Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "region:us" ]

[]

[]

[ "## Test\n test2" ]

[ "TAGS\n#region-us \n", "## Test\n test2" ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

[ "task_categories:object-detection", "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:expert-generated", "size_categories:1K<n<10K", "license:other", "lam", "historic", "arxiv:1405.0312", "region:us" ]

[ "1405.0312" ]

[]

[ "### Dataset Summary\n\n\nThis dataset comprises of images from chapbooks held by the National Library of Scotland and digitised and published as its Chapbooks Printed in Scotland dataset.\n\n\n\n> \n> \"Chapbooks were staple everyday reading material from the end of the 17th to the later 19th century. They were usually printed on a single sheet and then folded into books of 8, 12, 16 and 24 pages, and they were often illustrated with crude woodcuts. Their subjects range from news courtship, humour, occupations, fairy tales, apparitions, war, politics, crime, executions, historical figures, transvestites [*sic*] and freemasonry to religion and, of course, poetry. It has been estimated that around two thirds of chapbooks contain songs and poems, often under the title garlands.\" -Source\n> \n> \n> \n\n\nChapbooks were frequently illustrated, particularly on their title pages to attract customers, usually with a woodblock-printed illustration, or occasionally with a stereotyped woodcut or cast metal ornament. Apart from their artistic interest, these illustrations can also provide historical evidence such as the date, place or persons behind the publication of an item.\n\n\nThis dataset contains annotations for a subset of these chapbooks, created by Giles Bergel and Abhishek Dutta, based in the Visual Geometry Group in the University of Oxford. They were created under a National Librarian of Scotland's Fellowship in Digital Scholarship awarded to Giles Bergel in 2020. These annotations provide bounding boxes around illustrations printed on a subset of the chapbook pages, created using a combination of manual annotation and machine classification, described in this paper.\n\n\nThe dataset also includes computationally inferred 'visual groupings' to which illustrated chapbook pages may belong. These groupings are based on the recurrence of illustrations on chapbook pages, as determined through the use of the VGG Image Search Engine (VISE) software", "### Supported Tasks and Leaderboards\n\n\n* 'object-detection': the dataset contains bounding boxes for images contained in the Chapbooks\n* 'image-classification': a configuration for this dataset provides a classification label indicating if a page contains an illustration or not.\n* 'image-matching': a configuration for this dataset contains the annotations sorted into clusters or 'visual groupings' of illustrations that contain visually-matching content as determined by using the VGG Image Search Engine (VISE) software.\n\n\nThe performance on the 'object-detection' task reported in the paper Visual Analysis of Chapbooks Printed in Scotland is as follows:\n\n\nIOU threshold: 0.50, Precision: 0.993, Recall: 0.911\nIOU threshold: 0.75, Precision: 0.987, Recall: 0.905\nIOU threshold: 0.95, Precision: 0.973, Recall: 0.892\n\n\nThe performance on the 'image classification' task reported in the paper Visual Analysis of Chapbooks Printed in Scotland is as follows:\n\n\nImages in original dataset: 47329\nNumbers of images on which at least one illustration was detected: 3629\n\n\nNote that these figures do not represent images that contained multiple detections.\n\n\nSee the paper for examples of false-positive detections.\n\n\nThe performance on the 'image-matching' task is undergoing evaluation.", "### Languages\n\n\nText accompanying the illustrations is in English, Scots or Scottish Gaelic.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example instance from the 'illustration-detection' split:\n\n\nAn example instance from the 'image-classification' split:\n\n\nAn example from the 'image-matching' split:", "### Data Fields\n\n\nThe fields for the 'illustration-detection' config:\n\n\n* image\\_id: id for the image\n* height: height of the image\n* width: width of the image\n* image: image of the chapbook page\n* objects: annotations in COCO format, consisting of a list containing dictionaries with the following keys:\n\t+ bbox: bounding boxes for the images\n\t+ category\\_id: a label for the image\n\t+ image\\_id: id for the image\n\t+ iscrowd: COCO is a crowd flag\n\t+ segmentation: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)\n\n\nThe fields for the 'image-classification' config:\n\n\n* image: image\n* label: a label indicating if the page contains an illustration or not\n\n\nThe fields for the 'image-matching' config:\n\n\n* image: image of the chapbook page\n* label: an id for a particular instance of an image i.e. the same images will share the same id.", "### Data Splits\n\n\nThere is a single split 'train' for all configs. K-fold validation was used in the paper describing this dataset, so no existing splits were defined.