Datasets:

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ftopal

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huggingface-datasets

like 0

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:sl", "license:cc-by-nc-sa-4.0", "metaphor-classification", "metaphor-frame-classification", "multiword-expression-detection", "region:us" ]

[]

[ "sl" ]

[ "# Dataset Card for KOMET", "### Dataset Summary\n\nKOMET 1.0 is a hand-annotated Slovenian corpus of metaphorical expressions which contains about 200 000 words (across 13 963 sentences) from Slovene journalistic, fiction and online texts.", "### Supported Tasks and Leaderboards\n\nMetaphor detection, metaphor type classification, metaphor frame classification.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the dataset:\n\n\nThe sentence comes from the document 'URL', is the 60th sentence in the document and is the 1st sentence inside the 24th paragraph in the document.\nThe word \"po\" is annotated as an indirect metaphor-related word ('MRWi').\nThe phrase \"po svoje\" is annotated with the frame \"adverbial phrase\" and the word \"po\" is additionally annotated with the frame \"spatial_orientation\".", "### Data Fields\n\n- 'document_name': a string containing the name of the document in which the sentence appears; \n- 'idx': a uint32 containing the index of the sentence inside its document; \n- 'idx_paragraph': a uint32 containing the index of the paragraph in which the sentence appears;\n- 'idx_sentence': a uint32 containing the index of the sentence inside its paragraph;\ncontaining the consecutive number of the paragraph inside the current news article;\n- 'sentence_words': words in the sentence;\n- 'met_type': metaphors in the sentence, marked by their type and word indices;\n- 'met_frame': metaphor frames in the sentence, marked by their type (frame name) and word indices.", "## Dataset Creation\n\nThe texts were sampled from the Corpus of Slovene youth literature MAKS (journalistic, fiction and online texts). \nInitially, words whose meaning deviates from their primary meaning in the Dictionary of the standard Slovene Language were marked as metaphors.\nThen, their type was determined, i.e. whether they are an indirect (MRWi), direct (MRWd), borderline (WIDLI) metaphor or a metaphor flag (signal, marker; MFlag).\n\nFor more information, please check out the paper (which is in Slovenian language) or contact the dataset author", "## Additional Information", "### Dataset Curators\n\nŠpela Antloga.", "### Licensing Information\n\nCC BY-NC-SA 4.0", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Slovenian #license-cc-by-nc-sa-4.0 #metaphor-classification #metaphor-frame-classification #multiword-expression-detection #region-us \n", "# Dataset Card for KOMET", "### Dataset Summary\n\nKOMET 1.0 is a hand-annotated Slovenian corpus of metaphorical expressions which contains about 200 000 words (across 13 963 sentences) from Slovene journalistic, fiction and online texts.", "### Supported Tasks and Leaderboards\n\nMetaphor detection, metaphor type classification, metaphor frame classification.", "### Languages\n\nSlovenian.", "## Dataset Structure", "### Data Instances\n\nA sample instance from the dataset:\n\n\nThe sentence comes from the document 'URL', is the 60th sentence in the document and is the 1st sentence inside the 24th paragraph in the document.\nThe word \"po\" is annotated as an indirect metaphor-related word ('MRWi').\nThe phrase \"po svoje\" is annotated with the frame \"adverbial phrase\" and the word \"po\" is additionally annotated with the frame \"spatial_orientation\".", "### Data Fields\n\n- 'document_name': a string containing the name of the document in which the sentence appears; \n- 'idx': a uint32 containing the index of the sentence inside its document; \n- 'idx_paragraph': a uint32 containing the index of the paragraph in which the sentence appears;\n- 'idx_sentence': a uint32 containing the index of the sentence inside its paragraph;\ncontaining the consecutive number of the paragraph inside the current news article;\n- 'sentence_words': words in the sentence;\n- 'met_type': metaphors in the sentence, marked by their type and word indices;\n- 'met_frame': metaphor frames in the sentence, marked by their type (frame name) and word indices.", "## Dataset Creation\n\nThe texts were sampled from the Corpus of Slovene youth literature MAKS (journalistic, fiction and online texts). \nInitially, words whose meaning deviates from their primary meaning in the Dictionary of the standard Slovene Language were marked as metaphors.\nThen, their type was determined, i.e. whether they are an indirect (MRWi), direct (MRWd), borderline (WIDLI) metaphor or a metaphor flag (signal, marker; MFlag).\n\nFor more information, please check out the paper (which is in Slovenian language) or contact the dataset author", "## Additional Information", "### Dataset Curators\n\nŠpela Antloga.", "### Licensing Information\n\nCC BY-NC-SA 4.0", "### Contributions\n\nThanks to @matejklemen for adding this dataset." ]

[ "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\n*IMPORTANT*: This is the same dataset as relbert/semeval2012\\_relational\\_similarity,\nbut with a different train/validation split.\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\n*IMPORTANT*: This is the same dataset as relbert/semeval2012\\_relational\\_similarity,\nbut with a different train/validation split.\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Splits", "### Number of Positive/Negative Word-pairs in each Split" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:nl", "license:apache-2.0", "region:us" ]

[]

[ "nl" ]

[ "### Dataset Summary\n\n\nThe CNN / DailyMail Dutch 🇳🇱🇧🇪 Dataset is an English-language dataset translated to Dutch containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.\n\n\n*This dataset currently (Aug '22) has a single config, which is\nconfig '3.0.0' of cnn\\_dailymail translated to Dutch\nwith yhavinga/t5-base-36L-ccmatrix-multi.*", "### Supported Tasks and Leaderboards\n\n\n* 'summarization': Version 3.0.0 of the CNN / DailyMail Dataset can be used to train a model for abstractive and extractive summarization (Version 1.0.0 was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's ROUGE score for a given article is when compared to the highlight as written by the original article author. Zhong et al (2020) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the Papers With Code leaderboard for more models.", "### Languages\n\n\nThe BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is a string for the article, a string for the highlights, and a string for the id. See the CNN / Daily Mail dataset viewer to explore more examples.\n\n\nThe average token count for the articles and the highlights are provided below:", "### Data Fields\n\n\n* 'id': a string containing the heximal formated SHA1 hash of the url where the story was retrieved from\n* 'article': a string containing the body of the news article\n* 'highlights': a string containing the highlight of the article as written by the article author", "### Data Splits\n\n\nThe CNN/DailyMail dataset has 3 splits: *train*, *validation*, and *test*. Below are the statistics for Version 3.0.0 of the dataset.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015.\n\n\nThe code for the original data collection is available at <URL The articles were downloaded using archives of and on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <URL An updated version of the code that does not anonymize the data is available at <URL\n\n\nHermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.", "#### Who are the source language producers?\n\n\nThe text was written by journalists at CNN and the Daily Mail.", "### Annotations\n\n\nThe dataset does not contain any additional annotations.", "#### Annotation process\n\n\n[N/A]", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nVersion 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences.\n\n\nThis task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.", "### Discussion of Biases\n\n\nBordia and Bowman (2019) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.\n\n\nBecause the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published.", "### Other Known Limitations\n\n\nNews articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article (Kryściński et al, 2019). Chen et al (2016) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors.\n\n\nIt should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format.\n\n\nRamesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.\n\n\nThe code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <URL The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.", "### Licensing Information\n\n\nThe CNN / Daily Mail dataset version 1.0.0 is released under the Apache-2.0 License.", "### Contributions\n\n\nThanks to @thomwolf, @lewtun, @jplu, @jbragg, @patrickvonplaten and @mcmillanmajora for adding the English version of this dataset.\nThe dataset was translated on Cloud TPU compute generously provided by Google through the\nTPU Research Cloud." ]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Dutch #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nThe CNN / DailyMail Dutch 🇳🇱🇧🇪 Dataset is an English-language dataset translated to Dutch containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.\n\n\n*This dataset currently (Aug '22) has a single config, which is\nconfig '3.0.0' of cnn\\_dailymail translated to Dutch\nwith yhavinga/t5-base-36L-ccmatrix-multi.*", "### Supported Tasks and Leaderboards\n\n\n* 'summarization': Version 3.0.0 of the CNN / DailyMail Dataset can be used to train a model for abstractive and extractive summarization (Version 1.0.0 was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's ROUGE score for a given article is when compared to the highlight as written by the original article author. Zhong et al (2020) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the Papers With Code leaderboard for more models.", "### Languages\n\n\nThe BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor each instance, there is a string for the article, a string for the highlights, and a string for the id. See the CNN / Daily Mail dataset viewer to explore more examples.\n\n\nThe average token count for the articles and the highlights are provided below:", "### Data Fields\n\n\n* 'id': a string containing the heximal formated SHA1 hash of the url where the story was retrieved from\n* 'article': a string containing the body of the news article\n* 'highlights': a string containing the highlight of the article as written by the article author", "### Data Splits\n\n\nThe CNN/DailyMail dataset has 3 splits: *train*, *validation*, and *test*. Below are the statistics for Version 3.0.0 of the dataset.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015.\n\n\nThe code for the original data collection is available at <URL The articles were downloaded using archives of and on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <URL An updated version of the code that does not anonymize the data is available at <URL\n\n\nHermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.", "#### Who are the source language producers?\n\n\nThe text was written by journalists at CNN and the Daily Mail.", "### Annotations\n\n\nThe dataset does not contain any additional annotations.", "#### Annotation process\n\n\n[N/A]", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nVersion 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences.\n\n\nThis task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.", "### Discussion of Biases\n\n\nBordia and Bowman (2019) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.\n\n\nBecause the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published.", "### Other Known Limitations\n\n\nNews articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article (Kryściński et al, 2019). Chen et al (2016) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors.\n\n\nIt should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format.\n\n\nRamesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.\n\n\nThe code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <URL The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.", "### Licensing Information\n\n\nThe CNN / Daily Mail dataset version 1.0.0 is released under the Apache-2.0 License.", "### Contributions\n\n\nThanks to @thomwolf, @lewtun, @jplu, @jbragg, @patrickvonplaten and @mcmillanmajora for adding the English version of this dataset.\nThe dataset was translated on Cloud TPU compute generously provided by Google through the\nTPU Research Cloud." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:1k<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBroad Twitter Corpus NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'LOC', 'ORG', 'PER'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.", "### Data Splits" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-1k<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nBroad Twitter Corpus NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'LOC', 'ORG', 'PER'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.", "### Data Splits" ]

[ "task_categories:text-classification", "task_ids:acceptability-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:sentiment-classification", "task_ids:text-scoring", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "license:cc-by-4.0", "region:us" ]

[]

[]

[ "### Dataset Summ\n\n\nGLUE, the General Language Understanding Evaluation benchmark (URL is a collection of resources for training, evaluating, and analyzing natural language understanding systems.", "### Supported Tasks and Leaderboards\n\n\nThe leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:", "#### ax\n\n\nA manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.", "#### cola\n\n\nThe Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.", "#### mnli\n\n\nThe Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.", "#### mnli\\_matched\n\n\nThe matched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mnli\\_mismatched\n\n\nThe mismatched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mrpc\n\n\nThe Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.", "#### qnli\n\n\nThe Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.", "#### qqp\n\n\nThe Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.", "#### rte\n\n\nThe Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.", "#### sst2\n\n\nThe Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.", "#### stsb\n\n\nThe Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.", "#### wnli\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).", "### Languages\n\n\nThe language data in GLUE is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### ax\n\n\n* Size of downloaded dataset files: 0.21 MB\n* Size of the generated dataset: 0.23 MB\n* Total amount of disk used: 0.44 MB\n\n\nAn example of 'test' looks as follows.", "#### cola\n\n\n* Size of downloaded dataset files: 0.36 MB\n* Size of the generated dataset: 0.58 MB\n* Total amount of disk used: 0.94 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 78.65 MB\n* Total amount of disk used: 376.95 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\\_matched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.52 MB\n* Total amount of disk used: 301.82 MB\n\n\nAn example of 'test' looks as follows.", "#### mnli\\_mismatched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.73 MB\n* Total amount of disk used: 302.02 MB\n\n\nAn example of 'test' looks as follows.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### ax\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### cola\n\n\n* 'sentence': a 'string' feature.\n* 'label': a classification label, with possible values including 'unacceptable' (0), 'acceptable' (1).\n* 'idx': a 'int32' feature.", "#### mnli\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_matched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_mismatched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Splits", "#### ax", "#### cola", "#### mnli", "#### mnli\\_matched", "#### mnli\\_mismatched", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-acceptability-classification #task_ids-natural-language-inference #task_ids-semantic-similarity-scoring #task_ids-sentiment-classification #task_ids-text-scoring #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #license-cc-by-4.0 #region-us \n", "### Dataset Summ\n\n\nGLUE, the General Language Understanding Evaluation benchmark (URL is a collection of resources for training, evaluating, and analyzing natural language understanding systems.", "### Supported Tasks and Leaderboards\n\n\nThe leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:", "#### ax\n\n\nA manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.", "#### cola\n\n\nThe Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.", "#### mnli\n\n\nThe Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.", "#### mnli\\_matched\n\n\nThe matched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mnli\\_mismatched\n\n\nThe mismatched validation and test splits from MNLI. See the \"mnli\" BuilderConfig for additional information.", "#### mrpc\n\n\nThe Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.", "#### qnli\n\n\nThe Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.", "#### qqp\n\n\nThe Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.", "#### rte\n\n\nThe Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.", "#### sst2\n\n\nThe Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.", "#### stsb\n\n\nThe Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.", "#### wnli\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).", "### Languages\n\n\nThe language data in GLUE is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### ax\n\n\n* Size of downloaded dataset files: 0.21 MB\n* Size of the generated dataset: 0.23 MB\n* Total amount of disk used: 0.44 MB\n\n\nAn example of 'test' looks as follows.", "#### cola\n\n\n* Size of downloaded dataset files: 0.36 MB\n* Size of the generated dataset: 0.58 MB\n* Total amount of disk used: 0.94 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 78.65 MB\n* Total amount of disk used: 376.95 MB\n\n\nAn example of 'train' looks as follows.", "#### mnli\\_matched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.52 MB\n* Total amount of disk used: 301.82 MB\n\n\nAn example of 'test' looks as follows.", "#### mnli\\_mismatched\n\n\n* Size of downloaded dataset files: 298.29 MB\n* Size of the generated dataset: 3.73 MB\n* Total amount of disk used: 302.02 MB\n\n\nAn example of 'test' looks as follows.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### ax\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### cola\n\n\n* 'sentence': a 'string' feature.\n* 'label': a classification label, with possible values including 'unacceptable' (0), 'acceptable' (1).\n* 'idx': a 'int32' feature.", "#### mnli\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_matched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mnli\\_mismatched\n\n\n* 'premise': a 'string' feature.\n* 'hypothesis': a 'string' feature.\n* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'idx': a 'int32' feature.", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli", "### Data Splits", "#### ax", "#### cola", "#### mnli", "#### mnli\\_matched", "#### mnli\\_mismatched", "#### mrpc", "#### qnli", "#### qqp", "#### rte", "#### sst2", "#### stsb", "#### wnli\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "alignment", "text-classification", "summarisation", "human-feedback", "arxiv:2310.06452", "region:us" ]

[ "2310.06452" ]

[ "en" ]

[ "# Human feedback data\n\nThis is the version of the dataset used in URL\n\nIf starting a new project we would recommend using URL\n\nSee URL for original details of the dataset.\n\nHere the data is formatted to enable huggingface transformers sequence classification models to be trained as reward functions." ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #alignment #text-classification #summarisation #human-feedback #arxiv-2310.06452 #region-us \n", "# Human feedback data\n\nThis is the version of the dataset used in URL\n\nIf starting a new project we would recommend using URL\n\nSee URL for original details of the dataset.\n\nHere the data is formatted to enable huggingface transformers sequence classification models to be trained as reward functions." ]

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

[]

[]

