Datasets:

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ftopal

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huggingface-datasets

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[ "task_categories:conversational", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]

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[ "en" ]

[ "### Dataset Summary\n\n\nMultiWOZ 2.1 fixed the noise in state annotations and dialogue utterances. It also includes user dialogue acts from ConvLab (Lee et al., 2019) as well as multiple slot descriptions per dialogue state slot.\n\n\n* How to get the transformed data from original data:\n\t+ Download MultiWOZ\\_2.1.zip.\n\t+ Run 'python URL' in the current directory.\n* Main changes of the transformation:\n\t+ Create a new ontology in the unified format, taking slot descriptions from MultiWOZ 2.2.\n\t+ Correct some grammar errors in the text, mainly following 'URL' in MultiWOZ\\_2.1.\n\t+ Normalize slot name and value. See 'normalize\\_domain\\_slot\\_value' function in 'URL'.\n\t+ Correct some non-categorical slots' values and provide character level span annotation.\n\t+ Concatenate multiple values in user goal & state using '|'.\n\t+ Add 'booked' information in system turns from original belief states.\n\t+ Remove 'Booking' domain and remap all booking relevant dialog acts to unify the annotation of booking action in different domains, see 'booking\\_remapper.py'.\n* Annotations:\n\t+ user goal, dialogue acts, state.", "### Supported Tasks and Leaderboards\n\n\nNLU, DST, Policy, NLG, E2E, User simulator", "### Languages\n\n\nEnglish", "### Data Splits\n\n\n\n8 domains: ['attraction', 'hotel', 'taxi', 'restaurant', 'train', 'police', 'hospital', 'general']\n\n\n* cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.\n* non-cat slot span: how many values of non-categorical slots have span annotation in percentage.", "### Licensing Information\n\n\nApache License, Version 2.0" ]

[ "TAGS\n#task_categories-conversational #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nMultiWOZ 2.1 fixed the noise in state annotations and dialogue utterances. It also includes user dialogue acts from ConvLab (Lee et al., 2019) as well as multiple slot descriptions per dialogue state slot.\n\n\n* How to get the transformed data from original data:\n\t+ Download MultiWOZ\\_2.1.zip.\n\t+ Run 'python URL' in the current directory.\n* Main changes of the transformation:\n\t+ Create a new ontology in the unified format, taking slot descriptions from MultiWOZ 2.2.\n\t+ Correct some grammar errors in the text, mainly following 'URL' in MultiWOZ\\_2.1.\n\t+ Normalize slot name and value. See 'normalize\\_domain\\_slot\\_value' function in 'URL'.\n\t+ Correct some non-categorical slots' values and provide character level span annotation.\n\t+ Concatenate multiple values in user goal & state using '|'.\n\t+ Add 'booked' information in system turns from original belief states.\n\t+ Remove 'Booking' domain and remap all booking relevant dialog acts to unify the annotation of booking action in different domains, see 'booking\\_remapper.py'.\n* Annotations:\n\t+ user goal, dialogue acts, state.", "### Supported Tasks and Leaderboards\n\n\nNLU, DST, Policy, NLG, E2E, User simulator", "### Languages\n\n\nEnglish", "### Data Splits\n\n\n\n8 domains: ['attraction', 'hotel', 'taxi', 'restaurant', 'train', 'police', 'hospital', 'general']\n\n\n* cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.\n* non-cat slot span: how many values of non-categorical slots have span annotation in percentage.", "### Licensing Information\n\n\nApache License, Version 2.0" ]

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[ "# EAS Dataset\n on Instagram social network data\n\nNowadays, thanks to spread of social media, and large amount of data in Internet, the need for changing how we look and interpret data is evolving. Visualization is one of the most important fields in data science. About growing usage of social media, analyzing the data they contain is crucial. In this research, the Emotion Analytic System on Instagram social network data designed and developed. In this system, we analyze emotions and words that user writes, and visualize them by visualizing techniques. Over 370,000 Instagram comments have been collected with the help of data crawlers that we developed, after that we prepared the data and preprocessed them; including normalizing, finding the keywords and etc. The system is developed by Python.\n\nThis Dataset has over 370,000 preprocessed comments (that most of them are in Persian) from 40 instagram channels. These comments are crawled from 12 April 2017 (1396/01/26 A.H.S) to 29 July 2017 (1396/05/07 A.H.S).\n\nIf you use this dataset in your publications, please cite this paper:" ]

[ "TAGS\n#region-us \n", "# EAS Dataset\n on Instagram social network data\n\nNowadays, thanks to spread of social media, and large amount of data in Internet, the need for changing how we look and interpret data is evolving. Visualization is one of the most important fields in data science. About growing usage of social media, analyzing the data they contain is crucial. In this research, the Emotion Analytic System on Instagram social network data designed and developed. In this system, we analyze emotions and words that user writes, and visualize them by visualizing techniques. Over 370,000 Instagram comments have been collected with the help of data crawlers that we developed, after that we prepared the data and preprocessed them; including normalizing, finding the keywords and etc. The system is developed by Python.\n\nThis Dataset has over 370,000 preprocessed comments (that most of them are in Persian) from 40 instagram channels. These comments are crawled from 12 April 2017 (1396/01/26 A.H.S) to 29 July 2017 (1396/05/07 A.H.S).\n\nIf you use this dataset in your publications, please cite this paper:" ]

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[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:es", "license:unknown", "region:us" ]

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[ "es" ]

[ "# Dataset Card for [es_tweets_laboral]", "## 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\nDataset creado por @hucruz, @DanielaGarciaQuezada, @hylandude, @BloodBoy21\nEtiquetado por @DanielaGarciaQuezada\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\nespañol", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Spanish #license-unknown #region-us \n", "# Dataset Card for [es_tweets_laboral]", "## 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\nDataset creado por @hucruz, @DanielaGarciaQuezada, @hylandude, @BloodBoy21\nEtiquetado por @DanielaGarciaQuezada\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\nespañol", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "task_categories:image-classification", "task_categories:other", "task_ids:multi-label-image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "domain-generalization", "arxiv:2202.06523", "region:us" ]

[ "2202.06523" ]

[ "en" ]

[ "# Dataset Card for MetaShift", "## 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: MetaShift homepage\n- Repository: MetaShift repository\n- Paper: MetaShift paper\n- Point of Contact: Weixin Liang", "### Dataset Summary\n\nThe MetaShift dataset is a collection of 12,868 sets of natural images across 410 classes. It was created for understanding the performance of a machine learning model across diverse data distributions.\n\nThe authors leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift.\nThe key idea is to cluster images using its metadata which provides context for each image.\nFor example : cats with cars or cats in bathroom.\nThe main advantage is the dataset contains many more coherent sets of data compared to other benchmarks.\n\nTwo important benefits of MetaShift :\n- Contains orders of magnitude more natural data shifts than previously available.\n- Provides explicit explanations of what is unique about each of its data sets and a distance score that measures the amount of distribution shift between any two of its data sets.", "### Dataset Usage\n\nThe dataset has the following configuration parameters:\n- selected_classes: 'list[string]', optional, list of the classes to generate the MetaShift dataset for. If 'None', the list is equal to '['cat', 'dog', 'bus', 'truck', 'elephant', 'horse']'.\n- attributes_dataset: 'bool', default 'False', if 'True', the script generates the MetaShift-Attributes dataset. Refer MetaShift-Attributes Dataset for more information.\n- attributes: 'list[string]', optional, list of attributes classes included in the Attributes dataset. If 'None' and 'attributes_dataset' is 'True', it's equal to '[\"cat(orange)\", \"cat(white)\", \"dog(sitting)\", \"dog(jumping)\"]'. You can find the full attribute ontology in the above link. \n- with_image_metadata: 'bool', default 'False', whether to include image metadata. If set to 'True', this will give additional metadata about each image. See Scene Graph for more information.\n- image_subset_size_threshold: 'int', default '25', the number of images required to be considered a subset. If the number of images is less than this threshold, the subset is ignored.\n- min_local_groups: 'int', default '5', the minimum number of local groups required to be considered an object class.\n\nConsider the following examples to get an idea of how you can use the configuration parameters :\n\n1. To generate the MetaShift Dataset :\n\nThe full object vocabulary and its hierarchy can be seen here.\n\nThe default classes are '['cat', 'dog', 'bus', 'truck', 'elephant', 'horse']'\n\n2. To generate the MetaShift-Attributes Dataset (subsets defined by subject attributes) :\n\n\nThe default attributes are '[\"cat(orange)\", \"cat(white)\", \"dog(sitting)\", \"dog(jumping)\"]'\n\n3. To generate the dataset with additional image metadata information :\n\n4. Further, you can specify your own configuration different from those used in the papers as follows:", "### Dataset Meta-Graphs\n\nFrom the MetaShift Github Repo :\n> MetaShift splits the data points of each class (e.g., Cat) into many subsets based on visual contexts. Each node in the meta-graph represents one subset. The weight of each edge is the overlap coefficient between the corresponding two subsets. Node colors indicate the graph-based community detection results. Inter-community edges are colored. Intra-community edges are grayed out for better visualization. The border color of each example image indicates its community in the meta-graph. We have one such meta-graph for each of the 410 classes in the MetaShift.\n\nThe following are the metagraphs for the default classes, these have been generated using the 'generate_full_MetaShift.py' file.\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Cat Meta-graph\" /> </br>\n <b>Figure: Meta-graph: visualizing the diverse data distributions within the “cat” class. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Dog Meta-graph\" /> </br>\n<b>Figure: Meta-graph for the “Dog” class, which captures meaningful semantics of the multi-modal data distribution of “Dog”. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Bus Meta-graph\" /> </br>\n<b>Figure: Meta-graph for the “Bus” class. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Elephant Meta-graph\" /> </br>\n<b>Figure: Meta-graph for the \"Elephant\" class. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Horse Meta-graph\" /> </br>\n<b>Figure: Meta-graph for the \"Horse\" class. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Truck Meta-graph\"/> </br>\n<b>Figure: Meta-graph for the Truck class. </b> \n</p>", "### Supported Tasks and Leaderboards\n\nFrom the paper:\n> MetaShift supports evaluation on both :\n> - domain generalization and subpopulation shifts settings,\n> - assessing training conflicts.", "### Languages\n\n All the classes and subsets use English as their primary language.", "## Dataset Structure", "### Data Instances\n\nA sample from the MetaShift dataset is provided below:\n\n\n\nA sample from the MetaShift-Attributes dataset is provided below:\n\n\nThe format of the dataset with image metadata included by passing 'with_image_metadata=True' to 'load_dataset' is provided below:", "### Data Fields\n\n- 'image_id': Unique numeric ID of the image in Base Visual Genome dataset.\n- 'image': A PIL.Image.Image object containing the image. \n- 'label': an int classification label.\n- 'context': represents the context in which the label is seen. A given label could have multiple contexts.\n\nImage Metadata format can be seen here and a sample above has been provided for reference.", "### Data Splits\n\nAll the data is contained in training set.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> We present MetaShift as an important resource for studying the behavior of\nML algorithms and training dynamics across data with heterogeneous contexts. In order to assess the reliability and fairness of a model, we need to evaluate\nits performance and training behavior across heterogeneous types of data. MetaShift contains many more coherent sets of data compared to other benchmarks. Importantly, we have explicit annotations of what makes each subset unique (e.g. cats with cars or dogs next to a bench) as well as a score that measures the distance between any two subsets, which is not available in previous benchmarks of natural data.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper:\n> We leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift. Visual Genome contains over 100k images across 1,702 object classes. MetaShift is constructed on a class-by-class basis. For each class, say “cat”, we pull out all cat images and proceed with generating candidate subests, constructing meta-graphs and then duantify distances of distribution shifts.", "#### Who are the source language producers?", "### Annotations\n\nThe MetaShift dataset uses Visual Genome as its base, therefore the annotations process is same as the Visual Genome dataset.", "#### Annotation process\n\nFrom the Visual Genome paper :\n> We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over 33,000 unique workers contributed to the dataset. The dataset was collected over the course of 6 months after 15 months of experimentation and iteration on the data representation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where each HIT involved creating descriptions, questions and answers, or region graphs.", "#### Who are the annotators?\n\nFrom the Visual Genome paper :\n> Visual Genome was collected and verified entirely by crowd workers from Amazon Mechanical Turk.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nFrom the paper:\n> One limitation is that our MetaShift might inherit existing biases in Visual Genome, which is the\nbase dataset of our MetaShift. Potential concerns include minority groups being under-represented\nin certain classes (e.g., women with snowboard), or annotation bias where people in images are\nby default labeled as male when gender is unlikely to be identifiable. Existing work in analyzing,\nquantifying, and mitigating biases in general computer vision datasets can help with addressing this\npotential negative societal impact.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nFrom the paper :\n> Our MetaShift and the code would use the Creative Commons Attribution 4.0 International License. Visual Genome (Krishna et al., 2017) is licensed under a Creative Commons Attribution 4.0 International License. MS-COCO (Lin et al., 2014) is licensed under CC-BY 4.0. The Visual Genome dataset uses 108, 077 images from the intersection of the YFCC100M (Thomee et al., 2016) and MS-COCO. We use the pre-processed and cleaned version of Visual Genome by GQA (Hudson & Manning, 2019).", "### Contributions\n\nThanks to @dnaveenr for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_categories-other #task_ids-multi-label-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #domain-generalization #arxiv-2202.06523 #region-us \n", "# Dataset Card for MetaShift", "## 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: MetaShift homepage\n- Repository: MetaShift repository\n- Paper: MetaShift paper\n- Point of Contact: Weixin Liang", "### Dataset Summary\n\nThe MetaShift dataset is a collection of 12,868 sets of natural images across 410 classes. It was created for understanding the performance of a machine learning model across diverse data distributions.\n\nThe authors leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift.\nThe key idea is to cluster images using its metadata which provides context for each image.\nFor example : cats with cars or cats in bathroom.\nThe main advantage is the dataset contains many more coherent sets of data compared to other benchmarks.\n\nTwo important benefits of MetaShift :\n- Contains orders of magnitude more natural data shifts than previously available.\n- Provides explicit explanations of what is unique about each of its data sets and a distance score that measures the amount of distribution shift between any two of its data sets.", "### Dataset Usage\n\nThe dataset has the following configuration parameters:\n- selected_classes: 'list[string]', optional, list of the classes to generate the MetaShift dataset for. If 'None', the list is equal to '['cat', 'dog', 'bus', 'truck', 'elephant', 'horse']'.\n- attributes_dataset: 'bool', default 'False', if 'True', the script generates the MetaShift-Attributes dataset. Refer MetaShift-Attributes Dataset for more information.\n- attributes: 'list[string]', optional, list of attributes classes included in the Attributes dataset. If 'None' and 'attributes_dataset' is 'True', it's equal to '[\"cat(orange)\", \"cat(white)\", \"dog(sitting)\", \"dog(jumping)\"]'. You can find the full attribute ontology in the above link. \n- with_image_metadata: 'bool', default 'False', whether to include image metadata. If set to 'True', this will give additional metadata about each image. See Scene Graph for more information.\n- image_subset_size_threshold: 'int', default '25', the number of images required to be considered a subset. If the number of images is less than this threshold, the subset is ignored.\n- min_local_groups: 'int', default '5', the minimum number of local groups required to be considered an object class.\n\nConsider the following examples to get an idea of how you can use the configuration parameters :\n\n1. To generate the MetaShift Dataset :\n\nThe full object vocabulary and its hierarchy can be seen here.\n\nThe default classes are '['cat', 'dog', 'bus', 'truck', 'elephant', 'horse']'\n\n2. To generate the MetaShift-Attributes Dataset (subsets defined by subject attributes) :\n\n\nThe default attributes are '[\"cat(orange)\", \"cat(white)\", \"dog(sitting)\", \"dog(jumping)\"]'\n\n3. To generate the dataset with additional image metadata information :\n\n4. Further, you can specify your own configuration different from those used in the papers as follows:", "### Dataset Meta-Graphs\n\nFrom the MetaShift Github Repo :\n> MetaShift splits the data points of each class (e.g., Cat) into many subsets based on visual contexts. Each node in the meta-graph represents one subset. The weight of each edge is the overlap coefficient between the corresponding two subsets. Node colors indicate the graph-based community detection results. Inter-community edges are colored. Intra-community edges are grayed out for better visualization. The border color of each example image indicates its community in the meta-graph. We have one such meta-graph for each of the 410 classes in the MetaShift.\n\nThe following are the metagraphs for the default classes, these have been generated using the 'generate_full_MetaShift.py' file.\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Cat Meta-graph\" /> </br>\n <b>Figure: Meta-graph: visualizing the diverse data distributions within the “cat” class. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Dog Meta-graph\" /> </br>\n<b>Figure: Meta-graph for the “Dog” class, which captures meaningful semantics of the multi-modal data distribution of “Dog”. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Bus Meta-graph\" /> </br>\n<b>Figure: Meta-graph for the “Bus” class. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Elephant Meta-graph\" /> </br>\n<b>Figure: Meta-graph for the \"Elephant\" class. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Horse Meta-graph\" /> </br>\n<b>Figure: Meta-graph for the \"Horse\" class. </b> \n</p>\n\n<p align='center'>\n <img width='75%' src='https://i.URL alt=\"Truck Meta-graph\"/> </br>\n<b>Figure: Meta-graph for the Truck class. </b> \n</p>", "### Supported Tasks and Leaderboards\n\nFrom the paper:\n> MetaShift supports evaluation on both :\n> - domain generalization and subpopulation shifts settings,\n> - assessing training conflicts.", "### Languages\n\n All the classes and subsets use English as their primary language.", "## Dataset Structure", "### Data Instances\n\nA sample from the MetaShift dataset is provided below:\n\n\n\nA sample from the MetaShift-Attributes dataset is provided below:\n\n\nThe format of the dataset with image metadata included by passing 'with_image_metadata=True' to 'load_dataset' is provided below:", "### Data Fields\n\n- 'image_id': Unique numeric ID of the image in Base Visual Genome dataset.\n- 'image': A PIL.Image.Image object containing the image. \n- 'label': an int classification label.\n- 'context': represents the context in which the label is seen. A given label could have multiple contexts.\n\nImage Metadata format can be seen here and a sample above has been provided for reference.", "### Data Splits\n\nAll the data is contained in training set.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> We present MetaShift as an important resource for studying the behavior of\nML algorithms and training dynamics across data with heterogeneous contexts. In order to assess the reliability and fairness of a model, we need to evaluate\nits performance and training behavior across heterogeneous types of data. MetaShift contains many more coherent sets of data compared to other benchmarks. Importantly, we have explicit annotations of what makes each subset unique (e.g. cats with cars or dogs next to a bench) as well as a score that measures the distance between any two subsets, which is not available in previous benchmarks of natural data.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper:\n> We leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift. Visual Genome contains over 100k images across 1,702 object classes. MetaShift is constructed on a class-by-class basis. For each class, say “cat”, we pull out all cat images and proceed with generating candidate subests, constructing meta-graphs and then duantify distances of distribution shifts.", "#### Who are the source language producers?", "### Annotations\n\nThe MetaShift dataset uses Visual Genome as its base, therefore the annotations process is same as the Visual Genome dataset.", "#### Annotation process\n\nFrom the Visual Genome paper :\n> We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over 33,000 unique workers contributed to the dataset. The dataset was collected over the course of 6 months after 15 months of experimentation and iteration on the data representation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where each HIT involved creating descriptions, questions and answers, or region graphs.", "#### Who are the annotators?\n\nFrom the Visual Genome paper :\n> Visual Genome was collected and verified entirely by crowd workers from Amazon Mechanical Turk.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nFrom the paper:\n> One limitation is that our MetaShift might inherit existing biases in Visual Genome, which is the\nbase dataset of our MetaShift. Potential concerns include minority groups being under-represented\nin certain classes (e.g., women with snowboard), or annotation bias where people in images are\nby default labeled as male when gender is unlikely to be identifiable. Existing work in analyzing,\nquantifying, and mitigating biases in general computer vision datasets can help with addressing this\npotential negative societal impact.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nFrom the paper :\n> Our MetaShift and the code would use the Creative Commons Attribution 4.0 International License. Visual Genome (Krishna et al., 2017) is licensed under a Creative Commons Attribution 4.0 International License. MS-COCO (Lin et al., 2014) is licensed under CC-BY 4.0. The Visual Genome dataset uses 108, 077 images from the intersection of the YFCC100M (Thomee et al., 2016) and MS-COCO. We use the pre-processed and cleaned version of Visual Genome by GQA (Hudson & Manning, 2019).", "### Contributions\n\nThanks to @dnaveenr for adding this dataset." ]

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[ "proteins", "molecules", "chemistry", "SMILES", "complex structures", "region:us" ]

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[ "## How to use the data sets\n\nThis dataset contains about 36,000 unique pairs of protein sequences and ligand SMILES, and the coordinates\nof their complexes from the PDB.\n\nSMILES are assumed to be tokenized by the regex from P. Schwaller.", "## Ligand selection criteria\n\nOnly ligands\n\n- that have at least 3 atoms,\n- a molecular weight >= 100 Da,\n- and which are not among the 280 most common ligands in the PDB (this includes common additives like PEG, ADP, ..)\n\nare considered.", "### Use the already preprocessed data\n\nLoad a test/train split using\n\n\n\nReceptor features contain protein frames and side chain angles in OpenFold/AlphaFold format.\nLigand tokens which do not correspond to atoms have 'nan' as their coordinates.\n\nDocumentation by example:", "### Manual update from PDB" ]

[ "TAGS\n#proteins #molecules #chemistry #SMILES #complex structures #region-us \n", "## How to use the data sets\n\nThis dataset contains about 36,000 unique pairs of protein sequences and ligand SMILES, and the coordinates\nof their complexes from the PDB.\n\nSMILES are assumed to be tokenized by the regex from P. Schwaller.", "## Ligand selection criteria\n\nOnly ligands\n\n- that have at least 3 atoms,\n- a molecular weight >= 100 Da,\n- and which are not among the 280 most common ligands in the PDB (this includes common additives like PEG, ADP, ..)\n\nare considered.", "### Use the already preprocessed data\n\nLoad a test/train split using\n\n\n\nReceptor features contain protein frames and side chain angles in OpenFold/AlphaFold format.\nLigand tokens which do not correspond to atoms have 'nan' as their coordinates.\n\nDocumentation by example:", "### Manual update from PDB" ]

[ "arxiv:1912.05200", "region:us" ]

[ "1912.05200" ]

[]

[ "# Dataset Card for biomed_squad_es_v2\n\nThis Dataset was created as part of the \"Extractive QA Biomedicine\" project developed during the 2022 Hackathon organized by SOMOS NLP.", "## Table of Contents\n\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\nThis is a subset of the dev squad_es (v2) dataset (automatic translation of the Stanford Question Answering Dataset v2 into Spanish) containing questions related to the biomedical domain.\n\nLicense, distribution and usage conditions of the original Squad_es Dataset apply.", "### Languages\n\nSpanish", "## Dataset Structure", "### Data Fields", "### Data Splits\n\nValidation: 1137 examples", "## Team\nSantiago Maximo: smaximo" ]

[ "TAGS\n#arxiv-1912.05200 #region-us \n", "# Dataset Card for biomed_squad_es_v2\n\nThis Dataset was created as part of the \"Extractive QA Biomedicine\" project developed during the 2022 Hackathon organized by SOMOS NLP.", "## Table of Contents\n\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\nThis is a subset of the dev squad_es (v2) dataset (automatic translation of the Stanford Question Answering Dataset v2 into Spanish) containing questions related to the biomedical domain.\n\nLicense, distribution and usage conditions of the original Squad_es Dataset apply.", "### Languages\n\nSpanish", "## Dataset Structure", "### Data Fields", "### Data Splits\n\nValidation: 1137 examples", "## Team\nSantiago Maximo: smaximo" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "language:ru", "arxiv:2108.13112", "region:us" ]

[ "2108.13112" ]

[ "ru" ]

[ "# RuNNE dataset", "## Table of Contents\n- Dataset Description\n- Dataset Structure\n- Citation Information\n- Contacts", "## Dataset Description\nPart of NEREL dataset (URL a Russian dataset\nfor named entity recognition and relation extraction, used in RuNNE (2022)\ncompetition (URL\n\nEntities may be nested (see URL\n\nEntity types list:\n* AGE\n* AWARD\n* CITY\n* COUNTRY\n* CRIME\n* DATE\n* DISEASE\n* DISTRICT\n* EVENT\n* FACILITY\n* FAMILY\n* IDEOLOGY\n* LANGUAGE\n* LAW\n* LOCATION\n* MONEY\n* NATIONALITY\n* NUMBER\n* ORDINAL\n* ORGANIZATION\n* PENALTY\n* PERCENT\n* PERSON\n* PRODUCT\n* PROFESSION\n* RELIGION\n* STATE_OR_PROVINCE\n* TIME\n* WORK_OF_ART", "## Dataset Structure\nThere are two \"configs\" or \"subsets\" of the dataset. \nUsing \n'load_dataset('MalakhovIlya/RuNNE', 'ent_types')['ent_types']' \nyou can download list of entity types (\nDataset({\n features: ['type'],\n num_rows: 29\n})\n) \nUsing \n'load_dataset('MalakhovIlya/RuNNE', 'data')' or 'load_dataset('MalakhovIlya/RuNNE')'\nyou can download the data itself (DatasetDict)\n\nDataset consists of 3 splits: \"train\", \"test\" and \"dev\". Each of them contains text document. \"Train\" and \"test\" splits also contain annotated entities, \"dev\" doesn't.\nEach entity is represented by a string of the following format: \"\\<start> \\<stop> \\<type>\", where \\<start> is a position of the first symbol of entity in text, \\<stop> is the last symbol position in text and \\<type> is a one of the aforementioned list of types.\n\nP.S.\nOriginal NEREL dataset also contains relations, events and linked entities, but they were not added here yet ¯\\\\\\_(ツ)_/¯\n\n\n@article{Artemova2022runne, \n title={{RuNNE-2022 Shared Task: Recognizing Nested Named Entities}},\n author={Artemova, Ekaterina and Zmeev, Maksim and Loukachevitch, Natalia and Rozhkov, Igor and Batura, Tatiana and Braslavski, Pavel and Ivanov, Vladimir and Tutubalina, Elena},\n journal={Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference \"Dialog\"},\n year={2022}\n}" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #language-Russian #arxiv-2108.13112 #region-us \n", "# RuNNE dataset", "## Table of Contents\n- Dataset Description\n- Dataset Structure\n- Citation Information\n- Contacts", "## Dataset Description\nPart of NEREL dataset (URL a Russian dataset\nfor named entity recognition and relation extraction, used in RuNNE (2022)\ncompetition (URL\n\nEntities may be nested (see URL\n\nEntity types list:\n* AGE\n* AWARD\n* CITY\n* COUNTRY\n* CRIME\n* DATE\n* DISEASE\n* DISTRICT\n* EVENT\n* FACILITY\n* FAMILY\n* IDEOLOGY\n* LANGUAGE\n* LAW\n* LOCATION\n* MONEY\n* NATIONALITY\n* NUMBER\n* ORDINAL\n* ORGANIZATION\n* PENALTY\n* PERCENT\n* PERSON\n* PRODUCT\n* PROFESSION\n* RELIGION\n* STATE_OR_PROVINCE\n* TIME\n* WORK_OF_ART", "## Dataset Structure\nThere are two \"configs\" or \"subsets\" of the dataset. \nUsing \n'load_dataset('MalakhovIlya/RuNNE', 'ent_types')['ent_types']' \nyou can download list of entity types (\nDataset({\n features: ['type'],\n num_rows: 29\n})\n) \nUsing \n'load_dataset('MalakhovIlya/RuNNE', 'data')' or 'load_dataset('MalakhovIlya/RuNNE')'\nyou can download the data itself (DatasetDict)\n\nDataset consists of 3 splits: \"train\", \"test\" and \"dev\". Each of them contains text document. \"Train\" and \"test\" splits also contain annotated entities, \"dev\" doesn't.\nEach entity is represented by a string of the following format: \"\\<start> \\<stop> \\<type>\", where \\<start> is a position of the first symbol of entity in text, \\<stop> is the last symbol position in text and \\<type> is a one of the aforementioned list of types.\n\nP.S.\nOriginal NEREL dataset also contains relations, events and linked entities, but they were not added here yet ¯\\\\\\_(ツ)_/¯\n\n\n@article{Artemova2022runne, \n title={{RuNNE-2022 Shared Task: Recognizing Nested Named Entities}},\n author={Artemova, Ekaterina and Zmeev, Maksim and Loukachevitch, Natalia and Rozhkov, Igor and Batura, Tatiana and Braslavski, Pavel and Ivanov, Vladimir and Tutubalina, Elena},\n journal={Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference \"Dialog\"},\n year={2022}\n}" ]

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

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|fever", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for fever_gold_evidence", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nDataset for training classification-only fact checking with claims from the FEVER dataset.\nThis dataset is used in the paper \"Generating Label Cohesive and Well-Formed Adversarial Claims\", EMNLP 2020\n\nThe evidence is the gold evidence from the FEVER dataset for *REFUTE* and *SUPPORT* claims.\nFor *NEI* claims, we extract evidence sentences with the system in \"Christopher Malon. 2018. Team Papelo: Transformer Networks at FEVER. In Proceedings of the\nFirst Workshop on Fact Extraction and VERification (FEVER), pages 109\u0013113.\"\nMore details can be found in URL", "### 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" ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-machine-generated #annotations_creators-expert-generated #language_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|fever #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "# Dataset Card for fever_gold_evidence", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nDataset for training classification-only fact checking with claims from the FEVER dataset.\nThis dataset is used in the paper \"Generating Label Cohesive and Well-Formed Adversarial Claims\", EMNLP 2020\n\nThe evidence is the gold evidence from the FEVER dataset for *REFUTE* and *SUPPORT* claims.\nFor *NEI* claims, we extract evidence sentences with the system in \"Christopher Malon. 2018. Team Papelo: Transformer Networks at FEVER. In Proceedings of the\nFirst Workshop on Fact Extraction and VERification (FEVER), pages 109\u0013113.\"\nMore details can be found in URL", "### 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" ]

