Datasets:

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ftopal

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huggingface-datasets

like 0

[ "arxiv:1605.07683", "arxiv:1502.05698", "region:us" ]

[ "1605.07683", "1502.05698" ]

[]

[ "# Dialog bAbI tasks data\nIn this directory is the set of 6 tasks for testing end-to-end dialog systems in the restaurant domain as described in the paper \"Learning End-to-End Goal-Oriented Dialog\" by Bordes & Weston (URL The aim is that each task tests a unique aspect of dialog. Tasks are designed to complement the set of 20 bAbI tasks for story understanding already released with the paper \"Towards AI Complete Question Answering: A Set of Prerequisite Toy Tasks\" by Weston et al. (URL", "## Data\nFor each task, there are 1000 dialogs for training, 1000 for development and 1000 for testing. For tasks 1-5, we also include a second test set (with suffix -URL) that contains dialogs including entities not present in training and development sets.\n\nThe file format for each task is as follows:\n'ID user_utterance [tab] bot_utterances'\n\nThe IDs for a given dialog start at 1 and increase. When the IDs in a file reset back to 1 you can consider the following sentences as a new dialog. When the bot speaks two times in a row, we used the special token \"<SILENCE>\" to fill in for the missing user utterance.\n\nFor example (for task 1):\n\n\nThe goal of the tasks is to predict the bot utterances, that can be sentences or API calls (sentences starting with the special token \"api_call\").\n\nAlong with the train, dev and test sets, we also include a knowledge base file (URL) that contain all entities appearing in dialogs for tasks 1-5. We also include a file containing the candidates to select the answer from (URL) for tasks 1-5, that is simply made of all the bot utterances in train, dev, test for these tasks.\n\nTask 6 is a bit different since its data comes from the Dialog State Tracking Challenge 2 (URL which we modified to convert it into the same format as the other tasks. There is no OOV test set associated with this task and the knowledge base (URL) is imperfect. This task has its own candidates file (URL).", "## License\nThis dataset is released under Creative Commons Attribution 3.0 Unported license. A copy of this license is included with the data.", "## Contact\nThe author of this porting is Alessandro Suglia and he has only made available the dataset via\nHuggingface datasets.\nFor more details on the dataset and baselines, see the paper \"Learning End-to-End Goal-Oriented Dialog\" by Antoine Bordes and Jason Weston (URL For any information, contact Antoine Bordes : abordes (at) fb (dot) com ." ]

[ "TAGS\n#arxiv-1605.07683 #arxiv-1502.05698 #region-us \n", "# Dialog bAbI tasks data\nIn this directory is the set of 6 tasks for testing end-to-end dialog systems in the restaurant domain as described in the paper \"Learning End-to-End Goal-Oriented Dialog\" by Bordes & Weston (URL The aim is that each task tests a unique aspect of dialog. Tasks are designed to complement the set of 20 bAbI tasks for story understanding already released with the paper \"Towards AI Complete Question Answering: A Set of Prerequisite Toy Tasks\" by Weston et al. (URL", "## Data\nFor each task, there are 1000 dialogs for training, 1000 for development and 1000 for testing. For tasks 1-5, we also include a second test set (with suffix -URL) that contains dialogs including entities not present in training and development sets.\n\nThe file format for each task is as follows:\n'ID user_utterance [tab] bot_utterances'\n\nThe IDs for a given dialog start at 1 and increase. When the IDs in a file reset back to 1 you can consider the following sentences as a new dialog. When the bot speaks two times in a row, we used the special token \"<SILENCE>\" to fill in for the missing user utterance.\n\nFor example (for task 1):\n\n\nThe goal of the tasks is to predict the bot utterances, that can be sentences or API calls (sentences starting with the special token \"api_call\").\n\nAlong with the train, dev and test sets, we also include a knowledge base file (URL) that contain all entities appearing in dialogs for tasks 1-5. We also include a file containing the candidates to select the answer from (URL) for tasks 1-5, that is simply made of all the bot utterances in train, dev, test for these tasks.\n\nTask 6 is a bit different since its data comes from the Dialog State Tracking Challenge 2 (URL which we modified to convert it into the same format as the other tasks. There is no OOV test set associated with this task and the knowledge base (URL) is imperfect. This task has its own candidates file (URL).", "## License\nThis dataset is released under Creative Commons Attribution 3.0 Unported license. A copy of this license is included with the data.", "## Contact\nThe author of this porting is Alessandro Suglia and he has only made available the dataset via\nHuggingface datasets.\nFor more details on the dataset and baselines, see the paper \"Learning End-to-End Goal-Oriented Dialog\" by Antoine Bordes and Jason Weston (URL For any information, contact Antoine Bordes : abordes (at) fb (dot) com ." ]

[ "arxiv:2108.06897", "region:us" ]

[ "2108.06897" ]

[]

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[ "TAGS\n#arxiv-2108.06897 #region-us \n" ]

[ "arxiv:2203.06486", "region:us" ]

[ "2203.06486" ]

[]

[ "### Note\n\n\nThe original paper published two sub-datasets one collected from statista and the other from pew. The dataset upload here is from statista. Images can be downloaded from the github repo mentioned above.\n\n\nLangugage\n=========\n\n\nThe data is in english and the summaries are in english.\n\n\nDataset split\n=============\n\n\n\nName of Contributor: Saad Obaid ul Islam" ]

[ "TAGS\n#arxiv-2203.06486 #region-us \n", "### Note\n\n\nThe original paper published two sub-datasets one collected from statista and the other from pew. The dataset upload here is from statista. Images can be downloaded from the github repo mentioned above.\n\n\nLangugage\n=========\n\n\nThe data is in english and the summaries are in english.\n\n\nDataset split\n=============\n\n\n\nName of Contributor: Saad Obaid ul Islam" ]

[ "region:us" ]

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

[ "region:us" ]

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

[ "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: google/pegasus-large\n* Dataset: pn_summary\n* Config: 1.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @marsraker09 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/pegasus-large\n* Dataset: pn_summary\n* Config: 1.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @marsraker09 for evaluating this model." ]

[ "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: facebook/bart-large-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @AlekseyKorshuk 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: facebook/bart-large-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @AlekseyKorshuk for evaluating this model." ]

[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ro", "license:apache-2.0", "hate-speech-detection", "region:us" ]

[]

[ "ro" ]

[ "### Dataset Summary\n\n\nFB-RO-Offense corpus, an offensive speech dataset containing 4,455 user-generated comments from Facebook live broadcasts available in Romanian\n\n\nThe annotation follows the hierarchical tagset proposed in the Germeval 2018 Dataset.\nThe following Classes are available:\n\n\n* OTHER: Non-Offensive Language\n* OFFENSIVE:\n\t+ PROFANITY\n\t+ INSULT\n\t+ ABUSE", "### Languages\n\n\nRomanian\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'sender': a 'string' feature.\n* 'no\\_reacts': a 'integer'\n* 'text': a 'string'.\n* 'label': categorical 'OTHER', 'PROFANITY', 'INSULT', 'ABUSE'", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nCollecting data for abusive language classification for Romanian Language.", "### Source Data\n\n\nFacebook comments", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\nSocial media users", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nNative speakers", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. No PII removal has been performed.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThis data is available and distributed under Apache-2.0 license", "### Contributions" ]

[ "TAGS\n#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Romanian #license-apache-2.0 #hate-speech-detection #region-us \n", "### Dataset Summary\n\n\nFB-RO-Offense corpus, an offensive speech dataset containing 4,455 user-generated comments from Facebook live broadcasts available in Romanian\n\n\nThe annotation follows the hierarchical tagset proposed in the Germeval 2018 Dataset.\nThe following Classes are available:\n\n\n* OTHER: Non-Offensive Language\n* OFFENSIVE:\n\t+ PROFANITY\n\t+ INSULT\n\t+ ABUSE", "### Languages\n\n\nRomanian\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\n* 'sender': a 'string' feature.\n* 'no\\_reacts': a 'integer'\n* 'text': a 'string'.\n* 'label': categorical 'OTHER', 'PROFANITY', 'INSULT', 'ABUSE'", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nCollecting data for abusive language classification for Romanian Language.", "### Source Data\n\n\nFacebook comments", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?\n\n\nSocial media users", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nNative speakers", "### Personal and Sensitive Information\n\n\nThe data was public at the time of collection. No PII removal has been performed.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThis data is available and distributed under Apache-2.0 license", "### Contributions" ]

[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "source_datasets:original", "arxiv:2201.06642", "arxiv:2103.12028", "region:us" ]

[ "2201.06642", "2103.12028" ]

[]

[ "## Dataset Summary\n\nThis dataset obtained by language classification and filtering of the Common Crawl (May-2022) corpus using the ungoliant architecture. Data is distributed by taking Indonesian language only.", "### Loader\nTBD", "### Licensing Information\n These data are released under this licensing scheme\n We do not own any of the text from which these data has been extracted.\n We license the actual packaging of these data under the Creative Commons CC0 license (\"no rights reserved\") URL\n To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR\n This work is published from: France.\n Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:\n * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.\n * Clearly identify the copyrighted work claimed to be infringed.\n * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.\n We will comply to legitimate requests by removing the affected sources from the next release of the corpus." ]

[ "TAGS\n#task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #source_datasets-original #arxiv-2201.06642 #arxiv-2103.12028 #region-us \n", "## Dataset Summary\n\nThis dataset obtained by language classification and filtering of the Common Crawl (May-2022) corpus using the ungoliant architecture. Data is distributed by taking Indonesian language only.", "### Loader\nTBD", "### Licensing Information\n These data are released under this licensing scheme\n We do not own any of the text from which these data has been extracted.\n We license the actual packaging of these data under the Creative Commons CC0 license (\"no rights reserved\") URL\n To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR\n This work is published from: France.\n Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:\n * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.\n * Clearly identify the copyrighted work claimed to be infringed.\n * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.\n We will comply to legitimate requests by removing the affected sources from the next release of the corpus." ]

[ "task_categories:unconditional-image-generation", "size_categories:100K<n<1M", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-unconditional-image-generation #size_categories-100K<n<1M #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Questions-vs-Statements-Classification", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information", "## Dataset Description\n\n- Homepage: Kaggle\n- Point of Contact: Shahrukh Khan", "### Dataset Summary\n\nA dataset containing statements and questions with their corresponding labels.", "### Supported Tasks and Leaderboards\n\nmulti-class-classification", "### Languages\n\nen", "## Dataset Structure", "### Data Splits\n\nTrain Test Valid", "## Dataset Creation", "### Curation Rationale\n\n\nThe goal of this project is to classify sentences, based on type:\nStatement (Declarative Sentence)\nQuestion (Interrogative Sentence)", "### Source Data\nKaggle", "#### Initial Data Collection and Normalization\n\nThe dataset is created by parsing out the SQuAD dataset and combining it with the SPAADIA dataset.", "### Other Known Limitations\n\nQuestions in this case ar are only one sentence, statements are a single sentence or more. They are classified correctly but don't include sentences prior to questions.", "## Additional Information", "### Dataset Curators\n\nSHAHRUKH KHAN", "### Licensing Information\n\nCC0: Public Domain" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Questions-vs-Statements-Classification", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information", "## Dataset Description\n\n- Homepage: Kaggle\n- Point of Contact: Shahrukh Khan", "### Dataset Summary\n\nA dataset containing statements and questions with their corresponding labels.", "### Supported Tasks and Leaderboards\n\nmulti-class-classification", "### Languages\n\nen", "## Dataset Structure", "### Data Splits\n\nTrain Test Valid", "## Dataset Creation", "### Curation Rationale\n\n\nThe goal of this project is to classify sentences, based on type:\nStatement (Declarative Sentence)\nQuestion (Interrogative Sentence)", "### Source Data\nKaggle", "#### Initial Data Collection and Normalization\n\nThe dataset is created by parsing out the SQuAD dataset and combining it with the SPAADIA dataset.", "### Other Known Limitations\n\nQuestions in this case ar are only one sentence, statements are a single sentence or more. They are classified correctly but don't include sentences prior to questions.", "## Additional Information", "### Dataset Curators\n\nSHAHRUKH KHAN", "### Licensing Information\n\nCC0: Public Domain" ]

[ "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: t5-large\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @shahbazsyed 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: t5-large\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @shahbazsyed 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: saattrupdan/nbailab-base-ner-scandi\n* Dataset: dane\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @KennethEnevoldsen 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: saattrupdan/nbailab-base-ner-scandi\n* Dataset: dane\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @KennethEnevoldsen 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: patrickvonplaten/bert2bert_cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mumumumu 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: patrickvonplaten/bert2bert_cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mumumumu for evaluating this model." ]

[ "task_categories:image-classification", "task_categories:image-to-text", "task_categories:feature-extraction", "task_ids:multi-class-image-classification", "task_ids:multi-label-image-classification", "task_ids:image-captioning", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:other-iconclass-metadata", "size_categories:10K<n<100K", "license:cc0-1.0", "lam", "art", "region:us" ]

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[ "# Dataset Card for Brill Iconclass AI Test Set", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:URL\n- Paper:URL\n- Leaderboard:\n- Point of Contact:info@URL", "### Dataset Summary\n\n> A test dataset and challenge to apply machine learning to collections described with the Iconclass classification system.\n\nThis dataset contains '87749' images with Iconclass metadata assigned to the images. The iconclass metadata classification system is intended to provide 'the comprehensive classification system for the content of images.'.\n\n> Iconclass was developed in the Netherlands as a standard classification for recording collections, with the idea of assembling huge databases that will allow the retrieval of images featuring particular details, subjects or other common factors. It was developed in the 1970s and was loosely based on the Dewey Decimal System because it was meant to be used in art library card catalogs. source\n\nThe Iconclass \n\n> view of the world is subdivided in 10 main categories...An Iconclass concept consists of an alphanumeric class number (“notation”) and a corresponding content definition (“textual correlate”). An object can be tagged with as many concepts as the user sees fit. source\n\nThese ten divisions are as follows:\n\n- 0 Abstract, Non-representational Art\n- 1 Religion and Magic\n- 2 Nature\n- 3 Human being, Man in general\n- 4 Society, Civilization, Culture\n- 5 Abstract Ideas and Concepts\n- 6 History\n- 7 Bible\n- 8 Literature\n- 9 Classical Mythology and Ancient History\n\nWithin each of these divisions further subdivision's are possible (9 or 10 subdivisions). For example, under '4 Society, Civilization, Culture', one can find: \n\n- 41 · material aspects of daily life\n- 42 · family, descendance\n- 43 · recreation, amusement\n- 44 · state; law; political life\n- ... \n\nSee URL for the full list. \n\n\nTo illustrate we can look at some example Iconclass classifications. \n\n'41A12' represents 'castle'. This classification is generated via building from the 'base' division '4', with the following attributes: \n\n- 4 · Society, Civilization, Culture\n- 41 · material aspects of daily life\n- 41A · housing\n- 41A1 · civic architecture; edifices; dwellings \n\nsource\n\nThe construction of Iconclass of parts makes it particularly interesting (and challenging) to tackle via Machine Learning. Whilst one could tackle this dataset as a (multi) label image classification problem, this is only one way of tackling it. For example in the above label 'castle' giving the model the 'freedom' to predict only a partial label could result in the prediction '41A' i.e. housing. Whilst a very particular form of housing this prediction for 'castle' is not 'wrong' so much as it is not as precise as a human cataloguer may provide.", "### Supported Tasks and Leaderboards\n\nAs discussed above this dataset could be tackled in various ways:\n\n- as an image classification task\n- as a multi-label classification task \n- as an image to text task\n- as a task whereby a model predicts partial sequences of the label. \n\nThis list is not exhaustive.", "### Languages\n\nThis dataset doesn't have a natural language. The labels themselves can be treated as a form of language i.e. the label can be thought of as a sequence of tokens that construct a 'sentence'.", "## Dataset Structure\n\nThe dataset contains a single configuration.", "### Data Instances\n\nAn example instance of the dataset is as follows:", "### Data Fields\n\nThe dataset is made up of\n\n- an image \n- a sequence of Iconclass labels", "### Data Splits\n\nThe dataset doesn't provide any predefined train, validation or test splits.", "## Dataset Creation\n\n> To facilitate the creation of better models in the cultural heritage domain, and promote the research on tools and techniques using Iconclass, we are making this dataset freely available. All that we ask is that any use is acknowledged and results be shared so that we can all benefit. The content is sampled from the Arkyves database. source", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe images are samples from the Arkyves database. This collection includes images from \n\n> from libraries and museums in many countries, including the Rijksmuseum in Amsterdam, the Netherlands Institute for Art History (RKD), the Herzog August Bibliothek in Wolfenbüttel, and the university libraries of Milan, Utrecht and Glasgow. source", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\nThe annotations are derived from the source dataset see above. Most annotations were likely created by staff with experience with the Iconclass metadata schema.", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nIconclass as a metadata standard absorbs biases from the time and place of its creation (1940s Netherlands). In particular, '32B human races, peoples; nationalities' has been subject to criticism. '32B36 'primitive', 'pre-modern' peoples' is one example of a category which we may not wish to adopt. In general, there are components of the subdivisions of '32B' which reflect a belief that race is a scientific category rather than socially constructed. \n\nThe Iconclass community is actively exploring these limitations; for example, see Revising Iconclass section 32B human races, peoples; nationalities. \n\n\nOne should be aware of these limitations to Iconclass, and in particular, before deploying a model trained on this data in any production settings.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nEtienne Posthumus", "### Licensing Information\nCC0 1.0", "### Contributions\n\nThanks to @davanstrien for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_categories-image-to-text #task_categories-feature-extraction #task_ids-multi-class-image-classification #task_ids-multi-label-image-classification #task_ids-image-captioning #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-other-iconclass-metadata #size_categories-10K<n<100K #license-cc0-1.0 #lam #art #region-us \n", "# Dataset Card for Brill Iconclass AI Test Set", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:URL\n- Paper:URL\n- Leaderboard:\n- Point of Contact:info@URL", "### Dataset Summary\n\n> A test dataset and challenge to apply machine learning to collections described with the Iconclass classification system.\n\nThis dataset contains '87749' images with Iconclass metadata assigned to the images. The iconclass metadata classification system is intended to provide 'the comprehensive classification system for the content of images.'.\n\n> Iconclass was developed in the Netherlands as a standard classification for recording collections, with the idea of assembling huge databases that will allow the retrieval of images featuring particular details, subjects or other common factors. It was developed in the 1970s and was loosely based on the Dewey Decimal System because it was meant to be used in art library card catalogs. source\n\nThe Iconclass \n\n> view of the world is subdivided in 10 main categories...An Iconclass concept consists of an alphanumeric class number (“notation”) and a corresponding content definition (“textual correlate”). An object can be tagged with as many concepts as the user sees fit. source\n\nThese ten divisions are as follows:\n\n- 0 Abstract, Non-representational Art\n- 1 Religion and Magic\n- 2 Nature\n- 3 Human being, Man in general\n- 4 Society, Civilization, Culture\n- 5 Abstract Ideas and Concepts\n- 6 History\n- 7 Bible\n- 8 Literature\n- 9 Classical Mythology and Ancient History\n\nWithin each of these divisions further subdivision's are possible (9 or 10 subdivisions). For example, under '4 Society, Civilization, Culture', one can find: \n\n- 41 · material aspects of daily life\n- 42 · family, descendance\n- 43 · recreation, amusement\n- 44 · state; law; political life\n- ... \n\nSee URL for the full list. \n\n\nTo illustrate we can look at some example Iconclass classifications. \n\n'41A12' represents 'castle'. This classification is generated via building from the 'base' division '4', with the following attributes: \n\n- 4 · Society, Civilization, Culture\n- 41 · material aspects of daily life\n- 41A · housing\n- 41A1 · civic architecture; edifices; dwellings \n\nsource\n\nThe construction of Iconclass of parts makes it particularly interesting (and challenging) to tackle via Machine Learning. Whilst one could tackle this dataset as a (multi) label image classification problem, this is only one way of tackling it. For example in the above label 'castle' giving the model the 'freedom' to predict only a partial label could result in the prediction '41A' i.e. housing. Whilst a very particular form of housing this prediction for 'castle' is not 'wrong' so much as it is not as precise as a human cataloguer may provide.", "### Supported Tasks and Leaderboards\n\nAs discussed above this dataset could be tackled in various ways:\n\n- as an image classification task\n- as a multi-label classification task \n- as an image to text task\n- as a task whereby a model predicts partial sequences of the label. \n\nThis list is not exhaustive.", "### Languages\n\nThis dataset doesn't have a natural language. The labels themselves can be treated as a form of language i.e. the label can be thought of as a sequence of tokens that construct a 'sentence'.", "## Dataset Structure\n\nThe dataset contains a single configuration.", "### Data Instances\n\nAn example instance of the dataset is as follows:", "### Data Fields\n\nThe dataset is made up of\n\n- an image \n- a sequence of Iconclass labels", "### Data Splits\n\nThe dataset doesn't provide any predefined train, validation or test splits.", "## Dataset Creation\n\n> To facilitate the creation of better models in the cultural heritage domain, and promote the research on tools and techniques using Iconclass, we are making this dataset freely available. All that we ask is that any use is acknowledged and results be shared so that we can all benefit. The content is sampled from the Arkyves database. source", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe images are samples from the Arkyves database. This collection includes images from \n\n> from libraries and museums in many countries, including the Rijksmuseum in Amsterdam, the Netherlands Institute for Art History (RKD), the Herzog August Bibliothek in Wolfenbüttel, and the university libraries of Milan, Utrecht and Glasgow. source", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\nThe annotations are derived from the source dataset see above. Most annotations were likely created by staff with experience with the Iconclass metadata schema.", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nIconclass as a metadata standard absorbs biases from the time and place of its creation (1940s Netherlands). In particular, '32B human races, peoples; nationalities' has been subject to criticism. '32B36 'primitive', 'pre-modern' peoples' is one example of a category which we may not wish to adopt. In general, there are components of the subdivisions of '32B' which reflect a belief that race is a scientific category rather than socially constructed. \n\nThe Iconclass community is actively exploring these limitations; for example, see Revising Iconclass section 32B human races, peoples; nationalities. \n\n\nOne should be aware of these limitations to Iconclass, and in particular, before deploying a model trained on this data in any production settings.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nEtienne Posthumus", "### Licensing Information\nCC0 1.0", "### Contributions\n\nThanks to @davanstrien for adding this dataset." ]

