Datasets:

---

ftopal

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huggingface-datasets

like 0

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

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the\ncompositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005)\nand consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and\nincludes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.\n\n\nBinary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive\nwith neutral sentences discarded) refer to the dataset as SST-2 or SST binary.", "### Supported Tasks and Leaderboards\n\n\n* 'sentiment-classification'", "### Languages\n\n\nThe text in the dataset is in English ('en').\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'idx': Monotonically increasing index ID.\n* 'sentence': Complete sentence expressing an opinion about a film.\n* 'label': Sentiment of the opinion, either \"negative\" (0) or positive (1). The test set labels are hidden (-1).", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\nRotten Tomatoes reviewers.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nUnknown.", "### Contributions\n\n\nThanks to @albertvillanova for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us \n", "### Dataset Summary\n\n\nThe Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the\ncompositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005)\nand consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and\nincludes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.\n\n\nBinary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive\nwith neutral sentences discarded) refer to the dataset as SST-2 or SST binary.", "### Supported Tasks and Leaderboards\n\n\n* 'sentiment-classification'", "### Languages\n\n\nThe text in the dataset is in English ('en').\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'idx': Monotonically increasing index ID.\n* 'sentence': Complete sentence expressing an opinion about a film.\n* 'label': Sentiment of the opinion, either \"negative\" (0) or positive (1). The test set labels are hidden (-1).", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\nRotten Tomatoes reviewers.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nUnknown.", "### Contributions\n\n\nThanks to @albertvillanova for adding this dataset." ]

[ "arxiv:1611.09268", "region:us" ]

[ "1611.09268" ]

[]

[]

[ "TAGS\n#arxiv-1611.09268 #region-us \n" ]

[ "arxiv:1611.09268", "region:us" ]

[ "1611.09268" ]

[]

[]

[ "TAGS\n#arxiv-1611.09268 #region-us \n" ]

[ "task_categories:text-classification", "task_categories:text2text-generation", "task_categories:translation", "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", "idiomaticity-classification", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe MAGPIE corpus (Haagsma et al. 2020) is a large sense-annotated corpus of potentially idiomatic expressions (PIEs), based on the British National Corpus (BNC). Potentially idiomatic expressions are like idiomatic expressions, but the term also covers literal uses of idiomatic expressions, such as 'I leave work at the end of the day.' for the idiom 'at the end of the day'. This version of the dataset reflects the filtered subset used by Dankers et al. (2022) in their investigation on how PIEs are represented by NMT models. Authors use 37k samples annotated as fully figurative or literal, for 1482 idioms that contain nouns, numerals or adjectives that are colors (which they refer to as keywords). Because idioms show syntactic and morphological variability, the focus is mostly put on nouns. PIEs and their context are separated using the original corpus’s word-level annotations.", "### Languages\n\n\nThe language data in MAGPIE is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe 'magpie' configuration contains sentences with annotations for the presence, usage an type of potentially idiomatic expressions. An example from the 'train' split of the 'magpie' config (default) is provided below.\n\n\nThe text is provided as-is, without further preprocessing or tokenization.\n\n\nThe fields are the following:\n\n\n* 'sentence': The sentence containing a PIE.\n* 'annotation': List of 0s and 1s of the same length of the whitespace-tokenized sentence, with 1s corresponding to the position of the idiomatic expression.\n* 'idiom': The idiom contained in the sentence in its base form.\n* 'usage': Either 'figurative' or 'literal', depending on the usage of the PIE.\n* 'variant': 'identical' if the PIE matches the base form of the idiom, otherwise specifies the variation.\n* 'pos\\_tags': List of POS tags associated with words in the sentence.", "### Data Splits", "### Dataset Creation\n\n\nPlease refer to the original article MAGPIE: A Large Corpus of Potentially Idiomatic Expressions for additional information on dataset creation, and to the article Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation for further information on the filtering of selected idioms.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe original authors are the curators of the original dataset. For problems or updates on this Datasets version, please contact gabriele.sarti996@URL.", "### Licensing Information\n\n\nThe dataset is licensed under Creative Commons 4.0 license (CC-BY-4.0)\n\n\nPlease cite the authors if you use this corpus in your work:" ]

[ "TAGS\n#task_categories-text-classification #task_categories-text2text-generation #task_categories-translation #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 #idiomaticity-classification #region-us \n", "### Dataset Summary\n\n\nThe MAGPIE corpus (Haagsma et al. 2020) is a large sense-annotated corpus of potentially idiomatic expressions (PIEs), based on the British National Corpus (BNC). Potentially idiomatic expressions are like idiomatic expressions, but the term also covers literal uses of idiomatic expressions, such as 'I leave work at the end of the day.' for the idiom 'at the end of the day'. This version of the dataset reflects the filtered subset used by Dankers et al. (2022) in their investigation on how PIEs are represented by NMT models. Authors use 37k samples annotated as fully figurative or literal, for 1482 idioms that contain nouns, numerals or adjectives that are colors (which they refer to as keywords). Because idioms show syntactic and morphological variability, the focus is mostly put on nouns. PIEs and their context are separated using the original corpus’s word-level annotations.", "### Languages\n\n\nThe language data in MAGPIE is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe 'magpie' configuration contains sentences with annotations for the presence, usage an type of potentially idiomatic expressions. An example from the 'train' split of the 'magpie' config (default) is provided below.\n\n\nThe text is provided as-is, without further preprocessing or tokenization.\n\n\nThe fields are the following:\n\n\n* 'sentence': The sentence containing a PIE.\n* 'annotation': List of 0s and 1s of the same length of the whitespace-tokenized sentence, with 1s corresponding to the position of the idiomatic expression.\n* 'idiom': The idiom contained in the sentence in its base form.\n* 'usage': Either 'figurative' or 'literal', depending on the usage of the PIE.\n* 'variant': 'identical' if the PIE matches the base form of the idiom, otherwise specifies the variation.\n* 'pos\\_tags': List of POS tags associated with words in the sentence.", "### Data Splits", "### Dataset Creation\n\n\nPlease refer to the original article MAGPIE: A Large Corpus of Potentially Idiomatic Expressions for additional information on dataset creation, and to the article Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation for further information on the filtering of selected idioms.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe original authors are the curators of the original dataset. For problems or updates on this Datasets version, please contact gabriele.sarti996@URL.", "### Licensing Information\n\n\nThe dataset is licensed under Creative Commons 4.0 license (CC-BY-4.0)\n\n\nPlease cite the authors if you use this corpus in your work:" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nPR is a phrase retrieval task with the goal of finding a phrase t in a given document d such that t is semantically similar to the query phrase, which is the paraphrase q1 provided by annotators.\nWe release two versions of PR: PR-pass and PR-page, i.e., datasets of 3-tuples (query q1, target phrase t, document d) where d is a random 11-sentence passage that contains t or an entire Wikipedia page.\nWhile PR-pass contains 28,147 examples, PR-page contains slightly fewer examples (28,098) as we remove those trivial examples whose Wikipedia pages contain exactly the query phrase (in addition to the target phrase).\nBoth datasets are split into 5K/3K/~20K for test/dev/train, respectively.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nPR-pass\n\n\n* Size of downloaded dataset files: 43.61 MB\n* Size of the generated dataset: 36.98 MB\n* Total amount of disk used: 80.59 MB\n\n\nAn example of 'train' looks as follows.\n\n\nPR-page\n\n\n* Size of downloaded dataset files: 421.56 MB\n* Size of the generated dataset: 412.17 MB\n* Total amount of disk used: 833.73 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all subsets and splits.\n\n\n* id: a string feature.\n* title: a string feature.\n* context: a string feature.\n* question: a string feature.\n* answers: a dictionary feature containing:\n\t+ text: a list of string features.\n\t+ answer\\_start: a list of int32 features.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from URL.", "#### Who are the source language producers?\n\n\nWe hired 13 linguistic experts from URL for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\n13 linguistic experts from URL.", "### Personal and Sensitive Information\n\n\nNo annotator identifying details are provided.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset is a joint work between Adobe Research and Auburn University.\nCreators: Thang M. Pham, David Seunghyun Yoon, Trung Bui, and Anh Nguyen.\n\n\n@PMThangXAI added this dataset to HuggingFace.", "### Licensing Information\n\n\nThis dataset is distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)" ]

[ "TAGS\n#task_categories-text-retrieval #annotations_creators-expert-generated #language_creators-found #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us \n", "### Dataset Summary\n\n\nPR is a phrase retrieval task with the goal of finding a phrase t in a given document d such that t is semantically similar to the query phrase, which is the paraphrase q1 provided by annotators.\nWe release two versions of PR: PR-pass and PR-page, i.e., datasets of 3-tuples (query q1, target phrase t, document d) where d is a random 11-sentence passage that contains t or an entire Wikipedia page.\nWhile PR-pass contains 28,147 examples, PR-page contains slightly fewer examples (28,098) as we remove those trivial examples whose Wikipedia pages contain exactly the query phrase (in addition to the target phrase).\nBoth datasets are split into 5K/3K/~20K for test/dev/train, respectively.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nPR-pass\n\n\n* Size of downloaded dataset files: 43.61 MB\n* Size of the generated dataset: 36.98 MB\n* Total amount of disk used: 80.59 MB\n\n\nAn example of 'train' looks as follows.\n\n\nPR-page\n\n\n* Size of downloaded dataset files: 421.56 MB\n* Size of the generated dataset: 412.17 MB\n* Total amount of disk used: 833.73 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all subsets and splits.\n\n\n* id: a string feature.\n* title: a string feature.\n* context: a string feature.\n* question: a string feature.\n* answers: a dictionary feature containing:\n\t+ text: a list of string features.\n\t+ answer\\_start: a list of int32 features.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from URL.", "#### Who are the source language producers?\n\n\nWe hired 13 linguistic experts from URL for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\n13 linguistic experts from URL.", "### Personal and Sensitive Information\n\n\nNo annotator identifying details are provided.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset is a joint work between Adobe Research and Auburn University.\nCreators: Thang M. Pham, David Seunghyun Yoon, Trung Bui, and Anh Nguyen.\n\n\n@PMThangXAI added this dataset to HuggingFace.", "### Licensing Information\n\n\nThis dataset is distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-nc-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for financial_phrasebank", "## 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\nAuditor review data collected by News Department\n- Point of Contact:\nTalked to COE for Auditing", "### Dataset Summary\nAuditor sentiment dataset of sentences from financial news. The dataset consists of * sentences from English language financial news categorized by sentiment. The dataset is divided by agreement rate of 5-8 annotators.", "### Supported Tasks and Leaderboards\nSentiment Classification", "### Languages\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'", "### Data Splits\nA test train split was created randomly with a 75/25 split", "## Dataset Creation", "### Curation Rationale\n\nThe key arguments for the low utilization of statistical techniques in\nfinancial sentiment analysis have been the difficulty of implementation for\npractical applications and the lack of high quality training data for building\nsuch models. *", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe corpus used in this paper is made out of English news on all listed\ncompanies in", "#### Who are the source language producers?\n\nThe source data was written by various auditors", "### Annotations", "#### Annotation process\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement:", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nAll annotators were from the same institution and so interannotator agreement\nshould be understood with this taken into account.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nLicense: Creative Commons Attribution 4.0 International License (CC-BY)", "### Contributions" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-nc-sa-3.0 #region-us \n", "# Dataset Card for financial_phrasebank", "## 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\nAuditor review data collected by News Department\n- Point of Contact:\nTalked to COE for Auditing", "### Dataset Summary\nAuditor sentiment dataset of sentences from financial news. The dataset consists of * sentences from English language financial news categorized by sentiment. The dataset is divided by agreement rate of 5-8 annotators.", "### Supported Tasks and Leaderboards\nSentiment Classification", "### Languages\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'", "### Data Splits\nA test train split was created randomly with a 75/25 split", "## Dataset Creation", "### Curation Rationale\n\nThe key arguments for the low utilization of statistical techniques in\nfinancial sentiment analysis have been the difficulty of implementation for\npractical applications and the lack of high quality training data for building\nsuch models. *", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe corpus used in this paper is made out of English news on all listed\ncompanies in", "#### Who are the source language producers?\n\nThe source data was written by various auditors", "### Annotations", "#### Annotation process\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement:", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nAll annotators were from the same institution and so interannotator agreement\nshould be understood with this taken into account.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nLicense: Creative Commons Attribution 4.0 International License (CC-BY)", "### Contributions" ]

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

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:\n\n\nNote that, sadly, it flipped the boolean, using 1 for mundane and 0 for goblin.", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:\n\n\nNote that, sadly, it flipped the boolean, using 1 for mundane and 0 for goblin.", "### 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-retrieval", "annotations_creators:expert-generated", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nPSD is a phrase retrieval task like PR-pass and PR-page but more challenging since each example contains two short paragraphs (~11 sentences each) which trigger different senses of the same phrase.\nThe goal is to find the instance of the target phrase t that is semantically similar to a paraphrase q.\nThe dataset is split into 5,150/3,000/20,002 for test/dev/train, respectively.\n\n\n\n\n\n\n\nGiven document D, trained Longformer-large model correctly retrieves massive figure in the second paragraph for the query Q2 \"giant number\" but fails to retrieve the answer when the query Q1 is \"huge model\".\nThe correct answer for Q1 should be massive figure in the first passage since this phrase relates to a model rather than a number.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nPSD\n\n\n* Size of downloaded dataset files: 49.95 MB\n* Size of the generated dataset: 43.26 MB\n* Total amount of disk used: 93.20 MB\n\n\nAn example of 'test' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all subsets and splits.\n\n\n* id: a string feature.\n* title: a string feature.\n* context: a string feature.\n* question: a string feature.\n* answers: a dictionary feature containing:\n\t+ text: a list of string features.\n\t+ answer\\_start: a list of int32 features.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from URL.", "#### Who are the source language producers?\n\n\nWe hired 13 linguistic experts from URL for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\n13 linguistic experts from URL.", "### Personal and Sensitive Information\n\n\nNo annotator identifying details are provided.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset is a joint work between Adobe Research and Auburn University.\nCreators: Thang M. Pham, David Seunghyun Yoon, Trung Bui, and Anh Nguyen.\n\n\n@PMThangXAI added this dataset to HuggingFace.", "### Licensing Information\n\n\nThis dataset is distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)" ]

