Datasets:

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ftopal

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huggingface-datasets

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[ "task_categories:sentence-similarity", "size_categories:1K<n<10K", "language:en", "doi:10.57967/hf/0409", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"eclassQuery\"\n\nThis Dataset consists of paraphrases of ECLASS-standard pump-properties. It can be used to evaluate models on the task of matching these paraphrases to the actual ECLASS-standard pump-properties based on their semantics." ]

[ "TAGS\n#task_categories-sentence-similarity #size_categories-1K<n<10K #language-English #doi-10.57967/hf/0409 #region-us \n", "# Dataset Card for \"eclassQuery\"\n\nThis Dataset consists of paraphrases of ECLASS-standard pump-properties. It can be used to evaluate models on the task of matching these paraphrases to the actual ECLASS-standard pump-properties based on their semantics." ]

[ "size_categories:n<1K", "source_datasets:google", "language:en", "license:apache-2.0", "images ", "food", "vada sambhar", "dreambooth-hackathon", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for vada-sambhar", "## Dataset Description\n\n The dataset contains of images of my favorite south indian dish - Vada Sambhar.", "### Dataset Curators\n\nThe data has been downloaded from Google images.", "### Licensing Information\n\nThe vada-sambhar dataset version 1.0.0 is released under the Apache-2.0 License." ]

[ "TAGS\n#size_categories-n<1K #source_datasets-google #language-English #license-apache-2.0 #images #food #vada sambhar #dreambooth-hackathon #region-us \n", "# Dataset Card for vada-sambhar", "## Dataset Description\n\n The dataset contains of images of my favorite south indian dish - Vada Sambhar.", "### Dataset Curators\n\nThe data has been downloaded from Google images.", "### Licensing Information\n\nThe vada-sambhar dataset version 1.0.0 is released under the Apache-2.0 License." ]

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

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

[ "license:unknown", "doi:10.57967/hf/0284", "region:us" ]

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[ "### FrenchHateSpeechSuperset\n\nThis dataset is a superset of multiple datasets including hate speech, harasment, sexist, racist, etc...messages from various platforms.\n\nIncluded datasets :\n\n- MLMA dataset\n- CAA dataset\n- FTR dataset\n- \"An Annotated Corpus for Sexism Detection in French Tweets\" dataset\n- UC-Berkeley-Measuring-Hate-Speech dataset (translated from english*)", "#### References", "### Translation\n\nFrench datasets for hate speech are quite rare. To augment current dataset, messages from other languages (english only for now) have been integrated.\nTo integrate other languages dataset, MT model were used and manually selected for each dataset.\n\n- UC-Berkeley-Measuring-Hate-Speech dataset : Abelll/marian-finetuned-kde4-en-to-fr", "### Language verification\n\nSince MT models are not perfect, some messages are not entirely translated or not translated at all.\nTo check for obvious errors in pipeline, a general language detection model is used to prune non french texts.\n\nLanguage detection model : papluca/xlm-roberta-base-language-detection", "### Annotation\n\nSince \"hate speech\" dimension is highly subjective, and datasets comes with different annotations types, a conventional labeling stategy is required.\n\nEach sample is annotated with \"0\" if negative sample and \"1\" if positive sample.", "### Filtering rules :\n\n- FTR dataset : [wip]\n- MLMA dataset : [wip]\n- CAA dataset : [wip]\n- \"Annotated Corpus\" dataset : [wip]\n- UC-Berkeley Measuring Hate Speech dataset : average hate_speech_score > 0 -> 1" ]

[ "TAGS\n#license-unknown #doi-10.57967/hf/0284 #region-us \n", "### FrenchHateSpeechSuperset\n\nThis dataset is a superset of multiple datasets including hate speech, harasment, sexist, racist, etc...messages from various platforms.\n\nIncluded datasets :\n\n- MLMA dataset\n- CAA dataset\n- FTR dataset\n- \"An Annotated Corpus for Sexism Detection in French Tweets\" dataset\n- UC-Berkeley-Measuring-Hate-Speech dataset (translated from english*)", "#### References", "### Translation\n\nFrench datasets for hate speech are quite rare. To augment current dataset, messages from other languages (english only for now) have been integrated.\nTo integrate other languages dataset, MT model were used and manually selected for each dataset.\n\n- UC-Berkeley-Measuring-Hate-Speech dataset : Abelll/marian-finetuned-kde4-en-to-fr", "### Language verification\n\nSince MT models are not perfect, some messages are not entirely translated or not translated at all.\nTo check for obvious errors in pipeline, a general language detection model is used to prune non french texts.\n\nLanguage detection model : papluca/xlm-roberta-base-language-detection", "### Annotation\n\nSince \"hate speech\" dimension is highly subjective, and datasets comes with different annotations types, a conventional labeling stategy is required.\n\nEach sample is annotated with \"0\" if negative sample and \"1\" if positive sample.", "### Filtering rules :\n\n- FTR dataset : [wip]\n- MLMA dataset : [wip]\n- CAA dataset : [wip]\n- \"Annotated Corpus\" dataset : [wip]\n- UC-Berkeley Measuring Hate Speech dataset : average hate_speech_score > 0 -> 1" ]

[ "task_categories:image-classification", "language:ar", "license:cc-by-4.0", "image_classification", "Arabic_Sign_Language", "region:us" ]

[]

[ "ar" ]

[ "# Dataset Card for \"ArASL_Database_Grayscale\"", "## Dataset Description\n\n- Homepage: URL\n- Paper: ArASL: Arabic Alphabets Sign Language Dataset", "### Dataset Summary\n\nA new dataset consists of 54,049 images of ArSL alphabets performed by more than 40 people for 32 standard Arabic signs and alphabets.\nThe number of images per class differs from one class to another. Sample image of all Arabic Language Signs is also attached. The CSV file contains the Label of each corresponding Arabic Sign Language Image based on the image file name.", "### Supported Tasks and Leaderboards\n\n- 'image-classification': The goal of this task is to classify a given image into one of 32 classes.", "### Languages\n\nArabic", "### Data Instances\n\nA sample from the training set is provided below:", "### Contributions\n\nThanks to MOHAMMAD ALBARHAM for adding this dataset to huggingface hub." ]

[ "TAGS\n#task_categories-image-classification #language-Arabic #license-cc-by-4.0 #image_classification #Arabic_Sign_Language #region-us \n", "# Dataset Card for \"ArASL_Database_Grayscale\"", "## Dataset Description\n\n- Homepage: URL\n- Paper: ArASL: Arabic Alphabets Sign Language Dataset", "### Dataset Summary\n\nA new dataset consists of 54,049 images of ArSL alphabets performed by more than 40 people for 32 standard Arabic signs and alphabets.\nThe number of images per class differs from one class to another. Sample image of all Arabic Language Signs is also attached. The CSV file contains the Label of each corresponding Arabic Sign Language Image based on the image file name.", "### Supported Tasks and Leaderboards\n\n- 'image-classification': The goal of this task is to classify a given image into one of 32 classes.", "### Languages\n\nArabic", "### Data Instances\n\nA sample from the training set is provided below:", "### Contributions\n\nThanks to MOHAMMAD ALBARHAM for adding this dataset to huggingface hub." ]

[ "size_categories:n<1K", "source_datasets:original", "language:en", "license:creativeml-openrail-m", "images", "football stadium", "Manchester United", "Old Trafford", "dreambooth-hackathon", "region:us" ]

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[ "en" ]

[ "# Dataset Card for old-trafford", "## Dataset Description\n\n The dataset contains images of Old Trafford - a football stadium that belongs to Manchester United Football Club.", "### Dataset Curators\n\nThe data has been downloaded from Google Images.", "### Licensing Information\n\nThe old-trafford dataset version 1.0.0 is released under the creativeml-openrail-m License." ]