[ "multilinguality:monolingual", "source_datasets:aeslc", "language:en", "license:mit", "text2text generation", "email", "email generation", "enron", "region:us" ]

[]

[ "en" ]

[ "## about\n\n- aeslc dataset but cleaned and keywords extracted to a new column\n- an EDA website generated via pandas profiling is on netlify here", "## Python usage\n\n\nBasic example notebook here." ]

[ "TAGS\n#multilinguality-monolingual #source_datasets-aeslc #language-English #license-mit #text2text generation #email #email generation #enron #region-us \n", "## about\n\n- aeslc dataset but cleaned and keywords extracted to a new column\n- an EDA website generated via pandas profiling is on netlify here", "## Python usage\n\n\nBasic example notebook here." ]

[ "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: huggingface-course/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @jordyvl 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: huggingface-course/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @jordyvl for evaluating this model." ]

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

[]

[ "es" ]

[ "# 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#multilinguality-monolingual #language-Spanish #license-cc-by-nc-4.0 #region-us \n", "# 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", "task_ids:sentiment-classification", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc0-1.0", "arxiv:2005.11140", "region:us" ]

[ "2005.11140" ]

[ "en" ]

[ "# Dataset Card for atypical_animacy", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: Mariona Coll Ardanuy, Daniel CS Wilson", "### Dataset Summary\n\nAtypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library. This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence.", "### Supported Tasks and Leaderboards\n\n- 'text-classification' - This dataset can be used to determine if a mention of an entity in a document was humanlike or not\n- 'entity-recognition' - The dataset can be used to fine tune large models for NER, albeit for a very specific use case", "### Languages\n\nThe text in the dataset is in English, as written by authors of books digitized by the British Library. The associated BCP-47 code in 'en'", "## Dataset Structure\n\nThe dataset has a single configuration", "### Data Instances\n\nAn example data point", "### Data Fields\n\n- id: sentence identifier according to internal Living with Machines BL books indexing.\n- sentence: sentence where target expression occurs.\n- context: sentence where target expression occurs, plus one sentence to the left and one sentence to the right.\n- target: target expression\n- animacy: animacy of the target expression\n- humanness: humanness of the target expression", "### Data Splits\n\nTrain | 598", "## Dataset Creation\n\nThe dataset was created by manually annotating books that had been digitized by the British Library. According to the paper's authors, \n> \"we provide a basis for examining how machines were imagined during the nineteenth century as everything from lifeless mechanical objects to living beings, or even human-like agents that feel, think, and love. We focus on texts from nineteenth-century Britain, a society being transformed by industrialization, as a good candidate for studying the broader issue\"", "### Curation Rationale\n\nFrom the paper: \n> The Stories dataset is largely composed of target expressions that correspond to either typically animate or typically inanimate entities. Even though some cases of unconventional animacy can be found(folktales, in particular, are richer in typically inanimate entities that become animate), these accountfor a very small proportion of the data.6 We decided to create our own dataset (henceforth 19thC Machines dataset) to gain a better sense of the suitability of our method to the problem of atypical animacy detection, with particular attention to the case of animacy of machines in nineteenth-century texts.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe dataset was generated by manually annotating books that have been digitized by the British Library", "#### Who are the source language producers?\n\nThe data was originally produced by British authors in the 19th century. The books were then digitzed whcih produces some noise due to the OCR method. The annotators are from The Alan Turing Institute, The British Library, University of Cambridge, University of Exeter and Queen Mary University of London", "### Annotations", "#### Annotation process\nAnnotation was carried out in two parts. \nFor the intial annotation process, from the paper:\n> \"For human annotators, even history and literature experts, language subtleties made this task extremely subjective. In the first task, we masked the target word (i.e. the machine) in each sentence and asked the annotator to fill the slot with the most likely entity between ‘human’, ‘horse’, and ‘machine’, representing three levels in the animacy hierarchy: human, animal, and object (Comrie, 1989, 185). We asked annotators to stick to the most literal meaning and avoid metaphorical interpretations when possible. The second task was more straightforwardly related to determining the animacy of the target entity, given the same 100 sentences. We asked annotators to provide a score between -2 and 2, with -2 being definitely inanimate, -1 possibly inanimate, 1 possibly animate, and 2 definitely animate. Neutral judgements were not allowed. \"\n\nFor the final annotations, from the paper:\n> A subgroup of five annotators collaboratively wrote the guidelines based on their experience annotating the first batch of sentences, taking into account common discrepancies. After discussion, it was decided that a machine would be tagged as animate if it is described as having traits distinctive of biologically animate beings or human-specific skills, or portrayed as having feelings, emotions, or a soul. Sentences like the ones in example 2 would be considered animate, but an additional annotation layer would be provided to capture the notion of humanness, which would be true if the machine is portrayed as sentient and capable of specifically human emotions, and false if it used to suggest some degree of dehumanization.", "#### Who are the annotators?\n Annotations were carried out by the following people \n- Giorgia Tolfo\n- Ruth Ahnert\n- Kaspar Beelen\n- Mariona Coll Ardanuy\n- Jon Lawrence\n- Katherine McDonough\n- Federico Nanni\n- Daniel CS Wilson", "### Personal and Sensitive Information\n\nThis dataset does not have any personal information since they are digitizations of books from the 19th century. Some passages might be sensitive, but it is not explicitly mentioned in the paper.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThe curators for this dataset are:\n- Kaspar Beelen\n- Mariona Coll Ardanuy\n- Federico Nanni\n- Giorgia Tolfo", "### Licensing Information\nCC0 1.0 Universal Public Domain" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc0-1.0 #arxiv-2005.11140 #region-us \n", "# Dataset Card for atypical_animacy", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: Mariona Coll Ardanuy, Daniel CS Wilson", "### Dataset Summary\n\nAtypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library. This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence.", "### Supported Tasks and Leaderboards\n\n- 'text-classification' - This dataset can be used to determine if a mention of an entity in a document was humanlike or not\n- 'entity-recognition' - The dataset can be used to fine tune large models for NER, albeit for a very specific use case", "### Languages\n\nThe text in the dataset is in English, as written by authors of books digitized by the British Library. The associated BCP-47 code in 'en'", "## Dataset Structure\n\nThe dataset has a single configuration", "### Data Instances\n\nAn example data point", "### Data Fields\n\n- id: sentence identifier according to internal Living with Machines BL books indexing.\n- sentence: sentence where target expression occurs.\n- context: sentence where target expression occurs, plus one sentence to the left and one sentence to the right.\n- target: target expression\n- animacy: animacy of the target expression\n- humanness: humanness of the target expression", "### Data Splits\n\nTrain | 598", "## Dataset Creation\n\nThe dataset was created by manually annotating books that had been digitized by the British Library. According to the paper's authors, \n> \"we provide a basis for examining how machines were imagined during the nineteenth century as everything from lifeless mechanical objects to living beings, or even human-like agents that feel, think, and love. We focus on texts from nineteenth-century Britain, a society being transformed by industrialization, as a good candidate for studying the broader issue\"", "### Curation Rationale\n\nFrom the paper: \n> The Stories dataset is largely composed of target expressions that correspond to either typically animate or typically inanimate entities. Even though some cases of unconventional animacy can be found(folktales, in particular, are richer in typically inanimate entities that become animate), these accountfor a very small proportion of the data.6 We decided to create our own dataset (henceforth 19thC Machines dataset) to gain a better sense of the suitability of our method to the problem of atypical animacy detection, with particular attention to the case of animacy of machines in nineteenth-century texts.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe dataset was generated by manually annotating books that have been digitized by the British Library", "#### Who are the source language producers?\n\nThe data was originally produced by British authors in the 19th century. The books were then digitzed whcih produces some noise due to the OCR method. The annotators are from The Alan Turing Institute, The British Library, University of Cambridge, University of Exeter and Queen Mary University of London", "### Annotations", "#### Annotation process\nAnnotation was carried out in two parts. \nFor the intial annotation process, from the paper:\n> \"For human annotators, even history and literature experts, language subtleties made this task extremely subjective. In the first task, we masked the target word (i.e. the machine) in each sentence and asked the annotator to fill the slot with the most likely entity between ‘human’, ‘horse’, and ‘machine’, representing three levels in the animacy hierarchy: human, animal, and object (Comrie, 1989, 185). We asked annotators to stick to the most literal meaning and avoid metaphorical interpretations when possible. The second task was more straightforwardly related to determining the animacy of the target entity, given the same 100 sentences. We asked annotators to provide a score between -2 and 2, with -2 being definitely inanimate, -1 possibly inanimate, 1 possibly animate, and 2 definitely animate. Neutral judgements were not allowed. \"\n\nFor the final annotations, from the paper:\n> A subgroup of five annotators collaboratively wrote the guidelines based on their experience annotating the first batch of sentences, taking into account common discrepancies. After discussion, it was decided that a machine would be tagged as animate if it is described as having traits distinctive of biologically animate beings or human-specific skills, or portrayed as having feelings, emotions, or a soul. Sentences like the ones in example 2 would be considered animate, but an additional annotation layer would be provided to capture the notion of humanness, which would be true if the machine is portrayed as sentient and capable of specifically human emotions, and false if it used to suggest some degree of dehumanization.", "#### Who are the annotators?\n Annotations were carried out by the following people \n- Giorgia Tolfo\n- Ruth Ahnert\n- Kaspar Beelen\n- Mariona Coll Ardanuy\n- Jon Lawrence\n- Katherine McDonough\n- Federico Nanni\n- Daniel CS Wilson", "### Personal and Sensitive Information\n\nThis dataset does not have any personal information since they are digitizations of books from the 19th century. Some passages might be sensitive, but it is not explicitly mentioned in the paper.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThe curators for this dataset are:\n- Kaspar Beelen\n- Mariona Coll Ardanuy\n- Federico Nanni\n- Giorgia Tolfo", "### Licensing Information\nCC0 1.0 Universal Public Domain" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.\nModel is available here.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nen\n\n\nHow to use\n----------\n\n\nHow to load this dataset directly with the datasets library:\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'text': a 'string' feature.", "### Data Splits\n\n\n\n'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nAbout\n-----\n\n\n*Built by Aleksey Korshuk*\n\n\n\n\n\nFor more details, visit the project repository.\n\n\n![GitHub stars](URL" ]

[ "TAGS\n#language-English #huggingartists #lyrics #region-us \n", "### Dataset Summary\n\n\nThe Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.\nModel is available here.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nen\n\n\nHow to use\n----------\n\n\nHow to load this dataset directly with the datasets library:\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'text': a 'string' feature.", "### Data Splits\n\n\n\n'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nAbout\n-----\n\n\n*Built by Aleksey Korshuk*\n\n\n\n\n\nFor more details, visit the project repository.\n\n\n![GitHub stars](URL" ]

[ "language:lb", "language:de", "language:fr", "language:en", "language:pt", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "lb", "de", "fr", "en", "pt" ]

[ "#### This custom multilingual-multispeaker TTS speech corpus contains 12.800 balanced samples with audio files (wav format sampled with 16000 Hz) and related transcriptions (csv format with two columns) from 18 speakers. The dataset has been assembled from the following sources:\n\n* VCTK : 428 + 426 + 426 english male samples (p259, p274, p286) (CC BY 4.0) \n* LJSpeech : 1280 english female samples (public domain)\n* m-ailabs : 1280 french male samples (public free licence)\n* SIWIS : 1024 french female samples (CC BY 4.0) \n* Rhasspy : 1082 german female samples (CC0 1.0)\n* Thorsten : 1280 german male samples (CC0)\n* TTS-Portuguese-Corpus : 2560 portuguese male samples (CC BY 4.0) \n* Marylux : 663 luxembourgish & 198 german & 256 french female samples (CC BY-NC-SA 4.0) \n* URL : 409 luxembourgish female & 231 luxembourgish male samples (© URL)\n* URL : 1257 luxembourgish male samples (© RTL-CLT-UFA)\n* Charel : 11 luxembourgish boy samples from my grandchild", "#### The dataset has been manually checked and the transcriptions have been expanded and eventually corrected to comply with the audio files. The data structure is equivalent to the mailabs format. The folder nesting is shown below:", "#### Thanks to RTL and to the University of Luxembourg for permission to use and share selected copyrighted data." ]

[ "TAGS\n#language-Luxembourgish #language-German #language-French #language-English #language-Portuguese #license-cc-by-nc-sa-4.0 #region-us \n", "#### This custom multilingual-multispeaker TTS speech corpus contains 12.800 balanced samples with audio files (wav format sampled with 16000 Hz) and related transcriptions (csv format with two columns) from 18 speakers. The dataset has been assembled from the following sources:\n\n* VCTK : 428 + 426 + 426 english male samples (p259, p274, p286) (CC BY 4.0) \n* LJSpeech : 1280 english female samples (public domain)\n* m-ailabs : 1280 french male samples (public free licence)\n* SIWIS : 1024 french female samples (CC BY 4.0) \n* Rhasspy : 1082 german female samples (CC0 1.0)\n* Thorsten : 1280 german male samples (CC0)\n* TTS-Portuguese-Corpus : 2560 portuguese male samples (CC BY 4.0) \n* Marylux : 663 luxembourgish & 198 german & 256 french female samples (CC BY-NC-SA 4.0) \n* URL : 409 luxembourgish female & 231 luxembourgish male samples (© URL)\n* URL : 1257 luxembourgish male samples (© RTL-CLT-UFA)\n* Charel : 11 luxembourgish boy samples from my grandchild", "#### The dataset has been manually checked and the transcriptions have been expanded and eventually corrected to comply with the audio files. The data structure is equivalent to the mailabs format. The folder nesting is shown below:", "#### Thanks to RTL and to the University of Luxembourg for permission to use and share selected copyrighted 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: Question Answering\n* Model: autoevaluate/extractive-question-answering\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 @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/extractive-question-answering\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 @lewtun for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "### Data Splits\n\n\nThe ITN dataset has 3 splits: *train*, *validation*, and *test*. In *train*, *validation* splits, the input (src) and their label (tgt) are provided. In the *test* splits, only the input (src) is provided." ]

[ "TAGS\n#region-us \n", "### Data Splits\n\n\nThe ITN dataset has 3 splits: *train*, *validation*, and *test*. In *train*, *validation* splits, the input (src) and their label (tgt) are provided. In the *test* splits, only the input (src) is provided." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:fi", "license:gpl-3.0", "question-generation", "region:us" ]

[]

[ "fi" ]

[ "### Dataset Summary\n\n\nMachine translated and normalized Finnish version of the SQuAD-v2.0 dataset. Details about the translation and normalization processes can be found here.\n\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nExample data:", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Finnish #license-gpl-3.0 #question-generation #region-us \n", "### Dataset Summary\n\n\nMachine translated and normalized Finnish version of the SQuAD-v2.0 dataset. Details about the translation and normalization processes can be found here.\n\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nExample data:", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### plain\\_text\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits" ]

[ "region:us" ]

[]

[]

[ "# Egyptian hieroglyphs 𓂀", "## _Hieroglyphs image dataset along with Language Model !_\n\n!code", "## Features\n\n- This dataset is build from the hieroglyphs found in 10 different pictures from the book \"The Pyramid of Unas\" (Alexandre Piankoff, 1955). We therefore urge you to have access to this book before using the dataset.\n- The ten different pictures used throughout this dataset are: 3,5,7,9,20,21,22,23,39,41 (numbers represent the numbers used in the book \"The pyramid of Unas\".\n- Each hieroglyph is manually annotated and labelled according the Gardiner Sign List. The images are stored with their label and number in their name.\n\n\n\n> NOTE: The labelling may not be 100% correct.\n> This is out of my knowledge as an Egyptian\n> The hieroglyphs that I was unable to identify are labelled as \"UNKNOWN\".\n\n&emsp;", "## Process\n\nAside from the manual annotation, we used a text-detection method to extract the hieroglyphs automatically. The results are shown in 'Dataset/Automated/'\nThe labels on automatic detected images are based on a comparison with the manual detection, and are labelled according the the Pascal VOC overlap criteria (50% overlap).\n\nThe x/y position of each hieroglyph is stored in the Location-folder. Each file in this folder contains the exact position of all (raw) annotated hieroglyphs in their corresponding picture. \nExample: \"030000_S29.png,71,27,105,104,\" from Dataset/Manual/Locations/3.txt:\n - image = Dataset/Manual/Raw/3/030000_D35.png\n - Picture number = 3 \t(Dataset/Pictures/URL)\n - index number = 0\n - Gardiner label = D35\n - top-left position = 71,27\n - bottom-right position = 105,104\t\t(such that width = (105-71) = 34, and the height is (104-27) = 77)\n\nIncluded in this dataset are some tools to create the language model.\nin 'Dataset/LanguageModel/JSESH_EgyptianTexts/' are the Egyptian texts from the JSesh database. Jsesh is an open source program, used to write hieroglyphs Jsesh. The texts are written in a mixture of Gardiner labels and transliteration. Each text can be opened by Jsesh to view the hieroglyphs.\n\nFurthermore, a lexicon is included in 'Dataset/LanguageModel/URL'. Originally from OpenGlyp, but with added word-occurrence based on the EgyptianTexts. Each time a word is encoutered in the text, the word-occurrence is increased by 1 divided by the amount of other possible words that can be made with the surrounding hieroglyphs.\n\nThe lexicon is organised as follows: each line contains a word, that is made up by a number of hieroglyphs. Other information such as the translation, transliteration and word-occurrence is also stored. Each element is separated by a semicolon.\n'Example: D36,N35,D7,;an;beautiful;0.333333;'\n - The 3 hieroglyphs used to write this word: D36,N35,D7,\n - transliteration: an\n - English translation: beautiful\n - word-occurrence: 0.333333\n\nnGrams are included in this dataset as well, under Dataset/LanguageModel/URL\nEach line in this file contains an nGram (either uni-gram, bi-gram or tri-gram) accompanied by their occurrence. \n'Example: G17,N29,G1,;9;'\n - Hieroglyphs used to write this tri-gram: G17,N29,G1\n - number of occurrences in the EgyptianTexts database: 9", "## Structure\n\nThe dataset is organised as follows:\n\nDataset/\n|---Pictures/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains 10 pictures from the book \"The Pyramid of Unas\", which are used throughout this dataset'\n\n |---Manual/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the manually annotated images of hieroglyphs'\n |------Locations/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the location-files that hold the x/y position of each'\n |------hieroglyph.\n |------Preprocessed/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the pre-processed images'\n |------Raw/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the raw, un-pre-processed, images of hieroglyphs'\n\t\n |---Automated/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the result of the automatic hieroglpyh detection'\n |------Locations/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the location-files that hold the x/y position of each '\n |------hieroglyph.\n |------Preprocessed/'Contains the pre-processed images'\n |------Raw/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the raw, un-pre-processed, images of hieroglyphs'\n\n |---ExampleSet7/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'An example of how the test and train set can be separated.'\n |------test/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Simply contains all pre-processed images from picture #7'\n |------train/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains all the hieroglyphs images from other pictures.'\n\n |---Language Model/\n |------JSESH_EgyptianTexts/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the EgyptianTexts database of JSesh, which is a program used to write hieroglyphs' JSesh link.\n |------URL\n |------URL", "## License\n\nGPL - non commercial use\n\nWhat are you waiting for? Make some Magic !" ]