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset was created to facilitate research into Scottish chapbook illustration and publishing. Detected illustrations can be browsed under publication metadata: together with the use of VGG Image Search Engine (VISE) software, this allows researchers to identify matching imagery and to infer the source of a chapbook from partial evidence. This browse and search functionality is available in this public demo documented here", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe initial data was taken from the National Library of Scotland's Chapbooks Printed in Scotland dataset No normalisation was performed, but only the images and a subset of the metadata was used. OCR text was not used.", "#### Who are the source language producers?\n\n\nThe initial dataset was created by the National Library of Scotland from scans and in-house curated catalogue descriptions for the NLS Data Foundry under the direction of Dr. Sarah Ames.\n\n\nThis subset of the data was created by Dr. Giles Bergel and Dr. Abhishek Dutta using a combination of manual annotation and machine classification, described below.", "### Annotations", "#### Annotation process\n\n\nAnnotation was initially performed on a subset of 337 of the 47329 images, using the VGG List Annotator (LISA software. Detected illustrations, displayed as annotations in LISA, were reviewed and refined in a number of passes (see this paper for more details). Initial detections were performed with an EfficientDet object detector trained on COCO, the annotation of which is described in this paper", "#### Who are the annotators?\n\n\nAbhishek Dutta created the initial 337 annotations for retraining the EfficentDet model. Detections were reviewed and in some cases revised by Giles Bergel.", "### Personal and Sensitive Information\n\n\nNone\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nWe believe this dataset will assist in the training and benchmarking of illustration detectors. It is hoped that by automating a task that would otherwise require manual annotation it will save researchers time and labour in preparing data for both machine and human analysis. The dataset in question is based on a category of popular literature that reflected the learning, tastes and cultural faculties of both its large audiences and its largely-unknown creators - we hope that its use, reuse and adaptation will highlight the importance of cheap chapbooks in the spread of literature, knowledge and entertainment in both urban and rural regions of Scotland and the United Kingdom during this period.", "### Discussion of Biases\n\n\nWhile the original Chapbooks Printed in Scotland is the largest single collection of digitised chapbooks, it is as yet unknown if it is fully representative of all chapbooks printed in Scotland, or of cheap printed literature in general. It is known that a small number of chapbooks (less than 0.1%) within the original collection were not printed in Scotland but this is not expected to have a significant impact on the profile of the collection as a representation of the population of chapbooks as a whole.\n\n\nThe definition of an illustration as opposed to an ornament or other non-textual printed feature is somewhat arbitrary: edge-cases were evaluated by conformance with features that are most characteristic of the chapbook genre as a whole in terms of content, style or placement on the page.\n\n\nAs there is no consensus definition of the chapbook even among domain specialists, the composition of the original dataset is based on the judgement of those who assembled and curated the original collection.", "### Other Known Limitations\n\n\nWithin this dataset, illustrations are repeatedly reused to an unusually high degree compared to other printed forms. The positioning of illustrations on the page and the size and format of chapbooks as a whole is also characteristic of the chapbook format in particular. The extent to which these annotations may be generalised to other printed works is under evaluation: initial results have been promising for other letterpress illustrations surrounded by texts.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Giles Bergel\n* Abhishek Dutta", "### Licensing Information\n\n\nIn accordance with the original data, this dataset is in the public domain.", "### Contributions\n\n\nThanks to @davanstrien and Giles Bergel for adding this dataset." ]

