: A Diverse Named Entity Recognition Resource, for details.", "### Supported Tasks and Leaderboards\n\n\n* Named Entity Recognition\n* On PWC: Named Entity Recognition on Broad Twitter Corpus", "### Languages\n\n\nEnglish from UK, US, Australia, Canada, Ireland, New Zealand; 'bcp47:en'\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\nEach tweet contains an ID, a list of tokens, and a list of NER tags\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'strings'\n* 'ner\\_tags': a 'list' of class IDs ('int's) representing the NER class:", "### Data Splits\n\n\n\nThe most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.\n\n\nTest: Section F\n\n\nDevelopment: Section H (the paper says \"second half of Section H\" but ordinality could be ambiguous, so it all goes in. Bonne chance)\n\n\nTraining: everything else\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\nCreative Commons Attribution 4.0 International (CC BY 4.0)", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nThis is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.\n\n\nSee the paper, Broad Twitter Corpus: A Diverse Named Entity Recognition Resource, for details.", "### Supported Tasks and Leaderboards\n\n\n* Named Entity Recognition\n* On PWC: Named Entity Recognition on Broad Twitter Corpus", "### Languages\n\n\nEnglish from UK, US, Australia, Canada, Ireland, New Zealand; 'bcp47:en'\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\nEach tweet contains an ID, a list of tokens, and a list of NER tags\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'strings'\n* 'ner\\_tags': a 'list' of class IDs ('int's) representing the NER class:", "### Data Splits\n\n\n\nThe most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.\n\n\nTest: Section F\n\n\nDevelopment: Section H (the paper says \"second half of Section H\" but ordinality could be ambiguous, so it all goes in. Bonne chance)\n\n\nTraining: everything else\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\nCreative Commons Attribution 4.0 International (CC BY 4.0)", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0", "named-entity-linking", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis data is for the task of named entity recognition and linking/disambiguation over tweets. It comprises\nthe addition of an entity URI layer on top of an NER-annotated tweet dataset. The task is to detect entities\nand then provide a correct link to them in DBpedia, thus disambiguating otherwise ambiguous entity surface\nforms; for example, this means linking \"Paris\" to the correct instance of a city named that (e.g. Paris,\nFrance vs. Paris, Texas).\n\n\nThe data concentrates on ten types of named entities: company, facility, geographic location, movie, musical\nartist, person, product, sports team, TV show, and other.\n\n\nThe file is tab separated, in CoNLL format, with line breaks between tweets.\n\n\n* Data preserves the tokenisation used in the Ritter datasets.\n* PoS labels are not present for all tweets, but where they could be found in the Ritter data, they're given.\n* In cases where a URI could not be agreed, or was not present in DBpedia, the linking URI is 'NIL'.\n\n\nSee the paper, Analysis of Named Entity Recognition and Linking for Tweets for a full description of the methodology.", "### Supported Tasks and Leaderboards\n\n\n* Dataset leaderboard on PWC: Entity Linking on Derczynski", "### Languages\n\n\nEnglish of unknown region ('bcp47:en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### ipm\\_nel\n\n\n* Size of downloaded dataset files: 120 KB\n* Size of the generated dataset:\n* Total amount of disk used:\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'string' features.\n* 'ner\\_tags': a 'list' of classification labels ('int'). Full tagset with indices:\n* 'uris': a 'list' of URIs ('string') that disambiguate entities. Set to 'NIL' when an entity has no DBpedia entry, or blank for outside-of-entity tokens.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nTo gather a social media benchmark for named entity linking that is sufficiently different from newswire data.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is partly harvested from that distributed by Ritter / Named Entity Recognition in Tweets: An Experimental Study,\nand partly taken from Twitter by the authors.", "#### Who are the source language producers?\n\n\nEnglish-speaking Twitter users, between October 2011 and September 2013", "### Annotations", "#### Annotation process\n\n\nThe authors were allocated documents and marked them for named entities (where these were not already present) and then attempted to find\nthe best-fitting DBpedia entry for each entity found. Each entity mention was labelled by a random set of three volunteers.\nThe annotation task was mediated using Crowdflower (Biewald, 2012). Our interface design was to show each volunteer the text of the tweet, any URL links contained\ntherein, and a set of candidate targets from DBpedia. The volunteers were encouraged to click on the URL links from the\ntweet, to gain addition context and thus ensure that the correct DBpedia URI is chosen by them. Candidate entities were\nshown in random order, using the text from the corresponding DBpedia abstracts (where available) or the actual DBpedia\nURI otherwise. In addition, the options ‘‘none of the above’’, ‘‘not an entity’’ and ‘‘cannot decide’’ were added, to allow the\nvolunteers to indicate that this entity mention has no corresponding DBpedia URI (none of the above), the highlighted text\nis not an entity, or that the tweet text (and any links, if available) did not provide sufficient information to reliably disambiguate the entity mention.", "#### Who are the annotators?\n\n\nThe annotators are 10 volunteer NLP researchers, from the authors and the authors' institutions.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. User names are preserved.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThere's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.", "### Discussion of Biases\n\n\nThe data is annotated by NLP researchers; we know that this group has high agreement but low recall on English twitter text C16-1111.", "### Other Known Limitations\n\n\nThe above limitations apply.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0. You must\nacknowledge the author if you use this data, but apart from that, you're quite\nfree to do most things. See URL .", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #named-entity-linking #region-us \n", "### Dataset Summary\n\n\nThis data is for the task of named entity recognition and linking/disambiguation over tweets. It comprises\nthe addition of an entity URI layer on top of an NER-annotated tweet dataset. The task is to detect entities\nand then provide a correct link to them in DBpedia, thus disambiguating otherwise ambiguous entity surface\nforms; for example, this means linking \"Paris\" to the correct instance of a city named that (e.g. Paris,\nFrance vs. Paris, Texas).\n\n\nThe data concentrates on ten types of named entities: company, facility, geographic location, movie, musical\nartist, person, product, sports team, TV show, and other.\n\n\nThe file is tab separated, in CoNLL format, with line breaks between tweets.\n\n\n* Data preserves the tokenisation used in the Ritter datasets.\n* PoS labels are not present for all tweets, but where they could be found in the Ritter data, they're given.\n* In cases where a URI could not be agreed, or was not present in DBpedia, the linking URI is 'NIL'.\n\n\nSee the paper, Analysis of Named Entity Recognition and Linking for Tweets for a full description of the methodology.", "### Supported Tasks and Leaderboards\n\n\n* Dataset leaderboard on PWC: Entity Linking on Derczynski", "### Languages\n\n\nEnglish of unknown region ('bcp47:en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### ipm\\_nel\n\n\n* Size of downloaded dataset files: 120 KB\n* Size of the generated dataset:\n* Total amount of disk used:\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'string' features.\n* 'ner\\_tags': a 'list' of classification labels ('int'). Full tagset with indices:\n* 'uris': a 'list' of URIs ('string') that disambiguate entities. Set to 'NIL' when an entity has no DBpedia entry, or blank for outside-of-entity tokens.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nTo gather a social media benchmark for named entity linking that is sufficiently different from newswire data.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is partly harvested from that distributed by Ritter / Named Entity Recognition in Tweets: An Experimental Study,\nand partly taken from Twitter by the authors.", "#### Who are the source language producers?\n\n\nEnglish-speaking Twitter users, between October 2011 and September 2013", "### Annotations", "#### Annotation process\n\n\nThe authors were allocated documents and marked them for named entities (where these were not already present) and then attempted to find\nthe best-fitting DBpedia entry for each entity found. Each entity mention was labelled by a random set of three volunteers.\nThe annotation task was mediated using Crowdflower (Biewald, 2012). Our interface design was to show each volunteer the text of the tweet, any URL links contained\ntherein, and a set of candidate targets from DBpedia. The volunteers were encouraged to click on the URL links from the\ntweet, to gain addition context and thus ensure that the correct DBpedia URI is chosen by them. Candidate entities were\nshown in random order, using the text from the corresponding DBpedia abstracts (where available) or the actual DBpedia\nURI otherwise. In addition, the options ‘‘none of the above’’, ‘‘not an entity’’ and ‘‘cannot decide’’ were added, to allow the\nvolunteers to indicate that this entity mention has no corresponding DBpedia URI (none of the above), the highlighted text\nis not an entity, or that the tweet text (and any links, if available) did not provide sufficient information to reliably disambiguate the entity mention.", "#### Who are the annotators?\n\n\nThe annotators are 10 volunteer NLP researchers, from the authors and the authors' institutions.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. User names are preserved.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThere's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.", "### Discussion of Biases\n\n\nThe data is annotated by NLP researchers; we know that this group has high agreement but low recall on English twitter text C16-1111.", "### Other Known Limitations\n\n\nThe above limitations apply.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0. You must\nacknowledge the author if you use this data, but apart from that, you're quite\nfree to do most things. See URL .", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "task_ids:hate-speech-detection", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "arxiv:2107.13592", "doi:10.57967/hf/0514", "region:us" ]

[ "2107.13592" ]

[]

[ "### Dataset Summary\n\n\nThis is an abusive/offensive language detection dataset for Albanian. The data is formatted\nfollowing the OffensEval convention, with three tasks:\n\n\n* Subtask A: Offensive (OFF) or not (NOT)\n* Subtask B: Untargeted (UNT) or targeted insult (TIN)\n* Subtask C: Type of target: individual (IND), group (GRP), or other (OTH)\n\n\nNotes on the above:\n\n\n* The subtask A field should always be filled.\n* The subtask B field should only be filled if there's \"offensive\" (OFF) in A.\n* The subtask C field should only be filled if there's \"targeted\" (TIN) in B.\n\n\nThe dataset name is a backronym, also standing for \"Spoken Hate in the Albanian Jargon\"\n\n\nSee the paper URL for full details.", "### Supported Tasks and Leaderboards\n\n\n* Task A leaderboard at URL", "### Languages\n\n\nAlbanian ('bcp47:sq-AL')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### shaj\n\n\n* Size of downloaded dataset files: 769.21 KiB\n* Size of the generated dataset: 1.06 MiB\n* Total amount of disk used: 1.85 MiB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'text': a 'string'.\n* 'subtask\\_a': whether or not the instance is offensive; '0: OFF, 1: NOT'\n* 'subtask\\_b': whether an offensive instance is a targeted insult; '0: TIN, 1: UNT, 2: not applicable'\n* 'subtask\\_c': what a targeted insult is aimed at; '0: IND, 1: GRP, 2: OTH, 3: not applicable'", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nCollecting data for enabling offensive speech detection in Albanian", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe text is scraped from comments on popular Albanian YouTube and Instagram accounts.\nAn extended discussion is given in the paper in section 3.2.", "#### Who are the source language producers?\n\n\nPeople who comment on a selection of high-activity Albanian instagram and youtube profiles.", "### Annotations", "#### Annotation process\n\n\nThe annotation scheme was taken from OffensEval 2019 and applied by two native speaker authors of the paper as well as their friends and family.", "#### Who are the annotators?\n\n\nAlbanian native speakers, male and female, aged 20-60.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. No PII removal has been performed.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe data definitely contains abusive language.", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0.", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "TAGS\n#task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #arxiv-2107.13592 #doi-10.57967/hf/0514 #region-us \n", "### Dataset Summary\n\n\nThis is an abusive/offensive language detection dataset for Albanian. The data is formatted\nfollowing the OffensEval convention, with three tasks:\n\n\n* Subtask A: Offensive (OFF) or not (NOT)\n* Subtask B: Untargeted (UNT) or targeted insult (TIN)\n* Subtask C: Type of target: individual (IND), group (GRP), or other (OTH)\n\n\nNotes on the above:\n\n\n* The subtask A field should always be filled.\n* The subtask B field should only be filled if there's \"offensive\" (OFF) in A.\n* The subtask C field should only be filled if there's \"targeted\" (TIN) in B.\n\n\nThe dataset name is a backronym, also standing for \"Spoken Hate in the Albanian Jargon\"\n\n\nSee the paper URL for full details.", "### Supported Tasks and Leaderboards\n\n\n* Task A leaderboard at URL", "### Languages\n\n\nAlbanian ('bcp47:sq-AL')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### shaj\n\n\n* Size of downloaded dataset files: 769.21 KiB\n* Size of the generated dataset: 1.06 MiB\n* Total amount of disk used: 1.85 MiB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'text': a 'string'.\n* 'subtask\\_a': whether or not the instance is offensive; '0: OFF, 1: NOT'\n* 'subtask\\_b': whether an offensive instance is a targeted insult; '0: TIN, 1: UNT, 2: not applicable'\n* 'subtask\\_c': what a targeted insult is aimed at; '0: IND, 1: GRP, 2: OTH, 3: not applicable'", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nCollecting data for enabling offensive speech detection in Albanian", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe text is scraped from comments on popular Albanian YouTube and Instagram accounts.\nAn extended discussion is given in the paper in section 3.2.", "#### Who are the source language producers?\n\n\nPeople who comment on a selection of high-activity Albanian instagram and youtube profiles.", "### Annotations", "#### Annotation process\n\n\nThe annotation scheme was taken from OffensEval 2019 and applied by two native speaker authors of the paper as well as their friends and family.", "#### Who are the annotators?\n\n\nAlbanian native speakers, male and female, aged 20-60.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. No PII removal has been performed.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe data definitely contains abusive language.", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0.", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:da", "license:cc-by-4.0", "stance-detection", "region:us" ]

[]

[ "da" ]

[ "### Dataset Summary\n\n\nThis is an SDQC stance-annotated Reddit dataset for the Danish language generated within a thesis project. The dataset consists of over 5000 comments structured as comment trees and linked to 33 source posts.\n\n\nThe dataset is applicable for supervised stance classification and rumour veracity prediction.", "### Supported Tasks and Leaderboards\n\n\n* Stance prediction", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### DAST / dkstance\n\n\n* Size of downloaded dataset files: 4.72 MiB\n* Size of the generated dataset: 3.69 MiB\n* Total amount of disk used: 8.41 MiB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'native\\_id': a 'string' feature representing the native ID of the entry.\n* 'text': a 'string' of the comment text in which stance is annotated.\n* 'parent\\_id': the 'native\\_id' of this comment's parent.\n* 'parent\\_text': a 'string' of the parent comment's text.\n* 'parent\\_stance': the label of the stance in the comment towards its parent comment.\n* 'source\\_id': the 'native\\_id' of this comment's source / post.\n* 'source\\_text': a 'string' of the source / post text.\n* 'source\\_stance': the label of the stance in the comment towards the original source post.", "### Data Splits\n\n\n\nThese splits are specified after the original reserach was reported. The splits add an extra level of rigour, in that no source posts' comment tree is spread over more than one partition.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nComments around rumourous claims to enable rumour and stance analysis in Danish", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is from Reddit posts that relate to one of a specific set of news stories; these stories are enumerated in the paper.", "#### Who are the source language producers?\n\n\nDanish-speaking Twitter users.", "### Annotations", "#### Annotation process\n\n\nThere was multi-user annotation process mediated through a purpose-built interface for annotating stance in Reddit threads.", "#### Who are the annotators?\n\n\n* Age: 20-30.\n* Gender: male.\n* Race/ethnicity: white northern European.\n* Native language: Danish.\n* Socioeconomic status: higher education student.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. User names are not preserved.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThere's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.", "### Discussion of Biases\n\n\nThe source of the text has a strong demographic bias, being mostly young white men who are vocal their opinions. This constrains both the styles of language and discussion contained in the data, as well as the topics discussed and viewpoints held.", "### Other Known Limitations\n\n\nThe above limitations apply.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0.\nAn NLP data statement is included in the paper describing the work, URL", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Danish #license-cc-by-4.0 #stance-detection #region-us \n", "### Dataset Summary\n\n\nThis is an SDQC stance-annotated Reddit dataset for the Danish language generated within a thesis project. The dataset consists of over 5000 comments structured as comment trees and linked to 33 source posts.\n\n\nThe dataset is applicable for supervised stance classification and rumour veracity prediction.", "### Supported Tasks and Leaderboards\n\n\n* Stance prediction", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### DAST / dkstance\n\n\n* Size of downloaded dataset files: 4.72 MiB\n* Size of the generated dataset: 3.69 MiB\n* Total amount of disk used: 8.41 MiB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'native\\_id': a 'string' feature representing the native ID of the entry.\n* 'text': a 'string' of the comment text in which stance is annotated.\n* 'parent\\_id': the 'native\\_id' of this comment's parent.\n* 'parent\\_text': a 'string' of the parent comment's text.\n* 'parent\\_stance': the label of the stance in the comment towards its parent comment.\n* 'source\\_id': the 'native\\_id' of this comment's source / post.\n* 'source\\_text': a 'string' of the source / post text.\n* 'source\\_stance': the label of the stance in the comment towards the original source post.", "### Data Splits\n\n\n\nThese splits are specified after the original reserach was reported. The splits add an extra level of rigour, in that no source posts' comment tree is spread over more than one partition.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nComments around rumourous claims to enable rumour and stance analysis in Danish", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is from Reddit posts that relate to one of a specific set of news stories; these stories are enumerated in the paper.", "#### Who are the source language producers?\n\n\nDanish-speaking Twitter users.", "### Annotations", "#### Annotation process\n\n\nThere was multi-user annotation process mediated through a purpose-built interface for annotating stance in Reddit threads.", "#### Who are the annotators?\n\n\n* Age: 20-30.\n* Gender: male.\n* Race/ethnicity: white northern European.\n* Native language: Danish.\n* Socioeconomic status: higher education student.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. User names are not preserved.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThere's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.", "### Discussion of Biases\n\n\nThe source of the text has a strong demographic bias, being mostly young white men who are vocal their opinions. This constrains both the styles of language and discussion contained in the data, as well as the topics discussed and viewpoints held.", "### Other Known Limitations\n\n\nThe above limitations apply.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0.\nAn NLP data statement is included in the paper describing the work, URL", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "task_categories:text-classification", "task_ids:sentiment-analysis", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:da", "license:cc-by-4.0", "stance-detection", "region:us" ]

[]

[ "da" ]

[ "### Dataset Summary\n\n\nPolitical stance in Danish. Examples represent statements by\npoliticians and are annotated for, against, or neutral to a given topic/article.", "### Supported Tasks and Leaderboards\n\n\n*", "### Languages\n\n\nDanish, bcp47: 'da-DK'\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### polstance\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'topic': a 'string' expressing a topic.\n* 'quote': a 'string' to be classified for its stance to the topic.\n* 'label': a class label representing the stance the text expresses towards the target. Full tagset with indices:\n* 'quoteID': a 'string' of the internal quote ID.\n* 'party': a 'string' describing the party affiliation of the quote utterer at the time of utterance.\n* 'politician': a 'string' naming the politician who uttered the quote.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nCollection of quotes from politicians to allow detecting how political quotes orient to issues.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is taken from proceedings of the Danish parliament, the Folketing - URL.", "#### Who are the source language producers?\n\n\nDanish polticians", "### Annotations", "#### Annotation process\n\n\nAnnotators labelled comments for being against, neutral, or for a specified topic", "#### Who are the annotators?\n\n\nDanish native speakers, 20s, male, studying Software Design.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection and will remain open public record by law in Denmark.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe above limitations apply.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0.", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-analysis #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Danish #license-cc-by-4.0 #stance-detection #region-us \n", "### Dataset Summary\n\n\nPolitical stance in Danish. Examples represent statements by\npoliticians and are annotated for, against, or neutral to a given topic/article.", "### Supported Tasks and Leaderboards\n\n\n*", "### Languages\n\n\nDanish, bcp47: 'da-DK'\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### polstance\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'topic': a 'string' expressing a topic.\n* 'quote': a 'string' to be classified for its stance to the topic.\n* 'label': a class label representing the stance the text expresses towards the target. Full tagset with indices:\n* 'quoteID': a 'string' of the internal quote ID.\n* 'party': a 'string' describing the party affiliation of the quote utterer at the time of utterance.\n* 'politician': a 'string' naming the politician who uttered the quote.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nCollection of quotes from politicians to allow detecting how political quotes orient to issues.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is taken from proceedings of the Danish parliament, the Folketing - URL.", "#### Who are the source language producers?\n\n\nDanish polticians", "### Annotations", "#### Annotation process\n\n\nAnnotators labelled comments for being against, neutral, or for a specified topic", "#### Who are the annotators?\n\n\nDanish native speakers, 20s, male, studying Software Design.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection and will remain open public record by law in Denmark.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe above limitations apply.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0.", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:da", "license:cc-by-4.0", "region:us" ]

[]

[ "da" ]

[ "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: Leon Derczynski", "### Dataset Summary\n\nThis corpus introduces language processing resources and tools for Bornholmsk, a language spoken on the island of Bornholm, with roots in Danish and closely related to Scanian. \n\nSammenfattnijng på borrijnholmst: Dæjnna artikkelijn introduserer natursprågsresurser å varktoi for borrijnholmst, ed språg a dær snakkes på ön Borrijnholm me rødder i danst å i nær familia me skånst.\n\nFor more details, see the paper Bornholmsk Natural Language Processing: Resources and Tools.", "### Supported Tasks and Leaderboards\n\n*", "### Languages\n\nBornholmsk, a language variant of Danish spoken on the island of Bornholm. bcp47: 'da-bornholm'", "## Dataset Structure", "### Data Instances\n\n13169 lines, 175 167 words, 801 KB", "### Data Fields\n\n'id': the sentence ID, 'int'\n'text': the Bornholmsk text, 'string'", "### Data Splits\n\nMonolithic", "## Dataset Creation", "### Curation Rationale\n\nTo gather as much digital Bornholmsk together as possible", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom many places - see paper for details. Sources include poems, songs, translations from Danish, folk stories, dictionary entries.", "#### Who are the source language producers?\n\nNative speakers of Bornholmsk who have produced works in their native language, or translated them to Danish. Much of the data is the result of a community of Bornholmsk speakers volunteering their time across the island in an effort to capture this endangered language.", "### Annotations", "#### Annotation process\n\nNo annotations", "#### Who are the annotators?\n\nNo annotations", "### Personal and Sensitive Information\n\nUnknown, but low risk of presence, given the source material", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to capture Bornholmsk digitally and provide a way for NLP systems to interact with it, and perhaps even spark interest in dealing with the language.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis collection of Bornholmsk is curated by Leon Derczynski and Alex Speed Kjeldsen", "### Licensing Information\n\nCreative Commons Attribution 4.0" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Danish #license-cc-by-4.0 #region-us \n", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: Leon Derczynski", "### Dataset Summary\n\nThis corpus introduces language processing resources and tools for Bornholmsk, a language spoken on the island of Bornholm, with roots in Danish and closely related to Scanian. \n\nSammenfattnijng på borrijnholmst: Dæjnna artikkelijn introduserer natursprågsresurser å varktoi for borrijnholmst, ed språg a dær snakkes på ön Borrijnholm me rødder i danst å i nær familia me skånst.\n\nFor more details, see the paper Bornholmsk Natural Language Processing: Resources and Tools.", "### Supported Tasks and Leaderboards\n\n*", "### Languages\n\nBornholmsk, a language variant of Danish spoken on the island of Bornholm. bcp47: 'da-bornholm'", "## Dataset Structure", "### Data Instances\n\n13169 lines, 175 167 words, 801 KB", "### Data Fields\n\n'id': the sentence ID, 'int'\n'text': the Bornholmsk text, 'string'", "### Data Splits\n\nMonolithic", "## Dataset Creation", "### Curation Rationale\n\nTo gather as much digital Bornholmsk together as possible", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom many places - see paper for details. Sources include poems, songs, translations from Danish, folk stories, dictionary entries.", "#### Who are the source language producers?\n\nNative speakers of Bornholmsk who have produced works in their native language, or translated them to Danish. Much of the data is the result of a community of Bornholmsk speakers volunteering their time across the island in an effort to capture this endangered language.", "### Annotations", "#### Annotation process\n\nNo annotations", "#### Who are the annotators?\n\nNo annotations", "### Personal and Sensitive Information\n\nUnknown, but low risk of presence, given the source material", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to capture Bornholmsk digitally and provide a way for NLP systems to interact with it, and perhaps even spark interest in dealing with the language.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis collection of Bornholmsk is curated by Leon Derczynski and Alex Speed Kjeldsen", "### Licensing Information\n\nCreative Commons Attribution 4.0" ]