[ "TAGS\n#size_categories-n<1K #source_datasets-original #language-English #license-creativeml-openrail-m #images #football stadium #Manchester United #Old Trafford #dreambooth-hackathon #region-us \n", "# Dataset Card for old-trafford", "## Dataset Description\n\n The dataset contains images of Old Trafford - a football stadium that belongs to Manchester United Football Club.", "### Dataset Curators\n\nThe data has been downloaded from Google Images.", "### Licensing Information\n\nThe old-trafford dataset version 1.0.0 is released under the creativeml-openrail-m License." ]

[ "region:us" ]

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[ "# Dataset Card for \"untitled_goose_game\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"untitled_goose_game\"\n\nMore Information needed" ]

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

[]

[]

[]

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

[ "region:us" ]

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[ "# Dataset Card for \"alphafold_issues\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"alphafold_issues\"\n\nMore Information needed" ]

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

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is a custom train/test/validation split of the IMDb Large Movie Review Dataset available from URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "#### IMDb\\_movie\\_reviews\n\n\nAn example of 'train':", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### IMDb\\_movie\\_reviews\n\n\n* 'text': a 'string' feature.\n* 'label': a classification label, with values 'neg' (0), 'pos' (1).", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nThis is a custom train/test/validation split of the IMDb Large Movie Review Dataset available from URL", "### Supported Tasks and Leaderboards", "### Languages\n\n\nDataset Structure\n-----------------", "#### IMDb\\_movie\\_reviews\n\n\nAn example of 'train':", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### IMDb\\_movie\\_reviews\n\n\n* 'text': a 'string' feature.\n* 'label': a classification label, with values 'neg' (0), 'pos' (1).", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "task_categories:summarization", "size_categories:1K<n<10K", "language:en", "license:cc0-1.0", "superheroes", "heroes", "anime", "manga", "marvel", "region:us" ]

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[ "en" ]

[ "# Dataset Card for Superheroes", "## Dataset Description\n1400+ Superheroes history and powers description to apply text mining and NLP Original source", "## Context\nThe aim of this dataset is to make text analytics and NLP even funnier. All of us have dreamed to be like a superhero and save the world, yet we are still on Kaggle figuring out how python works. Then, why not improve our NLP competences by analyzing Superheros' history and powers?\n\nThe particularity of this dataset is that it contains categorical and numerical features such as overall_score, intelligence_score, creator, alignment, gender, eye_color but also text features history_text and powers_text. By combining the two, a lot of interesting insights can be gathered!", "## Content\nWe collected all data from superherodb and cooked for you in a nice and clean tabular format.\n\nThe dataset contains 1447 different Superheroes. Each superhero row has:\n\n* overall_score - derivated by superherodb from the power stats features. Can you find the relationship?\n* history_text - History of the Superhero (text features)\n* powers_text - Description of Superheros' powers (text features)\n* intelligence_score, strength_score, speed_score, durability_score, power_score and combat_score. (power stats features)\n* \"Origin\" (full_name, alter_egos, …)\n* \"Connections\" (occupation, base, teams, …)\n* \"Appareance\" (gender, type_race, height, weight, eye_color, …)", "## Acknowledgements\nThe following Github repository contains the code used to scrape this Dataset." ]

[ "TAGS\n#task_categories-summarization #size_categories-1K<n<10K #language-English #license-cc0-1.0 #superheroes #heroes #anime #manga #marvel #region-us \n", "# Dataset Card for Superheroes", "## Dataset Description\n1400+ Superheroes history and powers description to apply text mining and NLP Original source", "## Context\nThe aim of this dataset is to make text analytics and NLP even funnier. All of us have dreamed to be like a superhero and save the world, yet we are still on Kaggle figuring out how python works. Then, why not improve our NLP competences by analyzing Superheros' history and powers?\n\nThe particularity of this dataset is that it contains categorical and numerical features such as overall_score, intelligence_score, creator, alignment, gender, eye_color but also text features history_text and powers_text. By combining the two, a lot of interesting insights can be gathered!", "## Content\nWe collected all data from superherodb and cooked for you in a nice and clean tabular format.\n\nThe dataset contains 1447 different Superheroes. Each superhero row has:\n\n* overall_score - derivated by superherodb from the power stats features. Can you find the relationship?\n* history_text - History of the Superhero (text features)\n* powers_text - Description of Superheros' powers (text features)\n* intelligence_score, strength_score, speed_score, durability_score, power_score and combat_score. (power stats features)\n* \"Origin\" (full_name, alter_egos, …)\n* \"Connections\" (occupation, base, teams, …)\n* \"Appareance\" (gender, type_race, height, weight, eye_color, …)", "## Acknowledgements\nThe following Github repository contains the code used to scrape this Dataset." ]

[ "region:us" ]

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[]

[ "# Dataset Card for virus_dna_dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nA collection of full virus genome dna, the dataset was built from NCBI data", "### Supported Tasks and Leaderboards", "### Languages\n\nDNA", "## Dataset Structure", "### Data Instances\n\n{ 'Description' : 'NC_030848.1 Haloarcula californiae icosahedral...', 'dna_sequence' : 'TCATCTC TCTCTCT CTCTCTT GTTCCCG CGCCCGC CCGCCC...', \n'sequence_length':'35787', 'organism_id':' AB063393.2'}", "### Data Fields\n\n{ 'Description' : 'this contains the description about the DNA sequence contained in the NCBI dataset', 'dna_sequence' : 'this contains the dna sequence grouped by 7 nucleotides', \n'sequence_length':'this contains the length of the dna sequence'}", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nThe goal of this dataset was to make it easier to train an LLM on virus DNA", "### Source Data", "#### Initial Data Collection and Normalization\n\nDNA sequences were grouped by 7 nucleotides to make it easier to tokenize. Only full genomes were selected", "#### Who are the source language producers?\n\nViruses :)", "### Annotations", "#### Annotation process\n\nNCBI", "#### Who are the annotators?\n\nNCBI", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nMake it easier to train LLMs on virus DNA", "### Discussion of Biases\n\nOnly virus data that has been sequenced and upload into NCBI is contained in here", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nHassan Ahmed", "### Licensing Information" ]

[ "TAGS\n#region-us \n", "# Dataset Card for virus_dna_dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nA collection of full virus genome dna, the dataset was built from NCBI data", "### Supported Tasks and Leaderboards", "### Languages\n\nDNA", "## Dataset Structure", "### Data Instances\n\n{ 'Description' : 'NC_030848.1 Haloarcula californiae icosahedral...', 'dna_sequence' : 'TCATCTC TCTCTCT CTCTCTT GTTCCCG CGCCCGC CCGCCC...', \n'sequence_length':'35787', 'organism_id':' AB063393.2'}", "### Data Fields\n\n{ 'Description' : 'this contains the description about the DNA sequence contained in the NCBI dataset', 'dna_sequence' : 'this contains the dna sequence grouped by 7 nucleotides', \n'sequence_length':'this contains the length of the dna sequence'}", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nThe goal of this dataset was to make it easier to train an LLM on virus DNA", "### Source Data", "#### Initial Data Collection and Normalization\n\nDNA sequences were grouped by 7 nucleotides to make it easier to tokenize. Only full genomes were selected", "#### Who are the source language producers?\n\nViruses :)", "### Annotations", "#### Annotation process\n\nNCBI", "#### Who are the annotators?\n\nNCBI", "### Personal and Sensitive Information\n\nN/A", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nMake it easier to train LLMs on virus DNA", "### Discussion of Biases\n\nOnly virus data that has been sequenced and upload into NCBI is contained in here", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nHassan Ahmed", "### Licensing Information" ]

[ "task_categories:image-to-text", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"ui_refexp_saved_Jan2023\"\n\nThis is a saved snapshot of the dynamically generated UI Bert dataset. \nMuch faster download time than the dynamic version which pulls and filters large data files from remote sources." ]