[ "TAGS\n#task_categories-text-retrieval #annotations_creators-expert-generated #language_creators-found #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us \n", "### Dataset Summary\n\n\nPSD is a phrase retrieval task like PR-pass and PR-page but more challenging since each example contains two short paragraphs (~11 sentences each) which trigger different senses of the same phrase.\nThe goal is to find the instance of the target phrase t that is semantically similar to a paraphrase q.\nThe dataset is split into 5,150/3,000/20,002 for test/dev/train, respectively.\n\n\n\n\n\n\n\nGiven document D, trained Longformer-large model correctly retrieves massive figure in the second paragraph for the query Q2 \"giant number\" but fails to retrieve the answer when the query Q1 is \"huge model\".\nThe correct answer for Q1 should be massive figure in the first passage since this phrase relates to a model rather than a number.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nPSD\n\n\n* Size of downloaded dataset files: 49.95 MB\n* Size of the generated dataset: 43.26 MB\n* Total amount of disk used: 93.20 MB\n\n\nAn example of 'test' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all subsets and splits.\n\n\n* id: a string feature.\n* title: a string feature.\n* context: a string feature.\n* question: a string feature.\n* answers: a dictionary feature containing:\n\t+ text: a list of string features.\n\t+ answer\\_start: a list of int32 features.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from URL.", "#### Who are the source language producers?\n\n\nWe hired 13 linguistic experts from URL for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\n13 linguistic experts from URL.", "### Personal and Sensitive Information\n\n\nNo annotator identifying details are provided.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset is a joint work between Adobe Research and Auburn University.\nCreators: Thang M. Pham, David Seunghyun Yoon, Trung Bui, and Anh Nguyen.\n\n\n@PMThangXAI added this dataset to HuggingFace.", "### Licensing Information\n\n\nThis dataset is distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nPS is a binary classification task with the goal of predicting whether two multi-word noun phrases are semantically similar or not given *the same context* sentence.\nThis dataset contains ~10K pairs of two phrases along with their contexts used for disambiguation, since two phrases are not enough for semantic comparison.\nOur ~10K examples were annotated by linguistic experts on and verified in two rounds by 1000 Mturkers and 5 linguistic experts.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nPS\n\n\n* Size of downloaded dataset files: 4.60 MB\n* Size of the generated dataset: 2.96 MB\n* Total amount of disk used: 7.56 MB", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* phrase1: a string feature.\n* phrase2: a string feature.\n* sentence1: a string feature.\n* sentence2: a string feature.\n* label: a classification label, with negative (0) and positive (1).\n* idx: an int32 feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from URL.", "#### Who are the source language producers?\n\n\nWe hired 13 linguistic experts from URL for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\n13 linguistic experts from URL.", "### Personal and Sensitive Information\n\n\nNo annotator identifying details are provided.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset is a joint work between Adobe Research and Auburn University.\nCreators: Thang M. Pham, David Seunghyun Yoon, Trung Bui, and Anh Nguyen.\n\n\n@PMThangXAI added this dataset to HuggingFace.", "### Licensing Information\n\n\nThis dataset is distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)" ]

[ "TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #language_creators-found #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-nc-4.0 #region-us \n", "### Dataset Summary\n\n\nPS is a binary classification task with the goal of predicting whether two multi-word noun phrases are semantically similar or not given *the same context* sentence.\nThis dataset contains ~10K pairs of two phrases along with their contexts used for disambiguation, since two phrases are not enough for semantic comparison.\nOur ~10K examples were annotated by linguistic experts on and verified in two rounds by 1000 Mturkers and 5 linguistic experts.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nPS\n\n\n* Size of downloaded dataset files: 4.60 MB\n* Size of the generated dataset: 2.96 MB\n* Total amount of disk used: 7.56 MB", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* phrase1: a string feature.\n* phrase2: a string feature.\n* sentence1: a string feature.\n* sentence2: a string feature.\n* label: a classification label, with negative (0) and positive (1).\n* idx: an int32 feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from URL.", "#### Who are the source language producers?\n\n\nWe hired 13 linguistic experts from URL for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\n13 linguistic experts from URL.", "### Personal and Sensitive Information\n\n\nNo annotator identifying details are provided.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThis dataset is a joint work between Adobe Research and Auburn University.\nCreators: Thang M. Pham, David Seunghyun Yoon, Trung Bui, and Anh Nguyen.\n\n\n@PMThangXAI added this dataset to HuggingFace.", "### Licensing Information\n\n\nThis dataset is distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)" ]

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

[]

[ "en" ]

[ "# Dataset Card for Auditor_Review", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information", "## Dataset Description\nAuditor review data collected by News Department\n\n- Point of Contact:\nTalked to COE for Auditing, currently sue@URL", "### Dataset Summary\n\nAuditor sentiment dataset of sentences from financial news. The dataset consists of 3500 sentences from English language financial news categorized by sentiment. The dataset is divided by the agreement rate of 5-8 annotators.", "### Supported Tasks and Leaderboards\n\nSentiment Classification", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0) \n\nComplete data code is available here", "### Data Splits\n\nA train/test split was created randomly with a 75/25 split", "## Dataset Creation", "### Curation Rationale\n\nTo gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe corpus used in this paper is made out of English news reports.", "#### Who are the source language producers?\n\nThe source data was written by various auditors.", "### Annotations", "#### Annotation process\n\nThis release of the auditor reviews covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge of financial markets. The subset here is where inter-annotation agreement was greater than 75%.", "#### Who are the annotators?\n\nThey were pulled from the SME list, names are held by sue@URL", "### Personal and Sensitive Information\n\nThere is no personal or sensitive information in this dataset.", "## Considerations for Using the Data", "### Discussion of Biases\n\nAll annotators were from the same institution and so interannotator agreement\nshould be understood with this taken into account.\n\nThe Dataset Measurement tool identified these bias statistics:\n\n!Bias", "### Other Known Limitations", "### Licensing Information\n\nLicense: Demo.Org Proprietary - DO NOT SHARE" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #region-us \n", "# Dataset Card for Auditor_Review", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information", "## Dataset Description\nAuditor review data collected by News Department\n\n- Point of Contact:\nTalked to COE for Auditing, currently sue@URL", "### Dataset Summary\n\nAuditor sentiment dataset of sentences from financial news. The dataset consists of 3500 sentences from English language financial news categorized by sentiment. The dataset is divided by the agreement rate of 5-8 annotators.", "### Supported Tasks and Leaderboards\n\nSentiment Classification", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0) \n\nComplete data code is available here", "### Data Splits\n\nA train/test split was created randomly with a 75/25 split", "## Dataset Creation", "### Curation Rationale\n\nTo gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe corpus used in this paper is made out of English news reports.", "#### Who are the source language producers?\n\nThe source data was written by various auditors.", "### Annotations", "#### Annotation process\n\nThis release of the auditor reviews covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge of financial markets. The subset here is where inter-annotation agreement was greater than 75%.", "#### Who are the annotators?\n\nThey were pulled from the SME list, names are held by sue@URL", "### Personal and Sensitive Information\n\nThere is no personal or sensitive information in this dataset.", "## Considerations for Using the Data", "### Discussion of Biases\n\nAll annotators were from the same institution and so interannotator agreement\nshould be understood with this taken into account.\n\nThe Dataset Measurement tool identified these bias statistics:\n\n!Bias", "### Other Known Limitations", "### Licensing Information\n\nLicense: Demo.Org Proprietary - DO NOT SHARE" ]

[ "region:us" ]

[]

[]

[ "# Danbooru2020 Small 60GB sample dataset\n\nAggregating the kaggle dataset here but keeping their hosting for the raw files\n\nLinks:\n> URL\n\nSee the notebook file for a quick reference on how to extract info" ]

[ "TAGS\n#region-us \n", "# Danbooru2020 Small 60GB sample dataset\n\nAggregating the kaggle dataset here but keeping their hosting for the raw files\n\nLinks:\n> URL\n\nSee the notebook file for a quick reference on how to extract info" ]

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

[]

[]

[ "# Description\nThe dataset represents huge number of images of people wearing face masks or not to be used extensively for train/test splitting. Selected files were double-checked to avoid data collection bias using common sense.", "# Sources\nThe dataset obtained and combined from various open data sources, including following:\n\n- URL\n- URL\n- URL\n- URL\n- manually obtained under-represented observations using Google search engine", "# Structure\n\nThe dataset is curated and structured into three age groups (under 18, 18-65 and 65+) without initial test/train selection, which is achieved programmatically to allow manipulations with original data.\n<a href=\"URL target=\"_blank\"><img src=\"https://i.URL alt=\"2022-06-14-10-21-39\"/></a>\n\n<a href=\"URL target=\"_blank\"><img src=\"https://i.URL alt=\"Screenshot-2022-06-14-101707\"/></a>" ]

[ "TAGS\n#license-other #region-us \n", "# Description\nThe dataset represents huge number of images of people wearing face masks or not to be used extensively for train/test splitting. Selected files were double-checked to avoid data collection bias using common sense.", "# Sources\nThe dataset obtained and combined from various open data sources, including following:\n\n- URL\n- URL\n- URL\n- URL\n- manually obtained under-represented observations using Google search engine", "# Structure\n\nThe dataset is curated and structured into three age groups (under 18, 18-65 and 65+) without initial test/train selection, which is achieved programmatically to allow manipulations with original data.\n<a href=\"URL target=\"_blank\"><img src=\"https://i.URL alt=\"2022-06-14-10-21-39\"/></a>\n\n<a href=\"URL target=\"_blank\"><img src=\"https://i.URL alt=\"Screenshot-2022-06-14-101707\"/></a>" ]

[ "task_categories:conversational", "task_ids:dialogue-generation", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:zh", "license:mit", "arxiv:2008.03946", "region:us" ]

[ "2008.03946" ]

[ "zh" ]

[ "### Dataset Summary\n\n\nLCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.\n\n\nLCCC是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。 这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。 我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。", "### Supported Tasks and Leaderboards\n\n\n* dialogue-generation: The dataset can be used to train a model for generating dialogue responses.\n* response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.", "### Languages\n\n\nLCCC is in Chinese\n\n\nLCCC中的对话是中文的\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'dialog' (list of strings): List of utterances consisting of a dialogue.", "### Data Splits\n\n\nWe do not provide the offical split for LCCC-large.\nBut we provide a split for LCCC-base:\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\nMIT License\n\n\nCopyright (c) 2020 lemon234071\n\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.", "### Contributions\n\n\nThanks to Yinhe Zheng for adding this dataset." ]

[ "TAGS\n#task_categories-conversational #task_ids-dialogue-generation #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Chinese #license-mit #arxiv-2008.03946 #region-us \n", "### Dataset Summary\n\n\nLCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.\n\n\nLCCC是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。 这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。 我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。", "### Supported Tasks and Leaderboards\n\n\n* dialogue-generation: The dataset can be used to train a model for generating dialogue responses.\n* response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.", "### Languages\n\n\nLCCC is in Chinese\n\n\nLCCC中的对话是中文的\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'dialog' (list of strings): List of utterances consisting of a dialogue.", "### Data Splits\n\n\nWe do not provide the offical split for LCCC-large.\nBut we provide a split for LCCC-base:\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\nMIT License\n\n\nCopyright (c) 2020 lemon234071\n\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.", "### Contributions\n\n\nThanks to Yinhe Zheng for adding this dataset." ]

[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:da", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "da" ]

[ "# Dataset Card for \"dane-mini\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-Danish #license-cc-by-sa-4.0 #region-us \n", "# Dataset Card for \"dane-mini\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:nb", "license:other", "region:us" ]

[]

[ "nb" ]

[ "# Dataset Card for \"norne-nb-mini\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-Norwegian Bokmål #license-other #region-us \n", "# Dataset Card for \"norne-nb-mini\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:nn", "license:other", "region:us" ]

[]

[ "nn" ]

[ "# Dataset Card for \"norne-nn-mini\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-Norwegian Nynorsk #license-other #region-us \n", "# Dataset Card for \"norne-nn-mini\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:sv", "license:cc-by-4.0", "region:us" ]

[]

[ "sv" ]

[ "# Dataset Card for \"suc3-mini\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-Swedish #license-cc-by-4.0 #region-us \n", "# Dataset Card for \"suc3-mini\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:is", "license:other", "region:us" ]

[]

[ "is" ]

[ "# Dataset Card for \"mim-gold-ner-mini\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-Icelandic #license-other #region-us \n", "# Dataset Card for \"mim-gold-ner-mini\"\n\nMore Information needed" ]

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

[]

[ "fo" ]

[ "# Dataset Card for \"wikiann-fo-mini\"\n\nMore Information needed" ]

[ "TAGS\n#language-Faroese #region-us \n", "# Dataset Card for \"wikiann-fo-mini\"\n\nMore Information needed" ]

[ "task_categories:image-segmentation", "task_ids:semantic-segmentation", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:extended|ade20k", "language:en", "license:bsd-3-clause", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for ADE 20K Tiny\n\nThis is a tiny subset of the ADE 20K dataset, which you can find here." ]

[ "TAGS\n#task_categories-image-segmentation #task_ids-semantic-segmentation #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-extended|ade20k #language-English #license-bsd-3-clause #region-us \n", "# Dataset Card for ADE 20K Tiny\n\nThis is a tiny subset of the ADE 20K dataset, which you can find here." ]

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|yfcc100m", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Country211\n\nThe Country 211 Dataset from OpenAI.\n\nThis dataset was built by filtering the images from the YFCC100m dataset that have GPS coordinate corresponding to a ISO-3166 country code. The dataset is balanced by sampling 150 train images, 50 validation images, and 100 test images images for each country." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|yfcc100m #language-English #license-unknown #region-us \n", "# Dataset Card for Country211\n\nThe Country 211 Dataset from OpenAI.\n\nThis dataset was built by filtering the images from the YFCC100m dataset that have GPS coordinate corresponding to a ISO-3166 country code. The dataset is balanced by sampling 150 train images, 50 validation images, and 100 test images images for each country." ]

