Datasets:

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ftopal

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huggingface-datasets

like 0

[ "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<5M", "source_datasets:original", "language:qu", "license:apache-2.0", "region:us" ]

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[ "qu" ]

[ "# Dataset Card for Monolingual-Quechua-IIC", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: URL\n- Paper: Introducing QuBERT: A Large Monolingual Corpus and BERT Model for\nSouthern Quechua\n- Point of Contact: Rodolfo Zevallos\n- Size of downloaded dataset files: 373.28 MB", "### Dataset Summary\n We present Monolingual-Quechua-IIC, a monolingual corpus of Southern Quechua, which can be used to build language models using Transformers models. This corpus also includes the Wiki and OSCAR corpora. We used this corpus to build Llama-RoBERTa-Quechua, the first language model for Southern Quechua using Transformers.", "### Supported Tasks and Leaderboards", "### Languages\nSouthern Quechua", "## 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\nApache-2.0", "### Contributions\nThanks to @rjzevallos for adding this dataset." ]

[ "TAGS\n#task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<5M #source_datasets-original #language-Quechua #license-apache-2.0 #region-us \n", "# Dataset Card for Monolingual-Quechua-IIC", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n- Homepage: URL\n- Paper: Introducing QuBERT: A Large Monolingual Corpus and BERT Model for\nSouthern Quechua\n- Point of Contact: Rodolfo Zevallos\n- Size of downloaded dataset files: 373.28 MB", "### Dataset Summary\n We present Monolingual-Quechua-IIC, a monolingual corpus of Southern Quechua, which can be used to build language models using Transformers models. This corpus also includes the Wiki and OSCAR corpora. We used this corpus to build Llama-RoBERTa-Quechua, the first language model for Southern Quechua using Transformers.", "### Supported Tasks and Leaderboards", "### Languages\nSouthern Quechua", "## 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\nApache-2.0", "### Contributions\nThanks to @rjzevallos for adding this dataset." ]

[ "region:us" ]

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

[ "region:us" ]

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

[ "arxiv:1906.01155", "region:us" ]

[ "1906.01155" ]

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[ "### Contact\n\n\nParia Jamshid Lou [paria.jamshid-lou@URL](mailto:paria.jamshid-lou@URL)\n\n\nOmid Mohamad Nezami [omid.mohamad-nezami@URL](mailto:omid.mohamad-nezami@URL)" ]

[ "TAGS\n#arxiv-1906.01155 #region-us \n", "### Contact\n\n\nParia Jamshid Lou [paria.jamshid-lou@URL](mailto:paria.jamshid-lou@URL)\n\n\nOmid Mohamad Nezami [omid.mohamad-nezami@URL](mailto:omid.mohamad-nezami@URL)" ]

[ "region:us" ]

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[ "## QA4PC Dataset (paper: Cross-Policy Compliance Detection via Question Answering)", "### Train Sets\nTo create training set or entailment and QA tasks, download the ShARC data from here: URL After that, run the script from _create_train_from_sharc.py_, by providing the path to the ShARC train and development sets.", "### Evaluation Sets", "#### Entailment Data\nThe following files contain the data for the entailment task. This includes the policy + questions, a scenario and an answer (_Yes, No, Maybe_). Each datapoint also contain the information from the ShARC dataset such as tree_id and source_url.\n- __dev_entailment_qa4pc.json__\n- __test_entailment_qa4pc.json__", "#### QA Data\nThe following files contain the data for the QA task.\n- __dev_sc_qa4pc.json__\n- __test_sc_qa4pc.json__\n\nThe following file contains the expression tree data for the dev and test sets. Each tree includes a policy, a set of questions and a logical expression.\n- __trees_dev_test_qa4pc.json__" ]

[ "TAGS\n#region-us \n", "## QA4PC Dataset (paper: Cross-Policy Compliance Detection via Question Answering)", "### Train Sets\nTo create training set or entailment and QA tasks, download the ShARC data from here: URL After that, run the script from _create_train_from_sharc.py_, by providing the path to the ShARC train and development sets.", "### Evaluation Sets", "#### Entailment Data\nThe following files contain the data for the entailment task. This includes the policy + questions, a scenario and an answer (_Yes, No, Maybe_). Each datapoint also contain the information from the ShARC dataset such as tree_id and source_url.\n- __dev_entailment_qa4pc.json__\n- __test_entailment_qa4pc.json__", "#### QA Data\nThe following files contain the data for the QA task.\n- __dev_sc_qa4pc.json__\n- __test_sc_qa4pc.json__\n\nThe following file contains the expression tree data for the dev and test sets. Each tree includes a policy, a set of questions and a logical expression.\n- __trees_dev_test_qa4pc.json__" ]

[ "region:us" ]

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[ "# Dataset Card for mlquestions", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Instances\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: Devang Kulshreshtha", "### Dataset Summary\n\nThe MLQuestions dataset consists of questions from Google search queries and passages from Wikipedia pages related to Machine learning domain . The dataset was created to support research in domain adaptation of question generation and passage retrieval models.", "### Languages\n\nThe text in the dataset is in English", "## Dataset Structure", "### Data Instances\n\nWe release development and test sets where a typical data point comprises a passage denoted by 'input_text' label and a question, with a 'target_text' label. \n\nAn example from the MLQuestions test set looks as follows:\n\n{\n 'input_text': 'Bayesian learning uses Bayes' theorem to determine the conditional probability of a hypotheses given some evidence or observations.'\n 'target_text': 'What is Bayesian learning in machine learning'\n}\n\nWe also provide unsupervised questions and passages in two separate files - 'passages_unaligned.csv' and 'questions_unaligned.csv' with labels 'input_text' and 'target_text' respectively.", "## Additional Information", "### Licensing Information\n\nURL\n\n\n\nIf you find this useful in your research, please consider citing:\n\n @inproceedings{kulshreshtha-etal-2021-back,\n title = \"Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval\",\n author = \"Kulshreshtha, Devang and\n Belfer, Robert and\n Serban, Iulian Vlad and\n Reddy, Siva\",\n booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n month = nov,\n year = \"2021\",\n address = \"Online and Punta Cana, Dominican Republic\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"7064--7078\",\n abstract = \"In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.\",\n }" ]

[ "TAGS\n#region-us \n", "# Dataset Card for mlquestions", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Instances\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: Devang Kulshreshtha", "### Dataset Summary\n\nThe MLQuestions dataset consists of questions from Google search queries and passages from Wikipedia pages related to Machine learning domain . The dataset was created to support research in domain adaptation of question generation and passage retrieval models.", "### Languages\n\nThe text in the dataset is in English", "## Dataset Structure", "### Data Instances\n\nWe release development and test sets where a typical data point comprises a passage denoted by 'input_text' label and a question, with a 'target_text' label. \n\nAn example from the MLQuestions test set looks as follows:\n\n{\n 'input_text': 'Bayesian learning uses Bayes' theorem to determine the conditional probability of a hypotheses given some evidence or observations.'\n 'target_text': 'What is Bayesian learning in machine learning'\n}\n\nWe also provide unsupervised questions and passages in two separate files - 'passages_unaligned.csv' and 'questions_unaligned.csv' with labels 'input_text' and 'target_text' respectively.", "## Additional Information", "### Licensing Information\n\nURL\n\n\n\nIf you find this useful in your research, please consider citing:\n\n @inproceedings{kulshreshtha-etal-2021-back,\n title = \"Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval\",\n author = \"Kulshreshtha, Devang and\n Belfer, Robert and\n Serban, Iulian Vlad and\n Reddy, Siva\",\n booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n month = nov,\n year = \"2021\",\n address = \"Online and Punta Cana, Dominican Republic\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"7064--7078\",\n abstract = \"In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.\",\n }" ]

[ "region:us" ]

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

[ "region:us" ]

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[ "# English-Tamil parallel Corpus prepared by the National Languages Processing Center, University of Moratuwa. The data has been cleaned and then aligned." ]

[ "TAGS\n#region-us \n", "# English-Tamil parallel Corpus prepared by the National Languages Processing Center, University of Moratuwa. The data has been cleaned and then aligned." ]

[ "region:us" ]

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[ "# Sinhala-POS-Data\nPOS tagged Sinhala text\n\nnews- verified- final URL file contains the first version of our annotated data. There are 253636 word in it.\nURL contains the tag list.\nTagging URL contains a detailed description of the tags.\n\n\nIf you use this data set or the tag set, please cite one of these as apropriate:\n\nFernando, S., & Ranathunga, S. (2018, May). Evaluation of Different Classifiers for Sinhala POS Tagging. In 2018 Moratuwa Engineering Research Conference (MERCon) (pp. 96-101). IEEE.\n\n\nDilshani, N., Fernando, S., Ranathunga, S., Jayasena, S., & Dias, G. (2017). A Comprehensive Part of Speech (POS) Tag Set for Sinhala Language. The Third International Conference on Linguistics in Sri Lanka, ICLSL 2017. Department of Linguistics, University of Kelaniya, Sri Lanka.\n\n\nFernando, S., Ranathunga, S., Jayasena, S., & Dias, G. (2016, December). Comprehensive Part-Of-Speech Tag Set and SVM Based POS Tagger for Sinhala. In Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016) (pp. 173-182)." ]

[ "TAGS\n#region-us \n", "# Sinhala-POS-Data\nPOS tagged Sinhala text\n\nnews- verified- final URL file contains the first version of our annotated data. There are 253636 word in it.\nURL contains the tag list.\nTagging URL contains a detailed description of the tags.\n\n\nIf you use this data set or the tag set, please cite one of these as apropriate:\n\nFernando, S., & Ranathunga, S. (2018, May). Evaluation of Different Classifiers for Sinhala POS Tagging. In 2018 Moratuwa Engineering Research Conference (MERCon) (pp. 96-101). IEEE.\n\n\nDilshani, N., Fernando, S., Ranathunga, S., Jayasena, S., & Dias, G. (2017). A Comprehensive Part of Speech (POS) Tag Set for Sinhala Language. The Third International Conference on Linguistics in Sri Lanka, ICLSL 2017. Department of Linguistics, University of Kelaniya, Sri Lanka.\n\n\nFernando, S., Ranathunga, S., Jayasena, S., & Dias, G. (2016, December). Comprehensive Part-Of-Speech Tag Set and SVM Based POS Tagger for Sinhala. In Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016) (pp. 173-182)." ]

[ "task_ids:text-simplification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:hu", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "hu" ]

[ "### Dataset Summary\n\n\nThis is the dataset card for the Hungarian Corpus of Linguistic Acceptability (HuCOLA), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is aN id, a sentence and a label.\n\n\nAn example:", "### Data Fields\n\n\n* Sent\\_id: unique id of the instances, an integer between 1 and 1000;\n* Sent: a Hungarian sentence;\n* label: '0' for wrong, '1' for good sentences.", "### Data Splits\n\n\nHuCOLA has 3 splits: *train*, *validation* and *test*.\n\n\nDataset split: train, Number of sentences in the split: 7276, Proportion of the split: 80%\nDataset split: validation, Number of sentences in the split: 900, Proportion of the split: 10%\nDataset split: test, Number of sentences in the split: 900, Proportion of the split: 10%\n\n\nThe test data is distributed without the labels. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment). The evaluation metric is Matthew's correlation coefficient.\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data was collected by two human annotators from 3 main linguistic books on Hungarian language:\n\n\n* Kiefer Ferenc (ed.) (1992), Strukturális magyar nyelvtan 1. Mondattan. Budapest, Akadémiai Kiadó.\n* Alberti, Gábor and Laczkó, Tibor (eds) (2018), Syntax of Hungarian Nouns and Noun Phrases. I., II. Comprehensive grammar resources. Amsterdam University Press, Amsterdam.\n* Katalin É. Kiss and Veronika Hegedűs (eds) (2021), Postpositions and Postpositional Phrases. Amsterdam: Amsterdam University Press.\n\n\nThe process of collecting sentences partly followed the one described in Warstadt et. al (2018). The guideline of our process is available in the repository of HuCOLA.", "### Annotations", "#### Annotation process\n\n\nEach instance was annotated by 4 human annotators for its acceptability (see the annotation guidelines in the repository of HuCOLA).", "#### Who are the annotators?\n\n\nThe annotators were native Hungarian speakers (of various ages, from 20 to 67) without any linguistic backround.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nHuCOLA is released under the CC-BY-SA 4.0 licence.\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis\nkiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)", "### Contributions\n\n\nThanks to lnnoemi for adding this dataset." ]

[ "TAGS\n#task_ids-text-simplification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Hungarian #license-cc-by-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nThis is the dataset card for the Hungarian Corpus of Linguistic Acceptability (HuCOLA), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is aN id, a sentence and a label.\n\n\nAn example:", "### Data Fields\n\n\n* Sent\\_id: unique id of the instances, an integer between 1 and 1000;\n* Sent: a Hungarian sentence;\n* label: '0' for wrong, '1' for good sentences.", "### Data Splits\n\n\nHuCOLA has 3 splits: *train*, *validation* and *test*.\n\n\nDataset split: train, Number of sentences in the split: 7276, Proportion of the split: 80%\nDataset split: validation, Number of sentences in the split: 900, Proportion of the split: 10%\nDataset split: test, Number of sentences in the split: 900, Proportion of the split: 10%\n\n\nThe test data is distributed without the labels. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment). The evaluation metric is Matthew's correlation coefficient.\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data was collected by two human annotators from 3 main linguistic books on Hungarian language:\n\n\n* Kiefer Ferenc (ed.) (1992), Strukturális magyar nyelvtan 1. Mondattan. Budapest, Akadémiai Kiadó.\n* Alberti, Gábor and Laczkó, Tibor (eds) (2018), Syntax of Hungarian Nouns and Noun Phrases. I., II. Comprehensive grammar resources. Amsterdam University Press, Amsterdam.\n* Katalin É. Kiss and Veronika Hegedűs (eds) (2021), Postpositions and Postpositional Phrases. Amsterdam: Amsterdam University Press.\n\n\nThe process of collecting sentences partly followed the one described in Warstadt et. al (2018). The guideline of our process is available in the repository of HuCOLA.", "### Annotations", "#### Annotation process\n\n\nEach instance was annotated by 4 human annotators for its acceptability (see the annotation guidelines in the repository of HuCOLA).", "#### Who are the annotators?\n\n\nThe annotators were native Hungarian speakers (of various ages, from 20 to 67) without any linguistic backround.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nHuCOLA is released under the CC-BY-SA 4.0 licence.\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis\nkiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)", "### Contributions\n\n\nThanks to lnnoemi for adding this dataset." ]

[ "task_categories:other", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:bsd-2-clause", "commonsense-reasoning", "region:us" ]

[]

[ "hu" ]

[ "### Dataset Summary\n\n\nThis is the dataset card for the Hungarian Choice of Plausible Alternatives Corpus (HuCoPA), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The corpus was created by translating and re-annotating the original English CoPA corpus (Roemmele et al., 2011).", "### Supported Tasks and Leaderboards\n\n\n'commonsense reasoning'\n'question answering'", "### Languages\n\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is an id, a premise, a question ('cause' or 'effect'), two alternatives and a label (1 or 2).\n\n\nAn example:", "### Data Fields\n\n\n* id: unique id of the instances, an integer between 1 and 1000;\n* question: \"cause\" or \"effect\". It suggests what kind of causal relation are we looking for: in the case of \"cause\" we search for the more plausible alternative that may be a cause of the premise. In the case of \"effect\" we are looking for a plausible result of the premise;\n* premise: the premise, a sentence;\n* choice1: the first alternative, a sentence;\n* choice2: the second alternative, a sentence;\n* label: the number of the more plausible alternative (1 or 2).", "### Data Splits\n\n\nHuCoPA has 3 splits: *train*, *validation* and *test*.\n\n\n\nThe test data is distributed without the labels. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment).\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is a translation of the content of the CoPA corpus. Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator.", "### Annotations", "#### Annotation process\n\n\nThe instances initially inherited their original labels from the CoPA dataset. Each instance was annotated by a human annotator. If the original label and the human annotator's label did not match, we manually curated the instance and assigned a final label to that. This step was necessary to ensure that the causal realationship had not been changed or lost during the translation process.", "#### Who are the annotators?\n\n\nThe translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.\n\n\nAdditional Information\n----------------------\n\n\nThe human performance on the test set is 96% (accuracy).", "### Licensing Information\n\n\nHuCoPA is released under the BSD 2-Clause License.\n\n\nCopyright (c) 2010, University of Southern California\nAll rights reserved.\n\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n\n* Redistributions of source code must retain the above copyright notice, this\nlist of conditions and the following disclaimer.\n* Redistributions in binary form must reproduce the above copyright notice,\nthis list of conditions and the following disclaimer in the documentation\nand/or other materials provided with the distribution.\n\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis\nkiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. In: Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika (eds), XVIII. Magyar Számítógépes Nyelvészeti Konferencia. JATEPress, Szeged. 431–446.\n\n\nand to:\n\n\nRoemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011.", "### Contributions\n\n\nThanks to lnnoemi for adding this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|other #language-Hungarian #license-bsd-2-clause #commonsense-reasoning #region-us \n", "### Dataset Summary\n\n\nThis is the dataset card for the Hungarian Choice of Plausible Alternatives Corpus (HuCoPA), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The corpus was created by translating and re-annotating the original English CoPA corpus (Roemmele et al., 2011).", "### Supported Tasks and Leaderboards\n\n\n'commonsense reasoning'\n'question answering'", "### Languages\n\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is an id, a premise, a question ('cause' or 'effect'), two alternatives and a label (1 or 2).\n\n\nAn example:", "### Data Fields\n\n\n* id: unique id of the instances, an integer between 1 and 1000;\n* question: \"cause\" or \"effect\". It suggests what kind of causal relation are we looking for: in the case of \"cause\" we search for the more plausible alternative that may be a cause of the premise. In the case of \"effect\" we are looking for a plausible result of the premise;\n* premise: the premise, a sentence;\n* choice1: the first alternative, a sentence;\n* choice2: the second alternative, a sentence;\n* label: the number of the more plausible alternative (1 or 2).", "### Data Splits\n\n\nHuCoPA has 3 splits: *train*, *validation* and *test*.\n\n\n\nThe test data is distributed without the labels. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment).\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is a translation of the content of the CoPA corpus. Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator.", "### Annotations", "#### Annotation process\n\n\nThe instances initially inherited their original labels from the CoPA dataset. Each instance was annotated by a human annotator. If the original label and the human annotator's label did not match, we manually curated the instance and assigned a final label to that. This step was necessary to ensure that the causal realationship had not been changed or lost during the translation process.", "#### Who are the annotators?\n\n\nThe translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.\n\n\nAdditional Information\n----------------------\n\n\nThe human performance on the test set is 96% (accuracy).", "### Licensing Information\n\n\nHuCoPA is released under the BSD 2-Clause License.\n\n\nCopyright (c) 2010, University of Southern California\nAll rights reserved.\n\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n\n* Redistributions of source code must retain the above copyright notice, this\nlist of conditions and the following disclaimer.\n* Redistributions in binary form must reproduce the above copyright notice,\nthis list of conditions and the following disclaimer in the documentation\nand/or other materials provided with the distribution.\n\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis\nkiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. In: Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika (eds), XVIII. Magyar Számítógépes Nyelvészeti Konferencia. JATEPress, Szeged. 431–446.\n\n\nand to:\n\n\nRoemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011.", "### Contributions\n\n\nThanks to lnnoemi for adding this dataset." ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:abstractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:cc-by-4.0", "region:us" ]