[ "TAGS\n#region-us \n", "# Egyptian hieroglyphs 𓂀", "## _Hieroglyphs image dataset along with Language Model !_\n\n!code", "## Features\n\n- This dataset is build from the hieroglyphs found in 10 different pictures from the book \"The Pyramid of Unas\" (Alexandre Piankoff, 1955). We therefore urge you to have access to this book before using the dataset.\n- The ten different pictures used throughout this dataset are: 3,5,7,9,20,21,22,23,39,41 (numbers represent the numbers used in the book \"The pyramid of Unas\".\n- Each hieroglyph is manually annotated and labelled according the Gardiner Sign List. The images are stored with their label and number in their name.\n\n\n\n> NOTE: The labelling may not be 100% correct.\n> This is out of my knowledge as an Egyptian\n> The hieroglyphs that I was unable to identify are labelled as \"UNKNOWN\".\n\n&emsp;", "## Process\n\nAside from the manual annotation, we used a text-detection method to extract the hieroglyphs automatically. The results are shown in 'Dataset/Automated/'\nThe labels on automatic detected images are based on a comparison with the manual detection, and are labelled according the the Pascal VOC overlap criteria (50% overlap).\n\nThe x/y position of each hieroglyph is stored in the Location-folder. Each file in this folder contains the exact position of all (raw) annotated hieroglyphs in their corresponding picture. \nExample: \"030000_S29.png,71,27,105,104,\" from Dataset/Manual/Locations/3.txt:\n - image = Dataset/Manual/Raw/3/030000_D35.png\n - Picture number = 3 \t(Dataset/Pictures/URL)\n - index number = 0\n - Gardiner label = D35\n - top-left position = 71,27\n - bottom-right position = 105,104\t\t(such that width = (105-71) = 34, and the height is (104-27) = 77)\n\nIncluded in this dataset are some tools to create the language model.\nin 'Dataset/LanguageModel/JSESH_EgyptianTexts/' are the Egyptian texts from the JSesh database. Jsesh is an open source program, used to write hieroglyphs Jsesh. The texts are written in a mixture of Gardiner labels and transliteration. Each text can be opened by Jsesh to view the hieroglyphs.\n\nFurthermore, a lexicon is included in 'Dataset/LanguageModel/URL'. Originally from OpenGlyp, but with added word-occurrence based on the EgyptianTexts. Each time a word is encoutered in the text, the word-occurrence is increased by 1 divided by the amount of other possible words that can be made with the surrounding hieroglyphs.\n\nThe lexicon is organised as follows: each line contains a word, that is made up by a number of hieroglyphs. Other information such as the translation, transliteration and word-occurrence is also stored. Each element is separated by a semicolon.\n'Example: D36,N35,D7,;an;beautiful;0.333333;'\n - The 3 hieroglyphs used to write this word: D36,N35,D7,\n - transliteration: an\n - English translation: beautiful\n - word-occurrence: 0.333333\n\nnGrams are included in this dataset as well, under Dataset/LanguageModel/URL\nEach line in this file contains an nGram (either uni-gram, bi-gram or tri-gram) accompanied by their occurrence. \n'Example: G17,N29,G1,;9;'\n - Hieroglyphs used to write this tri-gram: G17,N29,G1\n - number of occurrences in the EgyptianTexts database: 9", "## Structure\n\nThe dataset is organised as follows:\n\nDataset/\n|---Pictures/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains 10 pictures from the book \"The Pyramid of Unas\", which are used throughout this dataset'\n\n |---Manual/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the manually annotated images of hieroglyphs'\n |------Locations/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the location-files that hold the x/y position of each'\n |------hieroglyph.\n |------Preprocessed/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the pre-processed images'\n |------Raw/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the raw, un-pre-processed, images of hieroglyphs'\n\t\n |---Automated/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the result of the automatic hieroglpyh detection'\n |------Locations/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the location-files that hold the x/y position of each '\n |------hieroglyph.\n |------Preprocessed/'Contains the pre-processed images'\n |------Raw/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the raw, un-pre-processed, images of hieroglyphs'\n\n |---ExampleSet7/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'An example of how the test and train set can be separated.'\n |------test/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Simply contains all pre-processed images from picture #7'\n |------train/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains all the hieroglyphs images from other pictures.'\n\n |---Language Model/\n |------JSESH_EgyptianTexts/&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'Contains the EgyptianTexts database of JSesh, which is a program used to write hieroglyphs' JSesh link.\n |------URL\n |------URL", "## License\n\nGPL - non commercial use\n\nWhat are you waiting for? Make some Magic !" ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "multilinguality:monolingual", "size_categories:n<1k", "language:fi", "license:gpl-3.0", "question-generation", "region:us" ]

[]

[ "fi" ]

[ "### Dataset Summary\n\n\nWikiQA-100-fi dataset contains 100 questions related to Finnish Wikipedia articles. The dataset is in the SQuAD format, and there are 10 questions for each category identified by the authors of SQuAD. Unlike SQuAD2.0, WikiQA-100-fi contains only answerable questions. The dataset is tiny compared to actual QA test sets, but it still gives an impression of the models' performance on purely native text data collected by a native speaker. The dataset was originally created as an evaluation set for models that had been mostly fine-tuned with automatically translated QA data. More information about the dataset and models created with it can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nExample data:", "### Data Fields", "#### plain\\_text\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-n<1k #language-Finnish #license-gpl-3.0 #question-generation #region-us \n", "### Dataset Summary\n\n\nWikiQA-100-fi dataset contains 100 questions related to Finnish Wikipedia articles. The dataset is in the SQuAD format, and there are 10 questions for each category identified by the authors of SQuAD. Unlike SQuAD2.0, WikiQA-100-fi contains only answerable questions. The dataset is tiny compared to actual QA test sets, but it still gives an impression of the models' performance on purely native text data collected by a native speaker. The dataset was originally created as an evaluation set for models that had been mostly fine-tuned with automatically translated QA data. More information about the dataset and models created with it can be found here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nExample data:", "### Data Fields", "#### plain\\_text\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits" ]

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

[]

[ "ko" ]

[ "### License\n\n\nCC BY-SA 4.0\n\n\nDataset Structure\n-----------------", "### Data Instance", "### Data Size\n\n\ndownload: 7.90 GiB \n\ngenerated: 11.86 GiB \n\ntotal: 19.76 GiB\n\n\n※ You can download this dataset from kaggle, and it's 5 GiB. (12.48 GiB when uncompressed)", "### Data Fields\n\n\n* text: 'string'", "### Data Splits" ]

[ "TAGS\n#task_categories-fill-mask #task_categories-text-generation #task_ids-masked-language-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Korean #license-cc-by-sa-4.0 #region-us \n", "### License\n\n\nCC BY-SA 4.0\n\n\nDataset Structure\n-----------------", "### Data Instance", "### Data Size\n\n\ndownload: 7.90 GiB \n\ngenerated: 11.86 GiB \n\ntotal: 19.76 GiB\n\n\n※ You can download this dataset from kaggle, and it's 5 GiB. (12.48 GiB when uncompressed)", "### Data Fields\n\n\n* text: 'string'", "### Data Splits" ]

[ "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: pszemraj/long-t5-tglobal-base-16384-book-summary\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: pszemraj/long-t5-tglobal-base-16384-book-summary\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." ]

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

[]

[]

[ "# Five standard datasets for few-shot classification\n- *miniImageNet*. It contains 100 classes with 600 images in each class, which are built upon the ImageNet dataset. The 100 classes are divided into 64, 16, 20 for meta-training, meta-validation and meta-testing, respectively.\n- *tieredImageNet*. TieredImageNet is also a subset of ImageNet, which includes 608 classes from 34 super-classes. Compared with miniImageNet, the splits of meta-training(20), meta-validation(6) and meta-testing(8) are set according to the super-classes to enlarge the domain difference between training and testing phase. The dataset also include more images for training and evaluation.\n- *CIFAR-FS*. CIFAR-FS is divided from CIFAR-100, which consists of 60,000 images in 100 categories. The CIFAR-FS is divided into 64, 16 and 20 for training, validation, and evaluation, respectively.\n- *FC100*. FC100 is also divided from CIFAR-100, which is more difficult because it is more diverse. The FC100 uses a split similar to tieredImageNet, where train, validation, and test splits contain 60, 20, and 20 classes. \n- *CUB*. CUB-200-2011 (CUB) is a fine-grained dataset of 200 bird species with total 11,788 images. It is is randomly divided into three disjoint sets of the training set (100 classes), validation set (50 classes), and testing set (50 classes)." ]

[ "TAGS\n#license-mit #region-us \n", "# Five standard datasets for few-shot classification\n- *miniImageNet*. It contains 100 classes with 600 images in each class, which are built upon the ImageNet dataset. The 100 classes are divided into 64, 16, 20 for meta-training, meta-validation and meta-testing, respectively.\n- *tieredImageNet*. TieredImageNet is also a subset of ImageNet, which includes 608 classes from 34 super-classes. Compared with miniImageNet, the splits of meta-training(20), meta-validation(6) and meta-testing(8) are set according to the super-classes to enlarge the domain difference between training and testing phase. The dataset also include more images for training and evaluation.\n- *CIFAR-FS*. CIFAR-FS is divided from CIFAR-100, which consists of 60,000 images in 100 categories. The CIFAR-FS is divided into 64, 16 and 20 for training, validation, and evaluation, respectively.\n- *FC100*. FC100 is also divided from CIFAR-100, which is more difficult because it is more diverse. The FC100 uses a split similar to tieredImageNet, where train, validation, and test splits contain 60, 20, and 20 classes. \n- *CUB*. CUB-200-2011 (CUB) is a fine-grained dataset of 200 bird species with total 11,788 images. It is is randomly divided into three disjoint sets of the training set (100 classes), validation set (50 classes), and testing set (50 classes)." ]

[ "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: 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: 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: Token Classification\n* Model: autoevaluate/entity-extraction\n* Dataset: conll2003\n* Config: conll2003\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: Token Classification\n* Model: autoevaluate/entity-extraction\n* Dataset: conll2003\n* Config: conll2003\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: wmt16\n* Config: ro-en\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: wmt16\n* Config: ro-en\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-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:unknown", "language:code", "license:other", "region:us" ]

[]

[ "code" ]

[]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-unknown #language-code #license-other #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: Multi-class Text Classification\n* Model: aatmasidha/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 @aatmasidha 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: aatmasidha/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 @aatmasidha 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: sarahmiller137/distilbert-base-uncased-ft-conll2003\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sarahmiller137 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: sarahmiller137/distilbert-base-uncased-ft-conll2003\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sarahmiller137 for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

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[ "# KENNSLURÓMUR - ICELANDIC LECTURES", "### [Icelandic]\n\nKennslurómur - Íslenskir fyrirlestrar er safn af hljóðskrám og samsvarandi texta úr kennslufyrirlestrum sem teknir voru upp í áföngum í Háskólanum í Reykjavík og Háskóla Íslands. Þetta safn má nota við þjálfun talgreina.\n\nFyrirlesararnir gáfu upptökurnar sínar sem síðan voru talgreindar með talgreini, næst var frálagið lesið og leiðrétt af hópi sumarnema og að lokum var allur texti yfirfarinn af prófarkalesara. \n\nÍ þessu safni eru 51 klukkustund af hljóðskrám sem dreifast á 171 fyrirlestur frá 11 fyrirlesurum.", "### [English]\n\nKennslurómur - Icelandic Lectures is a collection of audio recordings and their corresponding segmented transcripts from class lectures recorded at Reykjavik University and the University of Iceland. This material was compiled for the training of speech recognition models.\n\nThe lectures were donated by each lecturer, then transcribed with an Icelandic speech recognizer, then manually corrected by human transcribers and finally verified by a proofreader. \n\nThis release contains 51 hours divided between 171 lectures from 11 lecturers.", "## LECTURE TOPICS\nThe topic of the lextures cover a diverse range of university level subjects.", "## STRUCTURE\n\n URL - Lists the speakers (lecturers) and their IDs.\n URL - Lists all lectures. See header for the format.\n DOCS/\n transcription_guidelines_is.txt - Transcription guidelines in Icelandic.\n URL - Description of the license.\n prerp_for_training.py - An example data preparation script for KALDI.\n <SPK-ID>/ - A directory per speaker.\n <LECTURE-ID>.wav - Audio recording of the entire lecture.\n <LECTURE-ID>.txt - Transcript of the entire lecture in 1 to \n 40 second segments. Tab separated list with the\n fields: segment ID, start time in milliseconds, \n end time in milliseconds and utterance text.", "## Alignment and segmentation\nThe segments are mostly split on sentence boundaries. Each segment ranges from a few seconds to roughly 40 seconds in duration. The recordings and transcripts were automatically aligned using either Montreal Forced Aligner or the aligner Gentle. The alignment quality was tested by training an acoustic model in Kaldi and rejected segments due to alignment issues. Recordings with an abnormally high number of faulty segments were manually aligned. This means that there are likely still some imperfectly aligned segments, but due to resource constraints, they were not manually checked and verified.", "## Training, development and testing sets\nEvery segment has been marked as either train, dev or eval. This can be seen in the \\<SPK-ID\\>/\\<LECTURE-ID\\>.txt files. There are a few speakers in this dataset creating training sets without overlap of speakers is not possible without holding out a large portion of the data. Therefore, it was decided to randomly assign each speaker's segments proportionally 80/10/10 (train, dev, eval) based on the duration of each segment.", "## FORMAT\n Sampling rate 16000 Hz\n Audio format 16 bit PCM RIFF WAVE\n Language Icelandic\n Type of speech Single speaker spontaneous and scripted speech with minimal\n backspeech.\n Media type Recorded university lectures, a mixture of prerecorded \n classes and in-class recordings.", "## SPECIAL ANNOTATIONS\n\nThree types of special annotations are found the transcripts:\n\n\n [UNK] Unintelligible, spoken background noise\n\n [HIK: <stubs>] Hesitation, where <stubs> can be a comma separated list\n of false start (often partial) words.\n\n [<IPA sym>] Standalone IPA phones are transcribed in brackets which\n only appear in \"Icelandic linguistics\" lectures.\n E.g. \"Þannig fáum við eins og raddað b, [p] [p] [p] \n „bera bera“.\".", "## LICENSE\n\nThe audio recordings (.wav files) are attributed to the corresponding lecturer\nin the file 'URL'. Everything else is attributed to \nTiro ehf.\n\nPublished with a CC BY 4.0 license. You are free to copy and redistribute the \nmaterial in any medium or format, remix, transform and build upon the material \nfor any purpose, even commercially under the following terms: You must give \nappropriate credit, provide a link to the license, and indicate if changes were \nmade. You may do so in any reasonable manner, but not in any way that suggests \nthe licensor endorses you or your use. \n\nLink to the license: URL", "## ACKNOWLEDGMENTS\n\nThis project was funded by the Language Technology Programme for Icelandic\n2019-2023. The programme, which is managed and coordinated by Almannarómur, is\nfunded by the Icelandic Ministry of Education, Science and Culture." ]

[ "TAGS\n#region-us \n", "# KENNSLURÓMUR - ICELANDIC LECTURES", "### [Icelandic]\n\nKennslurómur - Íslenskir fyrirlestrar er safn af hljóðskrám og samsvarandi texta úr kennslufyrirlestrum sem teknir voru upp í áföngum í Háskólanum í Reykjavík og Háskóla Íslands. Þetta safn má nota við þjálfun talgreina.\n\nFyrirlesararnir gáfu upptökurnar sínar sem síðan voru talgreindar með talgreini, næst var frálagið lesið og leiðrétt af hópi sumarnema og að lokum var allur texti yfirfarinn af prófarkalesara. \n\nÍ þessu safni eru 51 klukkustund af hljóðskrám sem dreifast á 171 fyrirlestur frá 11 fyrirlesurum.", "### [English]\n\nKennslurómur - Icelandic Lectures is a collection of audio recordings and their corresponding segmented transcripts from class lectures recorded at Reykjavik University and the University of Iceland. This material was compiled for the training of speech recognition models.\n\nThe lectures were donated by each lecturer, then transcribed with an Icelandic speech recognizer, then manually corrected by human transcribers and finally verified by a proofreader. \n\nThis release contains 51 hours divided between 171 lectures from 11 lecturers.", "## LECTURE TOPICS\nThe topic of the lextures cover a diverse range of university level subjects.", "## STRUCTURE\n\n URL - Lists the speakers (lecturers) and their IDs.\n URL - Lists all lectures. See header for the format.\n DOCS/\n transcription_guidelines_is.txt - Transcription guidelines in Icelandic.\n URL - Description of the license.\n prerp_for_training.py - An example data preparation script for KALDI.\n <SPK-ID>/ - A directory per speaker.\n <LECTURE-ID>.wav - Audio recording of the entire lecture.\n <LECTURE-ID>.txt - Transcript of the entire lecture in 1 to \n 40 second segments. Tab separated list with the\n fields: segment ID, start time in milliseconds, \n end time in milliseconds and utterance text.", "## Alignment and segmentation\nThe segments are mostly split on sentence boundaries. Each segment ranges from a few seconds to roughly 40 seconds in duration. The recordings and transcripts were automatically aligned using either Montreal Forced Aligner or the aligner Gentle. The alignment quality was tested by training an acoustic model in Kaldi and rejected segments due to alignment issues. Recordings with an abnormally high number of faulty segments were manually aligned. This means that there are likely still some imperfectly aligned segments, but due to resource constraints, they were not manually checked and verified.", "## Training, development and testing sets\nEvery segment has been marked as either train, dev or eval. This can be seen in the \\<SPK-ID\\>/\\<LECTURE-ID\\>.txt files. There are a few speakers in this dataset creating training sets without overlap of speakers is not possible without holding out a large portion of the data. Therefore, it was decided to randomly assign each speaker's segments proportionally 80/10/10 (train, dev, eval) based on the duration of each segment.", "## FORMAT\n Sampling rate 16000 Hz\n Audio format 16 bit PCM RIFF WAVE\n Language Icelandic\n Type of speech Single speaker spontaneous and scripted speech with minimal\n backspeech.\n Media type Recorded university lectures, a mixture of prerecorded \n classes and in-class recordings.", "## SPECIAL ANNOTATIONS\n\nThree types of special annotations are found the transcripts:\n\n\n [UNK] Unintelligible, spoken background noise\n\n [HIK: <stubs>] Hesitation, where <stubs> can be a comma separated list\n of false start (often partial) words.\n\n [<IPA sym>] Standalone IPA phones are transcribed in brackets which\n only appear in \"Icelandic linguistics\" lectures.\n E.g. \"Þannig fáum við eins og raddað b, [p] [p] [p] \n „bera bera“.\".", "## LICENSE\n\nThe audio recordings (.wav files) are attributed to the corresponding lecturer\nin the file 'URL'. Everything else is attributed to \nTiro ehf.\n\nPublished with a CC BY 4.0 license. You are free to copy and redistribute the \nmaterial in any medium or format, remix, transform and build upon the material \nfor any purpose, even commercially under the following terms: You must give \nappropriate credit, provide a link to the license, and indicate if changes were \nmade. You may do so in any reasonable manner, but not in any way that suggests \nthe licensor endorses you or your use. \n\nLink to the license: URL", "## ACKNOWLEDGMENTS\n\nThis project was funded by the Language Technology Programme for Icelandic\n2019-2023. The programme, which is managed and coordinated by Almannarómur, is\nfunded by the Icelandic Ministry of Education, Science and Culture." ]

[ "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: issifuamajeed/distilbert-base-uncased-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @issifuamajeed 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: issifuamajeed/distilbert-base-uncased-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @issifuamajeed for evaluating this model." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "language:code", "license:cc-by-sa-4.0", "arxiv:2206.08474", "region:us" ]

[ "2206.08474" ]

[ "code" ]

[ "# XLCost for text-to-code synthesis", "## Dataset Description\nThis is a subset of XLCoST benchmark, for text-to-code generation at snippet level and program level for 7 programming languages: 'Python, C, C#, C++, Java, Javascript and PHP'.", "## Languages\n\nThe dataset contains text in English and its corresponding code translation. Each program is divided into several code snippets, so the snipppet-level subsets contain these code snippets with their corresponding comments, for program-level subsets, the comments were concatenated in one long description. Moreover, programs in all the languages are aligned at the snippet level and the comment for a particular snippet is the same across all the languages.", "## Dataset Structure\nTo load the dataset you need to specify a subset among the 14 exiting instances: 'LANGUAGE-snippet-level/LANGUAGE-program-level' for 'LANGUAGE' in '[Python, C, Csharp, C++, Java, Javascript and PHP]'. By default 'Python-snippet-level' is loaded. \n\n\n\n\nNote that the data undergo some tokenization hence the additional whitespaces and the use of NEW_LINE instead of '\\n' and INDENT instead of '\\t', DEDENT to cancel indentation...", "## Data Fields\n\n* text: natural language description/comment\n* code: code at snippet/program level", "## Data Splits\n\nEach subset has three splits: train, test and validation." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-cc-by-sa-4.0 #arxiv-2206.08474 #region-us \n", "# XLCost for text-to-code synthesis", "## Dataset Description\nThis is a subset of XLCoST benchmark, for text-to-code generation at snippet level and program level for 7 programming languages: 'Python, C, C#, C++, Java, Javascript and PHP'.", "## Languages\n\nThe dataset contains text in English and its corresponding code translation. Each program is divided into several code snippets, so the snipppet-level subsets contain these code snippets with their corresponding comments, for program-level subsets, the comments were concatenated in one long description. Moreover, programs in all the languages are aligned at the snippet level and the comment for a particular snippet is the same across all the languages.", "## Dataset Structure\nTo load the dataset you need to specify a subset among the 14 exiting instances: 'LANGUAGE-snippet-level/LANGUAGE-program-level' for 'LANGUAGE' in '[Python, C, Csharp, C++, Java, Javascript and PHP]'. By default 'Python-snippet-level' is loaded. \n\n\n\n\nNote that the data undergo some tokenization hence the additional whitespaces and the use of NEW_LINE instead of '\\n' and INDENT instead of '\\t', DEDENT to cancel indentation...", "## Data Fields\n\n* text: natural language description/comment\n* code: code at snippet/program level", "## Data Splits\n\nEach subset has three splits: train, test and validation." ]