[ "# This is a WIP repository for some experiments.", "# The official version of this dataset can be found at: URL", "# I worked on formating and uploading this dataset for the BIGLAM HACKATON. More info at : URL\n\n\n![DOI](URL", "# Corpus of Spanish Golden-Age Sonnets", "## Introduction\nThis corpus comprises sonnets written in Spanish between the 16th and 17th centuries.\n\nThis corpus is a dataset saved in .csv, from a previous one in .xml. \nAll the information of the original dataset can be consulted in its original repository.\n\n\nEach sonnet has been annotated in accordance with the TEI standard. Besides the header and structural information, each sonnet includes the formal representation of each verse’s particular metrical pattern.\n\nThe pattern consists of a sequence of unstressed syllables (represented by the \"-\" sign) and stressed syllables (\"+\" sign). Thus, each verse’s metrical pattern is represented as follows:\n\n\t\"---+---+-+-\"\n\t\nEach line in the metric_pattern codifies a line in the sonnet_text column.", "## Column description\n- 'author' (string): Author of the sonnet described\n- 'sonnet_title' (string): Sonnet title\n- 'sonnet_text' (string): Full text of the specific sonnet, divided by lines ('\\n')\n- 'metric_pattern' (string): Full metric pattern of the sonnet, in text, with TEI standard, divided by lines ('\\n')\n- 'reference_id' (int): Id of the original XML file where the sonnet is extracted\n- 'publisher' (string): Name of the publisher\n- 'editor' (string): Name of the editor\n- 'research_author' (string): Name of the principal research author\n- 'metrical_patterns_annotator' (string): Name of the annotation's checker\n- 'research_group' (string): Name of the research group that processed the sonnet", "## Poets\nWith the purpose of having a corpus as representative as possible, every author from the 16th and 17th centuries with more than 10 digitalized and available sonnets has been included.\n\nAll texts have been taken from the Biblioteca Virtual Miguel de Cervantes.\n\nCurrently, the corpus comprises more than 5,000 sonnets (more than 71,000 verses).", "## Annotation\nThe metrical pattern annotation has been carried out in a semi-automatic way. Firstly, all sonnets have been processed by an automatic metrical scansion system which assigns a distinct metrical pattern to each verse. Secondly, a part of the corpus has been manually checked and errors have been corrected.\n\nCurrently the corpus is going through the manual validation phase, and each sonnet includes information about whether it has already been manually checked or not.", "## How to cite this corpus\nIf you would like to cite this corpus for academic research purposes, please use this reference:\n\nNavarro-Colorado, Borja; Ribes Lafoz, María, and Sánchez, Noelia (2015) \"Metrical annotation of a large corpus of Spanish sonnets: representation, scansion and evaluation\" 10th edition of the Language Resources and Evaluation Conference 2016 Portorož, Slovenia. (PDF)", "## Further Information\nThis corpus is part of the ADSO project, developed at the University of Alicante and funded by Fundación BBVA.\n\nIf you require further information about the metrical annotation, please consult the Annotation Guide (in Spanish) or the following papers:\n\n- Navarro-Colorado, Borja; Ribes-Lafoz, María and Sánchez, Noelia (2016) \"Metrical Annotation of a Large Corpus of Spanish Sonnets: Representation, Scansion and Evaluation\" Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) Portorož, Slovenia.\n\n- Navarro-Colorado, Borja (2015) \"A computational linguistic approach to Spanish Golden Age Sonnets: metrical and semantic aspects\" Computational Linguistics for Literature NAACL 2015, Denver (Co), USA (PDF).", "## License\nThe metrical annotation of this corpus is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License.\n\nAbout the texts, \"this digital object is protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by the organization giving access to it. Further information available at URL \"." ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "# This is a WIP repository for some experiments.", "# The official version of this dataset can be found at: URL", "# I worked on formating and uploading this dataset for the BIGLAM HACKATON. More info at : URL\n\n\n![DOI](URL", "# Corpus of Spanish Golden-Age Sonnets", "## Introduction\nThis corpus comprises sonnets written in Spanish between the 16th and 17th centuries.\n\nThis corpus is a dataset saved in .csv, from a previous one in .xml. \nAll the information of the original dataset can be consulted in its original repository.\n\n\nEach sonnet has been annotated in accordance with the TEI standard. Besides the header and structural information, each sonnet includes the formal representation of each verse’s particular metrical pattern.\n\nThe pattern consists of a sequence of unstressed syllables (represented by the \"-\" sign) and stressed syllables (\"+\" sign). Thus, each verse’s metrical pattern is represented as follows:\n\n\t\"---+---+-+-\"\n\t\nEach line in the metric_pattern codifies a line in the sonnet_text column.", "## Column description\n- 'author' (string): Author of the sonnet described\n- 'sonnet_title' (string): Sonnet title\n- 'sonnet_text' (string): Full text of the specific sonnet, divided by lines ('\\n')\n- 'metric_pattern' (string): Full metric pattern of the sonnet, in text, with TEI standard, divided by lines ('\\n')\n- 'reference_id' (int): Id of the original XML file where the sonnet is extracted\n- 'publisher' (string): Name of the publisher\n- 'editor' (string): Name of the editor\n- 'research_author' (string): Name of the principal research author\n- 'metrical_patterns_annotator' (string): Name of the annotation's checker\n- 'research_group' (string): Name of the research group that processed the sonnet", "## Poets\nWith the purpose of having a corpus as representative as possible, every author from the 16th and 17th centuries with more than 10 digitalized and available sonnets has been included.\n\nAll texts have been taken from the Biblioteca Virtual Miguel de Cervantes.\n\nCurrently, the corpus comprises more than 5,000 sonnets (more than 71,000 verses).", "## Annotation\nThe metrical pattern annotation has been carried out in a semi-automatic way. Firstly, all sonnets have been processed by an automatic metrical scansion system which assigns a distinct metrical pattern to each verse. Secondly, a part of the corpus has been manually checked and errors have been corrected.\n\nCurrently the corpus is going through the manual validation phase, and each sonnet includes information about whether it has already been manually checked or not.", "## How to cite this corpus\nIf you would like to cite this corpus for academic research purposes, please use this reference:\n\nNavarro-Colorado, Borja; Ribes Lafoz, María, and Sánchez, Noelia (2015) \"Metrical annotation of a large corpus of Spanish sonnets: representation, scansion and evaluation\" 10th edition of the Language Resources and Evaluation Conference 2016 Portorož, Slovenia. (PDF)", "## Further Information\nThis corpus is part of the ADSO project, developed at the University of Alicante and funded by Fundación BBVA.\n\nIf you require further information about the metrical annotation, please consult the Annotation Guide (in Spanish) or the following papers:\n\n- Navarro-Colorado, Borja; Ribes-Lafoz, María and Sánchez, Noelia (2016) \"Metrical Annotation of a Large Corpus of Spanish Sonnets: Representation, Scansion and Evaluation\" Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) Portorož, Slovenia.\n\n- Navarro-Colorado, Borja (2015) \"A computational linguistic approach to Spanish Golden Age Sonnets: metrical and semantic aspects\" Computational Linguistics for Literature NAACL 2015, Denver (Co), USA (PDF).", "## License\nThe metrical annotation of this corpus is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License.\n\nAbout the texts, \"this digital object is protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by the organization giving access to it. Further information available at URL \"." ]

[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:extended|s2orc", "language:en", "license:cc-by-nc-4.0", "citation detection", "citation", "science", "scholarly documents", "bio", "medicine", "computer science", "citeworthiness", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for CiteWorth", "## Dataset Description\n\n- Repo URL\n- Paper URL", "### Dataset Summary\n\nScientific document understanding is challenging as the data is highly domain specific and diverse. However, datasets for tasks with scientific text require expensive manual annotation and tend to be small and limited to only one or a few fields. At the same time, scientific documents contain many potential training signals, such as citations, which can be used to build large labelled datasets. Given this, we present an in-depth study of cite-worthiness detection in English, where a sentence is labelled for whether or not it cites an external source. To accomplish this, we introduce CiteWorth, a large, contextualized, rigorously cleaned labelled dataset for cite-worthiness detection built from a massive corpus of extracted plain-text scientific documents. We show that CiteWorth is high-quality, challenging, and suitable for studying problems such as domain adaptation. Our best performing cite-worthiness detection model is a paragraph-level contextualized sentence labelling model based on Longformer, exhibiting a 5 F1 point improvement over SciBERT which considers only individual sentences. Finally, we demonstrate that language model fine-tuning with cite-worthiness as a secondary task leads to improved performance on downstream scientific document understanding tasks.", "## Dataset Structure\n\nThe data is structured as follows\n - 'paper_id': The S2ORC paper ID where the paragraph comes from\n - 'section_idx': An index into the section array in the original S2ORC data\n - 'file_index': The volume in the S2ORC dataset that the paper belongs to\n - 'file_offset': Byte offset to the start of the paper json in the S2ORC paper PDF file\n - 'mag_field_of_study': The field of study to which a paper belongs (an array, but each paper belongs to a single field)\n - 'original_text': The original text of the paragraph\n - 'section_title': Title of the section to which the paragraph belongs\n - 'samples': An array containing dicts of the cleaned sentences for the paragraph, in order. The fields for each dict are as follows\n - 'text': The cleaned text for the sentence\n - 'label': Label for the sentence, either 'check-worthy' for cite-worthy sentences or 'non-check-worthy' non-cite-worthy sentences\n - 'original_text': The original sentence text\n - 'ref_ids': List of the reference IDs in the S2ORC dataset for papers cited in this sentence\n - 'citation_text': List of all citation text in this sentence", "## Dataset Creation\n\nThe data is derived from the S2ORC dataset, specifically the 20200705v1 release of the data. It is licensed under the CC By-NC 2.0 license. For details on the dataset creation process, see section 3 of our paper\n.", "## Citing\nPlease use the following citation when referencing this work or using the data:" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-extended|s2orc #language-English #license-cc-by-nc-4.0 #citation detection #citation #science #scholarly documents #bio #medicine #computer science #citeworthiness #region-us \n", "# Dataset Card for CiteWorth", "## Dataset Description\n\n- Repo URL\n- Paper URL", "### Dataset Summary\n\nScientific document understanding is challenging as the data is highly domain specific and diverse. However, datasets for tasks with scientific text require expensive manual annotation and tend to be small and limited to only one or a few fields. At the same time, scientific documents contain many potential training signals, such as citations, which can be used to build large labelled datasets. Given this, we present an in-depth study of cite-worthiness detection in English, where a sentence is labelled for whether or not it cites an external source. To accomplish this, we introduce CiteWorth, a large, contextualized, rigorously cleaned labelled dataset for cite-worthiness detection built from a massive corpus of extracted plain-text scientific documents. We show that CiteWorth is high-quality, challenging, and suitable for studying problems such as domain adaptation. Our best performing cite-worthiness detection model is a paragraph-level contextualized sentence labelling model based on Longformer, exhibiting a 5 F1 point improvement over SciBERT which considers only individual sentences. Finally, we demonstrate that language model fine-tuning with cite-worthiness as a secondary task leads to improved performance on downstream scientific document understanding tasks.", "## Dataset Structure\n\nThe data is structured as follows\n - 'paper_id': The S2ORC paper ID where the paragraph comes from\n - 'section_idx': An index into the section array in the original S2ORC data\n - 'file_index': The volume in the S2ORC dataset that the paper belongs to\n - 'file_offset': Byte offset to the start of the paper json in the S2ORC paper PDF file\n - 'mag_field_of_study': The field of study to which a paper belongs (an array, but each paper belongs to a single field)\n - 'original_text': The original text of the paragraph\n - 'section_title': Title of the section to which the paragraph belongs\n - 'samples': An array containing dicts of the cleaned sentences for the paragraph, in order. The fields for each dict are as follows\n - 'text': The cleaned text for the sentence\n - 'label': Label for the sentence, either 'check-worthy' for cite-worthy sentences or 'non-check-worthy' non-cite-worthy sentences\n - 'original_text': The original sentence text\n - 'ref_ids': List of the reference IDs in the S2ORC dataset for papers cited in this sentence\n - 'citation_text': List of all citation text in this sentence", "## Dataset Creation\n\nThe data is derived from the S2ORC dataset, specifically the 20200705v1 release of the data. It is licensed under the CC By-NC 2.0 license. For details on the dataset creation process, see section 3 of our paper\n.", "## Citing\nPlease use the following citation when referencing this work or using the data:" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/xlm-roberta-base-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nickprock for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nickprock/xlm-roberta-base-banking77-classification\n* Dataset: banking77\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nickprock for evaluating this model." ]

[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:gpl-3.0", "scientific text", "scholarly text", "inference", "fact checking", "misinformation", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Scientific Exaggeration Detection", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nPublic trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task.", "## Dataset Structure\n\nThe training and test data are derived from the InSciOut studies from Sumner et al. 2014 and Bratton et al. 2019. The splits have the following fields:\n\n\n\nThe exaggeration label is one of 'same', 'exaggerates', or 'downplays'. The strength label is one of the following:", "## Dataset Creation\n\nSee section 4 of the paper for details on how the dataset was curated. The original InSciOut data can be found here\n\nThanks to @dwright37 for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #language-English #license-gpl-3.0 #scientific text #scholarly text #inference #fact checking #misinformation #region-us \n", "# Dataset Card for Scientific Exaggeration Detection", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nPublic trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task.", "## Dataset Structure\n\nThe training and test data are derived from the InSciOut studies from Sumner et al. 2014 and Bratton et al. 2019. The splits have the following fields:\n\n\n\nThe exaggeration label is one of 'same', 'exaggerates', or 'downplays'. The strength label is one of the following:", "## Dataset Creation\n\nSee section 4 of the paper for details on how the dataset was curated. The original InSciOut data can be found here\n\nThanks to @dwright37 for adding this dataset." ]

[ "task_categories:text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended", "language:en", "license:cc-by-4.0", "alignment", "text-classification", "summarisation", "human-feedback", "arxiv:2310.06452", "region:us" ]

[ "2310.06452" ]

[ "en" ]

[ "# Filtered TL;DR Dataset\n\nThis is the version of the dataset used in URL\n\nIf starting a new project we would recommend using URL\n\nFor more information see URL and for the original TL;DR dataset see URL" ]

[ "TAGS\n#task_categories-text-generation #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #alignment #text-classification #summarisation #human-feedback #arxiv-2310.06452 #region-us \n", "# Filtered TL;DR Dataset\n\nThis is the version of the dataset used in URL\n\nIf starting a new project we would recommend using URL\n\nFor more information see URL and for the original TL;DR dataset see URL" ]

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [COCO]", "## 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-scuwyh2000 for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# Dataset Card for [COCO]", "## 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-scuwyh2000 for adding this dataset." ]

[ "task_categories:text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended", "language:en", "license:cc-by-4.0", "alignment", "text-classification", "summarisation", "human-feedback", "arxiv:2310.06452", "region:us" ]

[ "2310.06452" ]

[ "en" ]

[ "# Filtered TL;DR Dataset\n\nThis is the version of the dataset used in URL\n\nIf starting a new project we would recommend using URL\n\nFor more information see URL and for the original TL;DR dataset see URL\n\nThis is the version of the dataset with only filtering on the queries, and hence there is more data than in URL which contains data with filtering on the queries and summaries." ]

[ "TAGS\n#task_categories-text-generation #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #alignment #text-classification #summarisation #human-feedback #arxiv-2310.06452 #region-us \n", "# Filtered TL;DR Dataset\n\nThis is the version of the dataset used in URL\n\nIf starting a new project we would recommend using URL\n\nFor more information see URL and for the original TL;DR dataset see URL\n\nThis is the version of the dataset with only filtering on the queries, and hence there is more data than in URL which contains data with filtering on the queries and summaries." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "source_datasets:original", "language:id", "license:cc", "region:us" ]

[]

[ "id" ]

[ "### Dataset Summary\n\nKoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter using fasttext\n\ndetail soon\n\n\n\n'''" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #source_datasets-original #language-Indonesian #license-cc #region-us \n", "### Dataset Summary\n\nKoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter using fasttext\n\ndetail soon\n\n\n\n'''" ]

[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "annotations_creators:crowdsourced", "language_creators:machinetranslation", "size_categories:1M<n<5", "source_datasets:multi_nli", "source_datasets:anli", "source_datasets:fever", "source_datasets:lingnli", "source_datasets:alisawuffles/WANLI", "language:multilingual", "language:zh", "language:ja", "language:ar", "language:ko", "language:de", "language:fr", "language:es", "language:pt", "language:hi", "language:id", "language:it", "language:tr", "language:ru", "language:bn", "language:ur", "language:mr", "language:ta", "language:vi", "language:fa", "language:pl", "language:uk", "language:nl", "language:sv", "language:he", "language:sw", "language:ps", "arxiv:2104.07179", "region:us" ]

[ "2104.07179" ]

[ "multilingual", "zh", "ja", "ar", "ko", "de", "fr", "es", "pt", "hi", "id", "it", "tr", "ru", "bn", "ur", "mr", "ta", "vi", "fa", "pl", "uk", "nl", "sv", "he", "sw", "ps" ]