[]

[ "hu" ]

[ "### Dataset Summary\n\n\nThis is the dataset card for the Hungarian Corpus for Reading Comprehension with Commonsense Reasoning (HuRC), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU.\nThe dataset contains 80 614 instances. Each instance is composed of a lead, a passage and a cloze-style query with a masked entity. The task is to select the named entity that is being masked in the query.\nThe data was automatically collected from the online news of Népszabadság online (URL).", "### Languages\n\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is an id, a lead, a passage, a query and a MASK.\nAn example:", "### Data Fields\n\n\n* id: unique id of the instances;\n* lead: a short summary of the article as it was extracted from the source texts;\n* passage: 3-6 paragraphs of texts as the body of the article;\n* query: the last paragraph of an article, some kind of summary or conclusion, with a named entity masked (with [MASK]) in it;\n* MASK: the masked named entity.", "### Data Splits\n\n\nHuRC has 3 splits: *train*, *validation* and *test*.\n\n\nDataset split: train, Number of instances in the split: 64614, Proportion of the split: 80%\nDataset split: validation, Number of instances in the split: 8000, Proportion of the split: 10%\nDataset split: test, Number of instances in the split: 8000, Proportion of the split: 10%\n\n\nThe test data is distributed without the MASK fields. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment).\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nTo produce the Hungarian material, we used the daily articles from Népszabadság Online which had titles and summaries as well. We selected 3-6 paragraphs from each article from the ones which contain proper nouns both in the main part and the summary as well. We trained a NER model using huBERT (Nemeskey 2021) for recognizing proper nouns. NerKor (Simon és Vadász 2021) and Huggingface’s token-level classification library were used to fine-tune the model. Our model achieved an F-score of 90.18 on the test material. As a final step, we found pairs of proper names which are present both in the main article and the summary. Multiple articles contained more than one such pairs so we used those more than once. This resulted in a database of 88655 instances (from 49782 articles).\n\n\nThe quantitative properties of our corpus are as follows: Number of articles: 88655 Number of different articles (type): 49782 Token: 27703631 Type: 1115.260 Average length of text (token): 249.42 (median: 229) Average question length (token): 63.07 (median: 56). We fine-tuned the corpus by hand.\n\n\nOne annotator per 100 unit checked and validated the dataset for which we provided our own demo interface. Automatic masking and the previous occurrence of the entity was checked. This resulted in a database of 80 614 validated entries.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nHuRC is released under the cc-by-4.0 license.\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)", "### Contributions\n\n\nThanks to lnnoemi for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #task_ids-abstractive-qa #annotations_creators-crowdsourced #language_creators-found #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|other #language-Hungarian #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nThis is the dataset card for the Hungarian Corpus for Reading Comprehension with Commonsense Reasoning (HuRC), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU.\nThe dataset contains 80 614 instances. Each instance is composed of a lead, a passage and a cloze-style query with a masked entity. The task is to select the named entity that is being masked in the query.\nThe data was automatically collected from the online news of Népszabadság online (URL).", "### Languages\n\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is an id, a lead, a passage, a query and a MASK.\nAn example:", "### Data Fields\n\n\n* id: unique id of the instances;\n* lead: a short summary of the article as it was extracted from the source texts;\n* passage: 3-6 paragraphs of texts as the body of the article;\n* query: the last paragraph of an article, some kind of summary or conclusion, with a named entity masked (with [MASK]) in it;\n* MASK: the masked named entity.", "### Data Splits\n\n\nHuRC has 3 splits: *train*, *validation* and *test*.\n\n\nDataset split: train, Number of instances in the split: 64614, Proportion of the split: 80%\nDataset split: validation, Number of instances in the split: 8000, Proportion of the split: 10%\nDataset split: test, Number of instances in the split: 8000, Proportion of the split: 10%\n\n\nThe test data is distributed without the MASK fields. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment).\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nTo produce the Hungarian material, we used the daily articles from Népszabadság Online which had titles and summaries as well. We selected 3-6 paragraphs from each article from the ones which contain proper nouns both in the main part and the summary as well. We trained a NER model using huBERT (Nemeskey 2021) for recognizing proper nouns. NerKor (Simon és Vadász 2021) and Huggingface’s token-level classification library were used to fine-tune the model. Our model achieved an F-score of 90.18 on the test material. As a final step, we found pairs of proper names which are present both in the main article and the summary. Multiple articles contained more than one such pairs so we used those more than once. This resulted in a database of 88655 instances (from 49782 articles).\n\n\nThe quantitative properties of our corpus are as follows: Number of articles: 88655 Number of different articles (type): 49782 Token: 27703631 Type: 1115.260 Average length of text (token): 249.42 (median: 229) Average question length (token): 63.07 (median: 56). We fine-tuned the corpus by hand.\n\n\nOne annotator per 100 unit checked and validated the dataset for which we provided our own demo interface. Automatic masking and the previous occurrence of the entity was checked. This resulted in a database of 80 614 validated entries.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nHuRC is released under the cc-by-4.0 license.\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)", "### Contributions\n\n\nThanks to lnnoemi for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "task_ids:text-scoring", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:bsd-2-clause", "region:us" ]

[]

[ "hu" ]

[ "### Dataset Summary\n\n\nThis is the dataset card for the Hungarian version of the Stanford Sentiment Treebank. This dataset which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The corpus was created by translating and re-annotating the original SST (Roemmele et al., 2011).", "### Supported Tasks and Leaderboards\n\n\n'sentiment classification'\n\n\n'sentiment scoring'", "### Language\n\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is an id, a sentence and a sentiment label.\n\n\nAn example:", "### Data Fields\n\n\n* Sent\\_id: unique id of the instances;\n* Sent: the sentence, translation of an instance of the SST dataset;\n* Label: \"negative\", \"neutral\", or \"positive\".", "### Data Splits\n\n\nHuSST has 3 splits: *train*, *validation* and *test*.\n\n\n\nThe test data is distributed without the labels. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment).\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is a translation of the content of the SST dataset (only the whole sentences were used). Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator.", "### Annotations", "#### Annotation process\n\n\nThe translated sentences were annotated by three human annotators with one of the following labels: negative, neutral and positive. Each sentence was then curated by a fourth annotator (the 'curator'). The final label is the decision of the curator based on the three labels of the annotators.", "#### Who are the annotators?\n\n\nThe translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Vadász, T. (2022) HuLU: magyar nyelvű benchmark adatbázis\nkiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. pp. 431–446.\n\n\nand to:\n\n\nSocher et al. (2013), Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1631--1642.", "### Contributions\n\n\nThanks to lnnoemi for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #task_ids-sentiment-scoring #task_ids-text-scoring #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|other #language-Hungarian #license-bsd-2-clause #region-us \n", "### Dataset Summary\n\n\nThis is the dataset card for the Hungarian version of the Stanford Sentiment Treebank. This dataset which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The corpus was created by translating and re-annotating the original SST (Roemmele et al., 2011).", "### Supported Tasks and Leaderboards\n\n\n'sentiment classification'\n\n\n'sentiment scoring'", "### Language\n\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is an id, a sentence and a sentiment label.\n\n\nAn example:", "### Data Fields\n\n\n* Sent\\_id: unique id of the instances;\n* Sent: the sentence, translation of an instance of the SST dataset;\n* Label: \"negative\", \"neutral\", or \"positive\".", "### Data Splits\n\n\nHuSST has 3 splits: *train*, *validation* and *test*.\n\n\n\nThe test data is distributed without the labels. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment).\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is a translation of the content of the SST dataset (only the whole sentences were used). Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator.", "### Annotations", "#### Annotation process\n\n\nThe translated sentences were annotated by three human annotators with one of the following labels: negative, neutral and positive. Each sentence was then curated by a fourth annotator (the 'curator'). The final label is the decision of the curator based on the three labels of the annotators.", "#### Who are the annotators?\n\n\nThe translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Vadász, T. (2022) HuLU: magyar nyelvű benchmark adatbázis\nkiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. pp. 431–446.\n\n\nand to:\n\n\nSocher et al. (2013), Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1631--1642.", "### Contributions\n\n\nThanks to lnnoemi for adding this dataset." ]

[ "task_categories:other", "task_ids:coreference-resolution", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|other", "language:hu", "license:cc-by-sa-4.0", "structure-prediction", "region:us" ]

[]

[ "hu" ]

[ "# Dataset Card for HuWNLI", "## Table of Contents\n\n- Table of Contents\n\n- Dataset Description\n\n - Dataset Summary\n\n - Supported Tasks and Leaderboards\n\n - Languages\n\n- Dataset Structure\n\n - Data Instances\n\n - Data Fields\n\n - Data Splits\n\n- Dataset Creation\n\n - Curation Rationale\n\n - Source Data\n\n - Annotations\n\n - Personal and Sensitive Information\n\n- Considerations for Using the Data\n\n - Social Impact of Dataset\n\n - Discussion of Biases\n\n - Other Known Limitations\n\n- Additional Information\n\n - Dataset Curators\n\n - Licensing Information\n\n - Citation Information\n\n - Contributions", "## Dataset Description\n- Homepage:\n- Repository:\nHuWNLI dataset\n- Paper:\n- Leaderboard:\n- Point of Contact:\nlnnoemi", "### Dataset Summary\n\nThis is the dataset card for the Hungarian translation of the Winograd schemata formatted as an inference task. A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution (Levesque et al. 2012). This dataset is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The corpus was created by translating and manually curating the original English Winograd schemata. The NLI format was created by replacing the ambiguous pronoun with each possible referent (the method is described in GLUE's paper, Wang et al. 2019). We extended the set of sentence pairs derived from the schemata by the translation of the sentence pairs that - together with the Winograd schema sentences - build up the WNLI dataset of GLUE.", "### Languages\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.", "## Dataset Structure", "### Data Instances\n\nFor each instance, there is an orig_id, an id, two sentences and a label. \n\nAn example:", "### Data Fields\n- orig_id: the original id of this sentence pair (more precisely, its English counterpart's) in GLUE's WNLI dataset;\n\n- id: unique id of the instances;\n\n- sentence1: the premise;\n\n- sentence2: the hypothesis; \n\n- label: \"1\" if sentence2 is entailed by sentence1, and \"0\" otherwise.", "### Data Splits\n\nThe data is distributed in three splits: training set (562), development set (59) and test set (134). The splits follow GLUE's WNLI's splits but contain fewer instances as many sentence pairs had to be thrown away for being untranslatable to Hungarian. The train and the development set have been extended from nli sentence pairs formatted from the Hungarian translation of 6 Winograd schemata left out from the original WNLI dataset.\nThe test set's sentence pairs are translated from GLUE's WNLI's test set. This set was distributed without labels. 3 annotators annotated the Hungarian sentence pairs.\nThe test set of HuWNLI is also distributed without labels. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment).", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data is a translation of the English Winograd schemata and the additional sentence pairs of GLUE's WNLI. Each schema and sentence pair was translated by a human translator. Each schema was manually curated by a linguistic expert. The schemata were transformed into nli format by a linguistic expert.\n\nDuring the adaption method, we found two erroneous labels in GLUE's WNLI's train set (id 347 and id 464). We corrected them in our dataset.", "## Additional Information\n\nAverage human performance on the test set is 92,78% (accuracy).", "### Licensing Information\n\nHuWNLI is released under the Creative Commons Attribution-ShareAlike 4.0 International License.\n\n\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\nLigeti-Nagy, N., Héja, E., Laki, L. J., Takács, D., Yang, Z. Gy. and Váradi, T. (2023) Hát te mekkorát nőttél! - A HuLU első életéve új adatbázisokkal és webszolgáltatással \\[Look at how much you have grown! - The first year of HuLU with new databases and with webservice\\]. In: Berend, G., Gosztolya, G. and Vincze, V. (eds), XIX. Magyar Számítógépes Nyelvészeti Konferencia. Szeged, Szegedi Tudományegyetem, Informatikai Intézet. 217-230.\n\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából \\[HuLU: Hungarian benchmark dataset to evaluate neural language models\\]. In: Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika (eds), XVIII. Magyar Számítógépes Nyelvészeti Konferencia. JATEPress, Szeged. 431–446.\n\n\n\nand to:\n\nLevesque, Hector, Davis, Ernest, Morgenstern, Leora (2012) he winograd schema challenge. In: Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning.", "### Contributions\n\nThanks to lnnoemi for adding this dataset." ]

[ "TAGS\n#task_categories-other #task_ids-coreference-resolution #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|other #language-Hungarian #license-cc-by-sa-4.0 #structure-prediction #region-us \n", "# Dataset Card for HuWNLI", "## Table of Contents\n\n- Table of Contents\n\n- Dataset Description\n\n - Dataset Summary\n\n - Supported Tasks and Leaderboards\n\n - Languages\n\n- Dataset Structure\n\n - Data Instances\n\n - Data Fields\n\n - Data Splits\n\n- Dataset Creation\n\n - Curation Rationale\n\n - Source Data\n\n - Annotations\n\n - Personal and Sensitive Information\n\n- Considerations for Using the Data\n\n - Social Impact of Dataset\n\n - Discussion of Biases\n\n - Other Known Limitations\n\n- Additional Information\n\n - Dataset Curators\n\n - Licensing Information\n\n - Citation Information\n\n - Contributions", "## Dataset Description\n- Homepage:\n- Repository:\nHuWNLI dataset\n- Paper:\n- Leaderboard:\n- Point of Contact:\nlnnoemi", "### Dataset Summary\n\nThis is the dataset card for the Hungarian translation of the Winograd schemata formatted as an inference task. A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution (Levesque et al. 2012). This dataset is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The corpus was created by translating and manually curating the original English Winograd schemata. The NLI format was created by replacing the ambiguous pronoun with each possible referent (the method is described in GLUE's paper, Wang et al. 2019). We extended the set of sentence pairs derived from the schemata by the translation of the sentence pairs that - together with the Winograd schema sentences - build up the WNLI dataset of GLUE.", "### Languages\n\nThe BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.", "## Dataset Structure", "### Data Instances\n\nFor each instance, there is an orig_id, an id, two sentences and a label. \n\nAn example:", "### Data Fields\n- orig_id: the original id of this sentence pair (more precisely, its English counterpart's) in GLUE's WNLI dataset;\n\n- id: unique id of the instances;\n\n- sentence1: the premise;\n\n- sentence2: the hypothesis; \n\n- label: \"1\" if sentence2 is entailed by sentence1, and \"0\" otherwise.", "### Data Splits\n\nThe data is distributed in three splits: training set (562), development set (59) and test set (134). The splits follow GLUE's WNLI's splits but contain fewer instances as many sentence pairs had to be thrown away for being untranslatable to Hungarian. The train and the development set have been extended from nli sentence pairs formatted from the Hungarian translation of 6 Winograd schemata left out from the original WNLI dataset.\nThe test set's sentence pairs are translated from GLUE's WNLI's test set. This set was distributed without labels. 3 annotators annotated the Hungarian sentence pairs.\nThe test set of HuWNLI is also distributed without labels. To evaluate your model, please contact us, or check HuLU's website for an automatic evaluation (this feature is under construction at the moment).", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data is a translation of the English Winograd schemata and the additional sentence pairs of GLUE's WNLI. Each schema and sentence pair was translated by a human translator. Each schema was manually curated by a linguistic expert. The schemata were transformed into nli format by a linguistic expert.\n\nDuring the adaption method, we found two erroneous labels in GLUE's WNLI's train set (id 347 and id 464). We corrected them in our dataset.", "## Additional Information\n\nAverage human performance on the test set is 92,78% (accuracy).", "### Licensing Information\n\nHuWNLI is released under the Creative Commons Attribution-ShareAlike 4.0 International License.\n\n\n\n\nIf you use this resource or any part of its documentation, please refer to:\n\nLigeti-Nagy, N., Héja, E., Laki, L. J., Takács, D., Yang, Z. Gy. and Váradi, T. (2023) Hát te mekkorát nőttél! - A HuLU első életéve új adatbázisokkal és webszolgáltatással \\[Look at how much you have grown! - The first year of HuLU with new databases and with webservice\\]. In: Berend, G., Gosztolya, G. and Vincze, V. (eds), XIX. Magyar Számítógépes Nyelvészeti Konferencia. Szeged, Szegedi Tudományegyetem, Informatikai Intézet. 217-230.\n\n\n\nLigeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából \\[HuLU: Hungarian benchmark dataset to evaluate neural language models\\]. In: Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika (eds), XVIII. Magyar Számítógépes Nyelvészeti Konferencia. JATEPress, Szeged. 431–446.\n\n\n\nand to:\n\nLevesque, Hector, Davis, Ernest, Morgenstern, Leora (2012) he winograd schema challenge. In: Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning.", "### Contributions\n\nThanks to lnnoemi for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "size_categories:2G<n<1B", "source_datasets:original", "language:en", "language:nb", "language:no", "language:nn", "language:sv", "language:da", "language:is", "language:fo", "license:other", "arxiv:2104.09617", "region:us" ]