[ "TAGS\n#task_categories-image-to-text #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for \"ui_refexp_saved_Jan2023\"\n\nThis is a saved snapshot of the dynamically generated UI Bert dataset. \nMuch faster download time than the dynamic version which pulls and filters large data files from remote sources." ]

[ "region:us" ]

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[ "# Dataset Card for \"bookcorpus_compact_1024_shard5\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_1024_shard5\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"chocolate-captioned-dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"chocolate-captioned-dataset\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"embedding_dataset_distilbert_base_uncased_ad_subwords\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"embedding_dataset_distilbert_base_uncased_ad_subwords\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"RedditProject\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"RedditProject\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"tagesschau\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"tagesschau\"\n\nMore Information needed" ]

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

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[ "en" ]

[ "# What is this dataset?\n\nThis dataset is a collection of Pull Requests that contain comments from the Accelerate.\nIt contains the full contextual comments as well as code suggestions that exist inside of a code review" ]

[ "TAGS\n#size_categories-n<1K #language-English #license-mit #region-us \n", "# What is this dataset?\n\nThis dataset is a collection of Pull Requests that contain comments from the Accelerate.\nIt contains the full contextual comments as well as code suggestions that exist inside of a code review" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:pt", "license:unknown", "region:us" ]

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[ "pt" ]

[ "# Dataset Card for BrWaC", "## 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: BrWaC homepage\n- Repository: BrWaC repository\n- Paper: The brWaC Corpus: A New Open Resource for Brazilian Portuguese\n- Point of Contact: Jorge A. Wagner Filho", "### Dataset Summary\n\nThe BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework, \nwhich was made public for research purposes. The current corpus version, released in January 2017, is composed by \n3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available \nsolely for academic research purposes, and you agreed not to use it for any commercial applications. No need to manually download external sources.", "### Supported Tasks and Leaderboards", "### Languages\n\nPortuguese", "## Dataset Structure", "### Data Instances\n\nAn example from the BrWaC dataset looks as follows:", "### Data Fields\n\n- 'doc_id': The document ID\n- 'title': The document title\n- 'uri': URI where the document was extracted from\n- 'text': A list of document paragraphs (with a list of sentences in it as a list of strings)", "### Data Splits\n\nThe data is only split into train set with size of 3530796 samples.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-Portuguese #license-unknown #region-us \n", "# Dataset Card for BrWaC", "## 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: BrWaC homepage\n- Repository: BrWaC repository\n- Paper: The brWaC Corpus: A New Open Resource for Brazilian Portuguese\n- Point of Contact: Jorge A. Wagner Filho", "### Dataset Summary\n\nThe BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework, \nwhich was made public for research purposes. The current corpus version, released in January 2017, is composed by \n3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available \nsolely for academic research purposes, and you agreed not to use it for any commercial applications. No need to manually download external sources.", "### Supported Tasks and Leaderboards", "### Languages\n\nPortuguese", "## Dataset Structure", "### Data Instances\n\nAn example from the BrWaC dataset looks as follows:", "### Data Fields\n\n- 'doc_id': The document ID\n- 'title': The document title\n- 'uri': URI where the document was extracted from\n- 'text': A list of document paragraphs (with a list of sentences in it as a list of strings)", "### Data Splits\n\nThe data is only split into train set with size of 3530796 samples.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

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

[]

[ "es" ]

[ "# Dataset Card for Dataset Name\ntitulos_noticias_rcn_clasificadas", "## Dataset Description\nSe tomo las noticias de la pagina de RCN y se clasifico los titulos por ['salud' 'tecnologia' 'colombia' 'economia' 'deportes']\n\nsalud= 1805 datos, \ntecnologia= 1805 datos, \ncolombia= 1805 datos, \neconomia= 1805 datos, \ndeportes= 1805 datos, \n\n\nPara dar un total de 9030 filas.\n\npagina: URL\n\n- Homepage: \n- Repository: \n- Point of Contact:", "### Languages\nEspañol", "## Dataset Structure\ntext, label, url" ]

[ "TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-Spanish #region-us \n", "# Dataset Card for Dataset Name\ntitulos_noticias_rcn_clasificadas", "## Dataset Description\nSe tomo las noticias de la pagina de RCN y se clasifico los titulos por ['salud' 'tecnologia' 'colombia' 'economia' 'deportes']\n\nsalud= 1805 datos, \ntecnologia= 1805 datos, \ncolombia= 1805 datos, \neconomia= 1805 datos, \ndeportes= 1805 datos, \n\n\nPara dar un total de 9030 filas.\n\npagina: URL\n\n- Homepage: \n- Repository: \n- Point of Contact:", "### Languages\nEspañol", "## Dataset Structure\ntext, label, url" ]

[ "task_categories:translation", "size_categories:100K<n<1M", "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"open-cm\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"open-cm\"\n\nMore Information needed" ]

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

[]

[ "lb" ]

[ "# About the Speech Corpus\n\n'luxembourgish-asr-rtl-lu' dataset is a speech corpus for the under-resourced Luxembourgish language. The audio-transcription pairs were collected from URL.\nWe used forced alignment to segment the audio files. The transcriptions were validated with the help of language experts at the Center for the Luxembourgish Language.", "# Copyright notice\n\nCopyright © 2022 URL. All rights reserved." ]

[ "TAGS\n#language-Luxembourgish #license-cc-by-nc-nd-4.0 #region-us \n", "# About the Speech Corpus\n\n'luxembourgish-asr-rtl-lu' dataset is a speech corpus for the under-resourced Luxembourgish language. The audio-transcription pairs were collected from URL.\nWe used forced alignment to segment the audio files. The transcriptions were validated with the help of language experts at the Center for the Luxembourgish Language.", "# Copyright notice\n\nCopyright © 2022 URL. All rights reserved." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_compact_1024_shard4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_1024_shard4\"\n\nMore Information needed" ]

[ "language:ru", "license:cc", "image", "captcha", "region:us" ]

[]

[ "ru" ]

[ "# Note\nCaptcha images are presented as base64 string.\nAll csv files have a \"\\t\" separator.", "# Dataset consists of several files", "## fssp_*.csv\n\nI am publishing an updated version of the archive of 40,310 pictures, which I have divided into 4 categories:\n- 4 symbols on the picture - 6 747 pcs.\n- 5 symbols - 18 403 pcs.\n- 6 characters - 7,038 pcs.\n- 7 characters - 7 589 pcs.\n\nSymbols used in captcha\n'б','в','г','д','ж','к','л','м','н','п','р','с','т','2','4','5','6','7','8','9'", "## URL\n\nAbout 15 thousand captcha imgs, which consists of 6 numbers.", "## URL\n\nAbout 10 thousand captcha, which consists of English characters and numbers with a length of 5 elements.", "## URL\n\nAbout 19 thousand captcha, which consists of Russian characters and numbers from 5 to 6 elements long. Images from social network URL", "# Kaggle\n\nThis Dataset is updated by the previous one, which I published on Kaggle", "### License\nPublic Domain" ]

[ "TAGS\n#language-Russian #license-cc #image #captcha #region-us \n", "# Note\nCaptcha images are presented as base64 string.\nAll csv files have a \"\\t\" separator.", "# Dataset consists of several files", "## fssp_*.csv\n\nI am publishing an updated version of the archive of 40,310 pictures, which I have divided into 4 categories:\n- 4 symbols on the picture - 6 747 pcs.\n- 5 symbols - 18 403 pcs.\n- 6 characters - 7,038 pcs.\n- 7 characters - 7 589 pcs.\n\nSymbols used in captcha\n'б','в','г','д','ж','к','л','м','н','п','р','с','т','2','4','5','6','7','8','9'", "## URL\n\nAbout 15 thousand captcha imgs, which consists of 6 numbers.", "## URL\n\nAbout 10 thousand captcha, which consists of English characters and numbers with a length of 5 elements.", "## URL\n\nAbout 19 thousand captcha, which consists of Russian characters and numbers from 5 to 6 elements long. Images from social network URL", "# Kaggle\n\nThis Dataset is updated by the previous one, which I published on Kaggle", "### License\nPublic Domain" ]