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|sst2", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Rendered SST-2\n\nThe Rendered SST-2 Dataset from Open AI.\n\nRendered SST2 is an image classification dataset used to evaluate the models capability on optical character recognition. This dataset was generated by rendering sentences in the Standford Sentiment Treebank v2 dataset.\n\nThis dataset contains two classes (positive and negative) and is divided in three splits: a train split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images (444 positive and 428 negative), and a test split containing 1821 images (909 positive and 912 negative)." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-extended|sst2 #language-English #license-unknown #region-us \n", "# Rendered SST-2\n\nThe Rendered SST-2 Dataset from Open AI.\n\nRendered SST2 is an image classification dataset used to evaluate the models capability on optical character recognition. This dataset was generated by rendering sentences in the Standford Sentiment Treebank v2 dataset.\n\nThis dataset contains two classes (positive and negative) and is divided in three splits: a train split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images (444 positive and 428 negative), and a test split containing 1821 images (909 positive and 912 negative)." ]

[ "task_categories:object-detection", "annotations_creators:found", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Kitti\n\nThe Kitti dataset.\n\nThe Kitti object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects" ]

[ "TAGS\n#task_categories-object-detection #annotations_creators-found #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-unknown #region-us \n", "# Dataset Card for Kitti\n\nThe Kitti dataset.\n\nThe Kitti object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects" ]

[ "annotations_creators:no-annotation", "language_creators:other", "size_categories:1M<n<10M", "license:lgpl-3.0", "region:us" ]

[]

[]

[ "### Dataset Summary\n\n\nThis dataset contains over 6M CAD 2D sketches extracted from Onshape. Sketches are stored as python objects in the custom SAM format.\nSAM leverages the Sketchgraphs dataset for industrial needs and allows for easier transfer learning on other CAD softwares.", "### Supported Tasks and Leaderboards\n\n\nTasks: Automatic Sketch Generation, Auto Constraint\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe presented npy files contain python pickled objects and require the flat\\_array module of Sketchgraphs to be loaded. The normalization\\_output\\_merged.npy file contains sketch sequences represented as a list of SAM Primitives and Constraints. The sg\\_merged\\_final\\_\\*.npy files contain encoded constraint graphs of the sketches represented as a dictionnary of arrays.", "### Data Fields", "### Data Splits\n\n\nTrain: 6M, Val: 50k, Test: 50k\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" ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-other #size_categories-1M<n<10M #license-lgpl-3.0 #region-us \n", "### Dataset Summary\n\n\nThis dataset contains over 6M CAD 2D sketches extracted from Onshape. Sketches are stored as python objects in the custom SAM format.\nSAM leverages the Sketchgraphs dataset for industrial needs and allows for easier transfer learning on other CAD softwares.", "### Supported Tasks and Leaderboards\n\n\nTasks: Automatic Sketch Generation, Auto Constraint\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe presented npy files contain python pickled objects and require the flat\\_array module of Sketchgraphs to be loaded. The normalization\\_output\\_merged.npy file contains sketch sequences represented as a list of SAM Primitives and Constraints. The sg\\_merged\\_final\\_\\*.npy files contain encoded constraint graphs of the sketches represented as a dictionnary of arrays.", "### Data Fields", "### Data Splits\n\n\nTrain: 6M, Val: 50k, Test: 50k\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" ]

[ "task_categories:token-classification", "annotations_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:fr", "license:wtfpl", "region:us" ]

[]

[ "fr" ]

[ "# Dataset Card for frwiki_good_pages_el", "## Dataset Description\n\n- Repository: frwiki_el\n- Point of Contact: Gaëtan Caillaut", "### Dataset Summary\n\nThis dataset contains articles from the French Wikipédia.\nIt is intended to be used to train Entity Linking (EL) systems. Links in articles are used to detect named entities.\n\nThe dataset 'frwiki' contains sentences of each Wikipedia pages.\n\nThe dataset 'entities' contains description for each Wikipedia pages.", "### Languages\n\n- French", "## Dataset Structure", "### frwiki", "### entities" ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-French #license-wtfpl #region-us \n", "# Dataset Card for frwiki_good_pages_el", "## Dataset Description\n\n- Repository: frwiki_el\n- Point of Contact: Gaëtan Caillaut", "### Dataset Summary\n\nThis dataset contains articles from the French Wikipédia.\nIt is intended to be used to train Entity Linking (EL) systems. Links in articles are used to detect named entities.\n\nThe dataset 'frwiki' contains sentences of each Wikipedia pages.\n\nThe dataset 'entities' contains description for each Wikipedia pages.", "### Languages\n\n- French", "## Dataset Structure", "### frwiki", "### entities" ]

[ "license:other", "automatic claim verification", "claims", "arxiv:1909.03242", "region:us" ]

[ "1909.03242" ]

[]

[ "# multiFC\n\n- a dataset for the task of automatic claim verification\n- License is currently unknown, please refer to the original paper/dataset site:\n\n- URL", "## Dataset contents\n\n- IMPORTANT: the 'label' column in the 'test' set has dummy values as these were not provided (see original readme section for explanation)", "## Paper Abstract / Citation\n> We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-depth analysis of the dataset, highlighting characteristics and challenges. Further, we present results for automatic veracity prediction, both with established baselines and with a novel method for joint ranking of evidence pages and predicting veracity that outperforms all baselines. Significant performance increases are achieved by encoding evidence, and by modelling metadata. Our best-performing model achieves a Macro F1 of 49.2%, showing that this is a challenging testbed for claim veracity prediction.", "## Original README\nReal-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims\n\nThe MultiFC is the largest publicly available dataset of naturally occurring factual claims for automatic claim verification.\nIt is collected from 26 English fact-checking websites paired with textual sources and rich metadata and labeled for veracity by human expert journalists.", "###### TRAIN and DEV #######\nThe train and dev files are (tab-separated) and contain the following metadata:\nclaimID, claim, label, claimURL, reason, categories, speaker, checker, tags, article title, publish date, climate, entities\n\nFields that could not be crawled were set as \"None.\" Please refer to Table 11 of our paper to see the summary statistics.", "###### TEST #######\nThe test file follows the same structure. However, we have removed the label. Thus, it only presents 12 metadata.\nclaimID, claim, claim, reason, categories, speaker, checker, tags, article title, publish date, climate, entities\n\nFields that could not be crawled were set as \"None.\" Please refer to Table 11 of our paper to see the summary statistics.", "###### Snippets ######\nThe text of each claim is submitted verbatim as a query to the Google Search API (without quotes).\nIn the folder snippet, we provide the top 10 snippets retrieved. In some cases, fewer snippets are provided\nsince we have excluded the claimURL from the snippets.\nEach file in the snippets folder is named after the claimID of the claim submitted as a query.\nSnippets file is (tab-separated) and contains the following metadata:\nrank_position, title, snippet, snippet_url\n\n\nFor more information, please refer to our paper:\nReferences:\nIsabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen. 2019. \nMultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims. In EMNLP. Association for Computational Linguistics.\n\nURL" ]

[ "TAGS\n#license-other #automatic claim verification #claims #arxiv-1909.03242 #region-us \n", "# multiFC\n\n- a dataset for the task of automatic claim verification\n- License is currently unknown, please refer to the original paper/dataset site:\n\n- URL", "## Dataset contents\n\n- IMPORTANT: the 'label' column in the 'test' set has dummy values as these were not provided (see original readme section for explanation)", "## Paper Abstract / Citation\n> We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-depth analysis of the dataset, highlighting characteristics and challenges. Further, we present results for automatic veracity prediction, both with established baselines and with a novel method for joint ranking of evidence pages and predicting veracity that outperforms all baselines. Significant performance increases are achieved by encoding evidence, and by modelling metadata. Our best-performing model achieves a Macro F1 of 49.2%, showing that this is a challenging testbed for claim veracity prediction.", "## Original README\nReal-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims\n\nThe MultiFC is the largest publicly available dataset of naturally occurring factual claims for automatic claim verification.\nIt is collected from 26 English fact-checking websites paired with textual sources and rich metadata and labeled for veracity by human expert journalists.", "###### TRAIN and DEV #######\nThe train and dev files are (tab-separated) and contain the following metadata:\nclaimID, claim, label, claimURL, reason, categories, speaker, checker, tags, article title, publish date, climate, entities\n\nFields that could not be crawled were set as \"None.\" Please refer to Table 11 of our paper to see the summary statistics.", "###### TEST #######\nThe test file follows the same structure. However, we have removed the label. Thus, it only presents 12 metadata.\nclaimID, claim, claim, reason, categories, speaker, checker, tags, article title, publish date, climate, entities\n\nFields that could not be crawled were set as \"None.\" Please refer to Table 11 of our paper to see the summary statistics.", "###### Snippets ######\nThe text of each claim is submitted verbatim as a query to the Google Search API (without quotes).\nIn the folder snippet, we provide the top 10 snippets retrieved. In some cases, fewer snippets are provided\nsince we have excluded the claimURL from the snippets.\nEach file in the snippets folder is named after the claimID of the claim submitted as a query.\nSnippets file is (tab-separated) and contains the following metadata:\nrank_position, title, snippet, snippet_url\n\n\nFor more information, please refer to our paper:\nReferences:\nIsabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen. 2019. \nMultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims. In EMNLP. Association for Computational Linguistics.\n\nURL" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "language:code", "license:mit", "arxiv:2105.09938", "arxiv:2203.07814", "region:us" ]

[ "2105.09938", "2203.07814" ]

[ "code" ]

[ "### How to use it\n\n\nYou can load and iterate through the dataset with the following two lines of code for the train split:\n\n\nEach sample consists of a programming problem formulation in English, some ground truth Python solutions, test cases that are defined by their inputs and outputs and function name if provided, as well as some metadata regarding the difficulty level of the problem and its source.\n\n\nIf a sample has non empty 'input\\_output' feature, you can read it as a dictionary with keys 'inputs' and 'outputs' and 'fn\\_name' if it exists, and similarily you can parse the solutions into a list of solutions as shown in the code above.\n\n\nYou can also filter the dataset for the difficulty level: Introductory, Interview and Competition. Just pass the list of difficulties as a list. E.g. if you want the most challenging problems, you need to select the competition level:", "### Data Fields\n\n\nField: problem\\_id, Type: int, Description: problem id\nField: question, Type: string, Description: problem description\nField: solutions, Type: string, Description: some python solutions\nField: input\\_output, Type: string, Description: Json string with \"inputs\" and \"outputs\" of the test cases, might also include \"fn\\_name\" the name of the function\nField: difficulty, Type: string, Description: difficulty level of the problem\nField: url, Type: string, Description: url of the source of the problem\nField: starter\\_code, Type: string, Description: starter code to include in prompts\n\n\nwe mention that only few samples have 'fn\\_name' and 'starter\\_code' specified", "### Data Splits\n\n\nThe dataset contains a train and test splits with 5000 samples each.", "### Dataset Statistics\n\n\n* 10000 coding problems\n* 131777 test cases\n* all problems have a least one test case except 195 samples in the train split\n* for tests split, the average number of test cases is 21.2\n* average length of a problem is 293.2 words\n* all files have ground-truth solutions except 1235 samples in the test split\n\n\nDataset Creation\n----------------\n\n\nTo create the APPS dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Codewars, AtCoder, Kattis, and Codeforces. For more details please refer to the original paper.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nIn AlphaCode the authors found that this dataset can generate many false positives during evaluation, where incorrect submissions are marked as correct due to lack of test coverage." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #language-code #license-mit #arxiv-2105.09938 #arxiv-2203.07814 #region-us \n", "### How to use it\n\n\nYou can load and iterate through the dataset with the following two lines of code for the train split:\n\n\nEach sample consists of a programming problem formulation in English, some ground truth Python solutions, test cases that are defined by their inputs and outputs and function name if provided, as well as some metadata regarding the difficulty level of the problem and its source.\n\n\nIf a sample has non empty 'input\\_output' feature, you can read it as a dictionary with keys 'inputs' and 'outputs' and 'fn\\_name' if it exists, and similarily you can parse the solutions into a list of solutions as shown in the code above.\n\n\nYou can also filter the dataset for the difficulty level: Introductory, Interview and Competition. Just pass the list of difficulties as a list. E.g. if you want the most challenging problems, you need to select the competition level:", "### Data Fields\n\n\nField: problem\\_id, Type: int, Description: problem id\nField: question, Type: string, Description: problem description\nField: solutions, Type: string, Description: some python solutions\nField: input\\_output, Type: string, Description: Json string with \"inputs\" and \"outputs\" of the test cases, might also include \"fn\\_name\" the name of the function\nField: difficulty, Type: string, Description: difficulty level of the problem\nField: url, Type: string, Description: url of the source of the problem\nField: starter\\_code, Type: string, Description: starter code to include in prompts\n\n\nwe mention that only few samples have 'fn\\_name' and 'starter\\_code' specified", "### Data Splits\n\n\nThe dataset contains a train and test splits with 5000 samples each.", "### Dataset Statistics\n\n\n* 10000 coding problems\n* 131777 test cases\n* all problems have a least one test case except 195 samples in the train split\n* for tests split, the average number of test cases is 21.2\n* average length of a problem is 293.2 words\n* all files have ground-truth solutions except 1235 samples in the test split\n\n\nDataset Creation\n----------------\n\n\nTo create the APPS dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Codewars, AtCoder, Kattis, and Codeforces. For more details please refer to the original paper.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nIn AlphaCode the authors found that this dataset can generate many false positives during evaluation, where incorrect submissions are marked as correct due to lack of test coverage." ]

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

[]

[]

[]

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

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

[]

[]

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

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

[ "task_categories:text2text-generation", "task_ids:explanation-generation", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:multilingual", "size_categories:1K<n<10K", "language:en", "language:ar", "license:mit", "region:us" ]

[]

[ "en", "ar" ]

[ "### Dataset Summary\n\n\nMAWPS: A Math Word Problem Repository", "### Supported Tasks\n\n\nMath Word Problem Solving", "### Languages\n\n\nSupports Arabic and English\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'text\\_en': a 'string' feature.\n* 'text\\_ar': a 'string' feature.\n* 'eqn': 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?\n\n\nRik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman and Hannaneh Hajishirzi.", "### 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\nSpecial thanks to Associate Professor Marwan Torki and all my colleagues in CC491N (NLP) class for helping me translate this dataset." ]

[ "TAGS\n#task_categories-text2text-generation #task_ids-explanation-generation #annotations_creators-crowdsourced #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #language-English #language-Arabic #license-mit #region-us \n", "### Dataset Summary\n\n\nMAWPS: A Math Word Problem Repository", "### Supported Tasks\n\n\nMath Word Problem Solving", "### Languages\n\n\nSupports Arabic and English\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'text\\_en': a 'string' feature.\n* 'text\\_ar': a 'string' feature.\n* 'eqn': 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?\n\n\nRik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman and Hannaneh Hajishirzi.", "### 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\nSpecial thanks to Associate Professor Marwan Torki and all my colleagues in CC491N (NLP) class for helping me translate this dataset." ]