[ "region:us" ]

[]

[]

[ "# Wordnet definitions for English\n\nDataset by Princeton WordNet and the Open English WordNet team\nURL\n\nThis dataset contains every entry in wordnet that has a definition and an example.\nBe aware that the word \"null\" can be misinterpreted as a null value if loading it in with e.g. pandas" ]

[ "TAGS\n#region-us \n", "# Wordnet definitions for English\n\nDataset by Princeton WordNet and the Open English WordNet team\nURL\n\nThis dataset contains every entry in wordnet that has a definition and an example.\nBe aware that the word \"null\" can be misinterpreted as a null value if loading it in with e.g. pandas" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:translation", "language:es", "language:qu", "region:us" ]

[]

[ "es", "qu" ]

[ "# Spanish to Quechua", "## Table of Contents\n- Dataset Description\n- Dataset Structure\n- Dataset Creation\n- Considerations for Using the Data\n- team members", "## Dataset Description\n\nThis dataset is a recopilation of webs and others datasets that shows in dataset creation section. This contains translations from spanish (es) to Qechua of Ayacucho (qu).", "## Dataset Structure", "### Data Fields\n- es: The sentence in Spanish.\n- qu: The sentence in Quechua of Ayacucho.", "### Data Splits\n- train: To train the model (102 747 sentences).\n- Validation: To validate the model during training (12 844 sentences).\n- test: To evaluate the model when the training is finished (12 843 sentences).", "## Dataset Creation", "### Source Data\nThis dataset has generated from:\n- \"Mundo Quechua\" by \"Ivan Acuña\" - available here\n- \"Kuyakuykim (Te quiero): Apps con las que podrías aprender quechua\" by \"El comercio\" - available here\n- \"Piropos y frases de amor en quechua\" by \"Soy Quechua\" - available here\n- \"Corazón en quechua\" by \"Soy Quechua\" - available here\n- \"Oraciones en Español traducidas a Quechua\" by \"Tatoeba\" - available here\n- \"AmericasNLP 2021 Shared Task on Open Machine Translation\" by \"americasnlp2021\" - available here", "### Data cleaning\n- The dataset was manually cleaned during compilation, as some words of one language were related to several words of the other language.", "## Considerations for Using the Data\n\nThis is a first version of the dataset, we expected improve it over time and especially to neutralize the biblical themes.", "## Team members\n- Sara Benel\n- Jose Vílchez" ]

[ "TAGS\n#task_categories-translation #language-Spanish #language-Quechua #region-us \n", "# Spanish to Quechua", "## Table of Contents\n- Dataset Description\n- Dataset Structure\n- Dataset Creation\n- Considerations for Using the Data\n- team members", "## Dataset Description\n\nThis dataset is a recopilation of webs and others datasets that shows in dataset creation section. This contains translations from spanish (es) to Qechua of Ayacucho (qu).", "## Dataset Structure", "### Data Fields\n- es: The sentence in Spanish.\n- qu: The sentence in Quechua of Ayacucho.", "### Data Splits\n- train: To train the model (102 747 sentences).\n- Validation: To validate the model during training (12 844 sentences).\n- test: To evaluate the model when the training is finished (12 843 sentences).", "## Dataset Creation", "### Source Data\nThis dataset has generated from:\n- \"Mundo Quechua\" by \"Ivan Acuña\" - available here\n- \"Kuyakuykim (Te quiero): Apps con las que podrías aprender quechua\" by \"El comercio\" - available here\n- \"Piropos y frases de amor en quechua\" by \"Soy Quechua\" - available here\n- \"Corazón en quechua\" by \"Soy Quechua\" - available here\n- \"Oraciones en Español traducidas a Quechua\" by \"Tatoeba\" - available here\n- \"AmericasNLP 2021 Shared Task on Open Machine Translation\" by \"americasnlp2021\" - available here", "### Data cleaning\n- The dataset was manually cleaned during compilation, as some words of one language were related to several words of the other language.", "## Considerations for Using the Data\n\nThis is a first version of the dataset, we expected improve it over time and especially to neutralize the biblical themes.", "## Team members\n- Sara Benel\n- Jose Vílchez" ]

[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "architecture", "architext", "arxiv:2303.07519", "region:us" ]

[ "2303.07519" ]

[ "en" ]

[ "# Dataset Card for Architext", "## Dataset Description\nThis is the raw training data used to train the Architext models referenced in \"Architext: Language-Driven Generative Architecture Design\" . \n\n- Homepage: URL\n- Paper: URL\n- Point of Contact: Theodoros Galanos (URL", "## Dataset Creation\nThe data were synthetically generated by a parametric design script in Grasshopper 3D, a virtual algorithmic environment in the design software Rhinoceros 3D.", "## Considerations for Using the Data\nThe data describe once instance of architectural design, specifically layout generation for residential appartments. Even in that case, the data is limited in the possible shapes they can represent, size, and typologies. Additionally, the annotations used as language prompts to generate a design are restricted to automatically generated annotations based on layout characteristics (adjacency, typology, number of spaces).", "### Licensing Information\nThe dataset is licensed under the Apache 2.0 license.\n\n\n\nIf you use the dataset please cite:" ]

[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #architecture #architext #arxiv-2303.07519 #region-us \n", "# Dataset Card for Architext", "## Dataset Description\nThis is the raw training data used to train the Architext models referenced in \"Architext: Language-Driven Generative Architecture Design\" . \n\n- Homepage: URL\n- Paper: URL\n- Point of Contact: Theodoros Galanos (URL", "## Dataset Creation\nThe data were synthetically generated by a parametric design script in Grasshopper 3D, a virtual algorithmic environment in the design software Rhinoceros 3D.", "## Considerations for Using the Data\nThe data describe once instance of architectural design, specifically layout generation for residential appartments. Even in that case, the data is limited in the possible shapes they can represent, size, and typologies. Additionally, the annotations used as language prompts to generate a design are restricted to automatically generated annotations based on layout characteristics (adjacency, typology, number of spaces).", "### Licensing Information\nThe dataset is licensed under the Apache 2.0 license.\n\n\n\nIf you use the dataset please cite:" ]

[ "region:us" ]

[]

[]

[ "# RockingFace\n\nA distribution of Amp-Space Dataset of x/y pairs audio pairs of input signals and recorded outputs after being processed by an audio effect (amplifier, stompbox, studio tools, etc.)" ]

[ "TAGS\n#region-us \n", "# RockingFace\n\nA distribution of Amp-Space Dataset of x/y pairs audio pairs of input signals and recorded outputs after being processed by an audio effect (amplifier, stompbox, studio tools, etc.)" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "## Description\n\nAn adaptation of eHealth-KD Challenge 2020 dataset, filtered only for the task of NER. Some adaptation of the original dataset have been made:\n\n- BIO annotations\n- Errors fixing\n- Overlapped entities has been processed as an unique entity", "## Dataset loading\n\ndatasets = load_dataset('json', data_files={'train': ['@YOUR_PATH@/training_anns_bio.json'], 'testing': ['@YOUR_PATH@/testing_anns_bio.json'], 'validation': ['@YOUR_PATH@/development_anns_bio.json']})" ]

[ "TAGS\n#license-afl-3.0 #region-us \n", "## Description\n\nAn adaptation of eHealth-KD Challenge 2020 dataset, filtered only for the task of NER. Some adaptation of the original dataset have been made:\n\n- BIO annotations\n- Errors fixing\n- Overlapped entities has been processed as an unique entity", "## Dataset loading\n\ndatasets = load_dataset('json', data_files={'train': ['@YOUR_PATH@/training_anns_bio.json'], 'testing': ['@YOUR_PATH@/testing_anns_bio.json'], 'validation': ['@YOUR_PATH@/development_anns_bio.json']})" ]

[ "task_categories:text-classification", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:es", "license:cc-by-4.0", "readability", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for [readability-es-caes]", "## Dataset Description", "### Dataset Summary\n\nThis dataset is a compilation of short articles from websites dedicated to learn Spanish as a second language. These articles have been compiled from the following sources:\n- CAES corpus (Martínez et al., 2019): the \"Corpus de Aprendices del Español\" is a collection of texts produced by Spanish L2 learners from Spanish learning centers and universities. These text are produced by students of all levels (A1 to C1), with different backgrounds (11 native languages) and levels of experience.", "### Languages\n\nSpanish", "## Dataset Structure\n\nTexts are tokenized to create a paragraph-based dataset", "### Data Fields\n\nThe dataset is formatted as a json lines and includes the following fields:\n- Category: when available, this includes the level of this text according to the Common European Framework of Reference for Languages (CEFR).\n- Level: standardized readability level: simple or complex.\n- Level-3: standardized readability level: basic, intermediate or advanced.\n- Text: original text formatted into sentences.", "## Additional Information", "### Licensing Information\n\nURL\n\n\n\nPlease cite this page to give credit to the authors :)", "### Team\n- Laura Vásquez-Rodríguez\n- Pedro Cuenca\n- Sergio Morales\n- Fernando Alva-Manchego" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Spanish #license-cc-by-4.0 #readability #region-us \n", "# Dataset Card for [readability-es-caes]", "## Dataset Description", "### Dataset Summary\n\nThis dataset is a compilation of short articles from websites dedicated to learn Spanish as a second language. These articles have been compiled from the following sources:\n- CAES corpus (Martínez et al., 2019): the \"Corpus de Aprendices del Español\" is a collection of texts produced by Spanish L2 learners from Spanish learning centers and universities. These text are produced by students of all levels (A1 to C1), with different backgrounds (11 native languages) and levels of experience.", "### Languages\n\nSpanish", "## Dataset Structure\n\nTexts are tokenized to create a paragraph-based dataset", "### Data Fields\n\nThe dataset is formatted as a json lines and includes the following fields:\n- Category: when available, this includes the level of this text according to the Common European Framework of Reference for Languages (CEFR).\n- Level: standardized readability level: simple or complex.\n- Level-3: standardized readability level: basic, intermediate or advanced.\n- Text: original text formatted into sentences.", "## Additional Information", "### Licensing Information\n\nURL\n\n\n\nPlease cite this page to give credit to the authors :)", "### Team\n- Laura Vásquez-Rodríguez\n- Pedro Cuenca\n- Sergio Morales\n- Fernando Alva-Manchego" ]

[ "task_categories:text-classification", "task_ids:language-modeling", "annotations_creators:MajorIsaiah", "annotations_creators:Ximyer", "annotations_creators:clavel", "annotations_creators:inoid", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:n=200", "source_datasets:original", "language:es", "license:apache-2.0", "region:us" ]

[]

[ "es" ]

[ "### Dataset Summary\n\n\nEl dataset unam\\_tesis cuenta con 1000 tesis de 5 carreras de la Universidad Nacional Autónoma de México (UNAM), 200 por carrera. Se pretende seguir incrementando este dataset con las demás carreras y más tesis.", "### Supported Tasks and Leaderboards\n\n\ntext-classification", "### Languages\n\n\nEspañol (es)\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nLas instancias del dataset son de la siguiente forma:\n\n\n\n```\nEl objetivo de esta tesis es elaborar un estudio de las condiciones asociadas al aprendizaje desde casa a nivel preescolar y primaria en el municipio de Nicolás Romero a partir de la cancelación de clases presenciales ante la contingencia sanitaria del Covid-19 y el entorno familiar del estudiante. En México, la Encuesta para la Medición del Impacto COVID-19 en la Educación (ECOVID-ED) 2020, es un proyecto que propone el INEGI y realiza de manera especial para conocer las necesidades de la población estudiantil de 3 a 29 años de edad, saber qué está sucediendo con su entorno inmediato, las condiciones en las que desarrollan sus actividades académicas y el apoyo que realizan padres, tutores o cuidadores principales de las personas en edad formativa. La ECOVID-ED 2020 se llevó a cabo de manera especial con el objetivo de conocer el impacto de la cancelación provisional de clases presenciales en las instituciones educativas del país para evitar los contagios por la pandemia COVID-19 en la experiencia educativa de niños, niñas, adolescentes y jóvenes de 3 a 29 años, tanto en el ciclo escolar 2019-2020, como en ciclo 2020-2021. En este ámbito de investigación, el Instituto de Investigaciones sobre la Universidad y la Educación (IISUE) de la Universidad Nacional Autónoma de México público en 2020 la obra “Educación y Pandemia: Una visión académica” que se integran 34 trabajos que abordan la muy amplia temática de la educación y la universidad con reflexiones y ejercicios analíticos estrechamente relacionadas en el marco coyuntural de la pandemia COVID-19. La tesis se presenta en tres capítulos: En el capítulo uno se realizará una descripción del aprendizaje de los estudiantes a nivel preescolar y primaria del municipio de NicolásRomero, Estado de México, que por motivo de la contingencia sanitaria contra el Covid-19 tuvieron que concluir su ciclo académico 2019-2020 y el actual ciclo 2020-2021 en su casa debido a la cancelación provisional de clases presenciales y bajo la tutoría de padres, familiar o ser cercano; así como las horas destinadas al estudio y las herramientas tecnológicas como teléfonos inteligentes, computadoras portátiles, computadoras de escritorio, televisión digital y tableta. En el capítulo dos, se presentarán las herramientas necesarias para la captación de la información mediante técnicas de investigación social, a través de las cuales se mencionará, la descripción, contexto y propuestas del mismo, considerando los diferentes tipos de cuestionarios, sus componentes y diseño, teniendo así de manera específica la diversidad de ellos, que llevarán como finalidad realizar el cuestionario en línea para la presente investigación. Posteriormente, se podrá destacar las fases del diseño de la investigación, que se realizarán mediante una prueba piloto tomando como muestra a distintos expertos en el tema. De esta manera se obtendrá la información relevante para estudiarla a profundidad. En el capítulo tres, se realizará el análisis apoyado de las herramientas estadísticas, las cuales ofrecen explorar la muestra de una manera relevante, se aplicará el método inferencial para expresar la información y predecir las condiciones asociadas al autoaprendizaje, la habilidad pedagógica de padres o tutores, la convivencia familiar, la carga académica y actividades escolares y condicionamiento tecnológico,con la finalidad de inferir en la población. Asimismo, se realizarán pruebas de hipótesis, tablas de contingencia y matriz de correlación. Por consiguiente, los resultados obtenidos de las estadísticas se interpretarán para describir las condiciones asociadas y como impactan en la enseñanza de preescolar y primaria desde casa.|María de los Ángeles|Blancas Regalado|Análisis de las condiciones del aprendizaje desde casa en los alumnos de preescolar y primaria del municipio de Nicolás Romero |2022|Actuaría\n\n```", "### Data Fields\n\n\nEl dataset está compuesto por los siguientes campos: \"texto|titulo|carrera\". \n\ntexto: Se refiere al texto de la introducción de la tesis. \n\ntitulo: Se refiere al título de la tesis. \n\ncarrera: Se refiere al nombre de la carrera a la que pertenece la tesis.", "### Data Splits\n\n\nEl dataset tiene 2 particiones: entrenamiento (train) y prueba (test).\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nLa creación de este dataset ha sido motivada por la participación en el Hackathon 2022 de PLN en Español organizado por Somos NLP, con el objetivo de democratizar el NLP en español y promover su aplicación a buenas causas y, debido a que no existe un dataset de tesis en español.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nEl dataset original (dataset\\_tesis) fue creado a partir de un proceso de scraping donde se extrajeron tesis de la Universidad Nacional Autónoma de México en el siguiente link: URL\n\n\nSe optó por realizar un scraper para conseguir la información. Se decidió usar la base de datos TESIUNAM, la cual es un catálogo en donde se pueden visualizar las tesis de los sustentantes que obtuvieron un grado en la UNAM, así como de las tesis de licenciatura de escuelas incorporadas a ella.\n\n\nPara ello, en primer lugar se consultó la Oferta Académica (URL de la Universidad, sitio de donde se extrajo cada una de las 131 licenciaturas en forma de lista. Después, se analizó cada uno de los casos presente en la base de datos, debido a que existen carreras con más de 10 tesis, otras con menos de 10, o con solo una o ninguna tesis disponible. Se usó Selenium para la interacción con un navegador Web (Edge) y está actualmente configurado para obtener las primeras 20 tesis, o menos, por carrera.\n\n\nEste scraper obtiene de esta base de datos:\n\n\n* Nombres del Autor\n* Apellidos del Autor\n* Título de la Tesis\n* Año de la Tesis\n* Carrera de la Tesis\n\n\nA la vez, este scraper descarga cada una de las tesis en la carpeta Downloads del equipo local. En el csv formado por el scraper se añadió el \"Resumen/Introduccion/Conclusion de la tesis\", dependiendo cual primero estuviera disponible, ya que la complejidad recae en la diferencia de la estructura y formato de cada una de las tesis.", "#### Who are the source language producers?\n\n\nLos datos son creados por humanos de forma manual, en este caso por estudiantes de la UNAM y revisados por sus supervisores.", "### Annotations\n\n\nEl dataset fue procesado para eliminar información innecesaria para los clasificadores. El dataset original cuenta con los siguientes campos: \"texto|autor\\_nombre|autor\\_apellido|titulo|año|carrera\".", "#### Annotation process\n\n\nSe extrajeron primeramente 200 tesis de 5 carreras de esta universidad: Actuaría, Derecho, Economía, Psicología y Química Farmacéutico Biológica. De estas se extrajo: introducción, nombre del autor, apellidos de autor, título de la tesis y la carrera. Los datos fueron revisados y limpiados por los autores.\n\n\nLuego, el dataset fue procesado con las siguientes tareas de Procesamiento de Lenguaje Natural (dataset\\_tesis\\_procesado):\n\n\n* convertir a minúsculas\n* tokenización\n* eliminar palabras que no son alfanuméricas\n* eliminar palabras vacías\n* stemming: eliminar plurales", "#### Who are the annotators?\n\n\nLas anotaciones fueron hechas por humanos, en este caso los autores del dataset, usando código de máquina en el lenguaje Python.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nEl presente conjunto de datos favorecerá la búsqueda e investigación relacionada con tesis en español, a partir de su categorización automática por un modelo entrenado con este dataset. Esta tarea favorece el cumplimiento del objetivo 4 de Desarrollo Sostenible de la ONU: Educación y Calidad (URL", "### Discussion of Biases\n\n\nEl texto tiene algunos errores en la codificación por lo que algunos caracteres como las tildes no se muestran correctamente. Las palabras con estos caracteres son eliminadas en el procesamiento hasta que se corrija el problema.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMiembros del equipo (user de Hugging Face):\n\n\n\n```\nIsacc Isahias López López \nYisel Clavel Quintero\nDionis López\nXimena Yeraldin López López\n\n```", "### Licensing Information\n\n\nLa versión 1.0.0 del dataset unam\\_tesis está liberada bajo la licencia <a href='URL Apache-2.0 License .\n\n\n\"Esta base de datos se ha creado en el marco del Hackathon 2022 de PLN en Español organizado por Somos NLP patrocinado por Platzi, Paperspace y Hugging Face: URL\n\n\nPara citar este dataset, por favor, use el siguiente formato de cita:\n\n\n\n```\n@inproceedings{Hackathon 2022 de PLN en Español, \n title={UNAM's Theses with BETO fine-tuning classify}, \n author={López López, Isaac Isaías; Clavel Quintero, Yisel; López Ramos, Dionis & López López, Ximena Yeraldin}, \n booktitle={Hackathon 2022 de PLN en Español}, \n year={2022}\n}\n\n```", "### Contributions\n\n\nGracias a @yiselclavel y @IsaacIsaias por agregar este dataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-language-modeling #annotations_creators-MajorIsaiah #annotations_creators-Ximyer #annotations_creators-clavel #annotations_creators-inoid #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n=200 #source_datasets-original #language-Spanish #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nEl dataset unam\\_tesis cuenta con 1000 tesis de 5 carreras de la Universidad Nacional Autónoma de México (UNAM), 200 por carrera. Se pretende seguir incrementando este dataset con las demás carreras y más tesis.", "### Supported Tasks and Leaderboards\n\n\ntext-classification", "### Languages\n\n\nEspañol (es)\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nLas instancias del dataset son de la siguiente forma:\n\n\n\n```\nEl objetivo de esta tesis es elaborar un estudio de las condiciones asociadas al aprendizaje desde casa a nivel preescolar y primaria en el municipio de Nicolás Romero a partir de la cancelación de clases presenciales ante la contingencia sanitaria del Covid-19 y el entorno familiar del estudiante. En México, la Encuesta para la Medición del Impacto COVID-19 en la Educación (ECOVID-ED) 2020, es un proyecto que propone el INEGI y realiza de manera especial para conocer las necesidades de la población estudiantil de 3 a 29 años de edad, saber qué está sucediendo con su entorno inmediato, las condiciones en las que desarrollan sus actividades académicas y el apoyo que realizan padres, tutores o cuidadores principales de las personas en edad formativa. La ECOVID-ED 2020 se llevó a cabo de manera especial con el objetivo de conocer el impacto de la cancelación provisional de clases presenciales en las instituciones educativas del país para evitar los contagios por la pandemia COVID-19 en la experiencia educativa de niños, niñas, adolescentes y jóvenes de 3 a 29 años, tanto en el ciclo escolar 2019-2020, como en ciclo 2020-2021. En este ámbito de investigación, el Instituto de Investigaciones sobre la Universidad y la Educación (IISUE) de la Universidad Nacional Autónoma de México público en 2020 la obra “Educación y Pandemia: Una visión académica” que se integran 34 trabajos que abordan la muy amplia temática de la educación y la universidad con reflexiones y ejercicios analíticos estrechamente relacionadas en el marco coyuntural de la pandemia COVID-19. La tesis se presenta en tres capítulos: En el capítulo uno se realizará una descripción del aprendizaje de los estudiantes a nivel preescolar y primaria del municipio de NicolásRomero, Estado de México, que por motivo de la contingencia sanitaria contra el Covid-19 tuvieron que concluir su ciclo académico 2019-2020 y el actual ciclo 2020-2021 en su casa debido a la cancelación provisional de clases presenciales y bajo la tutoría de padres, familiar o ser cercano; así como las horas destinadas al estudio y las herramientas tecnológicas como teléfonos inteligentes, computadoras portátiles, computadoras de escritorio, televisión digital y tableta. En el capítulo dos, se presentarán las herramientas necesarias para la captación de la información mediante técnicas de investigación social, a través de las cuales se mencionará, la descripción, contexto y propuestas del mismo, considerando los diferentes tipos de cuestionarios, sus componentes y diseño, teniendo así de manera específica la diversidad de ellos, que llevarán como finalidad realizar el cuestionario en línea para la presente investigación. Posteriormente, se podrá destacar las fases del diseño de la investigación, que se realizarán mediante una prueba piloto tomando como muestra a distintos expertos en el tema. De esta manera se obtendrá la información relevante para estudiarla a profundidad. En el capítulo tres, se realizará el análisis apoyado de las herramientas estadísticas, las cuales ofrecen explorar la muestra de una manera relevante, se aplicará el método inferencial para expresar la información y predecir las condiciones asociadas al autoaprendizaje, la habilidad pedagógica de padres o tutores, la convivencia familiar, la carga académica y actividades escolares y condicionamiento tecnológico,con la finalidad de inferir en la población. Asimismo, se realizarán pruebas de hipótesis, tablas de contingencia y matriz de correlación. Por consiguiente, los resultados obtenidos de las estadísticas se interpretarán para describir las condiciones asociadas y como impactan en la enseñanza de preescolar y primaria desde casa.|María de los Ángeles|Blancas Regalado|Análisis de las condiciones del aprendizaje desde casa en los alumnos de preescolar y primaria del municipio de Nicolás Romero |2022|Actuaría\n\n```", "### Data Fields\n\n\nEl dataset está compuesto por los siguientes campos: \"texto|titulo|carrera\". \n\ntexto: Se refiere al texto de la introducción de la tesis. \n\ntitulo: Se refiere al título de la tesis. \n\ncarrera: Se refiere al nombre de la carrera a la que pertenece la tesis.", "### Data Splits\n\n\nEl dataset tiene 2 particiones: entrenamiento (train) y prueba (test).\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nLa creación de este dataset ha sido motivada por la participación en el Hackathon 2022 de PLN en Español organizado por Somos NLP, con el objetivo de democratizar el NLP en español y promover su aplicación a buenas causas y, debido a que no existe un dataset de tesis en español.", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nEl dataset original (dataset\\_tesis) fue creado a partir de un proceso de scraping donde se extrajeron tesis de la Universidad Nacional Autónoma de México en el siguiente link: URL\n\n\nSe optó por realizar un scraper para conseguir la información. Se decidió usar la base de datos TESIUNAM, la cual es un catálogo en donde se pueden visualizar las tesis de los sustentantes que obtuvieron un grado en la UNAM, así como de las tesis de licenciatura de escuelas incorporadas a ella.\n\n\nPara ello, en primer lugar se consultó la Oferta Académica (URL de la Universidad, sitio de donde se extrajo cada una de las 131 licenciaturas en forma de lista. Después, se analizó cada uno de los casos presente en la base de datos, debido a que existen carreras con más de 10 tesis, otras con menos de 10, o con solo una o ninguna tesis disponible. Se usó Selenium para la interacción con un navegador Web (Edge) y está actualmente configurado para obtener las primeras 20 tesis, o menos, por carrera.\n\n\nEste scraper obtiene de esta base de datos:\n\n\n* Nombres del Autor\n* Apellidos del Autor\n* Título de la Tesis\n* Año de la Tesis\n* Carrera de la Tesis\n\n\nA la vez, este scraper descarga cada una de las tesis en la carpeta Downloads del equipo local. En el csv formado por el scraper se añadió el \"Resumen/Introduccion/Conclusion de la tesis\", dependiendo cual primero estuviera disponible, ya que la complejidad recae en la diferencia de la estructura y formato de cada una de las tesis.", "#### Who are the source language producers?\n\n\nLos datos son creados por humanos de forma manual, en este caso por estudiantes de la UNAM y revisados por sus supervisores.", "### Annotations\n\n\nEl dataset fue procesado para eliminar información innecesaria para los clasificadores. El dataset original cuenta con los siguientes campos: \"texto|autor\\_nombre|autor\\_apellido|titulo|año|carrera\".", "#### Annotation process\n\n\nSe extrajeron primeramente 200 tesis de 5 carreras de esta universidad: Actuaría, Derecho, Economía, Psicología y Química Farmacéutico Biológica. De estas se extrajo: introducción, nombre del autor, apellidos de autor, título de la tesis y la carrera. Los datos fueron revisados y limpiados por los autores.\n\n\nLuego, el dataset fue procesado con las siguientes tareas de Procesamiento de Lenguaje Natural (dataset\\_tesis\\_procesado):\n\n\n* convertir a minúsculas\n* tokenización\n* eliminar palabras que no son alfanuméricas\n* eliminar palabras vacías\n* stemming: eliminar plurales", "#### Who are the annotators?\n\n\nLas anotaciones fueron hechas por humanos, en este caso los autores del dataset, usando código de máquina en el lenguaje Python.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nEl presente conjunto de datos favorecerá la búsqueda e investigación relacionada con tesis en español, a partir de su categorización automática por un modelo entrenado con este dataset. Esta tarea favorece el cumplimiento del objetivo 4 de Desarrollo Sostenible de la ONU: Educación y Calidad (URL", "### Discussion of Biases\n\n\nEl texto tiene algunos errores en la codificación por lo que algunos caracteres como las tildes no se muestran correctamente. Las palabras con estos caracteres son eliminadas en el procesamiento hasta que se corrija el problema.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMiembros del equipo (user de Hugging Face):\n\n\n\n```\nIsacc Isahias López López \nYisel Clavel Quintero\nDionis López\nXimena Yeraldin López López\n\n```", "### Licensing Information\n\n\nLa versión 1.0.0 del dataset unam\\_tesis está liberada bajo la licencia <a href='URL Apache-2.0 License .\n\n\n\"Esta base de datos se ha creado en el marco del Hackathon 2022 de PLN en Español organizado por Somos NLP patrocinado por Platzi, Paperspace y Hugging Face: URL\n\n\nPara citar este dataset, por favor, use el siguiente formato de cita:\n\n\n\n```\n@inproceedings{Hackathon 2022 de PLN en Español, \n title={UNAM's Theses with BETO fine-tuning classify}, \n author={López López, Isaac Isaías; Clavel Quintero, Yisel; López Ramos, Dionis & López López, Ximena Yeraldin}, \n booktitle={Hackathon 2022 de PLN en Español}, \n year={2022}\n}\n\n```", "### Contributions\n\n\nGracias a @yiselclavel y @IsaacIsaias por agregar este dataset." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

[ "multilinguality:monolingual", "multilinguality:translation", "language:fr", "license:apache-2.0", "region:us" ]

[]

[ "fr" ]

[]

[ "TAGS\n#multilinguality-monolingual #multilinguality-translation #language-French #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "arxiv:2010.11910", "region:us" ]

[ "2010.11910" ]

[]