[ "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: pszemraj/led-base-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/led-base-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[ "en" ]

[ "# Dataset Card for 'reviews_with_drift'", "## 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", "### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.", "### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).", "### Languages\n\nText is mainly written in english.", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @fjcasti1 for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us \n", "# Dataset Card for 'reviews_with_drift'", "## 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", "### Dataset Summary\n\nThis dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction_ts' of when the inference took place.", "### Supported Tasks and Leaderboards\n\n'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).", "### Languages\n\nText is mainly written in english.", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @fjcasti1 for adding this dataset." ]

[ "task_categories:text2text-generation", "task_categories:translation", "annotations_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:unknown", "source_datasets:extended|flores", "language:ace", "language:acm", "language:acq", "language:aeb", "language:af", "language:ajp", "language:ak", "language:als", "language:am", "language:apc", "language:ar", "language:ars", "language:ary", "language:arz", "language:as", "language:ast", "language:awa", "language:ayr", "language:azb", "language:azj", "language:ba", "language:bm", "language:ban", "language:be", "language:bem", "language:bn", "language:bho", "language:bjn", "language:bo", "language:bs", "language:bug", "language:bg", "language:ca", "language:ceb", "language:cs", "language:cjk", "language:ckb", "language:crh", "language:cy", "language:da", "language:de", "language:dik", "language:dyu", "language:dz", "language:el", "language:en", "language:eo", "language:et", "language:eu", "language:ee", "language:fo", "language:fj", "language:fi", "language:fon", "language:fr", "language:fur", "language:fuv", "language:gaz", "language:gd", "language:ga", "language:gl", "language:gn", "language:gu", "language:ht", "language:ha", "language:he", "language:hi", "language:hne", "language:hr", "language:hu", "language:hy", "language:ig", "language:ilo", "language:id", "language:is", "language:it", "language:jv", "language:ja", "language:kab", "language:kac", "language:kam", "language:kn", "language:ks", "language:ka", "language:kk", "language:kbp", "language:kea", "language:khk", "language:km", "language:ki", "language:rw", "language:ky", "language:kmb", "language:kmr", "language:knc", "language:kg", "language:ko", "language:lo", "language:lij", "language:li", "language:ln", "language:lt", "language:lmo", "language:ltg", "language:lb", "language:lua", "language:lg", "language:luo", "language:lus", "language:lvs", "language:mag", "language:mai", "language:ml", "language:mar", "language:min", "language:mk", "language:mt", "language:mni", "language:mos", "language:mi", "language:my", "language:nl", "language:nn", "language:nb", "language:npi", "language:nso", "language:nus", "language:ny", "language:oc", "language:ory", "language:pag", "language:pa", "language:pap", "language:pbt", "language:pes", "language:plt", "language:pl", "language:pt", "language:prs", "language:quy", "language:ro", "language:rn", "language:ru", "language:sg", "language:sa", "language:sat", "language:scn", "language:shn", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:sd", "language:so", "language:st", "language:es", "language:sc", "language:sr", "language:ss", "language:su", "language:sv", "language:swh", "language:szl", "language:ta", "language:taq", "language:tt", "language:te", "language:tg", "language:tl", "language:th", "language:ti", "language:tpi", "language:tn", "language:ts", "language:tk", "language:tum", "language:tr", "language:tw", "language:tzm", "language:ug", "language:uk", "language:umb", "language:ur", "language:uzn", "language:vec", "language:vi", "language:war", "language:wo", "language:xh", "language:ydd", "language:yo", "language:yue", "language:zh", "language:zsm", "language:zu", "license:cc-by-sa-4.0", "conditional-text-generation", "arxiv:2207.04672", "region:us" ]

[ "2207.04672" ]

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[ "### Dataset Summary\n\n\nFLORES is a benchmark dataset for machine translation between English and low-resource languages.\n\n\n\n> \n> The creation of FLORES-200 doubles the existing language coverage of FLORES-101.\n> Given the nature of the new languages, which have less standardization and require\n> more specialized professional translations, the verification process became more complex.\n> This required modifications to the translation workflow. FLORES-200 has several languages\n> which were not translated from English. Specifically, several languages were translated\n> from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also\n> includes two script alternatives for four languages. FLORES-200 consists of translations\n> from 842 distinct web articles, totaling 3001 sentences. These sentences are divided\n> into three splits: dev, devtest, and test (hidden). On average, sentences are approximately\n> 21 words long.\n> \n> \n> \n\n\nDisclaimer: \\*The Flores-200 dataset is hosted by the Facebook and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.", "### Supported Tasks and Leaderboards", "#### Multilingual Machine Translation\n\n\nRefer to the Dynabench leaderboard) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.", "### Languages\n\n\nThe dataset contains parallel sentences for 200 languages, as mentioned in the original Github page for the project. Languages are identified with the ISO 639-3 code (e.g. 'eng', 'fra', 'rus') plus an additional code describing the script (e.g., \"eng\\_Latn\", \"ukr\\_Cyrl\"). See the webpage for code descriptions.\n\n\nUse the configuration 'all' to access the full set of parallel sentences for all the available languages in a single command.\n\n\nUse a hyphenated pairing to get two langauges in one datapoint (e.g., \"eng\\_Latn-ukr\\_Cyrl\" will provide sentences in the format below).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from the 'dev' split for the Ukrainian language ('ukr\\_Cyrl' config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.\n\n\nWhen using a hyphenated pairing or using the 'all' function, data will be presented as follows:\n\n\nThe text is provided as-in the original dataset, without further preprocessing or tokenization.", "### Data Fields\n\n\n* 'id': Row number for the data entry, starting at 1.\n* 'sentence': The full sentence in the specific language (may have \\_lang for pairings)\n* 'URL': The URL for the English article from which the sentence was extracted.\n* 'domain': The domain of the sentence.\n* 'topic': The topic of the sentence.\n* 'has\\_image': Whether the original article contains an image.\n* 'has\\_hyperlink': Whether the sentence contains a hyperlink.", "### Data Splits", "### Dataset Creation\n\n\nPlease refer to the original article No Language Left Behind: Scaling Human-Centered Machine Translation for additional information on dataset creation.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nSee paper for details.", "### Licensing Information\n\n\nLicensed with Creative Commons Attribution Share Alike 4.0. License available here.\n\n\nPlease cite the authors if you use these corpora in your work:\n\n\nPlease also cite prior work that this dataset builds on:" ]

[ "TAGS\n#task_categories-text2text-generation #task_categories-translation #annotations_creators-found #language_creators-expert-generated #multilinguality-multilingual #multilinguality-translation #size_categories-unknown #source_datasets-extended|flores #language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-sa-4.0 #conditional-text-generation #arxiv-2207.04672 #region-us \n", "### Dataset Summary\n\n\nFLORES is a benchmark dataset for machine translation between English and low-resource languages.\n\n\n\n> \n> The creation of FLORES-200 doubles the existing language coverage of FLORES-101.\n> Given the nature of the new languages, which have less standardization and require\n> more specialized professional translations, the verification process became more complex.\n> This required modifications to the translation workflow. FLORES-200 has several languages\n> which were not translated from English. Specifically, several languages were translated\n> from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also\n> includes two script alternatives for four languages. FLORES-200 consists of translations\n> from 842 distinct web articles, totaling 3001 sentences. These sentences are divided\n> into three splits: dev, devtest, and test (hidden). On average, sentences are approximately\n> 21 words long.\n> \n> \n> \n\n\nDisclaimer: \\*The Flores-200 dataset is hosted by the Facebook and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.", "### Supported Tasks and Leaderboards", "#### Multilingual Machine Translation\n\n\nRefer to the Dynabench leaderboard) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.", "### Languages\n\n\nThe dataset contains parallel sentences for 200 languages, as mentioned in the original Github page for the project. Languages are identified with the ISO 639-3 code (e.g. 'eng', 'fra', 'rus') plus an additional code describing the script (e.g., \"eng\\_Latn\", \"ukr\\_Cyrl\"). See the webpage for code descriptions.\n\n\nUse the configuration 'all' to access the full set of parallel sentences for all the available languages in a single command.\n\n\nUse a hyphenated pairing to get two langauges in one datapoint (e.g., \"eng\\_Latn-ukr\\_Cyrl\" will provide sentences in the format below).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from the 'dev' split for the Ukrainian language ('ukr\\_Cyrl' config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.\n\n\nWhen using a hyphenated pairing or using the 'all' function, data will be presented as follows:\n\n\nThe text is provided as-in the original dataset, without further preprocessing or tokenization.", "### Data Fields\n\n\n* 'id': Row number for the data entry, starting at 1.\n* 'sentence': The full sentence in the specific language (may have \\_lang for pairings)\n* 'URL': The URL for the English article from which the sentence was extracted.\n* 'domain': The domain of the sentence.\n* 'topic': The topic of the sentence.\n* 'has\\_image': Whether the original article contains an image.\n* 'has\\_hyperlink': Whether the sentence contains a hyperlink.", "### Data Splits", "### Dataset Creation\n\n\nPlease refer to the original article No Language Left Behind: Scaling Human-Centered Machine Translation for additional information on dataset creation.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nSee paper for details.", "### Licensing Information\n\n\nLicensed with Creative Commons Attribution Share Alike 4.0. License available here.\n\n\nPlease cite the authors if you use these corpora in your work:\n\n\nPlease also cite prior work that this dataset builds on:" ]

[ "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: lewiswatson/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 @lewiswatson 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: lewiswatson/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 @lewiswatson for evaluating this model." ]

[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:masked-language-modeling", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ko", "license:cc-by-nc-sa-2.0", "region:us" ]

[]

[ "ko" ]

[ "### Lisence\n\n\nCC BY-NC-SA 2.0 KR\n\n\nData Structure\n--------------", "### Data Instance", "### Data Size\n\n\ndownload: 3.26 GiB \n\ngenerated: 3.73 GiB \n\ntotal: 6.99 GiB", "### Data Field\n\n\n* title: 'string'\n* text: 'string'", "### Data Splits" ]

[ "TAGS\n#task_categories-fill-mask #task_categories-text-generation #task_ids-masked-language-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Korean #license-cc-by-nc-sa-2.0 #region-us \n", "### Lisence\n\n\nCC BY-NC-SA 2.0 KR\n\n\nData Structure\n--------------", "### Data Instance", "### Data Size\n\n\ndownload: 3.26 GiB \n\ngenerated: 3.73 GiB \n\ntotal: 6.99 GiB", "### Data Field\n\n\n* title: 'string'\n* text: 'string'", "### Data Splits" ]

[ "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: bhadresh-savani/bertweet-base-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 @bhadresh-savani 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: bhadresh-savani/bertweet-base-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 @bhadresh-savani 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: bhadresh-savani/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 @bhadresh-savani 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: bhadresh-savani/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 @bhadresh-savani 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: bhadresh-savani/electra-base-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 @bhadresh-savani 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: bhadresh-savani/electra-base-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 @bhadresh-savani 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: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: xsum\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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: xsum\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:summarization", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis dataset collects 47,723 aviation incident reports published in the Aviation Safety Reporting System (ASRS) database maintained by NASA.", "### Supported Tasks and Leaderboards\n\n\n* 'summarization': Dataset can be used to train a model for abstractive and extractive summarization. The model performance is measured by how high the output summary's ROUGE score for a given narrative account of an aviation incident is when compared to the synopsis as written by a NASA expert. Models and scores to follow.", "### 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 narrative account (Report 1\\_Narrative), a string for the synopsis (Report 1.2\\_Synopsis), and a string for the document id (acn\\_num\\_ACN). Some instances may have two narratives (Report 1\\_Narrative & Report 2\\_Narrative) and extended analyses produced by experts (Report 1.1\\_Callback & Report 2.1\\_Callback). Other fields contain metadata such as time, location, flight conditions, aircraft model name, etc. associated with the incident. See the ASRS Incident Reports dataset viewer to explore more examples.\n\n\nThe average token count for the articles and the highlights are provided below.\n\n\nFeature: Report 1\\_Narrative, Number of Instances: 47,723, Mean Token Count: 281\nFeature: Report 1.1\\_Callback, Number of Instances: 1,435, Mean Token Count: 103\nFeature: Report 2\\_Narrative, Number of Instances: 11,228, Mean Token Count: 169\nFeature: Report 2.1 Callback, Number of Instances: 85, Mean Token Count: 110\nFeature: ​ Report 1.2\\_Synopsis, Number of Instances: 47,723, Mean Token Count: 27", "### Data fields\n\n\nMore data explanation." ]

[ "TAGS\n#task_categories-summarization #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nThis dataset collects 47,723 aviation incident reports published in the Aviation Safety Reporting System (ASRS) database maintained by NASA.", "### Supported Tasks and Leaderboards\n\n\n* 'summarization': Dataset can be used to train a model for abstractive and extractive summarization. The model performance is measured by how high the output summary's ROUGE score for a given narrative account of an aviation incident is when compared to the synopsis as written by a NASA expert. Models and scores to follow.", "### 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 narrative account (Report 1\\_Narrative), a string for the synopsis (Report 1.2\\_Synopsis), and a string for the document id (acn\\_num\\_ACN). Some instances may have two narratives (Report 1\\_Narrative & Report 2\\_Narrative) and extended analyses produced by experts (Report 1.1\\_Callback & Report 2.1\\_Callback). Other fields contain metadata such as time, location, flight conditions, aircraft model name, etc. associated with the incident. See the ASRS Incident Reports dataset viewer to explore more examples.\n\n\nThe average token count for the articles and the highlights are provided below.\n\n\nFeature: Report 1\\_Narrative, Number of Instances: 47,723, Mean Token Count: 281\nFeature: Report 1.1\\_Callback, Number of Instances: 1,435, Mean Token Count: 103\nFeature: Report 2\\_Narrative, Number of Instances: 11,228, Mean Token Count: 169\nFeature: Report 2.1 Callback, Number of Instances: 85, Mean Token Count: 110\nFeature: ​ Report 1.2\\_Synopsis, Number of Instances: 47,723, Mean Token Count: 27", "### Data fields\n\n\nMore data explanation." ]

[ "task_categories:multiple-choice", "task_categories:text-generation", "task_categories:text-classification", "task_ids:multiple-choice-qa", "task_ids:language-modeling", "task_ids:text-scoring", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Moral Stories", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: Moral Stories repository\n- Repository: Moral Stories repository\n- Paper: Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences\n- Leaderboard: [N/A]\n- Point of Contact: Denis Emelin", "### Dataset Summary\n\nMoral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories: \n- Norm: A guideline for social conduct generally observed by most people in everyday situations.\n- Situation: Setting of the story that introduces story participants and describes their environment.\n- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.\n- Normative action: An action by the actor that fulfills the intention and observes the norm.\n- Normative consequence: Possible effect of the normative action on the actor's environment.\n- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.\n- Divergent consequence: Possible effect of the divergent action on the actor's environment. \n\nAccordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also *Section 2* in the dataset paper.", "### Supported Tasks and Leaderboards\n\n- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A BART model can be used for this purpose.\n- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A RoBERTa-based model can be used for this purpose.\n- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A GPT-based model can be used for this purpose.", "### Languages\n\n*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the *Ethical Considerations* section of the dataset paper.", "## Dataset Structure", "### Data Instances\n\nDue to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to *Section 2* in the dataset paper. Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context): \n\n\"full\" instance: \n{\"ID\": \"3OJSZ2ATDTUMG7NGR28931AHDQF57F\", \n\"norm\": \"It's kind to wait for people.\", \n\"situation\": \"Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.\", \n\"intention\": \"Josh needs to get to work.\", \n\"moral_action\": \"Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.\", \n\"moral_consequence\": \"Penelope appreciates Josh's patience and thanks him for waiting around.\", \n\"immoral_action\": \"Josh shrugs his shoulders and drives away to work after sitting around a moment.\", \n\"immoral_consequence\": \"Penelope comes running out of the house shouting to wait, but she's too late.\"} \n \npositive and negative \"cls-action+norm\" instances (i.e. classify an action as normative or divergent given the action and a relevant norm): \n{\"ID\": \"3EF8EXOTT2TGCKNZROVU3129RYNJ1G1\", \n\"norm\": \"It's bad to be too loud and wake people up.\", \n\"moral_action\": \"Jordan quietly makes himself a bowl of cereal for breakfast.\", \n\"label\": \"1\"} \n{\"ID\": \"3EF8EXOTT2TGCKNZROVU3129RYNJ1G0\", \n\"norm\": \"It's bad to be too loud and wake people up.\", \n\"immoral_action\": \"Jordan noisily bangs a lot of pots and pans when making his breakfast.\", \n\"label\": \"0\"} \n\n\"gen-consequence$action+context\" instance (i.e. generate either a plausible or implausible consequence given an action and its context): \n{\"ID\": \"33FOTY3KENJT46HTHWE8GXPE3LE1C03\", \n\"norm\": \"It's mean to hit a bear with a guitar.\", \n\"situation\": \"Joe was camping in Montana with his band when he came across a black bear.\", \n\"intention\": \"Joe wanted to scare away the black bear.\", \n\"immoral_action\": \"Joe grabbed his guitarist's guitar and hit the bear with it.\", \n\"immoral_consequence\": \"The bear suffers a concussion and doesn't understand why he was hit.\", \n\"label\": \"1\"}", "### Data Fields\n\n- \"ID\": Unique identifier ID for this dataset instance.\n- \"norm\": A guideline for social conduct generally observed by most people in everyday situations.\n- \"situation\": Setting of the story that introduces story participants and describes their environment.\n- \"intention\": Reasonable goal that one of the story participants (the actor), wants to fulfill.\n- \"moral_(i.e. 'normative')_action\": An action by the actor that fulfills the intention and observes the norm.\n- \"moral_consequence\": Possible effect of the normative action on the actor's environment.\n- \"immoral_(i.e. 'divergent')_action\": An action by the actor that fulfills the intention and diverges from the norm.\n- \"immoral_consequence\": Possible effect of the divergent action on the actor's environment. \n- \"label\": Data instance label; for action-related tasks, \"0\" corresponds to an immoral / divergent action while \"1\" corresponds to a moral / normative action, for consequence-related tasks, \"0\" corresponds to a plausible consequence while \"1\" corresponds to an implausible consequence (for generation tasks, label is always set to \"1\")", "### Data Splits\n\nFor classification tasks, we examined three data split strategies:\n\n- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding\nnorms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set. \n- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.\n- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set. \n \nFor generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to *Section 3* and *Appendix C* in the dataset paper.", "## Dataset Creation", "### Curation Rationale\n\nPlease refer to *Section 2* and the *Ethical Considerations* section in the dataset paper.", "### Source Data", "#### Initial Data Collection and Normalization\n\nPlease refer to *Section 2* in the dataset paper.", "#### Who are the source language producers?\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "### Annotations", "#### Annotation process\n\nPlease refer to *Section 2* and the *Ethical Considerations* section in the dataset paper.", "#### Who are the annotators?\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "### Personal and Sensitive Information\n\n[N/A]", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "### Discussion of Biases\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "### Other Known Limitations\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "## Additional Information", "### Dataset Curators\n\nDenis Emelin", "### Licensing Information\n\nMIT\n\n\n\n@article{Emelin2021MoralSS,\n title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},\n author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},\n journal={ArXiv},\n year={2021},\n volume={abs/2012.15738}\n}" ]