[ "task_categories:token-classification", "task_ids:part-of-speech", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nPart-of-speech information is basic NLP task. However, Twitter text\nis difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style.\nThis data is the vote-constrained bootstrapped data generate to support state-of-the-art results.\n\n\nThe data is about 1.5 million English tweets annotated for part-of-speech using Ritter's extension of the PTB tagset.\nThe tweets are from 2012 and 2013, tokenized using the GATE tokenizer and tagged\njointly using the CMU ARK tagger and Ritter's T-POS tagger. Only when both these taggers' outputs\nare completely compatible over a whole tweet, is that tweet added to the dataset.\n\n\nThis data is recommend for use a training data only, and not evaluation data.\n\n\nFor more details see URL and URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish, non-region-specific. 'bcp47:en'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### twitter\\_pos\\_vcb\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'string' features.\n* 'pos\\_tags': a 'list' of classification labels ('int'). Full tagset with indices:", "### 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\nCreative Commons Attribution 4.0 (CC-BY)", "### Contributions\n\n\nAuthor uploaded (@leondz)" ]

[ "TAGS\n#task_categories-token-classification #task_ids-part-of-speech #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nPart-of-speech information is basic NLP task. However, Twitter text\nis difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style.\nThis data is the vote-constrained bootstrapped data generate to support state-of-the-art results.\n\n\nThe data is about 1.5 million English tweets annotated for part-of-speech using Ritter's extension of the PTB tagset.\nThe tweets are from 2012 and 2013, tokenized using the GATE tokenizer and tagged\njointly using the CMU ARK tagger and Ritter's T-POS tagger. Only when both these taggers' outputs\nare completely compatible over a whole tweet, is that tweet added to the dataset.\n\n\nThis data is recommend for use a training data only, and not evaluation data.\n\n\nFor more details see URL and URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish, non-region-specific. 'bcp47:en'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### twitter\\_pos\\_vcb\n\n\n* 'id': a 'string' feature.\n* 'tokens': a 'list' of 'string' features.\n* 'pos\\_tags': a 'list' of classification labels ('int'). Full tagset with indices:", "### 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\nCreative Commons Attribution 4.0 (CC-BY)", "### Contributions\n\n\nAuthor uploaded (@leondz)" ]

[ "task_categories:text-classification", "task_ids:fact-checking", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:zu", "license:cc-by-4.0", "stance-detection", "arxiv:2205.03153", "region:us" ]

[ "2205.03153" ]

[ "zu" ]

[ "### Dataset Summary\n\n\nThis is a stance detection dataset in the Zulu language. The data is translated to Zulu by Zulu native speakers, from English source texts.\n\n\nOur paper aims at utilizing this progress made for English to transfers that knowledge into other languages, which is a non-trivial task due to the domain gap between English and the target languages. We propose a black-box non-intrusive method that utilizes techniques from Domain Adaptation to reduce the domain gap, without requiring any human expertise in the target language, by leveraging low-quality data in both a supervised and unsupervised manner. This allows us to rapidly achieve similar results for stance detection for the Zulu language, the target language in this work, as are found for English. A natively-translated dataset is used for evaluation of domain transfer.", "### Supported Tasks and Leaderboards\n\n\n*", "### Languages\n\n\nZulu ('bcp47:zu')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### zulu\\_stance\n\n\n* Size of downloaded dataset files: 212.54 KiB\n* Size of the generated dataset: 186.76 KiB\n* Total amount of disk used: 399.30KiB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'text': a 'string' expressing a stance.\n* 'target': a 'string' of the target/topic annotated here.\n* 'stance': a class label representing the stance the text expresses towards the target. Full tagset with indices:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nTo enable stance detection in Zulu and also to measure domain transfer in translation", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe original data is taken from Semeval2016 task 6: Detecting stance in tweets.,\nand then translated manually to Zulu.", "#### Who are the source language producers?\n\n\nEnglish-speaking Twitter users.", "### Annotations", "#### Annotation process\n\n\nSee Semeval2016 task 6: Detecting stance in tweets.; the annotations are taken from there.", "#### Who are the annotators?\n\n\nSee Semeval2016 task 6: Detecting stance in tweets.; the annotations are taken from there.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. User names are preserved.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThere's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.", "### Discussion of Biases\n\n\nWhile the data is in Zulu, the source text is not from or about Zulu-speakers, and so still expresses the social biases and topics found in English-speaking Twitter users. Further, some of the topics are USA-specific. The sentiments and ideas in this dataset do not represent Zulu speakers.", "### Other Known Limitations\n\n\nThe above limitations apply.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0.", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Zulu #license-cc-by-4.0 #stance-detection #arxiv-2205.03153 #region-us \n", "### Dataset Summary\n\n\nThis is a stance detection dataset in the Zulu language. The data is translated to Zulu by Zulu native speakers, from English source texts.\n\n\nOur paper aims at utilizing this progress made for English to transfers that knowledge into other languages, which is a non-trivial task due to the domain gap between English and the target languages. We propose a black-box non-intrusive method that utilizes techniques from Domain Adaptation to reduce the domain gap, without requiring any human expertise in the target language, by leveraging low-quality data in both a supervised and unsupervised manner. This allows us to rapidly achieve similar results for stance detection for the Zulu language, the target language in this work, as are found for English. A natively-translated dataset is used for evaluation of domain transfer.", "### Supported Tasks and Leaderboards\n\n\n*", "### Languages\n\n\nZulu ('bcp47:zu')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### zulu\\_stance\n\n\n* Size of downloaded dataset files: 212.54 KiB\n* Size of the generated dataset: 186.76 KiB\n* Total amount of disk used: 399.30KiB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'text': a 'string' expressing a stance.\n* 'target': a 'string' of the target/topic annotated here.\n* 'stance': a class label representing the stance the text expresses towards the target. Full tagset with indices:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nTo enable stance detection in Zulu and also to measure domain transfer in translation", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe original data is taken from Semeval2016 task 6: Detecting stance in tweets.,\nand then translated manually to Zulu.", "#### Who are the source language producers?\n\n\nEnglish-speaking Twitter users.", "### Annotations", "#### Annotation process\n\n\nSee Semeval2016 task 6: Detecting stance in tweets.; the annotations are taken from there.", "#### Who are the annotators?\n\n\nSee Semeval2016 task 6: Detecting stance in tweets.; the annotations are taken from there.", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. User names are preserved.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThere's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.", "### Discussion of Biases\n\n\nWhile the data is in Zulu, the source text is not from or about Zulu-speakers, and so still expresses the social biases and topics found in English-speaking Twitter users. Further, some of the topics are USA-specific. The sentiments and ideas in this dataset do not represent Zulu speakers.", "### Other Known Limitations\n\n\nThe above limitations apply.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset is curated by the paper's authors.", "### Licensing Information\n\n\nThe authors distribute this data under Creative Commons attribution license, CC-BY 4.0.", "### Contributions\n\n\nAuthor-added dataset @leondz" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

[ "language:ar", "language:en", "language:fr", "language:de", "language:hi", "language:ru", "language:es", "language:ta", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "ar", "en", "fr", "de", "hi", "ru", "es", "ta" ]

[ "# Data Statement for SHADES\n\n> How to use this document:\n> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.\n\n> For full details, the best source is the original Data Statements paper, here: URL .\n\n> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as \"URL\". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.\n\n> Only blockquoted content should be deleted; the final about statement should be left intact.\n\nData set name: Bias-Shades\n\nCitation (if available): TODO.\n\nData set developer(s): This dataset was compiled by dozens of research scientists through the BigScience open science collaboration. Collaborators, representing numerous cultures and languages, joined the project of their own volition.\n\nData statement author(s): Shayne Longpre, Aurélie Névéol, Shanya Sharma[Add name here if you add/edit the data statement :)].\n\nOthers who contributed to this document: N/A\n\nLicense: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).", "## A. CURATION RATIONALE \n\n> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.\n\nThis dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted. An initial set of sentences was inferred from stereotypes expressed in the crowS-pairs data set(Nangia et al.). Native speakers first crafted templates for sentences expressing a stereotype. These templates are marked for gender and plurality of the target nouns, so the template can be reused by substituting different targets. Next, the template-target noun pair combinations were annotated for the veracity/reliability of the expressed stereotype. The resulting sentences express common and less common stereotypes in a variety of cultures and languages.", "## B. LANGUAGE VARIETY/VARIETIES\n\n> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., \"English as spoken in Palo Alto, California\", or \"Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin\").\n\n* BCP-47 language tags: en-US, fr-FR, hi-IN, es-DO, ar-LY, ru-RU, de-DE, nl-NL, ta-IN.\n* Language variety description: English spoken by native speakers of the United States, native French people from metropolitan France, native Hindi and Tamil speakers from India, Spanish speakers from the Dominican Republic, Arabic speakers from Libya, Russian speakers from Russia, German speakers from Germany, and Dutch speakers from the Netherlands.", "## C. CONTRIBUTOR DEMOGRAPHIC\n> ## C. SPEAKER DEMOGRAPHIC\n\n> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include: \n\nParticipants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Listed below.\n\nSpeakers:\n* [ADD YOURSELF!]\n* Shayne Longpre: English-speaking, male, 28 years old, culturally Canadian.\n* Aurélie Névéol: French (native), English and Spanish speaking, female, 44 years old, culturally French (also familiar with American culture)\n* Shanya Sharma: Hindi(native), English speaking, female, 24 years old, culturally Indian\n* Margaret Mitchell: English, female, mid-30s, U.S.A.\n* Maraim Masoud: Arabic, English Speaking female.", "## D. ANNOTATOR DEMOGRAPHIC\n\n> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:\n\nParticipants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Speaker and annotator contributors listed in section C.", "## E. SPEECH SITUATION\n\nN/A", "## F. TEXT CHARACTERISTICS\n\n> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.\n\nCollected data is a collection of offensive stereotyped statements in numerous languages and cultures. They might be upsetting and/or offensive.\n\nAlong with these stereotyped statements are annotation judgements of how prevalent/real the expressed stereotypes are in the real world. Some statements were created from templates with substituted target nouns, and therefore may express an uncommon or unlikely stereotype.", "## G. RECORDING QUALITY\n\nN/A", "## H. OTHER\n\n> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.", "## I. PROVENANCE APPENDIX\n\nThis initiative is part of the BigScience Workshop: URL", "## About this document\n\nA data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.\n\nData Statements are from the University of Washington. Contact: datastatements@URL. This document template is licensed as CC0.\n\nThis version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the 2020 LREC workshop on Data Statements, by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski." ]

[ "TAGS\n#language-Arabic #language-English #language-French #language-German #language-Hindi #language-Russian #language-Spanish #language-Tamil #license-cc-by-sa-4.0 #region-us \n", "# Data Statement for SHADES\n\n> How to use this document:\n> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.\n\n> For full details, the best source is the original Data Statements paper, here: URL .\n\n> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as \"URL\". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.\n\n> Only blockquoted content should be deleted; the final about statement should be left intact.\n\nData set name: Bias-Shades\n\nCitation (if available): TODO.\n\nData set developer(s): This dataset was compiled by dozens of research scientists through the BigScience open science collaboration. Collaborators, representing numerous cultures and languages, joined the project of their own volition.\n\nData statement author(s): Shayne Longpre, Aurélie Névéol, Shanya Sharma[Add name here if you add/edit the data statement :)].\n\nOthers who contributed to this document: N/A\n\nLicense: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).", "## A. CURATION RATIONALE \n\n> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.\n\nThis dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted. An initial set of sentences was inferred from stereotypes expressed in the crowS-pairs data set(Nangia et al.). Native speakers first crafted templates for sentences expressing a stereotype. These templates are marked for gender and plurality of the target nouns, so the template can be reused by substituting different targets. Next, the template-target noun pair combinations were annotated for the veracity/reliability of the expressed stereotype. The resulting sentences express common and less common stereotypes in a variety of cultures and languages.", "## B. LANGUAGE VARIETY/VARIETIES\n\n> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., \"English as spoken in Palo Alto, California\", or \"Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin\").\n\n* BCP-47 language tags: en-US, fr-FR, hi-IN, es-DO, ar-LY, ru-RU, de-DE, nl-NL, ta-IN.\n* Language variety description: English spoken by native speakers of the United States, native French people from metropolitan France, native Hindi and Tamil speakers from India, Spanish speakers from the Dominican Republic, Arabic speakers from Libya, Russian speakers from Russia, German speakers from Germany, and Dutch speakers from the Netherlands.", "## C. CONTRIBUTOR DEMOGRAPHIC\n> ## C. SPEAKER DEMOGRAPHIC\n\n> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include: \n\nParticipants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Listed below.\n\nSpeakers:\n* [ADD YOURSELF!]\n* Shayne Longpre: English-speaking, male, 28 years old, culturally Canadian.\n* Aurélie Névéol: French (native), English and Spanish speaking, female, 44 years old, culturally French (also familiar with American culture)\n* Shanya Sharma: Hindi(native), English speaking, female, 24 years old, culturally Indian\n* Margaret Mitchell: English, female, mid-30s, U.S.A.\n* Maraim Masoud: Arabic, English Speaking female.", "## D. ANNOTATOR DEMOGRAPHIC\n\n> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:\n\nParticipants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Speaker and annotator contributors listed in section C.", "## E. SPEECH SITUATION\n\nN/A", "## F. TEXT CHARACTERISTICS\n\n> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.\n\nCollected data is a collection of offensive stereotyped statements in numerous languages and cultures. They might be upsetting and/or offensive.\n\nAlong with these stereotyped statements are annotation judgements of how prevalent/real the expressed stereotypes are in the real world. Some statements were created from templates with substituted target nouns, and therefore may express an uncommon or unlikely stereotype.", "## G. RECORDING QUALITY\n\nN/A", "## H. OTHER\n\n> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.", "## I. PROVENANCE APPENDIX\n\nThis initiative is part of the BigScience Workshop: URL", "## About this document\n\nA data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.\n\nData Statements are from the University of Washington. Contact: datastatements@URL. This document template is licensed as CC0.\n\nThis version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the 2020 LREC workshop on Data Statements, by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for sci-definition", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n\n- Point of Contact: Tal August (taugust@URL)", "### Dataset Summary\n\nThis is a dataset of scientific and medical definitions. They are drawn from two sources: \n\n(MedQuAD)[URL\n(Wikipedia Science Glossaries)[URL\n\nFor both sources, all terms and definitions are formatted as \"What is (are) X?\" or \"Do you have more information about X?\" with the answer being the definition of X.", "### Supported Tasks and Leaderboards\n\nThe task this dataset was created for was generating definitions of scientific terms. It has additionally be used to vary the complexity of generating definitions. Both versions of this task are described in the original paper.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n {\n \"question\": \"What is (are) complement component 2 deficiency?\",\n \"category\": \"medquad\",\n \"first_sentence\": \"Complement component 2 deficiency is a disorder that causes the immune system to malfunction, resulting in a form of immunodeficiency.\",\n \"support_doc_sparse_s2orc\":\"The occurrence of membranoproliferative glomerulonephritis in a 13 year old boy with inherited complete deficiency of the second component of complement (C2) is described here....<P>....\"\n }", "### Data Fields\n\nquestion: The question containing the term\n\ncategory: either medquad or wikipedia\n\nfirst_sentence: The answer (or definition)\n\nsupport_doc_sparse_s2orc: the support document, made up of 10 journal abstracts related to the term. Journal abstracts are drawn from S2ORC (URL using ElasticSearch.", "### Data Splits\n\nSplits are in a test, train, and dev. As a note, the original results reported in the paper on generating definitions was on the dev set and training was done on a split of the train set. The original test set was reserved for evaluating complexity control.", "## Dataset Creation", "### Curation Rationale\n\nWe collected from these two sources because they contain expert-written and factually correct information about scientific and medical terms. For the MedQuAD data, we filter for questions of the form: \u001cWhat is (are) X?\u001d or \u001cDo you have more information on X?\u001d We then format the Wikipedia science glossaries in the same form (\"What is (are) X?\u001d).", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was collected by filtering the original MedQuAD dataset and drawing on Wikpiedia science glossaries. This occurred between September 2020-March 2021.", "#### Who are the source language producers?\n\nNIH website (MedQuAD), and Wikpedia editors", "### Annotations", "#### Annotation process\n\nN/A", "#### Who are the annotators?\n\nN/A", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe goal of this dataset is to enable a wider audience of readers to understand and engage with scientific writing. A risk, though, is that such attempts might instead widen the gap to accessing scientific information.", "### Discussion of Biases\n\nThe texts in the datasets we train our models on are in General or Academic American English. Many people, especially those who have been historically underrepresented in STEM disciplines and medicine, may not be comfortable with this dialect of English. This risks further alienating the readers we hope to serve.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPlease refer to the (MedQuAD)[URL license\n\n\n\n @inproceedings{august-2022-definition-complexity,\n title={Generating Scientific Definitions with Controllable Complexity},\n author={Tal August, Katharina Reinecke, and Noah A. Smith},\n booktitle={ACL},\n year={2022}\n }" ]

[ "TAGS\n#region-us \n", "# Dataset Card for sci-definition", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n\n- Point of Contact: Tal August (taugust@URL)", "### Dataset Summary\n\nThis is a dataset of scientific and medical definitions. They are drawn from two sources: \n\n(MedQuAD)[URL\n(Wikipedia Science Glossaries)[URL\n\nFor both sources, all terms and definitions are formatted as \"What is (are) X?\" or \"Do you have more information about X?\" with the answer being the definition of X.", "### Supported Tasks and Leaderboards\n\nThe task this dataset was created for was generating definitions of scientific terms. It has additionally be used to vary the complexity of generating definitions. Both versions of this task are described in the original paper.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n {\n \"question\": \"What is (are) complement component 2 deficiency?\",\n \"category\": \"medquad\",\n \"first_sentence\": \"Complement component 2 deficiency is a disorder that causes the immune system to malfunction, resulting in a form of immunodeficiency.\",\n \"support_doc_sparse_s2orc\":\"The occurrence of membranoproliferative glomerulonephritis in a 13 year old boy with inherited complete deficiency of the second component of complement (C2) is described here....<P>....\"\n }", "### Data Fields\n\nquestion: The question containing the term\n\ncategory: either medquad or wikipedia\n\nfirst_sentence: The answer (or definition)\n\nsupport_doc_sparse_s2orc: the support document, made up of 10 journal abstracts related to the term. Journal abstracts are drawn from S2ORC (URL using ElasticSearch.", "### Data Splits\n\nSplits are in a test, train, and dev. As a note, the original results reported in the paper on generating definitions was on the dev set and training was done on a split of the train set. The original test set was reserved for evaluating complexity control.", "## Dataset Creation", "### Curation Rationale\n\nWe collected from these two sources because they contain expert-written and factually correct information about scientific and medical terms. For the MedQuAD data, we filter for questions of the form: \u001cWhat is (are) X?\u001d or \u001cDo you have more information on X?\u001d We then format the Wikipedia science glossaries in the same form (\"What is (are) X?\u001d).", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data was collected by filtering the original MedQuAD dataset and drawing on Wikpiedia science glossaries. This occurred between September 2020-March 2021.", "#### Who are the source language producers?\n\nNIH website (MedQuAD), and Wikpedia editors", "### Annotations", "#### Annotation process\n\nN/A", "#### Who are the annotators?\n\nN/A", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe goal of this dataset is to enable a wider audience of readers to understand and engage with scientific writing. A risk, though, is that such attempts might instead widen the gap to accessing scientific information.", "### Discussion of Biases\n\nThe texts in the datasets we train our models on are in General or Academic American English. Many people, especially those who have been historically underrepresented in STEM disciplines and medicine, may not be comfortable with this dialect of English. This risks further alienating the readers we hope to serve.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPlease refer to the (MedQuAD)[URL license\n\n\n\n @inproceedings{august-2022-definition-complexity,\n title={Generating Scientific Definitions with Controllable Complexity},\n author={Tal August, Katharina Reinecke, and Noah A. Smith},\n booktitle={ACL},\n year={2022}\n }" ]

[ "arxiv:2005.00614", "region:us" ]

[ "2005.00614" ]

[]

[ "### Dataset Summary\n\n\nEvaluation of Named Entity Recognition and Fact Extraction Systems for Russian.", "### Supported Tasks and Leaderboards\n\n\nFor each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').\n\n\n* 'token-classification': The dataset can be used to train a model for [NER], which consists in [Token Classification]. Success on this task is typically measured by achieving a *high/low* metric name. The (model name or model class) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at leaderboard url and ranks models based on metric name while also reporting other metric name.", "### Languages\n\n\nRU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nProvide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.\n\n\nProvide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.", "### Data Fields\n\n\nList and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.\n\n\n* 'id': order id\n* 'tokens': list of tokens\n* 'ner\\_tags': list of ner tags", "### Data Splits\n\n\nDescribe and name the splits in the dataset if there are more than one.\n\n\nDescribe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.\n\n\nProvide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWhat need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?", "### Source Data\n\n\nThis section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)", "#### Initial Data Collection and Normalization\n\n\nDescribe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.\n\n\nIf data was collected from other pre-existing datasets, link to source here and to their Hugging Face version.\n\n\nIf the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.", "#### Who are the source language producers?\n\n\nState whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.\n\n\nIf available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.\n\n\nDescribe other people represented or mentioned in the data. Where possible, link to references for the information.", "### Annotations\n\n\nIf the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.", "#### Annotation process\n\n\nIf applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.", "#### Who are the annotators?\n\n\nIf annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.\n\n\nDescribe the people or systems who originally created the annotations and their selection criteria if applicable.\n\n\nIf available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.", "### Personal and Sensitive Information\n\n\nState whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See Larson 2017 for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).\n\n\nState whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).\n\n\nIf efforts were made to anonymize the data, describe the anonymization process.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nPlease discuss some of the ways you believe the use of this dataset will impact society.\n\n\nThe statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.\n\n\nAlso describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.", "### Discussion of Biases\n\n\nProvide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.\n\n\nFor Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.\n\n\nIf analyses have been run quantifying these biases, please add brief summaries and links to the studies here.", "### Other Known Limitations\n\n\nIf studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nList the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.", "### Licensing Information\n\n\nMIT" ]