[ "# Neural Audio Fingerprint Dataset\n(c) 2021 by Sungkyun Chang\nURL\n\nThis dataset includes all music sources, background noise and impulse-reponses\n (IR) samples that have been used in the work [\"Neural Audio Fingerprint for\n High-specific Audio Retrieval based on Contrastive Learning\"]\n (URL", "### Format:\n16-bit PCM Mono WAV, Sampling rate 8000 Hz", "### Description:", "### Data source:\n • Bacgkound noise from Audioset was retrieved using key words ['subway',\n 'metro', 'underground', 'not music']\n • URL pub-noise was recorded at the Strabucks branches in Seoul by\n Jeongsoo Park.\n • Random noise was generated by Donmoon Lee.\n • Room/space IR data was collected from Aachen IR and OpenAIR 1.4 dataset.\n • Portions of MIC IRs were from Vintage MIC (URL and\n pre-processed with room/space IR data.\n • Portions of MIC IRs were recorded by Donmoon Lee, Jeonsu Park and Hyungui Lim\n using mobile devices in the anechoic chamber at Seoul National University.\n • All music sources were taken from the Free Music Archive (FMA) data set, \n and converted from 'stereo 44Khz' to 'mono 8Khz'.\n • train-10k-30s contains all 8K songs from FMA_small. The remaining 2K songs\n were from FMA_medium.\n • val- and test- data were isolated from train-, and taken from FMA_medium.\n • test-query-db-500-30s/query consists of the pre-synthesized 2,000 queries\n of 10s each (SNR 0~10dB) and their corresponding 500 songs of 30s each.\n • Additionally, query_fixed_SNR directory contains synthesized queries with\n fixed SNR of 0dB and -3dB.\n • dummy-db-100k was taken from FMA_full, and duplicates with other sets were\n removed.", "### License:\nThis dataset is distributed under the CC BY-SA 2.0 license separately from the\n github source code, and licenses for composites from other datasets are\n attached to each sub-directory." ]

[ "TAGS\n#arxiv-2010.11910 #region-us \n", "# Neural Audio Fingerprint Dataset\n(c) 2021 by Sungkyun Chang\nURL\n\nThis dataset includes all music sources, background noise and impulse-reponses\n (IR) samples that have been used in the work [\"Neural Audio Fingerprint for\n High-specific Audio Retrieval based on Contrastive Learning\"]\n (URL", "### Format:\n16-bit PCM Mono WAV, Sampling rate 8000 Hz", "### Description:", "### Data source:\n • Bacgkound noise from Audioset was retrieved using key words ['subway',\n 'metro', 'underground', 'not music']\n • URL pub-noise was recorded at the Strabucks branches in Seoul by\n Jeongsoo Park.\n • Random noise was generated by Donmoon Lee.\n • Room/space IR data was collected from Aachen IR and OpenAIR 1.4 dataset.\n • Portions of MIC IRs were from Vintage MIC (URL and\n pre-processed with room/space IR data.\n • Portions of MIC IRs were recorded by Donmoon Lee, Jeonsu Park and Hyungui Lim\n using mobile devices in the anechoic chamber at Seoul National University.\n • All music sources were taken from the Free Music Archive (FMA) data set, \n and converted from 'stereo 44Khz' to 'mono 8Khz'.\n • train-10k-30s contains all 8K songs from FMA_small. The remaining 2K songs\n were from FMA_medium.\n • val- and test- data were isolated from train-, and taken from FMA_medium.\n • test-query-db-500-30s/query consists of the pre-synthesized 2,000 queries\n of 10s each (SNR 0~10dB) and their corresponding 500 songs of 30s each.\n • Additionally, query_fixed_SNR directory contains synthesized queries with\n fixed SNR of 0dB and -3dB.\n • dummy-db-100k was taken from FMA_full, and duplicates with other sets were\n removed.", "### License:\nThis dataset is distributed under the CC BY-SA 2.0 license separately from the\n github source code, and licenses for composites from other datasets are\n attached to each sub-directory." ]

[ "task_categories:text-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:es", "license:cc-by-4.0", "readability", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for [readability-es-sentences]", "## Dataset Description\n\nCompilation of short Spanish articles for readability assessment.", "### Dataset Summary\n\nThis dataset is a compilation of short articles from websites dedicated to learn Spanish as a second language. These articles have been compiled from the following sources: \n- Coh-Metrix-Esp corpus (Quispesaravia, et al., 2016): collection of 100 parallel texts with simple and complex variants in Spanish. These texts include children's and adult stories to fulfill each category.\n- kwiziq: a language learner assistant\n- URL: Spanish resources for students and teachers. We have downloaded the available content in their websites.", "### Languages\n\nSpanish", "## Dataset Structure\n\nThe dataset includes 1019 text entries between 80 and 8714 characters long. The vast majority (97%) are below 4,000 characters long.", "### Data Fields\n\nThe dataset is formatted as a json lines and includes the following fields:\n- Category: when available, this includes the level of this text according to the Common European Framework of Reference for Languages (CEFR).\n- Level: standardized readability level: complex or simple. \n- Level-3: standardized readability level: basic, intermediate or advanced\n- Text: original text formatted into sentences.\n\nNot all the entries contain usable values for 'category', 'level' and 'level-3', but all of them should contain at least one of 'level', 'level-3'. When the corresponding information could not be derived, we use the special '\"N/A\"' value to indicate so.", "## Additional Information", "### Licensing Information\n\nURL\n\n\n\nPlease cite this page to give credit to the authors :)", "### Team\n- Laura Vásquez-Rodríguez\n- Pedro Cuenca\n- Sergio Morales\n- Fernando Alva-Manchego" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Spanish #license-cc-by-4.0 #readability #region-us \n", "# Dataset Card for [readability-es-sentences]", "## Dataset Description\n\nCompilation of short Spanish articles for readability assessment.", "### Dataset Summary\n\nThis dataset is a compilation of short articles from websites dedicated to learn Spanish as a second language. These articles have been compiled from the following sources: \n- Coh-Metrix-Esp corpus (Quispesaravia, et al., 2016): collection of 100 parallel texts with simple and complex variants in Spanish. These texts include children's and adult stories to fulfill each category.\n- kwiziq: a language learner assistant\n- URL: Spanish resources for students and teachers. We have downloaded the available content in their websites.", "### Languages\n\nSpanish", "## Dataset Structure\n\nThe dataset includes 1019 text entries between 80 and 8714 characters long. The vast majority (97%) are below 4,000 characters long.", "### Data Fields\n\nThe dataset is formatted as a json lines and includes the following fields:\n- Category: when available, this includes the level of this text according to the Common European Framework of Reference for Languages (CEFR).\n- Level: standardized readability level: complex or simple. \n- Level-3: standardized readability level: basic, intermediate or advanced\n- Text: original text formatted into sentences.\n\nNot all the entries contain usable values for 'category', 'level' and 'level-3', but all of them should contain at least one of 'level', 'level-3'. When the corresponding information could not be derived, we use the special '\"N/A\"' value to indicate so.", "## Additional Information", "### Licensing Information\n\nURL\n\n\n\nPlease cite this page to give credit to the authors :)", "### Team\n- Laura Vásquez-Rodríguez\n- Pedro Cuenca\n- Sergio Morales\n- Fernando Alva-Manchego" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "en" ]

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

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

[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:ab", "license:cc0-1.0", "region:us" ]

[]

[ "ab" ]

[ "# Dataset Card for \"Abkhaz text\"", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n- Considerations for Using the Data\n - Other Known Limitations", "## Dataset Description\n- Point of Contact: Nart Tlisha\n- Size of the generated dataset: 176 MB", "### Dataset Summary\n\nThe Abkhaz language monolingual dataset is a collection of 1,470,480 sentences extracted from different sources. The dataset is available under the Creative Commons Universal Public Domain License. Part of it is also available as part of Common Voice, another part is from the Abkhaz National Corpus", "## Dataset Creation", "### Source Data\nHere is a link to the source of a large part of the data on github", "## Considerations for Using the Data", "### Other Known Limitations\nThe accuracy of the dataset is around 95% (gramatical, arthographical errors)" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-expert-generated #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-Abkhazian #license-cc0-1.0 #region-us \n", "# Dataset Card for \"Abkhaz text\"", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n- Considerations for Using the Data\n - Other Known Limitations", "## Dataset Description\n- Point of Contact: Nart Tlisha\n- Size of the generated dataset: 176 MB", "### Dataset Summary\n\nThe Abkhaz language monolingual dataset is a collection of 1,470,480 sentences extracted from different sources. The dataset is available under the Creative Commons Universal Public Domain License. Part of it is also available as part of Common Voice, another part is from the Abkhaz National Corpus", "## Dataset Creation", "### Source Data\nHere is a link to the source of a large part of the data on github", "## Considerations for Using the Data", "### Other Known Limitations\nThe accuracy of the dataset is around 95% (gramatical, arthographical errors)" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "# Dataset Description", "## Dataset Summary\n\nThis dataset was parsed from the Human-HIV Interaction dataset maintained by the NCBI. \nIt contains a >16,000 pairs of interactions between HIV and Human proteins.\nSequences of the interacting proteins were retrieved from the NCBI protein database and added to the dataset.\nThe raw data is available from the NBCI FTP site and the curation strategy is described in the NAR Research paper announcing the dataset.", "## Dataset Structure", "### Data Instances\n\nData Fields: hiv_protein_product, hiv_protein_name, interaction_type, human_protein_product, human_protein_name, reference_list, description, hiv_protein_sequence, human_protein_sequence\n\nData Splits: None", "## Dataset Creation\n\nCuration Rationale: This dataset was curated train models to recognize proteins that interact with HIV.\n\nInitial Data Collection and Normalization: Dataset was downloaded and curated on 4/4/2022 but the most recent update of the underlying NCBI database was 2016.", "## Considerations for Using the Data\n\nDiscussion of Biases: This dataset of protein interactions was manually curated by experts utilizing published scientific literature.\nThis inherently biases the collection to well-studied proteins and known interactions.\nThe dataset does not contain _negative_ interactions.", "## Additional Information: \n - Dataset Curators: Will Dampier \n - Citation Information: TBA" ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset Description", "## Dataset Summary\n\nThis dataset was parsed from the Human-HIV Interaction dataset maintained by the NCBI. \nIt contains a >16,000 pairs of interactions between HIV and Human proteins.\nSequences of the interacting proteins were retrieved from the NCBI protein database and added to the dataset.\nThe raw data is available from the NBCI FTP site and the curation strategy is described in the NAR Research paper announcing the dataset.", "## Dataset Structure", "### Data Instances\n\nData Fields: hiv_protein_product, hiv_protein_name, interaction_type, human_protein_product, human_protein_name, reference_list, description, hiv_protein_sequence, human_protein_sequence\n\nData Splits: None", "## Dataset Creation\n\nCuration Rationale: This dataset was curated train models to recognize proteins that interact with HIV.\n\nInitial Data Collection and Normalization: Dataset was downloaded and curated on 4/4/2022 but the most recent update of the underlying NCBI database was 2016.", "## Considerations for Using the Data\n\nDiscussion of Biases: This dataset of protein interactions was manually curated by experts utilizing published scientific literature.\nThis inherently biases the collection to well-studied proteins and known interactions.\nThe dataset does not contain _negative_ interactions.", "## Additional Information: \n - Dataset Curators: Will Dampier \n - Citation Information: TBA" ]

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

[]

[]

[]

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

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

[]

[ "en" ]

[ "# Dataset Card for the-reddit-dataset-dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: Website", "### Dataset Summary\n\nA meta dataset of Reddit's own /r/datasets community.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## Additional Information", "### Licensing Information\n\nCC-BY v4.0" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for the-reddit-dataset-dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: Website", "### Dataset Summary\n\nA meta dataset of Reddit's own /r/datasets community.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## Additional Information", "### Licensing Information\n\nCC-BY v4.0" ]

[ "license:mit", "arxiv:2202.03772", "region:us" ]

[ "2202.03772" ]

[]

[ "# Dataset Card for JetClass", "## Table of Contents\r\n- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)\r\n - Table of Contents\r\n - Dataset Description\r\n - Dataset Summary\r\n - Supported Tasks and Leaderboards\r\n - Languages\r\n - Dataset Structure\r\n - Data Instances\r\n - Data Fields\r\n - Data Splits\r\n - Dataset Creation\r\n - Curation Rationale\r\n - Source Data\r\n - Initial Data Collection and Normalization\r\n - Who are the source language producers?\r\n - Annotations\r\n - Annotation process\r\n - Who are the annotators?\r\n - Personal and Sensitive Information\r\n - Considerations for Using the Data\r\n - Social Impact of Dataset\r\n - Discussion of Biases\r\n - Other Known Limitations\r\n - Additional Information\r\n - Dataset Curators\r\n - Licensing Information\r\n - Citation Information\r\n - Contributions", "## Dataset Description\r\n\r\n- Homepage:\r\n- Repository: URL\r\n- Paper: URL\r\n- Leaderboard:\r\n- Point of Contact: Huilin Qu", "### Dataset Summary\r\n\r\nJetClass is a large and comprehensive dataset to advance deep learning for jet tagging. The dataset consists of 100 million jets for training, with 10 different types of jets. The jets in this dataset generally fall into two categories:\r\n\r\n* The background jets are initiated by light quarks or gluons (q/g) and are ubiquitously produced at the\r\nLHC.\r\n* The signal jets are those arising either from the top quarks (t), or from the W, Z or Higgs (H) bosons. For top quarks and Higgs bosons, we further consider their different decay modes as separate types, because the resulting jets have rather distinct characteristics and are often tagged individually.\r\n\r\nJets in this dataset are simulated with standard Monte Carlo event generators used by LHC experiments. The production and decay of the top quarks and the W, Z and Higgs bosons are generated with MADGRAPH5_aMC@NLO. We use PYTHIA to evolve the produced particles, i.e., performing parton showering and hadronization, and produce the final outgoing particles. To be close to realistic jets reconstructed at the ATLAS or CMS experiment, detector effects are simulated with DELPHES using the CMS detector configuration provided in DELPHES. In addition, the impact parameters of electrically charged particles are smeared to match the resolution of the CMS tracking detector . Jets are clustered from DELPHES E-Flow objects with the anti-kT algorithm using a distance\r\nparameter R = 0.8. Only jets with transverse momentum in 500–1000 GeV and pseudorapidity |η| < 2 are considered. For signal jets, only the “high-quality” ones that fully contain the decay products of initial particles are included.", "### 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\r\n\r\n\r\n\r\n\r\n\r\nIf you use the JetClass dataset, please cite:", "### Contributions\r\n\r\nThanks to @lewtun for adding this dataset." ]

[ "TAGS\n#license-mit #arxiv-2202.03772 #region-us \n", "# Dataset Card for JetClass", "## Table of Contents\r\n- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)\r\n - Table of Contents\r\n - Dataset Description\r\n - Dataset Summary\r\n - Supported Tasks and Leaderboards\r\n - Languages\r\n - Dataset Structure\r\n - Data Instances\r\n - Data Fields\r\n - Data Splits\r\n - Dataset Creation\r\n - Curation Rationale\r\n - Source Data\r\n - Initial Data Collection and Normalization\r\n - Who are the source language producers?\r\n - Annotations\r\n - Annotation process\r\n - Who are the annotators?\r\n - Personal and Sensitive Information\r\n - Considerations for Using the Data\r\n - Social Impact of Dataset\r\n - Discussion of Biases\r\n - Other Known Limitations\r\n - Additional Information\r\n - Dataset Curators\r\n - Licensing Information\r\n - Citation Information\r\n - Contributions", "## Dataset Description\r\n\r\n- Homepage:\r\n- Repository: URL\r\n- Paper: URL\r\n- Leaderboard:\r\n- Point of Contact: Huilin Qu", "### Dataset Summary\r\n\r\nJetClass is a large and comprehensive dataset to advance deep learning for jet tagging. The dataset consists of 100 million jets for training, with 10 different types of jets. The jets in this dataset generally fall into two categories:\r\n\r\n* The background jets are initiated by light quarks or gluons (q/g) and are ubiquitously produced at the\r\nLHC.\r\n* The signal jets are those arising either from the top quarks (t), or from the W, Z or Higgs (H) bosons. For top quarks and Higgs bosons, we further consider their different decay modes as separate types, because the resulting jets have rather distinct characteristics and are often tagged individually.\r\n\r\nJets in this dataset are simulated with standard Monte Carlo event generators used by LHC experiments. The production and decay of the top quarks and the W, Z and Higgs bosons are generated with MADGRAPH5_aMC@NLO. We use PYTHIA to evolve the produced particles, i.e., performing parton showering and hadronization, and produce the final outgoing particles. To be close to realistic jets reconstructed at the ATLAS or CMS experiment, detector effects are simulated with DELPHES using the CMS detector configuration provided in DELPHES. In addition, the impact parameters of electrically charged particles are smeared to match the resolution of the CMS tracking detector . Jets are clustered from DELPHES E-Flow objects with the anti-kT algorithm using a distance\r\nparameter R = 0.8. Only jets with transverse momentum in 500–1000 GeV and pseudorapidity |η| < 2 are considered. For signal jets, only the “high-quality” ones that fully contain the decay products of initial particles are included.", "### 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\r\n\r\n\r\n\r\n\r\n\r\nIf you use the JetClass dataset, please cite:", "### Contributions\r\n\r\nThanks to @lewtun for adding this dataset." ]

[ "task_categories:automatic-speech-recognition", "task_ids:keyword-spotting", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "language:en", "language:fr", "language:it", "language:es", "language:pt", "language:de", "language:nl", "language:ru", "language:pl", "language:cs", "language:ko", "language:zh", "license:cc-by-4.0", "arxiv:2104.08524", "region:us" ]

[ "2104.08524" ]

[ "en", "fr", "it", "es", "pt", "de", "nl", "ru", "pl", "cs", "ko", "zh" ]

[ "# MInDS-14", "## Dataset Description\n\n- Fine-Tuning script: pytorch/audio-classification\n- Paper: Multilingual and Cross-Lingual Intent Detection from Spoken Data\n- Total amount of disk used: ca. 500 MB \n\nMINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14 \nintents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.", "## Example\n\nMInDS-14 can be downloaded and used as follows:", "## Dataset Structure\n\nWe show detailed information the example configurations 'fr-FR' of the dataset.\nAll other configurations have the same structure.", "### Data Instances\n\nfr-FR\n\n- Size of downloaded dataset files: 471 MB\n- Size of the generated dataset: 300 KB\n- Total amount of disk used: 471 MB\n\n\nAn example of a datainstance of the config 'fr-FR' looks as follows:", "### Data Fields\nThe data fields are the same among all splits.\n\n- path (str): Path to the audio file\n- audio (dict): Audio object including loaded audio array, sampling rate and path ot audio\n- transcription (str): Transcription of the audio file\n- english_transcription (str): English transcription of the audio file\n- intent_class (int): Class id of intent\n- lang_id (int): Id of language", "### Data Splits\nEvery config only has the '\"train\"' split containing of *ca.* 600 examples.", "## Dataset Creation", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nAll datasets are licensed under the Creative Commons license (CC-BY).", "### Contributions\n\nThanks to @patrickvonplaten for adding this dataset" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_ids-keyword-spotting #annotations_creators-expert-generated #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #language-English #language-French #language-Italian #language-Spanish #language-Portuguese #language-German #language-Dutch #language-Russian #language-Polish #language-Czech #language-Korean #language-Chinese #license-cc-by-4.0 #arxiv-2104.08524 #region-us \n", "# MInDS-14", "## Dataset Description\n\n- Fine-Tuning script: pytorch/audio-classification\n- Paper: Multilingual and Cross-Lingual Intent Detection from Spoken Data\n- Total amount of disk used: ca. 500 MB \n\nMINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14 \nintents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.", "## Example\n\nMInDS-14 can be downloaded and used as follows:", "## Dataset Structure\n\nWe show detailed information the example configurations 'fr-FR' of the dataset.\nAll other configurations have the same structure.", "### Data Instances\n\nfr-FR\n\n- Size of downloaded dataset files: 471 MB\n- Size of the generated dataset: 300 KB\n- Total amount of disk used: 471 MB\n\n\nAn example of a datainstance of the config 'fr-FR' looks as follows:", "### Data Fields\nThe data fields are the same among all splits.\n\n- path (str): Path to the audio file\n- audio (dict): Audio object including loaded audio array, sampling rate and path ot audio\n- transcription (str): Transcription of the audio file\n- english_transcription (str): English transcription of the audio file\n- intent_class (int): Class id of intent\n- lang_id (int): Id of language", "### Data Splits\nEvery config only has the '\"train\"' split containing of *ca.* 600 examples.", "## Dataset Creation", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nAll datasets are licensed under the Creative Commons license (CC-BY).", "### Contributions\n\nThanks to @patrickvonplaten for adding this dataset" ]

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

[]

[]

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

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

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

[]

[ "en" ]

[ "# Dataset Card for the-reddit-place-dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: Website", "### Dataset Summary\n\nThe written history or /r/Place, in posts and comments.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## Additional Information", "### Licensing Information\n\nCC-BY v4.0" ]

[ "TAGS\n#annotations_creators-lexyr #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for the-reddit-place-dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information", "## Dataset Description\n\n- Homepage: URL\n- Point of Contact: Website", "### Dataset Summary\n\nThe written history or /r/Place, in posts and comments.", "### Languages\n\nMainly English.", "## Dataset Structure", "### Data Instances\n\nA data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.", "### Data Fields\n\n- 'type': the type of the data point. Can be 'post' or 'comment'.\n- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.\n- 'URL': the base-36 Reddit ID of the data point's host subreddit. Unique.\n- 'URL': the human-readable name of the data point's host subreddit.\n- 'URL': a boolean marking the data point's host subreddit as NSFW or not.\n- 'created_utc': a UTC timestamp for the data point.\n- 'permalink': a reference link to the data point on Reddit.\n- 'score': score of the data point on Reddit.\n\n- 'domain': (Post only) the domain of the data point's link.\n- 'url': (Post only) the destination of the data point's link, if any.\n- 'selftext': (Post only) the self-text of the data point, if any.\n- 'title': (Post only) the title of the post data point.\n\n- 'body': (Comment only) the body of the comment data point.\n- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.", "## Additional Information", "### Licensing Information\n\nCC-BY v4.0" ]

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

[]

[]

[ "## Labels\r\n\r\n 0: advertissement \r\n 1: budget \r\n 2: email \r\n 3: file folder \r\n 4: form \r\n 5: handwritten \r\n 6: invoice \r\n 7: letter \r\n 8: memo \r\n 9: news article \r\n10: presentation \r\n11: questionnaire \r\n12: resume \r\n13: scientific publication \r\n14: scientific report \r\n15: specification \r\n\r\nThis dataset is from this paper 'A. W. Harley, A. Ufkes, K. G. Derpanis, \"Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval,\" in ICDAR, 2015'", "## License\r\n\r\nRVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.", "## References\r\n\r\n1. D. Lewis, G. Agam, S. Argamon, O. Frieder, D. Grossman, and J. Heard, \"Building a test collection for complex document information processing,\" in Proc. 29th Annual Int. ACM SIGIR Conference (SIGIR 2006), pp. 665-666, 2006\r\n2. The Legacy Tobacco Document Library (LTDL), University of California, San Francisco, 2007. URL" ]

[ "TAGS\n#license-other #region-us \n", "## Labels\r\n\r\n 0: advertissement \r\n 1: budget \r\n 2: email \r\n 3: file folder \r\n 4: form \r\n 5: handwritten \r\n 6: invoice \r\n 7: letter \r\n 8: memo \r\n 9: news article \r\n10: presentation \r\n11: questionnaire \r\n12: resume \r\n13: scientific publication \r\n14: scientific report \r\n15: specification \r\n\r\nThis dataset is from this paper 'A. W. Harley, A. Ufkes, K. G. Derpanis, \"Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval,\" in ICDAR, 2015'", "## License\r\n\r\nRVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.", "## References\r\n\r\n1. D. Lewis, G. Agam, S. Argamon, O. Frieder, D. Grossman, and J. Heard, \"Building a test collection for complex document information processing,\" in Proc. 29th Annual Int. ACM SIGIR Conference (SIGIR 2006), pp. 665-666, 2006\r\n2. The Legacy Tobacco Document Library (LTDL), University of California, San Francisco, 2007. URL" ]

[ "license:cc-by-4.0", "arxiv:2103.05639", "region:us" ]

[ "2103.05639" ]

[]

[ "# An Amharic News Text classification Dataset \r\n\r\n> In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments." ]

[ "TAGS\n#license-cc-by-4.0 #arxiv-2103.05639 #region-us \n", "# An Amharic News Text classification Dataset \r\n\r\n> In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments." ]

[ "task_categories:image-classification", "task_categories:text-to-image", "task_categories:image-to-text", "size_categories:10K<n<100K", "license:unknown", "art", "region:us" ]

[]

[]

[ "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nDataset containing 81,444 pieces of visual art from various artists, taken from URL,\nalong with class labels for each image :\n\n* \"artist\" : 129 artist classes, including a \"Unknown Artist\" class\n* \"genre\" : 11 genre classes, including a \"Unknown Genre\" class\n* \"style\" : 27 style classes\n\nOn URL, the description for the \"Artworks by Genre\" page reads :\n\nA genre system divides artworks according to depicted themes and objects. A classical hierarchy of genres was developed in European culture by the 17th century. It ranked genres in high – history painting and portrait, - and low – genre painting, landscape and still life. This hierarchy was based on the notion of man as the measure of all things. Landscape and still life were the lowest because they did not involve human subject matter. History was highest because it dealt with the noblest events of humanity. Genre system is not so much relevant for a contemporary art; there are just two genre definitions that are usually applied to it: abstract or figurative.\n\nThe \"Artworks by Style\" page reads :\n\nA style of an artwork refers to its distinctive visual elements, techniques and methods. It usually corresponds with an art movement or a school (group) that its author is associated with.", "## Dataset Structure\n\n* \"image\" : image\n* \"artist\" : 129 artist classes, including a \"Unknown Artist\" class\n* \"genre\" : 11 genre classes, including a \"Unknown Genre\" class\n* \"style\" : 27 style classes", "### Source Data\n\nFiles taken from this archive, curated from the WikiArt website.", "## Additional Information\n\nNote:\n\n* The WikiArt dataset can be used only for non-commercial research purpose.\n* The images in the WikiArt dataset were obtained from URL.\n* The authors are neither responsible for the content nor the meaning of these images.\n\nBy using the WikiArt dataset, you agree to obey the terms and conditions of URL.", "### Contributions\n\n'gigant' added this dataset to the hub." ]

[ "TAGS\n#task_categories-image-classification #task_categories-text-to-image #task_categories-image-to-text #size_categories-10K<n<100K #license-unknown #art #region-us \n", "## Dataset Description\n\n- Homepage: URL", "### Dataset Summary\n\nDataset containing 81,444 pieces of visual art from various artists, taken from URL,\nalong with class labels for each image :\n\n* \"artist\" : 129 artist classes, including a \"Unknown Artist\" class\n* \"genre\" : 11 genre classes, including a \"Unknown Genre\" class\n* \"style\" : 27 style classes\n\nOn URL, the description for the \"Artworks by Genre\" page reads :\n\nA genre system divides artworks according to depicted themes and objects. A classical hierarchy of genres was developed in European culture by the 17th century. It ranked genres in high – history painting and portrait, - and low – genre painting, landscape and still life. This hierarchy was based on the notion of man as the measure of all things. Landscape and still life were the lowest because they did not involve human subject matter. History was highest because it dealt with the noblest events of humanity. Genre system is not so much relevant for a contemporary art; there are just two genre definitions that are usually applied to it: abstract or figurative.\n\nThe \"Artworks by Style\" page reads :\n\nA style of an artwork refers to its distinctive visual elements, techniques and methods. It usually corresponds with an art movement or a school (group) that its author is associated with.", "## Dataset Structure\n\n* \"image\" : image\n* \"artist\" : 129 artist classes, including a \"Unknown Artist\" class\n* \"genre\" : 11 genre classes, including a \"Unknown Genre\" class\n* \"style\" : 27 style classes", "### Source Data\n\nFiles taken from this archive, curated from the WikiArt website.", "## Additional Information\n\nNote:\n\n* The WikiArt dataset can be used only for non-commercial research purpose.\n* The images in the WikiArt dataset were obtained from URL.\n* The authors are neither responsible for the content nor the meaning of these images.\n\nBy using the WikiArt dataset, you agree to obey the terms and conditions of URL.", "### Contributions\n\n'gigant' added this dataset to the hub." ]

[ "arxiv:2103.04264", "region:us" ]

[ "2103.04264" ]

[]

[]

[ "TAGS\n#arxiv-2103.04264 #region-us \n" ]

[ "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:1M<n<10M", "source_datasets:original", "source_datasets:extended|bible_para", "source_datasets:extended|kde4", "source_datasets:extended|opus_gnome", "source_datasets:extended|open_subtitles", "source_datasets:extended|tatoeba", "language:en", "language:vi", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "en", "vi" ]

[ "### Dataset Summary\n\n\nMTet (Multi-domain Translation for English-Vietnamese) dataset contains roughly 4.2 million English-Vietnamese pairs of\ntexts, ranging across multiple different domains such as medical publications, religious texts, engineering articles,\nliterature, news, and poems.\n\n\nThis dataset extends our previous SAT (Style Augmented Translation) dataset (v1.0) by adding more high-quality\nEnglish-Vietnamese sentence pairs on various domains.", "### Supported Tasks and Leaderboards\n\n\n* Machine Translation", "### Languages\n\n\nThe languages in the dataset are:\n\n\n* Vietnamese ('vi')\n* English ('en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'translation':\n\t+ 'en': Parallel text in English.\n\t+ 'vi': Parallel text in Vietnamese.", "### Data Splits\n\n\nThe dataset is in a single \"train\" split.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).", "### Contributions\n\n\nThanks to @albertvillanova for adding this dataset." ]