[ "TAGS\n#task_categories-multiple-choice #task_categories-text-generation #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-language-modeling #task_ids-text-scoring #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us \n", "# Dataset Card for Moral Stories", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: Moral Stories repository\n- Repository: Moral Stories repository\n- Paper: Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences\n- Leaderboard: [N/A]\n- Point of Contact: Denis Emelin", "### Dataset Summary\n\nMoral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories: \n- Norm: A guideline for social conduct generally observed by most people in everyday situations.\n- Situation: Setting of the story that introduces story participants and describes their environment.\n- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.\n- Normative action: An action by the actor that fulfills the intention and observes the norm.\n- Normative consequence: Possible effect of the normative action on the actor's environment.\n- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.\n- Divergent consequence: Possible effect of the divergent action on the actor's environment. \n\nAccordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also *Section 2* in the dataset paper.", "### Supported Tasks and Leaderboards\n\n- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A BART model can be used for this purpose.\n- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A RoBERTa-based model can be used for this purpose.\n- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A GPT-based model can be used for this purpose.", "### Languages\n\n*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the *Ethical Considerations* section of the dataset paper.", "## Dataset Structure", "### Data Instances\n\nDue to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to *Section 2* in the dataset paper. Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context): \n\n\"full\" instance: \n{\"ID\": \"3OJSZ2ATDTUMG7NGR28931AHDQF57F\", \n\"norm\": \"It's kind to wait for people.\", \n\"situation\": \"Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.\", \n\"intention\": \"Josh needs to get to work.\", \n\"moral_action\": \"Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.\", \n\"moral_consequence\": \"Penelope appreciates Josh's patience and thanks him for waiting around.\", \n\"immoral_action\": \"Josh shrugs his shoulders and drives away to work after sitting around a moment.\", \n\"immoral_consequence\": \"Penelope comes running out of the house shouting to wait, but she's too late.\"} \n \npositive and negative \"cls-action+norm\" instances (i.e. classify an action as normative or divergent given the action and a relevant norm): \n{\"ID\": \"3EF8EXOTT2TGCKNZROVU3129RYNJ1G1\", \n\"norm\": \"It's bad to be too loud and wake people up.\", \n\"moral_action\": \"Jordan quietly makes himself a bowl of cereal for breakfast.\", \n\"label\": \"1\"} \n{\"ID\": \"3EF8EXOTT2TGCKNZROVU3129RYNJ1G0\", \n\"norm\": \"It's bad to be too loud and wake people up.\", \n\"immoral_action\": \"Jordan noisily bangs a lot of pots and pans when making his breakfast.\", \n\"label\": \"0\"} \n\n\"gen-consequence$action+context\" instance (i.e. generate either a plausible or implausible consequence given an action and its context): \n{\"ID\": \"33FOTY3KENJT46HTHWE8GXPE3LE1C03\", \n\"norm\": \"It's mean to hit a bear with a guitar.\", \n\"situation\": \"Joe was camping in Montana with his band when he came across a black bear.\", \n\"intention\": \"Joe wanted to scare away the black bear.\", \n\"immoral_action\": \"Joe grabbed his guitarist's guitar and hit the bear with it.\", \n\"immoral_consequence\": \"The bear suffers a concussion and doesn't understand why he was hit.\", \n\"label\": \"1\"}", "### Data Fields\n\n- \"ID\": Unique identifier ID for this dataset instance.\n- \"norm\": A guideline for social conduct generally observed by most people in everyday situations.\n- \"situation\": Setting of the story that introduces story participants and describes their environment.\n- \"intention\": Reasonable goal that one of the story participants (the actor), wants to fulfill.\n- \"moral_(i.e. 'normative')_action\": An action by the actor that fulfills the intention and observes the norm.\n- \"moral_consequence\": Possible effect of the normative action on the actor's environment.\n- \"immoral_(i.e. 'divergent')_action\": An action by the actor that fulfills the intention and diverges from the norm.\n- \"immoral_consequence\": Possible effect of the divergent action on the actor's environment. \n- \"label\": Data instance label; for action-related tasks, \"0\" corresponds to an immoral / divergent action while \"1\" corresponds to a moral / normative action, for consequence-related tasks, \"0\" corresponds to a plausible consequence while \"1\" corresponds to an implausible consequence (for generation tasks, label is always set to \"1\")", "### Data Splits\n\nFor classification tasks, we examined three data split strategies:\n\n- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding\nnorms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set. \n- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.\n- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set. \n \nFor generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to *Section 3* and *Appendix C* in the dataset paper.", "## Dataset Creation", "### Curation Rationale\n\nPlease refer to *Section 2* and the *Ethical Considerations* section in the dataset paper.", "### Source Data", "#### Initial Data Collection and Normalization\n\nPlease refer to *Section 2* in the dataset paper.", "#### Who are the source language producers?\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "### Annotations", "#### Annotation process\n\nPlease refer to *Section 2* and the *Ethical Considerations* section in the dataset paper.", "#### Who are the annotators?\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "### Personal and Sensitive Information\n\n[N/A]", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "### Discussion of Biases\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "### Other Known Limitations\n\nPlease refer to the *Ethical Considerations* section in the dataset paper.", "## Additional Information", "### Dataset Curators\n\nDenis Emelin", "### Licensing Information\n\nMIT\n\n\n\n@article{Emelin2021MoralSS,\n title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},\n author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},\n journal={ArXiv},\n year={2021},\n volume={abs/2012.15738}\n}" ]

[ "task_categories:translation", "task_ids:multiple-choice-qa", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:machine-generated", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "language:de", "language:fr", "language:ru", "license:mit", "region:us" ]

[]

[ "en", "de", "fr", "ru" ]

[ "# Dataset Card for Wino-X", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: Wino-X repository\n- Repository: Wino-X repository\n- Paper: Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution\n- Leaderboard: [N/A]\n- Point of Contact: Denis Emelin", "### Dataset Summary\n\nWino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English\ncounterparts, used to examine whether neural machine translation models can perform coreference resolution that\nrequires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across\nmultiple languages.", "### Supported Tasks and Leaderboards\n\n- translation: The dataset can be used to evaluate translations of ambiguous source sentences, as produced by translation models . A pretrained transformer-based NMT model can be used for this purpose.\n- coreference-resolution: The dataset can be used to rank alternative translations of an ambiguous source sentence that differ in the chosen referent of an ambiguous source pronoun. A pretrained transformer-based NMT model can be used for this purpose.\n- commonsense-reasoning: The dataset can also be used evaluate whether pretrained multilingual language models can perform commonsense reasoning in (or across) multiple languages by identifying the correct filler in a cloze completion task. An XLM-based model can be used for this purpose.", "### Languages\n\nThe dataset (both its MT and LM portions) is available in the following translation pairs: English-German, English-French, English-Russian. All English sentences included in *Wino-X* were extracted from publicly available parallel corpora, as detailed in the accompanying paper, and represent the dataset-specific language varieties. All non-English sentences were obtained through machine translation and may, as such, exhibit features of translationese.", "## Dataset Structure", "### Data Instances\n\nThe following represents a typical *MT-Wino-X* instance (for the English-German translation pair): \n \n{\"qID\": \"3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1\", \n\"sentence\": \"The woman looked for a different vase for the bouquet because it was too small.\", \n\"translation1\": \"Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.\", \n\"translation2\": \"Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.\", \n\"answer\": 1, \n\"pronoun1\": \"sie\", \n\"pronoun2\": \"er\", \n\"referent1_en\": \"vase\", \n\"referent2_en\": \"bouquet\", \n\"true_translation_referent_of_pronoun1_de\": \"Vase\", \n\"true_translation_referent_of_pronoun2_de\": \"Blumenstrauß\", \n\"false_translation_referent_of_pronoun1_de\": \"Vase\", \n \"false_translation_referent_of_pronoun2_de\": \"Blumenstrauß\"} \n \n \nThe following represents a typical *LM-Wino-X* instance (for the English-French translation pair): \n \n{\"qID\": \"3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1\", \n\"sentence\": \"The woman looked for a different vase for the bouquet because it was too small.\", \n\"context_en\": \"The woman looked for a different vase for the bouquet because _ was too small.\", \n\"context_fr\": \"La femme a cherché un vase différent pour le bouquet car _ était trop petit.\", \n\"option1_en\": \"the bouquet\", \n\"option2_en\": \"the vase\", \n\"option1_fr\": \"le bouquet\", \n\"option2_fr\": \"le vase\", \n\"answer\": 2, \n\"context_referent_of_option1_fr\": \"bouquet\", \n\"context_referent_of_option2_fr\": \"vase\"}", "### Data Fields\n\nFor *MT-Wino-X*: \n \n- \"qID\": Unique identifier ID for this dataset instance.\n- \"sentence\": English sentence containing the ambiguous pronoun 'it'.\n- \"translation1\": First translation candidate.\n- \"translation2\": Second translation candidate.\n- \"answer\": ID of the correct translation.\n- \"pronoun1\": Translation of the ambiguous source pronoun in translation1.\n- \"pronoun2\": Translation of the ambiguous source pronoun in translation2.\n- \"referent1_en\": English referent of the translation of the ambiguous source pronoun in translation1.\n- \"referent2_en\": English referent of the translation of the ambiguous source pronoun in translation2.\n- \"true_translation_referent_of_pronoun1_[TGT-LANG]\": Target language referent of pronoun1 in the correct translation.\n- \"true_translation_referent_of_pronoun2_[TGT-LANG]\": Target language referent of pronoun2 in the correct translation.\n- \"false_translation_referent_of_pronoun1_[TGT-LANG]\": Target language referent of pronoun1 in the incorrect translation.\n- \"false_translation_referent_of_pronoun2_[TGT-LANG]\": Target language referent of pronoun2 in the incorrect translation.\n \n \nFor *LM-Wino-X*: \n \n- \"qID\": Unique identifier ID for this dataset instance.\n- \"sentence\": English sentence containing the ambiguous pronoun 'it'. \n- \"context_en\": Same English sentence, where 'it' is replaced by a gap. \n- \"context_fr\": Target language translation of the English sentence, where the translation of 'it' is replaced by a gap.\n- \"option1_en\": First filler option for the English sentence.\n- \"option2_en\": Second filler option for the English sentence.\n- \"option1_[TGT-LANG]\": First filler option for the target language sentence.\n- \"option2_[TGT-LANG]\": Second filler option for the target language sentence.\n- \"answer\": ID of the correct gap filler.\n- \"context_referent_of_option1_[TGT-LANG]\": English translation of option1_[TGT-LANG].\n- \"context_referent_of_option2_[TGT-LANG]\": English translation of option2_[TGT-LANG]", "### Data Splits\n\n*Wno-X* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .", "## Dataset Creation", "### Curation Rationale\n\nPlease refer to Section 2 in the dataset paper.", "### Source Data", "#### Initial Data Collection and Normalization\n\nPlease refer to Section 2 in the dataset paper.", "#### Who are the source language producers?\n\nPlease refer to Section 2 in the dataset paper.", "### Annotations", "#### Annotation process\n\nPlease refer to Section 2 in the dataset paper.", "#### Who are the annotators?\n\nAnnotations were generated automatically and verified by the dataset author / curator for correctness.", "### Personal and Sensitive Information\n\n[N/A]", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nPlease refer to 'Ethical Considerations' in the dataset paper.", "### Discussion of Biases\n\nPlease refer to 'Ethical Considerations' in the dataset paper.", "### Other Known Limitations\n\nPlease refer to 'Ethical Considerations' in the dataset paper.", "## Additional Information", "### Dataset Curators\n\nDenis Emelin", "### Licensing Information\n\nMIT\n\n\n\n@inproceedings{Emelin2021WinoXMW,\n title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},\n author={Denis Emelin and Rico Sennrich},\n booktitle={EMNLP},\n year={2021}\n}" ]

[ "TAGS\n#task_categories-translation #task_ids-multiple-choice-qa #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #multilinguality-translation #size_categories-1K<n<10K #source_datasets-original #language-English #language-German #language-French #language-Russian #license-mit #region-us \n", "# Dataset Card for Wino-X", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: Wino-X repository\n- Repository: Wino-X repository\n- Paper: Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution\n- Leaderboard: [N/A]\n- Point of Contact: Denis Emelin", "### Dataset Summary\n\nWino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English\ncounterparts, used to examine whether neural machine translation models can perform coreference resolution that\nrequires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across\nmultiple languages.", "### Supported Tasks and Leaderboards\n\n- translation: The dataset can be used to evaluate translations of ambiguous source sentences, as produced by translation models . A pretrained transformer-based NMT model can be used for this purpose.\n- coreference-resolution: The dataset can be used to rank alternative translations of an ambiguous source sentence that differ in the chosen referent of an ambiguous source pronoun. A pretrained transformer-based NMT model can be used for this purpose.\n- commonsense-reasoning: The dataset can also be used evaluate whether pretrained multilingual language models can perform commonsense reasoning in (or across) multiple languages by identifying the correct filler in a cloze completion task. An XLM-based model can be used for this purpose.", "### Languages\n\nThe dataset (both its MT and LM portions) is available in the following translation pairs: English-German, English-French, English-Russian. All English sentences included in *Wino-X* were extracted from publicly available parallel corpora, as detailed in the accompanying paper, and represent the dataset-specific language varieties. All non-English sentences were obtained through machine translation and may, as such, exhibit features of translationese.", "## Dataset Structure", "### Data Instances\n\nThe following represents a typical *MT-Wino-X* instance (for the English-German translation pair): \n \n{\"qID\": \"3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1\", \n\"sentence\": \"The woman looked for a different vase for the bouquet because it was too small.\", \n\"translation1\": \"Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.\", \n\"translation2\": \"Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.\", \n\"answer\": 1, \n\"pronoun1\": \"sie\", \n\"pronoun2\": \"er\", \n\"referent1_en\": \"vase\", \n\"referent2_en\": \"bouquet\", \n\"true_translation_referent_of_pronoun1_de\": \"Vase\", \n\"true_translation_referent_of_pronoun2_de\": \"Blumenstrauß\", \n\"false_translation_referent_of_pronoun1_de\": \"Vase\", \n \"false_translation_referent_of_pronoun2_de\": \"Blumenstrauß\"} \n \n \nThe following represents a typical *LM-Wino-X* instance (for the English-French translation pair): \n \n{\"qID\": \"3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1\", \n\"sentence\": \"The woman looked for a different vase for the bouquet because it was too small.\", \n\"context_en\": \"The woman looked for a different vase for the bouquet because _ was too small.\", \n\"context_fr\": \"La femme a cherché un vase différent pour le bouquet car _ était trop petit.\", \n\"option1_en\": \"the bouquet\", \n\"option2_en\": \"the vase\", \n\"option1_fr\": \"le bouquet\", \n\"option2_fr\": \"le vase\", \n\"answer\": 2, \n\"context_referent_of_option1_fr\": \"bouquet\", \n\"context_referent_of_option2_fr\": \"vase\"}", "### Data Fields\n\nFor *MT-Wino-X*: \n \n- \"qID\": Unique identifier ID for this dataset instance.\n- \"sentence\": English sentence containing the ambiguous pronoun 'it'.\n- \"translation1\": First translation candidate.\n- \"translation2\": Second translation candidate.\n- \"answer\": ID of the correct translation.\n- \"pronoun1\": Translation of the ambiguous source pronoun in translation1.\n- \"pronoun2\": Translation of the ambiguous source pronoun in translation2.\n- \"referent1_en\": English referent of the translation of the ambiguous source pronoun in translation1.\n- \"referent2_en\": English referent of the translation of the ambiguous source pronoun in translation2.\n- \"true_translation_referent_of_pronoun1_[TGT-LANG]\": Target language referent of pronoun1 in the correct translation.\n- \"true_translation_referent_of_pronoun2_[TGT-LANG]\": Target language referent of pronoun2 in the correct translation.\n- \"false_translation_referent_of_pronoun1_[TGT-LANG]\": Target language referent of pronoun1 in the incorrect translation.\n- \"false_translation_referent_of_pronoun2_[TGT-LANG]\": Target language referent of pronoun2 in the incorrect translation.\n \n \nFor *LM-Wino-X*: \n \n- \"qID\": Unique identifier ID for this dataset instance.\n- \"sentence\": English sentence containing the ambiguous pronoun 'it'. \n- \"context_en\": Same English sentence, where 'it' is replaced by a gap. \n- \"context_fr\": Target language translation of the English sentence, where the translation of 'it' is replaced by a gap.\n- \"option1_en\": First filler option for the English sentence.\n- \"option2_en\": Second filler option for the English sentence.\n- \"option1_[TGT-LANG]\": First filler option for the target language sentence.\n- \"option2_[TGT-LANG]\": Second filler option for the target language sentence.\n- \"answer\": ID of the correct gap filler.\n- \"context_referent_of_option1_[TGT-LANG]\": English translation of option1_[TGT-LANG].\n- \"context_referent_of_option2_[TGT-LANG]\": English translation of option2_[TGT-LANG]", "### Data Splits\n\n*Wno-X* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .", "## Dataset Creation", "### Curation Rationale\n\nPlease refer to Section 2 in the dataset paper.", "### Source Data", "#### Initial Data Collection and Normalization\n\nPlease refer to Section 2 in the dataset paper.", "#### Who are the source language producers?\n\nPlease refer to Section 2 in the dataset paper.", "### Annotations", "#### Annotation process\n\nPlease refer to Section 2 in the dataset paper.", "#### Who are the annotators?\n\nAnnotations were generated automatically and verified by the dataset author / curator for correctness.", "### Personal and Sensitive Information\n\n[N/A]", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nPlease refer to 'Ethical Considerations' in the dataset paper.", "### Discussion of Biases\n\nPlease refer to 'Ethical Considerations' in the dataset paper.", "### Other Known Limitations\n\nPlease refer to 'Ethical Considerations' in the dataset paper.", "## Additional Information", "### Dataset Curators\n\nDenis Emelin", "### Licensing Information\n\nMIT\n\n\n\n@inproceedings{Emelin2021WinoXMW,\n title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},\n author={Denis Emelin and Rico Sennrich},\n booktitle={EMNLP},\n year={2021}\n}" ]

[ "region:us" ]

[]

[]

[]

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

[ "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: pszemraj/led-large-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/led-large-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/bigbird-pegasus-large-K-booksum\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/bigbird-pegasus-large-K-booksum\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/pegasus-large-summary-explain\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/pegasus-large-summary-explain\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "task_categories:multiple-choice", "task_categories:text-generation", "task_ids:multiple-choice-qa", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:mit", "arxiv:2206.04615", "region:us" ]

[ "2206.04615" ]

[ "en" ]