[ "# Datasheet for the dataset: multilingual-NLI-26lang-2mil7", "## Dataset Summary\n\nThis dataset contains 2 730 000 NLI text pairs in 26 languages spoken by more than 4 billion people. The dataset can be used to train models for multilingual NLI (Natural Language Inference) or zero-shot classification. The dataset is based on the English datasets MultiNLI, Fever-NLI, ANLI, LingNLI and WANLI and was created using the latest open-source machine translation models. \n\nThe dataset is designed to complement the established multilingual XNLI dataset. XNLI contains older machine translations of the MultiNLI dataset from 2018 for 14 languages, as well as human translations of 2490 texts for validation and 5010 texts for testing per language. multilingual-NLI-26lang-2mil7 is sourced from 5 different NLI datasets and contains 105 000 machine translated texts for each of 26 languages, leading to 2 730 000 NLI text pairs. \n\nThe release of the dataset is accompanied by the fine-tuned mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 model, which can be used for NLI or zero-shot classification in 100 languages.", "## Dataset Creation\n\nThe languages in the dataset are: ['ar', 'bn', 'de', 'es', 'fa', 'fr', 'he', 'hi', 'id', 'it', 'ja', 'ko', 'mr', 'nl', 'pl', 'ps', 'pt', 'ru', 'sv', 'sw', 'ta', 'tr', 'uk', 'ur', 'vi', 'zh'] (see ISO language codes) plus the original English texts. The languages were chosen based on two criteria: (1) They are either included in the list of the 20 most spoken languages (excluding Telugu and Nigerian Pidgin, for which no machine translation model was available); (2) or they are spoken in polit-economically important countries such as the G20 or Iran and Israel.\n\nFor each of the 26 languages, a different random sample of 25 000 hypothesis-premise pairs was taken from each of the following four datasets: MultiNLI (392 702 texts in total), Fever-NLI (196 805 texts), ANLI (162 865 texts), WANLI (102 885 texts). Moreover, a sample of 5000 texts was taken from LingNLI (29 985 texts) given its smaller total size. This leads to a different random sample of 105 000 source texts per target language with a diverse distribution of data from 5 different NLI datasets. \n\nEach sample was then machine translated using the latest open-source machine translation models available for the respective language: \n- opus-mt-tc-big models were available for English to ['ar', 'es', 'fr', 'it', 'pt', 'tr']\n- opus-mt-models were available for English to ['de', 'he', 'hi', 'id', 'mr', 'nl', 'ru', 'sv', 'sw', 'uk', 'ur', 'vi', 'zh']\n- m2m100_1.2B was used for the remaining languages ['bn', 'fa', 'ja', 'ko', 'pl', 'ps', 'ta']", "## DatasetStructure", "### Data Splits\n\nThe dataset contains 130 splits (26 * 5), one for each language-dataset pair following the format '{language-iso}_{dataset}'. For example, split 'zh_mnli' contains the Chinese translation of 25 000 texts from the MultiNLI dataset etc.", "### Data Fields\n\n- 'premise_original': The original premise from the English source dataset\n- 'hypothesis_original': The original hypothesis from the English source dataset\n- 'label': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n- 'premise': The machine translated premise in the target language\n- 'hypothesis': The machine translated premise in the target language", "### Example of a data instance:", "## Limitations and bias \n\nMachine translation is not as good as human translation. Machine translation can introduce inaccuracies that can be problematic for complex tasks like NLI. In an ideal world, original NLI data would be available for many languages. Given the lack of NLI data, using the latest open-source machine translation seems like a good solution to improve multilingual NLI. You can use the Hugging Face data viewer to inspect the data and verify the translation quality for your language of interest. Note that grammatical errors are less problematic for zero-shot use-cases as grammar is less relevant for these applications.", "## Other\n\nThe machine translation for the full dataset took roughly 100 hours on an A100 GPU, especially due to the size of the m2m100_1.2B model.", "## Ideas for cooperation or questions?\nFor updates on new models and datasets, follow me on Twitter.\nIf you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or on LinkedIn\n\n\n\nIf the dataset is useful for you, please cite the following article:" ]

[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #annotations_creators-crowdsourced #language_creators-machinetranslation #size_categories-1M<n<5 #source_datasets-multi_nli #source_datasets-anli #source_datasets-fever #source_datasets-lingnli #source_datasets-alisawuffles/WANLI #language-multilingual #language-Chinese #language-Japanese #language-Arabic #language-Korean #language-German #language-French #language-Spanish #language-Portuguese #language-Hindi #language-Indonesian #language-Italian #language-Turkish #language-Russian #language-Bengali #language-Urdu #language-Marathi #language-Tamil #language-Vietnamese #language-Persian #language-Polish #language-Ukrainian #language-Dutch #language-Swedish #language-Hebrew #language-Swahili (macrolanguage) #language-Pushto #arxiv-2104.07179 #region-us \n", "# Datasheet for the dataset: multilingual-NLI-26lang-2mil7", "## Dataset Summary\n\nThis dataset contains 2 730 000 NLI text pairs in 26 languages spoken by more than 4 billion people. The dataset can be used to train models for multilingual NLI (Natural Language Inference) or zero-shot classification. The dataset is based on the English datasets MultiNLI, Fever-NLI, ANLI, LingNLI and WANLI and was created using the latest open-source machine translation models. \n\nThe dataset is designed to complement the established multilingual XNLI dataset. XNLI contains older machine translations of the MultiNLI dataset from 2018 for 14 languages, as well as human translations of 2490 texts for validation and 5010 texts for testing per language. multilingual-NLI-26lang-2mil7 is sourced from 5 different NLI datasets and contains 105 000 machine translated texts for each of 26 languages, leading to 2 730 000 NLI text pairs. \n\nThe release of the dataset is accompanied by the fine-tuned mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 model, which can be used for NLI or zero-shot classification in 100 languages.", "## Dataset Creation\n\nThe languages in the dataset are: ['ar', 'bn', 'de', 'es', 'fa', 'fr', 'he', 'hi', 'id', 'it', 'ja', 'ko', 'mr', 'nl', 'pl', 'ps', 'pt', 'ru', 'sv', 'sw', 'ta', 'tr', 'uk', 'ur', 'vi', 'zh'] (see ISO language codes) plus the original English texts. The languages were chosen based on two criteria: (1) They are either included in the list of the 20 most spoken languages (excluding Telugu and Nigerian Pidgin, for which no machine translation model was available); (2) or they are spoken in polit-economically important countries such as the G20 or Iran and Israel.\n\nFor each of the 26 languages, a different random sample of 25 000 hypothesis-premise pairs was taken from each of the following four datasets: MultiNLI (392 702 texts in total), Fever-NLI (196 805 texts), ANLI (162 865 texts), WANLI (102 885 texts). Moreover, a sample of 5000 texts was taken from LingNLI (29 985 texts) given its smaller total size. This leads to a different random sample of 105 000 source texts per target language with a diverse distribution of data from 5 different NLI datasets. \n\nEach sample was then machine translated using the latest open-source machine translation models available for the respective language: \n- opus-mt-tc-big models were available for English to ['ar', 'es', 'fr', 'it', 'pt', 'tr']\n- opus-mt-models were available for English to ['de', 'he', 'hi', 'id', 'mr', 'nl', 'ru', 'sv', 'sw', 'uk', 'ur', 'vi', 'zh']\n- m2m100_1.2B was used for the remaining languages ['bn', 'fa', 'ja', 'ko', 'pl', 'ps', 'ta']", "## DatasetStructure", "### Data Splits\n\nThe dataset contains 130 splits (26 * 5), one for each language-dataset pair following the format '{language-iso}_{dataset}'. For example, split 'zh_mnli' contains the Chinese translation of 25 000 texts from the MultiNLI dataset etc.", "### Data Fields\n\n- 'premise_original': The original premise from the English source dataset\n- 'hypothesis_original': The original hypothesis from the English source dataset\n- 'label': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n- 'premise': The machine translated premise in the target language\n- 'hypothesis': The machine translated premise in the target language", "### Example of a data instance:", "## Limitations and bias \n\nMachine translation is not as good as human translation. Machine translation can introduce inaccuracies that can be problematic for complex tasks like NLI. In an ideal world, original NLI data would be available for many languages. Given the lack of NLI data, using the latest open-source machine translation seems like a good solution to improve multilingual NLI. You can use the Hugging Face data viewer to inspect the data and verify the translation quality for your language of interest. Note that grammatical errors are less problematic for zero-shot use-cases as grammar is less relevant for these applications.", "## Other\n\nThe machine translation for the full dataset took roughly 100 hours on an A100 GPU, especially due to the size of the m2m100_1.2B model.", "## Ideas for cooperation or questions?\nFor updates on new models and datasets, follow me on Twitter.\nIf you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or on LinkedIn\n\n\n\nIf the dataset is useful for you, please cite the following article:" ]

[ "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2009.11462", "doi:10.57967/hf/0002", "region:us" ]

[ "2009.11462" ]

[ "en" ]

[ "# Dataset Card for Real Toxicity Prompts", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: Toxic Degeneration homepage\n- Repository: Code repository\n- Paper: RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models\n- Leaderboard:\n- Point of Contact: Sam Gerhman", "### Dataset Summary\n\nRealToxicityPrompts is a dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nEach instance represents a prompt and its metadata:\n\n\nThe scores accompanying the prompt and the continuation are generated using the Perspective API", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> We select our prompts from sentences in the OPEN-WEBTEXT CORPUS (Gokaslan and Cohen, 2019), a large corpus of English web text scraped from outbound URLs from Reddit, for which we extract TOXICITY scores with PERSPECTIVE API.\nTo obtain a stratified range of prompt toxicity,10 we sample 25K sentences from four equal-width toxicity ranges ([0,.25), ..., [.75,1]), for a total of 100K sentences. We then split sentences in half, yielding a prompt and a continuation, both of which we also score for toxicity.\nfined to one half of the sentence.", "### Licensing Information\n\nThe image metadata is licensed under the Apache License: URL" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #arxiv-2009.11462 #doi-10.57967/hf/0002 #region-us \n", "# Dataset Card for Real Toxicity Prompts", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: Toxic Degeneration homepage\n- Repository: Code repository\n- Paper: RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models\n- Leaderboard:\n- Point of Contact: Sam Gerhman", "### Dataset Summary\n\nRealToxicityPrompts is a dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\nEach instance represents a prompt and its metadata:\n\n\nThe scores accompanying the prompt and the continuation are generated using the Perspective API", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> We select our prompts from sentences in the OPEN-WEBTEXT CORPUS (Gokaslan and Cohen, 2019), a large corpus of English web text scraped from outbound URLs from Reddit, for which we extract TOXICITY scores with PERSPECTIVE API.\nTo obtain a stratified range of prompt toxicity,10 we sample 25K sentences from four equal-width toxicity ranges ([0,.25), ..., [.75,1]), for a total of 100K sentences. We then split sentences in half, yielding a prompt and a continuation, both of which we also score for toxicity.\nfined to one half of the sentence.", "### Licensing Information\n\nThe image metadata is licensed under the Apache License: URL" ]

[ "task_categories:automatic-speech-recognition", "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "arxiv:1906.11047", "region:us" ]

[ "1906.11047" ]

[ "en" ]

[ "### Example Usage\n\n\ngives:\n\n\nautomatically loads the audio into memory:\n\n\nThe dataset was tested for correctness by fine-tuning a Wav2Vec2-Large model on it, more explicitly the 'wav2vec2-large-lv60' checkpoint.\n\n\nAs can be seen in this experiments, training the model for less than 2 epochs gives\n\n\n*Result (WER)*:\n\n\n\nas can be seen here.\n\n\nThe results are in-line with results of published papers:\n\n\n* *Hybrid acoustic models for distant and multichannel large vocabulary speech recognition*\n* Multi-Span Acoustic Modelling using Raw Waveform Signals\n\n\nYou can run URL to reproduce the result.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits", "#### Transcribed Subsets Size\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @sanchit-gandhi, @patrickvonplaten,\nand @polinaeterna for adding this dataset.\n\n\nTerms of Usage\n--------------" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #multilinguality-monolingual #language-English #license-cc-by-4.0 #arxiv-1906.11047 #region-us \n", "### Example Usage\n\n\ngives:\n\n\nautomatically loads the audio into memory:\n\n\nThe dataset was tested for correctness by fine-tuning a Wav2Vec2-Large model on it, more explicitly the 'wav2vec2-large-lv60' checkpoint.\n\n\nAs can be seen in this experiments, training the model for less than 2 epochs gives\n\n\n*Result (WER)*:\n\n\n\nas can be seen here.\n\n\nThe results are in-line with results of published papers:\n\n\n* *Hybrid acoustic models for distant and multichannel large vocabulary speech recognition*\n* Multi-Span Acoustic Modelling using Raw Waveform Signals\n\n\nYou can run URL to reproduce the result.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits", "#### Transcribed Subsets Size\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @sanchit-gandhi, @patrickvonplaten,\nand @polinaeterna for adding this dataset.\n\n\nTerms of Usage\n--------------" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #region-us \n" ]

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

[]

[]

[ "# Dataset Card for Marriage and Divorce Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis data contains 31 columns (100x31). The first 30 columns are features (inputs), namely Age Gap, Education, Economic Similarity, Social Similarities, Cultural Similarities, Social Gap, Common Interests, Religion Compatibility, No of Children from Previous Marriage, Desire to Marry, Independency, Relationship with the Spouse Family, Trading in, Engagement Time, Love, Commitment, Mental Health, The Sense of Having Children, Previous Trading, Previous Marriage, The Proportion of Common Genes, Addiction, Loyalty, Height Ratio, Good Income, Self Confidence, Relation with Non-spouse Before Marriage, Spouse Confirmed by Family, Divorce in the Family of Grade 1 and Start Socializing with the Opposite Sex Age. The 31th column is Divorce Probability (Target).", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @hosseinmousavi", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# Dataset Card for Marriage and Divorce Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis data contains 31 columns (100x31). The first 30 columns are features (inputs), namely Age Gap, Education, Economic Similarity, Social Similarities, Cultural Similarities, Social Gap, Common Interests, Religion Compatibility, No of Children from Previous Marriage, Desire to Marry, Independency, Relationship with the Spouse Family, Trading in, Engagement Time, Love, Commitment, Mental Health, The Sense of Having Children, Previous Trading, Previous Marriage, The Proportion of Common Genes, Addiction, Loyalty, Height Ratio, Good Income, Self Confidence, Relation with Non-spouse Before Marriage, Spouse Confirmed by Family, Divorce in the Family of Grade 1 and Start Socializing with the Opposite Sex Age. The 31th column is Divorce Probability (Target).", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @hosseinmousavi", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

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

[]

[]

[ "# Dataset Card for Dataset: NetFlix Shows", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe raw data is Web Scrapped through Selenium. It contains Unlabelled text data of around 9000 Netflix Shows and Movies along with Full details like Cast, Release Year, Rating, Description, etc.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @infamouscoder", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# Dataset Card for Dataset: NetFlix Shows", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe raw data is Web Scrapped through Selenium. It contains Unlabelled text data of around 9000 Netflix Shows and Movies along with Full details like Cast, Release Year, Rating, Description, etc.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @infamouscoder", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Japanese Wikipedia Dataset\n\nThis dataset is a comprehensive pull of all Japanese wikipedia article data as of 20220808. \n\n*Note:* Right now its uploaded as a single cleaned gzip file (for faster usage), I'll update this in the future to include a huggingface datasets compatible class and better support for japanese than the existing wikipedia repo.", "### Example use case:\n\n\n\n\n\n\nThe wikipedia articles were processed from their compressed format into a 7 GB jsonl file with filtering removing extraneous characters using the repo: URL\n\nSample Text:", "## Usage\n\nClone this repo and unzip the jsonl file using:" ]

[ "TAGS\n#region-us \n", "# Japanese Wikipedia Dataset\n\nThis dataset is a comprehensive pull of all Japanese wikipedia article data as of 20220808. \n\n*Note:* Right now its uploaded as a single cleaned gzip file (for faster usage), I'll update this in the future to include a huggingface datasets compatible class and better support for japanese than the existing wikipedia repo.", "### Example use case:\n\n\n\n\n\n\nThe wikipedia articles were processed from their compressed format into a 7 GB jsonl file with filtering removing extraneous characters using the repo: URL\n\nSample Text:", "## Usage\n\nClone this repo and unzip the jsonl file using:" ]

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

[]

[]

[ "# Dataset Card for Depression: Reddit Dataset (Cleaned)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe raw data is collected through web scrapping Subreddits and is cleaned using multiple NLP techniques. The data is only in English language. It mainly targets mental health classification.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @infamouscoder", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# Dataset Card for Depression: Reddit Dataset (Cleaned)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe raw data is collected through web scrapping Subreddits and is cleaned using multiple NLP techniques. The data is only in English language. It mainly targets mental health classification.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @infamouscoder", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

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

[]

[]

[ "# Dataset Card for Russia Ukraine Conflict", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @hskhawaja", "### Licensing Information\n\nThe license for this dataset is cc-by-nc-sa-4.0", "### Contributions" ]

[ "TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for Russia Ukraine Conflict", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @hskhawaja", "### Licensing Information\n\nThe license for this dataset is cc-by-nc-sa-4.0", "### Contributions" ]

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

[]

[]

[ "# Dataset Card for amazon reviews for sentiment analysis", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nOne of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial losses and customer losses. In solving these 2 basic problems, e-commerce site and sellers will increase their sales, while customers will complete their purchasing journey without any problems. \n\nThis dataset consists of ranking product ratings and reviews on Amazon. Please review this notebook to observe how I came up with this dataset This dataset containing Amazon Product Data includes product categories and various metadata. \n\n----", "### What is expected of you?\n\nThe product with the most comments in the electronics category has user ratings and comments. In this way, we expect you to perform sentiment analysis with your specific methods.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @tarkkaanko", "### Licensing Information\n\nThe license for this dataset is cc-by-nc-sa-4.0", "### Contributions" ]

[ "TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for amazon reviews for sentiment analysis", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nOne of the most important problems in e-commerce is the correct calculation of the points given to after-sales products. The solution to this problem is to provide greater customer satisfaction for the e-commerce site, product prominence for sellers, and a seamless shopping experience for buyers. Another problem is the correct ordering of the comments given to the products. The prominence of misleading comments will cause both financial losses and customer losses. In solving these 2 basic problems, e-commerce site and sellers will increase their sales, while customers will complete their purchasing journey without any problems. \n\nThis dataset consists of ranking product ratings and reviews on Amazon. Please review this notebook to observe how I came up with this dataset This dataset containing Amazon Product Data includes product categories and various metadata. \n\n----", "### What is expected of you?\n\nThe product with the most comments in the electronics category has user ratings and comments. In this way, we expect you to perform sentiment analysis with your specific methods.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @tarkkaanko", "### Licensing Information\n\nThe license for this dataset is cc-by-nc-sa-4.0", "### Contributions" ]