[ "TAGS\n#arxiv-2005.00614 #region-us \n", "### Dataset Summary\n\n\nEvaluation of Named Entity Recognition and Fact Extraction Systems for Russian.", "### Supported Tasks and Leaderboards\n\n\nFor each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').\n\n\n* 'token-classification': The dataset can be used to train a model for [NER], which consists in [Token Classification]. Success on this task is typically measured by achieving a *high/low* metric name. The (model name or model class) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at leaderboard url and ranks models based on metric name while also reporting other metric name.", "### Languages\n\n\nRU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nProvide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.\n\n\nProvide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.", "### Data Fields\n\n\nList and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.\n\n\n* 'id': order id\n* 'tokens': list of tokens\n* 'ner\\_tags': list of ner tags", "### Data Splits\n\n\nDescribe and name the splits in the dataset if there are more than one.\n\n\nDescribe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.\n\n\nProvide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWhat need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?", "### Source Data\n\n\nThis section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)", "#### Initial Data Collection and Normalization\n\n\nDescribe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.\n\n\nIf data was collected from other pre-existing datasets, link to source here and to their Hugging Face version.\n\n\nIf the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.", "#### Who are the source language producers?\n\n\nState whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.\n\n\nIf available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.\n\n\nDescribe other people represented or mentioned in the data. Where possible, link to references for the information.", "### Annotations\n\n\nIf the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.", "#### Annotation process\n\n\nIf applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.", "#### Who are the annotators?\n\n\nIf annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.\n\n\nDescribe the people or systems who originally created the annotations and their selection criteria if applicable.\n\n\nIf available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See Larson 2017 for using identity categories as a variables, particularly gender.\n\n\nDescribe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.", "### Personal and Sensitive Information\n\n\nState whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See Larson 2017 for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).\n\n\nState whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).\n\n\nIf efforts were made to anonymize the data, describe the anonymization process.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nPlease discuss some of the ways you believe the use of this dataset will impact society.\n\n\nThe statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.\n\n\nAlso describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.", "### Discussion of Biases\n\n\nProvide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.\n\n\nFor Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.\n\n\nIf analyses have been run quantifying these biases, please add brief summaries and links to the studies here.", "### Other Known Limitations\n\n\nIf studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nList the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.", "### Licensing Information\n\n\nMIT" ]

[ "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:" ]

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

[]

[ "en" ]

[ "# WikiHow Entries\n\nContains wikiHow question, answer and summary in '.json' format." ]

[ "TAGS\n#language-English #license-mit #region-us \n", "# WikiHow Entries\n\nContains wikiHow question, answer and summary in '.json' format." ]

[ "task_categories:translation", "annotations_creators:expert-generated", "language_creators:found", "language_creators:machine-generated", "language_creators:expert-generated", "multilinguality:translation", "size_categories:100K<n<1M", "license:unknown", "region:us" ]

[]

[ "bn-hi", "cs-en", "de-en", "de-fr", "en-cs", "en-de", "en-ha", "en-is", "en-ja", "en-ru", "en-zh", "fr-de", "ha-en", "hi-bn", "is-en", "ja-en", "ru-en", "xh-zh", "zh-en", "zu-xh" ]

[ "# Dataset Card for WMT21 Metrics Task", "## 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: WMT21 Metrics Shared Task\n- Repository: MT Metrics Eval Github Repository\n- Paper: Paper", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\nThe dataset comprises twenty language pairs:\n- Bengali-Hindi ('bn-hi')\n- Czech-English ('cs-en')\n- German-English ('de-en')\n- German-French ('de-fr')\n- English-Czech ('en-cs')\n- English-German ('en-de')\n- English-Hausa ('en-ha')\n- English-Icelandic ('en-is')\n- English-Japanese ('en-ja')\n- English-Russian ('en-ru')\n- English-Chinese ('en-zh')\n- French-German ('fr-de')\n- Hausa-English ('ha-en')\n- Hindi-Bengali ('hi-bn')\n- Icelandic-English ('is-en')\n- Japenese-English ('ja-en')\n- Russian-English ('ru-en')\n- Xhosa-Zulu ('xh-zu')\n- Chinese-English ('zh-en')\n- Zulu-Xhosa ('zu-xh')", "## 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-translation #annotations_creators-expert-generated #language_creators-found #language_creators-machine-generated #language_creators-expert-generated #multilinguality-translation #size_categories-100K<n<1M #license-unknown #region-us \n", "# Dataset Card for WMT21 Metrics Task", "## 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: WMT21 Metrics Shared Task\n- Repository: MT Metrics Eval Github Repository\n- Paper: Paper", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\nThe dataset comprises twenty language pairs:\n- Bengali-Hindi ('bn-hi')\n- Czech-English ('cs-en')\n- German-English ('de-en')\n- German-French ('de-fr')\n- English-Czech ('en-cs')\n- English-German ('en-de')\n- English-Hausa ('en-ha')\n- English-Icelandic ('en-is')\n- English-Japanese ('en-ja')\n- English-Russian ('en-ru')\n- English-Chinese ('en-zh')\n- French-German ('fr-de')\n- Hausa-English ('ha-en')\n- Hindi-Bengali ('hi-bn')\n- Icelandic-English ('is-en')\n- Japenese-English ('ja-en')\n- Russian-English ('ru-en')\n- Xhosa-Zulu ('xh-zu')\n- Chinese-English ('zh-en')\n- Zulu-Xhosa ('zu-xh')", "## 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." ]

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

[ "1912.06670" ]

[]

[ "# Dataset Card for Common Voice Corpus 9.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 20217 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 14973 validated hours in 93 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 9.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 20217 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 14973 validated hours in 93 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:other", "source_datasets:Open Images", "license:cc-by-4.0", "license:cc-by-2.0", "license:unknown", "super-resolution", "image-enhancement", "region:us" ]

[]

[]

[ "# VQGAN Pairs\n\nThis dataset contains ~2.4 million image pairs intended for improvement of image quality in VQGAN predictions. Each pair consists of:\n- A 512x512 crop of an image taken from Open Images.\n- A 256x256 image encoded and decoded using VQGAN, corresponding to the same image crop as the original.\n\nThis is the VQGAN implementation that was used for encoding and decoding: URL", "# License\nThis dataset is created using Open Images, which has the following license:\nThe annotations are licensed by Google LLC under CC BY 4.0 license. The images are listed as having a CC BY 2.0 license. Note: while we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself." ]

[ "TAGS\n#task_categories-other #source_datasets-Open Images #license-cc-by-4.0 #license-cc-by-2.0 #license-unknown #super-resolution #image-enhancement #region-us \n", "# VQGAN Pairs\n\nThis dataset contains ~2.4 million image pairs intended for improvement of image quality in VQGAN predictions. Each pair consists of:\n- A 512x512 crop of an image taken from Open Images.\n- A 256x256 image encoded and decoded using VQGAN, corresponding to the same image crop as the original.\n\nThis is the VQGAN implementation that was used for encoding and decoding: URL", "# License\nThis dataset is created using Open Images, which has the following license:\nThe annotations are licensed by Google LLC under CC BY 4.0 license. The images are listed as having a CC BY 2.0 license. Note: while we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself." ]

[ "Protein", "Enzyme Commission", "EC", "region:us" ]

[]

[ "protein sequences" ]

[]

[ "TAGS\n#Protein #Enzyme Commission #EC #region-us \n" ]

[ "Protein", "PFam", "region:us" ]

[]

[ "protein sequences" ]

[]

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

[ "Protein", "Gene Ontology", "GO", "region:us" ]

[]

[ "protein sequences" ]

[]

[ "TAGS\n#Protein #Gene Ontology #GO #region-us \n" ]

[ "Protein", "Enzyme Commission", "region:us" ]

[]

[ "protein sequences" ]

[ "# Dataset\n\nSwissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Enzyme Commission labels.\n\nThe dataset is ported from Protinfer: URL\n\nThe leaf level EC-labels are extracted and indexed, the mapping is provided in 'idx_mapping.json'. Proteins without leaf-level-EC tags are removed.", "## Example\nThe protein Q87BZ2 have the following EC tags.\n\n EC:2.-.-.- (Transferases)\n EC:2.7.-.- (Transferring phosphorus-containing groups)\n EC:2.7.1.- (Phosphotransferases with an alcohol group as acceptor)\n EC:2.7.1.30 (Glycerol kinase)\n\nWe only extract the leaf level labels, here EC:2.7.1.30, corresponding to glycerol kinase." ]

[ "TAGS\n#Protein #Enzyme Commission #region-us \n", "# Dataset\n\nSwissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Enzyme Commission labels.\n\nThe dataset is ported from Protinfer: URL\n\nThe leaf level EC-labels are extracted and indexed, the mapping is provided in 'idx_mapping.json'. Proteins without leaf-level-EC tags are removed.", "## Example\nThe protein Q87BZ2 have the following EC tags.\n\n EC:2.-.-.- (Transferases)\n EC:2.7.-.- (Transferring phosphorus-containing groups)\n EC:2.7.1.- (Phosphotransferases with an alcohol group as acceptor)\n EC:2.7.1.30 (Glycerol kinase)\n\nWe only extract the leaf level labels, here EC:2.7.1.30, corresponding to glycerol kinase." ]

[ "task_categories:other", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "multilinguality:translation", "size_categories:1M<n<10M", "source_datasets:original", "license:cc-by-3.0", "region:us" ]

[]

[ "uk-UA" ]

[ "# Ukrainian Wikipedia Edits", "### Dataset summary\n\nA collection of over 5M sentence edits extracted from Ukrainian Wikipedia history revisions.\n\nEdits were filtered by edit distance and sentence length. This makes them usable for grammatical error correction (GEC) or spellchecker models pre-training.", "### Supported Tasks and Leaderboards\n\n* Ukrainian grammatical error correction (GEC) - see UA-GEC\n* Ukrainian spelling correction", "### Languages\n\nUkrainian", "## Dataset Structure", "### Data Fields\n\n* 'src' - sentence before edit\n* 'tgt' - sentence after edit", "### Data Splits\n\n* 'full/train' contains all the data (5,243,376 samples)\n* 'tiny/train' contains a 5000 examples sample.", "## Dataset Creation\n\nLatest full Ukrainian Wiki dump were used as of 2022-04-30.\n\nIt was processed with the wikiedits and custom scripts.", "### Source Data", "#### Initial Data Collection and Normalization\n\nWikipedia", "#### Who are the source language producers?\n\nWikipedia writers", "### Annotations", "#### Annotation process\n\nAnnotations inferred by comparing two subsequent page revisions.", "#### Who are the annotators?\n\nPeople who edit Wikipedia pages.", "### Personal and Sensitive Information\n\nNo", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\nThe data is noisy. In addition to GEC and spelling edits, it contains a good chunk of factual changes and vandalism.\n\nMore task-specific filters could help.", "## Additional Information", "### Dataset Curators\n\nOleksiy Syvokon", "### Licensing Information\n\nCC-BY-3.0", "### Contributions\n\n@snukky created tools for dataset processing.\n\n@asivokon generated this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #multilinguality-translation #size_categories-1M<n<10M #source_datasets-original #license-cc-by-3.0 #region-us \n", "# Ukrainian Wikipedia Edits", "### Dataset summary\n\nA collection of over 5M sentence edits extracted from Ukrainian Wikipedia history revisions.\n\nEdits were filtered by edit distance and sentence length. This makes them usable for grammatical error correction (GEC) or spellchecker models pre-training.", "### Supported Tasks and Leaderboards\n\n* Ukrainian grammatical error correction (GEC) - see UA-GEC\n* Ukrainian spelling correction", "### Languages\n\nUkrainian", "## Dataset Structure", "### Data Fields\n\n* 'src' - sentence before edit\n* 'tgt' - sentence after edit", "### Data Splits\n\n* 'full/train' contains all the data (5,243,376 samples)\n* 'tiny/train' contains a 5000 examples sample.", "## Dataset Creation\n\nLatest full Ukrainian Wiki dump were used as of 2022-04-30.\n\nIt was processed with the wikiedits and custom scripts.", "### Source Data", "#### Initial Data Collection and Normalization\n\nWikipedia", "#### Who are the source language producers?\n\nWikipedia writers", "### Annotations", "#### Annotation process\n\nAnnotations inferred by comparing two subsequent page revisions.", "#### Who are the annotators?\n\nPeople who edit Wikipedia pages.", "### Personal and Sensitive Information\n\nNo", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\nThe data is noisy. In addition to GEC and spelling edits, it contains a good chunk of factual changes and vandalism.\n\nMore task-specific filters could help.", "## Additional Information", "### Dataset Curators\n\nOleksiy Syvokon", "### Licensing Information\n\nCC-BY-3.0", "### Contributions\n\n@snukky created tools for dataset processing.\n\n@asivokon generated this dataset." ]

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

[]

[ "en" ]

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

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

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs).\n\n\n\n> \n> NOTES\n> \n> \n> * There are currently no examples for SDGs 16 and 17. See this GitHub issue.\n> * As of July 2023, there areexamples also for SDG 16.\n> \n> \n>", "### Supported Tasks and Leaderboards\n\n\nTBD", "### Languages\n\n\nThe language of the dataset is English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is a string for the text, a string for the SDG, and an integer for the label.\n\n\nThe average token count for the premises and hypotheses are given below:", "### Data Fields\n\n\n* 'doi': Digital Object Identifier of the original document\n* 'text\\_id': unique text identifier\n* 'text': text excerpt from the document\n* 'sdg': the SDG the text is validated against\n* 'label': an integer from '0' to '17' which corresponds to the 'sdg' field\n* 'labels\\_negative': the number of volunteers who rejected the suggested SDG label\n* 'labels\\_positive': the number of volunteers who accepted the suggested SDG label\n* 'agreement': agreement score based on the formula", "### Data Splits\n\n\nThe OSDG-CD dataset has 1 splits: *train*.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe The OSDG Community Dataset (OSDG-CD) was developed as a benchmark for ...\nwith the goal of producing a dataset large enough to train models using neural methodologies.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nTBD", "#### Who are the source language producers?\n\n\nTBD", "### Annotations", "#### Annotation process\n\n\nTBD", "#### Who are the annotators?\n\n\nTBD", "### 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\nTBD\n\n\nAdditional Information\n----------------------\n\n\nTBD", "### Dataset Curators\n\n\nTBD", "### Licensing Information\n\n\nThe OSDG Community Dataset (OSDG-CD) is licensed under a Creative Commons Attribution 4.0 International License.", "### Contributions\n\n\nTBD" ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nThe OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs).\n\n\n\n> \n> NOTES\n> \n> \n> * There are currently no examples for SDGs 16 and 17. See this GitHub issue.\n> * As of July 2023, there areexamples also for SDG 16.\n> \n> \n>", "### Supported Tasks and Leaderboards\n\n\nTBD", "### Languages\n\n\nThe language of the dataset is English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is a string for the text, a string for the SDG, and an integer for the label.\n\n\nThe average token count for the premises and hypotheses are given below:", "### Data Fields\n\n\n* 'doi': Digital Object Identifier of the original document\n* 'text\\_id': unique text identifier\n* 'text': text excerpt from the document\n* 'sdg': the SDG the text is validated against\n* 'label': an integer from '0' to '17' which corresponds to the 'sdg' field\n* 'labels\\_negative': the number of volunteers who rejected the suggested SDG label\n* 'labels\\_positive': the number of volunteers who accepted the suggested SDG label\n* 'agreement': agreement score based on the formula", "### Data Splits\n\n\nThe OSDG-CD dataset has 1 splits: *train*.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe The OSDG Community Dataset (OSDG-CD) was developed as a benchmark for ...\nwith the goal of producing a dataset large enough to train models using neural methodologies.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nTBD", "#### Who are the source language producers?\n\n\nTBD", "### Annotations", "#### Annotation process\n\n\nTBD", "#### Who are the annotators?\n\n\nTBD", "### 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\nTBD\n\n\nAdditional Information\n----------------------\n\n\nTBD", "### Dataset Curators\n\n\nTBD", "### Licensing Information\n\n\nThe OSDG Community Dataset (OSDG-CD) is licensed under a Creative Commons Attribution 4.0 International License.", "### Contributions\n\n\nTBD" ]

[ "benchmark:raft", "region:us" ]

[]

[]

[ "# RAFT submissions for my-raft-submission", "## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.", "### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.", "### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:", "### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:", "### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]

[ "TAGS\n#benchmark-raft #region-us \n", "# RAFT submissions for my-raft-submission", "## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.", "### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.", "### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:", "### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:", "### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for wikiner", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\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", "## Dataset Description\n\n- Homepage: \n- Repository: [Needs More Informatio]\n- Paper: Learning multilingual named entity recognition from Wikipedia\n- Leaderboard: \n- Point of Contact: NazaGara", "### Dataset Summary\n\nNamed entities are phrases that contain the names of persons, organizations, locations, times and quantities.\n\nExample: [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .", "### Supported Tasks and Leaderboards\n\n\nNamed Entity Recognition (NER) is a subtask of Information Extraction. Different NER systems were evaluated as a part of the Sixth Message Understanding Conference in 1995 (MUC6). The target language was English. The participating systems performed well. However, many of them used language-specific resources for performing the task and it is unknown how they would have performed on another language than English.\nAfter 1995 NER systems have been developed for some European languages and a few Asian languages. There have been at least two studies that have applied one NER system to different languages. Palmer and Day [PD97] have used statistical methods for finding named entities in newswire articles in Chinese, English, French, Japanese, Portuguese and Spanish. They found that the difficulty of the NER task was different for the six languages but that a large part of the task could be performed with simple methods. Cucerzan and Yarowsky [CY99] used both morphological and contextual clues for identifying named entities in English, Greek, Hindi, Rumanian and Turkish. With minimal supervision, they obtained overall F measures between 40 and 70, depending on the languages used.\n- 'named-entity-recognition': The performance in this task is measured with F1 (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.\n\nThis dataset was used in order to train a Spanish NER model using BETO.", "### Languages\n\nThe only supported language is spanish (es).", "## Dataset Structure", "### Data Fields\n\nThe dictionary to map the id to the Label names is:\n{\n 0: 'O',\n 1: 'B-PER',\n 2: 'I-PER',\n 3: 'B-ORG',\n 4: 'I-ORG',\n 5: 'B-LOC',\n 6: 'I-LOC',\n 7: 'B-MISC',\n 8: 'I-MISC'\n}", "### Data Splits\n\nThe only split is the train split.\nNumber of examples = 128355", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\nCreated by Nothman et al. at 2013.", "### 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 wikiner", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\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", "## Dataset Description\n\n- Homepage: \n- Repository: [Needs More Informatio]\n- Paper: Learning multilingual named entity recognition from Wikipedia\n- Leaderboard: \n- Point of Contact: NazaGara", "### Dataset Summary\n\nNamed entities are phrases that contain the names of persons, organizations, locations, times and quantities.\n\nExample: [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .", "### Supported Tasks and Leaderboards\n\n\nNamed Entity Recognition (NER) is a subtask of Information Extraction. Different NER systems were evaluated as a part of the Sixth Message Understanding Conference in 1995 (MUC6). The target language was English. The participating systems performed well. However, many of them used language-specific resources for performing the task and it is unknown how they would have performed on another language than English.\nAfter 1995 NER systems have been developed for some European languages and a few Asian languages. There have been at least two studies that have applied one NER system to different languages. Palmer and Day [PD97] have used statistical methods for finding named entities in newswire articles in Chinese, English, French, Japanese, Portuguese and Spanish. They found that the difficulty of the NER task was different for the six languages but that a large part of the task could be performed with simple methods. Cucerzan and Yarowsky [CY99] used both morphological and contextual clues for identifying named entities in English, Greek, Hindi, Rumanian and Turkish. With minimal supervision, they obtained overall F measures between 40 and 70, depending on the languages used.\n- 'named-entity-recognition': The performance in this task is measured with F1 (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.\n\nThis dataset was used in order to train a Spanish NER model using BETO.", "### Languages\n\nThe only supported language is spanish (es).", "## Dataset Structure", "### Data Fields\n\nThe dictionary to map the id to the Label names is:\n{\n 0: 'O',\n 1: 'B-PER',\n 2: 'I-PER',\n 3: 'B-ORG',\n 4: 'I-ORG',\n 5: 'B-LOC',\n 6: 'I-LOC',\n 7: 'B-MISC',\n 8: 'I-MISC'\n}", "### Data Splits\n\nThe only split is the train split.\nNumber of examples = 128355", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\nCreated by Nothman et al. at 2013.", "### 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-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "arxiv:2203.09509", "region:us" ]

[ "2203.09509" ]

[]

[ "# Dataset Card for ToxiGen", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Citation Information", "## Sign up for Data Access\nTo access ToxiGen, first fill out this form.", "## Dataset Description\n\n- Repository: URL\n- Paper: URL\n- Point of Contact #1: Tom Hartvigsen\n- Point of Contact #2: Saadia Gabriel", "### Dataset Summary\n\nThis dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in our paper.", "### Languages\n\nAll text is written in English.", "## Dataset Structure", "### Data Fields\n\nWe release TOXIGEN as a dataframe with the following fields:\n- prompt is the prompt used for generation.\n- generation is the TOXIGEN generated text.\n- generation_method denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used.\n- prompt_label is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign).\n- group indicates the target group of the prompt.\n- roberta_prediction is the probability predicted by our corresponding RoBERTa model for each instance." ]

[ "TAGS\n#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #arxiv-2203.09509 #region-us \n", "# Dataset Card for ToxiGen", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Citation Information", "## Sign up for Data Access\nTo access ToxiGen, first fill out this form.", "## Dataset Description\n\n- Repository: URL\n- Paper: URL\n- Point of Contact #1: Tom Hartvigsen\n- Point of Contact #2: Saadia Gabriel", "### Dataset Summary\n\nThis dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in our paper.", "### Languages\n\nAll text is written in English.", "## Dataset Structure", "### Data Fields\n\nWe release TOXIGEN as a dataframe with the following fields:\n- prompt is the prompt used for generation.\n- generation is the TOXIGEN generated text.\n- generation_method denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used.\n- prompt_label is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign).\n- group indicates the target group of the prompt.\n- roberta_prediction is the probability predicted by our corresponding RoBERTa model for each instance." ]

[ "task_categories:text-retrieval", "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "source_datasets:extended|wikipedia", "language:af", "language:ar", "language:ast", "language:azb", "language:be", "language:bg", "language:bn", "language:br", "language:ca", "language:cs", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:fy", "language:ga", "language:gl", "language:hr", "language:hu", "language:hy", "language:id", "language:it", "language:iw", "language:ja", "language:ka", "language:ko", "language:la", "language:lt", "language:lv", "language:mk", "language:ml", "language:ms", "language:nl", "language:nn", "language:no", "language:pl", "language:pt", "language:ro", "language:ru", "language:sk", "language:sl", "language:sr", "language:sv", "language:th", "language:tr", "language:uk", "language:ur", "language:vi", "language:vo", "language:zh", "license:cc-by-sa-3.0", "arxiv:2103.01913", "region:us" ]

[ "2103.01913" ]

[ "af", "ar", "ast", "azb", "be", "bg", "bn", "br", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "hr", "hu", "hy", "id", "it", "iw", "ja", "ka", "ko", "la", "lt", "lv", "mk", "ml", "ms", "nl", "nn", "no", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "th", "tr", "uk", "ur", "vi", "vo", "zh" ]