[ "TAGS\n#annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-1M<n<10M #source_datasets-original #source_datasets-extended|bible_para #source_datasets-extended|kde4 #source_datasets-extended|opus_gnome #source_datasets-extended|open_subtitles #source_datasets-extended|tatoeba #language-English #language-Vietnamese #license-cc-by-nc-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nMTet (Multi-domain Translation for English-Vietnamese) dataset contains roughly 4.2 million English-Vietnamese pairs of\ntexts, ranging across multiple different domains such as medical publications, religious texts, engineering articles,\nliterature, news, and poems.\n\n\nThis dataset extends our previous SAT (Style Augmented Translation) dataset (v1.0) by adding more high-quality\nEnglish-Vietnamese sentence pairs on various domains.", "### Supported Tasks and Leaderboards\n\n\n* Machine Translation", "### Languages\n\n\nThe languages in the dataset are:\n\n\n* Vietnamese ('vi')\n* English ('en')\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields\n\n\n* 'translation':\n\t+ 'en': Parallel text in English.\n\t+ 'vi': Parallel text in Vietnamese.", "### Data Splits\n\n\nThe dataset is in a single \"train\" split.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).", "### Contributions\n\n\nThanks to @albertvillanova for adding this dataset." ]

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

[]

[ "fr" ]

[ "# Dataset Card for french-open-fiscal-texts", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset is an extraction from the OPENDATA/JADE. A list of case laws from the French court \"Conseil d'Etat\".", "### Supported Tasks and Leaderboards", "### Languages\n\nfr-FR", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n'file': identifier on the JADE OPENDATA file\n\n'title': Name of the law case \n'summary': Summary provided by JADE (may be missing) \n'content': Text content of the case law", "### Data Splits\n\ntrain \ntest", "## Dataset Creation", "### Curation Rationale\n\nThis dataset is an attempt to gather multiple tax related french text law. \nThe first intent it to build model to summarize law cases", "### Source Data", "#### Initial Data Collection and Normalization\n\nCollected from the URL\n- Filtering xml files containing \"Code général des impôts\" (tax related)\n- Extracting content, summary, identifier, title", "#### Who are the source language producers?\n\nDILA", "### 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#language-French #region-us \n", "# Dataset Card for french-open-fiscal-texts", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset is an extraction from the OPENDATA/JADE. A list of case laws from the French court \"Conseil d'Etat\".", "### Supported Tasks and Leaderboards", "### Languages\n\nfr-FR", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n'file': identifier on the JADE OPENDATA file\n\n'title': Name of the law case \n'summary': Summary provided by JADE (may be missing) \n'content': Text content of the case law", "### Data Splits\n\ntrain \ntest", "## Dataset Creation", "### Curation Rationale\n\nThis dataset is an attempt to gather multiple tax related french text law. \nThe first intent it to build model to summarize law cases", "### Source Data", "#### Initial Data Collection and Normalization\n\nCollected from the URL\n- Filtering xml files containing \"Code général des impôts\" (tax related)\n- Extracting content, summary, identifier, title", "#### Who are the source language producers?\n\nDILA", "### 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" ]

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

[]

[]

[ "## Common User Intentions", "#### Greetings\r\n- Wasemaje\r\n- uko aje btw\r\n- oyah...\r\n- Form\r\n- Alafu niaje\r\n- Poa Sana Mambo\r\n- Niko poa\r\n- Pia Mimi Niko salama\r\n- Hope siku yako iko poa\r\n- Siko poa kabisa\r\n- Nimekuwa poa\r\n- Umeshindaje\r\n- Hope uko poa\r\n- uko poa\r\n- Sasa\r\n- Vipi vipi\r\n- Niko salama\r\n- ..its been long.\r\n- Nko fiti\r\n- niko fiti\r\n- Nmeamka fity..\r\n- Vipi\r\n- Unasemaje\r\n- Aaaah...itakuaje sasaa..\r\n - .iz vipi..itakuaje..\r\n - Form ni gani bro...\r\n - iz vipi", "#### Affirm\r\n- Hapo sawa...\r\n- Fty\r\n- sai\r\n- Hio si ni better hadi\r\n- Imebidi.\r\n- Eeeh mazee\r\n- mazeee\r\n- Fity fity\r\n- Oooh poapoa\r\n- Yap \r\n- Inakaa poa\r\n- Yeah itabidi\r\n- Ooooh...\r\n- Si ndo nadaaiii\r\n- Oooh sawa\r\n- Okay sawa basi\r\n- Venye utaamua ni sawa\r\n- Sawa wacha tungoje\r\n- lazima\r\n- apa umenena\r\n- Sawa basi\r\n- walai\r\n- Oooh\r\n- inaweza mbaya\r\n- itaweza mbaya\r\n- ni sawa\r\n- Iko poa\r\n- Iko tu sawa hivo\r\n- ilinbamba.\r\n- Nimemada\r\n- Btw hao ata mimi naona\r\n- but inaeleweka\r\n- pia mimi\r\n- iende ikiendaga\r\n- We jua ivo\r\n- Hata Mimi\r\n- Nataka\r\n- Ooh.\r\n- Chezea tu hapo\r\n- isorait\r\n- Ata yako ni kali\r\n- Ntaicheck out Leo\r\n- hmm. Okay\r\n- Mimi sina shida\r\n- ooooh io iko fity...\r\n- hii ni ngori\r\n- maze\r\n- sawa\r\n- banaa\r\n- Aaah kumbe\r\n- Safiii..\r\n- Sasawa\r\n- hio ni fityyy\r\n- Yeah nliona\r\n- Vizii...\r\n- Eeeeh nmekua naiona...\r\n- Yea\r\n- Haina nomA\r\n- katambe\r\n- accept basi\r\n- ni sawa\r\n- Issaplan\r\n- nmeget\r\n- nimedai tu\r\n- eeh\r\n- Hio ni poa\r\n- nadai sa hii\r\n- Eeeeh\r\n- mi nadai tu \r\n- firi\r\n- Hapo freshi", "#### Deny\r\n- Sipendi\r\n- aih\r\n- Nimegive up\r\n- Yangu bado\r\n- siezi make\r\n- Sina\r\n- Haileti\r\n- Haiwezi\r\n- Io sikuwa nikwambie\r\n- Sikuwa\r\n- Wacha ata\r\n- ata sijui\r\n- Sijasema\r\n- Sijai\r\n- hiyo haiezi\r\n- Bado.\r\n- Uku tricks...\r\n- sidai\r\n- achana nayo\r\n- ziii\r\n- si fityy\r\n- Nimekataa Mimi\r\n- Sijui\r\n- Aiwezekani\r\n- Bado sioni", "#### Courtesy\r\n- Imefika... shukran\r\n- Haina ngori\r\n- Inafaa hivo\r\n- Utakuwa umeniokolea manzee\r\n- Karibu\r\n- Nyc one\r\n- Hakuna pressure\r\n- Gai. Pole\r\n- Usijali I will\r\n- Nimekufeel hapo\r\n- Waah izaa\r\n- Pole lkn\r\n- Pole\r\n- plz\r\n- okay...pole\r\n- thanks for pulling up lkn..\r\n- shukran\r\n- Eeeeh nyc\r\n- Thanx for the info\r\n- Uko aje\r\n- haina pressure\r\n- eih, iko fiti.\r\n- vitu kama hizo\r\n- sahii", "#### Asking clarification\r\n- check alafu unishow\r\n- Sasa msee akishabuy anafanya aje\r\n- Umeenda wapi\r\n- nlikuwa nadai\r\n- Nlikua nataka\r\n- Ulipata\r\n- leo jioni utakuwa?\r\n- uko\r\n- umelostia wapi?\r\n- ingine?\r\n- hii inamaanisha?\r\n- Wewe Sasa ni nani?\r\n- warrathos\r\n- kwani nisiende sasa\r\n- unadai zingine?\r\n- Kwani\r\n- Haiya...\r\n- Unadu?\r\n- inakuanga mangapiii...\r\n- Kuna nn\r\n- Nauliza\r\n- Hakuna kwanini\r\n- Nadai kujua what\r\n- Kwanini hakuna\r\n- Kwa nini hakuna\r\n- Uliniambia\r\n- Mbona\r\n- Nlikua nashangaa\r\n- Unadu nini\r\n- Oooh mara moja\r\n- Unaeza taka?\r\n- unaeza make?\r\n- Umeipata?\r\n- wapi kwingine tena\r\n- kuna yenye natafuta\r\n- Sijajua bado\r\n- Niko na ingine\r\n- ulikuwa unataka\r\n- ulinishow?\r\n- ulinsho\r\n- Umepata\r\n- Ata stage hakuna?\r\n- Huku hakuna kibandaski?\r\n- Sai ndio uko available\r\n- Ivo\r\n- Inaeza\r\n- Naeza\r\n- Btw, nikuulize\r\n- Uliza\r\n- hadi sa hii\r\n- Nauliza ndio nijue kama bado iko\r\n- Btw ile hoteli tulienda na wewe apo kiimbo huendangi?", "#### Comedy\r\n- Ata kama\r\n- Wasikupee pressure\r\n- umeanza jokes\r\n- Ulisumbua sana\r\n- Unaeza niambia ivo\r\n- usinicheke\r\n- Hakunakwanini\r\n- aki wewe.\r\n- naskia mpaka ulipiga sherehe\r\n- sio?\r\n- uko na kakitu\r\n- Aaaaii \r\n- .uko fity nayo..\r\n- icome through mbaya...", "#### Small talk\r\n- Kuchil tu bana\r\n- Inafaa hivo\r\n- Acha niskizie\r\n- Skujua hii stuff\r\n- nacheza chini\r\n- hii imesink deep.\r\n- mi Niko\r\n- khai, gai, ghaiye\r\n- Woiye\r\n- ndo nmeland\r\n- Nimekuona\r\n- Kaaai\r\n- Nambie\r\n- bado nashangaa aliipull thru maze\r\n- Niambie\r\n- Najua uko kejani\r\n- Bado uko\r\n- Utakuwa sawa\r\n- Niko poa ata kama uniliacha hanging jana\r\n- issa deal\r\n- Walai io nilijua utasema\r\n- hujawai sahau hii\r\n- Sijajua bado\r\n- Ni maroundi tu\r\n- Enyewe imetoka mbali\r\n- Hadi nimekuwa Tao leo\r\n- Ni mnoma mbaya\r\n- Anyway mambo ni polepole\r\n- Imagine\r\n- Sina la kusema\r\n- Sai\r\n- Najua umeboeka", "#### Resolute\r\n- Nataka leo\r\n- hayo ndo maisha Sasa\r\n- vile itakuja maze\r\n- Acha tu\r\n- Waaah Leo haiwezi\r\n- Ni sawa tu\r\n- Imeisha\r\n- Itabidi\r\n- siendagi\r\n- siezi kuangusha\r\n- nachangamkia hii\r\n- Weno ivi...\r\n- Hii price iko poa...", "#### implore\r\n- but nimetry tena\r\n- aminia tu\r\n- Ebu try\r\n- Alafu\r\n- naona hufeel kuongea\r\n- Watu hawaongei?\r\n- Itabidi tu umesort\r\n- Naona huna shughuli yangu\r\n- tufanye pamoja\r\n- khai, gai, ghaiye\r\n- so kalunch\r\n- ama?\r\n- Sahii ni the best time\r\n- Kwanza sahii\r\n- hii weekend\r\n- Kaanza next weekend ni fity\r\n- this weekend\r\n- Acha ntacheki\r\n- izo sasa..\r\n- Acha tuone\r\n- So tunafikanga ivor morning mapemaa\r\n- naona uko rada\r\n- mapema kiasi\r\n- nimchapie niskie... \r\n- Naisaka walai", "#### Bye\r\n- Ama kesho\r\n- Ngoja nta rudi baadaye\r\n- nacheki tu rada ya kesho\r\n- Nitakusort kesho morning\r\n- Ni hivo nimekafunga\r\n- nitakushow\r\n- Nextweek ndio inaeza\r\n- Ntakuchapia kama ntamake\r\n- Freshi", "#### Sample Bot Responses\r\n- tulia tu hana mambo mob\r\n- si you know how we do it \r\n- Form ni gani\r\n- Oooh nmekuget\r\n- znaeza kupea stress\r\n- Hues make leo\r\n- nshow password\r\n- Nmeichangamkia design ya ngori\r\n- Oooh nmekuget...\r\n- ilicome through\r\n- Naisaka walai\r\n- kesho ntakuchapia\r\n- nichapie niskie\r\n- Aaaah..\r\n- Alafu ile story ya\r\n- Ooooh ebu ntasaka\r\n- Saa ngapi uko free..\r\n- Ama unasema ya\r\n- Safiii..naona uko rada\r\n- Ilkulemea\r\n- Acha ntacheki\r\n- imeharibia form..\r\n- Nmeitafuta\r\n- Ndio nimeget\r\n- inaeza saidia mtu\r\n- Email yako ni gani\r\n- Wacha niangalie\r\n- nangoja ulipe\r\n- nimeshikika\r\n- Sawa tuma email\r\n- Kwani ulimwambia nini\r\n- Najua ata most of the time\r\n- mara most btw\r\n- Unajua tu ni risky\r\n- unadai tu niseme mi ni robot\r\n- kwanini\r\n- ndio usiulizwe\r\n- Ukiangalia niambie\r\n- Last time ukinipigia nilikuwa nimeenda kuoshwa\r\n- ikishaenda kwa mganga hairudi\r\n- Hata Mimi ni hayo mambo madogo madogo ndio imenieka.\r\n- We jua nafikirianga mingi ni venye zingine huwa sisemi\r\n- Na najua\r\n- unarelax\r\n- mm ata sko tensed\r\n- sahii ata ni risky\r\n- but ntakuchapia\r\n- oooh waah..\r\n- aaaah ata ww\r\n- hii si fityy\r\n- maze itabidi tudunde virtual\r\n- tunadunda wapiiii..\r\n- kwani sa mi ndo nafaa kumshow kila time coz this is not the first time namwambia‍️\r\n- Wacha hizo.\r\n- Yeah niko hapa\r\n- Niko\r\n- Give me sometime.\r\n- Maze...nmecheza ki mimi\r\n- Uko busy\r\n- Chill kiasi\r\n- Wacha nikusort\r\n- ntakushow\r\n- looking for you hupatikani\r\n- Mnaniogopa ama\r\n- Wewe unapenda free\r\n- Nakusort sai chill mazee\r\n- Kiasi\r\n- relax mkubwa\r\n- Sahii uko sorted sindio\r\n- Ni juu\r\n- bringing the future to us\r\n- hiyo ni form yangu daily\r\n- Ata mimi sitaki ufala \r\n- Imagine\r\n- Uko sawa\r\n- Uko sawa ama unaitaji ingine\r\n- ka unaeza\r\n- utanichapia tu\r\n- unasemaje lakini\r\n- Niulize\r\n- Uko na number\r\n- Ukiboeka wewe nitext\r\n- unadai sa hii ?\r\n- skuwa nimeona\r\n- Acha nicheki\r\n- Ni Friday bana\r\n- Niko chilled tu\r\n- Unadai aje.\r\n- Utanichapia basi\r\n- Umenyamaza sana bana\r\n- imekam through ama\r\n- Nategea umalize ndo nikushow ile form\r\n- Guidance tu kiasi\r\n- Tutadiscuss pia stori\r\n- Nakwelewa\r\n- tujue niaje\r\n- itaweza mbaya\r\n- Kuna hopes za kulearn" ]

[ "TAGS\n#license-mit #region-us \n", "## Common User Intentions", "#### Greetings\r\n- Wasemaje\r\n- uko aje btw\r\n- oyah...\r\n- Form\r\n- Alafu niaje\r\n- Poa Sana Mambo\r\n- Niko poa\r\n- Pia Mimi Niko salama\r\n- Hope siku yako iko poa\r\n- Siko poa kabisa\r\n- Nimekuwa poa\r\n- Umeshindaje\r\n- Hope uko poa\r\n- uko poa\r\n- Sasa\r\n- Vipi vipi\r\n- Niko salama\r\n- ..its been long.\r\n- Nko fiti\r\n- niko fiti\r\n- Nmeamka fity..\r\n- Vipi\r\n- Unasemaje\r\n- Aaaah...itakuaje sasaa..\r\n - .iz vipi..itakuaje..\r\n - Form ni gani bro...\r\n - iz vipi", "#### Affirm\r\n- Hapo sawa...\r\n- Fty\r\n- sai\r\n- Hio si ni better hadi\r\n- Imebidi.\r\n- Eeeh mazee\r\n- mazeee\r\n- Fity fity\r\n- Oooh poapoa\r\n- Yap \r\n- Inakaa poa\r\n- Yeah itabidi\r\n- Ooooh...\r\n- Si ndo nadaaiii\r\n- Oooh sawa\r\n- Okay sawa basi\r\n- Venye utaamua ni sawa\r\n- Sawa wacha tungoje\r\n- lazima\r\n- apa umenena\r\n- Sawa basi\r\n- walai\r\n- Oooh\r\n- inaweza mbaya\r\n- itaweza mbaya\r\n- ni sawa\r\n- Iko poa\r\n- Iko tu sawa hivo\r\n- ilinbamba.\r\n- Nimemada\r\n- Btw hao ata mimi naona\r\n- but inaeleweka\r\n- pia mimi\r\n- iende ikiendaga\r\n- We jua ivo\r\n- Hata Mimi\r\n- Nataka\r\n- Ooh.\r\n- Chezea tu hapo\r\n- isorait\r\n- Ata yako ni kali\r\n- Ntaicheck out Leo\r\n- hmm. Okay\r\n- Mimi sina shida\r\n- ooooh io iko fity...\r\n- hii ni ngori\r\n- maze\r\n- sawa\r\n- banaa\r\n- Aaah kumbe\r\n- Safiii..\r\n- Sasawa\r\n- hio ni fityyy\r\n- Yeah nliona\r\n- Vizii...\r\n- Eeeeh nmekua naiona...\r\n- Yea\r\n- Haina nomA\r\n- katambe\r\n- accept basi\r\n- ni sawa\r\n- Issaplan\r\n- nmeget\r\n- nimedai tu\r\n- eeh\r\n- Hio ni poa\r\n- nadai sa hii\r\n- Eeeeh\r\n- mi nadai tu \r\n- firi\r\n- Hapo freshi", "#### Deny\r\n- Sipendi\r\n- aih\r\n- Nimegive up\r\n- Yangu bado\r\n- siezi make\r\n- Sina\r\n- Haileti\r\n- Haiwezi\r\n- Io sikuwa nikwambie\r\n- Sikuwa\r\n- Wacha ata\r\n- ata sijui\r\n- Sijasema\r\n- Sijai\r\n- hiyo haiezi\r\n- Bado.\r\n- Uku tricks...\r\n- sidai\r\n- achana nayo\r\n- ziii\r\n- si fityy\r\n- Nimekataa Mimi\r\n- Sijui\r\n- Aiwezekani\r\n- Bado sioni", "#### Courtesy\r\n- Imefika... shukran\r\n- Haina ngori\r\n- Inafaa hivo\r\n- Utakuwa umeniokolea manzee\r\n- Karibu\r\n- Nyc one\r\n- Hakuna pressure\r\n- Gai. Pole\r\n- Usijali I will\r\n- Nimekufeel hapo\r\n- Waah izaa\r\n- Pole lkn\r\n- Pole\r\n- plz\r\n- okay...pole\r\n- thanks for pulling up lkn..\r\n- shukran\r\n- Eeeeh nyc\r\n- Thanx for the info\r\n- Uko aje\r\n- haina pressure\r\n- eih, iko fiti.\r\n- vitu kama hizo\r\n- sahii", "#### Asking clarification\r\n- check alafu unishow\r\n- Sasa msee akishabuy anafanya aje\r\n- Umeenda wapi\r\n- nlikuwa nadai\r\n- Nlikua nataka\r\n- Ulipata\r\n- leo jioni utakuwa?\r\n- uko\r\n- umelostia wapi?\r\n- ingine?\r\n- hii inamaanisha?\r\n- Wewe Sasa ni nani?\r\n- warrathos\r\n- kwani nisiende sasa\r\n- unadai zingine?\r\n- Kwani\r\n- Haiya...\r\n- Unadu?\r\n- inakuanga mangapiii...\r\n- Kuna nn\r\n- Nauliza\r\n- Hakuna kwanini\r\n- Nadai kujua what\r\n- Kwanini hakuna\r\n- Kwa nini hakuna\r\n- Uliniambia\r\n- Mbona\r\n- Nlikua nashangaa\r\n- Unadu nini\r\n- Oooh mara moja\r\n- Unaeza taka?\r\n- unaeza make?\r\n- Umeipata?\r\n- wapi kwingine tena\r\n- kuna yenye natafuta\r\n- Sijajua bado\r\n- Niko na ingine\r\n- ulikuwa unataka\r\n- ulinishow?\r\n- ulinsho\r\n- Umepata\r\n- Ata stage hakuna?\r\n- Huku hakuna kibandaski?\r\n- Sai ndio uko available\r\n- Ivo\r\n- Inaeza\r\n- Naeza\r\n- Btw, nikuulize\r\n- Uliza\r\n- hadi sa hii\r\n- Nauliza ndio nijue kama bado iko\r\n- Btw ile hoteli tulienda na wewe apo kiimbo huendangi?", "#### Comedy\r\n- Ata kama\r\n- Wasikupee pressure\r\n- umeanza jokes\r\n- Ulisumbua sana\r\n- Unaeza niambia ivo\r\n- usinicheke\r\n- Hakunakwanini\r\n- aki wewe.\r\n- naskia mpaka ulipiga sherehe\r\n- sio?\r\n- uko na kakitu\r\n- Aaaaii \r\n- .uko fity nayo..\r\n- icome through mbaya...", "#### Small talk\r\n- Kuchil tu bana\r\n- Inafaa hivo\r\n- Acha niskizie\r\n- Skujua hii stuff\r\n- nacheza chini\r\n- hii imesink deep.\r\n- mi Niko\r\n- khai, gai, ghaiye\r\n- Woiye\r\n- ndo nmeland\r\n- Nimekuona\r\n- Kaaai\r\n- Nambie\r\n- bado nashangaa aliipull thru maze\r\n- Niambie\r\n- Najua uko kejani\r\n- Bado uko\r\n- Utakuwa sawa\r\n- Niko poa ata kama uniliacha hanging jana\r\n- issa deal\r\n- Walai io nilijua utasema\r\n- hujawai sahau hii\r\n- Sijajua bado\r\n- Ni maroundi tu\r\n- Enyewe imetoka mbali\r\n- Hadi nimekuwa Tao leo\r\n- Ni mnoma mbaya\r\n- Anyway mambo ni polepole\r\n- Imagine\r\n- Sina la kusema\r\n- Sai\r\n- Najua umeboeka", "#### Resolute\r\n- Nataka leo\r\n- hayo ndo maisha Sasa\r\n- vile itakuja maze\r\n- Acha tu\r\n- Waaah Leo haiwezi\r\n- Ni sawa tu\r\n- Imeisha\r\n- Itabidi\r\n- siendagi\r\n- siezi kuangusha\r\n- nachangamkia hii\r\n- Weno ivi...\r\n- Hii price iko poa...", "#### implore\r\n- but nimetry tena\r\n- aminia tu\r\n- Ebu try\r\n- Alafu\r\n- naona hufeel kuongea\r\n- Watu hawaongei?\r\n- Itabidi tu umesort\r\n- Naona huna shughuli yangu\r\n- tufanye pamoja\r\n- khai, gai, ghaiye\r\n- so kalunch\r\n- ama?\r\n- Sahii ni the best time\r\n- Kwanza sahii\r\n- hii weekend\r\n- Kaanza next weekend ni fity\r\n- this weekend\r\n- Acha ntacheki\r\n- izo sasa..\r\n- Acha tuone\r\n- So tunafikanga ivor morning mapemaa\r\n- naona uko rada\r\n- mapema kiasi\r\n- nimchapie niskie... \r\n- Naisaka walai", "#### Bye\r\n- Ama kesho\r\n- Ngoja nta rudi baadaye\r\n- nacheki tu rada ya kesho\r\n- Nitakusort kesho morning\r\n- Ni hivo nimekafunga\r\n- nitakushow\r\n- Nextweek ndio inaeza\r\n- Ntakuchapia kama ntamake\r\n- Freshi", "#### Sample Bot Responses\r\n- tulia tu hana mambo mob\r\n- si you know how we do it \r\n- Form ni gani\r\n- Oooh nmekuget\r\n- znaeza kupea stress\r\n- Hues make leo\r\n- nshow password\r\n- Nmeichangamkia design ya ngori\r\n- Oooh nmekuget...\r\n- ilicome through\r\n- Naisaka walai\r\n- kesho ntakuchapia\r\n- nichapie niskie\r\n- Aaaah..\r\n- Alafu ile story ya\r\n- Ooooh ebu ntasaka\r\n- Saa ngapi uko free..\r\n- Ama unasema ya\r\n- Safiii..naona uko rada\r\n- Ilkulemea\r\n- Acha ntacheki\r\n- imeharibia form..\r\n- Nmeitafuta\r\n- Ndio nimeget\r\n- inaeza saidia mtu\r\n- Email yako ni gani\r\n- Wacha niangalie\r\n- nangoja ulipe\r\n- nimeshikika\r\n- Sawa tuma email\r\n- Kwani ulimwambia nini\r\n- Najua ata most of the time\r\n- mara most btw\r\n- Unajua tu ni risky\r\n- unadai tu niseme mi ni robot\r\n- kwanini\r\n- ndio usiulizwe\r\n- Ukiangalia niambie\r\n- Last time ukinipigia nilikuwa nimeenda kuoshwa\r\n- ikishaenda kwa mganga hairudi\r\n- Hata Mimi ni hayo mambo madogo madogo ndio imenieka.\r\n- We jua nafikirianga mingi ni venye zingine huwa sisemi\r\n- Na najua\r\n- unarelax\r\n- mm ata sko tensed\r\n- sahii ata ni risky\r\n- but ntakuchapia\r\n- oooh waah..\r\n- aaaah ata ww\r\n- hii si fityy\r\n- maze itabidi tudunde virtual\r\n- tunadunda wapiiii..\r\n- kwani sa mi ndo nafaa kumshow kila time coz this is not the first time namwambia‍️\r\n- Wacha hizo.\r\n- Yeah niko hapa\r\n- Niko\r\n- Give me sometime.\r\n- Maze...nmecheza ki mimi\r\n- Uko busy\r\n- Chill kiasi\r\n- Wacha nikusort\r\n- ntakushow\r\n- looking for you hupatikani\r\n- Mnaniogopa ama\r\n- Wewe unapenda free\r\n- Nakusort sai chill mazee\r\n- Kiasi\r\n- relax mkubwa\r\n- Sahii uko sorted sindio\r\n- Ni juu\r\n- bringing the future to us\r\n- hiyo ni form yangu daily\r\n- Ata mimi sitaki ufala \r\n- Imagine\r\n- Uko sawa\r\n- Uko sawa ama unaitaji ingine\r\n- ka unaeza\r\n- utanichapia tu\r\n- unasemaje lakini\r\n- Niulize\r\n- Uko na number\r\n- Ukiboeka wewe nitext\r\n- unadai sa hii ?\r\n- skuwa nimeona\r\n- Acha nicheki\r\n- Ni Friday bana\r\n- Niko chilled tu\r\n- Unadai aje.\r\n- Utanichapia basi\r\n- Umenyamaza sana bana\r\n- imekam through ama\r\n- Nategea umalize ndo nikushow ile form\r\n- Guidance tu kiasi\r\n- Tutadiscuss pia stori\r\n- Nakwelewa\r\n- tujue niaje\r\n- itaweza mbaya\r\n- Kuna hopes za kulearn" ]

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

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[ "## Labels\n\n 0: letter \n 1: form \n 2: email \n 3: handwritten \n 4: advertissement \n 5: scientific report \n 6: scientific publication \n 7: specification \n 8: file folder \n 9: news article \n10: budget \n11: invoice \n12: presentation \n13: questionnaire \n14: resume \n15: memo \n\nThis dataset is from this paper 'A. W. Harley, A. Ufkes, K. G. Derpanis, \"Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval,\" in ICDAR, 2015'", "## License\n\nRVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.", "## References\n\n1. D. Lewis, G. Agam, S. Argamon, O. Frieder, D. Grossman, and J. Heard, \"Building a test collection for complex document information processing,\" in Proc. 29th Annual Int. ACM SIGIR Conference (SIGIR 2006), pp. 665-666, 2006\n2. The Legacy Tobacco Document Library (LTDL), University of California, San Francisco, 2007. URL" ]

[ "TAGS\n#license-other #region-us \n", "## Labels\n\n 0: letter \n 1: form \n 2: email \n 3: handwritten \n 4: advertissement \n 5: scientific report \n 6: scientific publication \n 7: specification \n 8: file folder \n 9: news article \n10: budget \n11: invoice \n12: presentation \n13: questionnaire \n14: resume \n15: memo \n\nThis dataset is from this paper 'A. W. Harley, A. Ufkes, K. G. Derpanis, \"Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval,\" in ICDAR, 2015'", "## License\n\nRVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.", "## References\n\n1. D. Lewis, G. Agam, S. Argamon, O. Frieder, D. Grossman, and J. Heard, \"Building a test collection for complex document information processing,\" in Proc. 29th Annual Int. ACM SIGIR Conference (SIGIR 2006), pp. 665-666, 2006\n2. The Legacy Tobacco Document Library (LTDL), University of California, San Francisco, 2007. URL" ]