[ "TAGS\n#task_categories-object-detection #task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-expert-generated #size_categories-1K<n<10K #license-other #lam #historic #arxiv-1405.0312 #region-us \n", "### Dataset Summary\n\n\nThis dataset comprises of images from chapbooks held by the National Library of Scotland and digitised and published as its Chapbooks Printed in Scotland dataset.\n\n\n\n> \n> \"Chapbooks were staple everyday reading material from the end of the 17th to the later 19th century. They were usually printed on a single sheet and then folded into books of 8, 12, 16 and 24 pages, and they were often illustrated with crude woodcuts. Their subjects range from news courtship, humour, occupations, fairy tales, apparitions, war, politics, crime, executions, historical figures, transvestites [*sic*] and freemasonry to religion and, of course, poetry. It has been estimated that around two thirds of chapbooks contain songs and poems, often under the title garlands.\" -Source\n> \n> \n> \n\n\nChapbooks were frequently illustrated, particularly on their title pages to attract customers, usually with a woodblock-printed illustration, or occasionally with a stereotyped woodcut or cast metal ornament. Apart from their artistic interest, these illustrations can also provide historical evidence such as the date, place or persons behind the publication of an item.\n\n\nThis dataset contains annotations for a subset of these chapbooks, created by Giles Bergel and Abhishek Dutta, based in the Visual Geometry Group in the University of Oxford. They were created under a National Librarian of Scotland's Fellowship in Digital Scholarship awarded to Giles Bergel in 2020. These annotations provide bounding boxes around illustrations printed on a subset of the chapbook pages, created using a combination of manual annotation and machine classification, described in this paper.\n\n\nThe dataset also includes computationally inferred 'visual groupings' to which illustrated chapbook pages may belong. These groupings are based on the recurrence of illustrations on chapbook pages, as determined through the use of the VGG Image Search Engine (VISE) software", "### Supported Tasks and Leaderboards\n\n\n* 'object-detection': the dataset contains bounding boxes for images contained in the Chapbooks\n* 'image-classification': a configuration for this dataset provides a classification label indicating if a page contains an illustration or not.\n* 'image-matching': a configuration for this dataset contains the annotations sorted into clusters or 'visual groupings' of illustrations that contain visually-matching content as determined by using the VGG Image Search Engine (VISE) software.\n\n\nThe performance on the 'object-detection' task reported in the paper Visual Analysis of Chapbooks Printed in Scotland is as follows:\n\n\nIOU threshold: 0.50, Precision: 0.993, Recall: 0.911\nIOU threshold: 0.75, Precision: 0.987, Recall: 0.905\nIOU threshold: 0.95, Precision: 0.973, Recall: 0.892\n\n\nThe performance on the 'image classification' task reported in the paper Visual Analysis of Chapbooks Printed in Scotland is as follows:\n\n\nImages in original dataset: 47329\nNumbers of images on which at least one illustration was detected: 3629\n\n\nNote that these figures do not represent images that contained multiple detections.\n\n\nSee the paper for examples of false-positive detections.\n\n\nThe performance on the 'image-matching' task is undergoing evaluation.", "### Languages\n\n\nText accompanying the illustrations is in English, Scots or Scottish Gaelic.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example instance from the 'illustration-detection' split:\n\n\nAn example instance from the 'image-classification' split:\n\n\nAn example from the 'image-matching' split:", "### Data Fields\n\n\nThe fields for the 'illustration-detection' config:\n\n\n* image\\_id: id for the image\n* height: height of the image\n* width: width of the image\n* image: image of the chapbook page\n* objects: annotations in COCO format, consisting of a list containing dictionaries with the following keys:\n\t+ bbox: bounding boxes for the images\n\t+ category\\_id: a label for the image\n\t+ image\\_id: id for the image\n\t+ iscrowd: COCO is a crowd flag\n\t+ segmentation: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)\n\n\nThe fields for the 'image-classification' config:\n\n\n* image: image\n* label: a label indicating if the page contains an illustration or not\n\n\nThe fields for the 'image-matching' config:\n\n\n* image: image of the chapbook page\n* label: an id for a particular instance of an image i.e. the same images will share the same id.", "### Data Splits\n\n\nThere is a single split 'train' for all configs. K-fold validation was used in the paper describing this dataset, so no existing splits were defined.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset was created to facilitate research into Scottish chapbook illustration and publishing. Detected illustrations can be browsed under publication metadata: together with the use of VGG Image Search Engine (VISE) software, this allows researchers to identify matching imagery and to infer the source of a chapbook from partial evidence. This browse and search functionality is available in this public demo documented here", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe initial data was taken from the National Library of Scotland's Chapbooks Printed in Scotland dataset No normalisation was performed, but only the images and a subset of the metadata was used. OCR text was not used.", "#### Who are the source language producers?\n\n\nThe initial dataset was created by the National Library of Scotland from scans and in-house curated catalogue descriptions for the NLS Data Foundry under the direction of Dr. Sarah Ames.\n\n\nThis subset of the data was created by Dr. Giles Bergel and Dr. Abhishek Dutta using a combination of manual annotation and machine classification, described below.", "### Annotations", "#### Annotation process\n\n\nAnnotation was initially performed on a subset of 337 of the 47329 images, using the VGG List Annotator (LISA software. Detected illustrations, displayed as annotations in LISA, were reviewed and refined in a number of passes (see this paper for more details). Initial detections were performed with an EfficientDet object detector trained on COCO, the annotation of which is described in this paper", "#### Who are the annotators?\n\n\nAbhishek Dutta created the initial 337 annotations for retraining the EfficentDet model. Detections were reviewed and in some cases revised by Giles Bergel.", "### Personal and Sensitive Information\n\n\nNone\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nWe believe this dataset will assist in the training and benchmarking of illustration detectors. It is hoped that by automating a task that would otherwise require manual annotation it will save researchers time and labour in preparing data for both machine and human analysis. The dataset in question is based on a category of popular literature that reflected the learning, tastes and cultural faculties of both its large audiences and its largely-unknown creators - we hope that its use, reuse and adaptation will highlight the importance of cheap chapbooks in the spread of literature, knowledge and entertainment in both urban and rural regions of Scotland and the United Kingdom during this period.", "### Discussion of Biases\n\n\nWhile the original Chapbooks Printed in Scotland is the largest single collection of digitised chapbooks, it is as yet unknown if it is fully representative of all chapbooks printed in Scotland, or of cheap printed literature in general. It is known that a small number of chapbooks (less than 0.1%) within the original collection were not printed in Scotland but this is not expected to have a significant impact on the profile of the collection as a representation of the population of chapbooks as a whole.\n\n\nThe definition of an illustration as opposed to an ornament or other non-textual printed feature is somewhat arbitrary: edge-cases were evaluated by conformance with features that are most characteristic of the chapbook genre as a whole in terms of content, style or placement on the page.\n\n\nAs there is no consensus definition of the chapbook even among domain specialists, the composition of the original dataset is based on the judgement of those who assembled and curated the original collection.", "### Other Known Limitations\n\n\nWithin this dataset, illustrations are repeatedly reused to an unusually high degree compared to other printed forms. The positioning of illustrations on the page and the size and format of chapbooks as a whole is also characteristic of the chapbook format in particular. The extent to which these annotations may be generalised to other printed works is under evaluation: initial results have been promising for other letterpress illustrations surrounded by texts.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Giles Bergel\n* Abhishek Dutta", "### Licensing Information\n\n\nIn accordance with the original data, this dataset is in the public domain.", "### Contributions\n\n\nThanks to @davanstrien and Giles Bergel for adding this dataset." ]