[ "# Dataset Card for Understanding Fables", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: Understanding Fables BIG-Bench entry\n- Repository: Understanding Fables BIG-Bench entry\n- Paper: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models\n- Leaderboard: [N/A]\n- Point of Contact: Denis Emelin", "### Dataset Summary\n\nFables are short narratives that aim to communicate a specific lesson or wisdom, referred to as the moral. Morals can be idiomatic or provide a succinct summary of the fable. Importantly, they make explicit the communicative intent of the fable and, thus, are highly relevant to its content. A computational model capable of natural language understanding should, when presented with a fable and a set of potentially relevant morals, rank the moral that accurately captures the message communicated by the fable above the rest. Additionally, fables represent a highly unusual narrative domain, where animals and inanimate objects are anthropomorphized and referred to with gendered pronouns, i.e., a rabbit may be a she, rather than an it. Thus, to understand fables, models must abstract away from patterns commonly encountered in their training data by applying human-like characteristics to non-human actors. Overall, for a computational model to perform well on this task, it must be capable of (1) successfully identifying the core message of a short narrative, (2) identifying a moral that expresses this message among a set of distractor morals, and (3) doing so within a narrative domain that is unlike the majority of pre-training data. Thus, the evaluated large language models would need to demonstrate cross-domain generalization capability in addition to narrative comprehension.\n\nThe dataset evaluates models' ability to comprehend written narratives by asking them to select the most appropriate moral for each fable, from a set of five alternatives. In addition to the correct answer, this set contains four distractor morals, which were selected semi-automatically. To identify challenging distractor morals for each fable, sentence similarity was computed between the embeddings of each sentence in the fable and all morals found in the entire dataset, keeping ten morals that were found to be most similar to any of the fable's sentences. From this set, four distractors were selected manually, so that neither of the final distractors represents a valid moral of the fable. By construction, such distractor items are likely to pose a challenge for models that disproportionally rely on shallow heuristics as opposed to more sophisticated language-understanding strategies.", "### Supported Tasks and Leaderboards\n\n- multiple-choice: The dataset can be used to evaluate models on their ability to rank a moral that is relevant to a specified fable above distractor morals, e.g. by assigning the correct choice a lower model perplexity. A RoBERTa-based model can be used for this purpose.\n- text-generation: The dataset can also be used to train models to generate appropriate morals conditioned on the fable text. A GPT-based model can be used for this purpose.", "### Languages\n\nThe text in the dataset is in contemporary American English.", "## Dataset Structure", "### Data Instances\n\nA typical data point consists of a single fable, five morals - four distractors and one that correctly captures the lesson imparted by the fable, and an integer label denoting the ID of the correct answer option. An example dataset entry looks as follows: \n\n{\"story\": \"On a warm winter's day, the ants were busy drying corn that they had collected during summer. While they were engaged in their work, a starving grasshopper passed by and begged them for a single grain to stop his hunger. The ants, in turn, asked him why he had not collected food during the summer to prepare for the harsh winter, to which the grasshopper replied that he spent the warm days singing. Mockingly, the ants said to him in unison: \"Since you spent your summer singing, then you must dance hungrily to bed in winter.\" What is the moral of this story?\", \n\"answer0\": \"Fine clothes may disguise, but silly words will disclose a fool.\", \n\"answer1\": \"Better starve free than be a fat slave.\", \n\"answer2\": \"Evil wishes, like chickens, come home to roost.\", \n\"answer3\": \"Grasp at the shadow and you will lose the substance.\", \n\"answer4\": \"It is thrifty to prepare today for the wants of tomorrow.\", \n\"label\": 4}", "### Data Fields\n\n- \"story': The fable for which the appropriate moral has to be identified\n- \"answerN\": Moral candidates to be evaluated by the model \n- \"label\": The ID of the moral belonging to the fable", "### Data Splits\n\n*Understanding Fables* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .", "## Dataset Creation", "### Curation Rationale\n\nTo comply with the requirements for inclusion in BIG-bench, each fable was manually paraphrased, to ensure that the task cannot be solved by memorising web data. Following sources were used for fable collection: \n- URL\n- URL\n- URL\n- URL", "### Source Data", "#### Initial Data Collection and Normalization\n\nParaphrasing was done by an English speaker with native-like language proficiency and an academic background in literature. The created paraphrases differ from the originals in the identity of their participants (lion was replaced with tiger, wolf with coyote etc.), their sentence and narrative structure, and their register (archaic terms such as thou have been replaced with their modern counterparts). The phrasing of the morals has also been updated in cases where the original language was noticeably archaic (e.g., o'er reach -> overreach), with changes kept to a minimum. The mean string similarity between original fables and their paraphrases is consequently low at 0.26, according to the word-level Damerau\u0013Levenshtein distance. At the same time, great care was taken not to alter the content of the fables and preserve the relevance of their respective morals. This is evidenced by the high semantic similarity between the originals and their paraphrases, with a mean of 0.78, computed as the cosine similarity between the embeddings of the originals and their paraphrases, obtained using the Sentence-Transformers library. Moreover, duplicate and near-duplicate fables were removed from the final collection, as were several thematically problematic stories, e.g., ones with sexist undertones. In total, the dataset includes 189 paraphrased, unique fables.", "#### Who are the source language producers?\n\nOriginal authors and transcribers of the fables (unknown), the dataset author.", "### Annotations", "#### Annotation process\n\n[N/A]", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\n[N/A]", "## Considerations for Using the Data", "### Social Impact of Dataset\n\n[N/A]", "### Discussion of Biases\n\nSeveral thematically problematic fables, e.g., ones with sexist undertones, were removed by the author during the dataset creation process. However, it can be that the author overlooked other problematic or harmful biases present within the dataset.", "### Other Known Limitations\n\nThe dataset is very limited in size due to the small number of thematically distinct fables available online. Similarly, the focus on English fables alone is a limiting factor to be addressed in future dataset iterations.", "## Additional Information", "### Dataset Curators\n\nDenis Emelin", "### Licensing Information\n\nMIT\n\n\n\n@article{Srivastava2022BeyondTI,\n title={Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},\n author={Aarohi Srivastava and Abhinav Rastogi and Abhishek B Rao and Abu Awal Md Shoeb and Abubakar Abid and Adam Fisch and Adam R. Brown and Adam Santoro and Aditya Gupta and Adri{\\'a} Garriga-Alonso and Agnieszka Kluska and Aitor Lewkowycz and Akshat Agarwal and Alethea Power and Alex Ray and Alex Warstadt and Alexander W. Kocurek and Ali Safaya and Ali Tazarv and Alice Xiang and Alicia Parrish and Allen Nie and Aman Hussain and Amanda Askell and Amanda Dsouza and Ameet Annasaheb Rahane and Anantharaman S. Iyer and Anders Johan Andreassen and Andrea Santilli and Andreas Stuhlmuller and Andrew M. Dai and Andrew D. La and Andrew Kyle Lampinen and Andy Zou and Angela Jiang and Angelica Chen and Anh Vuong and Animesh Gupta and Anna Gottardi and Antonio Norelli and Anu Venkatesh and Arash Gholamidavoodi and Arfa Tabassum and Arul Menezes and Arun Kirubarajan and Asher Mullokandov and Ashish Sabharwal and Austin Herrick and Avia Efrat and Aykut Erdem and Ayla Karakacs and Bridget R. Roberts and Bao Sheng Loe and Barret Zoph and Bartlomiej Bojanowski and Batuhan Ozyurt and Behnam Hedayatnia and Behnam Neyshabur and Benjamin Inden and Benno Stein and Berk Ekmekci and Bill Yuchen Lin and Blake Stephen Howald and Cameron Diao and Cameron Dour and Catherine Stinson and Cedrick Argueta and C'esar Ferri Ram'irez and Chandan Singh and Charles Rathkopf and Chenlin Meng and Chitta Baral and Chiyu Wu and Chris Callison-Burch and Chris Waites and Christian Voigt and Christopher D. Manning and Christopher Potts and Cindy Tatiana Ramirez and Clara Rivera and Clemencia Siro and Colin Raffel and Courtney Ashcraft and Cristina Garbacea and Damien Sileo and Daniel H Garrette and Dan Hendrycks and Dan Kilman and Dan Roth and Daniel Freeman and Daniel Khashabi and Daniel Levy and Daniel Gonz'alez and Danny Hernandez and Danqi Chen and Daphne Ippolito and Dar Gilboa and David Dohan and D. Drakard and David Jurgens and Debajyoti Datta and Deep Ganguli and Denis Emelin and Denis Kleyko and Deniz Yuret and Derek Chen and Derek Tam and Dieuwke Hupkes and Diganta Misra and Dilyar Buzan and Dimitri Coelho Mollo and Diyi Yang and Dong-Ho Lee and Ekaterina Shutova and Ekin Dogus Cubuk and Elad Segal and Eleanor Hagerman and Elizabeth Barnes and Elizabeth P. Donoway and Ellie Pavlick and Emanuele Rodol{\\'a} and Emma FC Lam and Eric Chu and Eric Tang and Erkut Erdem and Ernie Chang and Ethan A. Chi and Ethan Dyer and Ethan Jerzak and Ethan Kim and Eunice Engefu Manyasi and Evgenii Zheltonozhskii and Fan Xia and Fatemeh Siar and Fernando Mart'inez-Plumed and Francesca Happ'e and François Chollet and Frieda Rong and Gaurav Mishra and Genta Indra Winata and Gerard de Melo and Germ{\\'a}n Kruszewski and Giambattista Parascandolo and Giorgio Mariani and Gloria Wang and Gonzalo Jaimovitch-L'opez and Gregor Betz and Guy Gur-Ari and Hana Galijasevic and Han Sol Kim and Hannah Rashkin and Hanna Hajishirzi and Harsh Mehta and Hayden Bogar and Henry Shevlin and Hinrich Sch{\\\"u}tze and Hiromu Yakura and Hongming Zhang and Hubert Wong and Ian Aik-Soon Ng and Isaac Noble and Jaap Jumelet and Jack Geissinger and John Kernion and Jacob Hilton and Jaehoon Lee and Jaime Fern{\\'a}ndez Fisac and J. Brooker Simon and James Koppel and James Zheng and James Zou and Jan Koco'n and Jana Thompson and Jared Kaplan and Jarema Radom and Jascha Narain Sohl-Dickstein and Jason Phang and Jason Wei and Jason Yosinski and Jekaterina Novikova and Jelle Bosscher and Jenni Marsh and Jeremy Kim and Jeroen Taal and Jesse Engel and Jesujoba Oluwadara Alabi and Jiacheng Xu and Jiaming Song and Jillian Tang and Jane W Waweru and John Burden and John Miller and John U. Balis and Jonathan Berant and Jorg Frohberg and Jos Rozen and Jos{\\'e} Hern{\\'a}ndez-Orallo and Joseph Boudeman and Joseph Jones and Joshua B. Tenenbaum and Joshua S. Rule and Joyce Chua and Kamil Kanclerz and Karen Livescu and Karl Krauth and Karthik Gopalakrishnan and Katerina Ignatyeva and Katja Markert and Kaustubh D. Dhole and Kevin Gimpel and Kevin Ochieng’ Omondi and Kory Wallace Mathewson and Kristen Chiafullo and Ksenia Shkaruta and Kumar Shridhar and Kyle McDonell and Kyle Richardson and Laria Reynolds and Leo Gao and Li Zhang and Liam Dugan and Lianhui Qin and Lidia Contreras-Ochando and Louis-Philippe Morency and Luca Moschella and Luca Lam and Lucy Noble and Ludwig Schmidt and Luheng He and Luis Oliveros Col'on and Luke Metz and Lutfi Kerem cSenel and Maarten Bosma and Maarten Sap and Maartje ter Hoeve and Madotto Andrea and Maheen Saleem Farooqi and Manaal Faruqui and Mantas Mazeika and Marco Baturan and Marco Marelli and Marco Maru and M Quintana and Marie Tolkiehn and Mario Giulianelli and Martha Lewis and Martin Potthast and Matthew Leavitt and Matthias Hagen and M'aty'as Schubert and Medina Baitemirova and Melissa Arnaud and Melvin Andrew McElrath and Michael A. Yee and Michael Cohen and Mi Gu and Michael I. Ivanitskiy and Michael Starritt and Michael Strube and Michal Swkedrowski and Michele Bevilacqua and Michihiro Yasunaga and Mihir Kale and Mike Cain and Mimee Xu and Mirac Suzgun and Monica Tiwari and Mohit Bansal and Moin Aminnaseri and Mor Geva and Mozhdeh Gheini and T MukundVarma and Nanyun Peng and Nathan Chi and Nayeon Lee and Neta Gur-Ari Krakover and Nicholas Cameron and Nicholas S. Roberts and Nicholas Doiron and Nikita Nangia and Niklas Deckers and Niklas Muennighoff and Nitish Shirish Keskar and Niveditha Iyer and Noah Constant and Noah Fiedel and Nuan Wen and Oliver Zhang and Omar Agha and Omar Elbaghdadi and Omer Levy and Owain Evans and Pablo Antonio Moreno Casares and Parth Doshi and Pascale Fung and Paul Pu Liang and Paul Vicol and Pegah Alipoormolabashi and Peiyuan Liao and Percy Liang and Peter W. Chang and Peter Eckersley and Phu Mon Htut and Pi-Bei Hwang and P. Milkowski and Piyush S. Patil and Pouya Pezeshkpour and Priti Oli and Qiaozhu Mei and QING LYU and Qinlang Chen and Rabin Banjade and Rachel Etta Rudolph and Raefer Gabriel and Rahel Habacker and Ram'on Risco Delgado and Rapha{\\\"e}l Milli{\\'e}re and Rhythm Garg and Richard Barnes and Rif A. Saurous and Riku Arakawa and Robbe Raymaekers and Robert Frank and Rohan Sikand and Roman Novak and Roman Sitelew and Ronan Le Bras and Rosanne Liu and Rowan Jacobs and Rui Zhang and Ruslan Salakhutdinov and Ryan Chi and Ryan Lee and Ryan Stovall and Ryan Teehan and Rylan Yang and Sahib J. Singh and Saif M. Mohammad and Sajant Anand and Sam Dillavou and Sam Shleifer and Sam Wiseman and Samuel Gruetter and Sam Bowman and Samuel S. Schoenholz and Sanghyun Han and Sanjeev Kwatra and Sarah A. Rous and Sarik Ghazarian and Sayan Ghosh and Sean Casey and Sebastian Bischoff and Sebastian Gehrmann and Sebastian Schuster and Sepideh Sadeghi and Shadi Sameh Hamdan and Sharon Zhou and Shashank Srivastava and Sherry Shi and Shikhar Singh and Shima Asaadi and Shixiang Shane Gu and Shubh Pachchigar and Shubham Toshniwal and Shyam Upadhyay and Shyamolima Debnath and Siamak Shakeri and Simon Thormeyer and Simone Melzi and Siva Reddy and Sneha Priscilla Makini and Soo-hwan Lee and Spencer Bradley Torene and Sriharsha Hatwar and Stanislas Dehaene and Stefan Divic and Stefano Ermon and Stella Rose Biderman and Stephanie C. Lin and Stephen Prasad and Steven T. Piantadosi and Stuart M. Shieber and Summer Misherghi and Svetlana Kiritchenko and Swaroop Mishra and Tal Linzen and Tal Schuster and Tao Li and Tao Yu and Tariq A. Ali and Tatsuo Hashimoto and Te-Lin Wu and Theo Desbordes and Theodore Rothschild and Thomas Phan and Tianle Wang and Tiberius Nkinyili and Timo Schick and T. N. Kornev and Timothy Telleen-Lawton and Titus Tunduny and Tobias Gerstenberg and Trenton Chang and Trishala Neeraj and Tushar Khot and Tyler O. Shultz and Uri Shaham and Vedant Misra and Vera Demberg and Victoria Nyamai and Vikas Raunak and Vinay V. Ramasesh and Vinay Uday Prabhu and Vishakh Padmakumar and Vivek Srikumar and William Fedus and William Saunders and William Zhang and W Vossen and Xiang Ren and Xiaoyu F Tong and Xinyi Wu and Xudong Shen and Yadollah Yaghoobzadeh and Yair Lakretz and Yang Song and Yasaman Bahri and Ye Ji Choi and Yichi Yang and Yiding Hao and Yifu Chen and Yonatan Belinkov and Yu Hou and Yu Hou and Yushi Bai and Zachary Seid and Zhao Xinran and Zhuoye Zhao and Zi Fu Wang and Zijie J. Wang and Zirui Wang and Ziyi Wu and Sahib Singh and Uri Shaham},\n journal={ArXiv},\n year={2022},\n volume={abs/2206.04615}\n}" ]