[ "### Dataset Summary\n\n\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\n\n\nA few unique advantages of WIT:\n\n\n* The largest multimodal dataset (time of this writing) by the number of image-text examples.\n* A massively multilingual (first of its kind) with coverage for over 100+ languages.\n* A collection of diverse set of concepts and real world entities.\n* Brings forth challenging real-world test sets.", "### Dataset Preprocessing\n\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\n* 'image-captioning': This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.\n* 'text-retrieval': The goal in this task is to build a model that retrieves the text closest to an image.\n\n\nIn these tasks, any combination of the 'caption\\_reference\\_description', 'caption\\_attribution\\_description' and 'caption\\_alt\\_text\\_description' fields can be used as the input text/caption.", "### Languages\n\n\nThe dataset contains examples from all Wikipedia languages, with the following stats:\n\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'language': Language code depicting wikipedia language of the page\n* 'page\\_url': URL to wikipedia page\n* 'image\\_url': URL to wikipedia image\n* 'page\\_title': Wikipedia page's title\n* 'section\\_title': Section's title\n* 'hierarchical\\_section\\_title': Hierarchical section's title\n* 'caption\\_reference\\_description': This is the caption that is visible on the wiki page directly below the image.\n* 'caption\\_attribution\\_description': This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias and thus can be in a language different to the original page article.\n* 'caption\\_alt\\_text\\_description': This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers\n* 'mime\\_type': Mime type associated to the image.\n* 'original\\_height': Image height\n* 'original\\_width': Image width\n* 'is\\_main\\_image': Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.\n* 'attribution\\_passes\\_lang\\_id': Compared 'language' field with the attribution language (written in the prefix of the attribution description).\n* 'page\\_changed\\_recently':\n* 'context\\_page\\_description': Page description corresponds to the short description of the page. It provides a concise explanation of the scope of the page.\n* 'context\\_section\\_description': Text within the image's section.\n\n\n\n![](URL alt=\"Half Dome\" /> </br>\n<b>Figure: WIT annotation example. </b>\n</p>\n<p>Details on the field content can be found directly in the paper, figure 5 and table 12.</p>\n<h3>Data Splits</h3>\n<p>All data is held in )\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFrom the repository:\n\n\n\n> \n> Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding.\n> \n> \n> To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets.\n> \n> \n> The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs).\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nFrom the paper, section 3.1:\n\n\n\n> \n> We started with all Wikipedia content pages (i.e., ignoring other\n> pages that have discussions, comments and such). These number about ∼124M pages across 279 languages.\n> \n> \n>", "#### Who are the source language producers?\n\n\nText was extracted from Wikipedia.", "### Annotations", "#### Annotation process\n\n\nWIT was constructed using an automatic process. However it was human-validated.\n\n\nFrom the paper, section 3.7:\n\n\n\n> \n> To further verify the quality of the WIT dataset we performed a\n> study using (crowd-sourced) human annotators. As seen in Fig. 3,\n> we asked raters to answer 3 questions. Given an image and the page\n> title, raters first evaluate the quality of the attribution description\n> and reference description in the first two questions (order randomized). The third question understands the contextual quality of these\n> text descriptions given the page description and caption. Each response is on a 3-point scale: \"Yes\" if the text perfectly describes\n> the image, \"Maybe\" if it is sufficiently explanatory and \"No\" if it is\n> irrelevant or the image is inappropriate.\n> \n> \n>", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases\n\n\nFrom the paper, section 3.4:\n\n\n\n> \n> Lastly we found that certain image-text pairs occurred very\n> frequently. These were often generic images that did not have\n> much to do with the main article page. Common examples\n> included flags, logos, maps, insignia and such. To prevent\n> biasing the data, we heavily under-sampled all such images\n> \n> \n>", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @thomasw21, @nateraw and hassiahk for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_categories-image-to-text #task_ids-image-captioning #annotations_creators-machine-generated #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #source_datasets-extended|wikipedia #language-Afrikaans #language-Arabic #language-Asturian #language-South Azerbaijani #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Catalan #language-Czech #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Western Frisian #language-Irish #language-Galician #language-Croatian #language-Hungarian #language-Armenian #language-Indonesian #language-Italian #language-iw #language-Japanese #language-Georgian #language-Korean #language-Latin #language-Lithuanian #language-Latvian #language-Macedonian #language-Malayalam #language-Malay (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovak #language-Slovenian #language-Serbian #language-Swedish #language-Thai #language-Turkish #language-Ukrainian #language-Urdu #language-Vietnamese #language-Volapük #language-Chinese #license-cc-by-sa-3.0 #arxiv-2103.01913 #region-us \n", "### Dataset Summary\n\n\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\n\n\nA few unique advantages of WIT:\n\n\n* The largest multimodal dataset (time of this writing) by the number of image-text examples.\n* A massively multilingual (first of its kind) with coverage for over 100+ languages.\n* A collection of diverse set of concepts and real world entities.\n* Brings forth challenging real-world test sets.", "### Dataset Preprocessing\n\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\n* 'image-captioning': This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.\n* 'text-retrieval': The goal in this task is to build a model that retrieves the text closest to an image.\n\n\nIn these tasks, any combination of the 'caption\\_reference\\_description', 'caption\\_attribution\\_description' and 'caption\\_alt\\_text\\_description' fields can be used as the input text/caption.", "### Languages\n\n\nThe dataset contains examples from all Wikipedia languages, with the following stats:\n\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'language': Language code depicting wikipedia language of the page\n* 'page\\_url': URL to wikipedia page\n* 'image\\_url': URL to wikipedia image\n* 'page\\_title': Wikipedia page's title\n* 'section\\_title': Section's title\n* 'hierarchical\\_section\\_title': Hierarchical section's title\n* 'caption\\_reference\\_description': This is the caption that is visible on the wiki page directly below the image.\n* 'caption\\_attribution\\_description': This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias and thus can be in a language different to the original page article.\n* 'caption\\_alt\\_text\\_description': This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers\n* 'mime\\_type': Mime type associated to the image.\n* 'original\\_height': Image height\n* 'original\\_width': Image width\n* 'is\\_main\\_image': Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.\n* 'attribution\\_passes\\_lang\\_id': Compared 'language' field with the attribution language (written in the prefix of the attribution description).\n* 'page\\_changed\\_recently':\n* 'context\\_page\\_description': Page description corresponds to the short description of the page. It provides a concise explanation of the scope of the page.\n* 'context\\_section\\_description': Text within the image's section.\n\n\n\n![](URL alt=\"Half Dome\" /> </br>\n<b>Figure: WIT annotation example. </b>\n</p>\n<p>Details on the field content can be found directly in the paper, figure 5 and table 12.</p>\n<h3>Data Splits</h3>\n<p>All data is held in )\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFrom the repository:\n\n\n\n> \n> Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding.\n> \n> \n> To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets.\n> \n> \n> The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs).\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nFrom the paper, section 3.1:\n\n\n\n> \n> We started with all Wikipedia content pages (i.e., ignoring other\n> pages that have discussions, comments and such). These number about ∼124M pages across 279 languages.\n> \n> \n>", "#### Who are the source language producers?\n\n\nText was extracted from Wikipedia.", "### Annotations", "#### Annotation process\n\n\nWIT was constructed using an automatic process. However it was human-validated.\n\n\nFrom the paper, section 3.7:\n\n\n\n> \n> To further verify the quality of the WIT dataset we performed a\n> study using (crowd-sourced) human annotators. As seen in Fig. 3,\n> we asked raters to answer 3 questions. Given an image and the page\n> title, raters first evaluate the quality of the attribution description\n> and reference description in the first two questions (order randomized). The third question understands the contextual quality of these\n> text descriptions given the page description and caption. Each response is on a 3-point scale: \"Yes\" if the text perfectly describes\n> the image, \"Maybe\" if it is sufficiently explanatory and \"No\" if it is\n> irrelevant or the image is inappropriate.\n> \n> \n>", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases\n\n\nFrom the paper, section 3.4:\n\n\n\n> \n> Lastly we found that certain image-text pairs occurred very\n> frequently. These were often generic images that did not have\n> much to do with the main article page. Common examples\n> included flags, logos, maps, insignia and such. To prevent\n> biasing the data, we heavily under-sampled all such images\n> \n> \n>", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @thomasw21, @nateraw and hassiahk for adding this dataset." ]

[ "task_ids:image-captioning", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:afr", "language:af", "language:aaa", "language:abc", "language:ada", "language:adq", "language:aeu", "language:agq", "language:ags", "language:ahk", "language:aia", "language:ajz", "language:aka", "language:ak", "language:ame", "language:amh", "language:am", "language:amp", "language:amu", "language:ann", "language:aph", "language:awa", "language:awb", "language:azn", "language:azo", "language:bag", "language:bam", "language:bm", "language:baw", "language:bax", "language:bbk", "language:bcc", "language:bce", "language:bec", "language:bef", "language:ben", "language:bn", "language:bfd", "language:bfm", "language:bfn", "language:bgf", "language:bho", "language:bhs", "language:bis", "language:bi", "language:bjn", "language:bjr", "language:bkc", "language:bkh", "language:bkm", "language:bkx", "language:bob", "language:bod", "language:bo", "language:boz", "language:bqm", "language:bra", "language:brb", "language:bri", "language:brv", "language:bss", "language:bud", "language:buo", "language:bwt", "language:bwx", "language:bxa", "language:bya", "language:bze", "language:bzi", "language:cak", "language:cbr", "language:ceb", "language:cgc", "language:chd", "language:chp", "language:cim", "language:clo", "language:cmn", "language:zh", "language:cmo", "language:csw", "language:cuh", "language:cuv", "language:dag", "language:ddg", "language:ded", "language:deu", "language:de", "language:dig", "language:dje", "language:dmg", "language:dnw", "language:dtp", "language:dtr", "language:dty", "language:dug", "language:eee", "language:ekm", "language:enb", "language:enc", "language:eng", "language:en", "language:ewo", "language:fas", "language:fa", "language:fil", "language:fli", "language:fon", "language:fra", "language:fr", "language:fub", "language:fuh", "language:gal", "language:gbj", "language:gou", "language:gsw", "language:guc", "language:guj", "language:gu", "language:guz", "language:gwc", "language:hao", "language:hat", "language:ht", "language:hau", "language:ha", "language:hbb", "language:hig", "language:hil", "language:hin", "language:hi", "language:hla", "language:hna", "language:hre", "language:hro", "language:idt", "language:ilo", "language:ind", "language:id", "language:ino", "language:isu", "language:ita", "language:it", "language:jgo", "language:jmx", "language:jpn", "language:ja", "language:jra", "language:kak", "language:kam", "language:kan", "language:kn", "language:kau", "language:kr", "language:kbq", "language:kbx", "language:kby", "language:kek", "language:ken", "language:khb", "language:khm", "language:km", "language:kik", "language:ki", "language:kin", "language:rw", "language:kir", "language:ky", "language:kjb", "language:kmg", "language:kmr", "language:ku", "language:kms", "language:kmu", "language:kor", "language:ko", "language:kqr", "language:krr", "language:ksw", "language:kur", "language:kvt", "language:kwd", "language:kwu", "language:kwx", "language:kxp", "language:kyq", "language:laj", "language:lan", "language:lao", "language:lo", "language:lbr", "language:lfa", "language:lgg", "language:lgr", "language:lhm", "language:lhu", "language:lkb", "language:llg", "language:lmp", "language:lns", "language:loh", "language:lsi", "language:lts", "language:lug", "language:lg", "language:luy", "language:lwl", "language:mai", "language:mal", "language:ml", "language:mam", "language:mar", "language:mr", "language:mdr", "language:mfh", "language:mfj", "language:mgg", "language:mgm", "language:mgo", "language:mgq", "language:mhx", "language:miy", "language:mkz", "language:mle", "language:mlk", "language:mlw", "language:mmu", "language:mne", "language:mnf", "language:mnw", "language:mot", "language:mqj", "language:mrn", "language:mry", "language:msb", "language:muv", "language:mve", "language:mxu", "language:mya", "language:my", "language:myk", "language:myx", "language:mzm", "language:nas", "language:nco", "language:nep", "language:ne", "language:new", "language:nge", "language:ngn", "language:nhx", "language:njy", "language:nla", "language:nld", "language:nl", "language:nlv", "language:nod", "language:nsk", "language:nsn", "language:nso", "language:nst", "language:nuj", "language:nwe", "language:nwi", "language:nxa", "language:nxl", "language:nya", "language:ny", "language:nyo", "language:nyu", "language:nza", "language:odk", "language:oji", "language:oj", "language:oki", "language:omw", "language:ori", "language:or", "language:ozm", "language:pae", "language:pag", "language:pan", "language:pa", "language:pbt", "language:pce", "language:pcg", "language:pdu", "language:pea", "language:pex", "language:pis", "language:pkb", "language:pmf", "language:pnz", "language:por", "language:pt", "language:psp", "language:pwg", "language:qaa", "language:qub", "language:quc", "language:quf", "language:quz", "language:qve", "language:qvh", "language:qvm", "language:qvo", "language:qxh", "language:rel", "language:rnl", "language:ron", "language:ro", "language:roo", "language:rue", "language:rug", "language:rus", "language:ru", "language:san", "language:sa", "language:saq", "language:sat", "language:sdk", "language:sea", "language:sgd", "language:shn", "language:sml", "language:snk", "language:snl", "language:som", "language:so", "language:sot", "language:st", "language:sox", "language:spa", "language:es", "language:sps", "language:ssn", "language:stk", "language:swa", "language:sw", "language:swh", "language:sxb", "language:syw", "language:taj", "language:tam", "language:ta", "language:tbj", "language:tdb", "language:tdg", "language:tdt", "language:teo", "language:tet", "language:tgk", "language:tg", "language:tha", "language:th", "language:the", "language:thk", "language:thl", "language:thy", "language:tio", "language:tkd", "language:tnl", "language:tnn", "language:tnp", "language:tnt", "language:tod", "language:tom", "language:tpi", "language:tpl", "language:tpu", "language:tsb", "language:tsn", "language:tn", "language:tso", "language:ts", "language:tuv", "language:tuz", "language:tvs", "language:udg", "language:unr", "language:urd", "language:ur", "language:uzb", "language:uz", "language:ven", "language:ve", "language:vie", "language:vi", "language:vif", "language:war", "language:wbm", "language:wbr", "language:wms", "language:wni", "language:wnk", "language:wtk", "language:xho", "language:xh", "language:xkg", "language:xmd", "language:xmg", "language:xmm", "language:xog", "language:xty", "language:yas", "language:yav", "language:ybb", "language:ybh", "language:ybi", "language:ydd", "language:yea", "language:yet", "language:yid", "language:yi", "language:yin", "language:ymp", "language:zaw", "language:zho", "language:zlm", "language:zuh", "language:zul", "language:zu", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "afr", "af", "aaa", "abc", "ada", "adq", "aeu", "agq", "ags", "ahk", "aia", "ajz", "aka", "ak", "ame", "amh", "am", "amp", "amu", "ann", "aph", "awa", "awb", "azn", "azo", "bag", "bam", "bm", "baw", "bax", "bbk", "bcc", "bce", "bec", "bef", "ben", "bn", "bfd", "bfm", "bfn", "bgf", "bho", "bhs", "bis", "bi", "bjn", "bjr", "bkc", "bkh", "bkm", "bkx", "bob", "bod", "bo", "boz", "bqm", "bra", "brb", "bri", "brv", "bss", "bud", "buo", "bwt", "bwx", "bxa", "bya", "bze", "bzi", "cak", "cbr", "ceb", "cgc", "chd", "chp", "cim", "clo", "cmn", "zh", "cmo", "csw", "cuh", "cuv", "dag", "ddg", "ded", "deu", "de", "dig", "dje", "dmg", "dnw", "dtp", "dtr", "dty", "dug", "eee", "ekm", "enb", "enc", "eng", "en", "ewo", "fas", "fa", "fil", "fli", "fon", "fra", "fr", "fub", "fuh", "gal", "gbj", "gou", "gsw", "guc", "guj", "gu", "guz", "gwc", "hao", "hat", "ht", "hau", "ha", "hbb", "hig", "hil", "hin", "hi", "hla", "hna", "hre", "hro", "idt", "ilo", "ind", "id", "ino", "isu", "ita", "it", "jgo", "jmx", "jpn", "ja", "jra", "kak", "kam", "kan", "kn", "kau", "kr", "kbq", "kbx", "kby", "kek", "ken", "khb", "khm", "km", "kik", "ki", "kin", "rw", "kir", "ky", "kjb", "kmg", "kmr", "ku", "kms", "kmu", "kor", "ko", "kqr", "krr", "ksw", "kur", "kvt", "kwd", "kwu", "kwx", "kxp", "kyq", "laj", "lan", "lao", "lo", "lbr", "lfa", "lgg", "lgr", "lhm", "lhu", "lkb", "llg", "lmp", "lns", "loh", "lsi", "lts", "lug", "lg", "luy", "lwl", "mai", "mal", "ml", "mam", "mar", "mr", "mdr", "mfh", "mfj", "mgg", "mgm", "mgo", "mgq", "mhx", "miy", "mkz", "mle", "mlk", "mlw", "mmu", "mne", "mnf", "mnw", "mot", "mqj", "mrn", "mry", "msb", "muv", "mve", "mxu", "mya", "my", "myk", "myx", "mzm", "nas", "nco", "nep", "ne", "new", "nge", "ngn", "nhx", "njy", "nla", "nld", "nl", "nlv", "nod", "nsk", "nsn", "nso", "nst", "nuj", "nwe", "nwi", "nxa", "nxl", "nya", "ny", "nyo", "nyu", "nza", "odk", "oji", "oj", "oki", "omw", "ori", "or", "ozm", "pae", "pag", "pan", "pa", "pbt", "pce", "pcg", "pdu", "pea", "pex", "pis", "pkb", "pmf", "pnz", "por", "pt", "psp", "pwg", "qaa", "qub", "quc", "quf", "quz", "qve", "qvh", "qvm", "qvo", "qxh", "rel", "rnl", "ron", "ro", "roo", "rue", "rug", "rus", "ru", "san", "sa", "saq", "sat", "sdk", "sea", "sgd", "shn", "sml", "snk", "snl", "som", "so", "sot", "st", "sox", "spa", "es", "sps", "ssn", "stk", "swa", "sw", "swh", "sxb", "syw", "taj", "tam", "ta", "tbj", "tdb", "tdg", "tdt", "teo", "tet", "tgk", "tg", "tha", "th", "the", "thk", "thl", "thy", "tio", "tkd", "tnl", "tnn", "tnp", "tnt", "tod", "tom", "tpi", "tpl", "tpu", "tsb", "tsn", "tn", "tso", "ts", "tuv", "tuz", "tvs", "udg", "unr", "urd", "ur", "uzb", "uz", "ven", "ve", "vie", "vi", "vif", "war", "wbm", "wbr", "wms", "wni", "wnk", "wtk", "xho", "xh", "xkg", "xmd", "xmg", "xmm", "xog", "xty", "yas", "yav", "ybb", "ybh", "ybi", "ydd", "yea", "yet", "yid", "yi", "yin", "ymp", "zaw", "zho", "zlm", "zuh", "zul", "zu" ]

[ "### Data Instances\n\n\nThe examples look like this for Hausa:\n\n\nThis would produce an output:\n\n\nTo download all of the images locally directory 'images', you can do something similar to the following:", "### Data Fields\n\n\nThe metadata fields below are available:\n\n\n* image\\_id: a unique ID for the image\n* image\\_url: a link for downloading the image\n* caption: a caption corresponding to the image\n* story\\_id: a unique ID for the corresponding story in which the caption appears\n* album\\_id: a unique ID for the corresponding album in which the image appears\n* original\\_bloom\\_language\\_tag: the original language identification from the Bloom library\n* index\\_in\\_story: an index corresponding to the order of the image-caption pair in the corresponding story", "### Data Splits\n\n\nAll languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.\n\n\nNOTE: The captions for the test split are currently hidden due to on ongoing shared task competition. They have been replaced by a placeholder '' token.\n\n\nChangelog\n---------\n\n\n* 25 October 2022 - Initial release\n* 25 October 2022 - Update to include licenses on each data item." ]