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

[]

[]

[ "# Dataset Card for Twitter Dataset: Tesla", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis dataset contains all the Tweets regarding #Tesla or #tesla till 12/07/2022 (dd-mm-yyyy). It can be used for sentiment analysis research purpose or used in other NLP tasks or just for fun.\nIt contains 10,000 recent Tweets with the user ID, the hashtags used in the Tweets, and other important features.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @vishesh1412", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# Dataset Card for Twitter Dataset: Tesla", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis dataset contains all the Tweets regarding #Tesla or #tesla till 12/07/2022 (dd-mm-yyyy). It can be used for sentiment analysis research purpose or used in other NLP tasks or just for fun.\nIt contains 10,000 recent Tweets with the user ID, the hashtags used in the Tweets, and other important features.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @vishesh1412", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "task_categories:question-answering", "task_categories:text-retrieval", "task_categories:text2text-generation", "task_categories:other", "task_categories:translation", "task_categories:conversational", "task_ids:extractive-qa", "task_ids:closed-domain-qa", "task_ids:utterance-retrieval", "task_ids:document-retrieval", "task_ids:open-book-qa", "task_ids:closed-book-qa", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "chatbots", "e-commerce", "retail", "insurance", "consumer", "consumer goods", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nNSME-COM, the NeuralSpace Massive E-commerce Dataset is a collection of resources for training, evaluating, and analyzing natural language understanding systems.", "### Supported Tasks and Leaderboards\n\n\nThe leaderboard for the GLUE benchmark can be found [at this address. It comprises the following tasks:", "#### nsds\n\n\nA manually-curated domain specific dataset by Data Engineers at NeuralSpace for rare E-commerce domains such as Insurance and Retail for NL researchers and practitioners to evaluate state of the art models here in 100+ languages. The dataset files are available in JSON format.", "### Languages\n\n\nThe language data in NSME-COM is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 10.86 KB\n\n\nAn example of 'test' looks as follows.\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### nsds\n\n\n* 'text': a 'string' feature.\n* 'intent': a 'string' feature.\n* 'type': a classification label, with possible values including 'train' or 'test'.", "### Data Splits", "#### nsds", "### Contributions\n\n\nAnkur Saxena (ankursaxena@URL)" ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-retrieval #task_categories-text2text-generation #task_categories-other #task_categories-translation #task_categories-conversational #task_ids-extractive-qa #task_ids-closed-domain-qa #task_ids-utterance-retrieval #task_ids-document-retrieval #task_ids-open-book-qa #task_ids-closed-book-qa #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #chatbots #e-commerce #retail #insurance #consumer #consumer goods #region-us \n", "### Dataset Summary\n\n\nNSME-COM, the NeuralSpace Massive E-commerce Dataset is a collection of resources for training, evaluating, and analyzing natural language understanding systems.", "### Supported Tasks and Leaderboards\n\n\nThe leaderboard for the GLUE benchmark can be found [at this address. It comprises the following tasks:", "#### nsds\n\n\nA manually-curated domain specific dataset by Data Engineers at NeuralSpace for rare E-commerce domains such as Insurance and Retail for NL researchers and practitioners to evaluate state of the art models here in 100+ languages. The dataset files are available in JSON format.", "### Languages\n\n\nThe language data in NSME-COM is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 10.86 KB\n\n\nAn example of 'test' looks as follows.\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### nsds\n\n\n* 'text': a 'string' feature.\n* 'intent': a 'string' feature.\n* 'type': a classification label, with possible values including 'train' or 'test'.", "### Data Splits", "#### nsds", "### Contributions\n\n\nAnkur Saxena (ankursaxena@URL)" ]

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

[]

[]

[]

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

[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "multilinguality:monolingual", "size_categories:100M<n<1B", "language:fa", "license:mit", "arxiv:2208.13486", "region:us" ]

[ "2208.13486" ]

[ "fa" ]

[ "### Dataset Summary\n\n\nnaab is the biggest cleaned and ready-to-use open-source textual corpus in Farsi. It contains about 130GB of data, 250 million paragraphs, and 15 billion words. The project name is derived from the Farsi word ناب which means pure and high-grade. We also provide the raw version of the corpus called naab-raw and an easy-to-use pre-processor that can be employed by those who wanted to make a customized corpus.\n\n\nYou can use this corpus by the commands below:\n\n\nYou may need to download parts/splits of this corpus too, if so use the command below (You can find more ways to use it here):\n\n\nNote: be sure that your machine has at least 130 GB free space, also it may take a while to download. If you are facing disk or internet shortage, you can use below code snippet helping you download your costume sections of the naab:", "### Supported Tasks and Leaderboards\n\n\nThis corpus can be used for training all language models which can be trained by Masked Language Modeling (MLM) or any other self-supervised objective.\n\n\n* 'language-modeling'\n* 'masked-language-modeling'\n\n\nDataset Structure\n-----------------\n\n\nEach row of the dataset will look like something like the below:\n\n\n* 'text' : the textual paragraph.", "### Data Splits\n\n\nThis dataset includes two splits ('train' and 'test'). We split these two by dividing the randomly permuted version of the corpus into (95%, 5%) division respected to ('train', 'test'). Since 'validation' is usually occurring during training with the 'train' dataset we avoid proposing another split for it.\n\n\n\nBelow you can see the log-based histogram of word/paragraph over the two splits of the dataset.\n\n\n\n as the training corpus and 2.3GB (including nearly 11 million sequences and nearly 300 million words) as the test corpus.</p>\n<h3>Source Data</h3>\n<p>The textual corpora that we used as our source data are illustrated in the figure below. It contains 5 corpora which are linked in the coming sections.</p>\n<div align=)\n<img src=\"URL\n</div>", "#### Persian NLP\n\n\nThis corpus includes eight corpora that are sorted based on their volume as below:\n\n\n* Common Crawl: 65GB (link)\n* MirasText: 12G\n* W2C – Web to Corpus: 1GB (link)\n* Persian Wikipedia (March 2020 dump): 787MB (link)\n* Leipzig Corpora: 424M (link)\n* VOA corpus: 66MB (link)\n* Persian poems corpus: 61MB (link)\n* TEP: Tehran English-Persian parallel corpus: 33MB (link)", "#### AGP\n\n\nThis corpus was a formerly private corpus for ASR Gooyesh Pardaz which is now published for all users by this project. This corpus contains more than 140 million paragraphs summed up in 23GB (after cleaning). This corpus is a mixture of both formal and informal paragraphs that are crawled from different websites and/or social media.", "#### OSCAR-fa\n\n\nOSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the go classy architecture. Data is distributed by language in both original and deduplicated form. We used the unshuffled-deduplicated-fa from this corpus, after cleaning there were about 36GB remaining.", "#### Telegram\n\n\nTelegram, a cloud-based instant messaging service, is a widely used application in Iran. Following this hypothesis, we prepared a list of Telegram channels in Farsi covering various topics including sports, daily news, jokes, movies and entertainment, etc. The text data extracted from mentioned channels mainly contains informal data.", "#### LSCP\n\n\nThe Large Scale Colloquial Persian Language Understanding dataset has 120M sentences from 27M casual Persian sentences with its derivation tree, part-of-speech tags, sentiment polarity, and translations in English, German, Czech, Italian, and Hindi. However, we just used the Farsi part of it and after cleaning we had 2.3GB of it remaining. Since the dataset is casual, it may help our corpus have more informal sentences although its proportion to formal paragraphs is not comparable.", "#### Initial Data Collection and Normalization\n\n\nThe data collection process was separated into two parts. In the first part, we searched for existing corpora. After downloading these corpora we started to crawl data from some social networks. Then thanks to ASR Gooyesh Pardaz we were provided with enough textual data to start the naab journey.\n\n\nWe used a preprocessor based on some stream-based Linux kernel commands so that this process can be less time/memory-consuming. The code is provided here.", "### Personal and Sensitive Information\n\n\nSince this corpus is briefly a compilation of some former corpora we take no responsibility for personal information included in this corpus. If you detect any of these violations please let us know, we try our best to remove them from the corpus ASAP.\n\n\nWe tried our best to provide anonymity while keeping the crucial information. We shuffled some parts of the corpus so the information passing through possible conversations wouldn't be harmful.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Sadra Sabouri (Sharif University of Technology)\n* Elnaz Rahmati (Sharif University of Technology)", "### Licensing Information\n\n\nmit?\n\n\nDOI: URL", "### Contributions\n\n\nThanks to @sadrasabouri and @elnazrahmati for adding this dataset.", "### Keywords\n\n\n* Farsi\n* Persian\n* raw text\n* پیکره فارسی\n* پیکره متنی\n* آموزش مدل زبانی" ]

[ "TAGS\n#task_categories-fill-mask #task_categories-text-generation #task_ids-language-modeling #task_ids-masked-language-modeling #multilinguality-monolingual #size_categories-100M<n<1B #language-Persian #license-mit #arxiv-2208.13486 #region-us \n", "### Dataset Summary\n\n\nnaab is the biggest cleaned and ready-to-use open-source textual corpus in Farsi. It contains about 130GB of data, 250 million paragraphs, and 15 billion words. The project name is derived from the Farsi word ناب which means pure and high-grade. We also provide the raw version of the corpus called naab-raw and an easy-to-use pre-processor that can be employed by those who wanted to make a customized corpus.\n\n\nYou can use this corpus by the commands below:\n\n\nYou may need to download parts/splits of this corpus too, if so use the command below (You can find more ways to use it here):\n\n\nNote: be sure that your machine has at least 130 GB free space, also it may take a while to download. If you are facing disk or internet shortage, you can use below code snippet helping you download your costume sections of the naab:", "### Supported Tasks and Leaderboards\n\n\nThis corpus can be used for training all language models which can be trained by Masked Language Modeling (MLM) or any other self-supervised objective.\n\n\n* 'language-modeling'\n* 'masked-language-modeling'\n\n\nDataset Structure\n-----------------\n\n\nEach row of the dataset will look like something like the below:\n\n\n* 'text' : the textual paragraph.", "### Data Splits\n\n\nThis dataset includes two splits ('train' and 'test'). We split these two by dividing the randomly permuted version of the corpus into (95%, 5%) division respected to ('train', 'test'). Since 'validation' is usually occurring during training with the 'train' dataset we avoid proposing another split for it.\n\n\n\nBelow you can see the log-based histogram of word/paragraph over the two splits of the dataset.\n\n\n\n as the training corpus and 2.3GB (including nearly 11 million sequences and nearly 300 million words) as the test corpus.</p>\n<h3>Source Data</h3>\n<p>The textual corpora that we used as our source data are illustrated in the figure below. It contains 5 corpora which are linked in the coming sections.</p>\n<div align=)\n<img src=\"URL\n</div>", "#### Persian NLP\n\n\nThis corpus includes eight corpora that are sorted based on their volume as below:\n\n\n* Common Crawl: 65GB (link)\n* MirasText: 12G\n* W2C – Web to Corpus: 1GB (link)\n* Persian Wikipedia (March 2020 dump): 787MB (link)\n* Leipzig Corpora: 424M (link)\n* VOA corpus: 66MB (link)\n* Persian poems corpus: 61MB (link)\n* TEP: Tehran English-Persian parallel corpus: 33MB (link)", "#### AGP\n\n\nThis corpus was a formerly private corpus for ASR Gooyesh Pardaz which is now published for all users by this project. This corpus contains more than 140 million paragraphs summed up in 23GB (after cleaning). This corpus is a mixture of both formal and informal paragraphs that are crawled from different websites and/or social media.", "#### OSCAR-fa\n\n\nOSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the go classy architecture. Data is distributed by language in both original and deduplicated form. We used the unshuffled-deduplicated-fa from this corpus, after cleaning there were about 36GB remaining.", "#### Telegram\n\n\nTelegram, a cloud-based instant messaging service, is a widely used application in Iran. Following this hypothesis, we prepared a list of Telegram channels in Farsi covering various topics including sports, daily news, jokes, movies and entertainment, etc. The text data extracted from mentioned channels mainly contains informal data.", "#### LSCP\n\n\nThe Large Scale Colloquial Persian Language Understanding dataset has 120M sentences from 27M casual Persian sentences with its derivation tree, part-of-speech tags, sentiment polarity, and translations in English, German, Czech, Italian, and Hindi. However, we just used the Farsi part of it and after cleaning we had 2.3GB of it remaining. Since the dataset is casual, it may help our corpus have more informal sentences although its proportion to formal paragraphs is not comparable.", "#### Initial Data Collection and Normalization\n\n\nThe data collection process was separated into two parts. In the first part, we searched for existing corpora. After downloading these corpora we started to crawl data from some social networks. Then thanks to ASR Gooyesh Pardaz we were provided with enough textual data to start the naab journey.\n\n\nWe used a preprocessor based on some stream-based Linux kernel commands so that this process can be less time/memory-consuming. The code is provided here.", "### Personal and Sensitive Information\n\n\nSince this corpus is briefly a compilation of some former corpora we take no responsibility for personal information included in this corpus. If you detect any of these violations please let us know, we try our best to remove them from the corpus ASAP.\n\n\nWe tried our best to provide anonymity while keeping the crucial information. We shuffled some parts of the corpus so the information passing through possible conversations wouldn't be harmful.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Sadra Sabouri (Sharif University of Technology)\n* Elnaz Rahmati (Sharif University of Technology)", "### Licensing Information\n\n\nmit?\n\n\nDOI: URL", "### Contributions\n\n\nThanks to @sadrasabouri and @elnazrahmati for adding this dataset.", "### Keywords\n\n\n* Farsi\n* Persian\n* raw text\n* پیکره فارسی\n* پیکره متنی\n* آموزش مدل زبانی" ]

[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "multilinguality:monolingual", "language:fa", "license:mit", "arxiv:2208.13486", "region:us" ]

[ "2208.13486" ]

[ "fa" ]

[ "# naab-raw (raw version of the naab corpus)\n_If you want to join our community to keep up with news, models and datasets from naab, click on [this link.]_", "## Table of Contents\n- Dataset Card Creation Guide\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Changelog\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n - Dataset Creation\n - Curation Rationale\n - Contribution Guideline\n - Personal and Sensitive Information\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: Sharif Speech and Language Processing Lab\n- Paper: naab: A ready-to-use plug-and-play corpus for Farsi\n- Point of Contact: Sadra Sabouri", "### Dataset Summary\n\nThis is the raw (uncleaned) version of the naab corpus. You can use also customize our preprocess script and make your own cleaned corpus. This repository is a hub for all Farsi corpora. Feel free to add your corpus following the contribution guidelines.\n\nYou can download the dataset by the command below:\n\n\nIf you wanted to download a specific part of the corpus you can set the config name to the specific corpus name:", "### Supported Tasks and Leaderboards\n\nThis corpus can be used for training all language models trained by Masked Language Modeling (MLM) or any other self-supervised objective.\n\n- 'language-modeling'\n- 'masked-language-modeling'", "### Changelog\n\nIt's crucial to log changes on the projects which face changes periodically. Please refer to the URL for more details.", "## Dataset Structure\n\nEach row of the dataset will look like something like the below:\n\n+ 'text' : the textual paragraph.", "### Data Splits\n\nThis corpus contains only a split (the 'train' split).", "## Dataset Creation", "### Curation Rationale\n\nHere are some details about each part of this corpus.", "#### CC-fa\n\nThe Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata, and text extractions. We use the Farsi part of it here.", "#### W2C\n\nThe W2C stands for Web to Corpus and it contains several corpera. We contain the Farsi part of it in this corpus.", "### Contribution Guideline\n\nIn order to add your dataset, you should follow the below steps and make a pull request in order to be merged with the _naab-raw_:\n\n1. Add your dataset to '_CORPUS_URLS' in 'URL' like:\n\n2. Add a log of your changes to the URL.\n3. Add some minor descriptions to the Curation Rationale under a subsection with your dataset name.", "### Personal and Sensitive Information\n\nSince this corpus is briefly a compilation of some former corpora we take no responsibility for personal information included in this corpus. If you detect any of these violations please let us know, we try our best to remove them from the corpus ASAP.\n\nWe tried our best to provide anonymity while keeping the crucial information. We shuffled some parts of the corpus so the information passing through possible conversations wouldn't be harmful.", "## Additional Information", "### Dataset Curators\n\n+ Sadra Sabouri (Sharif University of Technology)\n+ Elnaz Rahmati (Sharif University of Technology)", "### Licensing Information\n\nmit\n\n\n\n\n\nDOI:URL", "### Contributions\n\nThanks to @sadrasabouri and @elnazrahmati for adding this dataset.", "### Keywords\n+ Farsi\n+ Persian\n+ raw text\n+ پیکره فارسی\n+ پیکره متنی\n+ آموزش مدل زبانی" ]