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

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[]

[ "## Labels\n\n 0: letter \n 1: form \n 2: email \n 3: handwritten \n 4: advertissement \n 5: scientific report \n 6: scientific publication \n 7: specification \n 8: file folder \n 9: news article \n10: budget \n11: invoice \n12: presentation \n13: questionnaire \n14: resume \n15: memo \n\nThis dataset is from this paper 'A. W. Harley, A. Ufkes, K. G. Derpanis, \"Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval,\" in ICDAR, 2015'", "## License\n\nRVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.", "## References\n\n1. D. Lewis, G. Agam, S. Argamon, O. Frieder, D. Grossman, and J. Heard, \"Building a test collection for complex document information processing,\" in Proc. 29th Annual Int. ACM SIGIR Conference (SIGIR 2006), pp. 665-666, 2006\n2. The Legacy Tobacco Document Library (LTDL), University of California, San Francisco, 2007. URL" ]

[ "TAGS\n#license-other #region-us \n", "## Labels\n\n 0: letter \n 1: form \n 2: email \n 3: handwritten \n 4: advertissement \n 5: scientific report \n 6: scientific publication \n 7: specification \n 8: file folder \n 9: news article \n10: budget \n11: invoice \n12: presentation \n13: questionnaire \n14: resume \n15: memo \n\nThis dataset is from this paper 'A. W. Harley, A. Ufkes, K. G. Derpanis, \"Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval,\" in ICDAR, 2015'", "## License\n\nRVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.", "## References\n\n1. D. Lewis, G. Agam, S. Argamon, O. Frieder, D. Grossman, and J. Heard, \"Building a test collection for complex document information processing,\" in Proc. 29th Annual Int. ACM SIGIR Conference (SIGIR 2006), pp. 665-666, 2006\n2. The Legacy Tobacco Document Library (LTDL), University of California, San Francisco, 2007. URL" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "task_ids:parsing", "task_ids:part-of-speech", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:uk", "license:mit", "region:us" ]

[]

[ "uk" ]

[ "### Dataset Summary\n\n\nLarge silver standard Ukrainian corpus annotated with morphology tags, syntax trees and PER, LOC, ORG NER-tags.\nRepresents a subsample of Leipzig Corpora Collection for Ukrainian Language. The source texts are newspaper texts split into sentences and shuffled. The sentrences are annotated using transformer-based models trained using gold standard Ukrainian language datasets.", "### Languages\n\n\nUkrainian\n\n\nDataset Structure\n-----------------", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data\n\n\nLeipzig Corpora Collection:\n\n\nD. Goldhahn, T. Eckart & U. Quasthoff: Building Large Monolingual Dictionaries at the Leipzig Corpora Collection: From 100 to 200 Languages.\nIn: Proceedings of the 8th International Language Resources and Evaluation (LREC'12), 2012\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nMIT License\n\n\nCopyright (c) 2022\n\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #task_ids-parsing #task_ids-part-of-speech #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #language-Ukrainian #license-mit #region-us \n", "### Dataset Summary\n\n\nLarge silver standard Ukrainian corpus annotated with morphology tags, syntax trees and PER, LOC, ORG NER-tags.\nRepresents a subsample of Leipzig Corpora Collection for Ukrainian Language. The source texts are newspaper texts split into sentences and shuffled. The sentrences are annotated using transformer-based models trained using gold standard Ukrainian language datasets.", "### Languages\n\n\nUkrainian\n\n\nDataset Structure\n-----------------", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data\n\n\nLeipzig Corpora Collection:\n\n\nD. Goldhahn, T. Eckart & U. Quasthoff: Building Large Monolingual Dictionaries at the Leipzig Corpora Collection: From 100 to 200 Languages.\nIn: Proceedings of the 8th International Language Resources and Evaluation (LREC'12), 2012\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nMIT License\n\n\nCopyright (c) 2022\n\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE." ]

[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:translation", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:aai", "language:aak", "language:aau", "language:aaz", "language:abt", "language:abx", "language:aby", "language:acf", "language:acr", "language:acu", "language:adz", "language:aer", "language:aey", "language:agd", "language:agg", "language:agm", "language:agn", "language:agr", "language:agt", "language:agu", "language:aia", "language:aii", "language:aka", "language:ake", "language:alp", "language:alq", "language:als", "language:aly", "language:ame", "language:amf", "language:amk", "language:amm", "language:amn", "language:amo", "language:amp", "language:amr", "language:amu", "language:amx", "language:anh", "language:anv", "language:aoi", "language:aoj", "language:aom", "language:aon", "language:apb", "language:ape", "language:apn", "language:apr", "language:apu", "language:apw", "language:apz", "language:arb", "language:are", "language:arl", "language:arn", "language:arp", "language:asm", "language:aso", "language:ata", "language:atb", "language:atd", "language:atg", "language:att", "language:auc", "language:aui", "language:auy", "language:avt", "language:awb", "language:awk", "language:awx", "language:azb", "language:azg", "language:azz", "language:bao", "language:bba", "language:bbb", "language:bbr", "language:bch", "language:bco", "language:bdd", "language:bea", "language:bef", "language:bel", "language:ben", "language:beo", "language:beu", "language:bgs", "language:bgt", "language:bhg", "language:bhl", "language:big", "language:bjk", "language:bjp", "language:bjr", "language:bjv", "language:bjz", "language:bkd", "language:bki", "language:bkq", "language:bkx", "language:bla", "language:blw", "language:blz", "language:bmh", "language:bmk", "language:bmr", "language:bmu", "language:bnp", "language:boa", "language:boj", "language:bon", "language:box", "language:bpr", "language:bps", "language:bqc", "language:bqp", "language:bre", "language:bsj", "language:bsn", "language:bsp", "language:bss", "language:buk", "language:bus", "language:bvd", "language:bvr", "language:bxh", "language:byr", "language:byx", "language:bzd", "language:bzh", "language:bzj", "language:caa", "language:cab", "language:cac", "language:caf", "language:cak", "language:cao", "language:cap", "language:car", "language:cav", "language:cax", "language:cbc", "language:cbi", "language:cbk", "language:cbr", "language:cbs", "language:cbt", "language:cbu", "language:cbv", "language:cco", "language:ceb", "language:cek", "language:ces", "language:cgc", "language:cha", "language:chd", "language:chf", "language:chk", "language:chq", "language:chz", "language:cjo", "language:cjv", "language:ckb", "language:cle", "language:clu", "language:cme", "language:cmn", "language:cni", "language:cnl", "language:cnt", "language:cof", "language:con", "language:cop", "language:cot", "language:cpa", "language:cpb", "language:cpc", "language:cpu", "language:cpy", "language:crn", "language:crx", "language:cso", "language:csy", "language:cta", "language:cth", "language:ctp", "language:ctu", "language:cub", "language:cuc", "language:cui", "language:cuk", "language:cut", "language:cux", "language:cwe", "language:cya", "language:daa", "language:dad", "language:dah", "language:dan", "language:ded", "language:deu", "language:dgc", "language:dgr", "language:dgz", "language:dhg", "language:dif", "language:dik", "language:dji", "language:djk", "language:djr", "language:dob", "language:dop", "language:dov", "language:dwr", "language:dww", "language:dwy", "language:ebk", "language:eko", "language:emi", "language:emp", "language:eng", "language:enq", "language:epo", "language:eri", "language:ese", "language:esk", "language:etr", "language:ewe", "language:faa", "language:fai", "language:far", "language:ffm", "language:for", "language:fra", "language:fue", "language:fuf", "language:fuh", "language:gah", "language:gai", "language:gam", "language:gaw", "language:gdn", "language:gdr", "language:geb", "language:gfk", "language:ghs", "language:glk", "language:gmv", "language:gng", "language:gnn", "language:gnw", "language:gof", "language:grc", "language:gub", "language:guh", "language:gui", "language:guj", "language:gul", "language:gum", "language:gun", "language:guo", "language:gup", "language:gux", "language:gvc", "language:gvf", "language:gvn", "language:gvs", "language:gwi", "language:gym", "language:gyr", "language:hat", "language:hau", "language:haw", "language:hbo", "language:hch", "language:heb", "language:heg", "language:hin", "language:hix", "language:hla", "language:hlt", "language:hmo", "language:hns", "language:hop", "language:hot", "language:hrv", "language:hto", "language:hub", "language:hui", "language:hun", "language:hus", "language:huu", "language:huv", "language:hvn", "language:ian", "language:ign", "language:ikk", "language:ikw", "language:ilo", "language:imo", "language:inb", "language:ind", "language:ino", "language:iou", "language:ipi", "language:isn", "language:ita", "language:iws", "language:ixl", "language:jac", "language:jae", "language:jao", "language:jic", "language:jid", "language:jiv", "language:jni", "language:jpn", "language:jvn", "language:kan", "language:kaq", "language:kbc", "language:kbh", "language:kbm", "language:kbq", "language:kdc", "language:kde", "language:kdl", "language:kek", "language:ken", "language:kew", "language:kgf", "language:kgk", "language:kgp", "language:khs", "language:khz", "language:kik", "language:kiw", "language:kiz", "language:kje", "language:kjn", "language:kjs", "language:kkc", "language:kkl", "language:klt", "language:klv", "language:kmg", "language:kmh", "language:kmk", "language:kmo", "language:kms", "language:kmu", "language:kne", "language:knf", "language:knj", "language:knv", "language:kos", "language:kpf", "language:kpg", "language:kpj", "language:kpr", "language:kpw", "language:kpx", "language:kqa", "language:kqc", "language:kqf", "language:kql", "language:kqw", "language:ksd", "language:ksj", "language:ksr", "language:ktm", "language:kto", "language:kud", "language:kue", "language:kup", "language:kvg", "language:kvn", "language:kwd", "language:kwf", "language:kwi", "language:kwj", "language:kyc", "language:kyf", "language:kyg", "language:kyq", "language:kyz", "language:kze", "language:lac", "language:lat", "language:lbb", "language:lbk", "language:lcm", "language:leu", "language:lex", "language:lgl", "language:lid", "language:lif", "language:lin", "language:lit", "language:llg", "language:lug", "language:luo", "language:lww", "language:maa", "language:maj", "language:mal", "language:mam", "language:maq", "language:mar", "language:mau", "language:mav", "language:maz", "language:mbb", "language:mbc", "language:mbh", "language:mbj", "language:mbl", "language:mbs", "language:mbt", "language:mca", "language:mcb", "language:mcd", "language:mcf", "language:mco", "language:mcp", "language:mcq", "language:mcr", "language:mdy", "language:med", "language:mee", "language:mek", "language:meq", "language:met", "language:meu", "language:mgc", "language:mgh", "language:mgw", "language:mhl", "language:mib", "language:mic", "language:mie", "language:mig", "language:mih", "language:mil", "language:mio", "language:mir", "language:mit", "language:miz", "language:mjc", "language:mkj", "language:mkl", "language:mkn", "language:mks", "language:mle", "language:mlh", "language:mlp", "language:mmo", "language:mmx", "language:mna", "language:mop", "language:mox", "language:mph", "language:mpj", "language:mpm", "language:mpp", "language:mps", "language:mpt", "language:mpx", "language:mqb", "language:mqj", "language:msb", "language:msc", "language:msk", "language:msm", "language:msy", "language:mti", "language:mto", "language:mux", "language:muy", "language:mva", "language:mvn", "language:mwc", "language:mwe", "language:mwf", "language:mwp", "language:mxb", "language:mxp", "language:mxq", "language:mxt", "language:mya", "language:myk", "language:myu", "language:myw", "language:myy", "language:mzz", "language:nab", "language:naf", "language:nak", "language:nas", "language:nay", "language:nbq", "language:nca", "language:nch", "language:ncj", "language:ncl", "language:ncu", "language:ndg", "language:ndj", "language:nfa", "language:ngp", "language:ngu", "language:nhe", "language:nhg", "language:nhi", "language:nho", "language:nhr", "language:nhu", "language:nhw", "language:nhy", "language:nif", "language:nii", "language:nin", "language:nko", "language:nld", "language:nlg", "language:nmw", "language:nna", "language:nnq", "language:noa", "language:nop", "language:not", "language:nou", "language:npi", "language:npl", "language:nsn", "language:nss", "language:ntj", "language:ntp", "language:ntu", "language:nuy", "language:nvm", "language:nwi", "language:nya", "language:nys", "language:nyu", "language:obo", "language:okv", "language:omw", "language:ong", "language:ons", "language:ood", "language:opm", "language:ory", "language:ote", "language:otm", "language:otn", "language:otq", "language:ots", "language:pab", "language:pad", "language:pah", "language:pan", "language:pao", "language:pes", "language:pib", "language:pio", "language:pir", "language:piu", "language:pjt", "language:pls", "language:plu", "language:pma", "language:poe", "language:poh", "language:poi", "language:pol", "language:pon", "language:por", "language:poy", "language:ppo", "language:prf", "language:pri", "language:ptp", "language:ptu", "language:pwg", "language:qub", "language:quc", "language:quf", "language:quh", "language:qul", "language:qup", "language:qvc", "language:qve", "language:qvh", "language:qvm", "language:qvn", "language:qvs", "language:qvw", "language:qvz", "language:qwh", "language:qxh", "language:qxn", "language:qxo", "language:rai", "language:reg", "language:rgu", "language:rkb", "language:rmc", "language:rmy", "language:ron", "language:roo", "language:rop", "language:row", "language:rro", "language:ruf", "language:rug", "language:rus", "language:rwo", "language:sab", "language:san", "language:sbe", "language:sbk", "language:sbs", "language:seh", "language:sey", "language:sgb", "language:sgz", "language:shj", "language:shp", "language:sim", "language:sja", "language:sll", "language:smk", "language:snc", "language:snn", "language:snp", "language:snx", "language:sny", "language:som", "language:soq", "language:soy", "language:spa", "language:spl", "language:spm", "language:spp", "language:sps", "language:spy", "language:sri", "language:srm", "language:srn", "language:srp", "language:srq", "language:ssd", "language:ssg", "language:ssx", "language:stp", "language:sua", "language:sue", "language:sus", "language:suz", "language:swe", "language:swh", "language:swp", "language:sxb", "language:tac", "language:taj", "language:tam", "language:tav", "language:taw", "language:tbc", "language:tbf", "language:tbg", "language:tbl", "language:tbo", "language:tbz", "language:tca", "language:tcs", "language:tcz", "language:tdt", "language:tee", "language:tel", "language:ter", "language:tet", "language:tew", "language:tfr", "language:tgk", "language:tgl", "language:tgo", "language:tgp", "language:tha", "language:thd", "language:tif", "language:tim", "language:tiw", "language:tiy", "language:tke", "language:tku", "language:tlf", "language:tmd", "language:tna", "language:tnc", "language:tnk", "language:tnn", "language:tnp", "language:toc", "language:tod", "language:tof", "language:toj", "language:ton", "language:too", "language:top", "language:tos", "language:tpa", "language:tpi", "language:tpt", "language:tpz", "language:trc", "language:tsw", "language:ttc", "language:tte", "language:tuc", "language:tue", "language:tuf", "language:tuo", "language:tur", "language:tvk", "language:twi", "language:txq", "language:txu", "language:tzj", "language:tzo", "language:ubr", "language:ubu", "language:udu", "language:uig", "language:ukr", "language:uli", "language:ulk", "language:upv", "language:ura", "language:urb", "language:urd", "language:uri", "language:urt", "language:urw", "language:usa", "language:usp", "language:uvh", "language:uvl", "language:vid", "language:vie", "language:viv", "language:vmy", "language:waj", "language:wal", "language:wap", "language:wat", "language:wbi", "language:wbp", "language:wed", "language:wer", "language:wim", "language:wiu", "language:wiv", "language:wmt", "language:wmw", "language:wnc", "language:wnu", "language:wol", "language:wos", "language:wrk", "language:wro", "language:wrs", "language:wsk", "language:wuv", "language:xav", "language:xbi", "language:xed", "language:xla", "language:xnn", "language:xon", "language:xsi", "language:xtd", "language:xtm", "language:yaa", "language:yad", "language:yal", "language:yap", "language:yaq", "language:yby", "language:ycn", "language:yka", "language:yle", "language:yml", "language:yon", "language:yor", "language:yrb", "language:yre", "language:yss", "language:yuj", "language:yut", "language:yuw", "language:yva", "language:zaa", "language:zab", "language:zac", "language:zad", "language:zai", "language:zaj", "language:zam", "language:zao", "language:zap", "language:zar", "language:zas", "language:zat", "language:zav", "language:zaw", "language:zca", "language:zga", "language:zia", "language:ziw", "language:zlm", "language:zos", "language:zpc", "language:zpl", "language:zpm", "language:zpo", "language:zpq", "language:zpu", "language:zpv", "language:zpz", "language:zsr", "language:ztq", "language:zty", "language:zyp", "language:be", "language:br", "language:cs", "language:ch", "language:zh", "language:de", "language:en", "language:eo", "language:fr", "language:ht", "language:he", "language:hr", "language:id", "language:it", "language:ja", "language:la", "language:nl", "language:ru", "language:sa", "language:so", "language:es", "language:sr", "language:sv", "language:to", "language:uk", "language:vi", "license:cc-by-4.0", "license:other", "region:us" ]

[]

[ "aai", "aak", "aau", "aaz", "abt", "abx", "aby", "acf", "acr", "acu", "adz", "aer", "aey", "agd", "agg", "agm", "agn", "agr", "agt", "agu", "aia", "aii", "aka", "ake", "alp", "alq", "als", "aly", "ame", "amf", "amk", "amm", "amn", "amo", "amp", "amr", "amu", "amx", "anh", "anv", "aoi", "aoj", "aom", "aon", "apb", "ape", "apn", "apr", "apu", "apw", "apz", "arb", "are", "arl", "arn", "arp", "asm", "aso", "ata", "atb", "atd", "atg", "att", "auc", "aui", "auy", "avt", "awb", "awk", "awx", "azb", "azg", "azz", "bao", "bba", "bbb", "bbr", "bch", "bco", "bdd", "bea", "bef", "bel", "ben", "beo", "beu", "bgs", "bgt", "bhg", "bhl", "big", "bjk", "bjp", "bjr", "bjv", "bjz", "bkd", "bki", "bkq", "bkx", "bla", "blw", "blz", "bmh", "bmk", "bmr", "bmu", "bnp", "boa", "boj", "bon", "box", "bpr", "bps", "bqc", "bqp", "bre", "bsj", "bsn", "bsp", "bss", "buk", "bus", "bvd", "bvr", "bxh", "byr", "byx", "bzd", "bzh", "bzj", "caa", "cab", "cac", "caf", "cak", "cao", "cap", "car", "cav", "cax", "cbc", "cbi", "cbk", "cbr", "cbs", "cbt", "cbu", "cbv", "cco", "ceb", "cek", "ces", "cgc", "cha", "chd", "chf", "chk", "chq", "chz", "cjo", "cjv", "ckb", "cle", "clu", "cme", "cmn", "cni", "cnl", "cnt", "cof", "con", "cop", "cot", "cpa", "cpb", "cpc", "cpu", "cpy", "crn", "crx", "cso", "csy", "cta", "cth", "ctp", "ctu", "cub", "cuc", "cui", "cuk", "cut", "cux", "cwe", "cya", "daa", "dad", "dah", "dan", "ded", "deu", "dgc", "dgr", "dgz", "dhg", "dif", "dik", "dji", "djk", "djr", "dob", "dop", "dov", "dwr", "dww", "dwy", "ebk", "eko", "emi", "emp", "eng", "enq", "epo", "eri", "ese", "esk", "etr", "ewe", "faa", "fai", "far", "ffm", "for", "fra", "fue", "fuf", "fuh", "gah", "gai", "gam", "gaw", "gdn", "gdr", "geb", "gfk", "ghs", "glk", "gmv", "gng", "gnn", "gnw", "gof", "grc", "gub", "guh", "gui", "guj", "gul", "gum", "gun", "guo", "gup", "gux", "gvc", "gvf", "gvn", "gvs", "gwi", "gym", "gyr", "hat", "hau", "haw", "hbo", "hch", "heb", "heg", "hin", "hix", "hla", "hlt", "hmo", "hns", "hop", "hot", "hrv", "hto", "hub", "hui", "hun", "hus", "huu", "huv", "hvn", "ian", "ign", "ikk", "ikw", "ilo", "imo", "inb", "ind", "ino", "iou", "ipi", "isn", "ita", "iws", "ixl", "jac", "jae", "jao", "jic", "jid", "jiv", "jni", "jpn", "jvn", "kan", "kaq", "kbc", "kbh", "kbm", "kbq", "kdc", "kde", "kdl", "kek", "ken", "kew", "kgf", "kgk", "kgp", "khs", "khz", "kik", "kiw", "kiz", "kje", "kjn", "kjs", "kkc", "kkl", "klt", "klv", "kmg", "kmh", "kmk", "kmo", "kms", "kmu", "kne", "knf", "knj", "knv", "kos", "kpf", "kpg", "kpj", "kpr", "kpw", "kpx", "kqa", "kqc", "kqf", "kql", "kqw", "ksd", "ksj", "ksr", "ktm", "kto", "kud", "kue", "kup", "kvg", "kvn", "kwd", "kwf", "kwi", "kwj", "kyc", "kyf", "kyg", "kyq", "kyz", "kze", "lac", "lat", "lbb", "lbk", "lcm", "leu", "lex", "lgl", "lid", "lif", "lin", "lit", "llg", "lug", "luo", "lww", "maa", "maj", "mal", "mam", "maq", "mar", "mau", "mav", "maz", "mbb", "mbc", "mbh", "mbj", "mbl", "mbs", "mbt", "mca", "mcb", "mcd", "mcf", "mco", "mcp", "mcq", "mcr", "mdy", "med", "mee", "mek", "meq", "met", "meu", "mgc", "mgh", "mgw", "mhl", "mib", "mic", "mie", "mig", "mih", "mil", "mio", "mir", "mit", "miz", "mjc", "mkj", "mkl", "mkn", "mks", "mle", "mlh", "mlp", "mmo", "mmx", "mna", "mop", "mox", "mph", "mpj", "mpm", "mpp", "mps", "mpt", "mpx", "mqb", "mqj", "msb", "msc", "msk", "msm", "msy", "mti", "mto", "mux", "muy", "mva", "mvn", "mwc", "mwe", "mwf", "mwp", "mxb", "mxp", "mxq", "mxt", "mya", "myk", "myu", "myw", "myy", "mzz", "nab", "naf", "nak", "nas", "nay", "nbq", "nca", "nch", "ncj", "ncl", "ncu", "ndg", "ndj", "nfa", "ngp", "ngu", "nhe", "nhg", "nhi", "nho", "nhr", "nhu", "nhw", "nhy", "nif", "nii", "nin", "nko", "nld", "nlg", "nmw", "nna", "nnq", "noa", "nop", "not", "nou", "npi", "npl", "nsn", "nss", "ntj", "ntp", "ntu", "nuy", "nvm", "nwi", "nya", "nys", "nyu", "obo", "okv", "omw", "ong", "ons", "ood", "opm", "ory", "ote", "otm", "otn", "otq", "ots", "pab", "pad", "pah", "pan", "pao", "pes", "pib", "pio", "pir", "piu", "pjt", "pls", "plu", "pma", "poe", "poh", "poi", "pol", "pon", "por", "poy", "ppo", "prf", "pri", "ptp", "ptu", "pwg", "qub", "quc", "quf", "quh", "qul", "qup", "qvc", "qve", "qvh", "qvm", "qvn", "qvs", "qvw", "qvz", "qwh", "qxh", "qxn", "qxo", "rai", "reg", "rgu", "rkb", "rmc", "rmy", "ron", "roo", "rop", "row", "rro", "ruf", "rug", "rus", "rwo", "sab", "san", "sbe", "sbk", "sbs", "seh", "sey", "sgb", "sgz", "shj", "shp", "sim", "sja", "sll", "smk", "snc", "snn", "snp", "snx", "sny", "som", "soq", "soy", "spa", "spl", "spm", "spp", "sps", "spy", "sri", "srm", "srn", "srp", "srq", "ssd", "ssg", "ssx", "stp", "sua", "sue", "sus", "suz", "swe", "swh", "swp", "sxb", "tac", "taj", "tam", "tav", "taw", "tbc", "tbf", "tbg", "tbl", "tbo", "tbz", "tca", "tcs", "tcz", "tdt", "tee", "tel", "ter", "tet", "tew", "tfr", "tgk", "tgl", "tgo", "tgp", "tha", "thd", "tif", "tim", "tiw", "tiy", "tke", "tku", "tlf", "tmd", "tna", "tnc", "tnk", "tnn", "tnp", "toc", "tod", "tof", "toj", "ton", "too", "top", "tos", "tpa", "tpi", "tpt", "tpz", "trc", "tsw", "ttc", "tte", "tuc", "tue", "tuf", "tuo", "tur", "tvk", "twi", "txq", "txu", "tzj", "tzo", "ubr", "ubu", "udu", "uig", "ukr", "uli", "ulk", "upv", "ura", "urb", "urd", "uri", "urt", "urw", "usa", "usp", "uvh", "uvl", "vid", "vie", "viv", "vmy", "waj", "wal", "wap", "wat", "wbi", "wbp", "wed", "wer", "wim", "wiu", "wiv", "wmt", "wmw", "wnc", "wnu", "wol", "wos", "wrk", "wro", "wrs", "wsk", "wuv", "xav", "xbi", "xed", "xla", "xnn", "xon", "xsi", "xtd", "xtm", "yaa", "yad", "yal", "yap", "yaq", "yby", "ycn", "yka", "yle", "yml", "yon", "yor", "yrb", "yre", "yss", "yuj", "yut", "yuw", "yva", "zaa", "zab", "zac", "zad", "zai", "zaj", "zam", "zao", "zap", "zar", "zas", "zat", "zav", "zaw", "zca", "zga", "zia", "ziw", "zlm", "zos", "zpc", "zpl", "zpm", "zpo", "zpq", "zpu", "zpv", "zpz", "zsr", "ztq", "zty", "zyp", "be", "br", "cs", "ch", "zh", "de", "en", "eo", "fr", "ht", "he", "hr", "id", "it", "ja", "la", "nl", "ru", "sa", "so", "es", "sr", "sv", "to", "uk", "vi" ]