[ "task_categories:text-classification", "language:code", "license:bsd", "classification", "region:us" ]

[]

[ "code" ]

[]

[ "TAGS\n#task_categories-text-classification #language-code #license-bsd #classification #region-us \n" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/minilm-uncased-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ghpkishore for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/minilm-uncased-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ghpkishore for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/tinyroberta-6l-768d\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ghpkishore for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/tinyroberta-6l-768d\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ghpkishore for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ghpkishore for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ghpkishore for evaluating this model." ]

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

[]

[]

[]

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

[ "task_categories:text-generation", "task_categories:text-to-image", "annotations_creators:no-annotation", "language_creators:machine-generated", "size_categories:10K<n<100K", "language:en", "language:code", "license:cc-by-nc-sa-3.0", "code", "region:us" ]

[]

[ "en", "code" ]

[ "# Dataset Card for Shadertoys", "## 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 - Source Data\n- Licensing Information", "## Dataset Description\n\n- Repository: URL (private placeholder)", "### Dataset Summary\n\nThe Shadertoys dataset contains over 44k renderpasses collected from the URL API. Some shader programm contain multiple render passes.\nTo browse a subset of this dataset, look at the ShaderEval space. A finer variant of this dataset is Shadertoys-fine.", "### Supported Tasks and Leaderboards\n\n 'text-generation' the dataset can be used to train generative language models, for code completion tasks.\n 'ShaderEval' task1 from ShaderEval uses a dataset derived from Shadertoys to test return completion of autoregressive language models.", "### Languages\n\n- English (title, description, tags, comments)\n- Shadercode programming language, a subset of GLSL specifically for URL", "## Dataset Structure", "### Data Instances\n\nA data point consists of the whole shadercode, some information from the API as well as additional metadata.", "### Data Fields\n- 'num_passes' number of passes the parent shader program has\n- 'has_inputs' if any inputs were used like textures, audio streams,\n- 'name' Name of the renderpass, usually Image, Buffer A, Common, etc\n- 'type' type of the renderpass; one of '{'buffer', 'common', 'cubemap', 'image', 'sound'}'\n- 'code' the raw code (including comments) the whole renderpass.\n- 'title' Name of the Shader\n- 'description' description given for the Shader\n- 'tags' List of tags assigned to the Shader (by it's creator); there are more than 10000 unique tags.\n- 'license' currently in development\n- 'author' username of the shader author\n- 'source' URL to the shader. Not to the specific renderpass.", "### Data Splits\n\nCurrently available (shuffled):\n - train (85.0%)\n - test (15.0%)", "## Dataset Creation\n\nData retrieved starting 2022-07-20", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll data was collected via the URL API and then iterated over the items in 'renderpass' while adding some of the fields from 'info'.\nThe code to generate these datasets should be published on the GitHub repository in the near future.", "#### Who are the source language producers?\n\nURL contributers which publish shaders as 'public+API'", "## Licensing Information\n\nThe Default license for each Shader is CC BY-NC-SA 3.0. However, some Shaders might have a different license attached.\nThe Dataset is currently not filtering for any licenses but gives a license tag, if easily recognizeable by naive means.\nPlease check the first comment of each shader program yourself as to not violate any copyrights for downstream use. The main license requires share alike and by attribution.\nAttribution of every data field can be found in the 'author' column, but might not include further attribution within the code itself or parents from forked shaders." ]