[ "TAGS\n#task_categories-fill-mask #task_categories-text-generation #task_ids-language-modeling #task_ids-masked-language-modeling #multilinguality-monolingual #language-Persian #license-mit #arxiv-2208.13486 #region-us \n", "# naab-raw (raw version of the naab corpus)\n_If you want to join our community to keep up with news, models and datasets from naab, click on [this link.]_", "## Table of Contents\n- Dataset Card Creation Guide\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Changelog\n - Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n - Dataset Creation\n - Curation Rationale\n - Contribution Guideline\n - Personal and Sensitive Information\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: Sharif Speech and Language Processing Lab\n- Paper: naab: A ready-to-use plug-and-play corpus for Farsi\n- Point of Contact: Sadra Sabouri", "### Dataset Summary\n\nThis is the raw (uncleaned) version of the naab corpus. You can use also customize our preprocess script and make your own cleaned corpus. This repository is a hub for all Farsi corpora. Feel free to add your corpus following the contribution guidelines.\n\nYou can download the dataset by the command below:\n\n\nIf you wanted to download a specific part of the corpus you can set the config name to the specific corpus name:", "### Supported Tasks and Leaderboards\n\nThis corpus can be used for training all language models trained by Masked Language Modeling (MLM) or any other self-supervised objective.\n\n- 'language-modeling'\n- 'masked-language-modeling'", "### Changelog\n\nIt's crucial to log changes on the projects which face changes periodically. Please refer to the URL for more details.", "## Dataset Structure\n\nEach row of the dataset will look like something like the below:\n\n+ 'text' : the textual paragraph.", "### Data Splits\n\nThis corpus contains only a split (the 'train' split).", "## Dataset Creation", "### Curation Rationale\n\nHere are some details about each part of this corpus.", "#### CC-fa\n\nThe Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata, and text extractions. We use the Farsi part of it here.", "#### W2C\n\nThe W2C stands for Web to Corpus and it contains several corpera. We contain the Farsi part of it in this corpus.", "### Contribution Guideline\n\nIn order to add your dataset, you should follow the below steps and make a pull request in order to be merged with the _naab-raw_:\n\n1. Add your dataset to '_CORPUS_URLS' in 'URL' like:\n\n2. Add a log of your changes to the URL.\n3. Add some minor descriptions to the Curation Rationale under a subsection with your dataset name.", "### Personal and Sensitive Information\n\nSince this corpus is briefly a compilation of some former corpora we take no responsibility for personal information included in this corpus. If you detect any of these violations please let us know, we try our best to remove them from the corpus ASAP.\n\nWe tried our best to provide anonymity while keeping the crucial information. We shuffled some parts of the corpus so the information passing through possible conversations wouldn't be harmful.", "## Additional Information", "### Dataset Curators\n\n+ Sadra Sabouri (Sharif University of Technology)\n+ Elnaz Rahmati (Sharif University of Technology)", "### Licensing Information\n\nmit\n\n\n\n\n\nDOI:URL", "### Contributions\n\nThanks to @sadrasabouri and @elnazrahmati for adding this dataset.", "### Keywords\n+ Farsi\n+ Persian\n+ raw text\n+ پیکره فارسی\n+ پیکره متنی\n+ آموزش مدل زبانی" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:auromatically-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "language:ca", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "ca" ]

[ "# WikiCAT_ca: Catalan Text Classification dataset", "## Dataset Description\n\n- Paper: \n\n- Point of Contact: carlos.rodriguez1@URL\n\n\nRepository\n\nURL", "### Dataset Summary\n\nWikiCAT_ca is a Catalan corpus for thematic Text Classification tasks. It is created automagically from Wikipedia and Wikidata sources, and contains 13201 articles from the Viquipedia classified under 13 different categories.\n\nThis dataset was developed by BSC TeMU as part of the AINA project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.\n\nThis work is licensed under a <a rel=\"license\" href=\"URL 4.0 International</a>.", "### Supported Tasks and Leaderboards\n\nText classification, Language Model", "### Languages\n\nThe dataset is in Catalan (ca-ES).", "## Dataset Structure", "### Data Instances\n\nTwo json files, one for each split.", "### Data Fields\n\nWe used a simple model with the article text and associated labels, without further metadata.", "#### Example:\n\n<pre>\n{\"version\": \"1.1.0\",\n \"data\":\n [\n {\n 'sentence': ' Celsius és conegut com l\\'inventor de l\\'escala centesimal del termòmetre. Encara que aquest instrument és un invent molt antic, la història de la seva gradació és molt més capritxosa. Durant el segle xvi era graduat com \"fred\" col·locant-lo (...)', \n 'label': 'Ciència'\n },\n .\n .\n .\n ]\n}\n\n\n</pre>", "#### Labels\n\n'Ciència_i_Tecnologia', 'Dret', 'Economia', 'Enginyeria', 'Entreteniment', 'Esport', 'Filosofia', 'Història', 'Humanitats', 'Matemàtiques', 'Música', 'Política', 'Religió'", "### Data Splits\n\n* dev_ca.json: 2484 label-document pairs\n* train_ca.json: 9907 label-document pairs", "## Dataset Creation", "### Methodology\n\n\n“Category” starting pages are chosen to represent the topics in each language.\n\nWe extract, for each category, the main pages, as well as the subcategories ones, and the individual pages under this first level.\nFor each page, the \"summary\" provided by Wikipedia is also extracted as the representative text.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe source data are thematic categories in the different Wikipedias", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\nAutomatic annotation", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe hope this corpus contributes to the development of language models in Catalan, a low-resource language.", "### Discussion of Biases\n\nWe are aware that this data might contain biases. We have not applied any steps to reduce their impact.", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL)\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.", "### Licensing Information\n\nThis work is licensed under a <a rel=\"license\" href=\"URL 4.0 International</a>.", "### Contributions\n\n[N/A]" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-auromatically-generated #language_creators-found #multilinguality-monolingual #size_categories-unknown #language-Catalan #license-cc-by-sa-3.0 #region-us \n", "# WikiCAT_ca: Catalan Text Classification dataset", "## Dataset Description\n\n- Paper: \n\n- Point of Contact: carlos.rodriguez1@URL\n\n\nRepository\n\nURL", "### Dataset Summary\n\nWikiCAT_ca is a Catalan corpus for thematic Text Classification tasks. It is created automagically from Wikipedia and Wikidata sources, and contains 13201 articles from the Viquipedia classified under 13 different categories.\n\nThis dataset was developed by BSC TeMU as part of the AINA project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.\n\nThis work is licensed under a <a rel=\"license\" href=\"URL 4.0 International</a>.", "### Supported Tasks and Leaderboards\n\nText classification, Language Model", "### Languages\n\nThe dataset is in Catalan (ca-ES).", "## Dataset Structure", "### Data Instances\n\nTwo json files, one for each split.", "### Data Fields\n\nWe used a simple model with the article text and associated labels, without further metadata.", "#### Example:\n\n<pre>\n{\"version\": \"1.1.0\",\n \"data\":\n [\n {\n 'sentence': ' Celsius és conegut com l\\'inventor de l\\'escala centesimal del termòmetre. Encara que aquest instrument és un invent molt antic, la història de la seva gradació és molt més capritxosa. Durant el segle xvi era graduat com \"fred\" col·locant-lo (...)', \n 'label': 'Ciència'\n },\n .\n .\n .\n ]\n}\n\n\n</pre>", "#### Labels\n\n'Ciència_i_Tecnologia', 'Dret', 'Economia', 'Enginyeria', 'Entreteniment', 'Esport', 'Filosofia', 'Història', 'Humanitats', 'Matemàtiques', 'Música', 'Política', 'Religió'", "### Data Splits\n\n* dev_ca.json: 2484 label-document pairs\n* train_ca.json: 9907 label-document pairs", "## Dataset Creation", "### Methodology\n\n\n“Category” starting pages are chosen to represent the topics in each language.\n\nWe extract, for each category, the main pages, as well as the subcategories ones, and the individual pages under this first level.\nFor each page, the \"summary\" provided by Wikipedia is also extracted as the representative text.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe source data are thematic categories in the different Wikipedias", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\nAutomatic annotation", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nWe hope this corpus contributes to the development of language models in Catalan, a low-resource language.", "### Discussion of Biases\n\nWe are aware that this data might contain biases. We have not applied any steps to reduce their impact.", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL)\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.", "### Licensing Information\n\nThis work is licensed under a <a rel=\"license\" href=\"URL 4.0 International</a>.", "### Contributions\n\n[N/A]" ]

[ "task_categories:audio-classification", "task_ids:audio-emotion-recognition", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for RAVDESS", "## 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:\nURL\n- Repository:\n- Paper:\nURL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nRyerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)\nSpeech audio-only files (16bit, 48kHz .wav) from the RAVDESS. Full dataset of speech and song, audio and video (24.8 GB) available from Zenodo. Construction and perceptual validation of the RAVDESS is described in our Open Access paper in PLoS ONE.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure\n\nThe dataset repository contains only preprocessing scripts. When loaded and a cached version is not found, the dataset will be automatically downloaded and a .tsv file created with all data instances saved as rows in a table.", "### Data Instances", "### Data Fields\n\n- \"audio\": a datasets.Audio representation of the spoken utterance,\n- \"text\": a datasets.Value string representation of spoken utterance,\n- \"labels\": a datasets.ClassLabel representation of the emotion label,\n- \"speaker_id\": a datasets.Value string representation of the speaker ID,\n- \"speaker_gender\": a datasets.Value string representation of the speaker gender", "### Data Splits\n\nAll data is in the train partition.", "## Dataset Creation", "### Curation Rationale", "### Source Data\n\nOriginal Data from the Zenodo release of the RAVDESS Dataset:\n\nFiles\n\nThis portion of the RAVDESS contains 1440 files: 60 trials per actor x 24 actors = 1440. The RAVDESS contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech emotions includes calm, happy, sad, angry, fearful, surprise, and disgust expressions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression.\n\nFile naming convention\n\nEach of the 1440 files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., URL). These identifiers define the stimulus characteristics:\n\nFilename identifiers\n\nModality (01 = full-AV, 02 = video-only, 03 = audio-only).\n\nVocal channel (01 = speech, 02 = song).\n\nEmotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).\n\nEmotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.\n\nStatement (01 = \"Kids are talking by the door\", 02 = \"Dogs are sitting by the door\").\n\nRepetition (01 = 1st repetition, 02 = 2nd repetition).\n\nActor (01 to 24. Odd numbered actors are male, even numbered actors are female).\n\nFilename example: URL\n\nAudio-only (03)\nSpeech (01)\nFearful (06)\nNormal intensity (01)\nStatement \"dogs\" (02)\n1st Repetition (01)\n12th Actor (12)\nFemale, as the actor ID number is even.", "#### 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\n(CC BY-NC-SA 4.0)[URL\n\n\n\nHow to cite the RAVDESS\n\nAcademic citation\n\nIf you use the RAVDESS in an academic publication, please use the following citation: Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. URL\n\nAll other attributions\n\nIf you use the RAVDESS in a form other than an academic publication, such as in a blog post, school project, or non-commercial product, please use the following attribution: \"The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)\" by Livingstone & Russo is licensed under CC BY-NA-SC 4.0.", "### Contributions\n\nThanks to @narad for adding this dataset." ]

[ "TAGS\n#task_categories-audio-classification #task_ids-audio-emotion-recognition #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset Card for RAVDESS", "## 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:\nURL\n- Repository:\n- Paper:\nURL\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nRyerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)\nSpeech audio-only files (16bit, 48kHz .wav) from the RAVDESS. Full dataset of speech and song, audio and video (24.8 GB) available from Zenodo. Construction and perceptual validation of the RAVDESS is described in our Open Access paper in PLoS ONE.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish", "## Dataset Structure\n\nThe dataset repository contains only preprocessing scripts. When loaded and a cached version is not found, the dataset will be automatically downloaded and a .tsv file created with all data instances saved as rows in a table.", "### Data Instances", "### Data Fields\n\n- \"audio\": a datasets.Audio representation of the spoken utterance,\n- \"text\": a datasets.Value string representation of spoken utterance,\n- \"labels\": a datasets.ClassLabel representation of the emotion label,\n- \"speaker_id\": a datasets.Value string representation of the speaker ID,\n- \"speaker_gender\": a datasets.Value string representation of the speaker gender", "### Data Splits\n\nAll data is in the train partition.", "## Dataset Creation", "### Curation Rationale", "### Source Data\n\nOriginal Data from the Zenodo release of the RAVDESS Dataset:\n\nFiles\n\nThis portion of the RAVDESS contains 1440 files: 60 trials per actor x 24 actors = 1440. The RAVDESS contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech emotions includes calm, happy, sad, angry, fearful, surprise, and disgust expressions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression.\n\nFile naming convention\n\nEach of the 1440 files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., URL). These identifiers define the stimulus characteristics:\n\nFilename identifiers\n\nModality (01 = full-AV, 02 = video-only, 03 = audio-only).\n\nVocal channel (01 = speech, 02 = song).\n\nEmotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).\n\nEmotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.\n\nStatement (01 = \"Kids are talking by the door\", 02 = \"Dogs are sitting by the door\").\n\nRepetition (01 = 1st repetition, 02 = 2nd repetition).\n\nActor (01 to 24. Odd numbered actors are male, even numbered actors are female).\n\nFilename example: URL\n\nAudio-only (03)\nSpeech (01)\nFearful (06)\nNormal intensity (01)\nStatement \"dogs\" (02)\n1st Repetition (01)\n12th Actor (12)\nFemale, as the actor ID number is even.", "#### 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\n(CC BY-NC-SA 4.0)[URL\n\n\n\nHow to cite the RAVDESS\n\nAcademic citation\n\nIf you use the RAVDESS in an academic publication, please use the following citation: Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. URL\n\nAll other attributions\n\nIf you use the RAVDESS in a form other than an academic publication, such as in a blog post, school project, or non-commercial product, please use the following attribution: \"The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)\" by Livingstone & Russo is licensed under CC BY-NA-SC 4.0.", "### Contributions\n\nThanks to @narad for adding this dataset." ]

[ "task_categories:image-segmentation", "task_ids:instance-segmentation", "size_categories:1M<n<10M", "license:cc-by-4.0", "segmentation", "coco", "region:us" ]

[]

[]

[ "# LVIS", "### Dataset Summary\n\nThis dataset is the implementation of LVIS dataset into Hugging Face datasets. Please visit the original website for more information. \n\n- URL", "### Loading\nThis code returns train, validation and test generators.\n\n\n\nObjects is a dictionary which contains annotation information like bbox, class.", "### Access Generators\n\n\nAn example row is as follows." ]