[ "2104.09617" ]

[ "en", "nb", "no", "nn", "sv", "da", "is", "fo" ]

[ "### Dataset Summary\n\n\nThe NCC dataset contains json lines with language training data. Here is an example json line:\n\n\nData Fields\n-----------", "### Dataset Creation\n\n\nWe are providing a train and a validation split. The standard size of the validation is a single 1GB file, while train is sharded in 1GB chunks.\nAll files are gzipped.\n\n\nBuild date: 21012022", "#### Initial Data Collection and Curation\n\n\nThe procedure for the dataset creation is described in detail in our paper.", "### Summary", "### Document Types", "### Languages", "### Publish Periode\n\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThis corpus contains data under copyright and is not allowed to be used outide the National Library of Norway. The dataset should not be distributed.", "### Discussion of Biases\n\n\nPlease refer to our paper.", "### Dataset Curators\n\n\nFreddy Wetjen and Per Egil Kummervold\n\n\nLicense\n-------\n\n\nVarious licences applies to different parts of the corpus. Every document in the corpus has a tag telling what \"doc\\_type\" it belongs to. If you are unable to accept any of the licenses, you should filter out the \"doc\\_type\" with a conflicting license.\n\n\n\nWe are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus:" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-multilingual #size_categories-2G<n<1B #source_datasets-original #language-English #language-Norwegian Bokmål #language-Norwegian #language-Norwegian Nynorsk #language-Swedish #language-Danish #language-Icelandic #language-Faroese #license-other #arxiv-2104.09617 #region-us \n", "### Dataset Summary\n\n\nThe NCC dataset contains json lines with language training data. Here is an example json line:\n\n\nData Fields\n-----------", "### Dataset Creation\n\n\nWe are providing a train and a validation split. The standard size of the validation is a single 1GB file, while train is sharded in 1GB chunks.\nAll files are gzipped.\n\n\nBuild date: 21012022", "#### Initial Data Collection and Curation\n\n\nThe procedure for the dataset creation is described in detail in our paper.", "### Summary", "### Document Types", "### Languages", "### Publish Periode\n\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThis corpus contains data under copyright and is not allowed to be used outide the National Library of Norway. The dataset should not be distributed.", "### Discussion of Biases\n\n\nPlease refer to our paper.", "### Dataset Curators\n\n\nFreddy Wetjen and Per Egil Kummervold\n\n\nLicense\n-------\n\n\nVarious licences applies to different parts of the corpus. Every document in the corpus has a tag telling what \"doc\\_type\" it belongs to. If you are unable to accept any of the licenses, you should filter out the \"doc\\_type\" with a conflicting license.\n\n\n\nWe are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus:" ]

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:2G<n<1B", "source_datasets:original", "language:no", "language:nb", "language:nn", "license:cc0-1.0", "speech-modeling", "region:us" ]

[]

[ "no", "nb", "nn" ]

[ "#### Initial Data Collection\n\n\nThe procedure for the dataset creation is described in detail in our paper.\n\n\nStatistics\n----------\n\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThis corpus contains speech data. All recordings are of members of Parliament in a public setting, and can be distributed without any restrains.", "### Dataset Creators and Curators\n\n\nThe content of the dataset was created by the Norwegian Language Bank (Språkbanken) at the National Library of Norway. Javier de la Rosa, Freddy Wetjen, Per Egil Kummervold, and Andre Kaasen all contributed in making this into a HuggingFace Dataset. Thanks to the HuggingFace team for assistance.\n\n\nLicense\n-------\n\n\nThe sound and the transcriptions are released under the CC-ZERO-license. The curation of the HuggingFace Dataset is released under CC-BY-SA-3-license.\n\n\nThe following article gives detailed information about the corpus. Please refer to the article and this page if you are using this dataset:" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-audio-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-2G<n<1B #source_datasets-original #language-Norwegian #language-Norwegian Bokmål #language-Norwegian Nynorsk #license-cc0-1.0 #speech-modeling #region-us \n", "#### Initial Data Collection\n\n\nThe procedure for the dataset creation is described in detail in our paper.\n\n\nStatistics\n----------\n\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThis corpus contains speech data. All recordings are of members of Parliament in a public setting, and can be distributed without any restrains.", "### Dataset Creators and Curators\n\n\nThe content of the dataset was created by the Norwegian Language Bank (Språkbanken) at the National Library of Norway. Javier de la Rosa, Freddy Wetjen, Per Egil Kummervold, and Andre Kaasen all contributed in making this into a HuggingFace Dataset. Thanks to the HuggingFace team for assistance.\n\n\nLicense\n-------\n\n\nThe sound and the transcriptions are released under the CC-ZERO-license. The curation of the HuggingFace Dataset is released under CC-BY-SA-3-license.\n\n\nThe following article gives detailed information about the corpus. Please refer to the article and this page if you are using this dataset:" ]

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:2G<n<1B", "source_datasets:original", "language:nb", "language:no", "language:nn", "license:cc0-1.0", "speech-modeling", "region:us" ]

[]

[ "nb", "no", "nn" ]

[ "### Version A\n\n\nThis verison has a short list of the metadata and includes the audio (48k mp3) encoded as a float32 array in the dataset itself.\n\n\nThe current dataloader script is associated with this version.\n\n\nOne line in URL looks like this:", "### Version B\n\n\nThis verison does not contain the audio encoded in the dataset. Instead it has the audio files placed in sub-directories. There are currently both samples in clips\\_48k\\_wav and clips\\_16k\\_mp3. Only the base filename is referred in the dataset. Please not that there are both sentence-based audio clips as well at meeting-based audio clips. The dataset contains referrals to both, the latter referral has start and stop time as well.\n\n\nOne line in the train/URL looks like this:", "### Dataset Creation\n\n\nWe are providing a train, dev and test split. These are the same as in the orginal corpus.\n\n\nBuild date: 20012022", "#### Initial Data Collection and Curation\n\n\nThe procedure for the dataset creation is described in detail in the paper.\n\n\nStatistics\n----------\n\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThis corpus contains speech data and is allowed to be used outside the National Library of Norway for speech recognition technology purposes.", "### Discussion of Biases\n\n\nPlease refer to our paper.", "### Dataset Curators\n\n\nPer Erik Solberg\n\n\nFreddy Wetjen, Andre Kaasen and Per Egil Kummervold has contributed to porting it to the Hugging Face Dataset format.", "### Licensing Information\n\n\nLicensed for use outside the National Library of Norway.\n\n\nLicense\n-------\n\n\nCC-ZERO(URL\n\n\nWe are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus:" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-audio-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-2G<n<1B #source_datasets-original #language-Norwegian Bokmål #language-Norwegian #language-Norwegian Nynorsk #license-cc0-1.0 #speech-modeling #region-us \n", "### Version A\n\n\nThis verison has a short list of the metadata and includes the audio (48k mp3) encoded as a float32 array in the dataset itself.\n\n\nThe current dataloader script is associated with this version.\n\n\nOne line in URL looks like this:", "### Version B\n\n\nThis verison does not contain the audio encoded in the dataset. Instead it has the audio files placed in sub-directories. There are currently both samples in clips\\_48k\\_wav and clips\\_16k\\_mp3. Only the base filename is referred in the dataset. Please not that there are both sentence-based audio clips as well at meeting-based audio clips. The dataset contains referrals to both, the latter referral has start and stop time as well.\n\n\nOne line in the train/URL looks like this:", "### Dataset Creation\n\n\nWe are providing a train, dev and test split. These are the same as in the orginal corpus.\n\n\nBuild date: 20012022", "#### Initial Data Collection and Curation\n\n\nThe procedure for the dataset creation is described in detail in the paper.\n\n\nStatistics\n----------\n\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nThis corpus contains speech data and is allowed to be used outside the National Library of Norway for speech recognition technology purposes.", "### Discussion of Biases\n\n\nPlease refer to our paper.", "### Dataset Curators\n\n\nPer Erik Solberg\n\n\nFreddy Wetjen, Andre Kaasen and Per Egil Kummervold has contributed to porting it to the Hugging Face Dataset format.", "### Licensing Information\n\n\nLicensed for use outside the National Library of Norway.\n\n\nLicense\n-------\n\n\nCC-ZERO(URL\n\n\nWe are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus:" ]

[ "task_categories:text-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-4.0", "arxiv:2011.02686", "region:us" ]

[ "2011.02686" ]

[ "en" ]

[ "### Dataset Summary\n\n\nAggregated NoRec\\_fine: A Fine-grained Sentiment Dataset for Norwegian.\nThis dataset was created by the Nordic Language Processing Laboratory by aggregating the fine-grained annotations in NoReC\\_fine and removing sentences with conflicting or no sentiment.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in Norwegian.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nExample of one instance in the dataset.", "### Data Fields\n\n\n* 'id': index of the example\n* 'text': Text of a sentence\n* 'label': The sentiment label. Here\n\t+ 0 = negative\n\t+ 1 = positive", "### Data Splits\n\n\nThe dataset is split into a 'train', 'validation', and 'test' split with the following sizes:\n\n\n\nDataset Creation\n----------------\n\n\nThis dataset is based largely on the original data described in the paper *A Fine-Grained Sentiment Dataset for Norwegian* by L. Øvrelid, P. Mæhlum, J. Barnes, and E. Velldal, accepted at LREC 2020, paper available. However, we have since added annotations for another 3476 sentences, increasing the overall size and scope of the dataset.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThis work is licensed under a Creative Commons Attribution 4.0 International License" ]

[ "TAGS\n#task_categories-text-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-4.0 #arxiv-2011.02686 #region-us \n", "### Dataset Summary\n\n\nAggregated NoRec\\_fine: A Fine-grained Sentiment Dataset for Norwegian.\nThis dataset was created by the Nordic Language Processing Laboratory by aggregating the fine-grained annotations in NoReC\\_fine and removing sentences with conflicting or no sentiment.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in Norwegian.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nExample of one instance in the dataset.", "### Data Fields\n\n\n* 'id': index of the example\n* 'text': Text of a sentence\n* 'label': The sentiment label. Here\n\t+ 0 = negative\n\t+ 1 = positive", "### Data Splits\n\n\nThe dataset is split into a 'train', 'validation', and 'test' split with the following sizes:\n\n\n\nDataset Creation\n----------------\n\n\nThis dataset is based largely on the original data described in the paper *A Fine-Grained Sentiment Dataset for Norwegian* by L. Øvrelid, P. Mæhlum, J. Barnes, and E. Velldal, accepted at LREC 2020, paper available. However, we have since added annotations for another 3476 sentences, increasing the overall size and scope of the dataset.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThis work is licensed under a Creative Commons Attribution 4.0 International License" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:part-of-speech", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:no", "license:other", "structure-prediction", "arxiv:1911.12146", "region:us" ]

[ "1911.12146" ]

[ "no" ]