[ "TAGS\n#task_categories-text-generation #task_categories-text-to-image #annotations_creators-no-annotation #language_creators-machine-generated #size_categories-10K<n<100K #language-English #language-code #license-cc-by-nc-sa-3.0 #code #region-us \n", "# Dataset Card for Shadertoys", "## 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 - Source Data\n- Licensing Information", "## Dataset Description\n\n- Repository: URL (private placeholder)", "### Dataset Summary\n\nThe Shadertoys dataset contains over 44k renderpasses collected from the URL API. Some shader programm contain multiple render passes.\nTo browse a subset of this dataset, look at the ShaderEval space. A finer variant of this dataset is Shadertoys-fine.", "### Supported Tasks and Leaderboards\n\n 'text-generation' the dataset can be used to train generative language models, for code completion tasks.\n 'ShaderEval' task1 from ShaderEval uses a dataset derived from Shadertoys to test return completion of autoregressive language models.", "### Languages\n\n- English (title, description, tags, comments)\n- Shadercode programming language, a subset of GLSL specifically for URL", "## Dataset Structure", "### Data Instances\n\nA data point consists of the whole shadercode, some information from the API as well as additional metadata.", "### Data Fields\n- 'num_passes' number of passes the parent shader program has\n- 'has_inputs' if any inputs were used like textures, audio streams,\n- 'name' Name of the renderpass, usually Image, Buffer A, Common, etc\n- 'type' type of the renderpass; one of '{'buffer', 'common', 'cubemap', 'image', 'sound'}'\n- 'code' the raw code (including comments) the whole renderpass.\n- 'title' Name of the Shader\n- 'description' description given for the Shader\n- 'tags' List of tags assigned to the Shader (by it's creator); there are more than 10000 unique tags.\n- 'license' currently in development\n- 'author' username of the shader author\n- 'source' URL to the shader. Not to the specific renderpass.", "### Data Splits\n\nCurrently available (shuffled):\n - train (85.0%)\n - test (15.0%)", "## Dataset Creation\n\nData retrieved starting 2022-07-20", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll data was collected via the URL API and then iterated over the items in 'renderpass' while adding some of the fields from 'info'.\nThe code to generate these datasets should be published on the GitHub repository in the near future.", "#### Who are the source language producers?\n\nURL contributers which publish shaders as 'public+API'", "## Licensing Information\n\nThe Default license for each Shader is CC BY-NC-SA 3.0. However, some Shaders might have a different license attached.\nThe Dataset is currently not filtering for any licenses but gives a license tag, if easily recognizeable by naive means.\nPlease check the first comment of each shader program yourself as to not violate any copyrights for downstream use. The main license requires share alike and by attribution.\nAttribution of every data field can be found in the 'author' column, but might not include further attribution within the code itself or parents from forked shaders." ]

[ "uk", "region:us" ]

[]

[]

[]

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

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

[]

[ "ko" ]

[]

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

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

[]

[ "ko" ]

[]

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-large-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-large-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-large-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-large-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: Shanny/bert-finetuned-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ola13 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: Shanny/bert-finetuned-squad\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ola13 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/deberta-v3-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/deberta-v3-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/tinyroberta-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/tinyroberta-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-large-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/roberta-large-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/deberta-v3-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/deberta-v3-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-large-uncased-whole-word-masking-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-large-uncased-whole-word-masking-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/deberta-v3-large-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @MichelBartels for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/deberta-v3-large-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @MichelBartels for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: AJGP/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @hrezaeim for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: AJGP/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @hrezaeim for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards\n\nThis dataset is to be served as a reference for QA tasks.", "### Languages\n\nPersian", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nAll annotations are done according to the SQuAD2.0 data format.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll context and some of questions are retrieved from Faradars Introductory Course to Stock Market.", "#### Who are the source language producers?\n\nPersian (farsi)", "### Annotations", "#### Annotation process\n\nAll annotations are done via Deepset Haystack annotation tool.", "#### Who are the annotators?\n\nHesam Damghanian (this HF account)", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards\n\nThis dataset is to be served as a reference for QA tasks.", "### Languages\n\nPersian", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nAll annotations are done according to the SQuAD2.0 data format.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll context and some of questions are retrieved from Faradars Introductory Course to Stock Market.", "#### Who are the source language producers?\n\nPersian (farsi)", "### Annotations", "#### Annotation process\n\nAll annotations are done via Deepset Haystack annotation tool.", "#### Who are the annotators?\n\nHesam Damghanian (this HF account)", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information" ]