[ "TAGS\n#task_ids-image-captioning #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Afrikaans #language-Afrikaans #language-Ghotuo #language-Ambala Ayta #language-Adangme #language-Adangbe #language-Akeu #language-Aghem #language-Esimbi #language-Akha #language-Arosi #language-Amri Karbi #language-Akan #language-Akan #language-Yanesha' #language-Amharic #language-Amharic #language-Alamblak #language-Guerrero Amuzgo #language-Obolo #language-Athpariya #language-Awadhi #language-Awa (Papua New Guinea) #language-Western Durango Nahuatl #language-Awing #language-Tuki #language-Bambara #language-Bambara #language-Bambili-Bambui #language-Bamun #language-Babanki #language-Southern Balochi #language-Bamenyam #language-Iceve-Maci #language-Benabena #language-Bengali #language-Bengali #language-Bafut #language-Mmen #language-Bunak #language-Bangandu #language-Bhojpuri #language-Buwal #language-Bislama #language-Bislama #language-Banjar #language-Binumarien #language-Baka (Cameroon) #language-Bakoko #language-Kom (Cameroon) #language-Baikeno #language-Aweer #language-Tibetan #language-Tibetan #language-Tiéyaxo Bozo #language-Wumboko #language-Braj #language-Brao #language-Mokpwe #language-Western Bru #language-Akoose #language-Ntcham #language-Terei #language-Bafaw-Balong #language-Bu-Nao Bunu #language-Tairaha #language-Batak #language-Jenaama Bozo #language-Bisu #language-Kaqchikel #language-Cashibo-Cacataibo #language-Cebuano #language-Kagayanen #language-Highland Oaxaca Chontal #language-Chipewyan #language-Cimbrian #language-Lowland Oaxaca Chontal #language-Mandarin Chinese #language-Chinese #language-Central Mnong #language-Swampy Cree #language-Chuka #language-Cuvok #language-Dagbani #language-Fataluku #language-Dedua #language-German #language-German #language-Digo #language-Zarma #language-Upper Kinabatangan #language-Western Dani #language-Kadazan Dusun #language-Lotud #language-Dotyali #language-Duruma #language-E #language-Elip #language-Markweeta #language-En #language-English #language-English #language-Ewondo #language-Persian #language-Persian #language-Filipino #language-Fali #language-Fon #language-French #language-French #language-Adamawa Fulfulde #language-Western Niger Fulfulde #language-Galolen #language-Bodo Gadaba #language-Gavar #language-Swiss German #language-Wayuu #language-Gujarati #language-Gujarati #language-Gusii #language-Gawri #language-Hakö #language-Haitian #language-Haitian #language-Hausa #language-Hausa #language-Huba #language-Kamwe #language-Hiligaynon #language-Hindi #language-Hindi #language-Halia #language-Mina (Cameroon) #language-Hre #language-Haroi #language-Idaté #language-Iloko #language-Indonesian #language-Indonesian #language-Inoke-Yate #language-Isu (Menchum Division) #language-Italian #language-Italian #language-Ngomba #language-Western Juxtlahuaca Mixtec #language-Japanese #language-Japanese #language-Jarai #language-Kalanguya #language-Kamba (Kenya) #language-Kannada #language-Kannada #language-Kanuri #language-Kanuri #language-Kamano #language-Ap Ma #language-Manga Kanuri #language-Kekchí #language-Kenyang #language-Lü #language-Khmer #language-Khmer #language-Kikuyu #language-Kikuyu #language-Kinyarwanda #language-Kinyarwanda #language-Kirghiz #language-Kirghiz #language-Q'anjob'al #language-Kâte #language-Northern Kurdish #language-Kurdish #language-Kamasau #language-Kanite #language-Korean #language-Korean #language-Kimaragang #language-Krung #language-S'gaw Karen #language-Kurdish #language-Lahta Karen #language-Kwaio #language-Kwakum #language-Khirwar #language-Wadiyara Koli #language-Kenga #language-Lango (Uganda) #language-Laru #language-Lao #language-Lao #language-Lohorung #language-Lefa #language-Lugbara #language-Lengo #language-Lhomi #language-Lahu #language-Kabras #language-Lole #language-Limbum #language-Lamnso' #language-Laarim #language-Lashi #language-Tachoni #language-Ganda #language-Ganda #language-Luyia #language-Eastern Lawa #language-Maithili #language-Malayalam #language-Malayalam #language-Mam #language-Marathi #language-Marathi #language-Mandar #language-Matal #language-Mefele #language-Mpumpong #language-Mambae #language-Meta' #language-Malila #language-Maru #language-Ayutla Mixtec #language-Makasae #language-Manambu #language-Ilwana #language-Moloko #language-Mmaala #language-Naba #language-Mundani #language-Mon #language-Barí #language-Mamasa #language-Cheke Holo #language-Mandaya #language-Masbatenyo #language-Muthuvan #language-Marwari (Pakistan) #language-Mada (Cameroon) #language-Burmese #language-Burmese #language-Mamara Senoufo #language-Masaaba #language-Mumuye #language-Naasioi #language-Sibe #language-Nepali (macrolanguage) #language-Nepali (macrolanguage) #language-Newari #language-Ngemba #language-Ngwo #language-Isthmus-Mecayapan Nahuatl #language-Njyem #language-Ngombale #language-Dutch #language-Dutch #language-Orizaba Nahuatl #language-Northern Thai #language-Naskapi #language-Nehan #language-Pedi #language-Tase Naga #language-Nyole #language-Ngwe #language-Southwest Tanna #language-Nauete #language-South Nuaulu #language-Nyanja #language-Nyanja #language-Nyoro #language-Nyungwe #language-Tigon Mbembe #language-Od #language-Ojibwa #language-Ojibwa #language-Okiek #language-South Tairora #language-Oriya (macrolanguage) #language-Oriya (macrolanguage) #language-Koonzime #language-Pagibete #language-Pangasinan #language-Panjabi #language-Panjabi #language-Southern Pashto #language-Ruching Palaung #language-Paniya #language-Kayan #language-Peranakan Indonesian #language-Petats #language-Pijin #language-Pokomo #language-Pamona #language-Pana (Central African Republic) #language-Portuguese #language-Portuguese #language-Philippine Sign Language #language-Gapapaiwa #language-qaa #language-Huallaga Huánuco Quechua #language-K'iche' #language-Lambayeque Quechua #language-Cusco Quechua #language-Eastern Apurímac Quechua #language-Huamalíes-Dos de Mayo Huánuco Quechua #language-Margos-Yarowilca-Lauricocha Quechua #language-Napo Lowland Quechua #language-Panao Huánuco Quechua #language-Rendille #language-Ranglong #language-Romanian #language-Romanian #language-Rotokas #language-Rusyn #language-Roviana #language-Russian #language-Russian #language-Sanskrit #language-Sanskrit #language-Samburu #language-Santali #language-Sos Kundi #language-Semai #language-Surigaonon #language-Shan #language-Central Sama #language-Soninke #language-Sangil #language-Somali #language-Somali #language-Southern Sotho #language-Southern Sotho #language-Swo #language-Spanish #language-Spanish #language-Saposa #language-Waata #language-Arammba #language-Swahili (macrolanguage) #language-Swahili (macrolanguage) #language-Swahili (individual language) #language-Suba #language-Kagate #language-Eastern Tamang #language-Tamil #language-Tamil #language-Tiang #language-Panchpargania #language-Western Tamang #language-Tetun Dili #language-Teso #language-Tetum #language-Tajik #language-Tajik #language-Thai #language-Thai #language-Chitwania Tharu #language-Tharaka #language-Dangaura Tharu #language-Tha #language-Teop #language-Tukudede #language-Lenakel #language-North Tanna #language-Whitesands #language-Tontemboan #language-Toma #language-Tombulu #language-Tok Pisin #language-Tlacoapa Me'phaa #language-Tampuan #language-Tsamai #language-Tswana #language-Tswana #language-Tsonga #language-Tsonga #language-Turkana #language-Turka #language-Taveta #language-Muduga #language-Mundari #language-Urdu #language-Urdu #language-Uzbek #language-Uzbek #language-Venda #language-Venda #language-Vietnamese #language-Vietnamese #language-Vili #language-Waray (Philippines) #language-Wa #language-Wagdi #language-Wambon #language-Ndzwani Comorian #language-Wanukaka #language-Watakataui #language-Xhosa #language-Xhosa #language-Kagoro #language-Mbudum #language-Mengaka #language-Manado Malay #language-Soga #language-Yoloxochitl Mixtec #language-Nugunu (Cameroon) #language-Yangben #language-Yemba #language-Yakha #language-Yamphu #language-Eastern Yiddish #language-Ravula #language-Yetfa #language-Yiddish #language-Yiddish #language-Riang Lai #language-Yamap #language-Mitla Zapotec #language-Chinese #language-Malay (individual language) #language-Tokano #language-Zulu #language-Zulu #license-cc-by-nc-4.0 #region-us \n", "### Data Instances\n\n\nThe examples look like this for Hausa:\n\n\nThis would produce an output:\n\n\nTo download all of the images locally directory 'images', you can do something similar to the following:", "### Data Fields\n\n\nThe metadata fields below are available:\n\n\n* image\\_id: a unique ID for the image\n* image\\_url: a link for downloading the image\n* caption: a caption corresponding to the image\n* story\\_id: a unique ID for the corresponding story in which the caption appears\n* album\\_id: a unique ID for the corresponding album in which the image appears\n* original\\_bloom\\_language\\_tag: the original language identification from the Bloom library\n* index\\_in\\_story: an index corresponding to the order of the image-caption pair in the corresponding story", "### Data Splits\n\n\nAll languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.\n\n\nNOTE: The captions for the test split are currently hidden due to on ongoing shared task competition. They have been replaced by a placeholder '' token.\n\n\nChangelog\n---------\n\n\n* 25 October 2022 - Initial release\n* 25 October 2022 - Update to include licenses on each data item." ]

[ "task_categories:summarization", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nStorytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.\nCritical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.\nThe dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding\nabstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player\ncollaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,\nand semantic ties to the previous dialogues.", "### Supported Tasks and Leaderboards\n\n\n'summarization': The dataset can be used to train a model for abstractive summarization. A fast abstractive summarization-RL model was presented as a baseline, which achieves ROUGE-L-F1 of 25.18.", "### Languages\n\n\nThe text in the dataset is in English, as spoken by actors on The Critical Role show, which is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game.\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for up to 5 configurations of the dataset.", "### Data Instances", "#### default\n\n\n* Size of downloaded dataset files: 279.93 MB\n* Size of the generated dataset: 4020.33 MB\n* Total amount of disk used: 4300.25 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### default\n\n\n* 'chunk': a 'string' feature.\n* 'chunk\\_id': a 'int32' feature.\n* 'turn\\_start': a 'int32' feature.\n* 'turn\\_end': a 'int32' feature.\n* 'alignment\\_score': a 'float32' feature.\n* 'turn\\_num': a 'int32' feature.\n* 'turns': a dictionary feature containing:\n\t+ 'names': a 'string' feature.\n\t+ 'utterances': a 'string' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nDialogue understanding and abstractive summarization remain both important and challenging problems for computational linguistics. Current paradigms in summarization modeling have specific failures in capturing semantics and pragmatics, content selection, rewriting, and evaluation in the domain of long, story-telling dialogue. CRD3 offers a linguistically rich dataset to explore these domains.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nDungeons and Dragons is a popular roleplaying game that is driven by structured storytelling. Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons. This dataset consists of 159 episodes of the show, where the episodes are transcribed. Inconsistencies (e.g. spelling of speaker names) were manually resolved.\n\n\nThe abstractive summaries were collected from the Critical Role Fandom wiki", "#### Who are the source language producers?\n\n\nThe language producers are actors on The Critical Role show, which is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game.", "### Annotations", "#### Annotation process\n\n\n[N/A]", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\n[N/A]\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\nCRTranscript provided transcripts of the show; contributors of the Critical Role Wiki provided the abstractive summaries.", "### Licensing Information\n\n\nThis work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa-4.0]., as corresponding to the Critical Role Wiki URL", "### Contributions\n\n\nThanks to @thomwolf, @lhoestq, @mariamabarham, @lewtun for adding this dataset." ]

[ "TAGS\n#task_categories-summarization #task_categories-text-generation #task_categories-fill-mask #task_ids-dialogue-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nStorytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.\nCritical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.\nThe dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding\nabstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player\ncollaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,\nand semantic ties to the previous dialogues.", "### Supported Tasks and Leaderboards\n\n\n'summarization': The dataset can be used to train a model for abstractive summarization. A fast abstractive summarization-RL model was presented as a baseline, which achieves ROUGE-L-F1 of 25.18.", "### Languages\n\n\nThe text in the dataset is in English, as spoken by actors on The Critical Role show, which is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game.\n\n\nDataset Structure\n-----------------\n\n\nWe show detailed information for up to 5 configurations of the dataset.", "### Data Instances", "#### default\n\n\n* Size of downloaded dataset files: 279.93 MB\n* Size of the generated dataset: 4020.33 MB\n* Total amount of disk used: 4300.25 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### default\n\n\n* 'chunk': a 'string' feature.\n* 'chunk\\_id': a 'int32' feature.\n* 'turn\\_start': a 'int32' feature.\n* 'turn\\_end': a 'int32' feature.\n* 'alignment\\_score': a 'float32' feature.\n* 'turn\\_num': a 'int32' feature.\n* 'turns': a dictionary feature containing:\n\t+ 'names': a 'string' feature.\n\t+ 'utterances': a 'string' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nDialogue understanding and abstractive summarization remain both important and challenging problems for computational linguistics. Current paradigms in summarization modeling have specific failures in capturing semantics and pragmatics, content selection, rewriting, and evaluation in the domain of long, story-telling dialogue. CRD3 offers a linguistically rich dataset to explore these domains.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nDungeons and Dragons is a popular roleplaying game that is driven by structured storytelling. Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons. This dataset consists of 159 episodes of the show, where the episodes are transcribed. Inconsistencies (e.g. spelling of speaker names) were manually resolved.\n\n\nThe abstractive summaries were collected from the Critical Role Fandom wiki", "#### Who are the source language producers?\n\n\nThe language producers are actors on The Critical Role show, which is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game.", "### Annotations", "#### Annotation process\n\n\n[N/A]", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\n[N/A]\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\nCRTranscript provided transcripts of the show; contributors of the Critical Role Wiki provided the abstractive summaries.", "### Licensing Information\n\n\nThis work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa-4.0]., as corresponding to the Critical Role Wiki URL", "### Contributions\n\n\nThanks to @thomwolf, @lhoestq, @mariamabarham, @lewtun for adding this dataset." ]

[ "task_categories:image-to-text", "task_categories:text-retrieval", "task_ids:image-captioning", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "source_datasets:extended|wikipedia", "language:af", "language:an", "language:ar", "language:arz", "language:ast", "language:az", "language:azb", "language:ba", "language:bar", "language:be", "language:bg", "language:bn", "language:br", "language:bs", "language:ca", "language:ce", "language:ceb", "language:ckb", "language:cs", "language:cv", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fil", "language:fr", "language:fy", "language:ga", "language:gl", "language:hi", "language:hr", "language:hsb", "language:ht", "language:hu", "language:hy", "language:ia", "language:id", "language:io", "language:is", "language:it", "language:iw", "language:ja", "language:jv", "language:ka", "language:kk", "language:kn", "language:ko", "language:la", "language:lah", "language:lb", "language:lmo", "language:lt", "language:lv", "language:mg", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:my", "language:nan", "language:nds", "language:ne", "language:nl", "language:nn", "language:no", "language:nv", "language:oc", "language:pa", "language:pl", "language:pt", "language:qu", "language:ro", "language:ru", "language:sco", "language:si", "language:sk", "language:sl", "language:sq", "language:sr", "language:sv", "language:sw", "language:ta", "language:te", "language:tg", "language:th", "language:tr", "language:tt", "language:uk", "language:ur", "language:uz", "language:vec", "language:vi", "language:vo", "language:war", "language:xmf", "language:yue", "language:zh", "license:cc-by-sa-4.0", "text-image-retrieval", "arxiv:2103.01913", "arxiv:1512.03385", "arxiv:1905.00641", "region:us" ]

[ "2103.01913", "1512.03385", "1905.00641" ]

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[ "# Dataset Card for WIT", "## 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: WIT homepage\n- Paper: WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\n\n- Leaderboard: WIT leaderboard and WIT Kaggle competition\n- Point of Contact: Miriam Redi", "### Dataset Summary\n\nWikimedia's version of the Wikipedia-based Image Text (WIT) Dataset, a large multimodal multilingual dataset.\n\nFrom the official blog post:\n\n> The core training data is taken from the Wikipedia Image-Text (WIT) Dataset, a large curated set of more than 37 million image-text associations extracted from Wikipedia articles in 108 languages that was recently released by Google Research.\n>\n> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images. However, due to licensing and data volume issues, the Google dataset only provides the image name and corresponding URL for download and not the raw image files.\n>\n> Getting easy access to the image files is crucial for participants to successfully develop competitive models. Therefore, today, the Wikimedia Research team is releasing its first large image dataset. It contains more than six million image files from Wikipedia articles in 100+ languages, which correspond to almost [1] all captioned images in the WIT dataset. Image files are provided at a 300-px resolution, a size that is suitable for most of the learning frameworks used to classify and analyze images.\n\n> [1] We are publishing all images having a non-null “reference description” in the WIT dataset. For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the RetinaFace detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are candidate for deletion on Commons from the dataset.\n\nNote: Compared to Google's version, which has contents of one Wikipedia page per data sample, this version groups contents of all Wikipedia pages available in different languages for the image in one single data sample to avoid duplication of image bytes.", "### Supported Tasks and Leaderboards\n\n- 'image-captioning': This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.\n\n- 'text-retrieval': The goal in this task is to build a model that retrieves the text ('caption_title_and_reference_description') closest to an image. The leaderboard for this task can be found here. This task also has a competition on Kaggle. \n\nIn these tasks, any combination of the 'caption_reference_description', 'caption_attribution_description' and 'caption_alt_text_description' fields can be used as the input text/caption.", "### Languages\n\nThe dataset contains examples from all Wikipedia languages.", "## Dataset Structure", "### Data Instances\n\nEach instance is an image, its representation in bytes, a pre-computed embedding, and the set of captions attached to the image in Wikipedia.\n\n\n\nNote: The dataset is stored in Parquet for better performance. This dataset was generated from the original files using this script. Additionally, 120 examples from the original files have incorrectly formatted one or more of the following fields: 'original_height', 'original_width', 'mime_type' and 'caption_attribution_description'. The fixed versions of these examples that were used in the generation script can be found here.", "### Data Fields\n\n- 'image': A 'PIL.Image.Image' object containing the image resized to a width of 300-px while preserving its aspect ratio. 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_url': URL to wikipedia image\n- 'embedding': Precomputed image embedding. Each image is described with a 2048-dimensional signature extracted from the second-to-last layer of a ResNet-50 neural network trained with Imagenet data. These embeddings contain rich information about the image content and layout, in a compact form\n- 'metadata_url': URL to wikimedia page containing the image and the metadata\n- 'original_height': Original image height before resizing\n- 'original_width': Original image width before resizing\n- 'mime_type': Mime type associated to the image\n- 'caption_attribution_description': This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias.\n- 'wit_features': Sequence of captions for the image with language, page URL, information about the page, caption text, etc.\n - 'language': Language code depicting wikipedia language of the page\n - 'page_url': URL to wikipedia page\n - 'attribution_passes_lang_id': Compared 'language' field with the attribution language (written in the prefix of the attribution description.\n - 'caption_alt_text_description': This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers\n - 'caption_reference_description': This is the caption that is visible on the wikipedia page directly below the image.\n - 'caption_title_and_reference_description': Concatenation of 'page_title' and 'caption_reference_description'.\n - 'context_page_description': Corresponds to the short description of the page. It provides a concise explanation of the scope of the page.\n - 'context_section_description': Text within the image's section\n - 'hierarchical_section_title': Hierarchical section's title\n - 'is_main_image': Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.\n - 'page_changed_recently': \n - 'page_title': Wikipedia page's title\n - 'section_title': Section's title\n\n<p align='center'>\n <img width='75%' src='URL alt=\"Half Dome\" /> </br>\n<b>Figure: WIT annotation example. </b>\n</p>\n\nDetails on the field content can be found directly in the paper, figure 5 and table 12.", "### Data Splits\n\nAll data is held in 'train' split, with a total of 6477255 examples.", "## Dataset Creation", "### Curation Rationale\n\nFrom the official blog post: \n\n> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images.\n\n> Getting easy access to the image files is crucial for participants to successfully develop competitive models.\n \n> With this large release of visual data, we aim to help the competition participants—as well as researchers and practitioners who are interested in working with Wikipedia images—find and download the large number of image files associated with the challenge, in a compact form.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper, section 3.1:\n\n> We started with all Wikipedia content pages (i.e., ignoring other\npages that have discussions, comments and such). These number about ~124M pages across 279 languages.", "#### Who are the source language producers?\n\nText was extracted from Wikipedia.", "### Annotations", "#### Annotation process\n\nWIT was constructed using an automatic process. However it was human-validated.\n\nFrom the paper, section 3.7:\n\n> To further verify the quality of the WIT dataset we performed a\nstudy using (crowd-sourced) human annotators. As seen in Fig. 3,\nwe asked raters to answer 3 questions. Given an image and the page\ntitle, raters first evaluate the quality of the attribution description\nand reference description in the first two questions (order randomized). The third question understands the contextual quality of these\ntext descriptions given the page description and caption. Each response is on a 3-point scale: \"Yes\" if the text perfectly describes\nthe image, \"Maybe\" if it is sufficiently explanatory and \"No\" if it is\nirrelevant or the image is inappropriate.", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nFrom the official blog post:\n\n> For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the RetinaFace detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are candidate for deletion on Commons from the dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nFrom the paper, section 3.4:\n\n> Lastly we found that certain image-text pairs occurred very\nfrequently. These were often generic images that did not have\nmuch to do with the main article page. Common examples\nincluded flags, logos, maps, insignia and such. To prevent\nbiasing the data, we heavily under-sampled all such images", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nMiriam Redi, Fabian Kaelin and Tiziano Piccardi.", "### Licensing Information\n\nCC BY-SA 4.0 international license", "### Contributions\n\nThanks to @nateraw, yjernite and mariosasko for adding this dataset." ]

[ "TAGS\n#task_categories-image-to-text #task_categories-text-retrieval #task_ids-image-captioning #annotations_creators-machine-generated #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #source_datasets-extended|wikipedia #language-Afrikaans #language-Aragonese #language-Arabic #language-Egyptian Arabic #language-Asturian #language-Azerbaijani #language-South Azerbaijani #language-Bashkir #language-Bavarian #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Bosnian #language-Catalan #language-Chechen #language-Cebuano #language-Central Kurdish #language-Czech #language-Chuvash #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-Filipino #language-French #language-Western Frisian #language-Irish #language-Galician #language-Hindi #language-Croatian #language-Upper Sorbian #language-Haitian #language-Hungarian #language-Armenian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Ido #language-Icelandic #language-Italian #language-iw #language-Japanese #language-Javanese #language-Georgian #language-Kazakh #language-Kannada #language-Korean #language-Latin #language-Lahnda #language-Luxembourgish #language-Lombard #language-Lithuanian #language-Latvian #language-Malagasy #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Burmese #language-Min Nan Chinese #language-Low German #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Navajo #language-Occitan (post 1500) #language-Panjabi #language-Polish #language-Portuguese #language-Quechua #language-Romanian #language-Russian #language-Scots #language-Sinhala #language-Slovak #language-Slovenian #language-Albanian #language-Serbian #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Tajik #language-Thai #language-Turkish #language-Tatar #language-Ukrainian #language-Urdu #language-Uzbek #language-Venetian #language-Vietnamese #language-Volapük #language-Waray (Philippines) #language-Mingrelian #language-Yue Chinese #language-Chinese #license-cc-by-sa-4.0 #text-image-retrieval #arxiv-2103.01913 #arxiv-1512.03385 #arxiv-1905.00641 #region-us \n", "# Dataset Card for WIT", "## 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: WIT homepage\n- Paper: WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\n\n- Leaderboard: WIT leaderboard and WIT Kaggle competition\n- Point of Contact: Miriam Redi", "### Dataset Summary\n\nWikimedia's version of the Wikipedia-based Image Text (WIT) Dataset, a large multimodal multilingual dataset.\n\nFrom the official blog post:\n\n> The core training data is taken from the Wikipedia Image-Text (WIT) Dataset, a large curated set of more than 37 million image-text associations extracted from Wikipedia articles in 108 languages that was recently released by Google Research.\n>\n> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images. However, due to licensing and data volume issues, the Google dataset only provides the image name and corresponding URL for download and not the raw image files.\n>\n> Getting easy access to the image files is crucial for participants to successfully develop competitive models. Therefore, today, the Wikimedia Research team is releasing its first large image dataset. It contains more than six million image files from Wikipedia articles in 100+ languages, which correspond to almost [1] all captioned images in the WIT dataset. Image files are provided at a 300-px resolution, a size that is suitable for most of the learning frameworks used to classify and analyze images.\n\n> [1] We are publishing all images having a non-null “reference description” in the WIT dataset. For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the RetinaFace detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are candidate for deletion on Commons from the dataset.\n\nNote: Compared to Google's version, which has contents of one Wikipedia page per data sample, this version groups contents of all Wikipedia pages available in different languages for the image in one single data sample to avoid duplication of image bytes.", "### Supported Tasks and Leaderboards\n\n- 'image-captioning': This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.\n\n- 'text-retrieval': The goal in this task is to build a model that retrieves the text ('caption_title_and_reference_description') closest to an image. The leaderboard for this task can be found here. This task also has a competition on Kaggle. \n\nIn these tasks, any combination of the 'caption_reference_description', 'caption_attribution_description' and 'caption_alt_text_description' fields can be used as the input text/caption.", "### Languages\n\nThe dataset contains examples from all Wikipedia languages.", "## Dataset Structure", "### Data Instances\n\nEach instance is an image, its representation in bytes, a pre-computed embedding, and the set of captions attached to the image in Wikipedia.\n\n\n\nNote: The dataset is stored in Parquet for better performance. This dataset was generated from the original files using this script. Additionally, 120 examples from the original files have incorrectly formatted one or more of the following fields: 'original_height', 'original_width', 'mime_type' and 'caption_attribution_description'. The fixed versions of these examples that were used in the generation script can be found here.", "### Data Fields\n\n- 'image': A 'PIL.Image.Image' object containing the image resized to a width of 300-px while preserving its aspect ratio. 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_url': URL to wikipedia image\n- 'embedding': Precomputed image embedding. Each image is described with a 2048-dimensional signature extracted from the second-to-last layer of a ResNet-50 neural network trained with Imagenet data. These embeddings contain rich information about the image content and layout, in a compact form\n- 'metadata_url': URL to wikimedia page containing the image and the metadata\n- 'original_height': Original image height before resizing\n- 'original_width': Original image width before resizing\n- 'mime_type': Mime type associated to the image\n- 'caption_attribution_description': This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias.\n- 'wit_features': Sequence of captions for the image with language, page URL, information about the page, caption text, etc.\n - 'language': Language code depicting wikipedia language of the page\n - 'page_url': URL to wikipedia page\n - 'attribution_passes_lang_id': Compared 'language' field with the attribution language (written in the prefix of the attribution description.\n - 'caption_alt_text_description': This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers\n - 'caption_reference_description': This is the caption that is visible on the wikipedia page directly below the image.\n - 'caption_title_and_reference_description': Concatenation of 'page_title' and 'caption_reference_description'.\n - 'context_page_description': Corresponds to the short description of the page. It provides a concise explanation of the scope of the page.\n - 'context_section_description': Text within the image's section\n - 'hierarchical_section_title': Hierarchical section's title\n - 'is_main_image': Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.\n - 'page_changed_recently': \n - 'page_title': Wikipedia page's title\n - 'section_title': Section's title\n\n<p align='center'>\n <img width='75%' src='URL alt=\"Half Dome\" /> </br>\n<b>Figure: WIT annotation example. </b>\n</p>\n\nDetails on the field content can be found directly in the paper, figure 5 and table 12.", "### Data Splits\n\nAll data is held in 'train' split, with a total of 6477255 examples.", "## Dataset Creation", "### Curation Rationale\n\nFrom the official blog post: \n\n> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images.\n\n> Getting easy access to the image files is crucial for participants to successfully develop competitive models.\n \n> With this large release of visual data, we aim to help the competition participants—as well as researchers and practitioners who are interested in working with Wikipedia images—find and download the large number of image files associated with the challenge, in a compact form.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper, section 3.1:\n\n> We started with all Wikipedia content pages (i.e., ignoring other\npages that have discussions, comments and such). These number about ~124M pages across 279 languages.", "#### Who are the source language producers?\n\nText was extracted from Wikipedia.", "### Annotations", "#### Annotation process\n\nWIT was constructed using an automatic process. However it was human-validated.\n\nFrom the paper, section 3.7:\n\n> To further verify the quality of the WIT dataset we performed a\nstudy using (crowd-sourced) human annotators. As seen in Fig. 3,\nwe asked raters to answer 3 questions. Given an image and the page\ntitle, raters first evaluate the quality of the attribution description\nand reference description in the first two questions (order randomized). The third question understands the contextual quality of these\ntext descriptions given the page description and caption. Each response is on a 3-point scale: \"Yes\" if the text perfectly describes\nthe image, \"Maybe\" if it is sufficiently explanatory and \"No\" if it is\nirrelevant or the image is inappropriate.", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nFrom the official blog post:\n\n> For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the RetinaFace detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are candidate for deletion on Commons from the dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nFrom the paper, section 3.4:\n\n> Lastly we found that certain image-text pairs occurred very\nfrequently. These were often generic images that did not have\nmuch to do with the main article page. Common examples\nincluded flags, logos, maps, insignia and such. To prevent\nbiasing the data, we heavily under-sampled all such images", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nMiriam Redi, Fabian Kaelin and Tiziano Piccardi.", "### Licensing Information\n\nCC BY-SA 4.0 international license", "### Contributions\n\nThanks to @nateraw, yjernite and mariosasko for adding this dataset." ]