[ "region:us" ]

[]

[]

[ "# CORD: A Consolidated Receipt Dataset for Post-OCR Parsing\n\nCORD dataset is cloned from clovaai GitHub repo\n\n- Box coordinates are normalized against image width/height\n- Labels with very few occurrences are replaced with O:\n\n\n\nCheck for more info Sparrow", "### CORD: A Consolidated Receipt Dataset for Post-OCR Parsing", "### Post-OCR parsing: building simple and robust parser via BIO tagging" ]

[ "TAGS\n#region-us \n", "# CORD: A Consolidated Receipt Dataset for Post-OCR Parsing\n\nCORD dataset is cloned from clovaai GitHub repo\n\n- Box coordinates are normalized against image width/height\n- Labels with very few occurrences are replaced with O:\n\n\n\nCheck for more info Sparrow", "### CORD: A Consolidated Receipt Dataset for Post-OCR Parsing", "### Post-OCR parsing: building simple and robust parser via BIO tagging" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# CodeParrot Dataset after near deduplication (validation)", "## Dataset Description\n\nA dataset of Python files from Github. We performed near deduplication of this dataset split codeparrot-clean-train from codeparrot-clean. Exact deduplication can miss a fair amount of nearly identical files. We used MinHash with a Jaccard threshold (default=0.85) to create duplicate clusters. Then these clusters are reduced to unique files based on the exact Jaccard similarity. Fore more details, please refer to this repo." ]

[ "TAGS\n#region-us \n", "# CodeParrot Dataset after near deduplication (validation)", "## Dataset Description\n\nA dataset of Python files from Github. We performed near deduplication of this dataset split codeparrot-clean-train from codeparrot-clean. Exact deduplication can miss a fair amount of nearly identical files. We used MinHash with a Jaccard threshold (default=0.85) to create duplicate clusters. Then these clusters are reduced to unique files based on the exact Jaccard similarity. Fore more details, please refer to this repo." ]

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

[]

[ "es" ]

[ "### Dataset Summary\n\n\nFocusing on young influencers from the well-known social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of Spanish comments manually labeled on offensive pre-defined categories. From the total corpus, we selected 30,416 posts to be publicly published, they correspond to the ones used in the MeOffendES competition at IberLEF 2021. The posts are labeled with the following categories:\n\n\n* Offensive, the target is a person (OFP). Offensive text targeting a specific individual.\n* Offensive, the target is a group of people or collective (OFG). Offensive text targeting a group of people belonging to the same ethnic group, gender or sexual orientation, political ideology, religious belief, or other common characteristics.\n* Non-offensive, but with expletive language (NOE). A text that contains rude words, blasphemes, or swearwords but without the aim of offending, and usually with a positive connotation.\n* Non-offensive (NO). Text that is neither offensive nor contains expletive language", "### Supported Tasks and Leaderboards\n\n\nThis dataset is intended for multi-class offensive classification and binary offensive classification.\nCompetition MeOffendES task on offensive detection for Spanish at IberLEF 2021", "### Languages\n\n\n* Spanish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is a string for the id of the tweet, a string for the emotion class, a string for the offensive class, and a string for the event. See the to explore more examples.", "### Data Fields\n\n\n* 'comment\\_id': a string to identify the comment\n* 'influencer': a string containing the influencer associated with the comment\n* 'comment': a string containing the text of the comment\n* 'label': a string containing the offensive gold label\n* 'influencer\\_gender': a string containing the genre of the influencer\n* 'media': a string containing the social media platform where the comment has been retrieved", "### Data Splits\n\n\nThe OffendES dataset contains 3 splits: *train*, *validation*, and *test*. Below are the statistics for each class.\n\n\n\nDataset Creation\n----------------", "### Source Data\n\n\nTwitter, Youtube, Instagram", "#### Who are the annotators?\n\n\nAmazon Mechanical Turkers\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe OffendES dataset is released under the Apache-2.0 License." ]

[ "TAGS\n#language-Spanish #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nFocusing on young influencers from the well-known social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of Spanish comments manually labeled on offensive pre-defined categories. From the total corpus, we selected 30,416 posts to be publicly published, they correspond to the ones used in the MeOffendES competition at IberLEF 2021. The posts are labeled with the following categories:\n\n\n* Offensive, the target is a person (OFP). Offensive text targeting a specific individual.\n* Offensive, the target is a group of people or collective (OFG). Offensive text targeting a group of people belonging to the same ethnic group, gender or sexual orientation, political ideology, religious belief, or other common characteristics.\n* Non-offensive, but with expletive language (NOE). A text that contains rude words, blasphemes, or swearwords but without the aim of offending, and usually with a positive connotation.\n* Non-offensive (NO). Text that is neither offensive nor contains expletive language", "### Supported Tasks and Leaderboards\n\n\nThis dataset is intended for multi-class offensive classification and binary offensive classification.\nCompetition MeOffendES task on offensive detection for Spanish at IberLEF 2021", "### Languages\n\n\n* Spanish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is a string for the id of the tweet, a string for the emotion class, a string for the offensive class, and a string for the event. See the to explore more examples.", "### Data Fields\n\n\n* 'comment\\_id': a string to identify the comment\n* 'influencer': a string containing the influencer associated with the comment\n* 'comment': a string containing the text of the comment\n* 'label': a string containing the offensive gold label\n* 'influencer\\_gender': a string containing the genre of the influencer\n* 'media': a string containing the social media platform where the comment has been retrieved", "### Data Splits\n\n\nThe OffendES dataset contains 3 splits: *train*, *validation*, and *test*. Below are the statistics for each class.\n\n\n\nDataset Creation\n----------------", "### Source Data\n\n\nTwitter, Youtube, Instagram", "#### Who are the annotators?\n\n\nAmazon Mechanical Turkers\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe OffendES dataset is released under the Apache-2.0 License." ]

[ "region:us" ]

[]

[]

[ "# CodeParrot Dataset after near deduplication (train)", "## Dataset Description\n\nA dataset of Python files from Github. We performed near deduplication of this dataset split codeparrot-clean-train from codeparrot-clean. Exact deduplication can miss a fair amount of nearly identical files. We used MinHash with a Jaccard threshold (default=0.85) to create duplicate clusters. Then these clusters are reduced to unique files based on the exact Jaccard similarity. Fore more details, please refer to this repo." ]

[ "TAGS\n#region-us \n", "# CodeParrot Dataset after near deduplication (train)", "## Dataset Description\n\nA dataset of Python files from Github. We performed near deduplication of this dataset split codeparrot-clean-train from codeparrot-clean. Exact deduplication can miss a fair amount of nearly identical files. We used MinHash with a Jaccard threshold (default=0.85) to create duplicate clusters. Then these clusters are reduced to unique files based on the exact Jaccard similarity. Fore more details, please refer to this repo." ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended|wikipedia", "language:en", "language:ar", "language:bn", "language:fi", "language:id", "language:ja", "language:sw", "language:ko", "language:ru", "language:te", "language:th", "license:apache-2.0", "region:us" ]

[]

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

[ "### Dataset Summary\n\n\nTyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.\nThe languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language\nexpresses -- such that we expect models performing well on this set to generalize across a large number of the languages\nin the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic\ninformation-seeking task and avoid priming effects, questions are written by people who want to know the answer, but\ndon’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without\nthe use of translation (unlike MLQA and XQuAD).", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### primary\\_task\n\n\n* Size of downloaded dataset files: 1863.37 MB\n* Size of the generated dataset: 5757.59 MB\n* Total amount of disk used: 7620.96 MB\n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### primary\\_task\n\n\n* 'passage\\_answer\\_candidates': a dictionary feature containing:\n\t+ 'plaintext\\_start\\_byte': a 'int32' feature.\n\t+ 'plaintext\\_end\\_byte': a 'int32' feature.\n* 'question\\_text': a 'string' feature.\n* 'document\\_title': a 'string' feature.\n* 'language': a 'string' feature.\n* 'annotations': a dictionary feature containing:\n\t+ 'passage\\_answer\\_candidate\\_index': a 'int32' feature.\n\t+ 'minimal\\_answers\\_start\\_byte': a 'int32' feature.\n\t+ 'minimal\\_answers\\_end\\_byte': a 'int32' feature.\n\t+ 'yes\\_no\\_answer': a 'string' feature.\n* 'document\\_plaintext': a 'string' feature.\n* 'document\\_url': 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" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #source_datasets-extended|wikipedia #language-English #language-Arabic #language-Bengali #language-Finnish #language-Indonesian #language-Japanese #language-Swahili (macrolanguage) #language-Korean #language-Russian #language-Telugu #language-Thai #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nTyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.\nThe languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language\nexpresses -- such that we expect models performing well on this set to generalize across a large number of the languages\nin the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic\ninformation-seeking task and avoid priming effects, questions are written by people who want to know the answer, but\ndon’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without\nthe use of translation (unlike MLQA and XQuAD).", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### primary\\_task\n\n\n* Size of downloaded dataset files: 1863.37 MB\n* Size of the generated dataset: 5757.59 MB\n* Total amount of disk used: 7620.96 MB\n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### primary\\_task\n\n\n* 'passage\\_answer\\_candidates': a dictionary feature containing:\n\t+ 'plaintext\\_start\\_byte': a 'int32' feature.\n\t+ 'plaintext\\_end\\_byte': a 'int32' feature.\n* 'question\\_text': a 'string' feature.\n* 'document\\_title': a 'string' feature.\n* 'language': a 'string' feature.\n* 'annotations': a dictionary feature containing:\n\t+ 'passage\\_answer\\_candidate\\_index': a 'int32' feature.\n\t+ 'minimal\\_answers\\_start\\_byte': a 'int32' feature.\n\t+ 'minimal\\_answers\\_end\\_byte': a 'int32' feature.\n\t+ 'yes\\_no\\_answer': a 'string' feature.\n* 'document\\_plaintext': a 'string' feature.\n* 'document\\_url': 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" ]

[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "arxiv:2204.12632", "region:us" ]

[ "2204.12632" ]

[ "en" ]

[ "# Dataset Card for Fig-QA", "## 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 Splits\n- Considerations for Using the Data\n - Discussion of Biases\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: URL \n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: emmy@URL", "### Dataset Summary\n\nThis is the dataset for the paper Testing the Ability of Language Models to Interpret Figurative Language. Fig-QA consists of 10256 examples of human-written creative metaphors that are paired as a Winograd schema. It can be used to evaluate the commonsense reasoning of models. The metaphors themselves can also be used as training data for other tasks, such as metaphor detection or generation.", "### Supported Tasks and Leaderboards\n\nYou can evaluate your models on the test set by submitting to the leaderboard on Explainaboard. Click on \"New\" and select 'qa-multiple-choice' for the task field. Select 'accuracy' for the metric. You should upload results in the form of a system output file in JSON or JSONL format.", "### Languages\n\nThis is the English version. Multilingual version can be found here.", "### Data Splits\n\nTrain-{S, M(no suffix), XL}: different training set sizes\nDev\nTest (labels not provided for test set)", "## Considerations for Using the Data", "### Discussion of Biases\n\nThese metaphors are human-generated and may contain insults or other explicit content. Authors of the paper manually removed offensive content, but users should keep in mind that some potentially offensive content may remain in the dataset.", "## Additional Information", "### Licensing Information\n\nMIT License\n\n\n\nIf you found the dataset useful, please cite this paper:\n\n @misc{URL\n doi = {10.48550/ARXIV.2204.12632},\n url = {URL\n author = {Liu, Emmy and Cui, Chen and Zheng, Kenneth and Neubig, Graham},\n keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},\n title = {Testing the Ability of Language Models to Interpret Figurative Language},\n publisher = {arXiv},\n year = {2022},\n copyright = {Creative Commons Attribution Share Alike 4.0 International}\n }" ]

[ "TAGS\n#task_categories-multiple-choice #task_ids-multiple-choice-qa #annotations_creators-expert-generated #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #arxiv-2204.12632 #region-us \n", "# Dataset Card for Fig-QA", "## 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 Splits\n- Considerations for Using the Data\n - Discussion of Biases\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: URL \n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: emmy@URL", "### Dataset Summary\n\nThis is the dataset for the paper Testing the Ability of Language Models to Interpret Figurative Language. Fig-QA consists of 10256 examples of human-written creative metaphors that are paired as a Winograd schema. It can be used to evaluate the commonsense reasoning of models. The metaphors themselves can also be used as training data for other tasks, such as metaphor detection or generation.", "### Supported Tasks and Leaderboards\n\nYou can evaluate your models on the test set by submitting to the leaderboard on Explainaboard. Click on \"New\" and select 'qa-multiple-choice' for the task field. Select 'accuracy' for the metric. You should upload results in the form of a system output file in JSON or JSONL format.", "### Languages\n\nThis is the English version. Multilingual version can be found here.", "### Data Splits\n\nTrain-{S, M(no suffix), XL}: different training set sizes\nDev\nTest (labels not provided for test set)", "## Considerations for Using the Data", "### Discussion of Biases\n\nThese metaphors are human-generated and may contain insults or other explicit content. Authors of the paper manually removed offensive content, but users should keep in mind that some potentially offensive content may remain in the dataset.", "## Additional Information", "### Licensing Information\n\nMIT License\n\n\n\nIf you found the dataset useful, please cite this paper:\n\n @misc{URL\n doi = {10.48550/ARXIV.2204.12632},\n url = {URL\n author = {Liu, Emmy and Cui, Chen and Zheng, Kenneth and Neubig, Graham},\n keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},\n title = {Testing the Ability of Language Models to Interpret Figurative Language},\n publisher = {arXiv},\n year = {2022},\n copyright = {Creative Commons Attribution Share Alike 4.0 International}\n }" ]

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

[]

[]