[ "# Dataset Card for BibleNLP Corpus", "### Dataset Summary\nPartial and complete Bible translations in 833 languages, aligned by verse.", "### Languages\naai, aak, aau, aaz, abt, abx, aby, acf, acr, acu, adz, aer, aey, agd, agg, agm, agn, agr, agt, agu, aia, aii, aka, ake, alp, alq, als, aly, ame, amf, amk, amm, amn, amo, amp, amr, amu, amx, anh, anv, aoi, aoj, aom, aon, apb, ape, apn, apr, apu, apw, apz, arb, are, arl, arn, arp, asm, aso, ata, atb, atd, atg, att, auc, aui, auy, avt, awb, awk, awx, azb, azg, azz, bao, bba, bbb, bbr, bch, bco, bdd, bea, bef, bel, ben, beo, beu, bgs, bgt, bhg, bhl, big, bjk, bjp, bjr, bjv, bjz, bkd, bki, bkq, bkx, bla, blw, blz, bmh, bmk, bmr, bmu, bnp, boa, boj, bon, box, bpr, bps, bqc, bqp, bre, bsj, bsn, bsp, bss, buk, bus, bvd, bvr, bxh, byr, byx, bzd, bzh, bzj, caa, cab, cac, caf, cak, cao, cap, car, cav, cax, cbc, cbi, cbk, cbr, cbs, cbt, cbu, cbv, cco, ceb, cek, ces, cgc, cha, chd, chf, chk, chq, chz, cjo, cjv, ckb, cle, clu, cme, cmn, cni, cnl, cnt, cof, con, cop, cot, cpa, cpb, cpc, cpu, cpy, crn, crx, cso, csy, cta, cth, ctp, ctu, cub, cuc, cui, cuk, cut, cux, cwe, cya, daa, dad, dah, dan, ded, deu, dgc, dgr, dgz, dhg, dif, dik, dji, djk, djr, dob, dop, dov, dwr, dww, dwy, ebk, eko, emi, emp, eng, enq, epo, eri, ese, esk, etr, ewe, faa, fai, far, ffm, for, fra, fue, fuf, fuh, gah, gai, gam, gaw, gdn, gdr, geb, gfk, ghs, glk, gmv, gng, gnn, gnw, gof, grc, gub, guh, gui, guj, gul, gum, gun, guo, gup, gux, gvc, gvf, gvn, gvs, gwi, gym, gyr, hat, hau, haw, hbo, hch, heb, heg, hin, hix, hla, hlt, hmo, hns, hop, hot, hrv, hto, hub, hui, hun, hus, huu, huv, hvn, ian, ign, ikk, ikw, ilo, imo, inb, ind, ino, iou, ipi, isn, ita, iws, ixl, jac, jae, jao, jic, jid, jiv, jni, jpn, jvn, kan, kaq, kbc, kbh, kbm, kbq, kdc, kde, kdl, kek, ken, kew, kgf, kgk, kgp, khs, khz, kik, kiw, kiz, kje, kjn, kjs, kkc, kkl, klt, klv, kmg, kmh, kmk, kmo, kms, kmu, kne, knf, knj, knv, kos, kpf, kpg, kpj, kpr, kpw, kpx, kqa, kqc, kqf, kql, kqw, ksd, ksj, ksr, ktm, kto, kud, kue, kup, kvg, kvn, kwd, kwf, kwi, kwj, kyc, kyf, kyg, kyq, kyz, kze, lac, lat, lbb, lbk, lcm, leu, lex, lgl, lid, lif, lin, lit, llg, lug, luo, lww, maa, maj, mal, mam, maq, mar, mau, mav, maz, mbb, mbc, mbh, mbj, mbl, mbs, mbt, mca, mcb, mcd, mcf, mco, mcp, mcq, mcr, mdy, med, mee, mek, meq, met, meu, mgc, mgh, mgw, mhl, mib, mic, mie, mig, mih, mil, mio, mir, mit, miz, mjc, mkj, mkl, mkn, mks, mle, mlh, mlp, mmo, mmx, mna, mop, mox, mph, mpj, mpm, mpp, mps, mpt, mpx, mqb, mqj, msb, msc, msk, msm, msy, mti, mto, mux, muy, mva, mvn, mwc, mwe, mwf, mwp, mxb, mxp, mxq, mxt, mya, myk, myu, myw, myy, mzz, nab, naf, nak, nas, nay, nbq, nca, nch, ncj, ncl, ncu, ndg, ndj, nfa, ngp, ngu, nhe, nhg, nhi, nho, nhr, nhu, nhw, nhy, nif, nii, nin, nko, nld, nlg, nmw, nna, nnq, noa, nop, not, nou, npi, npl, nsn, nss, ntj, ntp, ntu, nuy, nvm, nwi, nya, nys, nyu, obo, okv, omw, ong, ons, ood, opm, ory, ote, otm, otn, otq, ots, pab, pad, pah, pan, pao, pes, pib, pio, pir, piu, pjt, pls, plu, pma, poe, poh, poi, pol, pon, por, poy, ppo, prf, pri, ptp, ptu, pwg, qub, quc, quf, quh, qul, qup, qvc, qve, qvh, qvm, qvn, qvs, qvw, qvz, qwh, qxh, qxn, qxo, rai, reg, rgu, rkb, rmc, rmy, ron, roo, rop, row, rro, ruf, rug, rus, rwo, sab, san, sbe, sbk, sbs, seh, sey, sgb, sgz, shj, shp, sim, sja, sll, smk, snc, snn, snp, snx, sny, som, soq, soy, spa, spl, spm, spp, sps, spy, sri, srm, srn, srp, srq, ssd, ssg, ssx, stp, sua, sue, sus, suz, swe, swh, swp, sxb, tac, taj, tam, tav, taw, tbc, tbf, tbg, tbl, tbo, tbz, tca, tcs, tcz, tdt, tee, tel, ter, tet, tew, tfr, tgk, tgl, tgo, tgp, tha, thd, tif, tim, tiw, tiy, tke, tku, tlf, tmd, tna, tnc, tnk, tnn, tnp, toc, tod, tof, toj, ton, too, top, tos, tpa, tpi, tpt, tpz, trc, tsw, ttc, tte, tuc, tue, tuf, tuo, tur, tvk, twi, txq, txu, tzj, tzo, ubr, ubu, udu, uig, ukr, uli, ulk, upv, ura, urb, urd, uri, urt, urw, usa, usp, uvh, uvl, vid, vie, viv, vmy, waj, wal, wap, wat, wbi, wbp, wed, wer, wim, wiu, wiv, wmt, wmw, wnc, wnu, wol, wos, wrk, wro, wrs, wsk, wuv, xav, xbi, xed, xla, xnn, xon, xsi, xtd, xtm, yaa, yad, yal, yap, yaq, yby, ycn, yka, yle, yml, yon, yor, yrb, yre, yss, yuj, yut, yuw, yva, zaa, zab, zac, zad, zai, zaj, zam, zao, zap, zar, zas, zat, zav, zaw, zca, zga, zia, ziw, zlm, zos, zpc, zpl, zpm, zpo, zpq, zpu, zpv, zpz, zsr, ztq, zty, zyp", "## Dataset Structure", "### Data Fields\n\ntranslation \n- languages - an N length list of the languages of the translations, sorted alphabetically\n- translation - an N length list with the translations each corresponding to the language specified in the above field\nfiles\n- lang - an N length list of the languages of the files, in order of input\n- file - an N length list of the filenames from the corpus on github, each corresponding with the lang above\nref - the verse(s) contained in the record, as a list, with each represented with: ''<a three letter book code> <chapter number>:<verse number>'' \n\nlicenses - an N length list of licenses, corresponding to the list of files above\n\ncopyrights - information on copyright holders, corresponding to the list of files above", "### Usage\n\nThe dataset loading script requires installation of tqdm, ijson, and numpy\n\nSpecify the languages to be paired with a list and ISO 693-3 language codes, such as ''languages = ['eng', 'fra']''.\nBy default, the script will return individual verse pairs, as well as verses covering a full range. If only the individual verses is desired, use ''pair='single'''. If only the maximum range pairing is desired use ''pair='range''' (for example, if one text uses the verse range covering GEN 1:1-3, all texts would return only the full length pairing).", "## Sources\nURL" ]

[ "TAGS\n#task_categories-translation #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-translation #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Arifama-Miniafia #language-Ankave #language-Abau #language-Amarasi #language-Ambulas #language-Inabaknon #language-Aneme Wake #language-Saint Lucian Creole French #language-Achi #language-Achuar-Shiwiar #language-Adzera #language-Eastern Arrernte #language-Amele #language-Agarabi #language-Angor #language-Angaataha #language-Agutaynen #language-Aguaruna #language-Central Cagayan Agta #language-Aguacateco #language-Arosi #language-Assyrian Neo-Aramaic #language-Akan #language-Akawaio #language-Alune #language-Algonquin #language-Tosk Albanian #language-Alyawarr #language-Yanesha' #language-Hamer-Banna #language-Ambai #language-Ama (Papua New Guinea) #language-Amanab #language-Amo #language-Alamblak #language-Amarakaeri #language-Guerrero Amuzgo #language-Anmatyerre #language-Nend #language-Denya #language-Anindilyakwa #language-Mufian #language-Ömie #language-Bumbita Arapesh #language-Sa'a #language-Bukiyip #language-Apinayé #language-Arop-Lokep #language-Apurinã #language-Western Apache #language-Safeyoka #language-Standard Arabic #language-Western Arrarnta #language-Arabela #language-Mapudungun #language-Arapaho #language-Assamese #language-Dano #language-Pele-Ata #language-Zaiwa #language-Ata Manobo #language-Ivbie North-Okpela-Arhe #language-Pamplona Atta #language-Waorani #language-Anuki #language-Awiyaana #language-Au #language-Awa (Papua New Guinea) #language-Awabakal #language-Awara #language-South Azerbaijani #language-San Pedro Amuzgos Amuzgo #language-Highland Puebla Nahuatl #language-Waimaha #language-Baatonum #language-Barai #language-Girawa #language-Bariai #language-Kaluli #language-Bunama #language-Beaver #language-Benabena #language-Belarusian #language-Bengali #language-Beami #language-Blagar #language-Tagabawa #language-Bughotu #language-Binandere #language-Bimin #language-Biangai #language-Barok #language-Fanamaket #language-Binumarien #language-Bedjond #language-Baruga #language-Binukid #language-Baki #language-Bakairí #language-Baikeno #language-Siksika #language-Balangao #language-Balantak #language-Kein #language-Ghayavi #language-Muinane #language-Somba-Siawari #language-Bola #language-Bora #language-Anjam #language-Bine #language-Buamu #language-Koronadal Blaan #language-Sarangani Blaan #language-Boko (Benin) #language-Busa #language-Breton #language-Bangwinji #language-Barasana-Eduria #language-Baga Sitemu #language-Akoose #language-Bugawac #language-Bokobaru #language-Baeggu #language-Burarra #language-Buhutu #language-Baruya #language-Qaqet #language-Bribri #language-Mapos Buang #language-Belize Kriol English #language-Chortí #language-Garifuna #language-Chuj #language-Southern Carrier #language-Kaqchikel #language-Chácobo #language-Chipaya #language-Galibi Carib #language-Cavineña #language-Chiquitano #language-Carapana #language-Chachi #language-Chavacano #language-Cashibo-Cacataibo #language-Cashinahua #language-Chayahuita #language-Candoshi-Shapra #language-Cacua #language-Comaltepec Chinantec #language-Cebuano #language-Eastern Khumi Chin #language-Czech #language-Kagayanen #language-Chamorro #language-Highland Oaxaca Chontal #language-Tabasco Chontal #language-Chuukese #language-Quiotepec Chinantec #language-Ozumacín Chinantec #language-Ashéninka Pajonal #language-Chuave #language-Central Kurdish #language-Lealao Chinantec #language-Caluyanun #language-Cerma #language-Mandarin Chinese #language-Asháninka #language-Lalana Chinantec #language-Tepetotutla Chinantec #language-Colorado #language-Cofán #language-Coptic #language-Caquinte #language-Palantla Chinantec #language-Ucayali-Yurúa Ashéninka #language-Ajyíninka Apurucayali #language-Pichis Ashéninka #language-South Ucayali Ashéninka #language-El Nayar Cora #language-Carrier #language-Sochiapam Chinantec #language-Siyin Chin #language-Tataltepec Chatino #language-Thaiphum Chin #language-Western Highland Chatino #language-Chol #language-Cubeo #language-Usila Chinantec #language-Cuiba #language-San Blas Kuna #language-Teutila Cuicatec #language-Tepeuxila Cuicatec #language-Kwere #language-Nopala Chatino #language-Dangaléat #language-Marik #language-Gwahatike #language-Danish #language-Dedua #language-German #language-Casiguran Dumagat Agta #language-Dogrib #language-Daga #language-Dhangu-Djangu #language-Dieri #language-Southwestern Dinka #language-Djinang #language-Eastern Maroon Creole #language-Djambarrpuyngu #language-Dobu #language-Lukpa #language-Dombe #language-Dawro #language-Dawawa #language-Dhuwaya #language-Eastern Bontok #language-Koti #language-Mussau-Emira #language-Northern Emberá #language-English #language-Enga #language-Esperanto #language-Ogea #language-Ese Ejja #language-Northwest Alaska Inupiatun #language-Edolo #language-Ewe #language-Fasu #language-Faiwol #language-Fataleka #language-Maasina Fulfulde #language-Fore #language-French #language-Borgu Fulfulde #language-Pular #language-Western Niger Fulfulde #language-Alekano #language-Borei #language-Kandawo #language-Nobonob #language-Umanakaina #language-Wipi #language-Kire #language-Patpatar #language-Guhu-Samane #language-Gilaki #language-Gamo #language-Ngangam #language-Gumatj #language-Western Bolivian Guaraní #language-Gofa #language-Ancient Greek (to 1453) #language-Guajajára #language-Guahibo #language-Eastern Bolivian Guaraní #language-Gujarati #language-Sea Island Creole English #language-Guambiano #language-Mbyá Guaraní #language-Guayabero #language-Gunwinggu #language-Gourmanchéma #language-Guanano #language-Golin #language-Kuku-Yalanji #language-Gumawana #language-Gwichʼin #language-Ngäbere #language-Guarayu #language-Haitian #language-Hausa #language-Hawaiian #language-Ancient Hebrew #language-Huichol #language-Hebrew #language-Helong #language-Hindi #language-Hixkaryána #language-Halia #language-Matu Chin #language-Hiri Motu #language-Caribbean Hindustani #language-Hopi #language-Hote #language-Croatian #language-Minica Huitoto #language-Huambisa #language-Huli #language-Hungarian #language-Huastec #language-Murui Huitoto #language-San Mateo Del Mar Huave #language-Sabu #language-Iatmul #language-Ignaciano #language-Ika #language-Ikwere #language-Iloko #language-Imbongu #language-Inga #language-Indonesian #language-Inoke-Yate #language-Tuma-Irumu #language-Ipili #language-Isanzu #language-Italian #language-Sepik Iwam #language-Ixil #language-Popti' #language-Yabem #language-Yanyuwa #language-Tol #language-Bu (Kaduna State) #language-Shuar #language-Janji #language-Japanese #language-Caribbean Javanese #language-Kannada #language-Capanahua #language-Kadiwéu #language-Camsá #language-Iwal #language-Kamano #language-Kutu #language-Makonde #language-Tsikimba #language-Kekchí #language-Kenyang #language-West Kewa #language-Kube #language-Kaiwá #language-Kaingang #language-Kasua #language-Keapara #language-Kikuyu #language-Northeast Kiwai #language-Kisi #language-Kisar #language-Kunjen #language-East Kewa #language-Odoodee #language-Kosarek Yale #language-Nukna #language-Maskelynes #language-Kâte #language-Kalam #language-Limos Kalinga #language-Kwoma #language-Kamasau #language-Kanite #language-Kankanaey #language-Mankanya #language-Western Kanjobal #language-Tabo #language-Kosraean #language-Komba #language-Kapingamarangi #language-Karajá #language-Korafe-Yegha #language-Kobon #language-Mountain Koiali #language-Mum #language-Doromu-Koki #language-Kakabai #language-Kyenele #language-Kandas #language-Kuanua #language-Uare #language-Borong #language-Kurti #language-Kuot #language-'Auhelawa #language-Kuman (Papua New Guinea) #language-Kunimaipa #language-Kuni-Boazi #language-Border Kuna #language-Kwaio #language-Kwara'ae #language-Awa-Cuaiquer #language-Kwanga #language-Kyaka #language-Kouya #language-Keyagana #language-Kenga #language-Kayabí #language-Kosena #language-Lacandon #language-Latin #language-Label #language-Central Bontok #language-Tungag #language-Kara (Papua New Guinea) #language-Luang #language-Wala #language-Nyindrou #language-Limbu #language-Lingala #language-Lithuanian #language-Lole #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lewo #language-San Jerónimo Tecóatl Mazatec #language-Jalapa De Díaz Mazatec #language-Malayalam #language-Mam #language-Chiquihuitlán Mazatec #language-Marathi #language-Huautla Mazatec #language-Sateré-Mawé #language-Central Mazahua #language-Western Bukidnon Manobo #language-Macushi #language-Mangseng #language-Nadëb #language-Maxakalí #language-Sarangani Manobo #language-Matigsalug Manobo #language-Maca #language-Machiguenga #language-Sharanahua #language-Matsés #language-Coatlán Mixe #language-Makaa #language-Ese #language-Menya #language-Male (Ethiopia) #language-Melpa #language-Mengen #language-Mekeo #language-Merey #language-Mato #language-Motu #language-Morokodo #language-Makhuwa-Meetto #language-Matumbi #language-Mauwake #language-Atatláhuca Mixtec #language-Mi'kmaq #language-Ocotepec Mixtec #language-San Miguel El Grande Mixtec #language-Chayuco Mixtec #language-Peñoles Mixtec #language-Pinotepa Nacional Mixtec #language-Isthmus Mixe #language-Southern Puebla Mixtec #language-Coatzospan Mixtec #language-San Juan Colorado Mixtec #language-Mokilese #language-Mokole #language-Kupang Malay #language-Silacayoapan Mixtec #language-Manambu #language-Mape #language-Bargam #language-Mangga Buang #language-Madak #language-Mbula #language-Mopán Maya #language-Molima #language-Maung #language-Martu Wangka #language-Yosondúa Mixtec #language-Migabac #language-Dadibi #language-Mian #language-Misima-Panaeati #language-Mbuko #language-Mamasa #language-Masbatenyo #language-Sankaran Maninka #language-Mansaka #language-Agusan Manobo #language-Aruamu #language-Maiwa (Papua New Guinea) #language-Totontepec Mixe #language-Bo-Ung #language-Muyang #language-Manam #language-Minaveha #language-Are #language-Mwera (Chimwera) #language-Murrinh-Patha #language-Kala Lagaw Ya #language-Tezoatlán Mixtec #language-Tlahuitoltepec Mixe #language-Juquila Mixe #language-Jamiltepec Mixtec #language-Burmese #language-Mamara Senoufo #language-Mundurukú #language-Muyuw #language-Macuna #language-Maiadomu #language-Southern Nambikuára #language-Nabak #language-Nakanai #language-Naasioi #language-Ngarrindjeri #language-Nggem #language-Iyo #language-Central Huasteca Nahuatl #language-Northern Puebla Nahuatl #language-Michoacán Nahuatl #language-Chumburung #language-Ndengereko #language-Ndamba #language-Dhao #language-Ngulu #language-Guerrero Nahuatl #language-Eastern Huasteca Nahuatl #language-Tetelcingo Nahuatl #language-Zacatlán-Ahuacatlán-Tepetzintla Nahuatl #language-Takuu #language-Naro #language-Noone #language-Western Huasteca Nahuatl #language-Northern Oaxaca Nahuatl #language-Nek #language-Nii #language-Ninzo #language-Nkonya #language-Dutch #language-Gela #language-Nimoa #language-Nyangumarta #language-Ngindo #language-Woun Meu #language-Numanggang #language-Nomatsiguenga #language-Ewage-Notu #language-Nepali (individual language) #language-Southeastern Puebla Nahuatl #language-Nehan #language-Nali #language-Ngaanyatjarra #language-Northern Tepehuan #language-Natügu #language-Nunggubuyu #language-Namiae #language-Southwest Tanna #language-Nyanja #language-Nyungar #language-Nyungwe #language-Obo Manobo #language-Orokaiva #language-South Tairora #language-Olo #language-Ono #language-Tohono O'odham #language-Oksapmin #language-Odia #language-Mezquital Otomi #language-Eastern Highland Otomi #language-Tenango Otomi #language-Querétaro Otomi #language-Estado de México Otomi #language-Parecís #language-Paumarí #language-Tenharim #language-Panjabi #language-Northern Paiute #language-Iranian Persian #language-Yine #language-Piapoco #language-Piratapuyo #language-Pintupi-Luritja #language-Pitjantjatjara #language-San Marcos Tlacoyalco Popoloca #language-Palikúr #language-Paama #language-San Juan Atzingo Popoloca #language-Poqomchi' #language-Highland Popoluca #language-Polish #language-Pohnpeian #language-Portuguese #language-Pogolo #language-Folopa #language-Paranan #language-Paicî #language-Patep #language-Bambam #language-Gapapaiwa #language-Huallaga Huánuco Quechua #language-K'iche' #language-Lambayeque Quechua #language-South Bolivian Quechua #language-North Bolivian Quechua #language-Southern Pastaza Quechua #language-Cajamarca Quechua #language-Eastern Apurímac Quechua #language-Huamalíes-Dos de Mayo Huánuco Quechua #language-Margos-Yarowilca-Lauricocha Quechua #language-North Junín Quechua #language-San Martín Quechua #language-Huaylla Wanca Quechua #language-Northern Pastaza Quichua #language-Huaylas Ancash Quechua #language-Panao Huánuco Quechua #language-Northern Conchucos Ancash Quechua #language-Southern Conchucos Ancash Quechua #language-Ramoaaina #language-Kara (Tanzania) #language-Ringgou #language-Rikbaktsa #language-Carpathian Romani #language-Vlax Romani #language-Romanian #language-Rotokas #language-Kriol #language-Dela-Oenale #language-Waima #language-Luguru #language-Roviana #language-Russian #language-Rawa #language-Buglere #language-Sanskrit #language-Saliba #language-Safwa #language-Subiya #language-Sena #language-Secoya #language-Mag-antsi Ayta #language-Sursurunga #language-Shatt #language-Shipibo-Conibo #language-Mende (Papua New Guinea) #language-Epena #language-Salt-Yui #language-Bolinao #language-Sinaugoro #language-Siona #language-Siane #language-Sam #language-Saniyo-Hiyewe #language-Somali #language-Kanasi #language-Miyobe #language-Spanish #language-Selepet #language-Akukem #language-Supyire Senoufo #language-Saposa #language-Sabaot #language-Siriano #language-Saramaccan #language-Sranan Tongo #language-Serbian #language-Sirionó #language-Siroi #language-Seimat #language-Samberigi #language-Southeastern Tepehuan #language-Sulka #language-Suena #language-Susu #language-Sunwar #language-Swedish #language-Swahili (individual language) #language-Suau #language-Suba #language-Lowland Tarahumara #language-Eastern Tamang #language-Tamil #language-Tatuyo #language-Tai #language-Takia #language-Mandara #language-North Tairora #language-Tboli #language-Tawala #language-Ditammari #language-Ticuna #language-Torres Strait Creole #language-Thado Chin #language-Tetun Dili #language-Huehuetla Tepehua #language-Telugu #language-Tereno #language-Tetum #language-Tewa (USA) #language-Teribe #language-Tajik #language-Tagalog #language-Sudest #language-Tangoa #language-Thai #language-Kuuk Thaayorre #language-Tifal #language-Timbe #language-Tiwi #language-Tiruray #language-Takwane #language-Upper Necaxa Totonac #language-Telefol #language-Haruai #language-Tacana #language-Tanimuca-Retuarã #language-Kwamera #language-North Tanna #language-Whitesands #language-Coyutla Totonac #language-Toma #language-Gizrra #language-Tojolabal #language-Tonga (Tonga Islands) #language-Xicotepec De Juárez Totonac #language-Papantla Totonac #language-Highland Totonac #language-Taupota #language-Tok Pisin #language-Tlachichilco Tepehua #language-Tinputz #language-Copala Triqui #language-Tsishingini #language-Tektiteko #language-Bwanabwana #language-Mutu #language-Tuyuca #language-Central Tunebo #language-Tucano #language-Turkish #language-Southeast Ambrym #language-Twi #language-Tii #language-Kayapó #language-Tz'utujil #language-Tzotzil #language-Ubir #language-Umbu-Ungu #language-Uduk #language-Uighur #language-Ukrainian #language-Ulithian #language-Meriam Mir #language-Uripiv-Wala-Rano-Atchin #language-Urarina #language-Urubú-Kaapor #language-Urdu #language-Urim #language-Urat #language-Sop #language-Usarufa #language-Uspanteco #language-Uri #language-Lote #language-Vidunda #language-Vietnamese #language-Iduna #language-Ayautla Mazatec #language-Waffa #language-Wolaytta #language-Wapishana #language-Kaninuwa #language-Vwanji #language-Warlpiri #language-Wedau #language-Weri #language-Wik-Mungkan #language-Wiru #language-Vitu #language-Walmajarri #language-Mwani #language-Wantoat #language-Usan #language-Wolof #language-Hanga Hundi #language-Garrwa #language-Worrorra #language-Waris #language-Waskia #language-Wuvulu-Aua #language-Xavánte #language-Kombio #language-Hdi #language-Kamula #language-Northern Kankanay #language-Konkomba #language-Sio #language-Diuxi-Tilantongo Mixtec #language-Magdalena Peñasco Mixtec #language-Yaminahua #language-Yagua #language-Yalunka #language-Yapese #language-Yaqui #language-Yaweyuha #language-Yucuna #language-Yakan #language-Yele #language-Iamalele #language-Yongkom #language-Yoruba #language-Yareba #language-Yaouré #language-Yessan-Mayo #language-Karkar-Yuri #language-Yopno #language-Yau (Morobe Province) #language-Yawa #language-Sierra de Juárez Zapotec #language-Western Tlacolula Valley Zapotec #language-Ocotlán Zapotec #language-Cajonos Zapotec #language-Isthmus Zapotec #language-Zaramo #language-Miahuatlán Zapotec #language-Ozolotepec Zapotec #language-Zapotec #language-Rincón Zapotec #language-Santo Domingo Albarradas Zapotec #language-Tabaa Zapotec #language-Yatzachi Zapotec #language-Mitla Zapotec #language-Coatecas Altas Zapotec #language-Kinga #language-Zia #language-Zigula #language-Malay (individual language) #language-Francisco León Zoque #language-Choapan Zapotec #language-Lachixío Zapotec #language-Mixtepec Zapotec #language-Amatlán Zapotec #language-Zoogocho Zapotec #language-Yalálag Zapotec #language-Chichicapan Zapotec #language-Texmelucan Zapotec #language-Southern Rincon Zapotec #language-Quioquitani-Quierí Zapotec #language-Yatee Zapotec #language-Zyphe Chin #language-Belarusian #language-Breton #language-Czech #language-Chamorro #language-Chinese #language-German #language-English #language-Esperanto #language-French #language-Haitian #language-Hebrew #language-Croatian #language-Indonesian #language-Italian #language-Japanese #language-Latin #language-Dutch #language-Russian #language-Sanskrit #language-Somali #language-Spanish #language-Serbian #language-Swedish #language-Tonga (Tonga Islands) #language-Ukrainian #language-Vietnamese #license-cc-by-4.0 #license-other #region-us \n", "# Dataset Card for BibleNLP Corpus", "### Dataset Summary\nPartial and complete Bible translations in 833 languages, aligned by verse.", "### Languages\naai, aak, aau, aaz, abt, abx, aby, acf, acr, acu, adz, aer, aey, agd, agg, agm, agn, agr, agt, agu, aia, aii, aka, ake, alp, alq, als, aly, ame, amf, amk, amm, amn, amo, amp, amr, amu, amx, anh, anv, aoi, aoj, aom, aon, apb, ape, apn, apr, apu, apw, apz, arb, are, arl, arn, arp, asm, aso, ata, atb, atd, atg, att, auc, aui, auy, avt, awb, awk, awx, azb, azg, azz, bao, bba, bbb, bbr, bch, bco, bdd, bea, bef, bel, ben, beo, beu, bgs, bgt, bhg, bhl, big, bjk, bjp, bjr, bjv, bjz, bkd, bki, bkq, bkx, bla, blw, blz, bmh, bmk, bmr, bmu, bnp, boa, boj, bon, box, bpr, bps, bqc, bqp, bre, bsj, bsn, bsp, bss, buk, bus, bvd, bvr, bxh, byr, byx, bzd, bzh, bzj, caa, cab, cac, caf, cak, cao, cap, car, cav, cax, cbc, cbi, cbk, cbr, cbs, cbt, cbu, cbv, cco, ceb, cek, ces, cgc, cha, chd, chf, chk, chq, chz, cjo, cjv, ckb, cle, clu, cme, cmn, cni, cnl, cnt, cof, con, cop, cot, cpa, cpb, cpc, cpu, cpy, crn, crx, cso, csy, cta, cth, ctp, ctu, cub, cuc, cui, cuk, cut, cux, cwe, cya, daa, dad, dah, dan, ded, deu, dgc, dgr, dgz, dhg, dif, dik, dji, djk, djr, dob, dop, dov, dwr, dww, dwy, ebk, eko, emi, emp, eng, enq, epo, eri, ese, esk, etr, ewe, faa, fai, far, ffm, for, fra, fue, fuf, fuh, gah, gai, gam, gaw, gdn, gdr, geb, gfk, ghs, glk, gmv, gng, gnn, gnw, gof, grc, gub, guh, gui, guj, gul, gum, gun, guo, gup, gux, gvc, gvf, gvn, gvs, gwi, gym, gyr, hat, hau, haw, hbo, hch, heb, heg, hin, hix, hla, hlt, hmo, hns, hop, hot, hrv, hto, hub, hui, hun, hus, huu, huv, hvn, ian, ign, ikk, ikw, ilo, imo, inb, ind, ino, iou, ipi, isn, ita, iws, ixl, jac, jae, jao, jic, jid, jiv, jni, jpn, jvn, kan, kaq, kbc, kbh, kbm, kbq, kdc, kde, kdl, kek, ken, kew, kgf, kgk, kgp, khs, khz, kik, kiw, kiz, kje, kjn, kjs, kkc, kkl, klt, klv, kmg, kmh, kmk, kmo, kms, kmu, kne, knf, knj, knv, kos, kpf, kpg, kpj, kpr, kpw, kpx, kqa, kqc, kqf, kql, kqw, ksd, ksj, ksr, ktm, kto, kud, kue, kup, kvg, kvn, kwd, kwf, kwi, kwj, kyc, kyf, kyg, kyq, kyz, kze, lac, lat, lbb, lbk, lcm, leu, lex, lgl, lid, lif, lin, lit, llg, lug, luo, lww, maa, maj, mal, mam, maq, mar, mau, mav, maz, mbb, mbc, mbh, mbj, mbl, mbs, mbt, mca, mcb, mcd, mcf, mco, mcp, mcq, mcr, mdy, med, mee, mek, meq, met, meu, mgc, mgh, mgw, mhl, mib, mic, mie, mig, mih, mil, mio, mir, mit, miz, mjc, mkj, mkl, mkn, mks, mle, mlh, mlp, mmo, mmx, mna, mop, mox, mph, mpj, mpm, mpp, mps, mpt, mpx, mqb, mqj, msb, msc, msk, msm, msy, mti, mto, mux, muy, mva, mvn, mwc, mwe, mwf, mwp, mxb, mxp, mxq, mxt, mya, myk, myu, myw, myy, mzz, nab, naf, nak, nas, nay, nbq, nca, nch, ncj, ncl, ncu, ndg, ndj, nfa, ngp, ngu, nhe, nhg, nhi, nho, nhr, nhu, nhw, nhy, nif, nii, nin, nko, nld, nlg, nmw, nna, nnq, noa, nop, not, nou, npi, npl, nsn, nss, ntj, ntp, ntu, nuy, nvm, nwi, nya, nys, nyu, obo, okv, omw, ong, ons, ood, opm, ory, ote, otm, otn, otq, ots, pab, pad, pah, pan, pao, pes, pib, pio, pir, piu, pjt, pls, plu, pma, poe, poh, poi, pol, pon, por, poy, ppo, prf, pri, ptp, ptu, pwg, qub, quc, quf, quh, qul, qup, qvc, qve, qvh, qvm, qvn, qvs, qvw, qvz, qwh, qxh, qxn, qxo, rai, reg, rgu, rkb, rmc, rmy, ron, roo, rop, row, rro, ruf, rug, rus, rwo, sab, san, sbe, sbk, sbs, seh, sey, sgb, sgz, shj, shp, sim, sja, sll, smk, snc, snn, snp, snx, sny, som, soq, soy, spa, spl, spm, spp, sps, spy, sri, srm, srn, srp, srq, ssd, ssg, ssx, stp, sua, sue, sus, suz, swe, swh, swp, sxb, tac, taj, tam, tav, taw, tbc, tbf, tbg, tbl, tbo, tbz, tca, tcs, tcz, tdt, tee, tel, ter, tet, tew, tfr, tgk, tgl, tgo, tgp, tha, thd, tif, tim, tiw, tiy, tke, tku, tlf, tmd, tna, tnc, tnk, tnn, tnp, toc, tod, tof, toj, ton, too, top, tos, tpa, tpi, tpt, tpz, trc, tsw, ttc, tte, tuc, tue, tuf, tuo, tur, tvk, twi, txq, txu, tzj, tzo, ubr, ubu, udu, uig, ukr, uli, ulk, upv, ura, urb, urd, uri, urt, urw, usa, usp, uvh, uvl, vid, vie, viv, vmy, waj, wal, wap, wat, wbi, wbp, wed, wer, wim, wiu, wiv, wmt, wmw, wnc, wnu, wol, wos, wrk, wro, wrs, wsk, wuv, xav, xbi, xed, xla, xnn, xon, xsi, xtd, xtm, yaa, yad, yal, yap, yaq, yby, ycn, yka, yle, yml, yon, yor, yrb, yre, yss, yuj, yut, yuw, yva, zaa, zab, zac, zad, zai, zaj, zam, zao, zap, zar, zas, zat, zav, zaw, zca, zga, zia, ziw, zlm, zos, zpc, zpl, zpm, zpo, zpq, zpu, zpv, zpz, zsr, ztq, zty, zyp", "## Dataset Structure", "### Data Fields\n\ntranslation \n- languages - an N length list of the languages of the translations, sorted alphabetically\n- translation - an N length list with the translations each corresponding to the language specified in the above field\nfiles\n- lang - an N length list of the languages of the files, in order of input\n- file - an N length list of the filenames from the corpus on github, each corresponding with the lang above\nref - the verse(s) contained in the record, as a list, with each represented with: ''<a three letter book code> <chapter number>:<verse number>'' \n\nlicenses - an N length list of licenses, corresponding to the list of files above\n\ncopyrights - information on copyright holders, corresponding to the list of files above", "### Usage\n\nThe dataset loading script requires installation of tqdm, ijson, and numpy\n\nSpecify the languages to be paired with a list and ISO 693-3 language codes, such as ''languages = ['eng', 'fra']''.\nBy default, the script will return individual verse pairs, as well as verses covering a full range. If only the individual verses is desired, use ''pair='single'''. If only the maximum range pairing is desired use ''pair='range''' (for example, if one text uses the verse range covering GEN 1:1-3, all texts would return only the full length pairing).", "## Sources\nURL" ]