[ "task_categories:image-segmentation", "task_categories:image-classification", "task_categories:question-answering", "size_categories:10K<n<100K", "language:am", "license:afl-3.0", "region:us" ]

[]

[ "am" ]

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

[ "TAGS\n#task_categories-image-segmentation #task_categories-image-classification #task_categories-question-answering #size_categories-10K<n<100K #language-Amharic #license-afl-3.0 #region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "task_categories:question-answering", "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1k<n<10k", "source_datasets:original", "language:fr", "license:apache-2.0", "region:us" ]

[]

[ "fr" ]

[ "### Dataset Summary\n\n\nThis paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.\n\n\nEach instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).\n\n\nWe also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.", "### Supported Tasks and Leaderboards\n\n\nMultiple-Choice Question Answering (MCQA)", "### Languages\n\n\nThe questions and answers are available in French.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'id' : a string question identifier for each example\n* 'question' : question text (a string)\n* 'answer\\_a' : Option A\n* 'answer\\_b' : Option B\n* 'answer\\_c' : Option C\n* 'answer\\_d' : Option D\n* 'answer\\_e' : Option E\n* 'correct\\_answers' : Correct options, i.e., A, D and E\n* 'choice\\_type' ({\"single\", \"multiple\"}): Question choice type.\n\t+ \"single\": Single-choice question, where each choice contains a single option.\n\t+ \"multiple\": Multi-choice question, where each choice contains a combination of multiple options.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.", "### Personal and Sensitive Information\n\n\nThe corpora is free of personal or sensitive information.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.", "### Licensing Information\n\n\nApache 2.0\n\n\nIf you find this useful in your research, please consider citing the dataset paper :", "### Contact\n\n\nThanks to contact Yanis LABRAK for more information about this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_categories-multiple-choice #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-1k<n<10k #source_datasets-original #language-French #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nThis paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.\n\n\nEach instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).\n\n\nWe also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.", "### Supported Tasks and Leaderboards\n\n\nMultiple-Choice Question Answering (MCQA)", "### Languages\n\n\nThe questions and answers are available in French.\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'id' : a string question identifier for each example\n* 'question' : question text (a string)\n* 'answer\\_a' : Option A\n* 'answer\\_b' : Option B\n* 'answer\\_c' : Option C\n* 'answer\\_d' : Option D\n* 'answer\\_e' : Option E\n* 'correct\\_answers' : Correct options, i.e., A, D and E\n* 'choice\\_type' ({\"single\", \"multiple\"}): Question choice type.\n\t+ \"single\": Single-choice question, where each choice contains a single option.\n\t+ \"multiple\": Multi-choice question, where each choice contains a combination of multiple options.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.", "### Personal and Sensitive Information\n\n\nThe corpora is free of personal or sensitive information.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.", "### Licensing Information\n\n\nApache 2.0\n\n\nIf you find this useful in your research, please consider citing the dataset paper :", "### Contact\n\n\nThanks to contact Yanis LABRAK for more information about this dataset." ]

[ "task_categories:token-classification", "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Dataset cointelegraph English", "## Dataset Description\n\nIt is a dataset where information about the title, description, author, etc. is collected.\napprox: 10041 row\npage: URL\n\n\n\ncategorie: #cryptocurrency, #Bitcoin, #Ethereum ..." ]

[ "TAGS\n#task_categories-token-classification #task_categories-question-answering #size_categories-10K<n<100K #language-English #region-us \n", "# Dataset cointelegraph English", "## Dataset Description\n\nIt is a dataset where information about the title, description, author, etc. is collected.\napprox: 10041 row\npage: URL\n\n\n\ncategorie: #cryptocurrency, #Bitcoin, #Ethereum ..." ]

[ "task_categories:token-classification", "task_categories:question-answering", "size_categories:10K<n<100K", "language:es", "region:us" ]

[]

[ "es" ]

[ "# Dataset cointelegraph español\n\n\nDataset Description\n\n\nes un dataset donde se recopila informacion del titulo , descripcion , autor, etc.\n\n\ntiene aprox: 10738 fila \n\n\npagina: URL\n\n\n\ncategorie: #cryptocurrency, #Bitcoin, #Ethereum ..." ]

[ "TAGS\n#task_categories-token-classification #task_categories-question-answering #size_categories-10K<n<100K #language-Spanish #region-us \n", "# Dataset cointelegraph español\n\n\nDataset Description\n\n\nes un dataset donde se recopila informacion del titulo , descripcion , autor, etc.\n\n\ntiene aprox: 10738 fila \n\n\npagina: URL\n\n\n\ncategorie: #cryptocurrency, #Bitcoin, #Ethereum ..." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"sd_filtered_2m\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"sd_filtered_2m\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_compact_1024_shard7\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_1024_shard7\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:18K", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:cc-by-3.0", "region:us" ]

[]

[ "pl" ]

[ "# KPWr & CEN" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-18K #size_categories-10K<n<100K #source_datasets-original #language-Polish #license-cc-by-3.0 #region-us \n", "# KPWr & CEN" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"pairwise-code-review-instruct-critique-revision-python\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pairwise-code-review-instruct-critique-revision-python\"\n\nMore Information needed" ]

[ "size_categories:n<1K", "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name\n\nRexTheToy", "### Dataset Summary\n\nImages of the rex the toy from Toy Story, stored for huggingface dreambooth hackathon." ]

[ "TAGS\n#size_categories-n<1K #region-us \n", "# Dataset Card for Dataset Name\n\nRexTheToy", "### Dataset Summary\n\nImages of the rex the toy from Toy Story, stored for huggingface dreambooth hackathon." ]

[ "task_categories:text2text-generation", "size_categories:10K<n<100K", "language:en", "license:mit", "NLP", "Text2Text", "region:us" ]

[]

[ "en" ]

[ "# SpellGram", "## Dataset consisting of grammatical and spelling errors\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances\n\n[URL]", "### 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" ]

[ "TAGS\n#task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-mit #NLP #Text2Text #region-us \n", "# SpellGram", "## Dataset consisting of grammatical and spelling errors\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances\n\n[URL]", "### 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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"datasets-test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"datasets-test\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordPets_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_train\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordPets_test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_test\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Caltech101_not_background_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_train\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Caltech101_not_background_test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordFlowers_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordFlowers_train\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordFlowers_test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordFlowers_test\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"DTD_parition1_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_train\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"DTD_parition1_test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test\"\n\nMore Information needed" ]

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"AbduRozik\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"AbduRozik\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"eee543\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"eee543\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordPets_test_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_test_embeddings\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordPets_train_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_train_embeddings\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Caltech101_not_background_test_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_embeddings\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Caltech101_not_background_train_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_train_embeddings\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordFlowers_test_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordFlowers_test_embeddings\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"OxfordFlowers_train_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordFlowers_train_embeddings\"\n\nMore Information needed" ]

[ "task_categories:image-segmentation", "language:en", "license:mit", "building", "facade", "region:us" ]

[]

[ "en" ]

[ "# CMP Facade Database\nWe present a dataset of facade images assembled at the Center for Machine Perception, which includes 606 rectified images of facades from various sources, which have been manually annotated. The facades are from different cities around the world and diverse architectural styles.\nDocumentation\n\nData origin, format and processing, annotation principles for 12 classes are specified in the report.\n\n- facade \n- molding\n- cornice\n- pillar\n- window\n- door\n- sill\n- blind\n- balcony\n- shop\n- deco\n- background\n\nLink to original website:\nURL\n\nCitation\nPlease use the following reference to cite the dataset:" ]