[ "# Dataset Card for Rice Image Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nRice Image Dataset\nDATASET: URL\n\nCitation Request: See the articles for more detailed information on the data.\n\nKoklu, M., Cinar, I., & Taspinar, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. URL\n\nCinar, I., & Koklu, M. (2021). Determination of Effective and Specific Physical Features of Rice Varieties by Computer Vision In Exterior Quality Inspection. Selcuk Journal of Agriculture and Food Sciences, 35(3), 229-243. URL\n\nCinar, I., & Koklu, M. (2022). Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences URL\n\nCinar, I., & Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188-194. URL\n\nDATASET: URL\n\nHighlights\n• Arborio, Basmati, Ipsala, Jasmine and Karacadag rice varieties were used.\n• The dataset (1) has 75K images including 15K pieces from each rice variety. The dataset (2) has 12 morphological, 4 shape and 90 color features.\n• ANN, DNN and CNN models were used to classify rice varieties.\n• Classified with an accuracy rate of 100% through the CNN model created.\n• The models used achieved successful results in the classification of rice varieties.\n\nAbstract\nRice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field.", "### 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\n\nThis dataset was shared by @muratkokludataset", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# Dataset Card for Rice Image Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nRice Image Dataset\nDATASET: URL\n\nCitation Request: See the articles for more detailed information on the data.\n\nKoklu, M., Cinar, I., & Taspinar, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. URL\n\nCinar, I., & Koklu, M. (2021). Determination of Effective and Specific Physical Features of Rice Varieties by Computer Vision In Exterior Quality Inspection. Selcuk Journal of Agriculture and Food Sciences, 35(3), 229-243. URL\n\nCinar, I., & Koklu, M. (2022). Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences URL\n\nCinar, I., & Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), 188-194. URL\n\nDATASET: URL\n\nHighlights\n• Arborio, Basmati, Ipsala, Jasmine and Karacadag rice varieties were used.\n• The dataset (1) has 75K images including 15K pieces from each rice variety. The dataset (2) has 12 morphological, 4 shape and 90 color features.\n• ANN, DNN and CNN models were used to classify rice varieties.\n• Classified with an accuracy rate of 100% through the CNN model created.\n• The models used achieved successful results in the classification of rice varieties.\n\nAbstract\nRice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field.", "### 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\n\nThis dataset was shared by @muratkokludataset", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "region:us" ]

[]

[]

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

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

[ "task_categories:text-classification", "task_ids:multi-label-classification", "task_ids:semantic-similarity-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "language:de", "license:mit", "region:us" ]

[]

[ "en", "de" ]

[ "### Dataset Summary\n\n\nSV-Ident comprises 4,248 sentences from social science publications in English and German. The data is the official data for the Shared Task: “Survey Variable Identification in Social Science Publications” (SV-Ident) 2022. Visit the homepage to find out more details about the shared task.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports:\n\n\n* Variable Detection: identifying whether a sentence contains a variable mention or not.\n* Variable Disambiguation: identifying which variable from a given vocabulary is mentioned in a sentence. NOTE: for this task, you will need to also download the variable metadata from here.", "### Languages\n\n\nThe text in the dataset is in English and German, as written by researchers. The domain of the texts is scientific publications in the social sciences.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\nThe following data fields are provided for documents:\n\n\nThe language for each document can be found in the document-language mapping file here, which maps 'doc\\_id' to a language code ('en', 'de'). The variables metadata (i.e., the vocabulary) can be downloaded from this link. Note, that each 'research\\_data' contains hundreds of variables (these can be understood as the corpus of documents to choose the most relevant from). If the variable has an \"unk\" tag, it means that the sentence contains a variable that has not been disambiguated. Such sentences could be used for Task 1 and filtered out for Task 2. The metadata file has the following format:\n\n\nEach variable may contain all (or some) of the following values:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset was curated by the VADIS project (URL\nThe documents were annotated by two expert annotators.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe original data are available at GESIS (URL in an unprocessed format.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nThe documents were annotated by two expert annotators.", "### Personal and Sensitive Information\n\n\nThe dataset does not include personal or sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nVADIS project (URL", "### Licensing Information\n\n\nAll documents originate from the Social Science Open Access Repository (SSOAR) and are licensed accordingly. The original document URLs are provided in document\\_urls.json. For more information on licensing, please refer to the terms and conditions on the SSAOR Grant of Licenses page.", "### Contributions" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-English #language-German #license-mit #region-us \n", "### Dataset Summary\n\n\nSV-Ident comprises 4,248 sentences from social science publications in English and German. The data is the official data for the Shared Task: “Survey Variable Identification in Social Science Publications” (SV-Ident) 2022. Visit the homepage to find out more details about the shared task.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports:\n\n\n* Variable Detection: identifying whether a sentence contains a variable mention or not.\n* Variable Disambiguation: identifying which variable from a given vocabulary is mentioned in a sentence. NOTE: for this task, you will need to also download the variable metadata from here.", "### Languages\n\n\nThe text in the dataset is in English and German, as written by researchers. The domain of the texts is scientific publications in the social sciences.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\nThe following data fields are provided for documents:\n\n\nThe language for each document can be found in the document-language mapping file here, which maps 'doc\\_id' to a language code ('en', 'de'). The variables metadata (i.e., the vocabulary) can be downloaded from this link. Note, that each 'research\\_data' contains hundreds of variables (these can be understood as the corpus of documents to choose the most relevant from). If the variable has an \"unk\" tag, it means that the sentence contains a variable that has not been disambiguated. Such sentences could be used for Task 1 and filtered out for Task 2. The metadata file has the following format:\n\n\nEach variable may contain all (or some) of the following values:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset was curated by the VADIS project (URL\nThe documents were annotated by two expert annotators.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe original data are available at GESIS (URL in an unprocessed format.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nThe documents were annotated by two expert annotators.", "### Personal and Sensitive Information\n\n\nThe dataset does not include personal or sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nVADIS project (URL", "### Licensing Information\n\n\nAll documents originate from the Social Science Open Access Repository (SSOAR) and are licensed accordingly. The original document URLs are provided in document\\_urls.json. For more information on licensing, please refer to the terms and conditions on the SSAOR Grant of Licenses page.", "### Contributions" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

[ "TAGS\n#license-cc0-1.0 #region-us \n" ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "TAGS\n#task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.", "### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'", "### Data Instances\n\n\nA high level example of any beir dataset:", "### Data Fields\n\n\nExamples from all configurations have the following features:", "### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.", "### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.", "### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.", "### 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\nCite as:", "### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset." ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for NASA technical report server metadata", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Considerations for Using the Data\n- Additional Information\n - Licensing Information\n - Contributions", "## Dataset Description\n\nHomepage: URL", "### Dataset Summary\nThe NTRS collects scientific and technical information funded or created by NASA and provides metadata but also access to abstracts and full texts.\nThe dataset contains all abstracts, titles, and associated metadata indexed on the NTRS.\nThe most recent bulk download can be aquired via the NTRS directly at:\nURL\n\nThis repository does not claim any ownership on the provided data, it only is supposed to provide an easily accesible gateway to the data, through the Huggingface API. \nThe original author and source should always be credited.", "## Dataset Structure", "### Data Instances\n\nThe dataset contain over 508000 objects (abstracts) and associated metadata from NASA funded projects in time range of 1917 to today (18.06.2022). \nIt therefore is a rich data source for language modeling in the domain of spacecraft design and space science.", "### Data Fields", "## Dataset Creation", "### Curation Rationale\n\nThe last bulk download was done on 18.06.2022. The dataset was cleaned from abstracts that occur multiple times.", "## Considerations for Using the Data\n\nMain field that probably interest people:\n\n\"abstract\", \"subjectCategory\", \"keywords\", \"center\"", "## Additional Information", "### Licensing Information\n\"Generally, United States government works (works prepared by officers and employees of the U.S. Government as part of their official duties) are not protected by copyright in the U.S. (17 U.S.C. §105) and may be used without obtaining permission from NASA. However, U.S. government works may contain privately created, copyrighted works (e.g., quote, photograph, chart, drawing, etc.) used under license or with permission of the copyright owner. Incorporation in a U.S. government work does not place the private work in the public domain.\nplace the private work in the public domain.\n\nMoreover, not all materials on or available through download from this Web site are U.S. government works. Some materials available from this Web site may be protected by copyrights owned by private individuals or organizations and may be subject to restrictions on use. For example, contractors and grantees are not considered Government employees; generally, they hold copyright to works they produce for the Government. Other materials may be the result of joint authorship due to collaboration between a Government employee and a private individual wherein the private individual will hold a copyright to the work jointly with U.S. Government. The Government is granted a worldwide license to use, modify, reproduce, release, perform, display, or disclose these works by or on behalf of the Government.\n\nWhile NASA may publicly release copyrighted works in which it has government purpose licenses or specific permission to release, such licenses or permission do not necessarily transfer to others. Thus, such works are still protected by copyright, and recipients of the works must comply with the copyright law (Title 17 United States Code). Such copyrighted works may not be modified, reproduced, or redistributed without permission of the copyright owner.\"\n\nTaken from URL please visit for more information.", "### Contributions\n\nFor any any inquiries about this data set please contact @pauldrm" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-other #region-us \n", "# Dataset Card for NASA technical report server metadata", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Considerations for Using the Data\n- Additional Information\n - Licensing Information\n - Contributions", "## Dataset Description\n\nHomepage: URL", "### Dataset Summary\nThe NTRS collects scientific and technical information funded or created by NASA and provides metadata but also access to abstracts and full texts.\nThe dataset contains all abstracts, titles, and associated metadata indexed on the NTRS.\nThe most recent bulk download can be aquired via the NTRS directly at:\nURL\n\nThis repository does not claim any ownership on the provided data, it only is supposed to provide an easily accesible gateway to the data, through the Huggingface API. \nThe original author and source should always be credited.", "## Dataset Structure", "### Data Instances\n\nThe dataset contain over 508000 objects (abstracts) and associated metadata from NASA funded projects in time range of 1917 to today (18.06.2022). \nIt therefore is a rich data source for language modeling in the domain of spacecraft design and space science.", "### Data Fields", "## Dataset Creation", "### Curation Rationale\n\nThe last bulk download was done on 18.06.2022. The dataset was cleaned from abstracts that occur multiple times.", "## Considerations for Using the Data\n\nMain field that probably interest people:\n\n\"abstract\", \"subjectCategory\", \"keywords\", \"center\"", "## Additional Information", "### Licensing Information\n\"Generally, United States government works (works prepared by officers and employees of the U.S. Government as part of their official duties) are not protected by copyright in the U.S. (17 U.S.C. §105) and may be used without obtaining permission from NASA. However, U.S. government works may contain privately created, copyrighted works (e.g., quote, photograph, chart, drawing, etc.) used under license or with permission of the copyright owner. Incorporation in a U.S. government work does not place the private work in the public domain.\nplace the private work in the public domain.\n\nMoreover, not all materials on or available through download from this Web site are U.S. government works. Some materials available from this Web site may be protected by copyrights owned by private individuals or organizations and may be subject to restrictions on use. For example, contractors and grantees are not considered Government employees; generally, they hold copyright to works they produce for the Government. Other materials may be the result of joint authorship due to collaboration between a Government employee and a private individual wherein the private individual will hold a copyright to the work jointly with U.S. Government. The Government is granted a worldwide license to use, modify, reproduce, release, perform, display, or disclose these works by or on behalf of the Government.\n\nWhile NASA may publicly release copyrighted works in which it has government purpose licenses or specific permission to release, such licenses or permission do not necessarily transfer to others. Thus, such works are still protected by copyright, and recipients of the works must comply with the copyright law (Title 17 United States Code). Such copyrighted works may not be modified, reproduced, or redistributed without permission of the copyright owner.\"\n\nTaken from URL please visit for more information.", "### Contributions\n\nFor any any inquiries about this data set please contact @pauldrm" ]

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

[]

[]

[ "## Source: URL\n\nAlso published to URL using 'datasette'\n\nColumns:\n- name: The English name of the Pokemon\n- japanese_name: The Original Japanese name of the Pokemon\n- pokedex_number: The entry number of the Pokemon in the National Pokedex\n- percentage_male: The percentage of the species that are male. Blank if the Pokemon is genderless.\n- type1: The Primary Type of the Pokemon\n- type2: The Secondary Type of the Pokemon\n- classification: The Classification of the Pokemon as described by the Sun and Moon Pokedex\n- height_m: Height of the Pokemon in metres\n- weight_kg: The Weight of the Pokemon in kilograms\n- capture_rate: Capture Rate of the Pokemon\n- baseeggsteps: The number of steps required to hatch an egg of the Pokemon\n- abilities: A stringified list of abilities that the Pokemon is capable of having\n- experience_growth: The Experience Growth of the Pokemon\n- base_happiness: Base Happiness of the Pokemon\n- against_?: Eighteen features that denote the amount of damage taken against an attack of a particular type\n- hp: The Base HP of the Pokemon\n- attack: The Base Attack of the Pokemon\n- defense: The Base Defense of the Pokemon\n- sp_attack: The Base Special Attack of the Pokemon\n- sp_defense: The Base Special Defense of the Pokemon\n- speed: The Base Speed of the Pokemon\n- generation: The numbered generation which the Pokemon was first introduced\n- is_legendary: Denotes if the Pokemon is legendary." ]

[ "TAGS\n#license-cc0-1.0 #pokemon #region-us \n", "## Source: URL\n\nAlso published to URL using 'datasette'\n\nColumns:\n- name: The English name of the Pokemon\n- japanese_name: The Original Japanese name of the Pokemon\n- pokedex_number: The entry number of the Pokemon in the National Pokedex\n- percentage_male: The percentage of the species that are male. Blank if the Pokemon is genderless.\n- type1: The Primary Type of the Pokemon\n- type2: The Secondary Type of the Pokemon\n- classification: The Classification of the Pokemon as described by the Sun and Moon Pokedex\n- height_m: Height of the Pokemon in metres\n- weight_kg: The Weight of the Pokemon in kilograms\n- capture_rate: Capture Rate of the Pokemon\n- baseeggsteps: The number of steps required to hatch an egg of the Pokemon\n- abilities: A stringified list of abilities that the Pokemon is capable of having\n- experience_growth: The Experience Growth of the Pokemon\n- base_happiness: Base Happiness of the Pokemon\n- against_?: Eighteen features that denote the amount of damage taken against an attack of a particular type\n- hp: The Base HP of the Pokemon\n- attack: The Base Attack of the Pokemon\n- defense: The Base Defense of the Pokemon\n- sp_attack: The Base Special Attack of the Pokemon\n- sp_defense: The Base Special Defense of the Pokemon\n- speed: The Base Speed of the Pokemon\n- generation: The numbered generation which the Pokemon was first introduced\n- is_legendary: Denotes if the Pokemon is legendary." ]