[ "TAGS\n#task_categories-image-segmentation #task_ids-instance-segmentation #size_categories-1M<n<10M #license-cc-by-4.0 #segmentation #coco #region-us \n", "# LVIS", "### Dataset Summary\n\nThis dataset is the implementation of LVIS dataset into Hugging Face datasets. Please visit the original website for more information. \n\n- URL", "### Loading\nThis code returns train, validation and test generators.\n\n\n\nObjects is a dictionary which contains annotation information like bbox, class.", "### Access Generators\n\n\nAn example row is as follows." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: siddharthtumre/biobert-finetuned-jnlpba\n* Dataset: jnlpba\n* Config: jnlpba\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @siddharthtumre for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: siddharthtumre/biobert-finetuned-jnlpba\n* Dataset: jnlpba\n* Config: jnlpba\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @siddharthtumre for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: siddharthtumre/biobert-finetuned-jnlpba-ner\n* Dataset: jnlpba\n* Config: jnlpba\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @siddharthtumre for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: siddharthtumre/biobert-finetuned-jnlpba-ner\n* Dataset: jnlpba\n* Config: jnlpba\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @siddharthtumre for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #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:" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:automatically-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "language:en", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# WikiCAT_en (Text Classification) English dataset", "## Dataset Description\n\n- Paper: \n\n- Point of Contact: \n\ncarlos.rodriguez1@URL\n\n\nRepository\n\nURL", "### Dataset Summary\n\nWikiCAT_en is a English corpus for thematic Text Classification tasks. It is created automatically from Wikipedia and Wikidata sources, and contains 28921 article summaries from the Wikiipedia classified under 19 different categories.\n\nThis dataset was developed by BSC TeMU as part of the PlanTL project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.", "### Supported Tasks and Leaderboards\n\nText classification, Language Model", "### Languages\n\nEN - English", "## Dataset Structure", "### Data Instances\n\nTwo json files, one for each split.", "### Data Fields\n\nWe used a simple model with the article text and associated labels, without further metadata.", "#### Example:\n\n<pre>\n{\"version\": \"1.1.0\",\n \"data\":\n [\n {\n {'sentence': 'The IEEE Donald G. Fink Prize Paper Award was established in 1979 by the board of directors of the Institute of Electrical and Electronics Engineers (IEEE) in honor of Donald G. Fink. He was a past president of the Institute of Radio Engineers (IRE), and the first general manager and executive director of the IEEE. Recipients of this award received a certificate and an honorarium. The award was presented annually since 1981 and discontinued in 2016.', 'label': 'Engineering'\n },\n .\n .\n .\n ]\n}\n\n\n</pre>", "#### Labels\n\n'Health', 'Law', 'Entertainment', 'Religion', 'Business', 'Science', 'Engineering', 'Nature', 'Philosophy', 'Economy', 'Sports', 'Technology', 'Government', 'Mathematics', 'Military', 'Humanities', 'Music', 'Politics', 'History'", "### Data Splits\n\n* hftrain_en.json: 20237 label-document pairs\n* hfeval_en.json: 8684 label-document pairs", "## Dataset Creation", "### Methodology\n\nSe eligen páginas de partida “Category:” para representar los temas en cada lengua.\n\nSe extrae para cada categoría las páginas principales, así como las subcategorías, y las páginas individuales bajo estas subcategorías de primer nivel.\nPara cada página, se extrae también el “summary” que proporciona Wikipedia.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe source data are Wikipedia page summaries and thematic categories", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\nAutomatic annotation", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\n[N/A]", "### Discussion of Biases\n\n[N/A]", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators \nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL). \n\nFor further information, send an email to (plantl-gob-es@URL).\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\nThis work is licensed under CC Attribution 4.0 International License.\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n[N/A]" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-automatically-generated #language_creators-found #multilinguality-monolingual #size_categories-unknown #language-English #license-cc-by-sa-3.0 #region-us \n", "# WikiCAT_en (Text Classification) English dataset", "## Dataset Description\n\n- Paper: \n\n- Point of Contact: \n\ncarlos.rodriguez1@URL\n\n\nRepository\n\nURL", "### Dataset Summary\n\nWikiCAT_en is a English corpus for thematic Text Classification tasks. It is created automatically from Wikipedia and Wikidata sources, and contains 28921 article summaries from the Wikiipedia classified under 19 different categories.\n\nThis dataset was developed by BSC TeMU as part of the PlanTL project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.", "### Supported Tasks and Leaderboards\n\nText classification, Language Model", "### Languages\n\nEN - English", "## Dataset Structure", "### Data Instances\n\nTwo json files, one for each split.", "### Data Fields\n\nWe used a simple model with the article text and associated labels, without further metadata.", "#### Example:\n\n<pre>\n{\"version\": \"1.1.0\",\n \"data\":\n [\n {\n {'sentence': 'The IEEE Donald G. Fink Prize Paper Award was established in 1979 by the board of directors of the Institute of Electrical and Electronics Engineers (IEEE) in honor of Donald G. Fink. He was a past president of the Institute of Radio Engineers (IRE), and the first general manager and executive director of the IEEE. Recipients of this award received a certificate and an honorarium. The award was presented annually since 1981 and discontinued in 2016.', 'label': 'Engineering'\n },\n .\n .\n .\n ]\n}\n\n\n</pre>", "#### Labels\n\n'Health', 'Law', 'Entertainment', 'Religion', 'Business', 'Science', 'Engineering', 'Nature', 'Philosophy', 'Economy', 'Sports', 'Technology', 'Government', 'Mathematics', 'Military', 'Humanities', 'Music', 'Politics', 'History'", "### Data Splits\n\n* hftrain_en.json: 20237 label-document pairs\n* hfeval_en.json: 8684 label-document pairs", "## Dataset Creation", "### Methodology\n\nSe eligen páginas de partida “Category:” para representar los temas en cada lengua.\n\nSe extrae para cada categoría las páginas principales, así como las subcategorías, y las páginas individuales bajo estas subcategorías de primer nivel.\nPara cada página, se extrae también el “summary” que proporciona Wikipedia.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe source data are Wikipedia page summaries and thematic categories", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\nAutomatic annotation", "### Personal and Sensitive Information\n\nNo personal or sensitive information included.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\n[N/A]", "### Discussion of Biases\n\n[N/A]", "### Other Known Limitations\n\n[N/A]", "## Additional Information", "### Dataset Curators \nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL). \n\nFor further information, send an email to (plantl-gob-es@URL).\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\nThis work is licensed under CC Attribution 4.0 International License.\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)", "### Contributions\n[N/A]" ]

[ "license:apache-2.0", "arxiv:2209.02970", "region:us" ]

[ "2209.02970" ]

[]

[ "# PretrainCorpusDemo", "## 引用 Citation\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n\n也可以引用我们的网站:\n\nYou can also cite our website:\n\n'''text\n@misc{Fengshenbang-LM,\n title={Fengshenbang-LM},\n author={IDEA-CCNL},\n year={2021},\n howpublished={\\url{URL\n}" ]

[ "TAGS\n#license-apache-2.0 #arxiv-2209.02970 #region-us \n", "# PretrainCorpusDemo", "## 引用 Citation\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n\n也可以引用我们的网站:\n\nYou can also cite our website:\n\n'''text\n@misc{Fengshenbang-LM,\n title={Fengshenbang-LM},\n author={IDEA-CCNL},\n year={2021},\n howpublished={\\url{URL\n}" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: Ahmed007/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: Ahmed007/distilbert-base-uncased-finetuned-emotion\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Natural Language Inference\n* Model: autoevaluate/natural-language-inference\n* Dataset: glue\n* Config: mrpc\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-arxiv\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-arxiv\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "task_categories:translation", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:id", "language:en", "license:cc-by-4.0", "indonesian", "english", "translation", "region:us" ]

[]

[ "id", "en" ]

[ "# Dataset Card for Inglish: Indonesian English Translation Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe original dataset is from MSRP dataset. The translation was generated from google translate.\nFeel free to check the translation if you find any error and open new discussion.", "### Supported Tasks and Leaderboards\n\nMachine Translation", "### Languages\n\nEnglish - Indonesian", "## 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 @andreaschandra for adding this dataset." ]

[ "TAGS\n#task_categories-translation #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-translation #size_categories-10K<n<100K #source_datasets-original #language-Indonesian #language-English #license-cc-by-4.0 #indonesian #english #translation #region-us \n", "# Dataset Card for Inglish: Indonesian English Translation Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe original dataset is from MSRP dataset. The translation was generated from google translate.\nFeel free to check the translation if you find any error and open new discussion.", "### Supported Tasks and Leaderboards\n\nMachine Translation", "### Languages\n\nEnglish - Indonesian", "## 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 @andreaschandra for adding this dataset." ]

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

[]

[]

[]

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

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

[]

[ "en" ]

[ "# Light dataset prepared for zero-shot summarization.\n\nDialogues are preprocessed into a form:" ]

[ "TAGS\n#language-English #license-mit #region-us \n", "# Light dataset prepared for zero-shot summarization.\n\nDialogues are preprocessed into a form:" ]

[ "task_categories:question-answering", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "multilinguality:translation", "size_categories:n<1K", "source_datasets:extended|xcopa", "language:et", "license:cc-by-4.0", "region:us" ]

[]

[ "et" ]

[ "# Dataset Card for EstCOPA", "### Dataset Summary\n\nEstCOPA is an extended version of XCOPA that was created with a goal to further investigate Estonian language understanding of large language models. EstCOPA provides two new versions of train, eval and test datasets in Estonian: firstly, a machine translated (En->Et) version of original English COPA (Roemmele et al., 2011) and secondly, a manually post-edited version of the same machine translated data.", "### Supported Tasks and Leaderboards", "### Languages\n\n- et", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\n\n\n\n\nIf you use the dataset in your work, please cite", "### Contributions\n\nThanks to @helehh for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #multilinguality-translation #size_categories-n<1K #source_datasets-extended|xcopa #language-Estonian #license-cc-by-4.0 #region-us \n", "# Dataset Card for EstCOPA", "### Dataset Summary\n\nEstCOPA is an extended version of XCOPA that was created with a goal to further investigate Estonian language understanding of large language models. EstCOPA provides two new versions of train, eval and test datasets in Estonian: firstly, a machine translated (En->Et) version of original English COPA (Roemmele et al., 2011) and secondly, a manually post-edited version of the same machine translated data.", "### Supported Tasks and Leaderboards", "### Languages\n\n- et", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\n\n\n\n\nIf you use the dataset in your work, please cite", "### Contributions\n\nThanks to @helehh for adding this dataset." ]

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

[]

[]

[]

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

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/xdistil-l12-h384-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/xdistil-l12-h384-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deberta-v3-xsmall-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deberta-v3-xsmall-squad2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "# I've just ported the dataset from tfds to huggingface. All credits goes to original authors, readme is copied from URL\n\n\n\nLoad in huggingface using : \n\ndataset = datasets.load_dataset('djaym7/wiki_dialog','OQ', beam_runner='DirectRunner')", "# Dialog Inpainting: Turning Documents into Dialogs", "## Abstract\nMany important questions (e.g. \"How to eat healthier?\") require conversation to establish context and explore in depth.\nHowever, conversational question answering (ConvQA) systems have long been stymied by scarce training data that is expensive to collect.\nTo address this problem, we propose a new technique for synthetically generating diverse and high-quality dialog data: *dialog inpainting*.\nOur approach takes the text of any document and transforms it into a two-person dialog between the writer and an imagined reader:\nwe treat sentences from the article as utterances spoken by the writer, and then use a dialog inpainter to predict what the imagined reader asked or said in between each of the writer's utterances.\nBy applying this approach to passages from Wikipedia and the web, we produce 'WikiDialog' and 'WebDialog', two datasets totalling 19 million diverse information-seeking dialogs---1,000x larger than the largest existing ConvQA dataset.\nFurthermore, human raters judge the *answer adequacy* and *conversationality* of 'WikiDialog' to be as good or better than existing manually-collected datasets.\nUsing our inpainted data to pre-train ConvQA retrieval systems, we significantly advance state-of-the-art across three benchmarks ('QReCC', 'OR-QuAC', 'TREC CaST') yielding up to 40\\% relative gains on standard evaluation metrics.", "## Disclaimer\nThis is not an officially supported Google product.", "# 'WikiDialog-OQ'\n\nWe are making 'WikiDialog-OQ', a dataset containing 11M information-seeking conversations from passages in English Wikipedia, publicly available.\nEach conversation was generated using the dialog inpainting method detailed in the paper using the 'Inpaint-OQ' inpainter model, a T5-XXL model that was fine-tuned on 'OR-QuAC' and 'QReCC' using a dialog reconstruction loss. For a detailed summary of the dataset, please refer to the data card.\n\nThe passages in the dataset come from the 'OR-QuAC' retrieval corpus and share passage ids.\nYou can download the 'OR-QuAC' dataset and find more details about it here.", "## Download the raw JSON format data.\n\nThe dataset can be downloaded in (gzipped) JSON format from Google Cloud using the following commands:\n\n\n\nEach line contains a single conversation serialized as a JSON object, for example:\n\n\nThe fields are:\n* 'pid (string)': a unique identifier of the passage that corresponds to the passage ids in the public OR-QuAC dataset.\n* 'title (string)': Title of the source Wikipedia page for 'passage'\n* 'passage (string)': A passage from English Wikipedia\n* 'sentences (list of strings)': A list of all the sentences that were segmented from 'passage'.\n* 'utterances (list of strings)': A synthetic dialog generated from 'passage' by our Dialog Inpainter model. The list contains alternating utterances from each speaker ('[utterance_1, utterance_2, …, utterance_n]'). In this dataset, the first utterance is a \"prompt\" that was provided to the model, and every alternating utterance is a sentence from the passage.\n* 'author_num (list of ints)': a list of integers indicating the author number in 'text'. '[utterance_1_author, utterance_2_author, …, utterance_n_author]'. Author numbers are either 0 or 1. \n\nNote that the dialog in 'utterances' only uses the first 6 sentences of the passage; the remaining sentences are provided in the 'sentences' field and can be used to extend the dialog.", "## Download the processed dataset via TFDS.\n\nFirst, install the 'tfds-nightly' package and other dependencies.\n\n\n\nAfter installation, load the 'WikiDialog-OQ' dataset using the following snippet:", "## Citing WikiDialog" ]

[ "TAGS\n#region-us \n", "# I've just ported the dataset from tfds to huggingface. All credits goes to original authors, readme is copied from URL\n\n\n\nLoad in huggingface using : \n\ndataset = datasets.load_dataset('djaym7/wiki_dialog','OQ', beam_runner='DirectRunner')", "# Dialog Inpainting: Turning Documents into Dialogs", "## Abstract\nMany important questions (e.g. \"How to eat healthier?\") require conversation to establish context and explore in depth.\nHowever, conversational question answering (ConvQA) systems have long been stymied by scarce training data that is expensive to collect.\nTo address this problem, we propose a new technique for synthetically generating diverse and high-quality dialog data: *dialog inpainting*.\nOur approach takes the text of any document and transforms it into a two-person dialog between the writer and an imagined reader:\nwe treat sentences from the article as utterances spoken by the writer, and then use a dialog inpainter to predict what the imagined reader asked or said in between each of the writer's utterances.\nBy applying this approach to passages from Wikipedia and the web, we produce 'WikiDialog' and 'WebDialog', two datasets totalling 19 million diverse information-seeking dialogs---1,000x larger than the largest existing ConvQA dataset.\nFurthermore, human raters judge the *answer adequacy* and *conversationality* of 'WikiDialog' to be as good or better than existing manually-collected datasets.\nUsing our inpainted data to pre-train ConvQA retrieval systems, we significantly advance state-of-the-art across three benchmarks ('QReCC', 'OR-QuAC', 'TREC CaST') yielding up to 40\\% relative gains on standard evaluation metrics.", "## Disclaimer\nThis is not an officially supported Google product.", "# 'WikiDialog-OQ'\n\nWe are making 'WikiDialog-OQ', a dataset containing 11M information-seeking conversations from passages in English Wikipedia, publicly available.\nEach conversation was generated using the dialog inpainting method detailed in the paper using the 'Inpaint-OQ' inpainter model, a T5-XXL model that was fine-tuned on 'OR-QuAC' and 'QReCC' using a dialog reconstruction loss. For a detailed summary of the dataset, please refer to the data card.\n\nThe passages in the dataset come from the 'OR-QuAC' retrieval corpus and share passage ids.\nYou can download the 'OR-QuAC' dataset and find more details about it here.", "## Download the raw JSON format data.\n\nThe dataset can be downloaded in (gzipped) JSON format from Google Cloud using the following commands:\n\n\n\nEach line contains a single conversation serialized as a JSON object, for example:\n\n\nThe fields are:\n* 'pid (string)': a unique identifier of the passage that corresponds to the passage ids in the public OR-QuAC dataset.\n* 'title (string)': Title of the source Wikipedia page for 'passage'\n* 'passage (string)': A passage from English Wikipedia\n* 'sentences (list of strings)': A list of all the sentences that were segmented from 'passage'.\n* 'utterances (list of strings)': A synthetic dialog generated from 'passage' by our Dialog Inpainter model. The list contains alternating utterances from each speaker ('[utterance_1, utterance_2, …, utterance_n]'). In this dataset, the first utterance is a \"prompt\" that was provided to the model, and every alternating utterance is a sentence from the passage.\n* 'author_num (list of ints)': a list of integers indicating the author number in 'text'. '[utterance_1_author, utterance_2_author, …, utterance_n_author]'. Author numbers are either 0 or 1. \n\nNote that the dialog in 'utterances' only uses the first 6 sentences of the passage; the remaining sentences are provided in the 'sentences' field and can be used to extend the dialog.", "## Download the processed dataset via TFDS.\n\nFirst, install the 'tfds-nightly' package and other dependencies.\n\n\n\nAfter installation, load the 'WikiDialog-OQ' dataset using the following snippet:", "## Citing WikiDialog" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: yhavinga/long-t5-tglobal-small-dutch-cnn-bf16-test\n* Dataset: yhavinga/cnn_dailymail_dutch\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: yhavinga/long-t5-tglobal-small-dutch-cnn-bf16-test\n* Dataset: yhavinga/cnn_dailymail_dutch\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga for evaluating this model." ]

[ "task_categories:table-question-answering", "task_categories:fill-mask", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "Github", "region:us" ]

[]

[ "en" ]

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

[ "TAGS\n#task_categories-table-question-answering #task_categories-fill-mask #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #Github #region-us \n", "# Dataset Card for GitHub-Issues", "## 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." ]

[ "autotrain", "evaluation", "region:us" ]

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[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: yhavinga/t5-v1.1-large-dutch-cnn-test\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: yhavinga/t5-v1.1-large-dutch-cnn-test\n* Dataset: ml6team/cnn_dailymail_nl\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @yhavinga for evaluating this model." ]

[ "license:bsd-3-clause-clear", "region:us" ]

[]

[]

[ "### Indonesia BioNER Dataset\n\n\nThis dataset taken from online health consultation platform URL which has been annotated by two medical doctors. Data were annotated using IOB in CoNLL format.\n\n\nDataset contains 2600 medical answers by doctors from 2017-2020. Two medical experts were assigned to annotate the data into two entity types: DISORDERS and ANATOMY. The topics of answers are: diarrhea, HIV-AIDS, nephrolithiasis and TBC, which marked as high-risk dataset from WHO.\n\n\nThis work is possible by generous support from Dr. Diana Purwitasari and Safitri Juanita.\n\n\n\n> \n> Note: this data is provided as is in Bahasa Indonesia. No translations are provided.\n> \n> \n>" ]