[ "TAGS\n#task_categories-image-segmentation #language-English #license-mit #building #facade #region-us \n", "# CMP Facade Database\nWe present a dataset of facade images assembled at the Center for Machine Perception, which includes 606 rectified images of facades from various sources, which have been manually annotated. The facades are from different cities around the world and diverse architectural styles.\nDocumentation\n\nData origin, format and processing, annotation principles for 12 classes are specified in the report.\n\n- facade \n- molding\n- cornice\n- pillar\n- window\n- door\n- sill\n- blind\n- balcony\n- shop\n- deco\n- background\n\nLink to original website:\nURL\n\nCitation\nPlease use the following reference to cite the dataset:" ]

[ "region:us" ]

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[ "# Dataset Card for \"DTD_parition1_test_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_embeddings\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"DTD_parition1_train_embeddings\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_train_embeddings\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"M2BD\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"M2BD\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"rick-and-morty-s5\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"rick-and-morty-s5\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"openai_summarize_tldr\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"openai_summarize_tldr\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"pingu-images-dreambooth\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pingu-images-dreambooth\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"boostcamp-docvqa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"boostcamp-docvqa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"boostcamp-docvqa-test\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"boostcamp-docvqa-test\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"patents_green_plastics_10k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"patents_green_plastics_10k\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"images_first_day\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"images_first_day\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"EnglishLM_Chars_removed\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"EnglishLM_Chars_removed\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"pick_a_pic_ranked_images_first_day\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pick_a_pic_ranked_images_first_day\"\n\nMore Information needed" ]

[ "task_categories:fill-mask", "size_categories:1B<n<10B", "language:he", "arxiv:2304.11077", "region:us" ]

[ "2304.11077" ]

[ "he" ]

[ "### Dataset Summary\n\nA Hebrew Deduplicated and Cleaned Common Crawl Corpus. A thoroughly cleaned and\napproximately deduplicated dataset for unsupervised learning.", "### Citing\n\nIf you use HeDC4 in your research, please cite HeRo: RoBERTa and Longformer Hebrew Language Models." ]

[ "TAGS\n#task_categories-fill-mask #size_categories-1B<n<10B #language-Hebrew #arxiv-2304.11077 #region-us \n", "### Dataset Summary\n\nA Hebrew Deduplicated and Cleaned Common Crawl Corpus. A thoroughly cleaned and\napproximately deduplicated dataset for unsupervised learning.", "### Citing\n\nIf you use HeDC4 in your research, please cite HeRo: RoBERTa and Longformer Hebrew Language Models." ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"tr-wikihow-summ\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"tr-wikihow-summ\"\n\nMore Information needed" ]

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"hdb0110\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"hdb0110\"\n\nMore Information needed" ]

[ "task_categories:question-answering", "task_categories:sentence-similarity", "size_categories:100K<n<1M", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is a simplified version of vblagoje's *lfqa\\_support\\_docs* and *lfqa* datasets.\nIt was generated by me to have a more straight forward way to train Seq2Seq models on context based long form question answering tasks.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'context': a list feature containing 'string' features.", "### Data Splits\n\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThis dataset is distributed under the MIT licence." ]

[ "TAGS\n#task_categories-question-answering #task_categories-sentence-similarity #size_categories-100K<n<1M #language-English #license-mit #region-us \n", "### Dataset Summary\n\n\nThis is a simplified version of vblagoje's *lfqa\\_support\\_docs* and *lfqa* datasets.\nIt was generated by me to have a more straight forward way to train Seq2Seq models on context based long form question answering tasks.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'question': a 'string' feature.\n* 'answer': a 'string' feature.\n* 'context': a list feature containing 'string' features.", "### Data Splits\n\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThis dataset is distributed under the MIT licence." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"pick_a_pic_preferred_images_first_day\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pick_a_pic_preferred_images_first_day\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"IL2223_project\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"IL2223_project\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "task_ids:word-sense-disambiguation", "annotations_creators:expert-generated", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:pl", "license:cc-by-4.0", "region:us" ]

[]

[ "pl" ]

[ "# Word Sense Disambiguation Corpora for Polish", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper: URL\n- Point of Contact: URL@URL", "### Dataset Summary\n\n'WSD Polish Datasets' is a comprehensive benchmark for word sense disambiguation (WSD) classification task in Polish language.\nIt consists of 7 distinct datasets, manually annotated with senses from plWordNet-4.5 sense inventory. The following datasets\nwere annotated and included into our benchmark:\n- KPWr\n- KPWr-100\n- Sherlock (SPEC)\n- Skladnica\n- WikiGlex (a subset of GLEX corpus)\n- EmoGlex (a subset of GLEX corpus)\n- Walenty\n\nFor more details, please check the following publication:\n\n\n\nA new publication on Polish WSD corpora will be available soon", "### Supported Tasks and Leaderboards\n\nWord sense disambiguation task. We do not provide a leaderboard. However, we provide an example evaluation script for evaluating WSD models.", "### Languages\n\nPolish language, PL", "## Dataset Structure", "### Data Instances\n\nData are structured in JSONL format, each single text sample is divided by sentence.", "### Data Fields\n\nDescription of json keys:\n- 'text': text of the sentence\n- 'tokens': list of tokens made by tokenization process\n - 'index': token order index in sentence\n - 'position': token chars span indices <included, excluded>\n - 'orth': word\n - 'lemma': lemmatised word\n - 'pos': part of speech\n - 'ctag': morphosyntactic tag\n- 'phrases': list of multi-word \n- 'wsd': annotation labels for the WSD task", "### Data Splits\n\nWe do not specify an exact data split for training and evaluation. However, we suggest to use GLEX and Składnica for training and other datasets for testing.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection, Normalization and Post-processing\n\nSource corpora were initially pre-processed using morphosyntactic tagging and multi-word expression recognition tools.\nTo tokenize and tag the datasets we used MorphoDiTa adapted to Polish language. To recognize multi-word expressions\nwe applied pattern-based matching tool Corpus2-MWE - only MWEs from plWordNet were included. After manual annotation, \nsense indices of plWordNet 4.5 were mapped automatically to Princeton WordNet 3.0 and BabelNet 4.0 indices using plWordNet's interlingual mapping.", "### Annotations", "#### Annotation process\n\n* 2+1 annotation process with inter-annotator agreement score over 0.6 PSA\n* annotated with plWordNet 4.5\n* software: WordNet-Loom and Inforex\n* both single-word and multi-word expressions annotated\n* full-text sense annotation (excluding KPWr)", "#### Who are the annotators?\n\n- professional linguists from CLARIN-PL project", "### Personal and Sensitive Information\n\nThe datasets do not contain any personal or sensitive information.", "## Considerations for Using the Data", "### Discussion of Biases\n\nSome datasets are biased towards most frequent senses. No information about other biases - needs further analysis.", "### Other Known Limitations\n\n* sense inventories are usually incomplete therefore some word senses might be missing in plWordNet\n* single-word and multi-word terms expressing novel senses (missing in plWordNet) were not marked", "## Additional Information", "### Dataset Curators\n\nArkadiusz Janz (URL@URL)", "### Licensing Information\n\nKPWR-100 CC-BY-SA 4.0 \nKPWR CC-BY-SA 4.0 \nWalenty CC-BY-SA 4.0 \nSherlock CC-BY 4.0 \nSkladnica GNU GPL 3 \nGLEX plWordNet License \n\n\n\n\nMain source (all corpora as a unified benchmark) and published here on HuggingFace:\n\n'\n\nRelated work\n------------\n\nKPWr-100, Składnica, SPEC\n'\n\nGLEX (EmoGLEX)\n\n'\n\nKPWr\n'\n\nSkładnica\n'\n\nWalenty\n'\n\nMapping plWordNet onto Princeton WordNet\n'" ]