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

[]

[]

[ "## About Dataset", "### The ultimate Soccer database for data analysis and machine learning\n\nWhat you get:\n\n- +25,000 matches\n- +10,000 players\n- 11 European Countries with their lead championship\n- Seasons 2008 to 2016\n- Players and Teams' attributes* sourced from EA Sports' FIFA video game series, including the weekly updates\n- Team line up with squad formation (X, Y coordinates)\n- Betting odds from up to 10 providers\n- Detailed match events (goal types, possession, corner, cross, fouls, cards etc…) for +10,000 matches\n\n*16th Oct 2016: New table containing teams' attributes from FIFA !" ]

[ "TAGS\n#license-odbl #region-us \n", "## About Dataset", "### The ultimate Soccer database for data analysis and machine learning\n\nWhat you get:\n\n- +25,000 matches\n- +10,000 players\n- 11 European Countries with their lead championship\n- Seasons 2008 to 2016\n- Players and Teams' attributes* sourced from EA Sports' FIFA video game series, including the weekly updates\n- Team line up with squad formation (X, Y coordinates)\n- Betting odds from up to 10 providers\n- Detailed match events (goal types, possession, corner, cross, fouls, cards etc…) for +10,000 matches\n\n*16th Oct 2016: New table containing teams' attributes from FIFA !" ]

[ "task_categories:text-classification", "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Adult_Content_Detection", "## Table of Contents\n- Dataset Description\n- Languages\n- Dataset Structure\n- Source Data", "## Dataset Description\n\n850 Articles descriptions classified into two different categories namely: Adult, and Non_Adult", "## Languages\n\nThe text in the dataset is in English", "## Dataset Structure\n\nThe dataset consists of two columns namely Description and Category.\nThe Description column consists of the overview of the article and the Category column consists of the class each article belongs to", "## Source Data\n\nThe dataset is scrapped across different platforms" ]

[ "TAGS\n#task_categories-text-classification #multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for Adult_Content_Detection", "## Table of Contents\n- Dataset Description\n- Languages\n- Dataset Structure\n- Source Data", "## Dataset Description\n\n850 Articles descriptions classified into two different categories namely: Adult, and Non_Adult", "## Languages\n\nThe text in the dataset is in English", "## Dataset Structure\n\nThe dataset consists of two columns namely Description and Category.\nThe Description column consists of the overview of the article and the Category column consists of the class each article belongs to", "## Source Data\n\nThe dataset is scrapped across different platforms" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [Dataset Name]", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:\n- license: gpl-3.0", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for [Dataset Name]", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:\n- license: gpl-3.0", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "task_categories:other", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:unknown", "language:en", "license:mit", "deduplication", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for CORE Deduplication", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings\n- Point of Contact: CORE Team\n- Size of downloaded dataset files: 204 MB", "### Dataset Summary\n\nCORE 2020 Deduplication dataset (URL contains 100K scholarly documents labeled as duplicates/non-duplicates.", "### Languages\n\nThe dataset language is English (BCP-47 'en')" ]

[ "TAGS\n#task_categories-other #task_ids-natural-language-inference #task_ids-semantic-similarity-scoring #task_ids-text-scoring #annotations_creators-unknown #language_creators-unknown #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-unknown #language-English #license-mit #deduplication #region-us \n", "# Dataset Card for CORE Deduplication", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings\n- Point of Contact: CORE Team\n- Size of downloaded dataset files: 204 MB", "### Dataset Summary\n\nCORE 2020 Deduplication dataset (URL contains 100K scholarly documents labeled as duplicates/non-duplicates.", "### Languages\n\nThe dataset language is English (BCP-47 'en')" ]

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

[]

[]

[]

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

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

[]

[]

[]

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

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

[]

[]

[]

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

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

[]

[]

[]

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

[ "task_categories:text-generation", "task_categories:fill-mask", "multilinguality:multilingual", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "source_datasets:original", "language:ar", "language:en", "language:he", "language:de", "language:it", "language:fr", "language:pl", "language:pt", "language:ru", "language:uk", "region:us" ]

[]

[ "ar", "en", "he", "de", "it", "fr", "pl", "pt", "ru", "uk" ]

[ "## Leipzig Corpora Collection\n \nThe Leipzig Corpora Collection presents corpora in different languages using the same format and comparable sources. All data are available as plain text files and can be imported into a MySQL database by using the provided import script. They are intended both for scientific use by corpus linguists as well as for applications such as knowledge extraction programs.\nThe corpora are identical in format and similar in size and content. They contain randomly selected sentences in the language of the corpus and are available in sizes from 10,000 sentences up to 1 million sentences. The sources are either newspaper texts or texts randomly collected from the web. The texts are split into sentences. Non-sentences and foreign language material was removed. Because word co-occurrence information is useful for many applications, these data are precomputed and included as well. For each word, the most significant words appearing as immediate left or right neighbor or appearing anywhere within the same sentence are given. More information about the format and content of these files can be found here.\nThe corpora are automatically collected from carefully selected public sources without considering in detail the content of the contained text. No responsibility is taken for the content of the data. In particular, the views and opinions expressed in specific parts of the data remain exclusively with the authors.", "## Dataset Usage", "### Links \n\nA \"links\" subset contains URLs with corresponding language and id (based on 'URL \n\n\n\n \n\nwhere is possible to choose specific 'data_id' to load a specific dataset, where 'data_id' is name of the subset\n\nLinks possible to filter according to metdata attributes:", "### Corpus\n\nafter selecting 'data_id', let's say 'heb_wikipedia_2021_1M', we could load it:\n\n\n\n\nanother example: \n\n\n\nsample:\n\n\n\nIf you use one of these corpora in your work, please, to cite this work:" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-original #language-Arabic #language-English #language-Hebrew #language-German #language-Italian #language-French #language-Polish #language-Portuguese #language-Russian #language-Ukrainian #region-us \n", "## Leipzig Corpora Collection\n \nThe Leipzig Corpora Collection presents corpora in different languages using the same format and comparable sources. All data are available as plain text files and can be imported into a MySQL database by using the provided import script. They are intended both for scientific use by corpus linguists as well as for applications such as knowledge extraction programs.\nThe corpora are identical in format and similar in size and content. They contain randomly selected sentences in the language of the corpus and are available in sizes from 10,000 sentences up to 1 million sentences. The sources are either newspaper texts or texts randomly collected from the web. The texts are split into sentences. Non-sentences and foreign language material was removed. Because word co-occurrence information is useful for many applications, these data are precomputed and included as well. For each word, the most significant words appearing as immediate left or right neighbor or appearing anywhere within the same sentence are given. More information about the format and content of these files can be found here.\nThe corpora are automatically collected from carefully selected public sources without considering in detail the content of the contained text. No responsibility is taken for the content of the data. In particular, the views and opinions expressed in specific parts of the data remain exclusively with the authors.", "## Dataset Usage", "### Links \n\nA \"links\" subset contains URLs with corresponding language and id (based on 'URL \n\n\n\n \n\nwhere is possible to choose specific 'data_id' to load a specific dataset, where 'data_id' is name of the subset\n\nLinks possible to filter according to metdata attributes:", "### Corpus\n\nafter selecting 'data_id', let's say 'heb_wikipedia_2021_1M', we could load it:\n\n\n\n\nanother example: \n\n\n\nsample:\n\n\n\nIf you use one of these corpora in your work, please, to cite this work:" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:no-annotation", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:he", "license:other", "region:us" ]

[]

[ "he" ]

[ "## 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" ]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Hebrew #license-other #region-us \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" ]

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

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "annotations_creators:Andrew Thompson", "annotations_creators:components.one", "language:en", "region:us" ]

[]

[ "en" ]

[ "# 2.7 million news articles and essays", "## Table of Contents\n- Dataset Description", "## Dataset Description\n\n2.7 million news articles and essays from 27 American publications. Includes date, title, publication, article text, publication name, year, month, and URL (for some). Articles mostly span from 2016 to early 2020.\n\n- Type: CSV\n- Size: 3.4 GB compressed, 8.8 GB uncompressed\n- Created by: Andrew Thompson\n- Date added: 4/3/2020\n- Date modified: 4/3/2020\n- source: Component one Datasets 2.7 Millions\n- Date of Download and processed: 19/6/2022\n- Header was modified with the respective columns\n- Row number 2,324,812 was removed" ]

[ "TAGS\n#annotations_creators-Andrew Thompson #annotations_creators-components.one #language-English #region-us \n", "# 2.7 million news articles and essays", "## Table of Contents\n- Dataset Description", "## Dataset Description\n\n2.7 million news articles and essays from 27 American publications. Includes date, title, publication, article text, publication name, year, month, and URL (for some). Articles mostly span from 2016 to early 2020.\n\n- Type: CSV\n- Size: 3.4 GB compressed, 8.8 GB uncompressed\n- Created by: Andrew Thompson\n- Date added: 4/3/2020\n- Date modified: 4/3/2020\n- source: Component one Datasets 2.7 Millions\n- Date of Download and processed: 19/6/2022\n- Header was modified with the respective columns\n- Row number 2,324,812 was removed" ]

[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2112.04482", "arxiv:2111.11431", "region:us" ]

[ "2112.04482", "2111.11431" ]

[ "en" ]

[ "# Dataset Card for PMD", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Compared to original FLAVA paper \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: PMD homepage\n- Repository: PMD repository\n- Paper: FLAVA: A Foundational Language And Vision Alignment Model\n\n- Leaderboard:\n- Point of Contact: Amanpreet Singh", "### Dataset Summary\n\nIntroduced in the FLAVA paper, Public Multimodal Dataset (PMD) is a collection of publicly-available image-text pair datasets. PMD contains 70M image-text pairs in total with 68M unique images. The dataset contains pairs from Conceptual Captions, Conceptual Captions 12M, WIT, Localized Narratives, RedCaps, COCO, SBU Captions, Visual Genome and a subset of YFCC100M dataset.\n\nIf you use PMD, please cite the original FLAVA paper as follows, along with the individual datasets (!! - see below for references):\n\n\n\nYou can load this dataset by first logging into Hugging Face using 'huggingface-cli login' and then running the following commands:\n\n\n\nYou can also load the dataset in streaming mode if you don't want to download the big dataset files (> 50GB locally without the images):", "### Dataset Preprocessing\n\nThis dataset doesn't download all of the images locally by default. Instead, it exposes URLs for some of the images. To fetch the images, use the following code:", "#### Save to disk\nYou can also save the dataset to disk for faster and direct loading next time but beware of the space required:", "#### Load Subsets\n\nYou can also download a specific set from the PMD dataset by using \n \nThe choices are '", "#### Flickr30K Localized Narratives Subset\nThe Flickr30K subset of Localized Narratives is not included by default as it requires a manual download. You can include it by downloading the tar file from here after signing an agreement to '</path/to/Downloads>' and then loading it whole PMD or localized narratives subset by:", "#### Facing issues?\n\nIf you are facing issues, you can try loading a specific revision of the repo by using:", "### Supported Tasks and Leaderboards\n\nIn the FLAVA paper, the dataset has been used to pretrain the FLAVA model as a source of well-aligned image-text pairs. This allows having a generic vision-and-language model which can be fine-tuned for a variety of tasks.\n\nWe anticipate that the dataset can be used to train deep neural networks that perform image captioning and that learn transferable visual representations for a variety of downstream visual recognition tasks (image classification, object detection, instance segmentation). We also anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks, such as image or text retrieval or text-to-image synthesis.", "### Languages\n\nAll of the subsets in PMD use English as their primary language.", "## Dataset Structure", "### Data Instances\n\nEach instance in PMD represents a single image-text pair:", "### Data Fields\n\n- 'image_url': Static URL for downloading the image associated with the text. Can be 'None' if image is locally available.\n- 'image': A PIL Image object for the image associated with the text. Can be 'None' if image is not locally available.\n- 'text': 'str', A textual description corresponding to the image.\n- 'source': 'str', The PMD subset which this pair is from.\n- 'meta': 'str', A json representation of the original annotation from the dataset.", "### Data Splits\n\nAll the data is contained in the training set. The training set has nearly 70M instances. \n\nWe intend for this dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Thus, all of the instances should be used for pretraining. If required, we specifically make sure that there is no overlap with Karpathy's COCO validation set so users can use that subset for any validation purposes. Users can also load Karpathy's val subset by specifying the \"validation\" split while loading PMD. This will also load other \"validation\" splits for some subsets, if they are available.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> Purely contrastive methods, however, also have important shortcomings. Their cross-modal nature does not make them easily usable on multimodal problems that require dealing with both modalities at the same time. They require large corpora, which for both CLIP and ALIGN have not been made accessible to the research community and the details of which remain shrouded in mystery, notwithstanding well-known issues with the construction of such datasets", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper:\n> Data Collection Pipeline\n\n- For the YFCC100M dataset, we filter the image-text data by discarding non-English captions and only keeping captions that contain more than two words from the description field of each image, if this does not pass our filters we consider the title field. Other than that, we did not do any additional filtering.\n- For the VisualGenome, COCO and Localized Narratives subsets, we remove any overlaps with Karpathy's COCO val and test sets.\n- For Localized Narratives, we split the original caption which is a paragraph into multiple captions by using spaCy library and take the cartesan product leading to each sample as a separate image-text pair.", "#### Compared to original FLAVA paper\n\nThe PMD dataset in this repo doesn't correspond 1:1 exactly to the original PMD dataset used in the FLAVA paper though this repo is built by the same authors. This is due to difficulty in reproducing WiT and YFCC100M subsets exactly. This repo in general contains more data than the PMD in the FLAVA paper and hence should probably result in better performance.", "#### Who are the source language producers?\n\nPlease refer to the original dataset papers to understand where the content is coming from.", "### Annotations", "#### Annotation process\n\nThe dataset is a combination of existing public datasets with some filtering applied on top so there is no annotation process involved.", "#### Who are the annotators?\n\nPlease refer to the original dataset papers to understand where the content is coming from.", "### Personal and Sensitive Information\n\nPlease refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found here.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nFrom the paper:\n> Has an analysis of the potential impact of the dataset and its use on data subjects (e.g.,\na data protection impact analysis) been conducted?\n\nNo.", "### Discussion of Biases\n\nPlease refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found here.", "### Other Known Limitations\n\nFrom the paper:\n> Are there any errors, sources of noise, or redundancies in the dataset?\n\nPMD is noisy by design since image-text pairs on the internet are noisy and unstructured. Though, since it contains sources such as COCO, Visual Genome, and Localized Narratives which are hand-curated by annotators, it has a lot of well-aligned data as well. So, it is definitely more aligned compared to e.g. LAION.\n\nSome instances may also have duplicate images and captions but should have almost no effect in training large-scale models.\n\n> Does the dataset contain data that might be considered confidential (e.g., data that is\nprotected by legal privilege or by doctor-patient confidentiality, data that includes the\ncontent of individuals non-public communications)?\n\nNot that the authors know of. Please refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found here.", "## Additional Information", "### Dataset Curators\n\nThe authors of the original dataset papers, as well as the authors of the FLAVA paper (Amanpreet, Ronghang, Vedanuj, Guillaume, Wojciech, Marcus and Douwe).", "### Licensing Information\n\nHere are the individual licenses from each of the datasets that apply if you use this dataset:", "#### COCO\nThe annotations in the COCO dataset belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License.\n\nThe COCO Consortium does not own the copyright of the images. Use of the images must abide by the Flickr Terms of Use. The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset.", "#### Conceptual Captions\nThe dataset may be freely used for any purpose, although acknowledgement of Google LLC (\"Google\") as the data source would be appreciated. The dataset is provided \"AS IS\" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.", "#### WIT\nThis data is available under the Creative Commons Attribution-ShareAlike 3.0 Unported license.", "#### Visual Genome\nVisual Genome by Ranjay Krishna et al is licensed under a Creative Commons Attribution 4.0 International License.", "#### Localized Narratives\n\nAll the annotations available through this website are released under a CC BY 4.0 license. You are free to redistribute and modify the annotations, but we ask you to please keep the original attribution to our paper.", "#### YFCC100M\nUse of the original media files is subject to the Creative Commons licenses chosen by their creators/uploaders. License information for each media file can be found within the YFCC100M metadata. Use of the dataset is subject to the relevant Webscope License Agreement, which you need to agree to if you use this dataset.", "#### RedCaps\nThe image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (URL\napi-terms) and users must comply with Reddit User Agreeement, Content Policy,\nand Privacy Policy – all accessible at URL\n\nSimilar to RedCaps:\n> PMD should only be used for non-commercial research. PMD should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of PMD are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.\n\n\n\n\nPlease cite the main FLAVA paper in which PMD was introduced along with each of the subsets used in PMD as follows:", "### Contributions\n\nThanks to @aps, Thomas Wang, and @VictorSanh for adding this dataset." ]