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

[]

[ "en" ]

[ "# Dataset Card for reddit-r-bitcoin-data-for-jun-2022", "## 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- Reddit downloader used: URL\n- Point of Contact: Website", "### Dataset Summary\n\nLite version of our premium Reddit /r/Bitcoin dataset - CSV of all posts & comments to the /r/Bitcoin subreddit over Jun 2022.", "### 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-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for reddit-r-bitcoin-data-for-jun-2022", "## 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- Reddit downloader used: URL\n- Point of Contact: Website", "### Dataset Summary\n\nLite version of our premium Reddit /r/Bitcoin dataset - CSV of all posts & comments to the /r/Bitcoin subreddit over Jun 2022.", "### 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:unknown", "region:us" ]

[]

[]

[]

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

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-large-uncased-whole-word-masking-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mbartolo for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-large-uncased-whole-word-masking-squad2\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @mbartolo for evaluating this model." ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[ "en" ]

[ "# Snares \n\nFSD50K subset of just snares.\n\n\n\nIf you unpack as described above, 'URL' will have correct filepath to audio file when loaded in as CSV. Here we show with pandas..." ]

[ "TAGS\n#language-English #license-other #region-us \n", "# Snares \n\nFSD50K subset of just snares.\n\n\n\nIf you unpack as described above, 'URL' will have correct filepath to audio file when loaded in as CSV. Here we show with pandas..." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

[ "task_categories:translation", "annotations_creators:expert-generated", "language_creators:found", "language_creators:machine-generated", "language_creators:expert-generated", "multilinguality:translation", "size_categories:100K<n<1M", "license:unknown", "region:us" ]

[]

[ "de-cs", "de-en", "de-fr", "en-cs", "en-de", "en-fi", "en-gu", "en-kk", "en-lt", "en-ru", "en-zh", "fi-en", "fr-de", "gu-en", "kk-en", "lt-en", "ru-en", "zh-en" ]

[ "# Dataset Card for WMT19 Metrics Task", "## 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: WMT19 Metrics Shared Task\n- Repository: MT Metrics Eval Github Repository\n- Paper: Paper", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\n\n\nThe dataset comprises the following language pairs:\n- de-cs\n- de-en\n- de-fr\n- en-cs\n- en-de\n- en-fi\n- en-gu\n- en-kk\n- en-lt\n- en-ru\n- en-zh\n- fi-en\n- fr-de\n- gu-en\n- kk-en\n- lt-en\n- ru-en\n- zh-en", "## 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-translation #annotations_creators-expert-generated #language_creators-found #language_creators-machine-generated #language_creators-expert-generated #multilinguality-translation #size_categories-100K<n<1M #license-unknown #region-us \n", "# Dataset Card for WMT19 Metrics Task", "## 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: WMT19 Metrics Shared Task\n- Repository: MT Metrics Eval Github Repository\n- Paper: Paper", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\n\n\nThe dataset comprises the following language pairs:\n- de-cs\n- de-en\n- de-fr\n- en-cs\n- en-de\n- en-fi\n- en-gu\n- en-kk\n- en-lt\n- en-ru\n- en-zh\n- fi-en\n- fr-de\n- gu-en\n- kk-en\n- lt-en\n- ru-en\n- zh-en", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/electra-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/electra-base-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/tinybert-6l-768d-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/tinybert-6l-768d-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-base-uncased-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-base-uncased-squad2\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-medium-squad2-distilled\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/bert-medium-squad2-distilled\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-base-squad2-distilled\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: deepset/xlm-roberta-base-squad2-distilled\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sjrlee for evaluating this model." ]

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

[]

[]

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

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

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

[]

[]