[ "### Dataset Summary\n\n\nNorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons,organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.", "### Supported Tasks and Leaderboards\n\n\nNorNE ads named entity annotations on top of the Norwegian Dependency Treebank.", "### Languages\n\n\nBoth Norwegian Bokmål ('bokmaal') and Nynorsk ('nynorsk') are supported as different configs in this dataset. An extra config for the combined languages is also included ('combined'). See the Annotation section for details on accessing reduced tag sets for the NER feature.\n\n\nDataset Structure\n-----------------\n\n\nEach entry contains text sentences, their language, identifiers, tokens, lemmas, and corresponding NER and POS tag lists.", "### Data Instances\n\n\nAn example of the 'train' split of the 'bokmaal' config.", "### Data Fields\n\n\nEach entry is annotated with the next fields:\n\n\n* 'idx' ('int'), text (sentence) identifier from the NorNE dataset\n* 'lang' ('str'), language variety, either 'bokmaal', 'nynorsk' or 'combined'\n* 'text' ('str'), plain text\n* 'tokens' ('List[str]'), list of tokens extracted from 'text'\n* 'lemmas' ('List[str]'), list of lemmas extracted from 'tokens'\n* 'ner\\_tags' ('List[int]'), list of numeric NER tags for each token in 'tokens'\n* 'pos\\_tags' ('List[int]'), list of numeric PoS tags for each token in 'tokens'\n\n\nAn example DataFrame obtained from the dataset:\n\n\n\n\n| | idx | lang | text | tokens | lemmas | ner\\_tags | pos\\_tags |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| 0 | 000001 | bokmaal | Lam og piggvar på bryllupsmenyen | [Lam, og, piggvar, på, bryllupsmenyen] | [lam, og, piggvar, på, bryllupsmeny] | [0, 0, 0, 0, 0] | [0, 9, 0, 5, 0] |\n| 1 | 000002 | bokmaal | Kamskjell, piggvar og lammefilet sto på menyen... | [Kamskjell, ,, piggvar, og, lammefilet, sto, p... | [kamskjell, $,, piggvar, og, lammefilet, stå, ... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | [0, 1, 0, 9, 0, 15, 2, 0, 2, 8, 6, 0, 1] |\n| 2 | 000003 | bokmaal | Og til dessert: Parfait à la Mette-Marit. | [Og, til, dessert, :, Parfait, à, la, Mette-Ma... | [og, til, dessert, $:, Parfait, à, la, Mette-M... | [0, 0, 0, 0, 7, 8, 8, 8, 0] | [9, 2, 0, 1, 10, 12, 12, 10, 1] |", "### Data Splits\n\n\nThere are three splits: 'train', 'validation' and 'test'.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n1. A *name* in this context is close to Saul Kripke's definition of a name,\nin that a name has a unique reference and its meaning is constant (there are exceptions in the annotations, e.g. \"Regjeringen\" (en. \"Government\")).\n2. It is the usage of a name that determines the entity type, not the default/literal sense of the name,\n3. If there is an ambiguity in the type/sense of a name, then the the default/literal sense of the name is chosen\n(following Markert and Nissim, 2002).\n\n\nFor more details, see the \"Annotation URL\" distributed with the corpus.", "### Source Data\n\n\nData was collected using blogs and newspapers in Norwegian, as well as parliament speeches and governamental reports.", "#### Initial Data Collection and Normalization\n\n\nThe texts in the Norwegian Dependency Treebank (NDT) are manually annotated with morphological features, syntactic functions\nand hierarchical structure. The formalism used for the syntactic annotation is dependency grammar.\n\n\nThe treebanks consists of two parts, one part in Norwegian Bokmål ('nob') and one part in Norwegian Nynorsk ('nno').\nBoth parts contain around 300.000 tokens, and are a mix of different non-fictional genres.\n\n\nSee the NDT webpage for more details.", "### Annotations\n\n\nThe following types of entities are annotated:\n\n\n* Person ('PER'): Real or fictional characters and animals\n* Organization ('ORG'): Any collection of people, such as firms, institutions, organizations, music groups,\nsports teams, unions, political parties etc.\n* Location ('LOC'): Geographical places, buildings and facilities\n* Geo-political entity ('GPE'): Geographical regions defined by political and/or social groups.\nA GPE entity subsumes and does not distinguish between a nation, its region, its government, or its people\n* Product ('PROD'): Artificially produced entities are regarded products. This may include more abstract entities, such as speeches,\nradio shows, programming languages, contracts, laws and ideas.\n* Event ('EVT'): Festivals, cultural events, sports events, weather phenomena, wars, etc. Events are bounded in time and space.\n* Derived ('DRV'): Words (and phrases?) that are dervied from a name, but not a name in themselves. They typically contain a full name and are capitalized, but are not proper nouns. Examples (fictive) are \"Brann-treneren\" (\"the Brann coach\") or \"Oslo-mannen\" (\"the man from Oslo\").\n* Miscellaneous ('MISC'): Names that do not belong in the other categories. Examples are animals species and names of medical conditions. Entities that are manufactured or produced are of type Products, whereas thing naturally or spontaneously occurring are of type Miscellaneous.\n\n\nFurthermore, all 'GPE' entities are additionally sub-categorized as being either 'ORG' or 'LOC', with the two annotation levels separated by an underscore:\n\n\n* 'GPE\\_LOC': Geo-political entity, with a locative sense (e.g. \"John lives in *Spain*\")\n* 'GPE\\_ORG': Geo-political entity, with an organisation sense (e.g. \"*Spain* declined to meet with Belgium\")\n\n\nThe two special types 'GPE\\_LOC' and 'GPE\\_ORG' can easily be altered depending on the task, choosing either the more general 'GPE' tag or the more specific 'LOC'/'ORG' tags, conflating them with the other annotations of the same type. This means that the following sets of entity types can be derived:\n\n\n* 7 types, deleting '\\_GPE': 'ORG', 'LOC', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'\n* 8 types, deleting 'LOC\\_' and 'ORG\\_': 'ORG', 'LOC', 'GPE', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'\n* 9 types, keeping all types: 'ORG', 'LOC', 'GPE\\_LOC', 'GPE\\_ORG', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'\n\n\nThe class distribution is as follows, broken down across the data splits of the UD version of NDT, and sorted by total counts (i.e. the number of examples, not tokens within the spans of the annotatons):\n\n\n\nTo access these reduce versions of the dataset, you can use the configs 'bokmaal-7', 'nynorsk-7', 'combined-7' for the NER tag set with 7 tags ( 'ORG', 'LOC', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'), and 'bokmaal-8', 'nynorsk-8', 'combined-8' for the NER tag set with 8 tags ('LOC\\_' and 'ORG\\_': 'ORG', 'LOC', 'GPE', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'). By default, the full set (9 tags) will be used.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nNorNE was created as a collaboration between Schibsted Media Group, Språkbanken at the National Library of Norway and the Language Technology Group at the University of Oslo.\n\n\nNorNE was added to Huggingface Datasets by the AI-Lab at the National Library of Norway.", "### Licensing Information\n\n\nThe NorNE corpus is published under the same license as the Norwegian Dependency Treebank\n\n\nThis dataset is described in the paper *NorNE: Annotating Named Entities for Norwegian* by\nFredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal, accepted for LREC 2020 and available as pre-print here: URL" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #task_ids-part-of-speech #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Norwegian #license-other #structure-prediction #arxiv-1911.12146 #region-us \n", "### Dataset Summary\n\n\nNorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons,organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.", "### Supported Tasks and Leaderboards\n\n\nNorNE ads named entity annotations on top of the Norwegian Dependency Treebank.", "### Languages\n\n\nBoth Norwegian Bokmål ('bokmaal') and Nynorsk ('nynorsk') are supported as different configs in this dataset. An extra config for the combined languages is also included ('combined'). See the Annotation section for details on accessing reduced tag sets for the NER feature.\n\n\nDataset Structure\n-----------------\n\n\nEach entry contains text sentences, their language, identifiers, tokens, lemmas, and corresponding NER and POS tag lists.", "### Data Instances\n\n\nAn example of the 'train' split of the 'bokmaal' config.", "### Data Fields\n\n\nEach entry is annotated with the next fields:\n\n\n* 'idx' ('int'), text (sentence) identifier from the NorNE dataset\n* 'lang' ('str'), language variety, either 'bokmaal', 'nynorsk' or 'combined'\n* 'text' ('str'), plain text\n* 'tokens' ('List[str]'), list of tokens extracted from 'text'\n* 'lemmas' ('List[str]'), list of lemmas extracted from 'tokens'\n* 'ner\\_tags' ('List[int]'), list of numeric NER tags for each token in 'tokens'\n* 'pos\\_tags' ('List[int]'), list of numeric PoS tags for each token in 'tokens'\n\n\nAn example DataFrame obtained from the dataset:\n\n\n\n\n| | idx | lang | text | tokens | lemmas | ner\\_tags | pos\\_tags |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| 0 | 000001 | bokmaal | Lam og piggvar på bryllupsmenyen | [Lam, og, piggvar, på, bryllupsmenyen] | [lam, og, piggvar, på, bryllupsmeny] | [0, 0, 0, 0, 0] | [0, 9, 0, 5, 0] |\n| 1 | 000002 | bokmaal | Kamskjell, piggvar og lammefilet sto på menyen... | [Kamskjell, ,, piggvar, og, lammefilet, sto, p... | [kamskjell, $,, piggvar, og, lammefilet, stå, ... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | [0, 1, 0, 9, 0, 15, 2, 0, 2, 8, 6, 0, 1] |\n| 2 | 000003 | bokmaal | Og til dessert: Parfait à la Mette-Marit. | [Og, til, dessert, :, Parfait, à, la, Mette-Ma... | [og, til, dessert, $:, Parfait, à, la, Mette-M... | [0, 0, 0, 0, 7, 8, 8, 8, 0] | [9, 2, 0, 1, 10, 12, 12, 10, 1] |", "### Data Splits\n\n\nThere are three splits: 'train', 'validation' and 'test'.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n1. A *name* in this context is close to Saul Kripke's definition of a name,\nin that a name has a unique reference and its meaning is constant (there are exceptions in the annotations, e.g. \"Regjeringen\" (en. \"Government\")).\n2. It is the usage of a name that determines the entity type, not the default/literal sense of the name,\n3. If there is an ambiguity in the type/sense of a name, then the the default/literal sense of the name is chosen\n(following Markert and Nissim, 2002).\n\n\nFor more details, see the \"Annotation URL\" distributed with the corpus.", "### Source Data\n\n\nData was collected using blogs and newspapers in Norwegian, as well as parliament speeches and governamental reports.", "#### Initial Data Collection and Normalization\n\n\nThe texts in the Norwegian Dependency Treebank (NDT) are manually annotated with morphological features, syntactic functions\nand hierarchical structure. The formalism used for the syntactic annotation is dependency grammar.\n\n\nThe treebanks consists of two parts, one part in Norwegian Bokmål ('nob') and one part in Norwegian Nynorsk ('nno').\nBoth parts contain around 300.000 tokens, and are a mix of different non-fictional genres.\n\n\nSee the NDT webpage for more details.", "### Annotations\n\n\nThe following types of entities are annotated:\n\n\n* Person ('PER'): Real or fictional characters and animals\n* Organization ('ORG'): Any collection of people, such as firms, institutions, organizations, music groups,\nsports teams, unions, political parties etc.\n* Location ('LOC'): Geographical places, buildings and facilities\n* Geo-political entity ('GPE'): Geographical regions defined by political and/or social groups.\nA GPE entity subsumes and does not distinguish between a nation, its region, its government, or its people\n* Product ('PROD'): Artificially produced entities are regarded products. This may include more abstract entities, such as speeches,\nradio shows, programming languages, contracts, laws and ideas.\n* Event ('EVT'): Festivals, cultural events, sports events, weather phenomena, wars, etc. Events are bounded in time and space.\n* Derived ('DRV'): Words (and phrases?) that are dervied from a name, but not a name in themselves. They typically contain a full name and are capitalized, but are not proper nouns. Examples (fictive) are \"Brann-treneren\" (\"the Brann coach\") or \"Oslo-mannen\" (\"the man from Oslo\").\n* Miscellaneous ('MISC'): Names that do not belong in the other categories. Examples are animals species and names of medical conditions. Entities that are manufactured or produced are of type Products, whereas thing naturally or spontaneously occurring are of type Miscellaneous.\n\n\nFurthermore, all 'GPE' entities are additionally sub-categorized as being either 'ORG' or 'LOC', with the two annotation levels separated by an underscore:\n\n\n* 'GPE\\_LOC': Geo-political entity, with a locative sense (e.g. \"John lives in *Spain*\")\n* 'GPE\\_ORG': Geo-political entity, with an organisation sense (e.g. \"*Spain* declined to meet with Belgium\")\n\n\nThe two special types 'GPE\\_LOC' and 'GPE\\_ORG' can easily be altered depending on the task, choosing either the more general 'GPE' tag or the more specific 'LOC'/'ORG' tags, conflating them with the other annotations of the same type. This means that the following sets of entity types can be derived:\n\n\n* 7 types, deleting '\\_GPE': 'ORG', 'LOC', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'\n* 8 types, deleting 'LOC\\_' and 'ORG\\_': 'ORG', 'LOC', 'GPE', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'\n* 9 types, keeping all types: 'ORG', 'LOC', 'GPE\\_LOC', 'GPE\\_ORG', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'\n\n\nThe class distribution is as follows, broken down across the data splits of the UD version of NDT, and sorted by total counts (i.e. the number of examples, not tokens within the spans of the annotatons):\n\n\n\nTo access these reduce versions of the dataset, you can use the configs 'bokmaal-7', 'nynorsk-7', 'combined-7' for the NER tag set with 7 tags ( 'ORG', 'LOC', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'), and 'bokmaal-8', 'nynorsk-8', 'combined-8' for the NER tag set with 8 tags ('LOC\\_' and 'ORG\\_': 'ORG', 'LOC', 'GPE', 'PER', 'PROD', 'EVT', 'DRV', 'MISC'). By default, the full set (9 tags) will be used.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nNorNE was created as a collaboration between Schibsted Media Group, Språkbanken at the National Library of Norway and the Language Technology Group at the University of Oslo.\n\n\nNorNE was added to Huggingface Datasets by the AI-Lab at the National Library of Norway.", "### Licensing Information\n\n\nThe NorNE corpus is published under the same license as the Norwegian Dependency Treebank\n\n\nThis dataset is described in the paper *NorNE: Annotating Named Entities for Norwegian* by\nFredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal, accepted for LREC 2020 and available as pre-print here: URL" ]

[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:no", "license:cc-by-4.0", "region:us" ]

[]

[ "no" ]

[ "### Dataset Summary\n\n\nThe Norwegian Parliament Speeches is a collection of text passages from 1998 to 2016 and pronounced at the Norwegian Parliament (Storting) by members of the two major parties: Fremskrittspartiet and Sosialistisk Venstreparti. The dataset is annotated with the party the speaker was associated with at the time (dates of speeches are also included).", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in Norwegian.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nExample of one instance in the dataset.", "### Data Fields\n\n\n* 'id': index of the example\n* 'text': Text of a speech\n* 'date': Date ('YYYY-MM-DD') the speech was produced\n* 'label': Political party the speaker was associated with at the time\n\t+ 0 = Fremskrittspartiet\n\t+ 1 = Sosialistisk Venstreparti", "### Data Splits\n\n\nThe dataset is split into a 'train', 'validation', and 'test' split with the following sizes:\n\n\n\nThe dataset is balanced on political party.\n\n\nDataset Creation\n----------------\n\n\nThis dataset is based on the publicly available information by Norwegian Parliament (Storting) and created by the National Library of Norway AI-Lab to benchmark their language models.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThis work is licensed under a Creative Commons Attribution 4.0 International License" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Norwegian #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nThe Norwegian Parliament Speeches is a collection of text passages from 1998 to 2016 and pronounced at the Norwegian Parliament (Storting) by members of the two major parties: Fremskrittspartiet and Sosialistisk Venstreparti. The dataset is annotated with the party the speaker was associated with at the time (dates of speeches are also included).", "### Supported Tasks and Leaderboards", "### Languages\n\n\nThe text in the dataset is in Norwegian.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nExample of one instance in the dataset.", "### Data Fields\n\n\n* 'id': index of the example\n* 'text': Text of a speech\n* 'date': Date ('YYYY-MM-DD') the speech was produced\n* 'label': Political party the speaker was associated with at the time\n\t+ 0 = Fremskrittspartiet\n\t+ 1 = Sosialistisk Venstreparti", "### Data Splits\n\n\nThe dataset is split into a 'train', 'validation', and 'test' split with the following sizes:\n\n\n\nThe dataset is balanced on political party.\n\n\nDataset Creation\n----------------\n\n\nThis dataset is based on the publicly available information by Norwegian Parliament (Storting) and created by the National Library of Norway AI-Lab to benchmark their language models.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThis work is licensed under a Creative Commons Attribution 4.0 International License" ]

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

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

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

[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2012.15606", "region:us" ]

[ "2012.15606" ]

[ "en" ]

[ "# Dataset Card for HateCheck", "## Dataset Description\n\nHateCheck is a suite of functional test for hate speech detection models. \nThe dataset contains 3,728 validated test cases in 29 functional tests.\n19 functional tests correspond to distinct types of hate. The other 11 functional tests cover challenging types of non-hate.\nThis allows for targeted diagnostic insights into model performance.\n\nIn our ACL paper, we found critical weaknesses in all commercial and academic hate speech detection model that we tested with HateCheck. \nPlease refer to the paper (linked below) for results and further discussion, as well as further information on the dataset and a full data statement.\n\n- Paper: Röttger et al. (2021) - HateCheck: Functional Tests for Hate Speech Detection Model. URL or URL\n- Repository: URL\n- Point of Contact: paul.rottger@URL", "## Dataset Structure\n\n\"URL\" contains all 3,728 validated test cases. Each test case (row) has the following attributes:\n\nfunctionality\nThe shorthand for the functionality tested by the test case.\n\ncase_id\nThe unique ID of the test case (assigned to each of the 3,901 cases we initially generated)\n\ntest_case\nThe text of the test case.\n\nlabel_gold\nThe gold standard label (hateful/non-hateful) of the test case. All test cases within a given functionality have the same gold standard label.\n\ntarget_ident\nWhere applicable, the protected group targeted or referenced by the test case. We cover seven protected groups in the test suite: women, trans people, gay people, black people, disabled people, Muslims and immigrants.\n\ndirection\nFor hateful cases, the binary secondary label indicating whether they are *directed* at an individual as part of a protected group or aimed at the group in *general*.\n\nfocus_words\nWhere applicable, the key word or phrase in a given test case (e.g. \"cut their throats\").\n\nfocus_lemma\nWhere applicable, the corresponding lemma (e.g. \"cut sb. throat\").\n\nref_case_id\nFor hateful cases, where applicable, the ID of the simpler hateful case which was perturbed to generate them.\nFor non-hateful cases, where applicable, the ID of the hateful case which is contrasted.\n\nref_templ_id\nThe equivalent, but for template IDs.\n\ntempl_id\nThe unique ID of the template from which the test case was generated (assigned to each of the 866 cases and templates from which we generated the 3,901 initial cases).\n\n\n\n\nWhen using HateCheck, please cite our ACL paper:\n\n@inproceedings{rottger-etal-2021-hatecheck,\n title = \"{H}ate{C}heck: Functional Tests for Hate Speech Detection Models\",\n author = {R{\\\"o}ttger, Paul and\n Vidgen, Bertie and\n Nguyen, Dong and\n Waseem, Zeerak and\n Margetts, Helen and\n Pierrehumbert, Janet},\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n doi = \"10.18653/v1/URL-long.4\",\n pages = \"41--58\",\n abstract = \"Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck{'}s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.\",\n}" ]

[ "TAGS\n#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2012.15606 #region-us \n", "# Dataset Card for HateCheck", "## Dataset Description\n\nHateCheck is a suite of functional test for hate speech detection models. \nThe dataset contains 3,728 validated test cases in 29 functional tests.\n19 functional tests correspond to distinct types of hate. The other 11 functional tests cover challenging types of non-hate.\nThis allows for targeted diagnostic insights into model performance.\n\nIn our ACL paper, we found critical weaknesses in all commercial and academic hate speech detection model that we tested with HateCheck. \nPlease refer to the paper (linked below) for results and further discussion, as well as further information on the dataset and a full data statement.\n\n- Paper: Röttger et al. (2021) - HateCheck: Functional Tests for Hate Speech Detection Model. URL or URL\n- Repository: URL\n- Point of Contact: paul.rottger@URL", "## Dataset Structure\n\n\"URL\" contains all 3,728 validated test cases. Each test case (row) has the following attributes:\n\nfunctionality\nThe shorthand for the functionality tested by the test case.\n\ncase_id\nThe unique ID of the test case (assigned to each of the 3,901 cases we initially generated)\n\ntest_case\nThe text of the test case.\n\nlabel_gold\nThe gold standard label (hateful/non-hateful) of the test case. All test cases within a given functionality have the same gold standard label.\n\ntarget_ident\nWhere applicable, the protected group targeted or referenced by the test case. We cover seven protected groups in the test suite: women, trans people, gay people, black people, disabled people, Muslims and immigrants.\n\ndirection\nFor hateful cases, the binary secondary label indicating whether they are *directed* at an individual as part of a protected group or aimed at the group in *general*.\n\nfocus_words\nWhere applicable, the key word or phrase in a given test case (e.g. \"cut their throats\").\n\nfocus_lemma\nWhere applicable, the corresponding lemma (e.g. \"cut sb. throat\").\n\nref_case_id\nFor hateful cases, where applicable, the ID of the simpler hateful case which was perturbed to generate them.\nFor non-hateful cases, where applicable, the ID of the hateful case which is contrasted.\n\nref_templ_id\nThe equivalent, but for template IDs.\n\ntempl_id\nThe unique ID of the template from which the test case was generated (assigned to each of the 866 cases and templates from which we generated the 3,901 initial cases).\n\n\n\n\nWhen using HateCheck, please cite our ACL paper:\n\n@inproceedings{rottger-etal-2021-hatecheck,\n title = \"{H}ate{C}heck: Functional Tests for Hate Speech Detection Models\",\n author = {R{\\\"o}ttger, Paul and\n Vidgen, Bertie and\n Nguyen, Dong and\n Waseem, Zeerak and\n Margetts, Helen and\n Pierrehumbert, Janet},\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n doi = \"10.18653/v1/URL-long.4\",\n pages = \"41--58\",\n abstract = \"Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck{'}s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.\",\n}" ]

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[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:es", "license:cc-by-sa-4.0", "arxiv:1606.05250", "region:us" ]

[ "1606.05250" ]

[ "es" ]