[ "TAGS\n#task_categories-token-classification #task_ids-word-sense-disambiguation #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-Polish #license-cc-by-4.0 #region-us \n", "# Word Sense Disambiguation Corpora for Polish", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper: URL\n- Point of Contact: URL@URL", "### Dataset Summary\n\n'WSD Polish Datasets' is a comprehensive benchmark for word sense disambiguation (WSD) classification task in Polish language.\nIt consists of 7 distinct datasets, manually annotated with senses from plWordNet-4.5 sense inventory. The following datasets\nwere annotated and included into our benchmark:\n- KPWr\n- KPWr-100\n- Sherlock (SPEC)\n- Skladnica\n- WikiGlex (a subset of GLEX corpus)\n- EmoGlex (a subset of GLEX corpus)\n- Walenty\n\nFor more details, please check the following publication:\n\n\n\nA new publication on Polish WSD corpora will be available soon", "### Supported Tasks and Leaderboards\n\nWord sense disambiguation task. We do not provide a leaderboard. However, we provide an example evaluation script for evaluating WSD models.", "### Languages\n\nPolish language, PL", "## Dataset Structure", "### Data Instances\n\nData are structured in JSONL format, each single text sample is divided by sentence.", "### Data Fields\n\nDescription of json keys:\n- 'text': text of the sentence\n- 'tokens': list of tokens made by tokenization process\n - 'index': token order index in sentence\n - 'position': token chars span indices <included, excluded>\n - 'orth': word\n - 'lemma': lemmatised word\n - 'pos': part of speech\n - 'ctag': morphosyntactic tag\n- 'phrases': list of multi-word \n- 'wsd': annotation labels for the WSD task", "### Data Splits\n\nWe do not specify an exact data split for training and evaluation. However, we suggest to use GLEX and Składnica for training and other datasets for testing.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection, Normalization and Post-processing\n\nSource corpora were initially pre-processed using morphosyntactic tagging and multi-word expression recognition tools.\nTo tokenize and tag the datasets we used MorphoDiTa adapted to Polish language. To recognize multi-word expressions\nwe applied pattern-based matching tool Corpus2-MWE - only MWEs from plWordNet were included. After manual annotation, \nsense indices of plWordNet 4.5 were mapped automatically to Princeton WordNet 3.0 and BabelNet 4.0 indices using plWordNet's interlingual mapping.", "### Annotations", "#### Annotation process\n\n* 2+1 annotation process with inter-annotator agreement score over 0.6 PSA\n* annotated with plWordNet 4.5\n* software: WordNet-Loom and Inforex\n* both single-word and multi-word expressions annotated\n* full-text sense annotation (excluding KPWr)", "#### Who are the annotators?\n\n- professional linguists from CLARIN-PL project", "### Personal and Sensitive Information\n\nThe datasets do not contain any personal or sensitive information.", "## Considerations for Using the Data", "### Discussion of Biases\n\nSome datasets are biased towards most frequent senses. No information about other biases - needs further analysis.", "### Other Known Limitations\n\n* sense inventories are usually incomplete therefore some word senses might be missing in plWordNet\n* single-word and multi-word terms expressing novel senses (missing in plWordNet) were not marked", "## Additional Information", "### Dataset Curators\n\nArkadiusz Janz (URL@URL)", "### Licensing Information\n\nKPWR-100 CC-BY-SA 4.0 \nKPWR CC-BY-SA 4.0 \nWalenty CC-BY-SA 4.0 \nSherlock CC-BY 4.0 \nSkladnica GNU GPL 3 \nGLEX plWordNet License \n\n\n\n\nMain source (all corpora as a unified benchmark) and published here on HuggingFace:\n\n'\n\nRelated work\n------------\n\nKPWr-100, Składnica, SPEC\n'\n\nGLEX (EmoGLEX)\n\n'\n\nKPWr\n'\n\nSkładnica\n'\n\nWalenty\n'\n\nMapping plWordNet onto Princeton WordNet\n'" ]

[ "task_categories:token-classification", "size_categories:100K<n<1M", "language:en", "language:es", "language:de", "language:it", "language:pt", "language:fr", "region:us" ]

[]

[ "en", "es", "de", "it", "pt", "fr" ]

[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis is a multilingual dataset containing ~130k annotated sentence boundaries. It contains laws and court decision in 6 different languages.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish, French, Italian, German, Portuguese, Spanish", "## Dataset Structure\n\nIt is structured in the following format: {language}\\_{type}\\_{shard}.URL\n\ntype is one of the following:\n- laws\n- judgements\n\nUse the the dataset like this:", "### Data Instances", "### Data Fields\n\n- text: the original text\n- spans:\n - start: offset of the first character\n - end: offset of the last character\n - label: One label only -> Sentence\n - token_start: id of the first token\n - token_end: id of the last token\n- tokens:\n - text: token text\n - start: offset of the first character\n - end: offset of the last character\n - id: token id\n - ws: whether the token is followed by whitespace", "### Data Splits\n\nThere is only one split available", "## 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" ]

[ "TAGS\n#task_categories-token-classification #size_categories-100K<n<1M #language-English #language-Spanish #language-German #language-Italian #language-Portuguese #language-French #region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis is a multilingual dataset containing ~130k annotated sentence boundaries. It contains laws and court decision in 6 different languages.", "### Supported Tasks and Leaderboards", "### Languages\n\nEnglish, French, Italian, German, Portuguese, Spanish", "## Dataset Structure\n\nIt is structured in the following format: {language}\\_{type}\\_{shard}.URL\n\ntype is one of the following:\n- laws\n- judgements\n\nUse the the dataset like this:", "### Data Instances", "### Data Fields\n\n- text: the original text\n- spans:\n - start: offset of the first character\n - end: offset of the last character\n - label: One label only -> Sentence\n - token_start: id of the first token\n - token_end: id of the last token\n- tokens:\n - text: token text\n - start: offset of the first character\n - end: offset of the last character\n - id: token id\n - ws: whether the token is followed by whitespace", "### Data Splits\n\nThere is only one split available", "## 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" ]

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

[]

[ "bn" ]

[ "### Dataset Summary\n\n\nSUBESCO is an audio-only emotional speech corpus of 7000 sentence-level utterances of the Bangla language. 20 professional actors (10 males and 10 females) participated in the recordings of 10 sentences for 7 target emotions. The emotions are Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. Total duration of the corpus is 7 hours 40 min 40 sec. Total size of the dataset is 2.03 GB. The dataset was evaluated by 50 raters (25 males, 25 females). Human perception test achieved a raw accuracy of 71%. All the details relating to creation, evaluation and analysis of SUBESCO have been described in the corresponding journal paper which has been published in Plos One.\n\n\nURL", "### Downloading the data", "### Naming Convention\n\n\nEach audio file in the dataset has a unique name. There are eight parts in the file name where all the parts are connected by underscores. The order of all the parts is organized as: Gender-Speaker's serial number-Speaker's name-Unit of recording-Unit number- Emotion name- Repeating number and the File format.\n\n\nFor example, the filename F\\_02\\_MONIKA\\_S\\_1\\_NEUTRAL\\_5.wav refers to:", "### Languages\n\n\nThis dataset contains Bangla Audio Data.\n\n\nDataset Creation\n----------------\n\n\nThis database was created as a part of PhD thesis project of the author Sadia Sultana. It was designed and developed by the author in the Department of Computer Science and Engineering of Shahjalal University of Science and Technology. Financial grant was supported by the university. If you use the dataset please cite SUBESCO and the corresponding academic journal publication in Plos One.", "### Contributors\n\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#task_categories-audio-classification #size_categories-1K<n<10K #language-Bengali #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nSUBESCO is an audio-only emotional speech corpus of 7000 sentence-level utterances of the Bangla language. 20 professional actors (10 males and 10 females) participated in the recordings of 10 sentences for 7 target emotions. The emotions are Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. Total duration of the corpus is 7 hours 40 min 40 sec. Total size of the dataset is 2.03 GB. The dataset was evaluated by 50 raters (25 males, 25 females). Human perception test achieved a raw accuracy of 71%. All the details relating to creation, evaluation and analysis of SUBESCO have been described in the corresponding journal paper which has been published in Plos One.\n\n\nURL", "### Downloading the data", "### Naming Convention\n\n\nEach audio file in the dataset has a unique name. There are eight parts in the file name where all the parts are connected by underscores. The order of all the parts is organized as: Gender-Speaker's serial number-Speaker's name-Unit of recording-Unit number- Emotion name- Repeating number and the File format.\n\n\nFor example, the filename F\\_02\\_MONIKA\\_S\\_1\\_NEUTRAL\\_5.wav refers to:", "### Languages\n\n\nThis dataset contains Bangla Audio Data.\n\n\nDataset Creation\n----------------\n\n\nThis database was created as a part of PhD thesis project of the author Sadia Sultana. It was designed and developed by the author in the Department of Computer Science and Engineering of Shahjalal University of Science and Technology. Financial grant was supported by the university. If you use the dataset please cite SUBESCO and the corresponding academic journal publication in Plos One.", "### Contributors\n\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:token-classification", "size_categories:100K<n<1M", "license:mit", "smart contract", "ethereum", "blockchain", "security", "region:us" ]