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

[]

[ "ru" ]

[ "# NEREL dataset", "## Table of Contents\n- Dataset Description\n- Dataset Structure\n- Citation Information\n- Contacts", "## Dataset Description\nNEREL dataset (URL is\na Russian dataset for named entity recognition and relation extraction.\nNEREL is significantly larger than existing Russian datasets:\nto date it contains 56K annotated named entities and 39K annotated relations.\nIts important difference from previous datasets is annotation of nested named\nentities, as well as relations within nested entities and at the discourse\nlevel. NEREL can facilitate development of novel models that can extract\nrelations between nested named entities, as well as relations on both sentence\nand document levels. NEREL also contains the annotation of events involving\nnamed entities and their roles in the events.\n\nYou can see full entity types list in a subset \"ent_types\"\nand full list of relation types in a subset \"rel_types\".", "## Dataset Structure\nThere are three \"configs\" or \"subsets\" of the dataset.\n\nUsing \n'load_dataset('MalakhovIlya/NEREL', 'ent_types')['ent_types']' \nyou can download list of entity types (\nDataset({features: ['type', 'link']})\n) where \"link\" is a knowledge base name used in entity linking task.\n \nUsing \n'load_dataset('MalakhovIlya/NEREL', 'rel_types')['rel_types']' \nyou can download list of entity types (\nDataset({features: ['type', 'arg1', 'arg2']})\n) where \"arg1\" and \"arg2\" are lists of entity types that can take part in such\n\"type\" of relation. \\<ENTITY> stands for any type.\n\nUsing \n'load_dataset('MalakhovIlya/NEREL', 'data')' or 'load_dataset('MalakhovIlya/NEREL')'\nyou can download the data itself, \nDatasetDict with 3 splits: \"train\", \"test\" and \"dev\".\nEach of them contains text document with annotated entities, relations and\nlinks.\n\n\"entities\" are used in named-entity recognition task (see URL \n\"relations\" are used in relationship extraction task (see URL \n\"links\" are used in entity linking task (see URL\n\nEach entity is represented by a string of the following format: \n'\"<id>\\t<type> <start> <stop>\\t<text>\"', where \n'<id>' is an entity id, \n'<type>' is one of entity types, \n'<start>' is a position of the first symbol of entity in text, \n'<stop>' is the last symbol position in text +1.\n\nEach relation is represented by a string of the following format: \n'\"<id>\\t<type> Arg1:<arg1_id> Arg2:<arg2_id>\"', where \n'<id>' is a relation id, \n'<arg1_id>' and '<arg2_id>' are entity ids.\n\nEach link is represented by a string of the following format: \n'\"<id>\\tReference <ent_id> <link>\\t<text>\"', where \n'<id>' is a link id, \n'<ent_id>' is an entity id, \n'<link>' is a reference to knowledge base entity (example: \"Wikidata:Q1879675\" if link exists, else \"Wikidata:NULL\"), \n'<text>' is a name of entity in knowledge base if link exists, else empty string.\n\n\n@article{loukachevitch2021nerel, \n title={NEREL: A Russian Dataset with Nested Named Entities, Relations and Events}, \n author={Loukachevitch, Natalia and Artemova, Ekaterina and Batura, Tatiana and Braslavski, Pavel and Denisov, Ilia and Ivanov, Vladimir and Manandhar, Suresh and Pugachev, Alexander and Tutubalina, Elena}, \n journal={arXiv preprint arXiv:2108.13112}, \n year={2021} \n}" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #language-Russian #region-us \n", "# NEREL dataset", "## Table of Contents\n- Dataset Description\n- Dataset Structure\n- Citation Information\n- Contacts", "## Dataset Description\nNEREL dataset (URL is\na Russian dataset for named entity recognition and relation extraction.\nNEREL is significantly larger than existing Russian datasets:\nto date it contains 56K annotated named entities and 39K annotated relations.\nIts important difference from previous datasets is annotation of nested named\nentities, as well as relations within nested entities and at the discourse\nlevel. NEREL can facilitate development of novel models that can extract\nrelations between nested named entities, as well as relations on both sentence\nand document levels. NEREL also contains the annotation of events involving\nnamed entities and their roles in the events.\n\nYou can see full entity types list in a subset \"ent_types\"\nand full list of relation types in a subset \"rel_types\".", "## Dataset Structure\nThere are three \"configs\" or \"subsets\" of the dataset.\n\nUsing \n'load_dataset('MalakhovIlya/NEREL', 'ent_types')['ent_types']' \nyou can download list of entity types (\nDataset({features: ['type', 'link']})\n) where \"link\" is a knowledge base name used in entity linking task.\n \nUsing \n'load_dataset('MalakhovIlya/NEREL', 'rel_types')['rel_types']' \nyou can download list of entity types (\nDataset({features: ['type', 'arg1', 'arg2']})\n) where \"arg1\" and \"arg2\" are lists of entity types that can take part in such\n\"type\" of relation. \\<ENTITY> stands for any type.\n\nUsing \n'load_dataset('MalakhovIlya/NEREL', 'data')' or 'load_dataset('MalakhovIlya/NEREL')'\nyou can download the data itself, \nDatasetDict with 3 splits: \"train\", \"test\" and \"dev\".\nEach of them contains text document with annotated entities, relations and\nlinks.\n\n\"entities\" are used in named-entity recognition task (see URL \n\"relations\" are used in relationship extraction task (see URL \n\"links\" are used in entity linking task (see URL\n\nEach entity is represented by a string of the following format: \n'\"<id>\\t<type> <start> <stop>\\t<text>\"', where \n'<id>' is an entity id, \n'<type>' is one of entity types, \n'<start>' is a position of the first symbol of entity in text, \n'<stop>' is the last symbol position in text +1.\n\nEach relation is represented by a string of the following format: \n'\"<id>\\t<type> Arg1:<arg1_id> Arg2:<arg2_id>\"', where \n'<id>' is a relation id, \n'<arg1_id>' and '<arg2_id>' are entity ids.\n\nEach link is represented by a string of the following format: \n'\"<id>\\tReference <ent_id> <link>\\t<text>\"', where \n'<id>' is a link id, \n'<ent_id>' is an entity id, \n'<link>' is a reference to knowledge base entity (example: \"Wikidata:Q1879675\" if link exists, else \"Wikidata:NULL\"), \n'<text>' is a name of entity in knowledge base if link exists, else empty string.\n\n\n@article{loukachevitch2021nerel, \n title={NEREL: A Russian Dataset with Nested Named Entities, Relations and Events}, \n author={Loukachevitch, Natalia and Artemova, Ekaterina and Batura, Tatiana and Braslavski, Pavel and Denisov, Ilia and Ivanov, Vladimir and Manandhar, Suresh and Pugachev, Alexander and Tutubalina, Elena}, \n journal={arXiv preprint arXiv:2108.13112}, \n year={2021} \n}" ]

[ "task_categories:text-classification", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:si", "license:mit", "region:us" ]

[]

[ "si" ]

[]

[ "TAGS\n#task_categories-text-classification #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #language-Sinhala #license-mit #region-us \n" ]

[ "task_categories:text-classification", "language_creators:crowdsourced", "multilinguality:monolingual", "language:si", "license:mit", "region:us" ]

[]

[ "si" ]

[]

[ "TAGS\n#task_categories-text-classification #language_creators-crowdsourced #multilinguality-monolingual #language-Sinhala #license-mit #region-us \n" ]

[ "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ko", "license:cc-by-sa-4.0", "arxiv:2204.04541", "region:us" ]

[ "2204.04541" ]

[ "ko" ]

[ "# Dataset Card for KoBEST", "## 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- Repository: URL\n- Paper:\n- Point of Contact: URL", "### Dataset Summary\n\nKoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean.", "### Supported Tasks and Leaderboards\n\nBoolean Question Answering, Choice of Plausible Alternatives, Words-in-Context, HellaSwag, Sentiment Negation Recognition", "### Languages\n\n'ko-KR'", "## Dataset Structure", "### Data Instances", "#### KB-BoolQ\nAn example of a data point looks as follows.", "#### KB-COPA\nAn example of a data point looks as follows.", "#### KB-WiC\nAn example of a data point looks as follows.", "#### KB-HellaSwag\nAn example of a data point looks as follows.", "#### KB-SentiNeg\nAn example of a data point looks as follows.", "### Data Fields", "### KB-BoolQ\n+ 'paragraph': a 'string' feature\n+ 'question': a 'string' feature\n+ 'label': a classification label, with possible values 'False'(0) and 'True'(1)", "### KB-COPA\n+ 'premise': a 'string' feature\n+ 'question': a 'string' feature\n+ 'alternative_1': a 'string' feature\n+ 'alternative_2': a 'string' feature\n+ 'label': an answer candidate label, with possible values 'alternative_1'(0) and 'alternative_2'(1)", "### KB-WiC\n+ 'target_word': a 'string' feature\n+ 'context_1': a 'string' feature\n+ 'context_2': a 'string' feature\n+ 'label': a classification label, with possible values 'False'(0) and 'True'(1)", "### KB-HellaSwag\n+ 'target_word': a 'string' feature\n+ 'context_1': a 'string' feature\n+ 'context_2': a 'string' feature\n+ 'label': a classification label, with possible values 'False'(0) and 'True'(1)", "### KB-SentiNeg\n+ 'sentence': a 'string' feature\n+ 'label': a classification label, with possible values 'Negative'(0) and 'Positive'(1)", "### Data Splits", "#### KB-BoolQ\n\n+ train: 3,665\n+ dev: 700\n+ test: 1,404", "#### KB-COPA\n\n+ train: 3,076\n+ dev: 1,000\n+ test: 1,000", "#### KB-WiC\n\n+ train: 3,318\n+ dev: 1,260\n+ test: 1,260", "#### KB-HellaSwag\n\n+ train: 3,665\n+ dev: 700\n+ test: 1,404", "#### KB-SentiNeg\n\n+ train: 3,649\n+ dev: 400\n+ test: 397\n+ test_originated: 397 (Corresponding training data where the test set is originated from.)", "## 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 @MJ-Jang for adding this dataset." ]

[ "TAGS\n#annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Korean #license-cc-by-sa-4.0 #arxiv-2204.04541 #region-us \n", "# Dataset Card for KoBEST", "## 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- Repository: URL\n- Paper:\n- Point of Contact: URL", "### Dataset Summary\n\nKoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean.", "### Supported Tasks and Leaderboards\n\nBoolean Question Answering, Choice of Plausible Alternatives, Words-in-Context, HellaSwag, Sentiment Negation Recognition", "### Languages\n\n'ko-KR'", "## Dataset Structure", "### Data Instances", "#### KB-BoolQ\nAn example of a data point looks as follows.", "#### KB-COPA\nAn example of a data point looks as follows.", "#### KB-WiC\nAn example of a data point looks as follows.", "#### KB-HellaSwag\nAn example of a data point looks as follows.", "#### KB-SentiNeg\nAn example of a data point looks as follows.", "### Data Fields", "### KB-BoolQ\n+ 'paragraph': a 'string' feature\n+ 'question': a 'string' feature\n+ 'label': a classification label, with possible values 'False'(0) and 'True'(1)", "### KB-COPA\n+ 'premise': a 'string' feature\n+ 'question': a 'string' feature\n+ 'alternative_1': a 'string' feature\n+ 'alternative_2': a 'string' feature\n+ 'label': an answer candidate label, with possible values 'alternative_1'(0) and 'alternative_2'(1)", "### KB-WiC\n+ 'target_word': a 'string' feature\n+ 'context_1': a 'string' feature\n+ 'context_2': a 'string' feature\n+ 'label': a classification label, with possible values 'False'(0) and 'True'(1)", "### KB-HellaSwag\n+ 'target_word': a 'string' feature\n+ 'context_1': a 'string' feature\n+ 'context_2': a 'string' feature\n+ 'label': a classification label, with possible values 'False'(0) and 'True'(1)", "### KB-SentiNeg\n+ 'sentence': a 'string' feature\n+ 'label': a classification label, with possible values 'Negative'(0) and 'Positive'(1)", "### Data Splits", "#### KB-BoolQ\n\n+ train: 3,665\n+ dev: 700\n+ test: 1,404", "#### KB-COPA\n\n+ train: 3,076\n+ dev: 1,000\n+ test: 1,000", "#### KB-WiC\n\n+ train: 3,318\n+ dev: 1,260\n+ test: 1,260", "#### KB-HellaSwag\n\n+ train: 3,665\n+ dev: 700\n+ test: 1,404", "#### KB-SentiNeg\n\n+ train: 3,649\n+ dev: 400\n+ test: 397\n+ test_originated: 397 (Corresponding training data where the test set is originated from.)", "## 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 @MJ-Jang for adding this dataset." ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for airplanes", "## 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\nthree classes of airplanes: drone, UAV, and fighter", "### 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\n\n\n\n\nDrone images were taken from: \n\nWang, Ye, Yueru Chen, Jongmoo Choi, and C-C. Jay Kuo. “Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks.” APSIPA Transactions on Signal and Information Processing 8 (2019).\n\nmcl-drone-dataset" ]

[ "TAGS\n#region-us \n", "# Dataset Card for airplanes", "## 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\nthree classes of airplanes: drone, UAV, and fighter", "### 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\n\n\n\n\nDrone images were taken from: \n\nWang, Ye, Yueru Chen, Jongmoo Choi, and C-C. Jay Kuo. “Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks.” APSIPA Transactions on Signal and Information Processing 8 (2019).\n\nmcl-drone-dataset" ]

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

[]

[]

[ "# GEM Submission\n\nSubmission name: ENT" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: ENT" ]

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

[]

[]

[ "# GEM Submission\n\nSubmission name: NCP_CC" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: NCP_CC" ]

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

[]

[]

[ "# GEM Submission\n\nSubmission name: ENT" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: ENT" ]

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

[]

[]

[ "# GEM Submission\n\nSubmission name: NCP_CC" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: NCP_CC" ]

[ "task_categories:token-classification", "size_categories:10K<n<100K", "language:pt", "license:unknown", "legal", "legislative", "region:us" ]

[]

[ "pt" ]

[ "# Dataset Card for pl-corpus", "## 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: UlyssesNER-Br homepage\n- Repository: UlyssesNER-Br repository\n- Paper: UlyssesNER-Br: A corpus of brazilian legislative documents for named entity recognition. In: Computational Processing of the Portuguese Language\n- Point of Contact: Hidelberg O. Albuquerque", "### Dataset Summary\n\nPL-corpus is part of the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines The presented corpus consists of 150 public bills from Brazilian Chamber of Deputies, manually annotated. Its contains semantic categories and types.", "### Supported Tasks and Leaderboards", "### Languages\n\nBrazilian Portuguese.", "## 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" ]

[ "TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-Portuguese #license-unknown #legal #legislative #region-us \n", "# Dataset Card for pl-corpus", "## 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: UlyssesNER-Br homepage\n- Repository: UlyssesNER-Br repository\n- Paper: UlyssesNER-Br: A corpus of brazilian legislative documents for named entity recognition. In: Computational Processing of the Portuguese Language\n- Point of Contact: Hidelberg O. Albuquerque", "### Dataset Summary\n\nPL-corpus is part of the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines The presented corpus consists of 150 public bills from Brazilian Chamber of Deputies, manually annotated. Its contains semantic categories and types.", "### Supported Tasks and Leaderboards", "### Languages\n\nBrazilian Portuguese.", "## 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" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-retrieval", "task_categories:text-generation", "task_ids:language-modeling", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100k", "language:en", "license:cc-by-nc-sa-4.0", "conversational-question-answering", "arxiv:2110.00768", "region:us" ]

[ "2110.00768" ]

[ "en" ]

[ "# Dataset Card for TopiOCQA", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: TopiOCQA homepage\n- Repository: TopiOCQA Github\n- Paper: Open-domain Conversational Question Answering with Topic Switching\n- Point of Contact: Vaibhav Adlakha", "### Dataset Summary\n\nTopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena.", "### Languages\n\nThe language in the dataset is English as spoken by the crowdworkers. The BCP-47 code for English is en.", "## Additional Information", "### Licensing Information\n\nTopiOCQA is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License." ]

[ "TAGS\n#task_categories-text-retrieval #task_categories-text-generation #task_ids-language-modeling #task_ids-open-domain-qa #annotations_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100k #language-English #license-cc-by-nc-sa-4.0 #conversational-question-answering #arxiv-2110.00768 #region-us \n", "# Dataset Card for TopiOCQA", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Homepage: TopiOCQA homepage\n- Repository: TopiOCQA Github\n- Paper: Open-domain Conversational Question Answering with Topic Switching\n- Point of Contact: Vaibhav Adlakha", "### Dataset Summary\n\nTopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena.", "### Languages\n\nThe language in the dataset is English as spoken by the crowdworkers. The BCP-47 code for English is en.", "## Additional Information", "### Licensing Information\n\nTopiOCQA is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License." ]

[ "task_categories:image-classification", "task_ids:multi-label-image-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Smithsonian Butterflies]", "## 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: Smithsonian \"Education and Outreach\" & \"NMNH - Entomology Dept.\" collections here", "### Dataset Summary\n\nHigh-res images from Smithsonian \"Education and Outreach\" & \"NMNH - Entomology Dept.\" collections. Crawled", "### Supported Tasks and Leaderboards\n\nIncludes metadata about the scientific name of butterflies, but there maybe missing values. Might be good for classification.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "# Example data", "### Data Fields\n\nsim-score indicates clip score for \"pretty butterfly\". This is to eliminate non-butterfly images(just id card images etc)", "### Data Splits\n\nNo specific split exists.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\n\nCrawled from \"Education and Outreach\" & \"NMNH - Entomology Dept.\" collections found online here", "#### 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\n\nDoesn't include all butterfly species ## Additional Information", "### Dataset Curators\n\nSmithsonian \"Education and Outreach\" & \"NMNH - Entomology Dept.\" collections", "### Licensing Information\n\nOnly results marked: CC0", "### Contributions\n\nThanks to @cceyda for adding this dataset." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-label-image-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-cc0-1.0 #region-us \n", "# Dataset Card for [Smithsonian Butterflies]", "## 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: Smithsonian \"Education and Outreach\" & \"NMNH - Entomology Dept.\" collections here", "### Dataset Summary\n\nHigh-res images from Smithsonian \"Education and Outreach\" & \"NMNH - Entomology Dept.\" collections. Crawled", "### Supported Tasks and Leaderboards\n\nIncludes metadata about the scientific name of butterflies, but there maybe missing values. Might be good for classification.", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "# Example data", "### Data Fields\n\nsim-score indicates clip score for \"pretty butterfly\". This is to eliminate non-butterfly images(just id card images etc)", "### Data Splits\n\nNo specific split exists.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\n\nCrawled from \"Education and Outreach\" & \"NMNH - Entomology Dept.\" collections found online here", "#### 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\n\nDoesn't include all butterfly species ## Additional Information", "### Dataset Curators\n\nSmithsonian \"Education and Outreach\" & \"NMNH - Entomology Dept.\" collections", "### Licensing Information\n\nOnly results marked: CC0", "### Contributions\n\nThanks to @cceyda for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Data Summary\nThis dataset contains images of Moroccan Chebakia (Traditional Ramadan Sweets).", "# Data Source\nAll of the images were web scrapped using a google image search API.", "### Contributions\n'Ilyas Moutawwakil' added this dataset to the hub." ]

[ "TAGS\n#region-us \n", "# Data Summary\nThis dataset contains images of Moroccan Chebakia (Traditional Ramadan Sweets).", "# Data Source\nAll of the images were web scrapped using a google image search API.", "### Contributions\n'Ilyas Moutawwakil' added this dataset to the hub." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Flowers Dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Official VGG'S URL", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nVGG have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes. The categories can be seen in the figure below. We randomly split the dataset into 3 different training, validation and test sets. A subset of the images have been groundtruth labelled for segmentation.\nYou can find the split files in the link, as a mat file.", "### Official VGG's URL\n\n17 Flower Category Database\n----------------------------------------------\nThis set contains images of flowers belonging to 17 different categories. \nThe images were acquired by searching the web and taking pictures. There are\n80 images for each category. \n\nThe database was used in:\n\nNilsback, M-E. and Zisserman, A. A Visual Vocabulary for Flower Classification.\nProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2006) \nURL\n\nThe datasplits used in this paper are specified in URL\n\nThere are 3 separate splits. The results in the paper are averaged over the 3 splits.\nEach split has a training file (trn1,trn2,trn3), a validation file (val1, val2, val3)\nand a testfile (tst1, tst2 or tst3). \n\nSegmentation Ground Truth\n------------------------------------------------\nThe ground truth is given for a subset of the images from 13 different\ncategories. \n\nMore details can be found in:\n\nNilsback, M-E. and Zisserman, A. Delving into the whorl of flower segmentation.\nProceedings of the British Machine Vision Conference (2007)\nhttp:URL\n\nThe ground truth file also contains the file URL, which indicated\nwhich images in the original database that have been anotated.\n\nDistance matrices\n-----------------------------------------------\n\nWe provide two set of distance matrices:\n\n1. URL \n- Distance matrices using the same features and segmentation as detailed in:\n Nilsback, M-E. and Zisserman, A. A Visual Vocabulary for Flower Classification.\n Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2006)\n URL\n\n2. URL\n- Distance matrices using the same features as described in: \n Nilsback, M-E. and Zisserman, A. Automated flower classification over a large number of classes.\n Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008)\n URL\n and the iterative segmenation scheme detailed in \n Nilsback, M-E. and Zisserman, A. Delving into the whorl of flower segmentation.\n Proceedings of the British Machine Vision Conference (2007)\n http:URL" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Flowers Dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Official VGG'S URL", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nVGG have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes. The categories can be seen in the figure below. We randomly split the dataset into 3 different training, validation and test sets. A subset of the images have been groundtruth labelled for segmentation.\nYou can find the split files in the link, as a mat file.", "### Official VGG's URL\n\n17 Flower Category Database\n----------------------------------------------\nThis set contains images of flowers belonging to 17 different categories. \nThe images were acquired by searching the web and taking pictures. There are\n80 images for each category. \n\nThe database was used in:\n\nNilsback, M-E. and Zisserman, A. A Visual Vocabulary for Flower Classification.\nProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2006) \nURL\n\nThe datasplits used in this paper are specified in URL\n\nThere are 3 separate splits. The results in the paper are averaged over the 3 splits.\nEach split has a training file (trn1,trn2,trn3), a validation file (val1, val2, val3)\nand a testfile (tst1, tst2 or tst3). \n\nSegmentation Ground Truth\n------------------------------------------------\nThe ground truth is given for a subset of the images from 13 different\ncategories. \n\nMore details can be found in:\n\nNilsback, M-E. and Zisserman, A. Delving into the whorl of flower segmentation.\nProceedings of the British Machine Vision Conference (2007)\nhttp:URL\n\nThe ground truth file also contains the file URL, which indicated\nwhich images in the original database that have been anotated.\n\nDistance matrices\n-----------------------------------------------\n\nWe provide two set of distance matrices:\n\n1. URL \n- Distance matrices using the same features and segmentation as detailed in:\n Nilsback, M-E. and Zisserman, A. A Visual Vocabulary for Flower Classification.\n Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2006)\n URL\n\n2. URL\n- Distance matrices using the same features as described in: \n Nilsback, M-E. and Zisserman, A. Automated flower classification over a large number of classes.\n Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008)\n URL\n and the iterative segmenation scheme detailed in \n Nilsback, M-E. and Zisserman, A. Delving into the whorl of flower segmentation.\n Proceedings of the British Machine Vision Conference (2007)\n http:URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "license:mit", "huggingnft", "nft", "huggan", "gan", "image", "images", "region:us" ]

[]

[]

[ "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

[ "TAGS\n#license-mit #huggingnft #nft #huggan #gan #image #images #region-us \n", "# Dataset Card", "## Disclaimer\n\nAll rights belong to their owners.\nModels and datasets can be removed from the site at the request of the copyright holder.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- How to use\n- Dataset Structure\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- About", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nNFT images dataset for unconditional generation.\n\nNFT collection available here.\n\nModel is available here.\n\nCheck Space: link.", "### Supported Tasks and Leaderboards", "## How to use\n\nHow to load this dataset directly with the datasets library:", "## Dataset Structure", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'image': an 'image' feature.\n- 'id': an 'int' feature.\n- 'token_metadata': a 'str' feature.\n- 'image_original_url': a 'str' feature.", "### 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", "## About\n\n*Built by Aleksey Korshuk*\n\n\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]

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

[]

[ "ru" ]

[ "# RuREBus dataset", "## Table of Contents\n- Dataset Description\n- Dataset Structure\n- Citation Information\n- Contacts", "## Dataset Description\nRuREBus dataset (URL is\na Russian dataset for named entity recognition and relation extraction.", "## Dataset Structure\nThere are two subsets of the dataset.\n\nUsing \n'load_dataset('MalakhovIlya/RuREBus')' \nyou can download annotated data (DatasetDict) for named entity recognition task and\nrelation extraction tasks. \nThis subset consists of two splits: \"train\" and \"test\".\n \nUsing \n'load_dataset('MalakhovIlya/NEREL', 'raw_txt')['raw_txt']' \nyou can download (Dataset) large corpus (~3gb) raw texts of the same subject\narea, but without any annotations.\n\n\"entities\" are used in named-entity recognition task (see URL \n\"relations\" are used in relationship extraction task (see URL\n\nEach entity is represented by a string of the following format: \n'\"<id>\\t<type> <start> <stop>\\t<text>\"', where \n'<id>' is an entity id, \n'<type>' is one of entity types, \n'<start>' is a position of the first symbol of entity in text, \n'<stop>' is the last symbol position in text +1.\n\nEach relation is represented by a string of the following format: \n'\"<id>\\t<type> Arg1:<arg1_id> Arg2:<arg2_id>\"', where \n'<id>' is a relation id, \n'<arg1_id>' and '<arg2_id>' are entity ids.\n\n\n@inproceedings{rurebus,\n Address = {Moscow, Russia},\n Author = {Ivanin, Vitaly and Artemova, Ekaterina and Batura, Tatiana and Ivanov, Vladimir and Sarkisyan, Veronika and Tutubalina, Elena and Smurov, Ivan},\n Title = {RuREBus-2020 Shared Task: Russian Relation Extraction for Business},\n Booktitle = {Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialog” [Komp’iuternaia Lingvistika i Intellektual’nye Tehnologii: Trudy Mezhdunarodnoj Konferentsii “Dialog”]},\n Year = {2020}\n}" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #language-Russian #region-us \n", "# RuREBus dataset", "## Table of Contents\n- Dataset Description\n- Dataset Structure\n- Citation Information\n- Contacts", "## Dataset Description\nRuREBus dataset (URL is\na Russian dataset for named entity recognition and relation extraction.", "## Dataset Structure\nThere are two subsets of the dataset.\n\nUsing \n'load_dataset('MalakhovIlya/RuREBus')' \nyou can download annotated data (DatasetDict) for named entity recognition task and\nrelation extraction tasks. \nThis subset consists of two splits: \"train\" and \"test\".\n \nUsing \n'load_dataset('MalakhovIlya/NEREL', 'raw_txt')['raw_txt']' \nyou can download (Dataset) large corpus (~3gb) raw texts of the same subject\narea, but without any annotations.\n\n\"entities\" are used in named-entity recognition task (see URL \n\"relations\" are used in relationship extraction task (see URL\n\nEach entity is represented by a string of the following format: \n'\"<id>\\t<type> <start> <stop>\\t<text>\"', where \n'<id>' is an entity id, \n'<type>' is one of entity types, \n'<start>' is a position of the first symbol of entity in text, \n'<stop>' is the last symbol position in text +1.\n\nEach relation is represented by a string of the following format: \n'\"<id>\\t<type> Arg1:<arg1_id> Arg2:<arg2_id>\"', where \n'<id>' is a relation id, \n'<arg1_id>' and '<arg2_id>' are entity ids.\n\n\n@inproceedings{rurebus,\n Address = {Moscow, Russia},\n Author = {Ivanin, Vitaly and Artemova, Ekaterina and Batura, Tatiana and Ivanov, Vladimir and Sarkisyan, Veronika and Tutubalina, Elena and Smurov, Ivan},\n Title = {RuREBus-2020 Shared Task: Russian Relation Extraction for Business},\n Booktitle = {Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialog” [Komp’iuternaia Lingvistika i Intellektual’nye Tehnologii: Trudy Mezhdunarodnoj Konferentsii “Dialog”]},\n Year = {2020}\n}" ]