[ "TAGS\n#license-bsd-3-clause-clear #region-us \n", "### Indonesia BioNER Dataset\n\n\nThis dataset taken from online health consultation platform URL which has been annotated by two medical doctors. Data were annotated using IOB in CoNLL format.\n\n\nDataset contains 2600 medical answers by doctors from 2017-2020. Two medical experts were assigned to annotate the data into two entity types: DISORDERS and ANATOMY. The topics of answers are: diarrhea, HIV-AIDS, nephrolithiasis and TBC, which marked as high-risk dataset from WHO.\n\n\nThis work is possible by generous support from Dr. Diana Purwitasari and Safitri Juanita.\n\n\n\n> \n> Note: this data is provided as is in Bahasa Indonesia. No translations are provided.\n> \n> \n>" ]

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

[]

[]

[ "# Entailment bank dataset\nThis dataset raw source can be found at allenai's Github. \n\nIf you use this dataset, it is best to cite the original paper" ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "# Entailment bank dataset\nThis dataset raw source can be found at allenai's Github. \n\nIf you use this dataset, it is best to cite the original paper" ]

[ "task_categories:text-classification", "task_ids:sentiment-analysis", "task_ids:sentiment-classification", "multilinguality:monolingual", "multilinguality:multilingual", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "language:de", "language:en", "language:es", "language:fr", "language:ja", "language:zh", "language:id", "language:ar", "language:hi", "language:it", "language:ms", "language:pt", "license:apache-2.0", "region:us" ]

[]

[ "de", "en", "es", "fr", "ja", "zh", "id", "ar", "hi", "it", "ms", "pt" ]

[ "# Multilingual Sentiments Dataset\n\nA collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative.\n\nMost multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative classification is too naive, most of the text in the world is actually neutral in sentiment. Furthermore, most multilingual sentiment datasets don't include Asian languages (e.g. Malay, Indonesian) and are dominated by Western languages (e.g. English, German).\n\nGit repo: URL", "## Dataset Description\n\n- Webpage: URL" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-classification #multilinguality-monolingual #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-1M<n<10M #language-German #language-English #language-Spanish #language-French #language-Japanese #language-Chinese #language-Indonesian #language-Arabic #language-Hindi #language-Italian #language-Malay (macrolanguage) #language-Portuguese #license-apache-2.0 #region-us \n", "# Multilingual Sentiments Dataset\n\nA collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative.\n\nMost multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative classification is too naive, most of the text in the world is actually neutral in sentiment. Furthermore, most multilingual sentiment datasets don't include Asian languages (e.g. Malay, Indonesian) and are dominated by Western languages (e.g. English, German).\n\nGit repo: URL", "## Dataset Description\n\n- Webpage: URL" ]

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

[]

[]

[]

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

[ "task_categories:translation", "annotations_creators:other", "language_creators:found", "multilinguality:translation", "size_categories:1M<n<10M", "language:bn", "language:en", "license:cc-by-nc-sa-4.0", "bengali", "BanglaNMT", "region:us" ]

[]

[ "bn", "en" ]

[ "### Dataset Summary\n\n\nThis is the largest Machine Translation (MT) dataset for Bengali-English, curated using novel sentence alignment methods introduced here.\n\n\nNote: This is a filtered version of the original dataset that the authors used for NMT training. For the complete set, refer to the offical repository", "### Supported Tasks and Leaderboards\n\n\nMore information needed", "### Languages\n\n\n* 'Bengali'\n* 'English'", "### Usage\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOne example from the dataset is given below in JSON format.", "### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'bn': a 'string' feature indicating the Bengali sentence.\n* 'en': a 'string' feature indicating the English translation.", "### Data Splits\n\n\n\nDataset Creation\n----------------\n\n\nMore information needed", "### Curation Rationale\n\n\nMore information needed", "### Source Data\n\n\nMore information needed", "#### Initial Data Collection and Normalization\n\n\nMore information needed", "#### Who are the source language producers?\n\n\nMore information needed", "### Annotations\n\n\nMore information needed", "#### Annotation process\n\n\nMore information needed", "#### Who are the annotators?\n\n\nMore information needed", "### Personal and Sensitive Information\n\n\nMore information needed\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nMore information needed", "### Discussion of Biases\n\n\nMore information needed", "### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMore information needed", "### Licensing Information\n\n\nContents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.\n\n\nIf you use the dataset, please cite the following paper:", "### Contributions\n\n\nThanks to @abhik1505040 and @Tahmid for adding this dataset." ]

[ "TAGS\n#task_categories-translation #annotations_creators-other #language_creators-found #multilinguality-translation #size_categories-1M<n<10M #language-Bengali #language-English #license-cc-by-nc-sa-4.0 #bengali #BanglaNMT #region-us \n", "### Dataset Summary\n\n\nThis is the largest Machine Translation (MT) dataset for Bengali-English, curated using novel sentence alignment methods introduced here.\n\n\nNote: This is a filtered version of the original dataset that the authors used for NMT training. For the complete set, refer to the offical repository", "### Supported Tasks and Leaderboards\n\n\nMore information needed", "### Languages\n\n\n* 'Bengali'\n* 'English'", "### Usage\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOne example from the dataset is given below in JSON format.", "### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'bn': a 'string' feature indicating the Bengali sentence.\n* 'en': a 'string' feature indicating the English translation.", "### Data Splits\n\n\n\nDataset Creation\n----------------\n\n\nMore information needed", "### Curation Rationale\n\n\nMore information needed", "### Source Data\n\n\nMore information needed", "#### Initial Data Collection and Normalization\n\n\nMore information needed", "#### Who are the source language producers?\n\n\nMore information needed", "### Annotations\n\n\nMore information needed", "#### Annotation process\n\n\nMore information needed", "#### Who are the annotators?\n\n\nMore information needed", "### Personal and Sensitive Information\n\n\nMore information needed\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nMore information needed", "### Discussion of Biases\n\n\nMore information needed", "### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMore information needed", "### Licensing Information\n\n\nContents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.\n\n\nIf you use the dataset, please cite the following paper:", "### Contributions\n\n\nThanks to @abhik1505040 and @Tahmid for adding this dataset." ]

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

[]

[]

[ "# 中文文本分类数据集\n\n数据来源：\n\n今日头条客户端\n\n\n\n数据格式：\n\n\n\n每行为一条数据，以'_!_'分割的个字段，从前往后分别是 新闻ID，分类code（见下文），分类名称（见下文），新闻字符串（仅含标题），新闻关键词\n\n\n\n分类code与名称：\n\n\n\n\n\n数据规模：\n\n共382688条，分布于15个分类中。\n\n\n\n采集时间：\n\n2018年05月" ]

[ "TAGS\n#license-mit #region-us \n", "# 中文文本分类数据集\n\n数据来源：\n\n今日头条客户端\n\n\n\n数据格式：\n\n\n\n每行为一条数据，以'_!_'分割的个字段，从前往后分别是 新闻ID，分类code（见下文），分类名称（见下文），新闻字符串（仅含标题），新闻关键词\n\n\n\n分类code与名称：\n\n\n\n\n\n数据规模：\n\n共382688条，分布于15个分类中。\n\n\n\n采集时间：\n\n2018年05月" ]

[ "task_categories:summarization", "task_ids:news-articles-summarization", "language:nl", "region:us" ]

[]

[ "nl" ]

[ "### Dataset Summary\n\n\nExtreme Summarization (XSum) Dataset.\n\n\nThere are three features:\n\n\n* document: Input news article.\n* summary: One sentence summary of the article.\n* id: BBC ID of the article.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### default\n\n\n* Size of downloaded dataset files: 245.38 MB\n* Size of the generated dataset: 507.60 MB\n* Total amount of disk used: 752.98 MB\n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### default\n\n\n* 'document': a 'string' feature.\n* 'summary': a 'string' feature.\n* 'id': a 'string' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @thomwolf, @lewtun, @mariamabarham, @jbragg, @lhoestq, @patrickvonplaten for adding the English version of this dataset.\nThe dataset was translated on Cloud TPU compute generously provided by Google through the\nTPU Research Cloud." ]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #language-Dutch #region-us \n", "### Dataset Summary\n\n\nExtreme Summarization (XSum) Dataset.\n\n\nThere are three features:\n\n\n* document: Input news article.\n* summary: One sentence summary of the article.\n* id: BBC ID of the article.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### default\n\n\n* Size of downloaded dataset files: 245.38 MB\n* Size of the generated dataset: 507.60 MB\n* Total amount of disk used: 752.98 MB\n\n\nAn example of 'validation' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### default\n\n\n* 'document': a 'string' feature.\n* 'summary': a 'string' feature.\n* 'id': a 'string' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @thomwolf, @lewtun, @mariamabarham, @jbragg, @lhoestq, @patrickvonplaten for adding the English version of this dataset.\nThe dataset was translated on Cloud TPU compute generously provided by Google through the\nTPU Research Cloud." ]

[ "task_categories:question-answering", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "20Q", "Twenty Questions", "20 Questions", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for 20Q" ]

[ "TAGS\n#task_categories-question-answering #multilinguality-monolingual #size_categories-1K<n<10K #language-English #20Q #Twenty Questions #20 Questions #region-us \n", "# Dataset Card for 20Q" ]

[ "task_categories:question-answering", "task_categories:text-retrieval", "task_categories:text2text-generation", "task_categories:other", "task_categories:translation", "task_categories:conversational", "task_ids:extractive-qa", "task_ids:closed-domain-qa", "task_ids:utterance-retrieval", "task_ids:document-retrieval", "task_ids:open-book-qa", "task_ids:closed-book-qa", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "chatbots", "e-commerce", "retail", "insurance", "consumer", "consumer goods", "region:us" ]

[]

[ "en" ]

[ "### Dataset Description\n\n\n* Homepage: NeuralSpace Homepage\n* Repository: NSME-COM Dataset\n* Point of Contact: Ankur Saxena\n* Point of Contact: Ayushman Dash\n* Size of downloaded dataset files: 10.86 KB", "### Dataset Summary\n\n\nIn this digital age, the E-Commerce industry has increasingly become a vital component of business strategy and development. To streamline, enhance and take the customer experience to the highest level, NLP can help create surprisingly massive value in the E-Commerce industry.\n\n\nOne of the most popular NLP use-cases is a chatbot. With a chatbot you can automate your customer engagement saving yourself time and other resources. Offering an enhanced and simplified customer experience you can increase your sales and also offer your website visitors personalized recommendations.\nThe NSME-COM dataset (NeuralSpace Massive E-Comm) is a manually curated dataset by data engineers at NeuralSpace for the insurance and retail domain. The dataset contains intents (the action users want to execute) and examples (anything that a user sends to the chatbot) that can be used to build a chatbot. The files in this dataset are available in JSON format.", "### Supported Tasks", "#### nsme-com", "### Languages\n\n\nThe language data in NSME-COM is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 10.86 KB\n\n\nAn example of 'test' looks as follows.\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### nsme-com\n\n\n* 'text': a 'string' feature.\n* 'intent': a 'string' feature.\n* 'type': a classification label, with possible values including 'train' or 'test'.", "### Data Splits", "#### nsme-com", "### Contributions\n\n\nAnkur Saxena (ankursaxena@URL)" ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-retrieval #task_categories-text2text-generation #task_categories-other #task_categories-translation #task_categories-conversational #task_ids-extractive-qa #task_ids-closed-domain-qa #task_ids-utterance-retrieval #task_ids-document-retrieval #task_ids-open-book-qa #task_ids-closed-book-qa #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #chatbots #e-commerce #retail #insurance #consumer #consumer goods #region-us \n", "### Dataset Description\n\n\n* Homepage: NeuralSpace Homepage\n* Repository: NSME-COM Dataset\n* Point of Contact: Ankur Saxena\n* Point of Contact: Ayushman Dash\n* Size of downloaded dataset files: 10.86 KB", "### Dataset Summary\n\n\nIn this digital age, the E-Commerce industry has increasingly become a vital component of business strategy and development. To streamline, enhance and take the customer experience to the highest level, NLP can help create surprisingly massive value in the E-Commerce industry.\n\n\nOne of the most popular NLP use-cases is a chatbot. With a chatbot you can automate your customer engagement saving yourself time and other resources. Offering an enhanced and simplified customer experience you can increase your sales and also offer your website visitors personalized recommendations.\nThe NSME-COM dataset (NeuralSpace Massive E-Comm) is a manually curated dataset by data engineers at NeuralSpace for the insurance and retail domain. The dataset contains intents (the action users want to execute) and examples (anything that a user sends to the chatbot) that can be used to build a chatbot. The files in this dataset are available in JSON format.", "### Supported Tasks", "#### nsme-com", "### Languages\n\n\nThe language data in NSME-COM is in English (BCP-47 'en')\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 10.86 KB\n\n\nAn example of 'test' looks as follows.\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### nsme-com\n\n\n* 'text': a 'string' feature.\n* 'intent': a 'string' feature.\n* 'type': a classification label, with possible values including 'train' or 'test'.", "### Data Splits", "#### nsme-com", "### Contributions\n\n\nAnkur Saxena (ankursaxena@URL)" ]

[ "task_categories:question-answering", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:rus", "license:other", "region:us" ]

[]

[ "rus" ]

[ "# 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#task_categories-question-answering #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Russian #license-other #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:translation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:translation", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "language:fr", "language:am", "language:bm", "language:bbj", "language:ee", "language:fon", "language:ha", "language:ig", "language:lg", "language:mos", "language:ny", "language:pcm", "language:rw", "language:sn", "language:sw", "language:tn", "language:tw", "language:wo", "language:xh", "language:yo", "language:zu", "license:cc-by-nc-4.0", "news, mafand, masakhane", "region:us" ]

[]

[ "en", "fr", "am", "bm", "bbj", "ee", "fon", "ha", "ig", "lg", "mos", "ny", "pcm", "rw", "sn", "sw", "tn", "tw", "wo", "xh", "yo", "zu" ]

[ "### Dataset Summary\n\n\nMAFAND-MT is the largest MT benchmark for African languages in the news domain, covering 21 languages.", "### Supported Tasks and Leaderboards\n\n\nMachine Translation", "### Languages\n\n\nThe languages covered are:\n\n\n* Amharic\n* Bambara\n* Ghomala\n* Ewe\n* Fon\n* Hausa\n* Igbo\n* Kinyarwanda\n* Luganda\n* Luo\n* Mossi\n* Nigerian-Pidgin\n* Chichewa\n* Shona\n* Swahili\n* Setswana\n* Twi\n* Wolof\n* Xhosa\n* Yoruba\n* Zulu\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* \"translation\": name of the task\n* \"src\" : source language e.g en\n* \"tgt\": target language e.g yo", "### Data Splits\n\n\nTrain/dev/test split\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nMAFAND was created from the news domain, translated from English or French to an African language", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\n* Masakhane\n* Igbo\n* Swahili\n* Hausa\n* Yoruba", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nMasakhane members", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCC-BY-4.0-NC" ]

[ "TAGS\n#task_categories-translation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-translation #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-English #language-French #language-Amharic #language-Bambara #language-Ghomálá' #language-Ewe #language-Fon #language-Hausa #language-Igbo #language-Ganda #language-Mossi #language-Nyanja #language-Nigerian Pidgin #language-Kinyarwanda #language-Shona #language-Swahili (macrolanguage) #language-Tswana #language-Twi #language-Wolof #language-Xhosa #language-Yoruba #language-Zulu #license-cc-by-nc-4.0 #news, mafand, masakhane #region-us \n", "### Dataset Summary\n\n\nMAFAND-MT is the largest MT benchmark for African languages in the news domain, covering 21 languages.", "### Supported Tasks and Leaderboards\n\n\nMachine Translation", "### Languages\n\n\nThe languages covered are:\n\n\n* Amharic\n* Bambara\n* Ghomala\n* Ewe\n* Fon\n* Hausa\n* Igbo\n* Kinyarwanda\n* Luganda\n* Luo\n* Mossi\n* Nigerian-Pidgin\n* Chichewa\n* Shona\n* Swahili\n* Setswana\n* Twi\n* Wolof\n* Xhosa\n* Yoruba\n* Zulu\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* \"translation\": name of the task\n* \"src\" : source language e.g en\n* \"tgt\": target language e.g yo", "### Data Splits\n\n\nTrain/dev/test split\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nMAFAND was created from the news domain, translated from English or French to an African language", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\n* Masakhane\n* Igbo\n* Swahili\n* Hausa\n* Yoruba", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nMasakhane members", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCC-BY-4.0-NC" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: siddharthtumre/biobert-ner\n* Dataset: jnlpba\n* Config: jnlpba\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @siddharthtumre for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: siddharthtumre/biobert-ner\n* Dataset: jnlpba\n* Config: jnlpba\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @siddharthtumre for evaluating this model." ]

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

[]

[]

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

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

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: autoevaluate/multi-class-classification\n* Dataset: emotion\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/distilbert-base-cased-distilled-squad\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: autoevaluate/distilbert-base-cased-distilled-squad\n* Dataset: autoevaluate/squad-sample\n* Config: autoevaluate--squad-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: autoevaluate/summarization\n* Dataset: autoevaluate/xsum-sample\n* Config: autoevaluate--xsum-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: autoevaluate/summarization\n* Dataset: autoevaluate/xsum-sample\n* Config: autoevaluate--xsum-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Translation\n* Model: autoevaluate/translation\n* Dataset: autoevaluate/wmt16-ro-en-sample\n* Config: autoevaluate--wmt16-ro-en-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Translation\n* Model: autoevaluate/translation\n* Dataset: autoevaluate/wmt16-ro-en-sample\n* Config: autoevaluate--wmt16-ro-en-sample\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Binary Text Classification\n* Model: autoevaluate/binary-classification\n* Dataset: glue\n* Config: sst2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "movielens", "recommendation", "collaborative filtering", "region:us" ]