[ "### Dataset Summary\n\n\nContains 6,247 contexts and 18,817 questions with their respective answers, 1 to 5 for each fragment.\n\n\nThe sources of the contexts are:\n\n\n* Encyclopedic articles from the Spanish Wikipedia, used under CC-by-sa licence.\n* News articles from Wikinews, used under CC-by licence.\n* Newswire and literature text from the AnCora corpus, used under CC-by licence.", "### Supported Tasks\n\n\nExtractive-QA", "### Languages\n\n\n* Spanish (es)", "### Directory Structure\n\n\n* URL\n* URL\n* URL\n* URL\n* URL\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n\n```\n\n{\n\t'id': '6cf3dcd6-b5a3-4516-8f9e-c5c1c6b66628', \n\t'title': 'Historia de Japón', \n\t'context': 'La historia de Japón (日本の歴史 o 日本史, Nihon no rekishi / Nihonshi?) es la sucesión de hechos acontecidos dentro del archipiélago japonés. Algunos de estos hechos aparecen aislados e influenciados por la naturaleza geográfica de Japón como nación insular, en tanto que otra serie de hechos, obedece a influencias foráneas como en el caso del Imperio chino, el cual definió su idioma, su escritura y, también, su cultura política. Asimismo, otra de las influencias foráneas fue la de origen occidental, lo que convirtió al país en una nación industrial, ejerciendo con ello una esfera de influencia y una expansión territorial sobre el área del Pacífico. No obstante, dicho expansionismo se detuvo tras la Segunda Guerra Mundial y el país se posicionó en un esquema de nación industrial con vínculos a su tradición cultural.', \n\t'question': '¿Qué influencia convirtió Japón en una nación industrial?', \n\t'answers': {\n\t\t'text': ['la de origen occidental'], \n\t\t'answer_start': [473]\n\t}\n}\n\n```", "### Data Fields\n\n\n\n```\n\n{\n id: str\n title: str\n context: str\n question: str\n answers: {\n answer_start: [int]\n text: [str]\n }\n}\n\n```", "### Data Splits\n\n\n\nContent analysis\n----------------", "### Number of articles, paragraphs and questions\n\n\n* Number of articles: 3,834\n* Number of contexts: 6,247\n* Number of questions: 18,817\n* Number of sentences: 48,026\n* Questions/Context ratio: 3.01\n* Sentences/Context ratio: 7.70", "### Number of tokens\n\n\n* Total tokens in context: 1,561,616\n* Average tokens/context: 250\n* Total tokens in questions: 203,235\n* Average tokens/question: 10.80\n* Total tokens in answers: 90,307\n* Average tokens/answer: 4.80", "### Lexical variation\n\n\n46.38% of the words in the Question can be found in the Context.", "### Question type\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFor compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines from SQUAD 1.0 (Rajpurkar, Pranav et al.).", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe source data are scraped articles from Wikinews, the Spanish Wikipedia and the AnCora corpus.\n\n\n* Spanish Wikipedia\n* Spanish Wikinews\n* AnCora corpus", "#### Who are the source language producers?\n\n\nContributors to the aforementioned sites.", "### Annotations", "#### Annotation process\n\n\nWe commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 (Rajpurkar, Pranav et al.).", "#### Who are the annotators?\n\n\nNative language speakers.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis corpus contributes to the development of language models in Spanish.", "### Discussion of Biases\n\n\nNo postprocessing steps were applied to mitigate potential social biases.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL).\n\n\nFor further information, send an email to (plantl-gob-es@URL).\n\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing information\n\n\nThis work is licensed under CC Attribution 4.0 International License.\n\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n\n\n[N/A]" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Spanish #license-cc-by-sa-4.0 #arxiv-1606.05250 #region-us \n", "### Dataset Summary\n\n\nContains 6,247 contexts and 18,817 questions with their respective answers, 1 to 5 for each fragment.\n\n\nThe sources of the contexts are:\n\n\n* Encyclopedic articles from the Spanish Wikipedia, used under CC-by-sa licence.\n* News articles from Wikinews, used under CC-by licence.\n* Newswire and literature text from the AnCora corpus, used under CC-by licence.", "### Supported Tasks\n\n\nExtractive-QA", "### Languages\n\n\n* Spanish (es)", "### Directory Structure\n\n\n* URL\n* URL\n* URL\n* URL\n* URL\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n\n```\n\n{\n\t'id': '6cf3dcd6-b5a3-4516-8f9e-c5c1c6b66628', \n\t'title': 'Historia de Japón', \n\t'context': 'La historia de Japón (日本の歴史 o 日本史, Nihon no rekishi / Nihonshi?) es la sucesión de hechos acontecidos dentro del archipiélago japonés. Algunos de estos hechos aparecen aislados e influenciados por la naturaleza geográfica de Japón como nación insular, en tanto que otra serie de hechos, obedece a influencias foráneas como en el caso del Imperio chino, el cual definió su idioma, su escritura y, también, su cultura política. Asimismo, otra de las influencias foráneas fue la de origen occidental, lo que convirtió al país en una nación industrial, ejerciendo con ello una esfera de influencia y una expansión territorial sobre el área del Pacífico. No obstante, dicho expansionismo se detuvo tras la Segunda Guerra Mundial y el país se posicionó en un esquema de nación industrial con vínculos a su tradición cultural.', \n\t'question': '¿Qué influencia convirtió Japón en una nación industrial?', \n\t'answers': {\n\t\t'text': ['la de origen occidental'], \n\t\t'answer_start': [473]\n\t}\n}\n\n```", "### Data Fields\n\n\n\n```\n\n{\n id: str\n title: str\n context: str\n question: str\n answers: {\n answer_start: [int]\n text: [str]\n }\n}\n\n```", "### Data Splits\n\n\n\nContent analysis\n----------------", "### Number of articles, paragraphs and questions\n\n\n* Number of articles: 3,834\n* Number of contexts: 6,247\n* Number of questions: 18,817\n* Number of sentences: 48,026\n* Questions/Context ratio: 3.01\n* Sentences/Context ratio: 7.70", "### Number of tokens\n\n\n* Total tokens in context: 1,561,616\n* Average tokens/context: 250\n* Total tokens in questions: 203,235\n* Average tokens/question: 10.80\n* Total tokens in answers: 90,307\n* Average tokens/answer: 4.80", "### Lexical variation\n\n\n46.38% of the words in the Question can be found in the Context.", "### Question type\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFor compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines from SQUAD 1.0 (Rajpurkar, Pranav et al.).", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe source data are scraped articles from Wikinews, the Spanish Wikipedia and the AnCora corpus.\n\n\n* Spanish Wikipedia\n* Spanish Wikinews\n* AnCora corpus", "#### Who are the source language producers?\n\n\nContributors to the aforementioned sites.", "### Annotations", "#### Annotation process\n\n\nWe commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 (Rajpurkar, Pranav et al.).", "#### Who are the annotators?\n\n\nNative language speakers.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis corpus contributes to the development of language models in Spanish.", "### Discussion of Biases\n\n\nNo postprocessing steps were applied to mitigate potential social biases.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL).\n\n\nFor further information, send an email to (plantl-gob-es@URL).\n\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing information\n\n\nThis work is licensed under CC Attribution 4.0 International License.\n\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n\n\n[N/A]" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "biomedical", "clinical", "spanish", "region:us" ]

[]

[ "es" ]

[ "### Dataset Summary\n\n\nCollection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).\n\n\nThe training subset contains 501 documents, the development subsets 500, and the test subset 300. The original dataset is distributed in Brat format.\n\n\nThis dataset was designed for the CANcer TExt Mining Shared Task, sponsored by Plan-TL.\n\n\nFor further information, please visit the official website.", "### Supported Tasks\n\n\nNamed Entity Recognition (NER)", "### Languages\n\n\n* Spanish (es)", "### Directory Structure\n\n\n* URL\n* URL\n* URL\n* URL\n* URL\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThree four-column files, one for each split.", "### Data Fields\n\n\nEvery file has 4 columns:\n\n\n* 1st column: Word form or punctuation symbol\n* 2nd column: Original BRAT file name\n* 3rd column: Spans\n* 4th column: IOB tag", "#### Example\n\n\n\n```\n\nEl cc_onco101 662_664 O\ninforme cc_onco101 665_672 O\nHP cc_onco101 673_675 O\nes cc_onco101 676_678 O\ncompatible cc_onco101 679_689 O\ncon cc_onco101 690_693 O\nadenocarcinoma cc_onco101 694_708 B-MORFOLOGIA_NEOPLASIA\nmoderadamente cc_onco101 709_722 I-MORFOLOGIA_NEOPLASIA\ndiferenciado cc_onco101 723_735 I-MORFOLOGIA_NEOPLASIA\nque cc_onco101 736_739 O\nafecta cc_onco101 740_746 O\na cc_onco101 747_748 O\ngrasa cc_onco101 749_754 O\nperipancreática cc_onco101 755_770 O\nsobrepasando cc_onco101 771_783 O\nla cc_onco101 784_786 O\nserosa cc_onco101 787_793 O\n, cc_onco101 793_794 O\ninfiltración cc_onco101 795_807 O\nperineural cc_onco101 808_818 O\n. cc_onco101 818_819 O\n\n```", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFor compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe selected clinical case reports are fairly similar to hospital health records. To increase the usefulness and practical relevance of the CANTEMIST corpus, we selected clinical cases affecting all genders and that comprised most ages (from children to the elderly) and of various complexity levels (solid tumors, hemato-oncological malignancies, neuroendocrine cancer...).\n\n\nThe CANTEMIST cases include clinical signs and symptoms, personal and family history, current illness, physical examination, complementary tests (blood tests, imaging, pathology), diagnosis, treatment (including adverse effects of chemotherapy), evolution and outcome.", "#### Who are the source language producers?\n\n\nHumans, there is no machine generated data.", "### Annotations", "#### Annotation process\n\n\nThe manual annotation of the Cantemist corpus was performed by clinical experts following the Cantemist guidelines (for more detail refer to this paper). These guidelines contain rules for annotating morphology neoplasms in Spanish oncology clinical cases, as well as for mapping these annotations to eCIE-O.\n\n\nA medical doctor was regularly consulted by annotators (scientists with PhDs on cancer-related subjects) for the most difficult pathology expressions. This same doctor periodically checked a random selection of annotated clinical records and these annotations were compared and discussed with the annotators. To normalize a selection of very complex cases, MD specialists in pathology from one of the largest university hospitals in Spain were consulted.", "#### Who are the annotators?\n\n\nClinical experts.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis corpus contributes to the development of medical language models in Spanish.", "### Discussion of Biases\n\n\nNot applicable.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL).\n\n\nFor further information, send an email to (plantl-gob-es@URL).\n\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing information\n\n\nThis work is licensed under CC Attribution 4.0 International License.\n\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n\n\n[N/A]" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #biomedical #clinical #spanish #region-us \n", "### Dataset Summary\n\n\nCollection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).\n\n\nThe training subset contains 501 documents, the development subsets 500, and the test subset 300. The original dataset is distributed in Brat format.\n\n\nThis dataset was designed for the CANcer TExt Mining Shared Task, sponsored by Plan-TL.\n\n\nFor further information, please visit the official website.", "### Supported Tasks\n\n\nNamed Entity Recognition (NER)", "### Languages\n\n\n* Spanish (es)", "### Directory Structure\n\n\n* URL\n* URL\n* URL\n* URL\n* URL\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThree four-column files, one for each split.", "### Data Fields\n\n\nEvery file has 4 columns:\n\n\n* 1st column: Word form or punctuation symbol\n* 2nd column: Original BRAT file name\n* 3rd column: Spans\n* 4th column: IOB tag", "#### Example\n\n\n\n```\n\nEl cc_onco101 662_664 O\ninforme cc_onco101 665_672 O\nHP cc_onco101 673_675 O\nes cc_onco101 676_678 O\ncompatible cc_onco101 679_689 O\ncon cc_onco101 690_693 O\nadenocarcinoma cc_onco101 694_708 B-MORFOLOGIA_NEOPLASIA\nmoderadamente cc_onco101 709_722 I-MORFOLOGIA_NEOPLASIA\ndiferenciado cc_onco101 723_735 I-MORFOLOGIA_NEOPLASIA\nque cc_onco101 736_739 O\nafecta cc_onco101 740_746 O\na cc_onco101 747_748 O\ngrasa cc_onco101 749_754 O\nperipancreática cc_onco101 755_770 O\nsobrepasando cc_onco101 771_783 O\nla cc_onco101 784_786 O\nserosa cc_onco101 787_793 O\n, cc_onco101 793_794 O\ninfiltración cc_onco101 795_807 O\nperineural cc_onco101 808_818 O\n. cc_onco101 818_819 O\n\n```", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFor compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe selected clinical case reports are fairly similar to hospital health records. To increase the usefulness and practical relevance of the CANTEMIST corpus, we selected clinical cases affecting all genders and that comprised most ages (from children to the elderly) and of various complexity levels (solid tumors, hemato-oncological malignancies, neuroendocrine cancer...).\n\n\nThe CANTEMIST cases include clinical signs and symptoms, personal and family history, current illness, physical examination, complementary tests (blood tests, imaging, pathology), diagnosis, treatment (including adverse effects of chemotherapy), evolution and outcome.", "#### Who are the source language producers?\n\n\nHumans, there is no machine generated data.", "### Annotations", "#### Annotation process\n\n\nThe manual annotation of the Cantemist corpus was performed by clinical experts following the Cantemist guidelines (for more detail refer to this paper). These guidelines contain rules for annotating morphology neoplasms in Spanish oncology clinical cases, as well as for mapping these annotations to eCIE-O.\n\n\nA medical doctor was regularly consulted by annotators (scientists with PhDs on cancer-related subjects) for the most difficult pathology expressions. This same doctor periodically checked a random selection of annotated clinical records and these annotations were compared and discussed with the annotators. To normalize a selection of very complex cases, MD specialists in pathology from one of the largest university hospitals in Spain were consulted.", "#### Who are the annotators?\n\n\nClinical experts.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis corpus contributes to the development of medical language models in Spanish.", "### Discussion of Biases\n\n\nNot applicable.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL).\n\n\nFor further information, send an email to (plantl-gob-es@URL).\n\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing information\n\n\nThis work is licensed under CC Attribution 4.0 International License.\n\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n\n\n[N/A]" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "biomedical", "clinical", "spanish", "region:us" ]

[]

[ "es" ]

[ "### Dataset Summary\n\n\nManually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO.\n\n\nThe PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets.\nThe training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.\nIn terms of training examples, this translates to a total of 8129, 3787 and 3952 annotated sentences in each set.\nThe original dataset is distributed in Brat format.\n\n\nThe annotation of the entire set of entity mentions was carried out by domain experts.\nIt includes the following 4 entity types: NORMALIZABLES, NO\\_NORMALIZABLES, PROTEINAS and UNCLEAR.\n\n\nThis dataset was designed for the PharmaCoNER task, sponsored by Plan-TL.\n\n\nFor further information, please visit the official website.", "### Supported Tasks\n\n\nNamed Entity Recognition (NER)", "### Languages\n\n\n* Spanish (es)", "### Directory Structure\n\n\n* URL\n* URL\n* dev-set\\_1.1.conll\n* test-set\\_1.1.conll\n* train-set\\_1.1.conll\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThree four-column files, one for each split.", "### Data Fields\n\n\nEvery file has four columns:\n\n\n* 1st column: Word form or punctuation symbol\n* 2nd column: Original BRAT file name\n* 3rd column: Spans\n* 4th column: IOB tag", "#### Example\n\n\n\n```\n\nLa S0004-06142006000900008-1 123_125 O\npaciente S0004-06142006000900008-1 126_134 O\ntenía S0004-06142006000900008-1 135_140 O\nantecedentes S0004-06142006000900008-1 141_153 O\nde S0004-06142006000900008-1 154_156 O\nhipotiroidismo S0004-06142006000900008-1 157_171 O\n, S0004-06142006000900008-1 171_172 O\nhipertensión S0004-06142006000900008-1 173_185 O\narterial S0004-06142006000900008-1 186_194 O\nen S0004-06142006000900008-1 195_197 O\ntratamiento S0004-06142006000900008-1 198_209 O\nhabitual S0004-06142006000900008-1 210_218 O\ncon S0004-06142006000900008-1 219-222 O\natenolol S0004-06142006000900008-1 223_231 B-NORMALIZABLES\ny S0004-06142006000900008-1 232_233 O\nenalapril S0004-06142006000900008-1 234_243 B-NORMALIZABLES\n\n```", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFor compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nManually classified collection of clinical case report sections. The clinical cases were not restricted to a single medical discipline, covering a variety of medical disciplines, including oncology, urology, cardiology, pneumology or infectious diseases. This is key to cover a diverse set of chemicals and drugs.", "#### Who are the source language producers?\n\n\nHumans, there is no machine generated data.", "### Annotations", "#### Annotation process\n\n\nThe annotation process of the PharmaCoNER corpus was inspired by previous annotation schemes and corpora used for the BioCreative CHEMDNER and GPRO tracks, translating the guidelines used for these tracks into Spanish and adapting them to the characteristics and needs of clinically oriented documents by modifying the annotation criteria and rules to cover medical information needs. This adaptation was carried out in collaboration with practicing physicians and medicinal chemistry experts. The adaptation, translation and refinement of the guidelines was done on a sample set of the SPACCC corpus and linked to an iterative process of annotation consistency analysis through interannotator agreement (IAA) studies until a high annotation quality in terms of IAA was reached.", "#### Who are the annotators?\n\n\nPracticing physicians and medicinal chemistry experts.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis corpus contributes to the development of medical language models in Spanish.", "### Discussion of Biases\n\n\n[N/A]\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL).\n\n\nFor further information, send an email to (plantl-gob-es@URL).\n\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing information\n\n\nThis work is licensed under CC Attribution 4.0 International License.\n\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n\n\n[N/A]" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #biomedical #clinical #spanish #region-us \n", "### Dataset Summary\n\n\nManually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO.\n\n\nThe PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets.\nThe training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.\nIn terms of training examples, this translates to a total of 8129, 3787 and 3952 annotated sentences in each set.\nThe original dataset is distributed in Brat format.\n\n\nThe annotation of the entire set of entity mentions was carried out by domain experts.\nIt includes the following 4 entity types: NORMALIZABLES, NO\\_NORMALIZABLES, PROTEINAS and UNCLEAR.\n\n\nThis dataset was designed for the PharmaCoNER task, sponsored by Plan-TL.\n\n\nFor further information, please visit the official website.", "### Supported Tasks\n\n\nNamed Entity Recognition (NER)", "### Languages\n\n\n* Spanish (es)", "### Directory Structure\n\n\n* URL\n* URL\n* dev-set\\_1.1.conll\n* test-set\\_1.1.conll\n* train-set\\_1.1.conll\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThree four-column files, one for each split.", "### Data Fields\n\n\nEvery file has four columns:\n\n\n* 1st column: Word form or punctuation symbol\n* 2nd column: Original BRAT file name\n* 3rd column: Spans\n* 4th column: IOB tag", "#### Example\n\n\n\n```\n\nLa S0004-06142006000900008-1 123_125 O\npaciente S0004-06142006000900008-1 126_134 O\ntenía S0004-06142006000900008-1 135_140 O\nantecedentes S0004-06142006000900008-1 141_153 O\nde S0004-06142006000900008-1 154_156 O\nhipotiroidismo S0004-06142006000900008-1 157_171 O\n, S0004-06142006000900008-1 171_172 O\nhipertensión S0004-06142006000900008-1 173_185 O\narterial S0004-06142006000900008-1 186_194 O\nen S0004-06142006000900008-1 195_197 O\ntratamiento S0004-06142006000900008-1 198_209 O\nhabitual S0004-06142006000900008-1 210_218 O\ncon S0004-06142006000900008-1 219-222 O\natenolol S0004-06142006000900008-1 223_231 B-NORMALIZABLES\ny S0004-06142006000900008-1 232_233 O\nenalapril S0004-06142006000900008-1 234_243 B-NORMALIZABLES\n\n```", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nFor compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nManually classified collection of clinical case report sections. The clinical cases were not restricted to a single medical discipline, covering a variety of medical disciplines, including oncology, urology, cardiology, pneumology or infectious diseases. This is key to cover a diverse set of chemicals and drugs.", "#### Who are the source language producers?\n\n\nHumans, there is no machine generated data.", "### Annotations", "#### Annotation process\n\n\nThe annotation process of the PharmaCoNER corpus was inspired by previous annotation schemes and corpora used for the BioCreative CHEMDNER and GPRO tracks, translating the guidelines used for these tracks into Spanish and adapting them to the characteristics and needs of clinically oriented documents by modifying the annotation criteria and rules to cover medical information needs. This adaptation was carried out in collaboration with practicing physicians and medicinal chemistry experts. The adaptation, translation and refinement of the guidelines was done on a sample set of the SPACCC corpus and linked to an iterative process of annotation consistency analysis through interannotator agreement (IAA) studies until a high annotation quality in terms of IAA was reached.", "#### Who are the annotators?\n\n\nPracticing physicians and medicinal chemistry experts.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThis corpus contributes to the development of medical language models in Spanish.", "### Discussion of Biases\n\n\n[N/A]\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL).\n\n\nFor further information, send an email to (plantl-gob-es@URL).\n\n\nThis work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.", "### Licensing information\n\n\nThis work is licensed under CC Attribution 4.0 International License.\n\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n\n\n[N/A]" ]