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:other", "arxiv:1409.0575", "arxiv:1912.07726", "arxiv:1811.12231", "arxiv:2109.13228", "region:us" ]

[ "1409.0575", "1912.07726", "1811.12231", "2109.13228" ]

[ "en" ]

[ "### Dataset Summary\n\n\nILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a \"synonym set\" or \"synset\". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.\n\n\nThis dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available here.\n\n\nTo evaluate the 'imagenet-classification' accuracy on the test split, one must first create an account at URL. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at URL The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is \"Classification submission (top-5 cls error)\". A sample of an exported text file looks like the following:\n\n\nThe export format is described in full in \"URL\" within the 2013 development kit available here: URL Please see the section entitled \"3.3 CLS-LOC submission format\". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See 'imagenet2012\\_labels.txt'.", "### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example looks like below:", "### 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* 'label': an 'int' classification label. -1 for 'test' set as the labels are missing.\n\n\nThe labels are indexed based on a sorted list of synset ids such as 'n07565083' which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file LOC\\_synset\\_mapping.txt available on Kaggle challenge page. You can also use 'dataset\\_instance.features[\"labels\"].int2str' function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.\n\n\n\n\n Click here to see the full list of ImageNet class labels mapping:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nInitial data for ImageNet image classification task consists of photographs collected from Flickr and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs 1. The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.", "#### Who are the source language producers?\n\n\nWordNet synsets further quality controlled by human annotators. The images are from Flickr.", "### Annotations", "#### Annotation process\n\n\nThe annotation process of collecting ImageNet for image classification task is a three step process.\n\n\n1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.\n2. Collecting the candidate image for these object categories using a search engine.\n3. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.\n\n\nSee the section 3.1 in 1 for more details on data collection procedure and 2 for general information on ImageNet.", "#### Who are the annotators?\n\n\nImages are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See 1 for more details.", "### Personal and Sensitive Information\n\n\nThe 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](URL on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been an attempt at filtering and balancing the people subtree in the larger ImageNet.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in 1 for a discussion on social impact of the dataset.", "### Discussion of Biases\n\n\n1. A study of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.\n2. A study has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.\n3. Another study more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.\n4. ImageNet data with face obfuscation is also provided at this link\n5. A study on genealogy of ImageNet is can be found at this link about the \"norms, values, and assumptions\" in ImageNet.\n6. See this study on filtering and balancing the distribution of people subtree in the larger complete ImageNet.", "### Other Known Limitations\n\n\n1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](URL [[2]](URL [[3]](URL\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nAuthors of [[1]](URL and [[2]](URL\n\n\n* Olga Russakovsky\n* Jia Deng\n* Hao Su\n* Jonathan Krause\n* Sanjeev Satheesh\n* Wei Dong\n* Richard Socher\n* Li-Jia Li\n* Kai Li\n* Sean Ma\n* Zhiheng Huang\n* Andrej Karpathy\n* Aditya Khosla\n* Michael Bernstein\n* Alexander C Berg\n* Li Fei-Fei", "### Licensing Information\n\n\nIn exchange for permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:\n\n\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "### Contributions\n\n\nThanks to @apsdehal for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-other #arxiv-1409.0575 #arxiv-1912.07726 #arxiv-1811.12231 #arxiv-2109.13228 #region-us \n", "### Dataset Summary\n\n\nILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a \"synonym set\" or \"synset\". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.\n\n\nThis dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available here.\n\n\nTo evaluate the 'imagenet-classification' accuracy on the test split, one must first create an account at URL. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at URL The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is \"Classification submission (top-5 cls error)\". A sample of an exported text file looks like the following:\n\n\nThe export format is described in full in \"URL\" within the 2013 development kit available here: URL Please see the section entitled \"3.3 CLS-LOC submission format\". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See 'imagenet2012\\_labels.txt'.", "### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example looks like below:", "### 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* 'label': an 'int' classification label. -1 for 'test' set as the labels are missing.\n\n\nThe labels are indexed based on a sorted list of synset ids such as 'n07565083' which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file LOC\\_synset\\_mapping.txt available on Kaggle challenge page. You can also use 'dataset\\_instance.features[\"labels\"].int2str' function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing.\n\n\n\n\n Click here to see the full list of ImageNet class labels mapping:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nInitial data for ImageNet image classification task consists of photographs collected from Flickr and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs 1. The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow.", "#### Who are the source language producers?\n\n\nWordNet synsets further quality controlled by human annotators. The images are from Flickr.", "### Annotations", "#### Annotation process\n\n\nThe annotation process of collecting ImageNet for image classification task is a three step process.\n\n\n1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years.\n2. Collecting the candidate image for these object categories using a search engine.\n3. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for.\n\n\nSee the section 3.1 in 1 for more details on data collection procedure and 2 for general information on ImageNet.", "#### Who are the annotators?\n\n\nImages are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See 1 for more details.", "### Personal and Sensitive Information\n\n\nThe 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](URL on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been an attempt at filtering and balancing the people subtree in the larger ImageNet.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in 1 for a discussion on social impact of the dataset.", "### Discussion of Biases\n\n\n1. A study of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images.\n2. A study has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness.\n3. Another study more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent.\n4. ImageNet data with face obfuscation is also provided at this link\n5. A study on genealogy of ImageNet is can be found at this link about the \"norms, values, and assumptions\" in ImageNet.\n6. See this study on filtering and balancing the distribution of people subtree in the larger complete ImageNet.", "### Other Known Limitations\n\n\n1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](URL [[2]](URL [[3]](URL\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nAuthors of [[1]](URL and [[2]](URL\n\n\n* Olga Russakovsky\n* Jia Deng\n* Hao Su\n* Jonathan Krause\n* Sanjeev Satheesh\n* Wei Dong\n* Richard Socher\n* Li-Jia Li\n* Kai Li\n* Sean Ma\n* Zhiheng Huang\n* Andrej Karpathy\n* Aditya Khosla\n* Michael Bernstein\n* Alexander C Berg\n* Li Fei-Fei", "### Licensing Information\n\n\nIn exchange for permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:\n\n\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "### Contributions\n\n\nThanks to @apsdehal for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:cs", "license:cc-by-nc-sa-4.0", "arxiv:2204.13915", "region:us" ]

[ "2204.13915" ]

[ "cs" ]

[ "# Dataset Card for Czech Subjectivity Dataset", "### Dataset Summary\n\nCzech subjectivity dataset (Subj-CS) of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. See the paper description URL", "### Github\nURL", "### Supported Tasks and Leaderboards\n\nSubjectivity Analysis", "### Languages\n\nCzech", "### Data Instances\n\ntrain/dev/test", "### Licensing Information\n\nCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n\n\n\nIf you use our dataset or software for academic research, please cite the our paper", "### Contact\npribanp@URL", "### Contributions\n\nThanks to @pauli31 for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Czech #license-cc-by-nc-sa-4.0 #arxiv-2204.13915 #region-us \n", "# Dataset Card for Czech Subjectivity Dataset", "### Dataset Summary\n\nCzech subjectivity dataset (Subj-CS) of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. See the paper description URL", "### Github\nURL", "### Supported Tasks and Leaderboards\n\nSubjectivity Analysis", "### Languages\n\nCzech", "### Data Instances\n\ntrain/dev/test", "### Licensing Information\n\nCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n\n\n\nIf you use our dataset or software for academic research, please cite the our paper", "### Contact\npribanp@URL", "### Contributions\n\nThanks to @pauli31 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:mit", "arxiv:2109.12085", "region:us" ]

[ "2109.12085" ]

[ "en" ]

[ "# Dataset Card for Text-based NP Enrichment", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: TNE OOD\nTNE\n- Point of Contact: Yanai Elazar", "### Dataset Summary\n\nText-based NP Enrichment (TNE) is a natural language understanding (NLU) task, which focus on relations between noun phrases (NPs) that can be mediated via prepositions. The dataset contains 5,497 documents, annotated exhaustively with all possible links between the NPs in each document.\n\nThe main data comes from WikiNews, which is used for train/dev/test. We also collected an additional set of 509 documents to serve as out of distribution (OOD) data points, from the Book Corpus, IMDB reviews and Reddit.", "### Supported Tasks and Leaderboards\n\nThe data contain both the main data for the TNE task, as well as coreference resolution data.\nThere are two leaderboards for the TNE data, one for the standard test set, and another one for the OOD test set:\n- TNE Leaderboard\n- TNE OOD Leaderboard", "### Languages\n\nThe text in the dataset is in English, as spoken in the different domains we include. The associated BCP-47 code is 'en'.", "## Dataset Structure", "### Data Instances\n\nThe original files are in a jsonl format, containing a dictionary of a single document, in each line.\nEach document contain a different amount of labels, due to the different amount of NPs.\nThe test and ood splits come without the annotated labels.", "### Data Fields\n\nA document consists of:\n\n* 'id': a unique identifier of a document, beginning with 'r' and followed by a number\n* 'text': the text of the document. The title and subtitles (if exists) are separated with two new lines. The paragraphs\nare separated by a single new line.\n* 'tokens': a list of string, containing the tokenized tokens\n* 'nps': a list of dictionaries, containing the following entries:\n * 'text': the text of the np\n * 'start_index': an integer indicating the starting index in the text\n * 'end_index': an integer indicating the ending index in the text\n * 'start_token': an integer indicating the first token of the np out of the tokenized tokens\n * 'end_token': an integer indicating the last token of the np out of the tokenized tokens\n * 'id': the id of the np\n* 'np_relations': these are the relation labels of the document. It is a list of dictionaries, where each\ndictionary contains:\n * 'anchor': the id of the anchor np\n * 'complement': the id of the complement np\n * 'preposition': the preposition that links between the anchor and the complement. This can take one out of 24 pre-defined preposition (23 + member(s)-of)\n * 'complement_coref_cluster_id': the coreference id, which the complement is part of.\n* 'coref': the coreference labels. It contains a list of dictionaries, where each dictionary contains:\n * 'id': the id of the coreference cluster\n * 'members': the ids of the nps members of such cluster\n * 'np_type': the type of cluster. It can be either \n * 'standard': regular coreference cluster\n * 'time/date/measurement': a time / date / measurement np. These will be singletons.\n * 'idiomatic': an idiomatic expression\n* 'metadata': metadata of the document. It contains the following:\n * 'annotators': a dictionary with anonymized annotators id\n * 'coref_worker': the coreference worker id\n * 'consolidator_worker': the consolidator worker id\n * 'np-relations_worker': the np relations worker id\n * 'url': the url where the document was taken from (not always existing)\n * 'source': the original file name where the document was taken from", "### Data Splits\n\nThe dataset is spread across four files, for the four different splits: train, dev, test and test_ood.\nAdditional details on the data statistics can be found in the paper", "## Dataset Creation", "### Curation Rationale\n\nTNE was build as a new task for language understanding, focusing on extracting relations between nouns, moderated by prepositions.", "### 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\n\nThe dataset was created by Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty, during work done at Bar-Ilan University, and AI2.", "### Licensing Information\n\nThe data is released under the MIT license.", "### Contributions\n\nThanks to @yanaiela, who is also the first author of the paper, for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-document-retrieval #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #arxiv-2109.12085 #region-us \n", "# Dataset Card for Text-based NP Enrichment", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: TNE OOD\nTNE\n- Point of Contact: Yanai Elazar", "### Dataset Summary\n\nText-based NP Enrichment (TNE) is a natural language understanding (NLU) task, which focus on relations between noun phrases (NPs) that can be mediated via prepositions. The dataset contains 5,497 documents, annotated exhaustively with all possible links between the NPs in each document.\n\nThe main data comes from WikiNews, which is used for train/dev/test. We also collected an additional set of 509 documents to serve as out of distribution (OOD) data points, from the Book Corpus, IMDB reviews and Reddit.", "### Supported Tasks and Leaderboards\n\nThe data contain both the main data for the TNE task, as well as coreference resolution data.\nThere are two leaderboards for the TNE data, one for the standard test set, and another one for the OOD test set:\n- TNE Leaderboard\n- TNE OOD Leaderboard", "### Languages\n\nThe text in the dataset is in English, as spoken in the different domains we include. The associated BCP-47 code is 'en'.", "## Dataset Structure", "### Data Instances\n\nThe original files are in a jsonl format, containing a dictionary of a single document, in each line.\nEach document contain a different amount of labels, due to the different amount of NPs.\nThe test and ood splits come without the annotated labels.", "### Data Fields\n\nA document consists of:\n\n* 'id': a unique identifier of a document, beginning with 'r' and followed by a number\n* 'text': the text of the document. The title and subtitles (if exists) are separated with two new lines. The paragraphs\nare separated by a single new line.\n* 'tokens': a list of string, containing the tokenized tokens\n* 'nps': a list of dictionaries, containing the following entries:\n * 'text': the text of the np\n * 'start_index': an integer indicating the starting index in the text\n * 'end_index': an integer indicating the ending index in the text\n * 'start_token': an integer indicating the first token of the np out of the tokenized tokens\n * 'end_token': an integer indicating the last token of the np out of the tokenized tokens\n * 'id': the id of the np\n* 'np_relations': these are the relation labels of the document. It is a list of dictionaries, where each\ndictionary contains:\n * 'anchor': the id of the anchor np\n * 'complement': the id of the complement np\n * 'preposition': the preposition that links between the anchor and the complement. This can take one out of 24 pre-defined preposition (23 + member(s)-of)\n * 'complement_coref_cluster_id': the coreference id, which the complement is part of.\n* 'coref': the coreference labels. It contains a list of dictionaries, where each dictionary contains:\n * 'id': the id of the coreference cluster\n * 'members': the ids of the nps members of such cluster\n * 'np_type': the type of cluster. It can be either \n * 'standard': regular coreference cluster\n * 'time/date/measurement': a time / date / measurement np. These will be singletons.\n * 'idiomatic': an idiomatic expression\n* 'metadata': metadata of the document. It contains the following:\n * 'annotators': a dictionary with anonymized annotators id\n * 'coref_worker': the coreference worker id\n * 'consolidator_worker': the consolidator worker id\n * 'np-relations_worker': the np relations worker id\n * 'url': the url where the document was taken from (not always existing)\n * 'source': the original file name where the document was taken from", "### Data Splits\n\nThe dataset is spread across four files, for the four different splits: train, dev, test and test_ood.\nAdditional details on the data statistics can be found in the paper", "## Dataset Creation", "### Curation Rationale\n\nTNE was build as a new task for language understanding, focusing on extracting relations between nouns, moderated by prepositions.", "### 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\n\nThe dataset was created by Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty, during work done at Bar-Ilan University, and AI2.", "### Licensing Information\n\nThe data is released under the MIT license.", "### Contributions\n\nThanks to @yanaiela, who is also the first author of the paper, for adding this dataset." ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "multilinguality:multilingual", "size_categories:100B<n<1T", "source_datasets:original", "language:af", "language:ar", "language:bn", "language:ca", "language:cs", "language:da", "language:de", "language:el", "language:eu", "language:fa", "language:fi", "language:fr", "language:gl", "language:hi", "language:hr", "language:it", "language:ja", "language:ko", "language:mt", "language:nl", "language:no", "language:oc", "language:pa", "language:pl", "language:pt", "language:ro", "language:sl", "language:sr", "language:sv", "language:tr", "language:uk", "language:ur", "license:cc-by-nc-nd-4.0", "arxiv:2206.15147", "region:us" ]

[ "2206.15147" ]

[ "af", "ar", "bn", "ca", "cs", "da", "de", "el", "eu", "fa", "fi", "fr", "gl", "hi", "hr", "it", "ja", "ko", "mt", "nl", "no", "oc", "pa", "pl", "pt", "ro", "sl", "sr", "sv", "tr", "uk", "ur" ]

[ "# esCorpius Multilingual Raw\nIn the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, they present important shortcomings for languages different from English, as they are either too small, or present a low quality derived from sub-optimal cleaning and deduplication. In this repository, we introduce esCorpius-m, a multilingual crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in some of the languages covered with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius-m has been released under CC BY-NC-ND 4.0 license.", "# Usage", "# Intended use\nThis corpus is the *raw version* of the esCorpius-m corpus. This corpus can be used for benchmarking deduplication tools.", "## Other corpora\n- esCorpius multilingual corpus (deduplicated): URL\n- esCorpius original *Spanish-only* corpus (deduplicated): URL\n\nLink to paper: URL / URL\n\nCite this work:", "## Disclaimer\nWe did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #multilinguality-multilingual #size_categories-100B<n<1T #source_datasets-original #language-Afrikaans #language-Arabic #language-Bengali #language-Catalan #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-Basque #language-Persian #language-Finnish #language-French #language-Galician #language-Hindi #language-Croatian #language-Italian #language-Japanese #language-Korean #language-Maltese #language-Dutch #language-Norwegian #language-Occitan (post 1500) #language-Panjabi #language-Polish #language-Portuguese #language-Romanian #language-Slovenian #language-Serbian #language-Swedish #language-Turkish #language-Ukrainian #language-Urdu #license-cc-by-nc-nd-4.0 #arxiv-2206.15147 #region-us \n", "# esCorpius Multilingual Raw\nIn the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, they present important shortcomings for languages different from English, as they are either too small, or present a low quality derived from sub-optimal cleaning and deduplication. In this repository, we introduce esCorpius-m, a multilingual crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in some of the languages covered with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius-m has been released under CC BY-NC-ND 4.0 license.", "# Usage", "# Intended use\nThis corpus is the *raw version* of the esCorpius-m corpus. This corpus can be used for benchmarking deduplication tools.", "## Other corpora\n- esCorpius multilingual corpus (deduplicated): URL\n- esCorpius original *Spanish-only* corpus (deduplicated): URL\n\nLink to paper: URL / URL\n\nCite this work:", "## Disclaimer\nWe did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus." ]

[ "task_categories:fill-mask", "task_categories:summarization", "task_categories:text-classification", "task_ids:masked-language-modeling", "task_ids:news-articles-summarization", "task_ids:news-articles-headline-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2008.00774", "region:us" ]

[ "2008.00774" ]

[ "en" ]

[ "### Dataset Summary\n\n\nElsevier OA CC-By: This is a corpus of 40k (40,091) open access (OA) CC-BY articles from across Elsevier’s journals\nrepresenting a large scale, cross-discipline set of research data to support NLP and ML research. The corpus include full-text\narticles published in 2014 to 2020 and are categorized in 27 Mid Level ASJC Code (subject classification).\n\n\n*Distribution of Publication Years*\n\n\n\n*Distribution of Articles Per Mid Level ASJC Code. Each article can belong to multiple ASJC codes.*", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish ('en').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe original dataset was published with the following json structure:\n\n\n*docId* The docID is the identifier of the document. This is unique to the document, and can be resolved into a URL\nfor the document through the addition of 'https//URL\n\n\n*abstract* This is the author provided abstract for the document\n\n\n*body\\_text* The full text for the document. The text has been split on sentence boundaries, thus making it easier to\nuse across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and\nIDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the\ntitle of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article\nstructure. References have been extracted from the sentences. The IDs of the extracted reference and their respective\noffset within the sentence can be found in the “refoffsets” field. The complete list of references are can be found in\nthe “bib\\_entry” field along with the references’ respective metadata. Some will be missing as we only keep ‘clean’\nsentences,\n\n\n*bib\\_entities* All the references from within the document can be found in this section. If the meta data for the\nreference is available, it has been added against the key for the reference. Where possible information such as the\ndocument titles, authors, and relevant identifiers (DOI and PMID) are included. The keys for each reference can be\nfound in the sentence where the reference is used with the start and end offset of where in the sentence that reference\nwas used.\n\n\n*metadata* Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and\nPMID). Along with a number of classification schemes such as ASJC and Subject Classification.\n\n\n*author\\_highlights* Author highlights were included in the corpus where the author(s) have provided them. The\ncoverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with\nthe aim of summarising the core findings and results in the article.", "### Data Fields\n\n\n* *title*: This is the author provided title for the document. 100% coverage.\n* *abstract*: This is the author provided abstract for the document. 99.25% coverage.\n* *keywords*: This is the author and publisher provided keywords for the document. 100% coverage.\n* *asjc*: This is the disciplines for the document as represented by 334 ASJC (All Science Journal Classification) codes. 100% coverage.\n* *subjareas*: This is the Subject Classification for the document as represented by 27 ASJC top-level subject classifications. 100% coverage.\n* *body\\_text*: The full text for the document. 100% coverage.\n* *author\\_highlights*: This is the author provided highlights for the document. 61.31% coverage.", "### Data Splits\n\n\n*Distribution of Publication Years*\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nDate the data was collected: 2020-06-25T11:00:00.000Z\n\n\nSee the original paper for more detail on the data collection process.", "#### Who are the source language producers?\n\n\nSee '3.1 Data Sampling' in the original paper.", "### 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\nCC BY 4.0", "### Contributions\n\n\nThanks to @orieg for adding this dataset." ]