[ "region:us" ]

[]

[]

[]

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

[ "language:en", "license:other", "mri", "quantitative mri", "reconstruction", "segmentation", "detection", "arxiv:2203.06823", "region:us" ]

[ "2203.06823" ]

[ "en" ]

[ "# SKM-TEA Sample Data\nThis dataset consists of a subset of scans from the SKM-TEA dataset. It can be used to build tutorials / demos with the SKM-TEA dataset.\n\nTo access to the full dataset, please follow instructions on Github.\n\nNOTE: This dataset subset *should not* be used for reporting/publishing metrics. All metrics should be computed on the full SKM-TEA test split.", "## Details\nThis mini dataset (~30GB) consists of 2 training scans, 1 validation scan, and 1 test scan from the SKM-TEA dataset. HDF5 files for the Raw Data Track are lzf-compressed to reduce size while maximizing speed for decompression.", "## License\nBy using this dataset, you agree to the Stanford University Dataset Research Use Agreement.", "## Reference\nIf you use this dataset, please reference the SKM-TEA paper:" ]

[ "TAGS\n#language-English #license-other #mri #quantitative mri #reconstruction #segmentation #detection #arxiv-2203.06823 #region-us \n", "# SKM-TEA Sample Data\nThis dataset consists of a subset of scans from the SKM-TEA dataset. It can be used to build tutorials / demos with the SKM-TEA dataset.\n\nTo access to the full dataset, please follow instructions on Github.\n\nNOTE: This dataset subset *should not* be used for reporting/publishing metrics. All metrics should be computed on the full SKM-TEA test split.", "## Details\nThis mini dataset (~30GB) consists of 2 training scans, 1 validation scan, and 1 test scan from the SKM-TEA dataset. HDF5 files for the Raw Data Track are lzf-compressed to reduce size while maximizing speed for decompression.", "## License\nBy using this dataset, you agree to the Stanford University Dataset Research Use Agreement.", "## Reference\nIf you use this dataset, please reference the SKM-TEA paper:" ]

[ "region:us" ]

[]

[]

[ "# AirDrums Data\nThis dataset contains all data needed for training\n\n'2d_images' contains raw unsegmented image data for 2-dimensional dataset. filenames are representative of timestamp\n\n'3d_images' contains raw unsegmented image data (paired) for 3-dimensional dataset. filenames are representative of timestamp and camera angle\n\nimages from both of the previous sets are to be segmented and converted to a coordinate and direction\n\n'2d_imu' contains IMU data for training in 2-dimensional space (xy) with segmented images from above\n\n'3d_imu' contains IMU data for training in 3-dimensional space(xyz) with segmented images from above and front (xy and yz planes)\n\n\n---\nlanguage: \n- en\n\ntags:\n- sensor\n- location\n\ndatasets:\n- 2d_images\n- 3d_images\n- 2d_imu\n- 3d_imu\n---" ]

[ "TAGS\n#region-us \n", "# AirDrums Data\nThis dataset contains all data needed for training\n\n'2d_images' contains raw unsegmented image data for 2-dimensional dataset. filenames are representative of timestamp\n\n'3d_images' contains raw unsegmented image data (paired) for 3-dimensional dataset. filenames are representative of timestamp and camera angle\n\nimages from both of the previous sets are to be segmented and converted to a coordinate and direction\n\n'2d_imu' contains IMU data for training in 2-dimensional space (xy) with segmented images from above\n\n'3d_imu' contains IMU data for training in 3-dimensional space(xyz) with segmented images from above and front (xy and yz planes)\n\n\n---\nlanguage: \n- en\n\ntags:\n- sensor\n- location\n\ndatasets:\n- 2d_images\n- 3d_images\n- 2d_imu\n- 3d_imu\n---" ]

[ "task_ids:named-entity-recognition", "multilinguality:monolingual", "language:ru", "region:us" ]

[]

[ "ru" ]

[ "### About DataSet\n\nThe dataset based on NEREL corpus.\n\nFor more information about original data, please visit this source\n\nExample of preparing original data illustrated in <Prepare_original_data.ipynb>", "### Additional info\n\nThe dataset consist 29 entities, each of them can be as beginner part of entity \"B-\" as inner \"I-\".\n\nFrequency for each entity:\n\n- I-AGE: 284\n- B-AGE: 247\n- B-AWARD: 285\n- I-AWARD: 466\n- B-CITY: 1080\n- I-CITY: 39\n- B-COUNTRY: 2378\n- I-COUNTRY: 128\n- B-CRIME: 214\n- I-CRIME: 372\n- B-DATE: 2701\n- I-DATE: 5437\n- B-DISEASE: 136\n- I-DISEASE: 80\n- B-DISTRICT: 98\n- I-DISTRICT: 73\n- B-EVENT: 3369\n- I-EVENT: 2524\n- B-FACILITY: 376\n- I-FACILITY: 510\n- B-FAMILY: 27\n- I-FAMILY: 22\n- B-IDEOLOGY: 271\n- I-IDEOLOGY: 20\n- B-LANGUAGE: 32\n- I-LAW: 1196\n- B-LAW: 297\n- B-LOCATION: 242\n- I-LOCATION: 139\n- B-MONEY: 147\n- I-MONEY: 361\n- B-NATIONALITY: 437\n- I-NATIONALITY: 41\n- B-NUMBER: 1079\n- I-NUMBER: 328\n- B-ORDINAL: 485\n- I-ORDINAL: 6\n- B-ORGANIZATION: 3339\n- I-ORGANIZATION: 3354\n- B-PENALTY: 73\n- I-PENALTY: 104\n- B-PERCENT: 51\n- I-PERCENT: 37\n- B-PERSON: 5148\n- I-PERSON: 3635\n- I-PRODUCT: 48\n- B-PRODUCT: 197\n- B-PROFESSION: 3869\n- I-PROFESSION: 2598\n- B-RELIGION: 102\n- I-RELIGION: 1\n- B-STATE_OR_PROVINCE: 436\n- I-STATE_OR_PROVINCE: 154\n- B-TIME: 187\n- I-TIME: 529\n- B-WORK_OF_ART: 133\n- I-WORK_OF_ART: 194\n\nYou can find mapper for entity ids in <id_to_label_map.pickle> file:" ]

[ "TAGS\n#task_ids-named-entity-recognition #multilinguality-monolingual #language-Russian #region-us \n", "### About DataSet\n\nThe dataset based on NEREL corpus.\n\nFor more information about original data, please visit this source\n\nExample of preparing original data illustrated in <Prepare_original_data.ipynb>", "### Additional info\n\nThe dataset consist 29 entities, each of them can be as beginner part of entity \"B-\" as inner \"I-\".\n\nFrequency for each entity:\n\n- I-AGE: 284\n- B-AGE: 247\n- B-AWARD: 285\n- I-AWARD: 466\n- B-CITY: 1080\n- I-CITY: 39\n- B-COUNTRY: 2378\n- I-COUNTRY: 128\n- B-CRIME: 214\n- I-CRIME: 372\n- B-DATE: 2701\n- I-DATE: 5437\n- B-DISEASE: 136\n- I-DISEASE: 80\n- B-DISTRICT: 98\n- I-DISTRICT: 73\n- B-EVENT: 3369\n- I-EVENT: 2524\n- B-FACILITY: 376\n- I-FACILITY: 510\n- B-FAMILY: 27\n- I-FAMILY: 22\n- B-IDEOLOGY: 271\n- I-IDEOLOGY: 20\n- B-LANGUAGE: 32\n- I-LAW: 1196\n- B-LAW: 297\n- B-LOCATION: 242\n- I-LOCATION: 139\n- B-MONEY: 147\n- I-MONEY: 361\n- B-NATIONALITY: 437\n- I-NATIONALITY: 41\n- B-NUMBER: 1079\n- I-NUMBER: 328\n- B-ORDINAL: 485\n- I-ORDINAL: 6\n- B-ORGANIZATION: 3339\n- I-ORGANIZATION: 3354\n- B-PENALTY: 73\n- I-PENALTY: 104\n- B-PERCENT: 51\n- I-PERCENT: 37\n- B-PERSON: 5148\n- I-PERSON: 3635\n- I-PRODUCT: 48\n- B-PRODUCT: 197\n- B-PROFESSION: 3869\n- I-PROFESSION: 2598\n- B-RELIGION: 102\n- I-RELIGION: 1\n- B-STATE_OR_PROVINCE: 436\n- I-STATE_OR_PROVINCE: 154\n- B-TIME: 187\n- I-TIME: 529\n- B-WORK_OF_ART: 133\n- I-WORK_OF_ART: 194\n\nYou can find mapper for entity ids in <id_to_label_map.pickle> file:" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:semantic-similarity-scoring", "source_datasets:multi_nli", "region:us" ]

[]

[]

[ "## Dataset Description\n\nThis dataset provides easier accessibility to the original MNLI dataset.\n\nWe randomly choose 10% of the original 'validation_matched' split and use it as the validation split.\nThe remaining 90% are used for the test split.\nThe train split remains unchanged." ]

[ "TAGS\n#task_categories-text-classification #task_ids-text-scoring #task_ids-semantic-similarity-scoring #source_datasets-multi_nli #region-us \n", "## Dataset Description\n\nThis dataset provides easier accessibility to the original MNLI dataset.\n\nWe randomly choose 10% of the original 'validation_matched' split and use it as the validation split.\nThe remaining 90% are used for the test split.\nThe train split remains unchanged." ]

[ "task_categories:question-answering", "task_ids:open-domain-qa", "task_ids:extractive-qa", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended", "language:bn", "license:cc-by-nc-sa-4.0", "arxiv:2101.00204", "arxiv:2007.01852", "arxiv:1606.05250", "arxiv:2003.05002", "region:us" ]

[ "2101.00204", "2007.01852", "1606.05250", "2003.05002" ]

[ "bn" ]

[ "### Dataset Summary\n\n\nThis is a Question Answering (QA) dataset for Bengali, curated from the SQuAD 2.0, TyDI-QA datasets and using the state-of-the-art English to Bengali translation model introduced here.", "### Supported Tasks and Leaderboards\n\n\nMore information needed", "### Languages\n\n\n* 'Bengali'", "### Usage\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOne example from the dataset is given below in JSON format.", "### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------\n\n\nFor the training set, we translated the complete SQuAD 2.0 dataset using the English to Bangla translation model introduced here. Due to the possibility of incursions of error during automatic translation, we used the Language-Agnostic BERT Sentence Embeddings (LaBSE) of the translations and original sentences to compute their similarity. A datapoint was accepted if all of its constituent sentences had a similarity score over 0.7.\n\n\nSince the TyDI-QA Gold Passage task guarantees that the given context contains the answer and we want to pose our QA task analogous to SQuAD 2.0, we also consider examples from the Passage selection task that don't have an answer for the given question. We distribute the resultant examples from the TyDI-QA training and validation sets (which are publicly available) evenly to our test and validation sets.", "### Curation Rationale\n\n\nMore information needed", "### Source Data\n\n\nSQuAD 2.0, TyDi-QA", "#### Initial Data Collection and Normalization\n\n\nMore information needed", "#### Who are the source language producers?\n\n\nMore information needed", "### Annotations\n\n\nMore information needed", "#### Annotation process\n\n\nMore information needed", "#### Who are the annotators?\n\n\nMore information needed", "### Personal and Sensitive Information\n\n\nMore information needed\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nMore information needed", "### Discussion of Biases\n\n\nMore information needed", "### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMore information needed", "### Licensing Information\n\n\nContents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.\n\n\nIf you use the dataset, please cite the following paper:", "### Contributions\n\n\nThanks to @abhik1505040 and @Tahmid for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-open-domain-qa #task_ids-extractive-qa #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-Bengali #license-cc-by-nc-sa-4.0 #arxiv-2101.00204 #arxiv-2007.01852 #arxiv-1606.05250 #arxiv-2003.05002 #region-us \n", "### Dataset Summary\n\n\nThis is a Question Answering (QA) dataset for Bengali, curated from the SQuAD 2.0, TyDI-QA datasets and using the state-of-the-art English to Bengali translation model introduced here.", "### Supported Tasks and Leaderboards\n\n\nMore information needed", "### Languages\n\n\n* 'Bengali'", "### Usage\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nOne example from the dataset is given below in JSON format.", "### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'id': a 'string' feature.\n* 'title': a 'string' feature.\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------\n\n\nFor the training set, we translated the complete SQuAD 2.0 dataset using the English to Bangla translation model introduced here. Due to the possibility of incursions of error during automatic translation, we used the Language-Agnostic BERT Sentence Embeddings (LaBSE) of the translations and original sentences to compute their similarity. A datapoint was accepted if all of its constituent sentences had a similarity score over 0.7.\n\n\nSince the TyDI-QA Gold Passage task guarantees that the given context contains the answer and we want to pose our QA task analogous to SQuAD 2.0, we also consider examples from the Passage selection task that don't have an answer for the given question. We distribute the resultant examples from the TyDI-QA training and validation sets (which are publicly available) evenly to our test and validation sets.", "### Curation Rationale\n\n\nMore information needed", "### Source Data\n\n\nSQuAD 2.0, TyDi-QA", "#### Initial Data Collection and Normalization\n\n\nMore information needed", "#### Who are the source language producers?\n\n\nMore information needed", "### Annotations\n\n\nMore information needed", "#### Annotation process\n\n\nMore information needed", "#### Who are the annotators?\n\n\nMore information needed", "### Personal and Sensitive Information\n\n\nMore information needed\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nMore information needed", "### Discussion of Biases\n\n\nMore information needed", "### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nMore information needed", "### Licensing Information\n\n\nContents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.\n\n\nIf you use the dataset, please cite the following paper:", "### Contributions\n\n\nThanks to @abhik1505040 and @Tahmid for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for [FrozenLake-v1]" ]

[ "TAGS\n#region-us \n", "# Dataset Card for [FrozenLake-v1]" ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:text-generation", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #annotations_creators-unknown #language_creators-unknown #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-unknown #region-us \n" ]

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

[]

[]

[ "# Dataset Description\n\nOut of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their singlet distribution.\nAfterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 11,698 sequences.", "# Citation\nIf you use this dataset, please cite our paper:" ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset Description\n\nOut of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their singlet distribution.\nAfterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 11,698 sequences.", "# Citation\nIf you use this dataset, please cite our paper:" ]

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

[]

[]

[ "# Dataset Card for Snacks", "## Dataset Summary\n\nThis is a dataset of 20 different types of snack foods that accompanies the book Machine Learning by Tutorials.\n\nThe images were taken from the Google Open Images dataset, release 2017_11.", "## Dataset Structure\n\nNumber of images in the train/validation/test splits:\n\n\n\nTotal images in each category:\n\n\n\nTo save space in the download, the images were resized so that their smallest side is 256 pixels. All EXIF information was removed.", "### Data Splits\n\nTrain, Test, Validation", "## Licensing Information\n\nJust like the images from Google Open Images, the snacks dataset is licensed under the terms of the Creative Commons license. \n\nThe images are listed as having a CC BY 2.0 license. \n\nThe annotations are licensed by Google Inc. under a CC BY 4.0 license. \n\nThe URL file contains the original URL, author information and license for each image." ]

[ "TAGS\n#task_categories-image-classification #license-cc-by-4.0 #region-us \n", "# Dataset Card for Snacks", "## Dataset Summary\n\nThis is a dataset of 20 different types of snack foods that accompanies the book Machine Learning by Tutorials.\n\nThe images were taken from the Google Open Images dataset, release 2017_11.", "## Dataset Structure\n\nNumber of images in the train/validation/test splits:\n\n\n\nTotal images in each category:\n\n\n\nTo save space in the download, the images were resized so that their smallest side is 256 pixels. All EXIF information was removed.", "### Data Splits\n\nTrain, Test, Validation", "## Licensing Information\n\nJust like the images from Google Open Images, the snacks dataset is licensed under the terms of the Creative Commons license. \n\nThe images are listed as having a CC BY 2.0 license. \n\nThe annotations are licensed by Google Inc. under a CC BY 4.0 license. \n\nThe URL file contains the original URL, author information and license for each image." ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|fever", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Fever dataset (URL), pre-processed and ready to train and evaluate.\nThe training objective is a text classification task - given a claim and evidence, predict if evidence is related to claim." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|fever #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Fever dataset (URL), pre-processed and ready to train and evaluate.\nThe training objective is a text classification task - given a claim and evidence, predict if evidence is related to claim." ]

[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:mit", "math-word-problems", "arxiv:2110.14168", "region:us" ]

[ "2110.14168" ]

[ "en" ]

[ "### Dataset Summary\n\n\nGSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.\n\n\n* These problems take between 2 and 8 steps to solve.\n* Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer.\n* A bright middle school student should be able to solve every problem: from the paper, \"Problems require no concepts beyond the level of early Algebra, and the vast majority of problems can be solved without explicitly defining a variable.\"\n* Solutions are provided in natural language, as opposed to pure math expressions. From the paper: \"We believe this is the most generally useful data format, and we expect it to shed light on the properties of large language models’ internal monologues\"\"", "### Supported Tasks and Leaderboards\n\n\nThis dataset is generally used to test logic and math in language modelling.\nIt has been used for many benchmarks, including the LLM Leaderboard.", "### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor the 'main' configuration, each instance contains a string for the grade-school level math question and a string for the corresponding answer with multiple steps of reasoning and calculator annotations (explained here).\n\n\nFor the 'socratic' configuration, each instance contains a string for a grade-school level math question, a string for the corresponding answer with multiple steps of reasoning, calculator annotations (explained here), and *Socratic sub-questions*.", "### Data Fields\n\n\nThe data fields are the same among 'main' and 'socratic' configurations and their individual splits.\n\n\n* question: The question string to a grade school math problem.\n* answer: The full solution string to the 'question'. It contains multiple steps of reasoning with calculator annotations and the final numeric solution.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nFrom the paper, appendix A:\n\n\n\n> \n> We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (URL). We then worked with Surge AI (URL), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors.\n> \n> \n>", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nSurge AI (URL)", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe GSM8K dataset is licensed under the MIT License.", "### Contributions\n\n\nThanks to @jon-tow for adding this dataset." ]

[ "TAGS\n#task_categories-text2text-generation #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #math-word-problems #arxiv-2110.14168 #region-us \n", "### Dataset Summary\n\n\nGSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.\n\n\n* These problems take between 2 and 8 steps to solve.\n* Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer.\n* A bright middle school student should be able to solve every problem: from the paper, \"Problems require no concepts beyond the level of early Algebra, and the vast majority of problems can be solved without explicitly defining a variable.\"\n* Solutions are provided in natural language, as opposed to pure math expressions. From the paper: \"We believe this is the most generally useful data format, and we expect it to shed light on the properties of large language models’ internal monologues\"\"", "### Supported Tasks and Leaderboards\n\n\nThis dataset is generally used to test logic and math in language modelling.\nIt has been used for many benchmarks, including the LLM Leaderboard.", "### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nFor the 'main' configuration, each instance contains a string for the grade-school level math question and a string for the corresponding answer with multiple steps of reasoning and calculator annotations (explained here).\n\n\nFor the 'socratic' configuration, each instance contains a string for a grade-school level math question, a string for the corresponding answer with multiple steps of reasoning, calculator annotations (explained here), and *Socratic sub-questions*.", "### Data Fields\n\n\nThe data fields are the same among 'main' and 'socratic' configurations and their individual splits.\n\n\n* question: The question string to a grade school math problem.\n* answer: The full solution string to the 'question'. It contains multiple steps of reasoning with calculator annotations and the final numeric solution.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nFrom the paper, appendix A:\n\n\n\n> \n> We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (URL). We then worked with Surge AI (URL), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors.\n> \n> \n>", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?\n\n\nSurge AI (URL)", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe GSM8K dataset is licensed under the MIT License.", "### Contributions\n\n\nThanks to @jon-tow for adding this dataset." ]

[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for SBU Captioned Photo Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper: Im2Text: Describing Images Using 1 Million Captioned Photographs\n- Leaderboard:\n- Point of Contact: Vicente Ordóñez Román", "### Dataset Summary\n\nSBU Captioned Photo Dataset is a collection of associated captions and images from Flickr.", "### Dataset Preprocessing\n\nThis dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:", "### Supported Tasks and Leaderboards\n\n- 'image-to-text': This dataset can be used to train a model for Image Captioning where the goal is to predict a caption given the image.", "### Languages\n\nAll captions are in English.", "## Dataset Structure", "### Data Instances\n\nEach instance in SBU Captioned Photo Dataset represents a single image with a caption and a user_id:", "### Data Fields\n\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'caption': Textual description of the image.\n- 'user_id': Author of caption.", "### Data Splits\n\nAll the data is contained in training split. The training set has 1M instances.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually\nrelevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results.", "### Source Data\n\nThe source images come from Flickr.", "#### Initial Data Collection and Normalization\n\nOne key contribution of our paper is a novel web-scale database of photographs with associated\ndescriptive text. To enable effective captioning of novel images, this database must be good in two\nways: 1) It must be large so that image based matches to a query are reasonably similar, 2) The\ncaptions associated with the data base photographs must be visually relevant so that transferring\ncaptions between pictures is useful. To achieve the first requirement we query Flickr using a huge\nnumber of pairs of query terms (objects, attributes, actions, stuff, and scenes). This produces a very\nlarge, but noisy initial set of photographs with associated text.", "#### Who are the source language producers?\n\nThe Flickr users.", "### Annotations", "#### Annotation process\n\nText descriptions associated with the images are inherited as annotations/captions.", "#### Who are the annotators?\n\nThe Flickr users.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nVicente Ordonez, Girish Kulkarni and Tamara L. Berg.", "### Licensing Information\n\nNot specified.", "### Contributions\n\nThanks to @thomasw21 for adding this dataset" ]

[ "TAGS\n#task_categories-image-to-text #task_ids-image-captioning #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-unknown #region-us \n", "# Dataset Card for SBU Captioned Photo Dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Dataset Preprocessing\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper: Im2Text: Describing Images Using 1 Million Captioned Photographs\n- Leaderboard:\n- Point of Contact: Vicente Ordóñez Román", "### Dataset Summary\n\nSBU Captioned Photo Dataset is a collection of associated captions and images from Flickr.", "### Dataset Preprocessing\n\nThis dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:", "### Supported Tasks and Leaderboards\n\n- 'image-to-text': This dataset can be used to train a model for Image Captioning where the goal is to predict a caption given the image.", "### Languages\n\nAll captions are in English.", "## Dataset Structure", "### Data Instances\n\nEach instance in SBU Captioned Photo Dataset represents a single image with a caption and a user_id:", "### Data Fields\n\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'caption': Textual description of the image.\n- 'user_id': Author of caption.", "### Data Splits\n\nAll the data is contained in training split. The training set has 1M instances.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually\nrelevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results.", "### Source Data\n\nThe source images come from Flickr.", "#### Initial Data Collection and Normalization\n\nOne key contribution of our paper is a novel web-scale database of photographs with associated\ndescriptive text. To enable effective captioning of novel images, this database must be good in two\nways: 1) It must be large so that image based matches to a query are reasonably similar, 2) The\ncaptions associated with the data base photographs must be visually relevant so that transferring\ncaptions between pictures is useful. To achieve the first requirement we query Flickr using a huge\nnumber of pairs of query terms (objects, attributes, actions, stuff, and scenes). This produces a very\nlarge, but noisy initial set of photographs with associated text.", "#### Who are the source language producers?\n\nThe Flickr users.", "### Annotations", "#### Annotation process\n\nText descriptions associated with the images are inherited as annotations/captions.", "#### Who are the annotators?\n\nThe Flickr users.", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nVicente Ordonez, Girish Kulkarni and Tamara L. Berg.", "### Licensing Information\n\nNot specified.", "### Contributions\n\nThanks to @thomasw21 for adding this dataset" ]

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

[]

[]

[ "# Dataset Description\nOut of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their doublet distribution. The very few sequences for which uShuffle failed to create a shuffled version were eliminated.\nAfterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 11,658 sequences.\n\nIf you use this dataset, please cite our paper:" ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset Description\nOut of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their doublet distribution. The very few sequences for which uShuffle failed to create a shuffled version were eliminated.\nAfterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 11,658 sequences.\n\nIf you use this dataset, please cite our paper:" ]

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

[]

[]

[ "# Dataset Description\nOut of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their triplet distribution. The sequences for which uShuffle failed to create a shuffled version were eliminated.\nAfterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 3,688 sequences.\n\nIf you use this dataset, please cite our paper:" ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset Description\nOut of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their triplet distribution. The sequences for which uShuffle failed to create a shuffled version were eliminated.\nAfterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 3,688 sequences.\n\nIf you use this dataset, please cite our paper:" ]

[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|climate_fever", "language:en", "license:cc-by-sa-3.0", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\nThis dataset is extracted from Climate Fever dataset (URL pre-processed and ready to train and evaluate.\nThe training objective is a text classification task - given a claim and evidence, predict if evidence is related to claim." ]

[ "TAGS\n#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|climate_fever #language-English #license-cc-by-sa-3.0 #license-gpl-3.0 #region-us \n", "### Dataset Summary\nThis dataset is extracted from Climate Fever dataset (URL pre-processed and ready to train and evaluate.\nThe training objective is a text classification task - given a claim and evidence, predict if evidence is related to claim." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "# Dataset Card for Snacks (Detection)", "## Dataset Summary\n\nThis is a dataset of 20 different types of snack foods that accompanies the book Machine Learning by Tutorials.\n\nThe images were taken from the Google Open Images dataset, release 2017_11.", "## Dataset Structure\n\nIncluded in the data folder are three CSV files with bounding box annotations for the images in the dataset, although not all images have annotations and some images have multiple annotations. \n\nThe columns in the CSV files are: \n\n- 'image_id': the filename of the image without the .jpg extension\n- 'x_min, x_max, y_min, y_max': normalized bounding box coordinates, i.e. in the range [0, 1]\n- 'class_name': the class that belongs to the bounding box\n- 'folder': the class that belongs to the image as a whole, which is also the name of the folder that contains the image\n\nThe class names are:\n\n\n\nNote: The image files are not part of this repo but can be found here.", "### Data Splits\n\nTrain, Test, Validation", "## Licensing Information\n\nJust like the images from Google Open Images, the snacks dataset is licensed under the terms of the Creative Commons license. \n\nThe images are listed as having a CC BY 2.0 license. \n\nThe annotations are licensed by Google Inc. under a CC BY 4.0 license." ]

[ "TAGS\n#task_categories-object-detection #license-cc-by-4.0 #region-us \n", "# Dataset Card for Snacks (Detection)", "## Dataset Summary\n\nThis is a dataset of 20 different types of snack foods that accompanies the book Machine Learning by Tutorials.\n\nThe images were taken from the Google Open Images dataset, release 2017_11.", "## Dataset Structure\n\nIncluded in the data folder are three CSV files with bounding box annotations for the images in the dataset, although not all images have annotations and some images have multiple annotations. \n\nThe columns in the CSV files are: \n\n- 'image_id': the filename of the image without the .jpg extension\n- 'x_min, x_max, y_min, y_max': normalized bounding box coordinates, i.e. in the range [0, 1]\n- 'class_name': the class that belongs to the bounding box\n- 'folder': the class that belongs to the image as a whole, which is also the name of the folder that contains the image\n\nThe class names are:\n\n\n\nNote: The image files are not part of this repo but can be found here.", "### Data Splits\n\nTrain, Test, Validation", "## Licensing Information\n\nJust like the images from Google Open Images, the snacks dataset is licensed under the terms of the Creative Commons license. \n\nThe images are listed as having a CC BY 2.0 license. \n\nThe annotations are licensed by Google Inc. under a CC BY 4.0 license." ]