[ "region:us" ]

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

[ "region:us" ]

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[ "# Dataset for annotation", "## Files and their description\n\n1.\tcombined_all_stats_csv \n\t- This is the CSV File containing statistics for the patent dataset \n\t\t> can be downloaded from the Git Repo.\n\t- What are the statistics it shows?\t\n\t\t> This file contains columns such as filename, total publications, total positive samples, total negative samples, and total neutral samples per every week of the year from 2010-2020. \n\n2. combined_neut_data_csv \n\t- This is the CSV File, that contains details regarding neutral samples \n\t\t> Can be downloaded from here \t\n\t- What data this file contains?\n\t\t> This file contains columns such as publication_number, patent_title, appl_type, and neutral_text for each patents.\t\n\n3. combined_pos_data_csv \n\t- This is the CSV File, that contains details regarding positive samples \n\t\t> Can be downloaded from here. \t\n\t- What data this file contains?\n\t\t> This file contains columns such as publication_number, patent_title, appl_type, and positive_text for each patent.\t \n\n4. combined_neg_data_csv \n\t- This is the CSV File, that contains details regarding negative samples \n\t\t> Can be downloaded from here \t\n\t- What data this file contains?\n\t\t> This file contains columns such as publication_number, patent_title, appl_type, and negative_text for each patent.\t \n\n5. combined_data_150k\n\t- This is the CSV File, that contains 150k cleaned data samples(50k from each pos, neg, and neut) required to train models. \n\t\t> Can be downloaded from here \t\n\t- What data this file contains?\n\t\t> This file contains two columns text and target. \n\t\t> Text can be neutral, positive, or negative. \n\t\t> Target can be label 0, 1, 2 for neutral, positive, and negative respectively." ]

[ "TAGS\n#region-us \n", "# Dataset for annotation", "## Files and their description\n\n1.\tcombined_all_stats_csv \n\t- This is the CSV File containing statistics for the patent dataset \n\t\t> can be downloaded from the Git Repo.\n\t- What are the statistics it shows?\t\n\t\t> This file contains columns such as filename, total publications, total positive samples, total negative samples, and total neutral samples per every week of the year from 2010-2020. \n\n2. combined_neut_data_csv \n\t- This is the CSV File, that contains details regarding neutral samples \n\t\t> Can be downloaded from here \t\n\t- What data this file contains?\n\t\t> This file contains columns such as publication_number, patent_title, appl_type, and neutral_text for each patents.\t\n\n3. combined_pos_data_csv \n\t- This is the CSV File, that contains details regarding positive samples \n\t\t> Can be downloaded from here. \t\n\t- What data this file contains?\n\t\t> This file contains columns such as publication_number, patent_title, appl_type, and positive_text for each patent.\t \n\n4. combined_neg_data_csv \n\t- This is the CSV File, that contains details regarding negative samples \n\t\t> Can be downloaded from here \t\n\t- What data this file contains?\n\t\t> This file contains columns such as publication_number, patent_title, appl_type, and negative_text for each patent.\t \n\n5. combined_data_150k\n\t- This is the CSV File, that contains 150k cleaned data samples(50k from each pos, neg, and neut) required to train models. \n\t\t> Can be downloaded from here \t\n\t- What data this file contains?\n\t\t> This file contains two columns text and target. \n\t\t> Text can be neutral, positive, or negative. \n\t\t> Target can be label 0, 1, 2 for neutral, positive, and negative respectively." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "source_datasets:original", "language:fa", "region:us" ]

[]

[ "fa" ]

[ "# Persian Blog", "# Dataset Summary\n\npersian_news_dataset is a collection of 400k blog posts. these posts have been gathered from more than 10 websites. This dataset can be used in different NLP tasks like language modeling and text generation tasks.\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #source_datasets-original #language-Persian #region-us \n", "# Persian Blog", "# Dataset Summary\n\npersian_news_dataset is a collection of 400k blog posts. these posts have been gathered from more than 10 websites. This dataset can be used in different NLP tasks like language modeling and text generation tasks.\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ "source_datasets:original", "language:fa", "region:us" ]

[]

[ "fa" ]

[ "# Persian Daily News", "# Dataset Summary\n\npersian_daily_news is a collection of 2 million of unique news articles with the headline for each article. dataset can be used in abstractive summarization and paraphrasing tasks.\n\nThis effort is part of a bigger perspective to have several datasets in Persian language(and other low resources languages) for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 2M news articles. Each article has these two attributes: text and summary. Here is a sample of dataset:" ]

[ "TAGS\n#source_datasets-original #language-Persian #region-us \n", "# Persian Daily News", "# Dataset Summary\n\npersian_daily_news is a collection of 2 million of unique news articles with the headline for each article. dataset can be used in abstractive summarization and paraphrasing tasks.\n\nThis effort is part of a bigger perspective to have several datasets in Persian language(and other low resources languages) for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 2M news articles. Each article has these two attributes: text and summary. Here is a sample of dataset:" ]

[ "task_categories:text-classification", "task_ids:language-modeling", "task_ids:multi-class-classification", "source_datasets:original", "language:fa", "region:us" ]

[]

[ "fa" ]

[ "# Persian_News_Dataset", "# Dataset Summary\n\npersian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. This dataset can be used in different NLP tasks like language modeling, classification, supervised topic modeling,...\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ "TAGS\n#task_categories-text-classification #task_ids-language-modeling #task_ids-multi-class-classification #source_datasets-original #language-Persian #region-us \n", "# Persian_News_Dataset", "# Dataset Summary\n\npersian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. This dataset can be used in different NLP tasks like language modeling, classification, supervised topic modeling,...\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ "region:us" ]

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[]

[ "### Access/Download\n\n\n* You can find the data under the 'dataset/' directory. and use it like this\n* Alternatively, you can also access the data through the HuggingFace datasets library\n\n\n\t+ First, you need to install datasets using this command in your terminal:\n* Then import 'persian\\_qa' dataset using 'load\\_dataset':", "### Examples", "### Statistic\n\n\n\nThe lengths are on the token level.\n\n\n* for more about data and more example see here\n\n\nModels\n------\n\n\nCurrently, two models (baseline) on HuggingFace model hub are using the dataset.\n\n\nYet, we didn't publish any papers on the work.\nHowever, if you did, please cite us properly with an entry like one below." ]

[ "TAGS\n#region-us \n", "### Access/Download\n\n\n* You can find the data under the 'dataset/' directory. and use it like this\n* Alternatively, you can also access the data through the HuggingFace datasets library\n\n\n\t+ First, you need to install datasets using this command in your terminal:\n* Then import 'persian\\_qa' dataset using 'load\\_dataset':", "### Examples", "### Statistic\n\n\n\nThe lengths are on the token level.\n\n\n* for more about data and more example see here\n\n\nModels\n------\n\n\nCurrently, two models (baseline) on HuggingFace model hub are using the dataset.\n\n\nYet, we didn't publish any papers on the work.\nHowever, if you did, please cite us properly with an entry like one below." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "language_creators:other", "multilinguality:monolingual", "source_datasets:extended|oscar", "source_datasets:extended|cc100", "language:ne", "license:cc0-1.0", "region:us" ]

[]

[ "ne" ]

[ "### Dataset Summary\n\n\n\"NepaliText\" language modeling dataset is a collection of over 13 million Nepali text sequences (phrases/sentences/paragraphs) extracted by combining the datasets: OSCAR , cc100 and a set of scraped Nepali articles on Wikipedia.", "### Supported Tasks and Leaderboards\n\n\nThis dataset is intended to pre-train language models and word representations on Nepali Language.", "### Languages\n\n\nThe data is focused on Nepali language, but may have instances of other languages as well.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example:", "### Data Fields\n\n\nThe data fields are:\n\n\n* 'text': 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\n\n\nThe dataset does not contain any additional annotations.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nBeing extracted and scraped from variety of internet sources, Personal and sensitive information might be present. This must be considered before training deep learning models, specially in the case of text-generation models.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @Sakonii for adding this dataset." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #language_creators-other #multilinguality-monolingual #source_datasets-extended|oscar #source_datasets-extended|cc100 #language-Nepali (macrolanguage) #license-cc0-1.0 #region-us \n", "### Dataset Summary\n\n\n\"NepaliText\" language modeling dataset is a collection of over 13 million Nepali text sequences (phrases/sentences/paragraphs) extracted by combining the datasets: OSCAR , cc100 and a set of scraped Nepali articles on Wikipedia.", "### Supported Tasks and Leaderboards\n\n\nThis dataset is intended to pre-train language models and word representations on Nepali Language.", "### Languages\n\n\nThe data is focused on Nepali language, but may have instances of other languages as well.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example:", "### Data Fields\n\n\nThe data fields are:\n\n\n* 'text': 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\n\n\nThe dataset does not contain any additional annotations.", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nBeing extracted and scraped from variety of internet sources, Personal and sensitive information might be present. This must be considered before training deep learning models, specially in the case of text-generation models.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @Sakonii for adding this dataset." ]

[ "region:us" ]

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[]

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

[ "region:us" ]

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[]

[]

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

[ "task_categories:text-classification", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:unknown", "region:us" ]

[]

[ "en-US" ]

[ "# Dataset Card for GitHub Issues", "## Dataset Description\nthis was an example dataset made from the huggingface course" ]

[ "TAGS\n#task_categories-text-classification #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #region-us \n", "# Dataset Card for GitHub Issues", "## Dataset Description\nthis was an example dataset made from the huggingface course" ]

[ "region:us" ]

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[]

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

[ "region:us" ]

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[ "# TREC Question Classification\r\n\r\nQuestion classification in coarse and fine-grained categories.\r\n\r\nSource: \r\n\r\nExperimental Data for Question Classification\r\n\r\nXin Li, Dan Roth, Learning Question Classifiers. COLING'02, Aug., 2002." ]

[ "TAGS\n#region-us \n", "# TREC Question Classification\r\n\r\nQuestion classification in coarse and fine-grained categories.\r\n\r\nSource: \r\n\r\nExperimental Data for Question Classification\r\n\r\nXin Li, Dan Roth, Learning Question Classifiers. COLING'02, Aug., 2002." ]

[ "arxiv:2104.06893", "region:us" ]

[ "2104.06893" ]

[]

[ "# Amazon Multilingual Counterfactual Dataset \n\nThe dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form – If p was true, then q would be true (i.e. assertions whose antecedent (p) and consequent (q) are known or assumed to be false).\n\nThe key features of this dataset are:\n\n* The dataset is multilingual and contains sentences in English, German, and Japanese.\n* The labeling was done by professional linguists and high quality was ensured.\n* The dataset is supplemented with the annotation guidelines and definitions, which were worked out by professional linguists. We also provide the clue word lists, which are typical for counterfactual sentences and were used for initial data filtering. The clue word lists were also compiled by professional linguists.\n\nPlease see the paper for the data statistics, detailed description of data collection and annotation.\n\n\nGitHub repo URL: URL", "## Usage\n\nYou can load each of the languages as follows:" ]

[ "TAGS\n#arxiv-2104.06893 #region-us \n", "# Amazon Multilingual Counterfactual Dataset \n\nThe dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form – If p was true, then q would be true (i.e. assertions whose antecedent (p) and consequent (q) are known or assumed to be false).\n\nThe key features of this dataset are:\n\n* The dataset is multilingual and contains sentences in English, German, and Japanese.\n* The labeling was done by professional linguists and high quality was ensured.\n* The dataset is supplemented with the annotation guidelines and definitions, which were worked out by professional linguists. We also provide the clue word lists, which are typical for counterfactual sentences and were used for initial data filtering. The clue word lists were also compiled by professional linguists.\n\nPlease see the paper for the data statistics, detailed description of data collection and annotation.\n\n\nGitHub repo URL: URL", "## Usage\n\nYou can load each of the languages as follows:" ]

[ "arxiv:2104.06893", "region:us" ]

[ "2104.06893" ]

[]

[ "# Amazon Counterfactual Statements\r\n\r\nThis dataset is the *en-ext* split from SetFit/amazon_counterfactual. As the original test set is rather small (1333 examples), a different split was created with 50-50 for training & testing.\r\n\r\nThe dataset is described in amazon-multilingual-counterfactual-dataset / Paper\r\n\r\nIt contains statements from Amazon reviews about events that did not or cannot take place." ]

[ "TAGS\n#arxiv-2104.06893 #region-us \n", "# Amazon Counterfactual Statements\r\n\r\nThis dataset is the *en-ext* split from SetFit/amazon_counterfactual. As the original test set is rather small (1333 examples), a different split was created with 50-50 for training & testing.\r\n\r\nThe dataset is described in amazon-multilingual-counterfactual-dataset / Paper\r\n\r\nIt contains statements from Amazon reviews about events that did not or cannot take place." ]

[ "region:us" ]

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[ "# BBC News Topic Classification\r\n\r\nDataset on BBC News Topic Classification: 2225 articles, each labeled under one of 5 categories: business, entertainment, politics, sport or tech." ]

[ "TAGS\n#region-us \n", "# BBC News Topic Classification\r\n\r\nDataset on BBC News Topic Classification: 2225 articles, each labeled under one of 5 categories: business, entertainment, politics, sport or tech." ]

[ "region:us" ]

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

[ "region:us" ]

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

[ "region:us" ]

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[ "# Ethos\n\nThis dataset is a clone of the official 'ethos' dataset on the Hub. It contains both 'binary' and 'multilabel' subsets." ]

[ "TAGS\n#region-us \n", "# Ethos\n\nThis dataset is a clone of the official 'ethos' dataset on the Hub. It contains both 'binary' and 'multilabel' subsets." ]

[ "region:us" ]

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

[ "region:us" ]

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[ "# GoEmotions\n\nThis dataset is a port of the official 'go_emotions' dataset on the Hub. It only contains the 'simplified' subset as these are the only fields we need for text classification." ]

[ "TAGS\n#region-us \n", "# GoEmotions\n\nThis dataset is a port of the official 'go_emotions' dataset on the Hub. It only contains the 'simplified' subset as these are the only fields we need for text classification." ]

[ "region:us" ]

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[ "# hate_speech_offensive\r\nThis dataset is a version from hate_speech_offensive, splitted into train and test set." ]

[ "TAGS\n#region-us \n", "# hate_speech_offensive\r\nThis dataset is a version from hate_speech_offensive, splitted into train and test set." ]

[ "region:us" ]

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[]

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

[ "region:us" ]

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[ "# Glue MNLI\r\n\r\nThis dataset is a port of the official 'mnli' dataset on the Hub. \r\nIt contains the matched version.\r\nNote that the premise and hypothesis columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue MNLI\r\n\r\nThis dataset is a port of the official 'mnli' dataset on the Hub. \r\nIt contains the matched version.\r\nNote that the premise and hypothesis columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

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[ "# Glue MNLI\r\n\r\nThis dataset is a port of the official 'mnli' dataset on the Hub. \r\nIt contains the mismatched version.\r\nNote that the premise and hypothesis columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue MNLI\r\n\r\nThis dataset is a port of the official 'mnli' dataset on the Hub. \r\nIt contains the mismatched version.\r\nNote that the premise and hypothesis columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

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[ "# Glue MRPC\r\n\r\nThis dataset is a port of the official 'mrpc' dataset on the Hub. \r\nNote that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue MRPC\r\n\r\nThis dataset is a port of the official 'mrpc' dataset on the Hub. \r\nNote that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

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[ "# Glue QNLI\r\n\r\nThis dataset is a port of the official 'qnli' dataset on the Hub. \r\nNote that the question and sentence columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue QNLI\r\n\r\nThis dataset is a port of the official 'qnli' dataset on the Hub. \r\nNote that the question and sentence columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

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[ "# Glue QQP\r\n\r\nThis dataset is a port of the official 'qqp' dataset on the Hub. \r\nNote that the question1 and question2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue QQP\r\n\r\nThis dataset is a port of the official 'qqp' dataset on the Hub. \r\nNote that the question1 and question2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

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[ "# Glue RTE\r\n\r\nThis dataset is a port of the official 'rte' dataset on the Hub. \r\nNote that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue RTE\r\n\r\nThis dataset is a port of the official 'rte' dataset on the Hub. \r\nNote that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

[]

[]

[ "# Stanford Sentiment Treebank - Binary\r\n\r\nStanford Sentiment Treebank with 2 labels: negative, positive\r\n\r\nSplits are from: \r\nURL\r\n\r\nTraining data is on sentence level, not on phrase level!" ]