[ "TAGS\n#task_categories-multiple-choice #task_categories-text-generation #task_ids-multiple-choice-qa #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-mit #arxiv-2206.04615 #region-us \n", "# Dataset Card for Understanding Fables", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: Understanding Fables BIG-Bench entry\n- Repository: Understanding Fables BIG-Bench entry\n- Paper: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models\n- Leaderboard: [N/A]\n- Point of Contact: Denis Emelin", "### Dataset Summary\n\nFables are short narratives that aim to communicate a specific lesson or wisdom, referred to as the moral. Morals can be idiomatic or provide a succinct summary of the fable. Importantly, they make explicit the communicative intent of the fable and, thus, are highly relevant to its content. A computational model capable of natural language understanding should, when presented with a fable and a set of potentially relevant morals, rank the moral that accurately captures the message communicated by the fable above the rest. Additionally, fables represent a highly unusual narrative domain, where animals and inanimate objects are anthropomorphized and referred to with gendered pronouns, i.e., a rabbit may be a she, rather than an it. Thus, to understand fables, models must abstract away from patterns commonly encountered in their training data by applying human-like characteristics to non-human actors. Overall, for a computational model to perform well on this task, it must be capable of (1) successfully identifying the core message of a short narrative, (2) identifying a moral that expresses this message among a set of distractor morals, and (3) doing so within a narrative domain that is unlike the majority of pre-training data. Thus, the evaluated large language models would need to demonstrate cross-domain generalization capability in addition to narrative comprehension.\n\nThe dataset evaluates models' ability to comprehend written narratives by asking them to select the most appropriate moral for each fable, from a set of five alternatives. In addition to the correct answer, this set contains four distractor morals, which were selected semi-automatically. To identify challenging distractor morals for each fable, sentence similarity was computed between the embeddings of each sentence in the fable and all morals found in the entire dataset, keeping ten morals that were found to be most similar to any of the fable's sentences. From this set, four distractors were selected manually, so that neither of the final distractors represents a valid moral of the fable. By construction, such distractor items are likely to pose a challenge for models that disproportionally rely on shallow heuristics as opposed to more sophisticated language-understanding strategies.", "### Supported Tasks and Leaderboards\n\n- multiple-choice: The dataset can be used to evaluate models on their ability to rank a moral that is relevant to a specified fable above distractor morals, e.g. by assigning the correct choice a lower model perplexity. A RoBERTa-based model can be used for this purpose.\n- text-generation: The dataset can also be used to train models to generate appropriate morals conditioned on the fable text. A GPT-based model can be used for this purpose.", "### Languages\n\nThe text in the dataset is in contemporary American English.", "## Dataset Structure", "### Data Instances\n\nA typical data point consists of a single fable, five morals - four distractors and one that correctly captures the lesson imparted by the fable, and an integer label denoting the ID of the correct answer option. An example dataset entry looks as follows: \n\n{\"story\": \"On a warm winter's day, the ants were busy drying corn that they had collected during summer. While they were engaged in their work, a starving grasshopper passed by and begged them for a single grain to stop his hunger. The ants, in turn, asked him why he had not collected food during the summer to prepare for the harsh winter, to which the grasshopper replied that he spent the warm days singing. Mockingly, the ants said to him in unison: \"Since you spent your summer singing, then you must dance hungrily to bed in winter.\" What is the moral of this story?\", \n\"answer0\": \"Fine clothes may disguise, but silly words will disclose a fool.\", \n\"answer1\": \"Better starve free than be a fat slave.\", \n\"answer2\": \"Evil wishes, like chickens, come home to roost.\", \n\"answer3\": \"Grasp at the shadow and you will lose the substance.\", \n\"answer4\": \"It is thrifty to prepare today for the wants of tomorrow.\", \n\"label\": 4}", "### Data Fields\n\n- \"story': The fable for which the appropriate moral has to be identified\n- \"answerN\": Moral candidates to be evaluated by the model \n- \"label\": The ID of the moral belonging to the fable", "### Data Splits\n\n*Understanding Fables* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .", "## Dataset Creation", "### Curation Rationale\n\nTo comply with the requirements for inclusion in BIG-bench, each fable was manually paraphrased, to ensure that the task cannot be solved by memorising web data. Following sources were used for fable collection: \n- URL\n- URL\n- URL\n- URL", "### Source Data", "#### Initial Data Collection and Normalization\n\nParaphrasing was done by an English speaker with native-like language proficiency and an academic background in literature. The created paraphrases differ from the originals in the identity of their participants (lion was replaced with tiger, wolf with coyote etc.), their sentence and narrative structure, and their register (archaic terms such as thou have been replaced with their modern counterparts). The phrasing of the morals has also been updated in cases where the original language was noticeably archaic (e.g., o'er reach -> overreach), with changes kept to a minimum. The mean string similarity between original fables and their paraphrases is consequently low at 0.26, according to the word-level Damerau\u0013Levenshtein distance. At the same time, great care was taken not to alter the content of the fables and preserve the relevance of their respective morals. This is evidenced by the high semantic similarity between the originals and their paraphrases, with a mean of 0.78, computed as the cosine similarity between the embeddings of the originals and their paraphrases, obtained using the Sentence-Transformers library. Moreover, duplicate and near-duplicate fables were removed from the final collection, as were several thematically problematic stories, e.g., ones with sexist undertones. In total, the dataset includes 189 paraphrased, unique fables.", "#### Who are the source language producers?\n\nOriginal authors and transcribers of the fables (unknown), the dataset author.", "### Annotations", "#### Annotation process\n\n[N/A]", "#### Who are the annotators?\n\n[N/A]", "### Personal and Sensitive Information\n\n[N/A]", "## Considerations for Using the Data", "### Social Impact of Dataset\n\n[N/A]", "### Discussion of Biases\n\nSeveral thematically problematic fables, e.g., ones with sexist undertones, were removed by the author during the dataset creation process. However, it can be that the author overlooked other problematic or harmful biases present within the dataset.", "### Other Known Limitations\n\nThe dataset is very limited in size due to the small number of thematically distinct fables available online. Similarly, the focus on English fables alone is a limiting factor to be addressed in future dataset iterations.", "## Additional Information", "### Dataset Curators\n\nDenis Emelin", "### Licensing Information\n\nMIT\n\n\n\n@article{Srivastava2022BeyondTI,\n title={Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},\n author={Aarohi Srivastava and Abhinav Rastogi and Abhishek B Rao and Abu Awal Md Shoeb and Abubakar Abid and Adam Fisch and Adam R. Brown and Adam Santoro and Aditya Gupta and Adri{\\'a} Garriga-Alonso and Agnieszka Kluska and Aitor Lewkowycz and Akshat Agarwal and Alethea Power and Alex Ray and Alex Warstadt and Alexander W. Kocurek and Ali Safaya and Ali Tazarv and Alice Xiang and Alicia Parrish and Allen Nie and Aman Hussain and Amanda Askell and Amanda Dsouza and Ameet Annasaheb Rahane and Anantharaman S. Iyer and Anders Johan Andreassen and Andrea Santilli and Andreas Stuhlmuller and Andrew M. Dai and Andrew D. La and Andrew Kyle Lampinen and Andy Zou and Angela Jiang and Angelica Chen and Anh Vuong and Animesh Gupta and Anna Gottardi and Antonio Norelli and Anu Venkatesh and Arash Gholamidavoodi and Arfa Tabassum and Arul Menezes and Arun Kirubarajan and Asher Mullokandov and Ashish Sabharwal and Austin Herrick and Avia Efrat and Aykut Erdem and Ayla Karakacs and Bridget R. Roberts and Bao Sheng Loe and Barret Zoph and Bartlomiej Bojanowski and Batuhan Ozyurt and Behnam Hedayatnia and Behnam Neyshabur and Benjamin Inden and Benno Stein and Berk Ekmekci and Bill Yuchen Lin and Blake Stephen Howald and Cameron Diao and Cameron Dour and Catherine Stinson and Cedrick Argueta and C'esar Ferri Ram'irez and Chandan Singh and Charles Rathkopf and Chenlin Meng and Chitta Baral and Chiyu Wu and Chris Callison-Burch and Chris Waites and Christian Voigt and Christopher D. Manning and Christopher Potts and Cindy Tatiana Ramirez and Clara Rivera and Clemencia Siro and Colin Raffel and Courtney Ashcraft and Cristina Garbacea and Damien Sileo and Daniel H Garrette and Dan Hendrycks and Dan Kilman and Dan Roth and Daniel Freeman and Daniel Khashabi and Daniel Levy and Daniel Gonz'alez and Danny Hernandez and Danqi Chen and Daphne Ippolito and Dar Gilboa and David Dohan and D. Drakard and David Jurgens and Debajyoti Datta and Deep Ganguli and Denis Emelin and Denis Kleyko and Deniz Yuret and Derek Chen and Derek Tam and Dieuwke Hupkes and Diganta Misra and Dilyar Buzan and Dimitri Coelho Mollo and Diyi Yang and Dong-Ho Lee and Ekaterina Shutova and Ekin Dogus Cubuk and Elad Segal and Eleanor Hagerman and Elizabeth Barnes and Elizabeth P. Donoway and Ellie Pavlick and Emanuele Rodol{\\'a} and Emma FC Lam and Eric Chu and Eric Tang and Erkut Erdem and Ernie Chang and Ethan A. Chi and Ethan Dyer and Ethan Jerzak and Ethan Kim and Eunice Engefu Manyasi and Evgenii Zheltonozhskii and Fan Xia and Fatemeh Siar and Fernando Mart'inez-Plumed and Francesca Happ'e and François Chollet and Frieda Rong and Gaurav Mishra and Genta Indra Winata and Gerard de Melo and Germ{\\'a}n Kruszewski and Giambattista Parascandolo and Giorgio Mariani and Gloria Wang and Gonzalo Jaimovitch-L'opez and Gregor Betz and Guy Gur-Ari and Hana Galijasevic and Han Sol Kim and Hannah Rashkin and Hanna Hajishirzi and Harsh Mehta and Hayden Bogar and Henry Shevlin and Hinrich Sch{\\\"u}tze and Hiromu Yakura and Hongming Zhang and Hubert Wong and Ian Aik-Soon Ng and Isaac Noble and Jaap Jumelet and Jack Geissinger and John Kernion and Jacob Hilton and Jaehoon Lee and Jaime Fern{\\'a}ndez Fisac and J. Brooker Simon and James Koppel and James Zheng and James Zou and Jan Koco'n and Jana Thompson and Jared Kaplan and Jarema Radom and Jascha Narain Sohl-Dickstein and Jason Phang and Jason Wei and Jason Yosinski and Jekaterina Novikova and Jelle Bosscher and Jenni Marsh and Jeremy Kim and Jeroen Taal and Jesse Engel and Jesujoba Oluwadara Alabi and Jiacheng Xu and Jiaming Song and Jillian Tang and Jane W Waweru and John Burden and John Miller and John U. Balis and Jonathan Berant and Jorg Frohberg and Jos Rozen and Jos{\\'e} Hern{\\'a}ndez-Orallo and Joseph Boudeman and Joseph Jones and Joshua B. Tenenbaum and Joshua S. Rule and Joyce Chua and Kamil Kanclerz and Karen Livescu and Karl Krauth and Karthik Gopalakrishnan and Katerina Ignatyeva and Katja Markert and Kaustubh D. Dhole and Kevin Gimpel and Kevin Ochieng’ Omondi and Kory Wallace Mathewson and Kristen Chiafullo and Ksenia Shkaruta and Kumar Shridhar and Kyle McDonell and Kyle Richardson and Laria Reynolds and Leo Gao and Li Zhang and Liam Dugan and Lianhui Qin and Lidia Contreras-Ochando and Louis-Philippe Morency and Luca Moschella and Luca Lam and Lucy Noble and Ludwig Schmidt and Luheng He and Luis Oliveros Col'on and Luke Metz and Lutfi Kerem cSenel and Maarten Bosma and Maarten Sap and Maartje ter Hoeve and Madotto Andrea and Maheen Saleem Farooqi and Manaal Faruqui and Mantas Mazeika and Marco Baturan and Marco Marelli and Marco Maru and M Quintana and Marie Tolkiehn and Mario Giulianelli and Martha Lewis and Martin Potthast and Matthew Leavitt and Matthias Hagen and M'aty'as Schubert and Medina Baitemirova and Melissa Arnaud and Melvin Andrew McElrath and Michael A. Yee and Michael Cohen and Mi Gu and Michael I. Ivanitskiy and Michael Starritt and Michael Strube and Michal Swkedrowski and Michele Bevilacqua and Michihiro Yasunaga and Mihir Kale and Mike Cain and Mimee Xu and Mirac Suzgun and Monica Tiwari and Mohit Bansal and Moin Aminnaseri and Mor Geva and Mozhdeh Gheini and T MukundVarma and Nanyun Peng and Nathan Chi and Nayeon Lee and Neta Gur-Ari Krakover and Nicholas Cameron and Nicholas S. Roberts and Nicholas Doiron and Nikita Nangia and Niklas Deckers and Niklas Muennighoff and Nitish Shirish Keskar and Niveditha Iyer and Noah Constant and Noah Fiedel and Nuan Wen and Oliver Zhang and Omar Agha and Omar Elbaghdadi and Omer Levy and Owain Evans and Pablo Antonio Moreno Casares and Parth Doshi and Pascale Fung and Paul Pu Liang and Paul Vicol and Pegah Alipoormolabashi and Peiyuan Liao and Percy Liang and Peter W. Chang and Peter Eckersley and Phu Mon Htut and Pi-Bei Hwang and P. Milkowski and Piyush S. Patil and Pouya Pezeshkpour and Priti Oli and Qiaozhu Mei and QING LYU and Qinlang Chen and Rabin Banjade and Rachel Etta Rudolph and Raefer Gabriel and Rahel Habacker and Ram'on Risco Delgado and Rapha{\\\"e}l Milli{\\'e}re and Rhythm Garg and Richard Barnes and Rif A. Saurous and Riku Arakawa and Robbe Raymaekers and Robert Frank and Rohan Sikand and Roman Novak and Roman Sitelew and Ronan Le Bras and Rosanne Liu and Rowan Jacobs and Rui Zhang and Ruslan Salakhutdinov and Ryan Chi and Ryan Lee and Ryan Stovall and Ryan Teehan and Rylan Yang and Sahib J. Singh and Saif M. Mohammad and Sajant Anand and Sam Dillavou and Sam Shleifer and Sam Wiseman and Samuel Gruetter and Sam Bowman and Samuel S. Schoenholz and Sanghyun Han and Sanjeev Kwatra and Sarah A. Rous and Sarik Ghazarian and Sayan Ghosh and Sean Casey and Sebastian Bischoff and Sebastian Gehrmann and Sebastian Schuster and Sepideh Sadeghi and Shadi Sameh Hamdan and Sharon Zhou and Shashank Srivastava and Sherry Shi and Shikhar Singh and Shima Asaadi and Shixiang Shane Gu and Shubh Pachchigar and Shubham Toshniwal and Shyam Upadhyay and Shyamolima Debnath and Siamak Shakeri and Simon Thormeyer and Simone Melzi and Siva Reddy and Sneha Priscilla Makini and Soo-hwan Lee and Spencer Bradley Torene and Sriharsha Hatwar and Stanislas Dehaene and Stefan Divic and Stefano Ermon and Stella Rose Biderman and Stephanie C. Lin and Stephen Prasad and Steven T. Piantadosi and Stuart M. Shieber and Summer Misherghi and Svetlana Kiritchenko and Swaroop Mishra and Tal Linzen and Tal Schuster and Tao Li and Tao Yu and Tariq A. Ali and Tatsuo Hashimoto and Te-Lin Wu and Theo Desbordes and Theodore Rothschild and Thomas Phan and Tianle Wang and Tiberius Nkinyili and Timo Schick and T. N. Kornev and Timothy Telleen-Lawton and Titus Tunduny and Tobias Gerstenberg and Trenton Chang and Trishala Neeraj and Tushar Khot and Tyler O. Shultz and Uri Shaham and Vedant Misra and Vera Demberg and Victoria Nyamai and Vikas Raunak and Vinay V. Ramasesh and Vinay Uday Prabhu and Vishakh Padmakumar and Vivek Srikumar and William Fedus and William Saunders and William Zhang and W Vossen and Xiang Ren and Xiaoyu F Tong and Xinyi Wu and Xudong Shen and Yadollah Yaghoobzadeh and Yair Lakretz and Yang Song and Yasaman Bahri and Ye Ji Choi and Yichi Yang and Yiding Hao and Yifu Chen and Yonatan Belinkov and Yu Hou and Yu Hou and Yushi Bai and Zachary Seid and Zhao Xinran and Zhuoye Zhao and Zi Fu Wang and Zijie J. Wang and Zirui Wang and Ziyi Wu and Sahib Singh and Uri Shaham},\n journal={ArXiv},\n year={2022},\n volume={abs/2206.04615}\n}" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2112.01488", "region:us" ]

[ "2112.01488" ]

[ "en" ]

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[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #arxiv-2112.01488 #region-us \n" ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2112.01488", "region:us" ]

[ "2112.01488" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-apache-2.0 #arxiv-2112.01488 #region-us \n" ]

[ "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: pszemraj/led-large-book-summary\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 @pszemraj 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: pszemraj/led-large-book-summary\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 @pszemraj 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: pszemraj/led-base-book-summary\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 @pszemraj 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: pszemraj/led-base-book-summary\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 @pszemraj 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: pszemraj/led-base-book-summary\n* Dataset: cnn_dailymail\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 @pszemraj 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: pszemraj/led-base-book-summary\n* Dataset: cnn_dailymail\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 @pszemraj for evaluating this model." ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nPublished version of dataset used for paper 'Towards an automatic requirements classification in a new Spanish dataset'", "### Languages\n\nSpanish", "## Dataset Structure", "### Data Fields\n\nProject: Project's Identifier from which the requirements were obtained.\nRequirement: Description of the software requirement.\nFinal label: Label of the requirement: F (functional requirement) and NF (non-functional requirement).", "## Dataset Creation", "### Initial Data Collection and Normalization\n\nThis dataset was created from a collection of functional and non-functional requirements extracted from 13 final degree and 2 master’s projects carried out from the University of A Coruna. It consist in 300 functional and 89 non-funtcional requirements.", "## Additional Information\n\n\n\nURL" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Spanish #license-cc-by-4.0 #region-us \n", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nPublished version of dataset used for paper 'Towards an automatic requirements classification in a new Spanish dataset'", "### Languages\n\nSpanish", "## Dataset Structure", "### Data Fields\n\nProject: Project's Identifier from which the requirements were obtained.\nRequirement: Description of the software requirement.\nFinal label: Label of the requirement: F (functional requirement) and NF (non-functional requirement).", "## Dataset Creation", "### Initial Data Collection and Normalization\n\nThis dataset was created from a collection of functional and non-functional requirements extracted from 13 final degree and 2 master’s projects carried out from the University of A Coruna. It consist in 300 functional and 89 non-funtcional requirements.", "## Additional Information\n\n\n\nURL" ]

[ "multilinguality:other-music", "language:en", "language:zh", "region:us" ]

[]

[ "en", "zh" ]

[]

[ "TAGS\n#multilinguality-other-music #language-English #language-Chinese #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: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: cnn_dailymail\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 @pszemraj 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: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: cnn_dailymail\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 @pszemraj 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: Abdelrahman-Rezk/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 @postpandas 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: Abdelrahman-Rezk/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 @postpandas 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: pszemraj/bigbird-pegasus-large-K-booksum\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/bigbird-pegasus-large-K-booksum\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/pegasus-large-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/pegasus-large-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: jsoutherland/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 @jsoutherland 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: jsoutherland/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 @jsoutherland 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: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: cnn_dailymail\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 @pszemraj 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: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: cnn_dailymail\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 @pszemraj for evaluating this model." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

[ "TAGS\n#task_categories-image-classification #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: Summarization\n* Model: pszemraj/led-base-book-summary\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 @pszemraj 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: pszemraj/led-base-book-summary\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 @pszemraj 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: pszemraj/led-large-book-summary\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 @pszemraj 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: pszemraj/led-large-book-summary\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 @pszemraj 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: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:cc-by-nc-sa-4.0", "arxiv:2103.06410", "region:us" ]

[ "2103.06410" ]

[ "en" ]

[ "# MediaSum", "## Description\nThis large-scale media interview dataset contains 463.6K transcripts with abstractive summaries, \ncollected from interview transcripts and overview / topic descriptions from NPR and CNN.", "### NOTE: The authors have requested that this dataset be used for research purposes only", "## Homepage\nURL", "## Paper\nURL", "## Authors", "### Chenguang Zhu*, Yang Liu*, Jie Mei, Michael Zeng", "#### Microsoft Cognitive Services Research Group\n{chezhu,yaliu10,jimei,nzeng}@URL\n\n@article{zhu2021mediasum, \n title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization}, \n author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael}, \n journal={arXiv preprint arXiv:2103.06410}, \n year={2021} \n}", "## Dataset size\nTrain: 443,596 \nValidation: 10,000 \nTest: 10,000 \n\nThe splits were made by using the file located here: URL", "## Data details\n- id (string): unique identifier\n- program (string): the program this transcript came from\n- date (string): date of program\n- url (string): link to where audio and transcript are located\n- title (string): title of the program. some datapoints do not have a title\n- summary (string): summary of the program\n- utt (list of string): list of utterances by the speakers in the program. corresponds with 'speaker'\n- speaker (list of string): list of speakers, corresponds with 'utt'\n\n\nExample:", "## Using the dataset", "## Data location\nURL", "## License\nNo license specified, but the authors have requested that this dataset be used for research purposes only." ]

[ "TAGS\n#task_categories-summarization #multilinguality-monolingual #size_categories-100K<n<1M #language-English #license-cc-by-nc-sa-4.0 #arxiv-2103.06410 #region-us \n", "# MediaSum", "## Description\nThis large-scale media interview dataset contains 463.6K transcripts with abstractive summaries, \ncollected from interview transcripts and overview / topic descriptions from NPR and CNN.", "### NOTE: The authors have requested that this dataset be used for research purposes only", "## Homepage\nURL", "## Paper\nURL", "## Authors", "### Chenguang Zhu*, Yang Liu*, Jie Mei, Michael Zeng", "#### Microsoft Cognitive Services Research Group\n{chezhu,yaliu10,jimei,nzeng}@URL\n\n@article{zhu2021mediasum, \n title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization}, \n author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael}, \n journal={arXiv preprint arXiv:2103.06410}, \n year={2021} \n}", "## Dataset size\nTrain: 443,596 \nValidation: 10,000 \nTest: 10,000 \n\nThe splits were made by using the file located here: URL", "## Data details\n- id (string): unique identifier\n- program (string): the program this transcript came from\n- date (string): date of program\n- url (string): link to where audio and transcript are located\n- title (string): title of the program. some datapoints do not have a title\n- summary (string): summary of the program\n- utt (list of string): list of utterances by the speakers in the program. corresponds with 'speaker'\n- speaker (list of string): list of speakers, corresponds with 'utt'\n\n\nExample:", "## Using the dataset", "## Data location\nURL", "## License\nNo license specified, but the authors have requested that this dataset be used for research purposes only." ]

[ "region:us" ]

[]

[]

[ "# SCP Text+ Embeddings\n\nThis dataset is adapted from the SCP 1to 7 corpus from Kaggle\n\nWe concatenated the title, state, text, and image captions columns. We also removed any rows that contained a deleted page, which trims the results down from 6999 -> 6618. \n\nThe embeddings were generated using sentence-transformers/multi-qa-mpnet-base-dot-v1\n\nFeel free to use the dataset for semantic search or text generation tasks!" ]

[ "TAGS\n#region-us \n", "# SCP Text+ Embeddings\n\nThis dataset is adapted from the SCP 1to 7 corpus from Kaggle\n\nWe concatenated the title, state, text, and image captions columns. We also removed any rows that contained a deleted page, which trims the results down from 6999 -> 6618. \n\nThe embeddings were generated using sentence-transformers/multi-qa-mpnet-base-dot-v1\n\nFeel free to use the dataset for semantic search or text generation tasks!" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nCoNLL-2003 NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'ORG', 'PER', 'LOC', 'MISC'\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", "### Licensing Information\n\n\nFrom the CoNLL2003 shared task page:\n\n\n\n> \n> The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done by people of the University of Antwerp. Because of copyright reasons we only make available the annotations. In order to build the complete data sets you will need access to the Reuters Corpus. It can be obtained for research purposes without any charge from NIST.\n> \n> \n> \n\n\nThe copyrights are defined below, from the Reuters Corpus page:\n\n\n\n> \n> The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements:\n> \n> \n> Organizational agreement\n> \n> \n> This agreement must be signed by the person responsible for the data at your organization, and sent to NIST.\n> \n> \n> Individual agreement\n> \n> \n> This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization.\n> \n> \n>" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nCoNLL-2003 NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'ORG', 'PER', 'LOC', 'MISC'\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", "### Licensing Information\n\n\nFrom the CoNLL2003 shared task page:\n\n\n\n> \n> The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done by people of the University of Antwerp. Because of copyright reasons we only make available the annotations. In order to build the complete data sets you will need access to the Reuters Corpus. It can be obtained for research purposes without any charge from NIST.\n> \n> \n> \n\n\nThe copyrights are defined below, from the Reuters Corpus page:\n\n\n\n> \n> The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements:\n> \n> \n> Organizational agreement\n> \n> \n> This agreement must be signed by the person responsible for the data at your organization, and sent to NIST.\n> \n> \n> Individual agreement\n> \n> \n> This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization.\n> \n> \n>" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nOntonotes5 NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'CARDINAL', 'DATE', 'PERSON', 'NORP', 'GPE', 'LAW', 'PERCENT', 'ORDINAL', 'MONEY', 'WORK\\_OF\\_ART', 'FAC', 'TIME', 'QUANTITY', 'PRODUCT', 'LANGUAGE', 'ORG', 'LOC', 'EVENT'\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-10K<n<100K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nOntonotes5 NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'CARDINAL', 'DATE', 'PERSON', 'NORP', 'GPE', 'LAW', 'PERCENT', 'ORDINAL', 'MONEY', 'WORK\\_OF\\_ART', 'FAC', 'TIME', 'QUANTITY', 'PRODUCT', 'LANGUAGE', 'ORG', 'LOC', 'EVENT'\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:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:1k<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nWNUT 2017 NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'creative-work', 'corporation', 'group', 'location', 'person', 'product'\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\nWNUT 2017 NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'creative-work', 'corporation', 'group', 'location', 'person', 'product'\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:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nFIN NER dataset formatted in a part of TNER project.\nFIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.\n\n\n* Entity Types: 'ORG', 'LOC', 'PER', 'MISC'\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<n<10K #language-English #license-mit #region-us \n", "### Dataset Summary\n\n\nFIN NER dataset formatted in a part of TNER project.\nFIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.\n\n\n* Entity Types: 'ORG', 'LOC', 'PER', 'MISC'\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:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBioNLP2004 NER dataset formatted in a part of TNER project.\nBioNLP2004 dataset contains training and test only, so we randomly sample a half size of test instances from the training set to create validation set.\n\n\n* Entity Types: 'DNA', 'protein', 'cell\\_type', 'cell\\_line', 'RNA'\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-10K<n<100K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nBioNLP2004 NER dataset formatted in a part of TNER project.\nBioNLP2004 dataset contains training and test only, so we randomly sample a half size of test instances from the training set to create validation set.\n\n\n* Entity Types: 'DNA', 'protein', 'cell\\_type', 'cell\\_line', 'RNA'\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:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nBioCreative V CDR NER dataset formatted in a part of TNER project.\nThe original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.\n\n\n* Entity Types: 'Chemical', 'Disease'\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-10K<n<100K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nBioCreative V CDR NER dataset formatted in a part of TNER project.\nThe original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.\n\n\n* Entity Types: 'Chemical', 'Disease'\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:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nMIT Movie NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'Actor', 'Plot', 'Opinion', 'Award', 'Year', 'Genre', 'Origin', 'Director', 'Soundtrack', 'Relationship', 'Character\\_Name', 'Quote'\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<n<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nMIT Movie NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'Actor', 'Plot', 'Opinion', 'Award', 'Year', 'Genre', 'Origin', 'Director', 'Soundtrack', 'Relationship', 'Character\\_Name', 'Quote'\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:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nMIT Restaurant NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'Rating', 'Amenity', 'Location', 'Restaurant\\_Name', 'Price', 'Hours', 'Dish', 'Cuisine'.\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<n<10K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nMIT Restaurant NER dataset formatted in a part of TNER project.\n\n\n* Entity Types: 'Rating', 'Amenity', 'Location', 'Restaurant\\_Name', 'Price', 'Hours', 'Dish', 'Cuisine'.\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" ]