[]

[]

[ "# Malicious Smart Contract Classification Dataset\n\nThis dataset includes malicious and benign smart contracts deployed on Ethereum. \n\n\nCode used to collect this data: data collection notebook\n\nFor more details on how this dataset can be used, please check out this blog: How Forta’s Predictive ML Models Detect Attacks Before Exploitation" ]

[ "TAGS\n#task_categories-token-classification #size_categories-100K<n<1M #license-mit #smart contract #ethereum #blockchain #security #region-us \n", "# Malicious Smart Contract Classification Dataset\n\nThis dataset includes malicious and benign smart contracts deployed on Ethereum. \n\n\nCode used to collect this data: data collection notebook\n\nFor more details on how this dataset can be used, please check out this blog: How Forta’s Predictive ML Models Detect Attacks Before Exploitation" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"raw-commit-diffs\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"raw-commit-diffs\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"analysed-diff-metadata\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"analysed-diff-metadata\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"bookcorpus_compact_1024_shard9_of_10\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_1024_shard9_of_10\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:summarization", "size_categories:n<1K", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"talkrl-podcast\"\n\nThis dataset is sourced from the TalkRL Podcast website and contains English transcripts of wonderful TalkRL podcast episodes. The transcripts were generated using OpenAI's base Whisper model" ]

[ "TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-summarization #size_categories-n<1K #language-English #region-us \n", "# Dataset Card for \"talkrl-podcast\"\n\nThis dataset is sourced from the TalkRL Podcast website and contains English transcripts of wonderful TalkRL podcast episodes. The transcripts were generated using OpenAI's base Whisper model" ]

[ "task_categories:text-generation", "size_categories:100K<n<1M", "license:mit", "music", "region:us" ]

[]

[]

[ "## ABC Notation\n\nABC notation is an ASCII-based plain text musical notation system that is commonly used for transcribing traditional music and sharing sheet music online. It provides a simple and concise way to represent musical elements such as notes, rhythms, chords, and more.\n\nFor those looking to interact with ABC notation in various ways, there are several tools available:\n\n1. Online ABC Player: This web-based tool allows you to input ABC notation and hear the corresponding audio playback. By pasting your ABC code into the player, you can instantly listen to the tune as it would sound when played.\n\n2. ABC Sheet Music Editor - EasyABC: EasyABC is a user-friendly software application designed for creating, editing, and formatting ABC notation. Its graphical interface enables you to input your ABC code, preview the sheet music, and make adjustments as necessary.", "## Dataset Summary\n\nThe Irish Massive ABC Notation (IrishMAN) dataset includes 216,284 Irish tunes in ABC notation, divided into 99\\% (214,122 tunes) for training and 1\\% (2,162 tunes) for validation. These tunes were collected from URL and URL, both renowned for sharing traditional music. To ensure uniformity in formatting, all tunes were converted to XML and then back to ABC using scripts, and fields containing natural language (e.g., titles and lyrics) were removed.\n\nEach tune is automatically annotated with control codes derived from ABC symbols, as described in the below section. These control codes offer insights into the musical forms and structures of each composition.\n\nIn the IrishMAN dataset, a music21-filtered subset includes 34,211 lead sheets, each human-annotated with chord symbols. It is from this very subset that TunesFormer developed its capacity to generate melodies with harmonies.\n\nA noteworthy aspect is the copyright status. All tunes in the dataset are in the public domain, ensuring ethical and legal usage for research and creative projects.", "## Control Codes\n\nInspired by CTRL, we incorporate control codes into TunesFormer to represent musical forms. These codes, positioned ahead of the ABC notation, enable users to specify the structures of the generated tunes. The following control codes are introduced:\n\n- S:number of sections: determines the number of sections in the entire melody. It counts on several symbols that can be used to represent section boundaries: '[|', '||', '|]', '|:', '::', and ':|'. In our dataset, the range is 1 to 8 (e.g., 'S:1' for a single-section melody, and 'S:8' for a melody with eight sections).\n\n- B:number of bars: specifies the desired number of bars within a section. It counts on the bar symbol '|'. In our dataset, the range is 1 to 32 (e.g., 'B:1' for a one-bar section, and 'B:32' for a section with 32 bars).\n\n- E:edit distance similarity: controls the similarity level between the current section $c$ and a previous section $p$ in the melody. It is based on the Levenshtein distance $lev(c,p)$ , quantifying the difference between sections for creating variations or contrasts. Mathematically, it can be expressed as:\n \n where $|c|$ and $|p|$ are the string lengths of the two sections. It is discretized into 11 levels, ranging from no match at all to an exact match (e.g., 'E:0' for no similarity, and 'E:10' for an exact match).", "## Copyright Disclaimer\nThis dataset is for research use only and not for commercial purposes. We believe all data in this dataset is in the public domain. If you own the copyright to any musical composition in the IrishMAN dataset and have concerns, please contact us at shangda@URL. We will address your concerns and take appropriate action if needed.", "## Special Thanks\nWe would like to extend a special thanks to URL and URL for their contributions to the development and promotion of ABC notation, as well as their significant impact on the field of music information retrieval. Their platforms have become invaluable resources for the traditional and folk music community. We also wish to express our gratitude to Willem (Wim) for providing the essential conversion tools that enabled the transformation of the tunes into a uniform format. Together, these collaborations have made it possible for researchers like us to create and study extensive datasets like IrishMAN." ]