[ "TAGS\n#task_categories-fill-mask #task_categories-summarization #task_categories-text-classification #task_ids-masked-language-modeling #task_ids-news-articles-summarization #task_ids-news-articles-headline-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2008.00774 #region-us \n", "### Dataset Summary\n\n\nElsevier OA CC-By: This is a corpus of 40k (40,091) open access (OA) CC-BY articles from across Elsevier’s journals\nrepresenting a large scale, cross-discipline set of research data to support NLP and ML research. The corpus include full-text\narticles published in 2014 to 2020 and are categorized in 27 Mid Level ASJC Code (subject classification).\n\n\n*Distribution of Publication Years*\n\n\n\n*Distribution of Articles Per Mid Level ASJC Code. Each article can belong to multiple ASJC codes.*", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish ('en').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe original dataset was published with the following json structure:\n\n\n*docId* The docID is the identifier of the document. This is unique to the document, and can be resolved into a URL\nfor the document through the addition of 'https//URL\n\n\n*abstract* This is the author provided abstract for the document\n\n\n*body\\_text* The full text for the document. The text has been split on sentence boundaries, thus making it easier to\nuse across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and\nIDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the\ntitle of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article\nstructure. References have been extracted from the sentences. The IDs of the extracted reference and their respective\noffset within the sentence can be found in the “refoffsets” field. The complete list of references are can be found in\nthe “bib\\_entry” field along with the references’ respective metadata. Some will be missing as we only keep ‘clean’\nsentences,\n\n\n*bib\\_entities* All the references from within the document can be found in this section. If the meta data for the\nreference is available, it has been added against the key for the reference. Where possible information such as the\ndocument titles, authors, and relevant identifiers (DOI and PMID) are included. The keys for each reference can be\nfound in the sentence where the reference is used with the start and end offset of where in the sentence that reference\nwas used.\n\n\n*metadata* Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and\nPMID). Along with a number of classification schemes such as ASJC and Subject Classification.\n\n\n*author\\_highlights* Author highlights were included in the corpus where the author(s) have provided them. The\ncoverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with\nthe aim of summarising the core findings and results in the article.", "### Data Fields\n\n\n* *title*: This is the author provided title for the document. 100% coverage.\n* *abstract*: This is the author provided abstract for the document. 99.25% coverage.\n* *keywords*: This is the author and publisher provided keywords for the document. 100% coverage.\n* *asjc*: This is the disciplines for the document as represented by 334 ASJC (All Science Journal Classification) codes. 100% coverage.\n* *subjareas*: This is the Subject Classification for the document as represented by 27 ASJC top-level subject classifications. 100% coverage.\n* *body\\_text*: The full text for the document. 100% coverage.\n* *author\\_highlights*: This is the author provided highlights for the document. 61.31% coverage.", "### Data Splits\n\n\n*Distribution of Publication Years*\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nDate the data was collected: 2020-06-25T11:00:00.000Z\n\n\nSee the original paper for more detail on the data collection process.", "#### Who are the source language producers?\n\n\nSee '3.1 Data Sampling' in the original paper.", "### 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\nCC BY 4.0", "### Contributions\n\n\nThanks to @orieg for adding this dataset." ]

[ "task_categories:sentence-similarity", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:afl-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Ukhushn/home-depot" ]

[ "TAGS\n#task_categories-sentence-similarity #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-afl-3.0 #region-us \n", "# Dataset Card for Ukhushn/home-depot" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:visual-question-answering", "task_ids:visual-question-answering", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:1904.08920", "arxiv:2007.00398", "region:us" ]

[ "1904.08920", "2007.00398" ]

[ "en" ]

[ "# Dataset Card for TextVQA", "## 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\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: mailto:amanpreet@URL", "### Dataset Summary\n\nTextVQA requires models to read and reason about text in images to answer questions about them.\nSpecifically, models need to incorporate a new modality of text present in the images and reason\nover it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images\nfrom the OpenImages dataset. The dataset uses VQA accuracy metric for evaluation.", "### Supported Tasks and Leaderboards\n\n- 'visual-question-answering': The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image.", "### Languages\n\nThe questions in the dataset are in English.", "## Dataset Structure", "### Data Instances\n\nA typical sample mainly contains the question in 'question' field, an image object in 'image' field, OpenImage image id in 'image_id' and lot of other useful metadata. 10 answers per questions are contained in the 'answers' attribute. For test set, 10 empty strings are contained in the 'answers' field as the answers are not available for it.\n\nAn example look like below:", "### Data Fields\n\n- 'question': string, the question that is being asked about the image\n- 'image_id': string, id of the image which is same as the OpenImages id\n- 'image': A 'PIL.Image.Image' object containing the image about which the question is being asked. 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_classes': List[str], The OpenImages classes to which the image belongs to.\n- 'flickr_original_url': string, URL to original image on Flickr\n- 'flickr_300k_url': string, URL to resized and low-resolution image on Flickr.\n- 'image_width': int, Width of the original image.\n- 'image_height': int, Height of the original image.\n- 'question_tokens': List[str], A pre-tokenized list of question.\n- 'answers': List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers.\n- 'question_id': int, Unique id of the question.\n- 'set_name': string, the set to which this question belongs.", "### Data Splits\n\nThere are three splits. 'train', 'validation' and 'test'. The 'train' and 'validation' sets share images with OpenImages 'train' set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on 'test' set, you need to go to the EvalAI leaderboard and upload your predictions there. Please see instructions at URL", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n\n> Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial images were sourced from OpenImages v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See annotation process section to understand the next stages.", "#### Who are the source language producers?\n\nEnglish Crowdsource Annotators", "### Annotations", "#### Annotation process\n\n After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage.", "#### Who are the annotators?\n\nAnnotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper.", "### Personal and Sensitive Information\n\nThe dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided 'image_classes' data field is one option to try to filter out some of this information.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including TextCaps and TextOCR. Similar datasets were introduced over the time which specifically focus on sight-disabled users such as VizWiz or focusing specifically on the same problem as TextVQA like STVQA, DocVQA and OCRVQA. Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets.", "### Discussion of Biases\n\nQuestion-only bias where a model is able to answer the question without even looking at the image is discussed in the paper which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric.", "### Other Known Limitations\n\n- The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding.\n- The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers.\n- The metric used for calculating accuracy is same as VQA accuracy. This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR.", "## Additional Information", "### Dataset Curators\n\n- Amanpreet Singh\n- Vivek Natarjan\n- Meet Shah\n- Yu Jiang\n- Xinlei Chen\n- Dhruv Batra\n- Devi Parikh\n- Marcus Rohrbach", "### Licensing Information\n\nCC by 4.0", "### Contributions\n\nThanks to @apsdehal for adding this dataset." ]

[ "TAGS\n#task_categories-visual-question-answering #task_ids-visual-question-answering #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-1904.08920 #arxiv-2007.00398 #region-us \n", "# Dataset Card for TextVQA", "## 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\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: mailto:amanpreet@URL", "### Dataset Summary\n\nTextVQA requires models to read and reason about text in images to answer questions about them.\nSpecifically, models need to incorporate a new modality of text present in the images and reason\nover it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images\nfrom the OpenImages dataset. The dataset uses VQA accuracy metric for evaluation.", "### Supported Tasks and Leaderboards\n\n- 'visual-question-answering': The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image.", "### Languages\n\nThe questions in the dataset are in English.", "## Dataset Structure", "### Data Instances\n\nA typical sample mainly contains the question in 'question' field, an image object in 'image' field, OpenImage image id in 'image_id' and lot of other useful metadata. 10 answers per questions are contained in the 'answers' attribute. For test set, 10 empty strings are contained in the 'answers' field as the answers are not available for it.\n\nAn example look like below:", "### Data Fields\n\n- 'question': string, the question that is being asked about the image\n- 'image_id': string, id of the image which is same as the OpenImages id\n- 'image': A 'PIL.Image.Image' object containing the image about which the question is being asked. 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_classes': List[str], The OpenImages classes to which the image belongs to.\n- 'flickr_original_url': string, URL to original image on Flickr\n- 'flickr_300k_url': string, URL to resized and low-resolution image on Flickr.\n- 'image_width': int, Width of the original image.\n- 'image_height': int, Height of the original image.\n- 'question_tokens': List[str], A pre-tokenized list of question.\n- 'answers': List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers.\n- 'question_id': int, Unique id of the question.\n- 'set_name': string, the set to which this question belongs.", "### Data Splits\n\nThere are three splits. 'train', 'validation' and 'test'. The 'train' and 'validation' sets share images with OpenImages 'train' set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on 'test' set, you need to go to the EvalAI leaderboard and upload your predictions there. Please see instructions at URL", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n\n> Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial images were sourced from OpenImages v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See annotation process section to understand the next stages.", "#### Who are the source language producers?\n\nEnglish Crowdsource Annotators", "### Annotations", "#### Annotation process\n\n After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage.", "#### Who are the annotators?\n\nAnnotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper.", "### Personal and Sensitive Information\n\nThe dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided 'image_classes' data field is one option to try to filter out some of this information.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including TextCaps and TextOCR. Similar datasets were introduced over the time which specifically focus on sight-disabled users such as VizWiz or focusing specifically on the same problem as TextVQA like STVQA, DocVQA and OCRVQA. Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets.", "### Discussion of Biases\n\nQuestion-only bias where a model is able to answer the question without even looking at the image is discussed in the paper which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric.", "### Other Known Limitations\n\n- The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding.\n- The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers.\n- The metric used for calculating accuracy is same as VQA accuracy. This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR.", "## Additional Information", "### Dataset Curators\n\n- Amanpreet Singh\n- Vivek Natarjan\n- Meet Shah\n- Yu Jiang\n- Xinlei Chen\n- Dhruv Batra\n- Devi Parikh\n- Marcus Rohrbach", "### Licensing Information\n\nCC by 4.0", "### Contributions\n\nThanks to @apsdehal for adding this dataset." ]

[ "task_categories:time-series-forecasting", "task_ids:univariate-time-series-forecasting", "task_ids:multivariate-time-series-forecasting", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "license:cc-by-4.0", "arxiv:2012.07436", "region:us" ]

[ "2012.07436" ]

[]

[ "# Dataset Card for Electricity Transformer Temperature", "## 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: Electricity Transformer Dataset\n- Repository: URL\n- Paper: Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting\n- Point of Contact: Haoyi Zhou", "### Dataset Summary\n\nThe electric power distribution problem is the distribution of electricity to different areas depending on its sequential usage. But predicting the future demand of a specific area is difficult, as it varies with weekdays, holidays, seasons, weather, temperatures, etc. However, no existing method can perform a long-term prediction based on super long-term real-world data with high precision. Any false predictions may damage the electrical transformer. So currently, without an efficient method to predict future electric usage, managers have to make decisions based on the empirical number, which is much higher than the real-world demands. It causes unnecessary waste of electric and equipment depreciation. On the other hand, the oil temperatures can reflect the condition of the Transformer. One of the most efficient strategies is to predict how the electrical transformers' oil temperature is safe and avoid unnecessary waste. As a result, to address this problem, the authors and Beijing Guowang Fuda Science & Technology Development Company have provided 2-years worth of data.\n\nSpecifically, the dataset combines short-term periodical patterns, long-term periodical patterns, long-term trends, and many irregular patterns. The dataset are obtained from 2 Electricity Transformers at 2 stations and come in an '1H' (hourly) or '15T' (15-minute) frequency containing 2 year * 365 days * 24 hours * (4 for 15T) times = 17,520 (70,080 for 15T) data points.\n\nThe target time series is the Oil Temperature and the dataset comes with the following 6 covariates in the univariate setup:\n* High UseFul Load \n* High UseLess Load\n* Middle UseFul Load \n* Middle UseLess Load \n* Low UseFul Load \n* Low UseLess Load", "### Dataset Usage\n\nTo load a particular variant of the dataset just specify its name e.g:\n\n\n\nor to specify a prediction length:", "### Supported Tasks and Leaderboards\n\nThe time series data is split into train/val/test set of 12/4/4 months respectively. Given the prediction length (default: 1 day (24 hours or 24*4 15T)) we create rolling windows of this size for the val/test sets.", "#### 'time-series-forecasting'", "##### 'univariate-time-series-forecasting'\n\nThe univariate time series forecasting tasks involves learning the future one dimensional 'target' values of a time series in a dataset for some 'prediction_length' time steps. The performance of the forecast models can then be validated via the ground truth in the 'validation' split and tested via the 'test' split. The covriates are stored in the 'feat_dynamic_real' key of each time series.", "##### 'multivariate-time-series-forecasting'\n\nThe multivariate time series forecasting task involves learning the future vector of 'target' values of a time series in a dataset for some 'prediction_length' time steps. Similar to the univariate setting the performance of a multivariate model can be validated via the ground truth in the 'validation' split and tested via the 'test' split.", "### Languages", "## Dataset Structure", "### Data Instances\n\nA sample from the training set is provided below:", "### Data Fields\n\nFor the univariate regular time series each series has the following keys:\n\n* 'start': a datetime of the first entry of each time series in the dataset\n* 'target': an array[float32] of the actual target values\n* 'feat_static_cat': an array[uint64] which contains a categorical identifier of each time series in the dataset\n* 'feat_dynamic_real': optional array of covariate features\n* 'item_id': a string identifier of each time series in a dataset for reference\n\nFor the multivariate time series the 'target' is a vector of the multivariate dimension for each time point.", "### Data Splits\n\nThe time series data is split into train/val/test set of 12/4/4 months respectively.", "## Dataset Creation", "### Curation Rationale\n\nDevelop time series methods that can perform a long-term prediction based on super long-term real-world data with high precision.", "### 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\n\n* Haoyi Zhou", "### Licensing Information\n\nCreative Commons Attribution 4.0 International", "### Contributions\n\nThanks to @kashif for adding this dataset." ]

[ "TAGS\n#task_categories-time-series-forecasting #task_ids-univariate-time-series-forecasting #task_ids-multivariate-time-series-forecasting #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #license-cc-by-4.0 #arxiv-2012.07436 #region-us \n", "# Dataset Card for Electricity Transformer Temperature", "## 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: Electricity Transformer Dataset\n- Repository: URL\n- Paper: Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting\n- Point of Contact: Haoyi Zhou", "### Dataset Summary\n\nThe electric power distribution problem is the distribution of electricity to different areas depending on its sequential usage. But predicting the future demand of a specific area is difficult, as it varies with weekdays, holidays, seasons, weather, temperatures, etc. However, no existing method can perform a long-term prediction based on super long-term real-world data with high precision. Any false predictions may damage the electrical transformer. So currently, without an efficient method to predict future electric usage, managers have to make decisions based on the empirical number, which is much higher than the real-world demands. It causes unnecessary waste of electric and equipment depreciation. On the other hand, the oil temperatures can reflect the condition of the Transformer. One of the most efficient strategies is to predict how the electrical transformers' oil temperature is safe and avoid unnecessary waste. As a result, to address this problem, the authors and Beijing Guowang Fuda Science & Technology Development Company have provided 2-years worth of data.\n\nSpecifically, the dataset combines short-term periodical patterns, long-term periodical patterns, long-term trends, and many irregular patterns. The dataset are obtained from 2 Electricity Transformers at 2 stations and come in an '1H' (hourly) or '15T' (15-minute) frequency containing 2 year * 365 days * 24 hours * (4 for 15T) times = 17,520 (70,080 for 15T) data points.\n\nThe target time series is the Oil Temperature and the dataset comes with the following 6 covariates in the univariate setup:\n* High UseFul Load \n* High UseLess Load\n* Middle UseFul Load \n* Middle UseLess Load \n* Low UseFul Load \n* Low UseLess Load", "### Dataset Usage\n\nTo load a particular variant of the dataset just specify its name e.g:\n\n\n\nor to specify a prediction length:", "### Supported Tasks and Leaderboards\n\nThe time series data is split into train/val/test set of 12/4/4 months respectively. Given the prediction length (default: 1 day (24 hours or 24*4 15T)) we create rolling windows of this size for the val/test sets.", "#### 'time-series-forecasting'", "##### 'univariate-time-series-forecasting'\n\nThe univariate time series forecasting tasks involves learning the future one dimensional 'target' values of a time series in a dataset for some 'prediction_length' time steps. The performance of the forecast models can then be validated via the ground truth in the 'validation' split and tested via the 'test' split. The covriates are stored in the 'feat_dynamic_real' key of each time series.", "##### 'multivariate-time-series-forecasting'\n\nThe multivariate time series forecasting task involves learning the future vector of 'target' values of a time series in a dataset for some 'prediction_length' time steps. Similar to the univariate setting the performance of a multivariate model can be validated via the ground truth in the 'validation' split and tested via the 'test' split.", "### Languages", "## Dataset Structure", "### Data Instances\n\nA sample from the training set is provided below:", "### Data Fields\n\nFor the univariate regular time series each series has the following keys:\n\n* 'start': a datetime of the first entry of each time series in the dataset\n* 'target': an array[float32] of the actual target values\n* 'feat_static_cat': an array[uint64] which contains a categorical identifier of each time series in the dataset\n* 'feat_dynamic_real': optional array of covariate features\n* 'item_id': a string identifier of each time series in a dataset for reference\n\nFor the multivariate time series the 'target' is a vector of the multivariate dimension for each time point.", "### Data Splits\n\nThe time series data is split into train/val/test set of 12/4/4 months respectively.", "## Dataset Creation", "### Curation Rationale\n\nDevelop time series methods that can perform a long-term prediction based on super long-term real-world data with high precision.", "### 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\n\n* Haoyi Zhou", "### Licensing Information\n\nCreative Commons Attribution 4.0 International", "### Contributions\n\nThanks to @kashif for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# hotpotExtendedAno-LM\n\nVersion of hotpotExtended-Annotated formatted for Language Modelling." ]

[ "TAGS\n#region-us \n", "# hotpotExtendedAno-LM\n\nVersion of hotpotExtended-Annotated formatted for Language Modelling." ]

[ "language:en", "NLP", "conservation", "dialogue", "region:us" ]

[]

[ "en" ]

[ "# Doctor Who Dialogues\n\nThis dataset contains all the script lines of Eleventh Doctor from Doctor Who which is a popular TV series. It can be processed and used for chatbots or relevant stuff." ]

[ "TAGS\n#language-English #NLP #conservation #dialogue #region-us \n", "# Doctor Who Dialogues\n\nThis dataset contains all the script lines of Eleventh Doctor from Doctor Who which is a popular TV series. It can be processed and used for chatbots or relevant stuff." ]