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

[]

[ "en" ]

[ "# Dataset Card for \"SumPubmed\"", "## Original Dataset Description\n\n- Repository: URL \n- Paper:", "## Description of dataset processing\n5 rows were dropped from the original dataset taken from KAGGLE as they were missing the respective 'shorter_abstract' entries.\n\nThe 'line_text' and 'filename_text' columns were left untouched while the remaining ones were processed to remove the '\\n' (many repetitions of those present in the original dataset), '\\<dig\\>', '\\<cit\\>', 'BACKGROUND', 'RESULTS' and 'CONCLUSIONS' matching strings which were deemed not necessary for the purpose of summarization. Additionally, extra spaces were removed and spacing around punctuations was fixed." ]

[ "TAGS\n#language-English #region-us \n", "# Dataset Card for \"SumPubmed\"", "## Original Dataset Description\n\n- Repository: URL \n- Paper:", "## Description of dataset processing\n5 rows were dropped from the original dataset taken from KAGGLE as they were missing the respective 'shorter_abstract' entries.\n\nThe 'line_text' and 'filename_text' columns were left untouched while the remaining ones were processed to remove the '\\n' (many repetitions of those present in the original dataset), '\\<dig\\>', '\\<cit\\>', 'BACKGROUND', 'RESULTS' and 'CONCLUSIONS' matching strings which were deemed not necessary for the purpose of summarization. Additionally, extra spaces were removed and spacing around punctuations was fixed." ]

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

[]

[]

[ "# Data source\nThis data has been collected through a standard scraping process from the Medium website, looking for published articles.", "# Data description\nEach row in the data is a different article published on Medium. For each article, you have the following features:\n- title *[string]*: The title of the article.\n- text *[string]*: The text content of the article.\n- url *[string]*: The URL associated to the article.\n- authors *[list of strings]*: The article authors.\n- timestamp *[string]*: The publication datetime of the article.\n- tags *[list of strings]*: List of tags associated to the article.", "# Data analysis\nYou can find a very quick data analysis in this notebook.", "# What can I do with this data?\n- A multilabel classification model that assigns tags to articles.\n- A seq2seq model that generates article titles.\n- Text analysis.\n- Finetune text generation models on the general domain of Medium, or on specific domains by filtering articles by the appropriate tags.", "# Collection methodology\nScraping has been done with Python and the requests library. Starting from a random article on Medium, the next articles to scrape are selected by visiting:\n1. The author archive pages.\n2. The publication archive pages (if present).\n3. The tags archives (if present).\n\nThe article HTML pages have been parsed with the newspaper Python library.\n\nPublished articles have been filtered for English articles only, using the Python langdetect library.\n\nAs a consequence of the collection methodology, the scraped articles are coming from a not uniform publication date distribution. This means that there are articles published in 2016 and in 2022, but the number of articles in this dataset published in 2016 is not the same as the number of articles published in 2022. In particular, there is a strong prevalence of articles published in 2020. Have a look at the accompanying notebook to see the distribution of the publication dates." ]

[ "TAGS\n#license-mit #region-us \n", "# Data source\nThis data has been collected through a standard scraping process from the Medium website, looking for published articles.", "# Data description\nEach row in the data is a different article published on Medium. For each article, you have the following features:\n- title *[string]*: The title of the article.\n- text *[string]*: The text content of the article.\n- url *[string]*: The URL associated to the article.\n- authors *[list of strings]*: The article authors.\n- timestamp *[string]*: The publication datetime of the article.\n- tags *[list of strings]*: List of tags associated to the article.", "# Data analysis\nYou can find a very quick data analysis in this notebook.", "# What can I do with this data?\n- A multilabel classification model that assigns tags to articles.\n- A seq2seq model that generates article titles.\n- Text analysis.\n- Finetune text generation models on the general domain of Medium, or on specific domains by filtering articles by the appropriate tags.", "# Collection methodology\nScraping has been done with Python and the requests library. Starting from a random article on Medium, the next articles to scrape are selected by visiting:\n1. The author archive pages.\n2. The publication archive pages (if present).\n3. The tags archives (if present).\n\nThe article HTML pages have been parsed with the newspaper Python library.\n\nPublished articles have been filtered for English articles only, using the Python langdetect library.\n\nAs a consequence of the collection methodology, the scraped articles are coming from a not uniform publication date distribution. This means that there are articles published in 2016 and in 2022, but the number of articles in this dataset published in 2016 is not the same as the number of articles published in 2022. In particular, there is a strong prevalence of articles published in 2020. Have a look at the accompanying notebook to see the distribution of the publication dates." ]

[ "region:us" ]

[]

[]

[]

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

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

[ "2206.15147" ]

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

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

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

[ "task_categories:text-classification", "task_categories:text-generation", "task_ids:multi-class-classification", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Old Bailey Proceedings", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact: The University of Sheffield\nDigital Humanities Institute\n34 Gell Street\nSheffield S3 7QY", "### Dataset Summary\n\nNote We are making this dataset available via the HuggingFace hub to open it up to more users and use cases. We have focused primarily on making an initial version of this dataset available, focusing on some potential use cases. If you think there are other configurations this dataset should support, please use the community tab to open an issue. \n\nThe dataset consists of 2,163 transcriptions of the Proceedings and 475 Ordinary's Accounts marked up in TEI-XML, and contains some documentation covering the data structure and variables. Each Proceedings file represents one session of the court (1674-1913), and each Ordinary's Account file represents a single pamphlet (1676-1772).", "### Supported Tasks and Leaderboards\n\n- 'language-modeling': This dataset can be used to contribute to the training or evaluation of language models for historical texts. Since it represents transcription from court proceedings, the language in this dataset may better represent the variety of language used at the time.\n- 'text-classification': This dataset can be used to classify what style of English some text is in\n- 'named-entity-recognition': Some of the text contains names of people and places. We don't currently provide the token IDs for these entities but do provide the tokens themselves. This means this dataset has the potential to be used to evaluate the performance of other Named Entity Recognition models on this dataset.", "### Languages\n\n'en'", "## Dataset Structure", "### Data Instances\n\nAn example of one instance from the dataset:", "### Data Fields\n\n- 'id': A unique identifier for the data point (in this case, a trial)\n- 'text': The text of the proceeding\n- 'places': The places mentioned in the text\n- 'type': This can be either 'OA' or 'OBP'. OA is \"Ordinary's Accounts\" and OBP is \"Sessions Proceedings\"\n- 'persons': The persons named in the text\n- 'date': The date of the text", "### Data Splits\nThis dataset only contains a single split:\n\nTrain: '2638' examples", "## Dataset Creation", "### Curation Rationale\n\nBetween 1674 and 1913 the Proceedings of the Central Criminal Court in London, the Old Bailey, were published eight times a year. These records detail 197,000 individual trials and contain 127 million words in 182,000 pages. They represent the largest single source of information about non-elite lives and behaviour ever published and provide a wealth of detail about everyday life, as well as valuable systematic evidence of the circumstances surrounding the crimes and lives of victims and the accused, and their trial outcomes. This project created a fully digitised and structured version of all surviving published trial accounts between 1674 and 1913, and made them available as a searchable online resource.", "### Source Data", "#### Initial Data Collection and Normalization\n\nStarting with microfilms of the original Proceedings and Ordinary's Accounts, page images were scanned to create high definition, 400dpi TIFF files, from which GIF and JPEG files have been created for transmission over the internet. The uncompressed TIFF files will be preserved for archival purposes and should eventually be accessible over the web once data transmission speeds improve. A GIF format has been used to transmit image files for the Proceedings published between 1674 and 1834.", "#### Who are the source language producers?\n\nThe text of the 1674 to October 1834 Proceedings was manually typed by the process known as \"double rekeying\", whereby the text is typed in twice, by two different typists. Then the two transcriptions are compared by computer. Differences are identified and then resolved manually. This process was also used to create a transcription of the Ordinary's Accounts. This process means this text data contains fewer errors than many historical text corpora produced using Optical Character Recognition.", "### Annotations", "#### Annotation process\n\nThe markup was done by a combination of automated and manual processes.\n\nMost of the 1674 to October 1834 markup was done manually by a team of five data developers working at the Humanities Research Institute at the University of Sheffield (see project staff).\n\nHowever, person names were tagged using an automated markup programme, GATE, developed by the Department of Computer Science at the University of Sheffield and specially customised to process the text of the Proceedings. Most of the 1674-1834 trial proceedings were run through GATE, which was able to identify approximately 80-90% of the names in the text. GATE was asked only to identify names where both a forename (not just an initial) and surname were given. The names not identified by this programme were not regularly marked up manually unless they were the names of defendants or victims.\n\nThe November 1834 to 1913 text was first run through an automated markup process. This process was carried out by the Digital Humanities Institute Sheffield.\n\nRemaining markup, including checking of the results of the automated markup, was carried out by a team of eight data developers employed by the University of Hertfordshire (see project staff).", "#### Who are the annotators?\n\n- The directors of this project, and authors of all the historical background pages, are Professor Clive Emsley (Open University), Professor Tim Hitchcock (University of Sussex) and Professor Robert Shoemaker (University of Sheffield).\n- The Project Manager is Dr Sharon Howard.\n- The technical officer responsible for programming the search engines is Jamie McLaughlin.\n- The Senior Data Developer, in charge of all the tagging procedures, was Dr Philippa Hardman.\n- The other Data Developers were Anna Bayman, Eilidh Garrett, Carol Lewis-Roylance, Susan Parkinson, Anna Simmons, Gwen Smithson, Nicola Wilcox, and Catherine Wright.\n- The London researcher was Mary Clayton.\n- The technical officers responsible for the automated markup were Ed MacKenzie and Katherine Rogers.\n- Project staff who worked on the 1674-1834 phase of the project include Dr Louise Henson (Senior Data Developer), Dr John Black, Dr Edwina Newman, Kay O'Flaherty, and Gwen Smithson.", "### Personal and Sensitive Information\n\n-This dataset contains personal information of people involved in criminal proceedings during the time period", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\n- \"Virtually every aspect of English life between 1674 and 1913 was influenced by gender, and this includes behaviour documented in the Old Bailey Proceedings. Long-held views about the particular strengths, weaknesses, and appropriate responsibilities of each sex shaped everyday lives, patterns of crime, and responses to crime.\" This dataset contains text that adheres to those stereotypes.\n- \"The make-up of London's population changed and changed again during the course of the two and a half centuries after 1674. European Protestant refugees, blacks discharged from the armies of a growing empire, and Jews from Spain and Eastern Europe, Irish men and women, Lascars and political refugees from the revolutions of the nineteenth century contributed to the ragout of communities that made up this world city. Information about all these communities, and several more besides, can be found in the Proceedings\"", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\n- The directors of this project, and authors of all the historical background pages, are Professor Clive Emsley (Open University), Professor Tim Hitchcock (University of Sussex) and Professor Robert Shoemaker (University of Sheffield).\n- The Project Manager is Dr Sharon Howard.\n- The technical officer responsible for programming the search engines is Jamie McLaughlin.\n- The Senior Data Developer, in charge of all the tagging procedures, was Dr Philippa Hardman.\n- The other Data Developers were Anna Bayman, Eilidh Garrett, Carol Lewis-Roylance, Susan Parkinson, Anna Simmons, Gwen Smithson, - Nicola Wilcox, and Catherine Wright.", "### Licensing Information\n\nCC-NY-04\n\n\n\n@article{Howard2017,\nauthor = \"Sharon Howard\",\ntitle = \"{Old Bailey Online XML Data}\",\nyear = \"2017\",\nmonth = \"4\",\nurl = \"URL\ndoi = \"10.15131/URL.4775434.v2\"\n}\n\nThanks to @shamikbose for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_categories-text-generation #task_ids-multi-class-classification #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for Old Bailey Proceedings", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact: The University of Sheffield\nDigital Humanities Institute\n34 Gell Street\nSheffield S3 7QY", "### Dataset Summary\n\nNote We are making this dataset available via the HuggingFace hub to open it up to more users and use cases. We have focused primarily on making an initial version of this dataset available, focusing on some potential use cases. If you think there are other configurations this dataset should support, please use the community tab to open an issue. \n\nThe dataset consists of 2,163 transcriptions of the Proceedings and 475 Ordinary's Accounts marked up in TEI-XML, and contains some documentation covering the data structure and variables. Each Proceedings file represents one session of the court (1674-1913), and each Ordinary's Account file represents a single pamphlet (1676-1772).", "### Supported Tasks and Leaderboards\n\n- 'language-modeling': This dataset can be used to contribute to the training or evaluation of language models for historical texts. Since it represents transcription from court proceedings, the language in this dataset may better represent the variety of language used at the time.\n- 'text-classification': This dataset can be used to classify what style of English some text is in\n- 'named-entity-recognition': Some of the text contains names of people and places. We don't currently provide the token IDs for these entities but do provide the tokens themselves. This means this dataset has the potential to be used to evaluate the performance of other Named Entity Recognition models on this dataset.", "### Languages\n\n'en'", "## Dataset Structure", "### Data Instances\n\nAn example of one instance from the dataset:", "### Data Fields\n\n- 'id': A unique identifier for the data point (in this case, a trial)\n- 'text': The text of the proceeding\n- 'places': The places mentioned in the text\n- 'type': This can be either 'OA' or 'OBP'. OA is \"Ordinary's Accounts\" and OBP is \"Sessions Proceedings\"\n- 'persons': The persons named in the text\n- 'date': The date of the text", "### Data Splits\nThis dataset only contains a single split:\n\nTrain: '2638' examples", "## Dataset Creation", "### Curation Rationale\n\nBetween 1674 and 1913 the Proceedings of the Central Criminal Court in London, the Old Bailey, were published eight times a year. These records detail 197,000 individual trials and contain 127 million words in 182,000 pages. They represent the largest single source of information about non-elite lives and behaviour ever published and provide a wealth of detail about everyday life, as well as valuable systematic evidence of the circumstances surrounding the crimes and lives of victims and the accused, and their trial outcomes. This project created a fully digitised and structured version of all surviving published trial accounts between 1674 and 1913, and made them available as a searchable online resource.", "### Source Data", "#### Initial Data Collection and Normalization\n\nStarting with microfilms of the original Proceedings and Ordinary's Accounts, page images were scanned to create high definition, 400dpi TIFF files, from which GIF and JPEG files have been created for transmission over the internet. The uncompressed TIFF files will be preserved for archival purposes and should eventually be accessible over the web once data transmission speeds improve. A GIF format has been used to transmit image files for the Proceedings published between 1674 and 1834.", "#### Who are the source language producers?\n\nThe text of the 1674 to October 1834 Proceedings was manually typed by the process known as \"double rekeying\", whereby the text is typed in twice, by two different typists. Then the two transcriptions are compared by computer. Differences are identified and then resolved manually. This process was also used to create a transcription of the Ordinary's Accounts. This process means this text data contains fewer errors than many historical text corpora produced using Optical Character Recognition.", "### Annotations", "#### Annotation process\n\nThe markup was done by a combination of automated and manual processes.\n\nMost of the 1674 to October 1834 markup was done manually by a team of five data developers working at the Humanities Research Institute at the University of Sheffield (see project staff).\n\nHowever, person names were tagged using an automated markup programme, GATE, developed by the Department of Computer Science at the University of Sheffield and specially customised to process the text of the Proceedings. Most of the 1674-1834 trial proceedings were run through GATE, which was able to identify approximately 80-90% of the names in the text. GATE was asked only to identify names where both a forename (not just an initial) and surname were given. The names not identified by this programme were not regularly marked up manually unless they were the names of defendants or victims.\n\nThe November 1834 to 1913 text was first run through an automated markup process. This process was carried out by the Digital Humanities Institute Sheffield.\n\nRemaining markup, including checking of the results of the automated markup, was carried out by a team of eight data developers employed by the University of Hertfordshire (see project staff).", "#### Who are the annotators?\n\n- The directors of this project, and authors of all the historical background pages, are Professor Clive Emsley (Open University), Professor Tim Hitchcock (University of Sussex) and Professor Robert Shoemaker (University of Sheffield).\n- The Project Manager is Dr Sharon Howard.\n- The technical officer responsible for programming the search engines is Jamie McLaughlin.\n- The Senior Data Developer, in charge of all the tagging procedures, was Dr Philippa Hardman.\n- The other Data Developers were Anna Bayman, Eilidh Garrett, Carol Lewis-Roylance, Susan Parkinson, Anna Simmons, Gwen Smithson, Nicola Wilcox, and Catherine Wright.\n- The London researcher was Mary Clayton.\n- The technical officers responsible for the automated markup were Ed MacKenzie and Katherine Rogers.\n- Project staff who worked on the 1674-1834 phase of the project include Dr Louise Henson (Senior Data Developer), Dr John Black, Dr Edwina Newman, Kay O'Flaherty, and Gwen Smithson.", "### Personal and Sensitive Information\n\n-This dataset contains personal information of people involved in criminal proceedings during the time period", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\n- \"Virtually every aspect of English life between 1674 and 1913 was influenced by gender, and this includes behaviour documented in the Old Bailey Proceedings. Long-held views about the particular strengths, weaknesses, and appropriate responsibilities of each sex shaped everyday lives, patterns of crime, and responses to crime.\" This dataset contains text that adheres to those stereotypes.\n- \"The make-up of London's population changed and changed again during the course of the two and a half centuries after 1674. European Protestant refugees, blacks discharged from the armies of a growing empire, and Jews from Spain and Eastern Europe, Irish men and women, Lascars and political refugees from the revolutions of the nineteenth century contributed to the ragout of communities that made up this world city. Information about all these communities, and several more besides, can be found in the Proceedings\"", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\n- The directors of this project, and authors of all the historical background pages, are Professor Clive Emsley (Open University), Professor Tim Hitchcock (University of Sussex) and Professor Robert Shoemaker (University of Sheffield).\n- The Project Manager is Dr Sharon Howard.\n- The technical officer responsible for programming the search engines is Jamie McLaughlin.\n- The Senior Data Developer, in charge of all the tagging procedures, was Dr Philippa Hardman.\n- The other Data Developers were Anna Bayman, Eilidh Garrett, Carol Lewis-Roylance, Susan Parkinson, Anna Simmons, Gwen Smithson, - Nicola Wilcox, and Catherine Wright.", "### Licensing Information\n\nCC-NY-04\n\n\n\n@article{Howard2017,\nauthor = \"Sharon Howard\",\ntitle = \"{Old Bailey Online XML Data}\",\nyear = \"2017\",\nmonth = \"4\",\nurl = \"URL\ndoi = \"10.15131/URL.4775434.v2\"\n}\n\nThanks to @shamikbose for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Large Logo Dataset (LLD)", "## Description\n\nAdapted from the original LLD dataset. Original description:\n\n> Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve the creative task of the designer. For this, we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web. Training Generative Adversarial Networks (GANs) for logo synthesis on such multi-modal data is not straightforward and results in mode collapse for some state-of-the-art methods. We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training. We are able to generate a high diversity of plausible logos and we demonstrate latent space exploration techniques to ease the logo design task in an interactive manner. Moreover, we validate the proposed clustered GAN training on CIFAR 10, achieving state-of-the-art Inception scores when using synthetic labels obtained via clustering the features of an ImageNet classifier. GANs can cope with multi-modal data by means of synthetic labels achieved through clustering, and our results show the creative potential of such techniques for logo synthesis and manipulation.", "## Schema\n\n\n\ns" ]

[ "TAGS\n#multilinguality-monolingual #size_categories-100K<n<1M #language-English #license-mit #region-us \n", "# Dataset Card for Large Logo Dataset (LLD)", "## Description\n\nAdapted from the original LLD dataset. Original description:\n\n> Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve the creative task of the designer. For this, we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web. Training Generative Adversarial Networks (GANs) for logo synthesis on such multi-modal data is not straightforward and results in mode collapse for some state-of-the-art methods. We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training. We are able to generate a high diversity of plausible logos and we demonstrate latent space exploration techniques to ease the logo design task in an interactive manner. Moreover, we validate the proposed clustered GAN training on CIFAR 10, achieving state-of-the-art Inception scores when using synthetic labels obtained via clustering the features of an ImageNet classifier. GANs can cope with multi-modal data by means of synthetic labels achieved through clustering, and our results show the creative potential of such techniques for logo synthesis and manipulation.", "## Schema\n\n\n\ns" ]