[]

[ "en" ]

[ "# MovieLens User Ratings\n\nThis dataset contains ~1M user ratings, consisting of ~10k of the most recent movies from the MovieLens 25M dataset, for which over 30k unique users have rated. The dataset is streamed from the MovieLens 25M dataset, filters for the recent movies, and returns the user ratings for those. After a few joins and checks, we get this dataset. Included are the URLs of the respective movie posters.\n\nThe dataset is part of an example on building a movie recommendation engine with vector search." ]

[ "TAGS\n#annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #language-English #movielens #recommendation #collaborative filtering #region-us \n", "# MovieLens User Ratings\n\nThis dataset contains ~1M user ratings, consisting of ~10k of the most recent movies from the MovieLens 25M dataset, for which over 30k unique users have rated. The dataset is streamed from the MovieLens 25M dataset, filters for the recent movies, and returns the user ratings for those. After a few joins and checks, we get this dataset. Included are the URLs of the respective movie posters.\n\nThe dataset is part of an example on building a movie recommendation engine with vector search." ]

[ "license:cc-by-nc-4.0", "arxiv:2208.07846", "region:us" ]

[ "2208.07846" ]

[]

[ "### Dataset Summary\n\n\nThis dataset contains dialogues collected from German factory workers at the *Center for industrial productivity* (CiP). The dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.", "### Supported Tasks and Leaderboards\n\n\nThis dataset supports the following tasks:\n\n\n* Sentence classification\n* Named entity recognition (will be updated soon with the new indexing)\n* Dialog generation (so far not evaluated)", "### Languages\n\n\nGerman\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOn sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit.\n\n\nOn token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.", "### Data Fields\n\n\nSentence level:\n\n\n* dialog-id: unique identifier for the dialog\n* turn-id: unique identifier for the turn\n* sentence-id: unique identifier for the dialog\n* sentence: the respective sentence\n* label: the label (*P* for Problem, *C* for Cause, *S* for solution, and *O* for Other)\n* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).\n* subsplit: the respective subsplit of the data (see below)\n\n\nToken level:\n\n\n* id: the identifier\n* tokens: a list of tokens (i.e., the tokenized dialogue)\n* entities: the named entity in a BIO scheme (*B-X*, *I-X*, or O).\n* subsplit: the respective subsplit of the data (see below)", "### Data Splits\n\n\nThe dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory.\n\n\nTrain:\n\n\n* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line\n* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line\n* Batch 2: data collected in-between October 2021-June 2022 from all workers\n\n\nTest:\n\n\n* Batch 3: data collected in July 2022 together with the system usability study run\n\n\nSentence level statistics:\n\n\n\nToken level statistics:\n[Needs to be added]\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset provides task-oriented dialogues that solve a very domain specific problem.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data was generated by workers at the CiP. The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the paper.", "#### Who are the source language producers?\n\n\nGerman factory workers working at the CiP", "### Annotations", "#### Annotation process\n\n\nToken level. Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.\n\n\nSentence level. Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the TexPrax demo paper.", "#### Who are the annotators?\n\n\nToken level. Researchers working at the CiP.\n\n\nSentence level. The factory workers themselves.", "### Personal and Sensitive Information\n\n\nThis dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nInformal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold.", "### Discussion of Biases\n\n\nThe dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines).", "### Other Known Limitations\n\n\nAdditional Information\n----------------------\n\n\nYou can download the data via:\n\n\nPlease find more information about the code and how the data was collected on GitHub.", "### Dataset Curators\n\n\nCuration is managed by our data manager at UKP.", "### Licensing Information\n\n\nCC-by-NC 4.0\n\n\nPlease cite this data using:", "### Contributions\n\n\nThanks to @Wuhn for adding this dataset.\n\n\nTags\n----\n\n\nannotations\\_creators:\n\n\n* expert-generated\n\n\nlanguage:\n\n\n* de\n\n\nlanguage\\_creators:\n\n\n* expert-generated\n\n\nlicense:\n\n\n* cc-by-nc-4.0\n\n\nmultilinguality:\n\n\n* monolingual\n\n\npretty\\_name: TexPrax-Conversations\n\n\nsize\\_categories:\n\n\n* n<1K\n* 1K<n<10K\n\n\nsource\\_datasets:\n\n\n* original\n\n\ntags:\n\n\n* dialog\n* expert to expert conversations\n* task-oriented\n\n\ntask\\_categories:\n\n\n* token-classification\n* text-classification\n\n\ntask\\_ids:\n\n\n* named-entity-recognition\n* multi-class-classification" ]

[ "TAGS\n#license-cc-by-nc-4.0 #arxiv-2208.07846 #region-us \n", "### Dataset Summary\n\n\nThis dataset contains dialogues collected from German factory workers at the *Center for industrial productivity* (CiP). The dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.", "### Supported Tasks and Leaderboards\n\n\nThis dataset supports the following tasks:\n\n\n* Sentence classification\n* Named entity recognition (will be updated soon with the new indexing)\n* Dialog generation (so far not evaluated)", "### Languages\n\n\nGerman\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOn sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit.\n\n\nOn token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.", "### Data Fields\n\n\nSentence level:\n\n\n* dialog-id: unique identifier for the dialog\n* turn-id: unique identifier for the turn\n* sentence-id: unique identifier for the dialog\n* sentence: the respective sentence\n* label: the label (*P* for Problem, *C* for Cause, *S* for solution, and *O* for Other)\n* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).\n* subsplit: the respective subsplit of the data (see below)\n\n\nToken level:\n\n\n* id: the identifier\n* tokens: a list of tokens (i.e., the tokenized dialogue)\n* entities: the named entity in a BIO scheme (*B-X*, *I-X*, or O).\n* subsplit: the respective subsplit of the data (see below)", "### Data Splits\n\n\nThe dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory.\n\n\nTrain:\n\n\n* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line\n* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line\n* Batch 2: data collected in-between October 2021-June 2022 from all workers\n\n\nTest:\n\n\n* Batch 3: data collected in July 2022 together with the system usability study run\n\n\nSentence level statistics:\n\n\n\nToken level statistics:\n[Needs to be added]\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset provides task-oriented dialogues that solve a very domain specific problem.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data was generated by workers at the CiP. The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the paper.", "#### Who are the source language producers?\n\n\nGerman factory workers working at the CiP", "### Annotations", "#### Annotation process\n\n\nToken level. Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.\n\n\nSentence level. Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the TexPrax demo paper.", "#### Who are the annotators?\n\n\nToken level. Researchers working at the CiP.\n\n\nSentence level. The factory workers themselves.", "### Personal and Sensitive Information\n\n\nThis dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nInformal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold.", "### Discussion of Biases\n\n\nThe dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines).", "### Other Known Limitations\n\n\nAdditional Information\n----------------------\n\n\nYou can download the data via:\n\n\nPlease find more information about the code and how the data was collected on GitHub.", "### Dataset Curators\n\n\nCuration is managed by our data manager at UKP.", "### Licensing Information\n\n\nCC-by-NC 4.0\n\n\nPlease cite this data using:", "### Contributions\n\n\nThanks to @Wuhn for adding this dataset.\n\n\nTags\n----\n\n\nannotations\\_creators:\n\n\n* expert-generated\n\n\nlanguage:\n\n\n* de\n\n\nlanguage\\_creators:\n\n\n* expert-generated\n\n\nlicense:\n\n\n* cc-by-nc-4.0\n\n\nmultilinguality:\n\n\n* monolingual\n\n\npretty\\_name: TexPrax-Conversations\n\n\nsize\\_categories:\n\n\n* n<1K\n* 1K<n<10K\n\n\nsource\\_datasets:\n\n\n* original\n\n\ntags:\n\n\n* dialog\n* expert to expert conversations\n* task-oriented\n\n\ntask\\_categories:\n\n\n* token-classification\n* text-classification\n\n\ntask\\_ids:\n\n\n* named-entity-recognition\n* multi-class-classification" ]

[ "task_categories:automatic-speech-recognition", "annotations_creators:machine-generated", "language_creators:other", "multilinguality:multilingual", "source_datasets:extended|common_voice", "source_datasets:MLCommons/ml_spoken_words", "language:eng", "language:en", "language:spa", "language:es", "language:ind", "language:id", "license:cc-by-4.0", "other-keyword-spotting", "region:us" ]

[]

[ "eng", "en", "spa", "es", "ind", "id" ]

[ "# Dataset Card for Audio Keyword Spotting", "## Table of Contents\n- Table of Contents", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: SIL AI email\n- Source Data: MLCommons/ml_spoken_words, trabina GitHub \n\n!sil-ai logo", "## Dataset Summary\n\nThe initial version of this dataset is a subset of MLCommons/ml_spoken_words, which is derived from Common Voice, designed for easier loading. Specifically, the subset consists of 'ml_spoken_words' files filtered by the names and placenames transliterated in Bible translations, as found in trabina. For our initial experiment, we have focused only on English, Spanish, and Indonesian, three languages whose name spellings are frequently used in other translations. We anticipate growing this dataset in the future to include additional keywords and other languages as the experiment progresses.", "### Data Fields\n\n* file: strinrelative audio path inside the archive\n* is_valid: if a sample is valid\n* language: language of an instance. \n* speaker_id: unique id of a speaker. Can be \"NA\" if an instance is invalid\n* gender: speaker gender. Can be one of '[\"MALE\", \"FEMALE\", \"OTHER\", \"NAN\"]'\n* keyword: word spoken in a current sample\n* audio: a dictionary containing the relative path to the audio file, \nthe decoded audio array, and the sampling rate. \nNote that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically \ndecoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of \na large number of audio files might take a significant amount of time. \nThus, it is important to first query the sample index before the \"audio\" column, \ni.e. 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'", "### Data Splits\n\nThe data for each language is splitted into train / validation / test parts.", "## Supported Tasks\nKeyword spotting and spoken term search", "### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. \nYou agree to not attempt to determine the identity of speakers.", "### Licensing Information\n\nThe dataset is licensed under CC-BY 4.0 and can be used for academic\nresearch and commercial applications in keyword spotting and spoken term search." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-machine-generated #language_creators-other #multilinguality-multilingual #source_datasets-extended|common_voice #source_datasets-MLCommons/ml_spoken_words #language-English #language-English #language-Spanish #language-Spanish #language-Indonesian #language-Indonesian #license-cc-by-4.0 #other-keyword-spotting #region-us \n", "# Dataset Card for Audio Keyword Spotting", "## Table of Contents\n- Table of Contents", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: SIL AI email\n- Source Data: MLCommons/ml_spoken_words, trabina GitHub \n\n!sil-ai logo", "## Dataset Summary\n\nThe initial version of this dataset is a subset of MLCommons/ml_spoken_words, which is derived from Common Voice, designed for easier loading. Specifically, the subset consists of 'ml_spoken_words' files filtered by the names and placenames transliterated in Bible translations, as found in trabina. For our initial experiment, we have focused only on English, Spanish, and Indonesian, three languages whose name spellings are frequently used in other translations. We anticipate growing this dataset in the future to include additional keywords and other languages as the experiment progresses.", "### Data Fields\n\n* file: strinrelative audio path inside the archive\n* is_valid: if a sample is valid\n* language: language of an instance. \n* speaker_id: unique id of a speaker. Can be \"NA\" if an instance is invalid\n* gender: speaker gender. Can be one of '[\"MALE\", \"FEMALE\", \"OTHER\", \"NAN\"]'\n* keyword: word spoken in a current sample\n* audio: a dictionary containing the relative path to the audio file, \nthe decoded audio array, and the sampling rate. \nNote that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically \ndecoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of \na large number of audio files might take a significant amount of time. \nThus, it is important to first query the sample index before the \"audio\" column, \ni.e. 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'", "### Data Splits\n\nThe data for each language is splitted into train / validation / test parts.", "## Supported Tasks\nKeyword spotting and spoken term search", "### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. \nYou agree to not attempt to determine the identity of speakers.", "### Licensing Information\n\nThe dataset is licensed under CC-BY 4.0 and can be used for academic\nresearch and commercial applications in keyword spotting and spoken term search." ]

[ "region:us" ]

[]

[]

[ "### Dataset Summary\n\nInput data for the first phase of BERT pretraining (sequence length 128). All text is tokenized with bert-base-uncased tokenizer. \nData is obtained by concatenating and shuffling wikipedia (split: 'URL') and bookcorpusopen datasets and running reference BERT data preprocessor without masking and input duplication ('dupe_factor = 1'). Documents are split into sentences with the NLTK sentence tokenizer ('nltk.tokenize.sent_tokenize').\n\nSee the dataset for the second phase of pretraining: bert_pretrain_phase2." ]

[ "TAGS\n#region-us \n", "### Dataset Summary\n\nInput data for the first phase of BERT pretraining (sequence length 128). All text is tokenized with bert-base-uncased tokenizer. \nData is obtained by concatenating and shuffling wikipedia (split: 'URL') and bookcorpusopen datasets and running reference BERT data preprocessor without masking and input duplication ('dupe_factor = 1'). Documents are split into sentences with the NLTK sentence tokenizer ('nltk.tokenize.sent_tokenize').\n\nSee the dataset for the second phase of pretraining: bert_pretrain_phase2." ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:other", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ru", "license:other", "region:us" ]

[]

[ "ru" ]

[ "### Dataset Summary\n\n\nCollection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags. Dataset is based on collection Persons-1000 originally containing 1000 news documents labeled only with names of persons.\n\n\nAdditional labels were obtained using guidelines similar to MUC-7 with web-based tool Brat for collaborative text annotation.\n\n\nCurrently dataset contains 26K annotated named entities (11K Persons, 7K Locations and 8K Organizations).\n\n\nConversion to the IOB2 format and splitting into train, validation and test sets was done by DeepPavlov team.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nRussian\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* id: a string feature.\n* tokens: a list of string features.\n* ner\\_tags: a list of classification labels (int). Full tagset with indices:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-other #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Russian #license-other #region-us \n", "### Dataset Summary\n\n\nCollection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags. Dataset is based on collection Persons-1000 originally containing 1000 news documents labeled only with names of persons.\n\n\nAdditional labels were obtained using guidelines similar to MUC-7 with web-based tool Brat for collaborative text annotation.\n\n\nCurrently dataset contains 26K annotated named entities (11K Persons, 7K Locations and 8K Organizations).\n\n\nConversion to the IOB2 format and splitting into train, validation and test sets was done by DeepPavlov team.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nRussian\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* id: a string feature.\n* tokens: a list of string features.\n* ner\\_tags: a list of classification labels (int). Full tagset with indices:", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information" ]

[ "region:us" ]

[]

[]

[ "### Dataset Summary\n\nInput data for the second phase of BERT pretraining (sequence length 512). All text is tokenized with bert-base-uncased tokenizer. \nData is obtained by concatenating and shuffling wikipedia (split: 'URL') and bookcorpusopen datasets and running reference BERT data preprocessor without masking and input duplication ('dupe_factor = 1'). Documents are split into sentences with the NLTK sentence tokenizer ('nltk.tokenize.sent_tokenize').\n\nSee the dataset for the first phase of pretraining: bert_pretrain_phase1." ]

[ "TAGS\n#region-us \n", "### Dataset Summary\n\nInput data for the second phase of BERT pretraining (sequence length 512). All text is tokenized with bert-base-uncased tokenizer. \nData is obtained by concatenating and shuffling wikipedia (split: 'URL') and bookcorpusopen datasets and running reference BERT data preprocessor without masking and input duplication ('dupe_factor = 1'). Documents are split into sentences with the NLTK sentence tokenizer ('nltk.tokenize.sent_tokenize').\n\nSee the dataset for the first phase of pretraining: bert_pretrain_phase1." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: Graphcore/deberta-base-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: Graphcore/deberta-base-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: yuvraj/summarizer-cnndm\n* Dataset: sepidmnorozy/Urdu_sentiment\n* Config: sepidmnorozy--Urdu_sentiment\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mwz for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: yuvraj/summarizer-cnndm\n* Dataset: sepidmnorozy/Urdu_sentiment\n* Config: sepidmnorozy--Urdu_sentiment\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mwz for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa2\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-gc1\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-gc1\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deb-base-gc2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deb-base-gc2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-gc1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-gc1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deb-base-gc2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deb-base-gc2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-gc1\n* Dataset: quoref\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-gc1\n* Dataset: quoref\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deb-base-gc2\n* Dataset: quoref\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/deb-base-gc2\n* Dataset: quoref\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa1\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa1\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa1\n* Dataset: quoref\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nbroad/rob-base-superqa1\n* Dataset: quoref\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "license:apache-2.0", "arxiv:2205.09073", "region:us" ]

[ "2205.09073" ]

[]

[]

[ "TAGS\n#license-apache-2.0 #arxiv-2205.09073 #region-us \n" ]

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

[]

[ "en" ]

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

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