[ "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:afr", "language:af", "language:aaa", "language:abc", "language:ada", "language:adq", "language:aeu", "language:agq", "language:ags", "language:ahk", "language:aia", "language:ajz", "language:aka", "language:ak", "language:ame", "language:amh", "language:am", "language:amp", "language:amu", "language:ann", "language:aph", "language:awa", "language:awb", "language:azn", "language:azo", "language:bag", "language:bam", "language:bm", "language:baw", "language:bax", "language:bbk", "language:bcc", "language:bce", "language:bec", "language:bef", "language:ben", "language:bn", "language:bfd", "language:bfm", "language:bfn", "language:bgf", "language:bho", "language:bhs", "language:bis", "language:bi", "language:bjn", "language:bjr", "language:bkc", "language:bkh", "language:bkm", "language:bkx", "language:bob", "language:bod", "language:bo", "language:boz", "language:bqm", "language:bra", "language:brb", "language:bri", "language:brv", "language:bss", "language:bud", "language:buo", "language:bwt", "language:bwx", "language:bxa", "language:bya", "language:bze", "language:bzi", "language:cak", "language:cbr", "language:ceb", "language:cgc", "language:chd", "language:chp", "language:cim", "language:clo", "language:cmn", "language:zh", "language:cmo", "language:csw", "language:cuh", "language:cuv", "language:dag", "language:ddg", "language:ded", "language:deu", "language:de", "language:dig", "language:dje", "language:dmg", "language:dnw", "language:dtp", "language:dtr", "language:dty", "language:dug", "language:eee", "language:ekm", "language:enb", "language:enc", "language:eng", "language:en", "language:ewo", "language:fas", "language:fa", "language:fil", "language:fli", "language:fon", "language:fra", "language:fr", "language:fub", "language:fuh", "language:gal", "language:gbj", "language:gou", "language:gsw", "language:guc", "language:guj", "language:gu", "language:guz", "language:gwc", "language:hao", "language:hat", "language:ht", "language:hau", "language:ha", "language:hbb", "language:hig", "language:hil", "language:hin", "language:hi", "language:hla", "language:hna", "language:hre", "language:hro", "language:idt", "language:ilo", "language:ind", "language:id", "language:ino", "language:isu", "language:ita", "language:it", "language:jgo", "language:jmx", "language:jpn", "language:ja", "language:jra", "language:kak", "language:kam", "language:kan", "language:kn", "language:kau", "language:kr", "language:kbq", "language:kbx", "language:kby", "language:kek", "language:ken", "language:khb", "language:khm", "language:km", "language:kik", "language:ki", "language:kin", "language:rw", "language:kir", "language:ky", "language:kjb", "language:kmg", "language:kmr", "language:ku", "language:kms", "language:kmu", "language:kor", "language:ko", "language:kqr", "language:krr", "language:ksw", "language:kur", "language:kvt", "language:kwd", "language:kwu", "language:kwx", "language:kxp", "language:kyq", "language:laj", "language:lan", "language:lao", "language:lo", "language:lbr", "language:lfa", "language:lgg", "language:lgr", "language:lhm", "language:lhu", "language:lkb", "language:llg", "language:lmp", "language:lns", "language:loh", "language:lsi", "language:lts", "language:lug", "language:lg", "language:luy", "language:lwl", "language:mai", "language:mal", "language:ml", "language:mam", "language:mar", "language:mr", "language:mdr", "language:mfh", "language:mfj", "language:mgg", "language:mgm", "language:mgo", "language:mgq", "language:mhx", "language:miy", "language:mkz", "language:mle", "language:mlk", "language:mlw", "language:mmu", "language:mne", "language:mnf", "language:mnw", "language:mot", "language:mqj", "language:mrn", "language:mry", "language:msb", "language:muv", "language:mve", "language:mxu", "language:mya", "language:my", "language:myk", "language:myx", "language:mzm", "language:nas", "language:nco", "language:nep", "language:ne", "language:new", "language:nge", "language:ngn", "language:nhx", "language:njy", "language:nla", "language:nld", "language:nl", "language:nlv", "language:nod", "language:nsk", "language:nsn", "language:nso", "language:nst", "language:nuj", "language:nwe", "language:nwi", "language:nxa", "language:nxl", "language:nya", "language:ny", "language:nyo", "language:nyu", "language:nza", "language:odk", "language:oji", "language:oj", "language:oki", "language:omw", "language:ori", "language:or", "language:ozm", "language:pae", "language:pag", "language:pan", "language:pa", "language:pbt", "language:pce", "language:pcg", "language:pdu", "language:pea", "language:pex", "language:pis", "language:pkb", "language:pmf", "language:pnz", "language:por", "language:pt", "language:psp", "language:pwg", "language:qaa", "language:qub", "language:quc", "language:quf", "language:quz", "language:qve", "language:qvh", "language:qvm", "language:qvo", "language:qxh", "language:rel", "language:rnl", "language:ron", "language:ro", "language:roo", "language:rue", "language:rug", "language:rus", "language:ru", "language:san", "language:sa", "language:saq", "language:sat", "language:sdk", "language:sea", "language:sgd", "language:shn", "language:sml", "language:snk", "language:snl", "language:som", "language:so", "language:sot", "language:st", "language:sox", "language:spa", "language:es", "language:sps", "language:ssn", "language:stk", "language:swa", "language:sw", "language:swh", "language:sxb", "language:syw", "language:taj", "language:tam", "language:ta", "language:tbj", "language:tdb", "language:tdg", "language:tdt", "language:teo", "language:tet", "language:tgk", "language:tg", "language:tha", "language:th", "language:the", "language:thk", "language:thl", "language:thy", "language:tio", "language:tkd", "language:tnl", "language:tnn", "language:tnp", "language:tnt", "language:tod", "language:tom", "language:tpi", "language:tpl", "language:tpu", "language:tsb", "language:tsn", "language:tn", "language:tso", "language:ts", "language:tuv", "language:tuz", "language:tvs", "language:udg", "language:unr", "language:urd", "language:ur", "language:uzb", "language:uz", "language:ven", "language:ve", "language:vie", "language:vi", "language:vif", "language:war", "language:wbm", "language:wbr", "language:wms", "language:wni", "language:wnk", "language:wtk", "language:xho", "language:xh", "language:xkg", "language:xmd", "language:xmg", "language:xmm", "language:xog", "language:xty", "language:yas", "language:yav", "language:ybb", "language:ybh", "language:ybi", "language:ydd", "language:yea", "language:yet", "language:yid", "language:yi", "language:yin", "language:ymp", "language:zaw", "language:zho", "language:zlm", "language:zuh", "language:zul", "language:zu", "license:cc-by-4.0", "license:cc-by-nc-4.0", "license:cc-by-nd-4.0", "license:cc-by-sa-4.0", "license:cc-by-nc-nd-4.0", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "afr", "af", "aaa", "abc", "ada", "adq", "aeu", "agq", "ags", "ahk", "aia", "ajz", "aka", "ak", "ame", "amh", "am", "amp", "amu", "ann", "aph", "awa", "awb", "azn", "azo", "bag", "bam", "bm", "baw", "bax", "bbk", "bcc", "bce", "bec", "bef", "ben", "bn", "bfd", "bfm", "bfn", "bgf", "bho", "bhs", "bis", "bi", "bjn", "bjr", "bkc", "bkh", "bkm", "bkx", "bob", "bod", "bo", "boz", "bqm", "bra", "brb", "bri", "brv", "bss", "bud", "buo", "bwt", "bwx", "bxa", "bya", "bze", "bzi", "cak", "cbr", "ceb", "cgc", "chd", "chp", "cim", "clo", "cmn", "zh", "cmo", "csw", "cuh", "cuv", "dag", "ddg", "ded", "deu", "de", "dig", "dje", "dmg", "dnw", "dtp", "dtr", "dty", "dug", "eee", "ekm", "enb", "enc", "eng", "en", "ewo", "fas", "fa", "fil", "fli", "fon", "fra", "fr", "fub", "fuh", "gal", "gbj", "gou", "gsw", "guc", "guj", "gu", "guz", "gwc", "hao", "hat", "ht", "hau", "ha", "hbb", "hig", "hil", "hin", "hi", "hla", "hna", "hre", "hro", "idt", "ilo", "ind", "id", "ino", "isu", "ita", "it", "jgo", "jmx", "jpn", "ja", "jra", "kak", "kam", "kan", "kn", "kau", "kr", "kbq", "kbx", "kby", "kek", "ken", "khb", "khm", "km", "kik", "ki", "kin", "rw", "kir", "ky", "kjb", "kmg", "kmr", "ku", "kms", "kmu", "kor", "ko", "kqr", "krr", "ksw", "kur", "kvt", "kwd", "kwu", "kwx", "kxp", "kyq", "laj", "lan", "lao", "lo", "lbr", "lfa", "lgg", "lgr", "lhm", "lhu", "lkb", "llg", "lmp", "lns", "loh", "lsi", "lts", "lug", "lg", "luy", "lwl", "mai", "mal", "ml", "mam", "mar", "mr", "mdr", "mfh", "mfj", "mgg", "mgm", "mgo", "mgq", "mhx", "miy", "mkz", "mle", "mlk", "mlw", "mmu", "mne", "mnf", "mnw", "mot", "mqj", "mrn", "mry", "msb", "muv", "mve", "mxu", "mya", "my", "myk", "myx", "mzm", "nas", "nco", "nep", "ne", "new", "nge", "ngn", "nhx", "njy", "nla", "nld", "nl", "nlv", "nod", "nsk", "nsn", "nso", "nst", "nuj", "nwe", "nwi", "nxa", "nxl", "nya", "ny", "nyo", "nyu", "nza", "odk", "oji", "oj", "oki", "omw", "ori", "or", "ozm", "pae", "pag", "pan", "pa", "pbt", "pce", "pcg", "pdu", "pea", "pex", "pis", "pkb", "pmf", "pnz", "por", "pt", "psp", "pwg", "qaa", "qub", "quc", "quf", "quz", "qve", "qvh", "qvm", "qvo", "qxh", "rel", "rnl", "ron", "ro", "roo", "rue", "rug", "rus", "ru", "san", "sa", "saq", "sat", "sdk", "sea", "sgd", "shn", "sml", "snk", "snl", "som", "so", "sot", "st", "sox", "spa", "es", "sps", "ssn", "stk", "swa", "sw", "swh", "sxb", "syw", "taj", "tam", "ta", "tbj", "tdb", "tdg", "tdt", "teo", "tet", "tgk", "tg", "tha", "th", "the", "thk", "thl", "thy", "tio", "tkd", "tnl", "tnn", "tnp", "tnt", "tod", "tom", "tpi", "tpl", "tpu", "tsb", "tsn", "tn", "tso", "ts", "tuv", "tuz", "tvs", "udg", "unr", "urd", "ur", "uzb", "uz", "ven", "ve", "vie", "vi", "vif", "war", "wbm", "wbr", "wms", "wni", "wnk", "wtk", "xho", "xh", "xkg", "xmd", "xmg", "xmm", "xog", "xty", "yas", "yav", "ybb", "ybh", "ybi", "ydd", "yea", "yet", "yid", "yi", "yin", "ymp", "zaw", "zho", "zlm", "zuh", "zul", "zu" ]

[ "### Data Instances\n\n\nThe examples look like this for Hindi:\n\n\nThis would produce an output:", "### Data Fields\n\n\nThe metadata fields below are available. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).\n\n\n* id: id of the sample\n* title: title of the book, e.g. \"Going to Buy a Book\".\n* license: specific license used, e.g. \"cc-by-sa\" for \"Creative Commons, by attribution, share-alike\".\n* album\\_id: an ID value corresponding to the set of images corresponding to the given story\n* story: the sequenced story data including lists of image IDs, image URLs, and corresponding text", "### Data Splits\n\n\nCurrently all languages include a train split only. In the future, we will be creating manual splits of the data.\n\n\nChangelog\n---------\n\n\n* 6 December 2022 - dataset is made public" ]

[ "TAGS\n#annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Afrikaans #language-Afrikaans #language-Ghotuo #language-Ambala Ayta #language-Adangme #language-Adangbe #language-Akeu #language-Aghem #language-Esimbi #language-Akha #language-Arosi #language-Amri Karbi #language-Akan #language-Akan #language-Yanesha' #language-Amharic #language-Amharic #language-Alamblak #language-Guerrero Amuzgo #language-Obolo #language-Athpariya #language-Awadhi #language-Awa (Papua New Guinea) #language-Western Durango Nahuatl #language-Awing #language-Tuki #language-Bambara #language-Bambara #language-Bambili-Bambui #language-Bamun #language-Babanki #language-Southern Balochi #language-Bamenyam #language-Iceve-Maci #language-Benabena #language-Bengali #language-Bengali #language-Bafut #language-Mmen #language-Bunak #language-Bangandu #language-Bhojpuri #language-Buwal #language-Bislama #language-Bislama #language-Banjar #language-Binumarien #language-Baka (Cameroon) #language-Bakoko #language-Kom (Cameroon) #language-Baikeno #language-Aweer #language-Tibetan #language-Tibetan #language-Tiéyaxo Bozo #language-Wumboko #language-Braj #language-Brao #language-Mokpwe #language-Western Bru #language-Akoose #language-Ntcham #language-Terei #language-Bafaw-Balong #language-Bu-Nao Bunu #language-Tairaha #language-Batak #language-Jenaama Bozo #language-Bisu #language-Kaqchikel #language-Cashibo-Cacataibo #language-Cebuano #language-Kagayanen #language-Highland Oaxaca Chontal #language-Chipewyan #language-Cimbrian #language-Lowland Oaxaca Chontal #language-Mandarin Chinese #language-Chinese #language-Central Mnong #language-Swampy Cree #language-Chuka #language-Cuvok #language-Dagbani #language-Fataluku #language-Dedua #language-German #language-German #language-Digo #language-Zarma #language-Upper Kinabatangan #language-Western Dani #language-Kadazan Dusun #language-Lotud #language-Dotyali #language-Duruma #language-E #language-Elip #language-Markweeta #language-En #language-English #language-English #language-Ewondo #language-Persian #language-Persian #language-Filipino #language-Fali #language-Fon #language-French #language-French #language-Adamawa Fulfulde #language-Western Niger Fulfulde #language-Galolen #language-Bodo Gadaba #language-Gavar #language-Swiss German #language-Wayuu #language-Gujarati #language-Gujarati #language-Gusii #language-Gawri #language-Hakö #language-Haitian #language-Haitian #language-Hausa #language-Hausa #language-Huba #language-Kamwe #language-Hiligaynon #language-Hindi #language-Hindi #language-Halia #language-Mina (Cameroon) #language-Hre #language-Haroi #language-Idaté #language-Iloko #language-Indonesian #language-Indonesian #language-Inoke-Yate #language-Isu (Menchum Division) #language-Italian #language-Italian #language-Ngomba #language-Western Juxtlahuaca Mixtec #language-Japanese #language-Japanese #language-Jarai #language-Kalanguya #language-Kamba (Kenya) #language-Kannada #language-Kannada #language-Kanuri #language-Kanuri #language-Kamano #language-Ap Ma #language-Manga Kanuri #language-Kekchí #language-Kenyang #language-Lü #language-Khmer #language-Khmer #language-Kikuyu #language-Kikuyu #language-Kinyarwanda #language-Kinyarwanda #language-Kirghiz #language-Kirghiz #language-Q'anjob'al #language-Kâte #language-Northern Kurdish #language-Kurdish #language-Kamasau #language-Kanite #language-Korean #language-Korean #language-Kimaragang #language-Krung #language-S'gaw Karen #language-Kurdish #language-Lahta Karen #language-Kwaio #language-Kwakum #language-Khirwar #language-Wadiyara Koli #language-Kenga #language-Lango (Uganda) #language-Laru #language-Lao #language-Lao #language-Lohorung #language-Lefa #language-Lugbara #language-Lengo #language-Lhomi #language-Lahu #language-Kabras #language-Lole #language-Limbum #language-Lamnso' #language-Laarim #language-Lashi #language-Tachoni #language-Ganda #language-Ganda #language-Luyia #language-Eastern Lawa #language-Maithili #language-Malayalam #language-Malayalam #language-Mam #language-Marathi #language-Marathi #language-Mandar #language-Matal #language-Mefele #language-Mpumpong #language-Mambae #language-Meta' #language-Malila #language-Maru #language-Ayutla Mixtec #language-Makasae #language-Manambu #language-Ilwana #language-Moloko #language-Mmaala #language-Naba #language-Mundani #language-Mon #language-Barí #language-Mamasa #language-Cheke Holo #language-Mandaya #language-Masbatenyo #language-Muthuvan #language-Marwari (Pakistan) #language-Mada (Cameroon) #language-Burmese #language-Burmese #language-Mamara Senoufo #language-Masaaba #language-Mumuye #language-Naasioi #language-Sibe #language-Nepali (macrolanguage) #language-Nepali (macrolanguage) #language-Newari #language-Ngemba #language-Ngwo #language-Isthmus-Mecayapan Nahuatl #language-Njyem #language-Ngombale #language-Dutch #language-Dutch #language-Orizaba Nahuatl #language-Northern Thai #language-Naskapi #language-Nehan #language-Pedi #language-Tase Naga #language-Nyole #language-Ngwe #language-Southwest Tanna #language-Nauete #language-South Nuaulu #language-Nyanja #language-Nyanja #language-Nyoro #language-Nyungwe #language-Tigon Mbembe #language-Od #language-Ojibwa #language-Ojibwa #language-Okiek #language-South Tairora #language-Oriya (macrolanguage) #language-Oriya (macrolanguage) #language-Koonzime #language-Pagibete #language-Pangasinan #language-Panjabi #language-Panjabi #language-Southern Pashto #language-Ruching Palaung #language-Paniya #language-Kayan #language-Peranakan Indonesian #language-Petats #language-Pijin #language-Pokomo #language-Pamona #language-Pana (Central African Republic) #language-Portuguese #language-Portuguese #language-Philippine Sign Language #language-Gapapaiwa #language-qaa #language-Huallaga Huánuco Quechua #language-K'iche' #language-Lambayeque Quechua #language-Cusco Quechua #language-Eastern Apurímac Quechua #language-Huamalíes-Dos de Mayo Huánuco Quechua #language-Margos-Yarowilca-Lauricocha Quechua #language-Napo Lowland Quechua #language-Panao Huánuco Quechua #language-Rendille #language-Ranglong #language-Romanian #language-Romanian #language-Rotokas #language-Rusyn #language-Roviana #language-Russian #language-Russian #language-Sanskrit #language-Sanskrit #language-Samburu #language-Santali #language-Sos Kundi #language-Semai #language-Surigaonon #language-Shan #language-Central Sama #language-Soninke #language-Sangil #language-Somali #language-Somali #language-Southern Sotho #language-Southern Sotho #language-Swo #language-Spanish #language-Spanish #language-Saposa #language-Waata #language-Arammba #language-Swahili (macrolanguage) #language-Swahili (macrolanguage) #language-Swahili (individual language) #language-Suba #language-Kagate #language-Eastern Tamang #language-Tamil #language-Tamil #language-Tiang #language-Panchpargania #language-Western Tamang #language-Tetun Dili #language-Teso #language-Tetum #language-Tajik #language-Tajik #language-Thai #language-Thai #language-Chitwania Tharu #language-Tharaka #language-Dangaura Tharu #language-Tha #language-Teop #language-Tukudede #language-Lenakel #language-North Tanna #language-Whitesands #language-Tontemboan #language-Toma #language-Tombulu #language-Tok Pisin #language-Tlacoapa Me'phaa #language-Tampuan #language-Tsamai #language-Tswana #language-Tswana #language-Tsonga #language-Tsonga #language-Turkana #language-Turka #language-Taveta #language-Muduga #language-Mundari #language-Urdu #language-Urdu #language-Uzbek #language-Uzbek #language-Venda #language-Venda #language-Vietnamese #language-Vietnamese #language-Vili #language-Waray (Philippines) #language-Wa #language-Wagdi #language-Wambon #language-Ndzwani Comorian #language-Wanukaka #language-Watakataui #language-Xhosa #language-Xhosa #language-Kagoro #language-Mbudum #language-Mengaka #language-Manado Malay #language-Soga #language-Yoloxochitl Mixtec #language-Nugunu (Cameroon) #language-Yangben #language-Yemba #language-Yakha #language-Yamphu #language-Eastern Yiddish #language-Ravula #language-Yetfa #language-Yiddish #language-Yiddish #language-Riang Lai #language-Yamap #language-Mitla Zapotec #language-Chinese #language-Malay (individual language) #language-Tokano #language-Zulu #language-Zulu #license-cc-by-4.0 #license-cc-by-nc-4.0 #license-cc-by-nd-4.0 #license-cc-by-sa-4.0 #license-cc-by-nc-nd-4.0 #license-cc-by-nc-sa-4.0 #region-us \n", "### Data Instances\n\n\nThe examples look like this for Hindi:\n\n\nThis would produce an output:", "### Data Fields\n\n\nThe metadata fields below are available. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).\n\n\n* id: id of the sample\n* title: title of the book, e.g. \"Going to Buy a Book\".\n* license: specific license used, e.g. \"cc-by-sa\" for \"Creative Commons, by attribution, share-alike\".\n* album\\_id: an ID value corresponding to the set of images corresponding to the given story\n* story: the sequenced story data including lists of image IDs, image URLs, and corresponding text", "### Data Splits\n\n\nCurrently all languages include a train split only. In the future, we will be creating manual splits of the data.\n\n\nChangelog\n---------\n\n\n* 6 December 2022 - dataset is made public" ]

[ "region:us" ]

[]

[]

[ "# Gcode (Geometric code)", "## Details\n\nUsage: 3D printing <br>\nSource: URL <br>\nSlicer: Prusa <br>\nCategory: Art & Design <br>\nSubcategory: Sculptures <br>\nModels: 400 <br>\nSliced files: 740 (some models have many) <br>\nData format: txt <br>\nTrain-test split: 90/10 <br>\nSize: 11GB" ]

[ "TAGS\n#region-us \n", "# Gcode (Geometric code)", "## Details\n\nUsage: 3D printing <br>\nSource: URL <br>\nSlicer: Prusa <br>\nCategory: Art & Design <br>\nSubcategory: Sculptures <br>\nModels: 400 <br>\nSliced files: 740 (some models have many) <br>\nData format: txt <br>\nTrain-test split: 90/10 <br>\nSize: 11GB" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset for the first <a href=\"URL style=\"color:blue\">Workshop on Information Extraction from Scientific Publications (WIESP/2022)</a>.", "## Dataset Description\nDatasets with text fragments from astrophysics papers, provided by the NASA Astrophysical Data System with manually tagged astronomical facilities and other entities of interest (e.g., celestial objects). \nDatasets are in JSON Lines format (each line is a json dictionary). \nThe datasets are formatted similarly to the CONLL2003 format. Each token is associated with an NER tag. The tags follow the \"B-\" and \"I-\" convention from the IOB2 syntax\")\n\nEach entry consists of a dictionary with the following keys:\n- '\"unique_id\"': a unique identifier for this data sample. Must be included in the predictions.\n- '\"tokens\"': the list of tokens (strings) that form the text of this sample. Must be included in the predictions.\n- '\"ner_tags\"': the list of NER tags (in IOB2 format)\n\nThe following keys are not strictly needed by the participants:\n- '\"ner_ids\"': the pre-computed list of ids corresponding ner_tags, as given by the dictionary in ner_tags.json\n- '\"label_studio_id\"', '\"section\"', '\"bibcode\"': references for internal NASA/ADS use.", "## Instructions for Workshop participants:\nHow to load the data using the Huggingface library:\n \n\nHow to load the data if you cloned the repository locally: \n(assuming './URL' is in the current directory, change as needed)\n- python (as list of dictionaries):\n\n - into Huggingface (as a Huggingface Dataset):\n\n\n\nHow to compute your scores on the training data:\n1. format your predictions as a list of dictionaries, each with the same '\"unique_id\"' and '\"tokens\"' keys from the dataset, as well as the list of predicted NER tags under the '\"pred_ner_tags\"' key (see 'URL' for an example).\n2. pass the references and predictions datasets to the 'compute_MCC()' and 'compute_seqeval()' functions (from the '.py' files with the same names).\n\nRequirement to run the scoring scripts: \nNumPy \nscikit-learn \nseqeval\n\nTo get scores on the validation data, zip your predictions file (a single '.jsonl' file formatted following the same instructions as above) and upload the '.zip' file to the Codalabs competition.", "## File list", "## Cite as\nOverview of the First Shared Task on Detecting Entities in the Astrophysics Literature (DEAL) (Grezes et al., WIESP 2022)" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #region-us \n", "# Dataset for the first <a href=\"URL style=\"color:blue\">Workshop on Information Extraction from Scientific Publications (WIESP/2022)</a>.", "## Dataset Description\nDatasets with text fragments from astrophysics papers, provided by the NASA Astrophysical Data System with manually tagged astronomical facilities and other entities of interest (e.g., celestial objects). \nDatasets are in JSON Lines format (each line is a json dictionary). \nThe datasets are formatted similarly to the CONLL2003 format. Each token is associated with an NER tag. The tags follow the \"B-\" and \"I-\" convention from the IOB2 syntax\")\n\nEach entry consists of a dictionary with the following keys:\n- '\"unique_id\"': a unique identifier for this data sample. Must be included in the predictions.\n- '\"tokens\"': the list of tokens (strings) that form the text of this sample. Must be included in the predictions.\n- '\"ner_tags\"': the list of NER tags (in IOB2 format)\n\nThe following keys are not strictly needed by the participants:\n- '\"ner_ids\"': the pre-computed list of ids corresponding ner_tags, as given by the dictionary in ner_tags.json\n- '\"label_studio_id\"', '\"section\"', '\"bibcode\"': references for internal NASA/ADS use.", "## Instructions for Workshop participants:\nHow to load the data using the Huggingface library:\n \n\nHow to load the data if you cloned the repository locally: \n(assuming './URL' is in the current directory, change as needed)\n- python (as list of dictionaries):\n\n - into Huggingface (as a Huggingface Dataset):\n\n\n\nHow to compute your scores on the training data:\n1. format your predictions as a list of dictionaries, each with the same '\"unique_id\"' and '\"tokens\"' keys from the dataset, as well as the list of predicted NER tags under the '\"pred_ner_tags\"' key (see 'URL' for an example).\n2. pass the references and predictions datasets to the 'compute_MCC()' and 'compute_seqeval()' functions (from the '.py' files with the same names).\n\nRequirement to run the scoring scripts: \nNumPy \nscikit-learn \nseqeval\n\nTo get scores on the validation data, zip your predictions file (a single '.jsonl' file formatted following the same instructions as above) and upload the '.zip' file to the Codalabs competition.", "## File list", "## Cite as\nOverview of the First Shared Task on Detecting Entities in the Astrophysics Literature (DEAL) (Grezes et al., WIESP 2022)" ]

[ "license:afl-3.0", "arxiv:2203.15867", "region:us" ]

[ "2203.15867" ]

[]

[ "### Dataset Summary\n\n\nWe introduce ImageCoDe, a vision-and-language benchmark that requires contextual language understanding in the form of pragmatics, temporality, long descriptions and visual nuances. The task: Given a detailed description, retrieve the target image among 10 minimally contrastive images. ImageCoDe contains 21K descriptions and 94K images. THe images are primarily frames based on video datasets.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn instance contains a description, the corresponding image set name, and the target index:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe main goal of ImageCoDe is to highlight weaknesses of recent Vision-and-Language models regarding complex language and fine-grained visual representations. In addition, we found that the dataset offers plenty of pragmatic examples and is therefore suitable for studying pragmatics." ]

[ "TAGS\n#license-afl-3.0 #arxiv-2203.15867 #region-us \n", "### Dataset Summary\n\n\nWe introduce ImageCoDe, a vision-and-language benchmark that requires contextual language understanding in the form of pragmatics, temporality, long descriptions and visual nuances. The task: Given a detailed description, retrieve the target image among 10 minimally contrastive images. ImageCoDe contains 21K descriptions and 94K images. THe images are primarily frames based on video datasets.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn instance contains a description, the corresponding image set name, and the target index:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe main goal of ImageCoDe is to highlight weaknesses of recent Vision-and-Language models regarding complex language and fine-grained visual representations. In addition, we found that the dataset offers plenty of pragmatic examples and is therefore suitable for studying pragmatics." ]

[ "region:us" ]

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[ "# test-imagefolder-metadata" ]

[ "TAGS\n#region-us \n", "# test-imagefolder-metadata" ]