[ "TAGS\n#region-us \n", "# Stanford Sentiment Treebank - Binary\r\n\r\nStanford Sentiment Treebank with 2 labels: negative, positive\r\n\r\nSplits are from: \r\nURL\r\n\r\nTraining data is on sentence level, not on phrase level!" ]

[ "region:us" ]

[]

[]

[ "# Stanford Sentiment Treebank - Fine-Grained\r\n\r\nStanford Sentiment Treebank with 5 labels: very positive, positive, neutral, negative, very negative\r\n\r\nSplits are from: \r\nURL\r\n\r\nTraining data is on sentence level, not on phrase level!" ]

[ "TAGS\n#region-us \n", "# Stanford Sentiment Treebank - Fine-Grained\r\n\r\nStanford Sentiment Treebank with 5 labels: very positive, positive, neutral, negative, very negative\r\n\r\nSplits are from: \r\nURL\r\n\r\nTraining data is on sentence level, not on phrase level!" ]

[ "region:us" ]

[]

[]

[ "# Glue STS-B\r\n\r\nThis dataset is a port of the official 'sts-b' dataset on the Hub.\r\nThis is not a classification task, so the label_text column is only included for consistency \r\nNote that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue STS-B\r\n\r\nThis dataset is a port of the official 'sts-b' dataset on the Hub.\r\nThis is not a classification task, so the label_text column is only included for consistency \r\nNote that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Subjective vs Objective\r\n\r\nThis is the SUBJ dataset as used in SentEval. It contains sentences with an annotation if they sentence describes something subjective about a movie or something objective" ]

[ "TAGS\n#region-us \n", "# Subjective vs Objective\r\n\r\nThis is the SUBJ dataset as used in SentEval. It contains sentences with an annotation if they sentence describes something subjective about a movie or something objective" ]

[ "region:us" ]

[]

[]

[ "# Toxic Conversation\r\nThis is a version of the Jigsaw Unintended Bias in Toxicity Classification dataset. It contains comments from the Civil Comments platform together with annotations if the comment is toxic or not.\r\n\r\n10 annotators annotated each example and, as recommended in the task page, set a comment as toxic when target >= 0.5\r\n\r\nThe dataset is inbalanced, with only about 8% of the comments marked as toxic." ]

[ "TAGS\n#region-us \n", "# Toxic Conversation\r\nThis is a version of the Jigsaw Unintended Bias in Toxicity Classification dataset. It contains comments from the Civil Comments platform together with annotations if the comment is toxic or not.\r\n\r\n10 annotators annotated each example and, as recommended in the task page, set a comment as toxic when target >= 0.5\r\n\r\nThe dataset is inbalanced, with only about 8% of the comments marked as toxic." ]

[ "region:us" ]

[]

[]

[ "# tweet_eval_stance_abortion\r\n\r\nThis is the stance_abortion subset of tweet_eval" ]

[ "TAGS\n#region-us \n", "# tweet_eval_stance_abortion\r\n\r\nThis is the stance_abortion subset of tweet_eval" ]

[ "region:us" ]

[]

[]

[ "# Tweet Sentiment Extraction\n\nSource: URL" ]

[ "TAGS\n#region-us \n", "# Tweet Sentiment Extraction\n\nSource: URL" ]

[ "region:us" ]

[]

[]

[ "# Glue WNLI\r\n\r\nThis dataset is a port of the official 'wnli' dataset on the Hub. \r\nNote that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "TAGS\n#region-us \n", "# Glue WNLI\r\n\r\nThis dataset is a port of the official 'wnli' dataset on the Hub. \r\nNote that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.\r\nAlso, the test split is not labeled; the label column values are always -1." ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ja", "license:cc-by-sa-3.0", "arxiv:2202.01764", "region:us" ]

[ "2202.01764" ]

[ "ja" ]

[ "### Dataset Summary\n\n\nJapanese Question Answering Dataset (JaQuAD), released in 2022, is a\nhuman-annotated dataset created for Japanese Machine Reading Comprehension.\nJaQuAD is developed to provide a SQuAD-like QA dataset in Japanese.\nJaQuAD contains 39,696 question-answer pairs.\nQuestions and answers are manually curated by human annotators.\nContexts are collected from Japanese Wikipedia articles.\nFine-tuning BERT-Japanese\non JaQuAD achieves 78.92% for an F1 score and 63.38% for an exact match.", "### Supported Tasks\n\n\n* 'extractive-qa': This dataset is intended to be used for 'extractive-qa'.", "### Languages\n\n\nJapanese ('ja')\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of dataset files: 24.6 MB\n* Size of the generated dataset: 48.6 MB\n* Total amount of disk used: 73.2 MB\n\n\nAn example of 'validation':", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'question\\_type': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'answer\\_type': a 'string' feature.", "### Data Splitting\n\n\nJaQuAD consists of three sets, 'train', 'validation', and 'test'. They were\ncreated from disjoint sets of Wikipedia articles. The 'test' set is not publicly\nreleased yet. The following table shows statistics for each set.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe JaQuAD dataset was created by Skelter Labs to\nprovide a SQuAD-like QA dataset in Japanese. Questions are original and based\non Japanese Wikipedia articles.", "### Source Data\n\n\nThe articles used for the contexts are from Japanese Wikipedia.\n88.7% of articles are from the curated list of Japanese high-quality Wikipedia\narticles, e.g., featured articles\nand good articles.", "### Annotations\n\n\nWikipedia articles were scrapped and divided into one more multiple paragraphs\nas contexts. Annotations (questions and answer spans) are written by fluent\nJapanese speakers, including natives and non-natives. Annotators were given a\ncontext and asked to generate non-trivial questions about information in the\ncontext.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information is included in this dataset. Dataset\nannotators has been manually verified it.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nUsers should consider that the articles are sampled from Wikipedia articles but\nnot representative of all Wikipedia articles.", "### Social Impact of Dataset\n\n\nThe social biases of this dataset have not yet been investigated.", "### Discussion of Biases\n\n\nThe social biases of this dataset have not yet been investigated. Articles and\nquestions have been selected for quality and diversity.", "### Other Known Limitations\n\n\nThe JaQuAD dataset has limitations as follows:\n\n\n* Most of them are short answers.\n* Assume that a question is answerable using the corresponding context.\n\n\nThis dataset is incomplete yet. If you find any errors in JaQuAD, please contact\nus.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nSkelter Labs: URL", "### Licensing Information\n\n\nThe JaQuAD dataset is licensed under the CC BY-SA 3.0 license.", "### Acknowledgements\n\n\nThis work was supported by TPU Research Cloud (TRC) program.\nFor training models, we used cloud TPUs provided by TRC. We also thank\nannotators who generated JaQuAD." ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Japanese #license-cc-by-sa-3.0 #arxiv-2202.01764 #region-us \n", "### Dataset Summary\n\n\nJapanese Question Answering Dataset (JaQuAD), released in 2022, is a\nhuman-annotated dataset created for Japanese Machine Reading Comprehension.\nJaQuAD is developed to provide a SQuAD-like QA dataset in Japanese.\nJaQuAD contains 39,696 question-answer pairs.\nQuestions and answers are manually curated by human annotators.\nContexts are collected from Japanese Wikipedia articles.\nFine-tuning BERT-Japanese\non JaQuAD achieves 78.92% for an F1 score and 63.38% for an exact match.", "### Supported Tasks\n\n\n* 'extractive-qa': This dataset is intended to be used for 'extractive-qa'.", "### Languages\n\n\nJapanese ('ja')\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of dataset files: 24.6 MB\n* Size of the generated dataset: 48.6 MB\n* Total amount of disk used: 73.2 MB\n\n\nAn example of 'validation':", "### Data Fields\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'question\\_type': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'answer\\_type': a 'string' feature.", "### Data Splitting\n\n\nJaQuAD consists of three sets, 'train', 'validation', and 'test'. They were\ncreated from disjoint sets of Wikipedia articles. The 'test' set is not publicly\nreleased yet. The following table shows statistics for each set.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe JaQuAD dataset was created by Skelter Labs to\nprovide a SQuAD-like QA dataset in Japanese. Questions are original and based\non Japanese Wikipedia articles.", "### Source Data\n\n\nThe articles used for the contexts are from Japanese Wikipedia.\n88.7% of articles are from the curated list of Japanese high-quality Wikipedia\narticles, e.g., featured articles\nand good articles.", "### Annotations\n\n\nWikipedia articles were scrapped and divided into one more multiple paragraphs\nas contexts. Annotations (questions and answer spans) are written by fluent\nJapanese speakers, including natives and non-natives. Annotators were given a\ncontext and asked to generate non-trivial questions about information in the\ncontext.", "### Personal and Sensitive Information\n\n\nNo personal or sensitive information is included in this dataset. Dataset\nannotators has been manually verified it.\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nUsers should consider that the articles are sampled from Wikipedia articles but\nnot representative of all Wikipedia articles.", "### Social Impact of Dataset\n\n\nThe social biases of this dataset have not yet been investigated.", "### Discussion of Biases\n\n\nThe social biases of this dataset have not yet been investigated. Articles and\nquestions have been selected for quality and diversity.", "### Other Known Limitations\n\n\nThe JaQuAD dataset has limitations as follows:\n\n\n* Most of them are short answers.\n* Assume that a question is answerable using the corresponding context.\n\n\nThis dataset is incomplete yet. If you find any errors in JaQuAD, please contact\nus.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nSkelter Labs: URL", "### Licensing Information\n\n\nThe JaQuAD dataset is licensed under the CC BY-SA 3.0 license.", "### Acknowledgements\n\n\nThis work was supported by TPU Research Cloud (TRC) program.\nFor training models, we used cloud TPUs provided by TRC. We also thank\nannotators who generated JaQuAD." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:found", "multilinguality:translation", "size_categories:unknown", "source_datasets:original", "language:code", "language:en", "license:gpl-3.0", "arxiv:2104.13100", "region:us" ]

[ "2104.13100" ]

[ "code", "en" ]

[ "# Shellcode_IA32\n\n___Shellcode_IA32___ is a dataset containing _20_ years of shellcodes from a variety of sources is the largest collection of shellcodes in assembly available to date.\n\nThis dataset consists of 3,200 examples of instructions in assembly language for _IA-32_ (the 32-bit version of the x86 Intel Architecture) from publicly available security exploits. We collected assembly programs used to generate shellcode from exploit-db and from shell-storm.\nWe enriched the dataset by adding examples of assembly programs for the _IA-32_ architecture from popular tutorials and books. This allowed us to understand how different authors and assembly experts comment and, thus, how to deal with the ambiguity of natural language in this specific context. Our dataset consists of 10% of instructions collected from books and guidelines, and the rest from real shellcodes. \n\nOur focus is on Linux, the most common OS for security-critical network services. Accordingly, we added assembly instructions written with _Netwide Assembler_ (NASM) for Linux.\n\nEach line of _Shellcode\\_IA32_ dataset represents a snippet - intent pair. The _snippet_ is a line or a combination of multiple lines of assembly code, built by following the NASM syntax. The _intent_ is a comment in the English language.\n\nFurther statistics on the dataset and a set of preliminary experiments performed with a neural machine translation (NMT) model are described in the following paper: Shellcode_IA32: A Dataset for Automatic Shellcode Generation.\n\n\nNote: This work was done in collaboration with the DESSERT Lab.\n\nThe dataset is also hosted on the DESSERT Lab Github.\n\n\nPlease consider citing our work:" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-found #multilinguality-translation #size_categories-unknown #source_datasets-original #language-code #language-English #license-gpl-3.0 #arxiv-2104.13100 #region-us \n", "# Shellcode_IA32\n\n___Shellcode_IA32___ is a dataset containing _20_ years of shellcodes from a variety of sources is the largest collection of shellcodes in assembly available to date.\n\nThis dataset consists of 3,200 examples of instructions in assembly language for _IA-32_ (the 32-bit version of the x86 Intel Architecture) from publicly available security exploits. We collected assembly programs used to generate shellcode from exploit-db and from shell-storm.\nWe enriched the dataset by adding examples of assembly programs for the _IA-32_ architecture from popular tutorials and books. This allowed us to understand how different authors and assembly experts comment and, thus, how to deal with the ambiguity of natural language in this specific context. Our dataset consists of 10% of instructions collected from books and guidelines, and the rest from real shellcodes. \n\nOur focus is on Linux, the most common OS for security-critical network services. Accordingly, we added assembly instructions written with _Netwide Assembler_ (NASM) for Linux.\n\nEach line of _Shellcode\\_IA32_ dataset represents a snippet - intent pair. The _snippet_ is a line or a combination of multiple lines of assembly code, built by following the NASM syntax. The _intent_ is a comment in the English language.\n\nFurther statistics on the dataset and a set of preliminary experiments performed with a neural machine translation (NMT) model are described in the following paper: Shellcode_IA32: A Dataset for Automatic Shellcode Generation.\n\n\nNote: This work was done in collaboration with the DESSERT Lab.\n\nThe dataset is also hosted on the DESSERT Lab Github.\n\n\nPlease consider citing our work:" ]

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

[]

[ "en" ]

[ "# Dataset Card for one-million-reddit-confessions", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains a million posts from the following subreddits:\n - /r/trueoffmychest\n - /r/confession\n - /r/confessions\n - /r/offmychest\n\nPosts are annotated with their score.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a Reddit post.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n- 'domain': the domain of the data point's link.\n- 'url': the destination of the data point's link, if any.\n- 'selftext': the self-text of the data point, if any.\n- 'title': the title of the post data point.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for one-million-reddit-confessions", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains a million posts from the following subreddits:\n - /r/trueoffmychest\n - /r/confession\n - /r/confessions\n - /r/offmychest\n\nPosts are annotated with their score.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a Reddit post.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n- 'domain': the domain of the data point's link.\n- 'url': the destination of the data point's link, if any.\n- 'selftext': the self-text of the data point, if any.\n- 'title': the title of the post data point.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

[]

[ "en" ]

[ "# Dataset Card for one-million-reddit-jokes", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains a million posts from /r/jokes.\n\nPosts are annotated with their score.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a Reddit post.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n- 'domain': the domain of the data point's link.\n- 'url': the destination of the data point's link, if any.\n- 'selftext': the self-text of the data point, if any.\n- 'title': the title of the post data point.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for one-million-reddit-jokes", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains a million posts from /r/jokes.\n\nPosts are annotated with their score.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a Reddit post.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n- 'domain': the domain of the data point's link.\n- 'url': the destination of the data point's link, if any.\n- 'selftext': the self-text of the data point, if any.\n- 'title': the title of the post data point.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

[]

[ "en" ]

[ "# Dataset Card for one-million-reddit-questions", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains a million posts on /r/AskReddit, annotated with their score.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a Reddit post.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n- 'domain': the domain of the data point's link.\n- 'url': the destination of the data point's link, if any.\n- 'selftext': the self-text of the data point, if any.\n- 'title': the title of the post data point.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for one-million-reddit-questions", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains a million posts on /r/AskReddit, annotated with their score.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a Reddit post.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n- 'domain': the domain of the data point's link.\n- 'url': the destination of the data point's link, if any.\n- 'selftext': the self-text of the data point, if any.\n- 'title': the title of the post data point.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

[]

[ "en" ]

[ "# Dataset Card for one-year-of-r-india", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity of the subreddit /r/India from Sep 30, 2020 to Sep 30, 2021.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for one-year-of-r-india", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity of the subreddit /r/India from Sep 30, 2020 to Sep 30, 2021.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

[]

[ "en" ]

[ "# Dataset Card for reddit-crypto-aug-2021", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity on the following subreddits for the entire month of August 2021:\n- /r/cryptocurrency\n- /r/cryptocurrencyclassic\n- /r/cryptocurrencyico\n- /r/cryptomars\n- /r/cryptomoon\n- /r/cryptomoonshots\n- /r/satoshistreetbets", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for reddit-crypto-aug-2021", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity on the following subreddits for the entire month of August 2021:\n- /r/cryptocurrency\n- /r/cryptocurrencyclassic\n- /r/cryptocurrencyico\n- /r/cryptomars\n- /r/cryptomoon\n- /r/cryptomoonshots\n- /r/satoshistreetbets", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

[]

[ "en" ]

[ "# Dataset Card for reddit-nonewnormal-complete", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity on subreddit /r/NoNewNormal for the entire duration of its existence.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

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

[]

[ "en" ]

[ "# Dataset Card for reddit-wallstreetbets-aug-2021", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity on subreddit /r/WallStreetBets for the entire month of August.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for reddit-wallstreetbets-aug-2021", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity on subreddit /r/WallStreetBets for the entire month of August.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

[]

[ "en" ]

[ "# Dataset Card for ten-million-reddit-answers", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains ten million question-answer pairs, labeled with score and pre-packaged with results of a basic sentiment predictor.\n\nThe data was procured from /r/AskReddit using SocialGrep.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for ten-million-reddit-answers", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains ten million question-answer pairs, labeled with score and pre-packaged with results of a basic sentiment predictor.\n\nThe data was procured from /r/AskReddit using SocialGrep.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

[]

[ "en" ]

[ "# Dataset Card for the-2022-trucker-strike-on-reddit", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains all the comments under the /r/Ottawa convoy megathreads.\n\nComments are annotated with their score.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a Reddit comment.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n- 'sentiment': the evaluated sentiment of the data point, if any.\n- 'body': the text of the data point.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for the-2022-trucker-strike-on-reddit", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains all the comments under the /r/Ottawa convoy megathreads.\n\nComments are annotated with their score.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a Reddit comment.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n- 'sentiment': the evaluated sentiment of the data point, if any.\n- 'body': the text of the data point.", "## 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\nCC-BY v4.0", "### Contributions" ]

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

[]

[ "en" ]