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

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

[ "task_categories:image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "size_categories:1K<n<10K", "source_datasets:extended", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nA smaller subset of 10 classes from Imagenet that aren't so easy to classify, since they're all dog breeds.\nThis dataset was created by Jeremy Howard, and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The dataset can be used to train a model for Image Classification.", "### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA data point comprises an image URL and its classification label.", "### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'label': the expected class label of the image.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\ncf. URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nImagewoof is a subset of ImageNet. Information about data collection of the source data can be found here.", "### Annotations", "#### Annotation process\n\n\ncf. URL", "#### Who are the annotators?\n\n\ncf. URL", "### Personal and Sensitive Information\n\n\ncf. URL\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\ncf. URL", "### Discussion of Biases\n\n\ncf. URL", "### Other Known Limitations\n\n\ncf. URL\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\ncf. URL\nand Jeremy Howard", "### Licensing Information\n\n\nApache License 2.0.", "### Contributions\n\n\nThis dataset was created by Jeremy Howard and published on Github. It was then only integrated into HuggingFace Datasets by @frgfm." ]

[ "TAGS\n#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-1K<n<10K #source_datasets-extended #language-English #license-apache-2.0 #region-us \n", "### Dataset Summary\n\n\nA smaller subset of 10 classes from Imagenet that aren't so easy to classify, since they're all dog breeds.\nThis dataset was created by Jeremy Howard, and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.", "### Supported Tasks and Leaderboards\n\n\n* 'image-classification': The dataset can be used to train a model for Image Classification.", "### Languages\n\n\nThe class labels in the dataset are in English.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA data point comprises an image URL and its classification label.", "### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'label': the expected class label of the image.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\ncf. URL", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nImagewoof is a subset of ImageNet. Information about data collection of the source data can be found here.", "### Annotations", "#### Annotation process\n\n\ncf. URL", "#### Who are the annotators?\n\n\ncf. URL", "### Personal and Sensitive Information\n\n\ncf. URL\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\ncf. URL", "### Discussion of Biases\n\n\ncf. URL", "### Other Known Limitations\n\n\ncf. URL\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\ncf. URL\nand Jeremy Howard", "### Licensing Information\n\n\nApache License 2.0.", "### Contributions\n\n\nThis dataset was created by Jeremy Howard and published on Github. It was then only integrated into HuggingFace Datasets by @frgfm." ]

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: philschmid/distilbart-cnn-12-6-samsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: philschmid/distilbart-cnn-12-6-samsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-6-6\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-6-6\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "task_categories:fill-mask", "task_categories:text-generation", "task_ids:masked-language-modeling", "task_ids:language-modeling", "annotations_creators:machine-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:1M<n<10M", "language:de", "language:nl", "language:en", "language:fr", "language:es", "license:cc-by-4.0", "ocr", "library", "region:us" ]

[]

[ "de", "nl", "en", "fr", "es" ]

[ "### Dataset Summary\n\n\n\n> \n> The digital collections of the SBB contain 153,942 digitized works from the time period of 1470 to 1945.\n> \n> \n> \n\n\n\n> \n> At the time of publication, 28,909 works have been OCR-processed resulting in 4,988,099 full-text pages.\n> For each page with OCR text, the language has been determined by langid (Lui/Baldwin 2012).\n> \n> \n>", "### Supported Tasks and Leaderboards\n\n\n* 'language-modeling': this dataset has the potential to be used for training language models on historical/OCR'd text. Since it contains OCR confidence, language and date information for many examples, it is also possible to filter this dataset to more closely match the requirements for training data.\n*", "### Languages\n\n\nThe collection includes material across a large number of languages. The languages of the OCR text have been detected using URL: An Off-the-shelf Language Identification Tool (Lui & Baldwin, ACL 2012). The dataset includes a confidence score for the language prediction. Note: not all examples may have been successfully matched to the language prediction table from the original data.\n\n\nThe frequency of the top ten languages in the dataset is shown below:\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach example represents a single page of OCR'd text.\n\n\nA single example of the dataset is as follows:", "### Data Fields\n\n\n* 'file name': filename of the original XML file\n* 'text': OCR'd text for that page of the item\n* 'wc': the word confidence for each token predicted by the OCR engine\n* 'ppn': 'Pica production numbers' an internal ID used by the library. See \n-'language\\_confidence': confidence score given by 'URL'\n* publisher: publisher of the item in which the text appears\n* place: place of publication of the item in which the text appears\n* date: date of the item in which the text appears\n* title: title of the item in which the text appears\n* aut: author of the item in which the text appears", "### Data Splits\n\n\nThis dataset contains only a single split 'train'.\n\n\nDataset Creation\n----------------\n\n\nThe dataset is created from OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB) hosted on Zenodo.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset is created from OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB) hosted on Zenodo. This dataset includes text content produced through running Optical Character Recognition across 153,942 digitized works held by the Berlin State Library.\n\n\nThe URL was used to create this dataset.\n\n\nTo make the dataset more useful for training language models, the following steps were carried out:\n\n\n* the CSV 'xml2csv\\_alto.csv', which contains the full text corpus per document page (incl.OCR word confidences) was loaded using the 'datasets' library\n* this CSV was augmented with language information from 'URL' note some examples don't find a match for this. Sometimes this is because a text is blank, but some actual text may be missing predicted language information\n* the CSV was further augmented by trying to map the PPN to fields in a metadata download created using URL note not all examples are successfully matched to this metadata download.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nThis dataset contains machine-produced annotations for:\n\n\n* the confidence scores the OCR engines used to produce the full-text materials.\n* the predicted languages and associated confidence scores produced by 'URL'\n\n\nThe dataset also contains metadata for the following fields:\n\n\n* author\n* publisher\n* the place of publication\n* title", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nThis dataset contains historical material, potentially including names, addresses etc., but these are not likely to refer to living individuals.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases\n\n\nAs with any historical material, the views and attitudes expressed in some texts will likely diverge from contemporary beliefs. One should consider carefully how this potential bias may become reflected in language models trained on this data.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nInitial data created by: Labusch, Kai; Zellhöfer, David", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International", "### Contributions\n\n\nThanks to @davanstrien for adding this dataset." ]