[ "TAGS\n#task_categories-image-to-text #task_ids-image-captioning #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2112.04482 #arxiv-2111.11431 #region-us \n", "# Dataset Card for PMD", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Compared to original FLAVA paper \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: PMD homepage\n- Repository: PMD repository\n- Paper: FLAVA: A Foundational Language And Vision Alignment Model\n\n- Leaderboard:\n- Point of Contact: Amanpreet Singh", "### Dataset Summary\n\nIntroduced in the FLAVA paper, Public Multimodal Dataset (PMD) is a collection of publicly-available image-text pair datasets. PMD contains 70M image-text pairs in total with 68M unique images. The dataset contains pairs from Conceptual Captions, Conceptual Captions 12M, WIT, Localized Narratives, RedCaps, COCO, SBU Captions, Visual Genome and a subset of YFCC100M dataset.\n\nIf you use PMD, please cite the original FLAVA paper as follows, along with the individual datasets (!! - see below for references):\n\n\n\nYou can load this dataset by first logging into Hugging Face using 'huggingface-cli login' and then running the following commands:\n\n\n\nYou can also load the dataset in streaming mode if you don't want to download the big dataset files (> 50GB locally without the images):", "### Dataset Preprocessing\n\nThis dataset doesn't download all of the images locally by default. Instead, it exposes URLs for some of the images. To fetch the images, use the following code:", "#### Save to disk\nYou can also save the dataset to disk for faster and direct loading next time but beware of the space required:", "#### Load Subsets\n\nYou can also download a specific set from the PMD dataset by using \n \nThe choices are '", "#### Flickr30K Localized Narratives Subset\nThe Flickr30K subset of Localized Narratives is not included by default as it requires a manual download. You can include it by downloading the tar file from here after signing an agreement to '</path/to/Downloads>' and then loading it whole PMD or localized narratives subset by:", "#### Facing issues?\n\nIf you are facing issues, you can try loading a specific revision of the repo by using:", "### Supported Tasks and Leaderboards\n\nIn the FLAVA paper, the dataset has been used to pretrain the FLAVA model as a source of well-aligned image-text pairs. This allows having a generic vision-and-language model which can be fine-tuned for a variety of tasks.\n\nWe anticipate that the dataset can be used to train deep neural networks that perform image captioning and that learn transferable visual representations for a variety of downstream visual recognition tasks (image classification, object detection, instance segmentation). We also anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks, such as image or text retrieval or text-to-image synthesis.", "### Languages\n\nAll of the subsets in PMD use English as their primary language.", "## Dataset Structure", "### Data Instances\n\nEach instance in PMD represents a single image-text pair:", "### Data Fields\n\n- 'image_url': Static URL for downloading the image associated with the text. Can be 'None' if image is locally available.\n- 'image': A PIL Image object for the image associated with the text. Can be 'None' if image is not locally available.\n- 'text': 'str', A textual description corresponding to the image.\n- 'source': 'str', The PMD subset which this pair is from.\n- 'meta': 'str', A json representation of the original annotation from the dataset.", "### Data Splits\n\nAll the data is contained in the training set. The training set has nearly 70M instances. \n\nWe intend for this dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Thus, all of the instances should be used for pretraining. If required, we specifically make sure that there is no overlap with Karpathy's COCO validation set so users can use that subset for any validation purposes. Users can also load Karpathy's val subset by specifying the \"validation\" split while loading PMD. This will also load other \"validation\" splits for some subsets, if they are available.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> Purely contrastive methods, however, also have important shortcomings. Their cross-modal nature does not make them easily usable on multimodal problems that require dealing with both modalities at the same time. They require large corpora, which for both CLIP and ALIGN have not been made accessible to the research community and the details of which remain shrouded in mystery, notwithstanding well-known issues with the construction of such datasets", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper:\n> Data Collection Pipeline\n\n- For the YFCC100M dataset, we filter the image-text data by discarding non-English captions and only keeping captions that contain more than two words from the description field of each image, if this does not pass our filters we consider the title field. Other than that, we did not do any additional filtering.\n- For the VisualGenome, COCO and Localized Narratives subsets, we remove any overlaps with Karpathy's COCO val and test sets.\n- For Localized Narratives, we split the original caption which is a paragraph into multiple captions by using spaCy library and take the cartesan product leading to each sample as a separate image-text pair.", "#### Compared to original FLAVA paper\n\nThe PMD dataset in this repo doesn't correspond 1:1 exactly to the original PMD dataset used in the FLAVA paper though this repo is built by the same authors. This is due to difficulty in reproducing WiT and YFCC100M subsets exactly. This repo in general contains more data than the PMD in the FLAVA paper and hence should probably result in better performance.", "#### Who are the source language producers?\n\nPlease refer to the original dataset papers to understand where the content is coming from.", "### Annotations", "#### Annotation process\n\nThe dataset is a combination of existing public datasets with some filtering applied on top so there is no annotation process involved.", "#### Who are the annotators?\n\nPlease refer to the original dataset papers to understand where the content is coming from.", "### Personal and Sensitive Information\n\nPlease refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found here.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nFrom the paper:\n> Has an analysis of the potential impact of the dataset and its use on data subjects (e.g.,\na data protection impact analysis) been conducted?\n\nNo.", "### Discussion of Biases\n\nPlease refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found here.", "### Other Known Limitations\n\nFrom the paper:\n> Are there any errors, sources of noise, or redundancies in the dataset?\n\nPMD is noisy by design since image-text pairs on the internet are noisy and unstructured. Though, since it contains sources such as COCO, Visual Genome, and Localized Narratives which are hand-curated by annotators, it has a lot of well-aligned data as well. So, it is definitely more aligned compared to e.g. LAION.\n\nSome instances may also have duplicate images and captions but should have almost no effect in training large-scale models.\n\n> Does the dataset contain data that might be considered confidential (e.g., data that is\nprotected by legal privilege or by doctor-patient confidentiality, data that includes the\ncontent of individuals non-public communications)?\n\nNot that the authors know of. Please refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found here.", "## Additional Information", "### Dataset Curators\n\nThe authors of the original dataset papers, as well as the authors of the FLAVA paper (Amanpreet, Ronghang, Vedanuj, Guillaume, Wojciech, Marcus and Douwe).", "### Licensing Information\n\nHere are the individual licenses from each of the datasets that apply if you use this dataset:", "#### COCO\nThe annotations in the COCO dataset belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License.\n\nThe COCO Consortium does not own the copyright of the images. Use of the images must abide by the Flickr Terms of Use. The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset.", "#### Conceptual Captions\nThe dataset may be freely used for any purpose, although acknowledgement of Google LLC (\"Google\") as the data source would be appreciated. The dataset is provided \"AS IS\" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.", "#### WIT\nThis data is available under the Creative Commons Attribution-ShareAlike 3.0 Unported license.", "#### Visual Genome\nVisual Genome by Ranjay Krishna et al is licensed under a Creative Commons Attribution 4.0 International License.", "#### Localized Narratives\n\nAll the annotations available through this website are released under a CC BY 4.0 license. You are free to redistribute and modify the annotations, but we ask you to please keep the original attribution to our paper.", "#### YFCC100M\nUse of the original media files is subject to the Creative Commons licenses chosen by their creators/uploaders. License information for each media file can be found within the YFCC100M metadata. Use of the dataset is subject to the relevant Webscope License Agreement, which you need to agree to if you use this dataset.", "#### RedCaps\nThe image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (URL\napi-terms) and users must comply with Reddit User Agreeement, Content Policy,\nand Privacy Policy – all accessible at URL\n\nSimilar to RedCaps:\n> PMD should only be used for non-commercial research. PMD should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of PMD are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.\n\n\n\n\nPlease cite the main FLAVA paper in which PMD was introduced along with each of the subsets used in PMD as follows:", "### Contributions\n\nThanks to @aps, Thomas Wang, and @VictorSanh for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# AudioMNIST\n\nThis is a HuggingFace Datasets adaptation of the AudioMNIST dataset\n\nOriginal Dataset:\nURL\n\n---\nlicense: mit\n---" ]

[ "TAGS\n#region-us \n", "# AudioMNIST\n\nThis is a HuggingFace Datasets adaptation of the AudioMNIST dataset\n\nOriginal Dataset:\nURL\n\n---\nlicense: mit\n---" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for jbo-corpus", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nParallel corpus of Lojban sentences", "### Licensing Information\n\nUnknown" ]

[ "TAGS\n#region-us \n", "# Dataset Card for jbo-corpus", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nParallel corpus of Lojban sentences", "### Licensing Information\n\nUnknown" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/American_English_Speech_Data_by_Mobile_Phone_Reading", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data set contains 349 American English speakers' speech data, all of whom are American locals. It is recorded in quiet environment. The recording contents cover various categories like economics, entertainment, news and spoken language. It is manually transcribed and annotated with the starting and ending time points. \n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nAmerican English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/American_English_Speech_Data_by_Mobile_Phone_Reading", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data set contains 349 American English speakers' speech data, all of whom are American locals. It is recorded in quiet environment. The recording contents cover various categories like economics, entertainment, news and spoken language. It is manually transcribed and annotated with the starting and ending time points. \n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nAmerican English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

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

[]

[]

[ "## Code snippet to visualise the position of the box\n\n\n\nResult: \n!Sample with box position" ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "## Code snippet to visualise the position of the box\n\n\n\nResult: \n!Sample with box position" ]

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

[]

[]

[]

[ "TAGS\n#license-cc0-1.0 #region-us \n" ]

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

[]

[]

[ "## Breast Cancer Wisconsin Diagnostic Dataset\n\nFollowing description was retrieved from breast cancer dataset on UCI machine learning repository).\n\nFeatures are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at here.\n\nSeparating plane described above was obtained using Multisurface Method-Tree (MSM-T), a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\", Optimization Methods and Software 1, 1992, 23-34].\n\nAttribute Information:\n\n- ID number\n- Diagnosis (M = malignant, B = benign)\n\nTen real-valued features are computed for each cell nucleus:\n\n- radius (mean of distances from center to points on the perimeter)\n- texture (standard deviation of gray-scale values)\n- perimeter\n- area\n- smoothness (local variation in radius lengths)\n- compactness (perimeter^2 / area - 1.0)\n- concavity (severity of concave portions of the contour)\n- concave points (number of concave portions of the contour)\n- symmetry\n- fractal dimension (\"coastline approximation\" - 1)" ]

[ "TAGS\n#license-cc-by-sa-4.0 #region-us \n", "## Breast Cancer Wisconsin Diagnostic Dataset\n\nFollowing description was retrieved from breast cancer dataset on UCI machine learning repository).\n\nFeatures are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at here.\n\nSeparating plane described above was obtained using Multisurface Method-Tree (MSM-T), a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\", Optimization Methods and Software 1, 1992, 23-34].\n\nAttribute Information:\n\n- ID number\n- Diagnosis (M = malignant, B = benign)\n\nTen real-valued features are computed for each cell nucleus:\n\n- radius (mean of distances from center to points on the perimeter)\n- texture (standard deviation of gray-scale values)\n- perimeter\n- area\n- smoothness (local variation in radius lengths)\n- compactness (perimeter^2 / area - 1.0)\n- concavity (severity of concave portions of the contour)\n- concave points (number of concave portions of the contour)\n- symmetry\n- fractal dimension (\"coastline approximation\" - 1)" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "region:us" ]

[]

[]