[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #license-mit #music #region-us \n", "## ABC Notation\n\nABC notation is an ASCII-based plain text musical notation system that is commonly used for transcribing traditional music and sharing sheet music online. It provides a simple and concise way to represent musical elements such as notes, rhythms, chords, and more.\n\nFor those looking to interact with ABC notation in various ways, there are several tools available:\n\n1. Online ABC Player: This web-based tool allows you to input ABC notation and hear the corresponding audio playback. By pasting your ABC code into the player, you can instantly listen to the tune as it would sound when played.\n\n2. ABC Sheet Music Editor - EasyABC: EasyABC is a user-friendly software application designed for creating, editing, and formatting ABC notation. Its graphical interface enables you to input your ABC code, preview the sheet music, and make adjustments as necessary.", "## Dataset Summary\n\nThe Irish Massive ABC Notation (IrishMAN) dataset includes 216,284 Irish tunes in ABC notation, divided into 99\\% (214,122 tunes) for training and 1\\% (2,162 tunes) for validation. These tunes were collected from URL and URL, both renowned for sharing traditional music. To ensure uniformity in formatting, all tunes were converted to XML and then back to ABC using scripts, and fields containing natural language (e.g., titles and lyrics) were removed.\n\nEach tune is automatically annotated with control codes derived from ABC symbols, as described in the below section. These control codes offer insights into the musical forms and structures of each composition.\n\nIn the IrishMAN dataset, a music21-filtered subset includes 34,211 lead sheets, each human-annotated with chord symbols. It is from this very subset that TunesFormer developed its capacity to generate melodies with harmonies.\n\nA noteworthy aspect is the copyright status. All tunes in the dataset are in the public domain, ensuring ethical and legal usage for research and creative projects.", "## Control Codes\n\nInspired by CTRL, we incorporate control codes into TunesFormer to represent musical forms. These codes, positioned ahead of the ABC notation, enable users to specify the structures of the generated tunes. The following control codes are introduced:\n\n- S:number of sections: determines the number of sections in the entire melody. It counts on several symbols that can be used to represent section boundaries: '[|', '||', '|]', '|:', '::', and ':|'. In our dataset, the range is 1 to 8 (e.g., 'S:1' for a single-section melody, and 'S:8' for a melody with eight sections).\n\n- B:number of bars: specifies the desired number of bars within a section. It counts on the bar symbol '|'. In our dataset, the range is 1 to 32 (e.g., 'B:1' for a one-bar section, and 'B:32' for a section with 32 bars).\n\n- E:edit distance similarity: controls the similarity level between the current section $c$ and a previous section $p$ in the melody. It is based on the Levenshtein distance $lev(c,p)$ , quantifying the difference between sections for creating variations or contrasts. Mathematically, it can be expressed as:\n \n where $|c|$ and $|p|$ are the string lengths of the two sections. It is discretized into 11 levels, ranging from no match at all to an exact match (e.g., 'E:0' for no similarity, and 'E:10' for an exact match).", "## Copyright Disclaimer\nThis dataset is for research use only and not for commercial purposes. We believe all data in this dataset is in the public domain. If you own the copyright to any musical composition in the IrishMAN dataset and have concerns, please contact us at shangda@URL. We will address your concerns and take appropriate action if needed.", "## Special Thanks\nWe would like to extend a special thanks to URL and URL for their contributions to the development and promotion of ABC notation, as well as their significant impact on the field of music information retrieval. Their platforms have become invaluable resources for the traditional and folk music community. We also wish to express our gratitude to Willem (Wim) for providing the essential conversion tools that enabled the transformation of the tunes into a uniform format. Together, these collaborations have made it possible for researchers like us to create and study extensive datasets like IrishMAN." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"chocolate-captioned-dataset-100\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"chocolate-captioned-dataset-100\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"chocolate-captioned-dataset-200\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"chocolate-captioned-dataset-200\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"chocolate-captioned-dataset-300\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"chocolate-captioned-dataset-300\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"chocolate-captioned-dataset-400\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"chocolate-captioned-dataset-400\"\n\nMore Information needed" ]

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Lemon_filter\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Lemon_filter\"\n\nMore Information needed" ]

[ "size_categories:1M<n<10M", "not-for-all-audiences", "region:us" ]

[]

[]

[ "# Deprecation Notice!\n> This dataset has been superseded by v2. Use v2 instead of this dataset.\n\n\nWarning: THIS dataset is NOT suitable for use by minors. The dataset contains X-rated/NFSW content.", "# E621 Rising: Raw Image Dataset v1\n\n2,905,671 images (~1.1TB) downloaded from 'URL' with tags.\n\nThis is a raw, uncurated, and largely unprocessed dataset. You likely want to use the curated version, available here. This dataset contains all kinds of NFSW material. You have been warned.", "## Image Processing\n* Only 'jpg' and 'png' images were considered\n* Image width and height have been clamped to '(0, 4096]px'; larger images have been resized to meet the limit\n* Alpha channels have been removed\n* All images have been converted to 'jpg' format\n* All images have been converted to TrueColor 'RGB'\n* All images have been verified to load with 'Pillow'\n* Metadata from E621 is available here.", "## Tags\nFor a comprehensive list of tags and counts, see here.", "### Changes From E621\n* Symbols have been prefixed with 'symbol:', e.g. 'symbol:<3'\n* Aspect ratio has been prefixed with 'aspect_ratio:', e.g. 'aspect_ratio:16_9'\n* All categories except 'general' have been prefixed with the category name, e.g. 'artist:somename'. The categories are:\n * 'artist'\n * 'copyright'\n * 'character'\n * 'species'\n * 'invalid'\n * 'meta'\n * 'lore'", "### Additional Tags\n* Image rating\n * 'rating:explicit'\n * 'rating:questionable'\n * 'rating:safe'\n* Image score\n * 'score:above_250'\n * 'score:above_500'\n * 'score:above_1000'\n * 'score:above_1500'\n * 'score:above_2000'\n * 'score:below_250'\n * 'score:below_100'\n * 'score:below_50'\n * 'score:below_25'\n * 'score:below_0'\n* Image favorites\n * 'favorites:above_4000'\n * 'favorites:above_3000'\n * 'favorites:above_2000'\n * 'favorites:above_1000'\n * 'favorites:below_1000'\n * 'favorites:below_500'\n * 'favorites:below_250'\n * 'favorites:below_100'\n * 'favorites:below_50'\n * 'favorites:below_25'" ]

[ "TAGS\n#size_categories-1M<n<10M #not-for-all-audiences #region-us \n", "# Deprecation Notice!\n> This dataset has been superseded by v2. Use v2 instead of this dataset.\n\n\nWarning: THIS dataset is NOT suitable for use by minors. The dataset contains X-rated/NFSW content.", "# E621 Rising: Raw Image Dataset v1\n\n2,905,671 images (~1.1TB) downloaded from 'URL' with tags.\n\nThis is a raw, uncurated, and largely unprocessed dataset. You likely want to use the curated version, available here. This dataset contains all kinds of NFSW material. You have been warned.", "## Image Processing\n* Only 'jpg' and 'png' images were considered\n* Image width and height have been clamped to '(0, 4096]px'; larger images have been resized to meet the limit\n* Alpha channels have been removed\n* All images have been converted to 'jpg' format\n* All images have been converted to TrueColor 'RGB'\n* All images have been verified to load with 'Pillow'\n* Metadata from E621 is available here.", "## Tags\nFor a comprehensive list of tags and counts, see here.", "### Changes From E621\n* Symbols have been prefixed with 'symbol:', e.g. 'symbol:<3'\n* Aspect ratio has been prefixed with 'aspect_ratio:', e.g. 'aspect_ratio:16_9'\n* All categories except 'general' have been prefixed with the category name, e.g. 'artist:somename'. The categories are:\n * 'artist'\n * 'copyright'\n * 'character'\n * 'species'\n * 'invalid'\n * 'meta'\n * 'lore'", "### Additional Tags\n* Image rating\n * 'rating:explicit'\n * 'rating:questionable'\n * 'rating:safe'\n* Image score\n * 'score:above_250'\n * 'score:above_500'\n * 'score:above_1000'\n * 'score:above_1500'\n * 'score:above_2000'\n * 'score:below_250'\n * 'score:below_100'\n * 'score:below_50'\n * 'score:below_25'\n * 'score:below_0'\n* Image favorites\n * 'favorites:above_4000'\n * 'favorites:above_3000'\n * 'favorites:above_2000'\n * 'favorites:above_1000'\n * 'favorites:below_1000'\n * 'favorites:below_500'\n * 'favorites:below_250'\n * 'favorites:below_100'\n * 'favorites:below_50'\n * 'favorites:below_25'" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"vul_lines\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"vul_lines\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:question-answering", "language:en", "license:cc-by-4.0", "Question Answering", "Aviation", "Knowledge Graphs", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #language-English #license-cc-by-4.0 #Question Answering #Aviation #Knowledge Graphs #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"npsc_dataset_tmp\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"npsc_dataset_tmp\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Colection of models trained on axis cameras" ]

[ "TAGS\n#region-us \n", "# Colection of models trained on axis cameras" ]