[ "# Dataset Card for the-reddit-covid-dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Disclaimer\nDue to file size limitations, we are not able to provide the comments for this dataset. Please feel free to download them from the website - no registration required.", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains all the mentions of the term 'covid' in post titles on the social media platform Reddit, up until the 25th of October, 2021.\n\nThe data was procured from Reddit using SocialGrep.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for the-reddit-covid-dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Disclaimer\nDue to file size limitations, we are not able to provide the comments for this dataset. Please feel free to download them from the website - no registration required.", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains all the mentions of the term 'covid' in post titles on the social media platform Reddit, up until the 25th of October, 2021.\n\nThe data was procured from Reddit using SocialGrep.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "annotations_creators:lexyr", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for top-american-universities-on-reddit", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity of the subreddits of the top 10 US colleges, according to the 2019 Forbes listing.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for top-american-universities-on-reddit", "## 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- Point of Contact: Website", "### Dataset Summary\n\nThis corpus contains the complete data for the activity of the subreddits of the top 10 US colleges, according to the 2019 Forbes listing.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## 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\nCC-BY v4.0", "### Contributions" ]

[ "region:us" ]

[]

[]

[]

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

[ "license:mit", "question-generation dataset", "qa dataset", "region:us" ]

[]

[ "thai", "th" ]

[]

[ "TAGS\n#license-mit #question-generation dataset #qa dataset #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "## Dataset Summary\r\n\r\nIt includes list of Arabic names with meaning and origin of most names" ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "## Dataset Summary\r\n\r\nIt includes list of Arabic names with meaning and origin of most names" ]

[ "task_categories:question-answering", "multilinguality:monolingual", "language:bn", "region:us" ]

[]

[ "bn" ]

[ "# Overview \nThis dataset contains the data for the paper Deep learning based question answering system in Bengali. It is a translated version of SQuAD 2.0 dataset to bengali language. Preprocessing details can be found in the paper." ]

[ "TAGS\n#task_categories-question-answering #multilinguality-monolingual #language-Bengali #region-us \n", "# Overview \nThis dataset contains the data for the paper Deep learning based question answering system in Bengali. It is a translated version of SQuAD 2.0 dataset to bengali language. Preprocessing details can be found in the paper." ]

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

[]

[ "ru" ]

[ "# Dataset with sentiment of Russian text\nContains aggregated dataset of Russian texts from 6 datasets.", "## Labels meaning\n 0: NEUTRAL\n 1: POSITIVE\n 2: NEGATIVE", "## Datasets\n\nSentiment Analysis in Russian\n\n> Sentiments (positive, negative or neutral) of news in russian language from Kaggle competition.\n\nRussian Language Toxic Comments\n\n> Small dataset with labeled comments from URL and URL.\n\nDataset of car reviews for machine learning (sentiment analysis)\n\n> Glazkova A. The evaluation of the proximity of text categories for solving electronic documents classification tasks //VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE. – 2015. – Т. 31. – №. 2. – С. 18-25.\n\nSentiment datasets by Blinov\n\n> Datasets contain reviews from different scopes.\n\nLINIS Crowd\n\n> Произведение «LINIS Crowd SENT - тональный словарь и коллекция текстов с тональной разметкой» созданное автором по имени Sergei Koltcov, Olessia Koltsova и Svetlana Alexeeva.\n\nRussian Hotel Reviews Dataset\n\n> Hotel reviews in Russian" ]

[ "TAGS\n#language-Russian #sentiment #text-classification #region-us \n", "# Dataset with sentiment of Russian text\nContains aggregated dataset of Russian texts from 6 datasets.", "## Labels meaning\n 0: NEUTRAL\n 1: POSITIVE\n 2: NEGATIVE", "## Datasets\n\nSentiment Analysis in Russian\n\n> Sentiments (positive, negative or neutral) of news in russian language from Kaggle competition.\n\nRussian Language Toxic Comments\n\n> Small dataset with labeled comments from URL and URL.\n\nDataset of car reviews for machine learning (sentiment analysis)\n\n> Glazkova A. The evaluation of the proximity of text categories for solving electronic documents classification tasks //VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE. – 2015. – Т. 31. – №. 2. – С. 18-25.\n\nSentiment datasets by Blinov\n\n> Datasets contain reviews from different scopes.\n\nLINIS Crowd\n\n> Произведение «LINIS Crowd SENT - тональный словарь и коллекция текстов с тональной разметкой» созданное автором по имени Sergei Koltcov, Olessia Koltsova и Svetlana Alexeeva.\n\nRussian Hotel Reviews Dataset\n\n> Hotel reviews in Russian" ]

[ "region:us" ]

[]

[]

[ "# JSFakes (Dr. Tristan Behrens).\n\nThis is a tokenized version of the JS-Fakes dataset by Omar Peracha. The original dataset can be found here:\nURL\n\nThe representation is four tracks with four bars per track.", "## Purpose.\n\nThis dataset is a good starting point for Music Generation. You could train GPT-2 on the samples to compose music.", "## Contact.\n\nFind me on LinkedIn and say hello.\n\nIf you find and issue or have a feature request, please contact me. \n\nPlease be so kind and like this dataset if you find it useful." ]

[ "TAGS\n#region-us \n", "# JSFakes (Dr. Tristan Behrens).\n\nThis is a tokenized version of the JS-Fakes dataset by Omar Peracha. The original dataset can be found here:\nURL\n\nThe representation is four tracks with four bars per track.", "## Purpose.\n\nThis dataset is a good starting point for Music Generation. You could train GPT-2 on the samples to compose music.", "## Contact.\n\nFind me on LinkedIn and say hello.\n\nIf you find and issue or have a feature request, please contact me. \n\nPlease be so kind and like this dataset if you find it useful." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for register_oscar", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields", "### Dataset Summary\n\nThe Register Oscar dataset is a multilingual dataset, containing languaegs from the Oscar dataset that have been tagged with register information. \n\n8 main-level registers:\n* Narrative (NA)\n* Informational Description (IN)\n* Opinion (OP)\n* Interactive Discussion (ID)\n* How-to/Instruction (HI)\n* Informational Persuasion (IP)\n* Lyrical (LY)\n* Spoken (SP)\n\nFor further description of the labels, see (Douglas Biber and Jesse Egbert. 2018. Register variation online)\n\nCode used to tag Register Oscar can be found at URL", "### Languages\n\nCurrently contains the following languages: Arabic, Bengali, Catalan, English, Spanish, Basque, French, Hindi, Indonesian, Portuguese, Swahili, Urdu, Vietnamese and Chinese.\n\nFor further information on the languages and data, see URL", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n* id: unique id of the document (from the Oscar dataset)\n* labels: the list of labels assigned to the text\n* text: the original text of the document (as appears in the Oscar dataset)", "### Citing" ]

[ "TAGS\n#region-us \n", "# Dataset Card for register_oscar", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields", "### Dataset Summary\n\nThe Register Oscar dataset is a multilingual dataset, containing languaegs from the Oscar dataset that have been tagged with register information. \n\n8 main-level registers:\n* Narrative (NA)\n* Informational Description (IN)\n* Opinion (OP)\n* Interactive Discussion (ID)\n* How-to/Instruction (HI)\n* Informational Persuasion (IP)\n* Lyrical (LY)\n* Spoken (SP)\n\nFor further description of the labels, see (Douglas Biber and Jesse Egbert. 2018. Register variation online)\n\nCode used to tag Register Oscar can be found at URL", "### Languages\n\nCurrently contains the following languages: Arabic, Bengali, Catalan, English, Spanish, Basque, French, Hindi, Indonesian, Portuguese, Swahili, Urdu, Vietnamese and Chinese.\n\nFor further information on the languages and data, see URL", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n* id: unique id of the document (from the Oscar dataset)\n* labels: the list of labels assigned to the text\n* text: the original text of the document (as appears in the Oscar dataset)", "### Citing" ]

[ "task_categories:text-classification", "task_categories:sentence-similarity", "task_categories:text2text-generation", "task_categories:other", "task_ids:semantic-similarity-classification", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:fi", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "fi" ]

[ "# 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: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: Not available\n- Point of Contact: Jenna Kanerva, Filip Ginter", "### Dataset Summary\n\nThe project gathered a large dataset of Finnish paraphrase pairs (over 100,000). The paraphrases are selected and classified manually, so as to minimize lexical overlap, and provide examples that are maximally structurally and lexically different. The objective is to create a dataset which is challenging and better tests the capabilities of natural language understanding. An important feature of the data is that most paraphrase pairs are distributed in their document context. The primary application for the dataset is the development and evaluation of deep language models, and representation learning in general.\n\nUsage:\n\nwhere 'name' is one of the supported loading options: 'plain', 'plain-context', 'classification', 'classification-context', or 'generation'. See Data Fields for more information.", "### Supported Tasks and Leaderboards\n\n* Paraphrase classification\n* Paraphrase generation", "### Languages\n\nFinnish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nThe dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example includes two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset includes three different configurations: 'plain', 'classification', and 'generation'. The 'plain' configuration loads the original data without any additional preprocessing or transformations, while the 'classification' configuration directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the 'generation' configuration, the examples are preprocessed to be directly suitable for the paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).\n\nEach pair in the 'plain' and 'classification' configurations will include fields:\n\n'id': \nIdentifier of the paraphrase pair (string)\n\n'gem_id':\nIdentifier of the paraphrase pair in the GEM dataset (string)\n\n'goeswith': \nIdentifier of the document from which the paraphrase was extracted, can be 'not available' in case the source of the paraphrase is not from document-structured data. All examples with the same 'goeswith' value (other than 'not available') should be kept together in any train/dev/test split; most users won't need this (string)\n\n'fold': \n0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold, most users won't need this (int)\n\n'text1': \nFirst paraphrase passage (string)\n\n'text2': \nSecond paraphrase passage (string)\n\n'label': \nManually annotated labels (string)\n\n'binary_label': \nLabel turned into binary with values 'positive' (paraphrase) and 'negative' (not-paraphrase) (string)\n\n'is_rewrite': \nIndicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)\n\nEach pair in the 'generation' config will include the same fields except 'text1' and 'text2' are renamed to 'input' and 'output' in order to indicate the generation direction. Thus the fields are: 'id', 'gem_id', 'goeswith', 'fold', 'input', 'output', 'label', 'binary_label', and 'is_rewrite'\n\nContext: Most (but not all) of the paraphrase pairs are identified in their document context. By default, these contexts are not included to conserve memory, but can be accessed using the configurations 'plain-context' and 'classification-context'. These are exactly like 'plain' and 'classification' with these additional fields:\n\n'context1': \na dictionary with the fields 'doctext' (string), 'begin' (int), 'end' (int). These mean that the paraphrase in 'text1' was extracted from 'doctext[begin:end]'. In most cases, 'doctext[begin:end]' and 'text1' are the exact same string, but occassionally that is not the case when e.g. intervening punctuations or other unrelated texts were \"cleaned\" from 'text1' during annotation. In case the context is not available, 'doctext' is an empty string and 'beg==end==0'\n\n'context2': \nsame as 'context1' but for 'text2'", "### 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 @jmnybl and @fginter for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_categories-sentence-similarity #task_categories-text2text-generation #task_categories-other #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Finnish #license-cc-by-sa-4.0 #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: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: Not available\n- Point of Contact: Jenna Kanerva, Filip Ginter", "### Dataset Summary\n\nThe project gathered a large dataset of Finnish paraphrase pairs (over 100,000). The paraphrases are selected and classified manually, so as to minimize lexical overlap, and provide examples that are maximally structurally and lexically different. The objective is to create a dataset which is challenging and better tests the capabilities of natural language understanding. An important feature of the data is that most paraphrase pairs are distributed in their document context. The primary application for the dataset is the development and evaluation of deep language models, and representation learning in general.\n\nUsage:\n\nwhere 'name' is one of the supported loading options: 'plain', 'plain-context', 'classification', 'classification-context', or 'generation'. See Data Fields for more information.", "### Supported Tasks and Leaderboards\n\n* Paraphrase classification\n* Paraphrase generation", "### Languages\n\nFinnish", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nThe dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example includes two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset includes three different configurations: 'plain', 'classification', and 'generation'. The 'plain' configuration loads the original data without any additional preprocessing or transformations, while the 'classification' configuration directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the 'generation' configuration, the examples are preprocessed to be directly suitable for the paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).\n\nEach pair in the 'plain' and 'classification' configurations will include fields:\n\n'id': \nIdentifier of the paraphrase pair (string)\n\n'gem_id':\nIdentifier of the paraphrase pair in the GEM dataset (string)\n\n'goeswith': \nIdentifier of the document from which the paraphrase was extracted, can be 'not available' in case the source of the paraphrase is not from document-structured data. All examples with the same 'goeswith' value (other than 'not available') should be kept together in any train/dev/test split; most users won't need this (string)\n\n'fold': \n0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold, most users won't need this (int)\n\n'text1': \nFirst paraphrase passage (string)\n\n'text2': \nSecond paraphrase passage (string)\n\n'label': \nManually annotated labels (string)\n\n'binary_label': \nLabel turned into binary with values 'positive' (paraphrase) and 'negative' (not-paraphrase) (string)\n\n'is_rewrite': \nIndicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)\n\nEach pair in the 'generation' config will include the same fields except 'text1' and 'text2' are renamed to 'input' and 'output' in order to indicate the generation direction. Thus the fields are: 'id', 'gem_id', 'goeswith', 'fold', 'input', 'output', 'label', 'binary_label', and 'is_rewrite'\n\nContext: Most (but not all) of the paraphrase pairs are identified in their document context. By default, these contexts are not included to conserve memory, but can be accessed using the configurations 'plain-context' and 'classification-context'. These are exactly like 'plain' and 'classification' with these additional fields:\n\n'context1': \na dictionary with the fields 'doctext' (string), 'begin' (int), 'end' (int). These mean that the paraphrase in 'text1' was extracted from 'doctext[begin:end]'. In most cases, 'doctext[begin:end]' and 'text1' are the exact same string, but occassionally that is not the case when e.g. intervening punctuations or other unrelated texts were \"cleaned\" from 'text1' during annotation. In case the context is not available, 'doctext' is an empty string and 'beg==end==0'\n\n'context2': \nsame as 'context1' but for 'text2'", "### 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 @jmnybl and @fginter for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# IndoParaCrawl\n\nIndoParaCrawl is ParaCrawl v7.1 dataset bulk-translated to Indonesian using Google Translate.\n\nThanks HuggingFace for providing free storage for datasets <3." ]

[ "TAGS\n#region-us \n", "# IndoParaCrawl\n\nIndoParaCrawl is ParaCrawl v7.1 dataset bulk-translated to Indonesian using Google Translate.\n\nThanks HuggingFace for providing free storage for datasets <3." ]

[ "task_categories:text-classification", "task_ids:sentiment-classification", "multilinguality:monolingual", "size_categories:100K<n<1m", "language:ko", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "ko" ]

[ "### KR3: Korean Restaurant Reviews with Ratings\nKorean sentiment classification dataset \n\n- Size: 460K(+180K)\n- Language: Korean-centric", "### ️ Caution with 'Rating' Column\n0 stands for negative review, 1 stands for positive review, and 2 stands for ambiguous review. \nNote that rating 2 is not intended to be used directly for supervised learning(classification). This data is included for additional pre-training purpose or other usage. \nIn other words, this dataset is basically a binary sentiment classification task where labels are 0 and 1.", "### See More\nSee all the codes for crawling/preprocessing the dataset and experiments with KR3 in GitHub Repo. \nSee Kaggle dataset in Kaggle Dataset.", "### Usage", "### License\nCC BY-NC-SA 4.0", "### Legal Issues\nWe concluded that the non-commerical usage and release of KR3 fall into the range of fair use (공정 이용) stated in the Korean copyright act (저작권법). We further clarify that we did not agree to the terms of service from any websites which might prohibit web crawling. In other words, web crawling we've done was proceeded without logging in to the website. Despite all of these, feel free to contact to any of the contributors if you notice any legal issues.", "### Contributors & Acknowledgement\n(Alphabetical order)\n\nDongin Jung\n\nHyunwoo Kwak\n\nKaeun Lee\n\nYejoon Lee\n\nThis work was done as DIYA 4기. Compute resources needed for the work was supported by DIYA and URL." ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-100K<n<1m #language-Korean #license-cc-by-nc-sa-4.0 #region-us \n", "### KR3: Korean Restaurant Reviews with Ratings\nKorean sentiment classification dataset \n\n- Size: 460K(+180K)\n- Language: Korean-centric", "### ️ Caution with 'Rating' Column\n0 stands for negative review, 1 stands for positive review, and 2 stands for ambiguous review. \nNote that rating 2 is not intended to be used directly for supervised learning(classification). This data is included for additional pre-training purpose or other usage. \nIn other words, this dataset is basically a binary sentiment classification task where labels are 0 and 1.", "### See More\nSee all the codes for crawling/preprocessing the dataset and experiments with KR3 in GitHub Repo. \nSee Kaggle dataset in Kaggle Dataset.", "### Usage", "### License\nCC BY-NC-SA 4.0", "### Legal Issues\nWe concluded that the non-commerical usage and release of KR3 fall into the range of fair use (공정 이용) stated in the Korean copyright act (저작권법). We further clarify that we did not agree to the terms of service from any websites which might prohibit web crawling. In other words, web crawling we've done was proceeded without logging in to the website. Despite all of these, feel free to contact to any of the contributors if you notice any legal issues.", "### Contributors & Acknowledgement\n(Alphabetical order)\n\nDongin Jung\n\nHyunwoo Kwak\n\nKaeun Lee\n\nYejoon Lee\n\nThis work was done as DIYA 4기. Compute resources needed for the work was supported by DIYA and URL." ]

[ "region:us" ]

[]

[]

[ "# Archive Of Our Own Original Works (AO4W)\n\nWarning! Many/most of these files may be NSFW!\n\nApproximately 2GB of text files from Archive of Our Own; specifically, files labeled \"original work\" or some variation. For training fiction models. I recommend that you clean the text as needed for your purposes." ]

[ "TAGS\n#region-us \n", "# Archive Of Our Own Original Works (AO4W)\n\nWarning! Many/most of these files may be NSFW!\n\nApproximately 2GB of text files from Archive of Our Own; specifically, files labeled \"original work\" or some variation. For training fiction models. I recommend that you clean the text as needed for your purposes." ]

[ "region:us" ]

[]

[]

[]

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