[ "# Dataset description\n \nThis dataset was created for fine-tuning the model robbert-base-v2-NER-NL-legislation-refs and consists of 512 token long examples which each contain one or more legislation references. These examples were created from a weakly labelled corpus of Dutch case law which was scraped from Linked Data Overheid, pre-tokenized and labelled (biluo_tags_from_offsets) through spaCy and further tokenized through applying Hugging Face's AutoTokenizer.from_pretrained() for pdelobelle/robbert-v2-dutch-base's tokenizer." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #region-us \n", "# Dataset description\n \nThis dataset was created for fine-tuning the model robbert-base-v2-NER-NL-legislation-refs and consists of 512 token long examples which each contain one or more legislation references. These examples were created from a weakly labelled corpus of Dutch case law which was scraped from Linked Data Overheid, pre-tokenized and labelled (biluo_tags_from_offsets) through spaCy and further tokenized through applying Hugging Face's AutoTokenizer.from_pretrained() for pdelobelle/robbert-v2-dutch-base's tokenizer." ]

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

[]

[]

[ "## Adult Census Income Dataset\nThe following was retrieved from UCI machine learning repository.\n\nThis data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)). The prediction task is to determine whether a person makes over $50K a year.\n\nDescription of fnlwgt (final weight)\n\nThe weights on the Current Population Survey (CPS) files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are:\n\n- A single cell estimate of the population 16+ for each state.\n- Controls for Hispanic Origin by age and sex.\n- Controls by Race, age and sex.\n\nWe use all three sets of controls in our weighting program and \"rake\" through them 6 times so that by the end we come back to all the controls we used. The term estimate refers to population totals derived from CPS by creating \"weighted tallies\" of any specified socio-economic characteristics of the population. People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state." ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "## Adult Census Income Dataset\nThe following was retrieved from UCI machine learning repository.\n\nThis data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)). The prediction task is to determine whether a person makes over $50K a year.\n\nDescription of fnlwgt (final weight)\n\nThe weights on the Current Population Survey (CPS) files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are:\n\n- A single cell estimate of the population 16+ for each state.\n- Controls for Hispanic Origin by age and sex.\n- Controls by Race, age and sex.\n\nWe use all three sets of controls in our weighting program and \"rake\" through them 6 times so that by the end we come back to all the controls we used. The term estimate refers to population totals derived from CPS by creating \"weighted tallies\" of any specified socio-economic characteristics of the population. People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state." ]

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

[]

[]

[]

[ "TAGS\n#license-cc0-1.0 #region-us \n" ]

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

[]

[]

[]

[ "TAGS\n#license-cc0-1.0 #region-us \n" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.\nModel is available here.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nen\n\n\nHow to use\n----------\n\n\nHow to load this dataset directly with the datasets library:\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'text': a 'string' feature.", "### Data Splits\n\n\n\n'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:\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\nAbout\n-----\n\n\n*Built by Aleksey Korshuk*\n\n\n\n\n\nFor more details, visit the project repository.\n\n\n![GitHub stars](URL" ]

[ "TAGS\n#language-English #huggingartists #lyrics #region-us \n", "### Dataset Summary\n\n\nThe Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.\nModel is available here.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nen\n\n\nHow to use\n----------\n\n\nHow to load this dataset directly with the datasets library:\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'text': a 'string' feature.", "### Data Splits\n\n\n\n'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:\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\nAbout\n-----\n\n\n*Built by Aleksey Korshuk*\n\n\n\n\n\nFor more details, visit the project repository.\n\n\n![GitHub stars](URL" ]

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

[]

[ "en" ]

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

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

[ "region:us" ]

[]

[]

[ "# Try to include an iframe\n\nfrom observable:\n\n<iframe width=\"100%\" height=\"635\" frameborder=\"0\"\n src=\"URL\n\n\nfrom an HF space:\n \n<iframe src=\"URL data-src=\"URL data-sdk=\"gradio\" title=\"Gradio app\" class=\"container p-0 flex-grow overflow-hidden space-iframe\" allow=\"accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking\" sandbox=\"allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads\" scrolling=\"no\" id=\"iFrameResizer0\" style=\"overflow: hidden; height: 725px;\"></iframe>" ]

[ "TAGS\n#region-us \n", "# Try to include an iframe\n\nfrom observable:\n\n<iframe width=\"100%\" height=\"635\" frameborder=\"0\"\n src=\"URL\n\n\nfrom an HF space:\n \n<iframe src=\"URL data-src=\"URL data-sdk=\"gradio\" title=\"Gradio app\" class=\"container p-0 flex-grow overflow-hidden space-iframe\" allow=\"accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking\" sandbox=\"allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads\" scrolling=\"no\" id=\"iFrameResizer0\" style=\"overflow: hidden; height: 725px;\"></iframe>" ]

[ "region:us" ]

[]

[]

[ "## Code snippet to visualise the position of the box\n\n\n\nResult: \n!Sample with box position" ]

[ "TAGS\n#region-us \n", "## Code snippet to visualise the position of the box\n\n\n\nResult: \n!Sample with box position" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "en" ]

[ "# Dataset Card for the Dog vs. Food (a.k.a. Dog Food) Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:: URL\n- Repository: : URL\n- Paper: : N/A\n- Leaderboard:: N/A\n- Point of Contact:: @sasha", "### Dataset Summary\n\nThis is a dataset for binary image classification, between 'dog' and 'food' classes. \n\nThe 'dog' class contains images of dogs that look like fried chicken and some that look like images of muffins, and the 'food' class contains images of (you guessed it) fried chicken and muffins", "### Supported Tasks and Leaderboards\n\nTBC", "### Languages\n\nThe labels are in English (['dog', 'food'])", "## Dataset Structure", "### Data Instances\nA sample from the training set is provided below:", "### Data Fields\n\n\n- img: A 'PIL.JpegImageFile' object containing the 300x470. 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: 0-1 with the following correspondence\n 0 dog\n 1 food", "### Data Splits\n\nTrain (2100 images) and Test (900 images)", "## Dataset Creation", "### Curation Rationale\n\nN/A", "### Source Data", "#### Initial Data Collection and Normalization\n\nThis dataset was taken from the qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins? Github repository, merging the 'chicken' and 'muffin' categories into a single 'food' category, and randomly splitting 10% of the data for validation.", "### Annotations", "#### Annotation process\n\nThis data was scraped from the internet and annotated based on the query words.", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nN/A", "### Discussion of Biases\n\nThis dataset is imbalanced -- it has more images of food (2000) compared to dogs (1000), due to the original labeling. This should be taken into account when evaluating models.", "### Other Known Limitations\n\nN/A", "## Additional Information", "### Dataset Curators\n\nThis dataset was created by @lanceyjt, @yl3829, @wesleytao, @qw2243c and @asyouhaveknown", "### Licensing Information\n\nNo information is indicated on the original github repository.\n\n\n\nN/A", "### Contributions\n\nThanks to @sashavor for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-unknown #region-us \n", "# Dataset Card for the Dog vs. Food (a.k.a. Dog Food) Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:: URL\n- Repository: : URL\n- Paper: : N/A\n- Leaderboard:: N/A\n- Point of Contact:: @sasha", "### Dataset Summary\n\nThis is a dataset for binary image classification, between 'dog' and 'food' classes. \n\nThe 'dog' class contains images of dogs that look like fried chicken and some that look like images of muffins, and the 'food' class contains images of (you guessed it) fried chicken and muffins", "### Supported Tasks and Leaderboards\n\nTBC", "### Languages\n\nThe labels are in English (['dog', 'food'])", "## Dataset Structure", "### Data Instances\nA sample from the training set is provided below:", "### Data Fields\n\n\n- img: A 'PIL.JpegImageFile' object containing the 300x470. 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: 0-1 with the following correspondence\n 0 dog\n 1 food", "### Data Splits\n\nTrain (2100 images) and Test (900 images)", "## Dataset Creation", "### Curation Rationale\n\nN/A", "### Source Data", "#### Initial Data Collection and Normalization\n\nThis dataset was taken from the qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins? Github repository, merging the 'chicken' and 'muffin' categories into a single 'food' category, and randomly splitting 10% of the data for validation.", "### Annotations", "#### Annotation process\n\nThis data was scraped from the internet and annotated based on the query words.", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nN/A", "### Discussion of Biases\n\nThis dataset is imbalanced -- it has more images of food (2000) compared to dogs (1000), due to the original labeling. This should be taken into account when evaluating models.", "### Other Known Limitations\n\nN/A", "## Additional Information", "### Dataset Curators\n\nThis dataset was created by @lanceyjt, @yl3829, @wesleytao, @qw2243c and @asyouhaveknown", "### Licensing Information\n\nNo information is indicated on the original github repository.\n\n\n\nN/A", "### Contributions\n\nThanks to @sashavor for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Playlist Generator Dataset\n\nThis dataset contains three files, used in the Playlist Generator space. Visit the blog post to learn more about the project: URL\n\n1. 'URL' contains Sentence Transformer embeddings for each verse for each song in a private (unreleased) dataset of song lyrics. The embeddings were generated using this model: URL\n2. 'URL' maps each verse to a song ID. The indices in 'URL' correspond with the indices in this CSV file.\n3. 'songs_new.csv' contains information about each song, such as the title, artist, and a link to the album art (if available)" ]

[ "TAGS\n#region-us \n", "# Playlist Generator Dataset\n\nThis dataset contains three files, used in the Playlist Generator space. Visit the blog post to learn more about the project: URL\n\n1. 'URL' contains Sentence Transformer embeddings for each verse for each song in a private (unreleased) dataset of song lyrics. The embeddings were generated using this model: URL\n2. 'URL' maps each verse to a song ID. The indices in 'URL' correspond with the indices in this CSV file.\n3. 'songs_new.csv' contains information about each song, such as the title, artist, and a link to the album art (if available)" ]

[ "task_categories:text-classification", "task_ids:fact-checking", "task_ids:natural-language-inference", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# Details\nFact Verification dataset created for Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21') based on Wikipedia edits (revisions).\n\nFor more details see: URL\n\nWhen using this dataset, please cite the paper:", "# BibTeX entry and citation info" ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #task_ids-natural-language-inference #multilinguality-monolingual #size_categories-100K<n<1M #language-English #license-cc-by-sa-3.0 #region-us \n", "# Details\nFact Verification dataset created for Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., NAACL 21') based on Wikipedia edits (revisions).\n\nFor more details see: URL\n\nWhen using this dataset, please cite the paper:", "# BibTeX entry and citation info" ]

[ "task_categories:text-generation", "language:si", "language:ta", "language:en", "license:mit", "region:us" ]

[]

[ "si", "ta", "en" ]

[ "# MWP-Dataset\nEnglish-Sinhala-Tamil Math Word Problem Dataset", "## File Structure\n\n- URL -> Simple English Math Word Problems\n- URL -> Simple Sinhala Math Word Problems\n- URL -> Simple Tamil Math Word Problems\n- URL -> Algebraic English Math Word Problems\n- URL -> Algebraic Sinhala Math Word Problems\n- URL -> Algebraic Tamil Math Word Problems\n\nAuthors:" ]

[ "TAGS\n#task_categories-text-generation #language-Sinhala #language-Tamil #language-English #license-mit #region-us \n", "# MWP-Dataset\nEnglish-Sinhala-Tamil Math Word Problem Dataset", "## File Structure\n\n- URL -> Simple English Math Word Problems\n- URL -> Simple Sinhala Math Word Problems\n- URL -> Simple Tamil Math Word Problems\n- URL -> Algebraic English Math Word Problems\n- URL -> Algebraic Sinhala Math Word Problems\n- URL -> Algebraic Tamil Math Word Problems\n\nAuthors:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/North_American_English_Speech_Data_by_Mobile_Phone_and_PC", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data set contains 302 North American speakers' speech data. The recording contents include phrases and sentences with rich scenes. The valid time is 201 hours. The recording environment is quiet indoor. The recording device includes PC, android cellphone, and iPhone. This data can be used in speech recognition research in North American area. \n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nNorth American English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/North_American_English_Speech_Data_by_Mobile_Phone_and_PC", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data set contains 302 North American speakers' speech data. The recording contents include phrases and sentences with rich scenes. The valid time is 201 hours. The recording environment is quiet indoor. The recording device includes PC, android cellphone, and iPhone. This data can be used in speech recognition research in North American area. \n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nNorth American English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Mandarin_Conversational_Speech_Data_by_Mobile_Phone_and_Voice_Recorder", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1950 speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy. \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nMandarin Chinese", "## 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\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Mandarin_Conversational_Speech_Data_by_Mobile_Phone_and_Voice_Recorder", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1950 speakers participated in the recording, and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy. \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nMandarin Chinese", "## 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\nCommerical License: URL", "### Contributions" ]

[ "task_categories:conversational", "task_categories:automatic-speech-recognition", "language:en", "region:us" ]

[]

[ "en" ]

[ "# 1000-Hours-American-English-Conversational-Speech-Data-by-Mobile-Phone", "## Description\n2000 speakers participated in the recording and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy.\n\nFor more details, please refer to the link: URL", "## Format\n16kHz, 16bit, uncompressed wav, mono channel", "## Recording Environment\nquiet indoor environment, without echo", "## Recording Content\ndozens of topics are specified, and the speakers make dialogue under those topics while the recording is performed", "## Population\n2,000 Americans, balance for gender;", "## Annotation\nannotating for the transcription text, speaker identification and gender", "## Device\nAndroid mobile phone, iPhone", "## Language\nAmerican English", "## Application scene\nspeech recognition, voiceprint recognition", "## Accuracy rate\n95%", "# Licensing Information\nCommercial License" ]

[ "TAGS\n#task_categories-conversational #task_categories-automatic-speech-recognition #language-English #region-us \n", "# 1000-Hours-American-English-Conversational-Speech-Data-by-Mobile-Phone", "## Description\n2000 speakers participated in the recording and conducted face-to-face communication in a natural way. They had free discussion on a number of given topics, with a wide range of fields; the voice was natural and fluent, in line with the actual dialogue scene. Text is transferred manually, with high accuracy.\n\nFor more details, please refer to the link: URL", "## Format\n16kHz, 16bit, uncompressed wav, mono channel", "## Recording Environment\nquiet indoor environment, without echo", "## Recording Content\ndozens of topics are specified, and the speakers make dialogue under those topics while the recording is performed", "## Population\n2,000 Americans, balance for gender;", "## Annotation\nannotating for the transcription text, speaker identification and gender", "## Device\nAndroid mobile phone, iPhone", "## Language\nAmerican English", "## Application scene\nspeech recognition, voiceprint recognition", "## Accuracy rate\n95%", "# Licensing Information\nCommercial License" ]