[ "TAGS\n#task_categories-fill-mask #task_categories-text-generation #task_ids-masked-language-modeling #task_ids-language-modeling #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-1M<n<10M #language-German #language-Dutch #language-English #language-French #language-Spanish #license-cc-by-4.0 #ocr #library #region-us \n", "### Dataset Summary\n\n\n\n> \n> The digital collections of the SBB contain 153,942 digitized works from the time period of 1470 to 1945.\n> \n> \n> \n\n\n\n> \n> At the time of publication, 28,909 works have been OCR-processed resulting in 4,988,099 full-text pages.\n> For each page with OCR text, the language has been determined by langid (Lui/Baldwin 2012).\n> \n> \n>", "### Supported Tasks and Leaderboards\n\n\n* 'language-modeling': this dataset has the potential to be used for training language models on historical/OCR'd text. Since it contains OCR confidence, language and date information for many examples, it is also possible to filter this dataset to more closely match the requirements for training data.\n*", "### Languages\n\n\nThe collection includes material across a large number of languages. The languages of the OCR text have been detected using URL: An Off-the-shelf Language Identification Tool (Lui & Baldwin, ACL 2012). The dataset includes a confidence score for the language prediction. Note: not all examples may have been successfully matched to the language prediction table from the original data.\n\n\nThe frequency of the top ten languages in the dataset is shown below:\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nEach example represents a single page of OCR'd text.\n\n\nA single example of the dataset is as follows:", "### Data Fields\n\n\n* 'file name': filename of the original XML file\n* 'text': OCR'd text for that page of the item\n* 'wc': the word confidence for each token predicted by the OCR engine\n* 'ppn': 'Pica production numbers' an internal ID used by the library. See \n-'language\\_confidence': confidence score given by 'URL'\n* publisher: publisher of the item in which the text appears\n* place: place of publication of the item in which the text appears\n* date: date of the item in which the text appears\n* title: title of the item in which the text appears\n* aut: author of the item in which the text appears", "### Data Splits\n\n\nThis dataset contains only a single split 'train'.\n\n\nDataset Creation\n----------------\n\n\nThe dataset is created from OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB) hosted on Zenodo.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe dataset is created from OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB) hosted on Zenodo. This dataset includes text content produced through running Optical Character Recognition across 153,942 digitized works held by the Berlin State Library.\n\n\nThe URL was used to create this dataset.\n\n\nTo make the dataset more useful for training language models, the following steps were carried out:\n\n\n* the CSV 'xml2csv\\_alto.csv', which contains the full text corpus per document page (incl.OCR word confidences) was loaded using the 'datasets' library\n* this CSV was augmented with language information from 'URL' note some examples don't find a match for this. Sometimes this is because a text is blank, but some actual text may be missing predicted language information\n* the CSV was further augmented by trying to map the PPN to fields in a metadata download created using URL note not all examples are successfully matched to this metadata download.", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nThis dataset contains machine-produced annotations for:\n\n\n* the confidence scores the OCR engines used to produce the full-text materials.\n* the predicted languages and associated confidence scores produced by 'URL'\n\n\nThe dataset also contains metadata for the following fields:\n\n\n* author\n* publisher\n* the place of publication\n* title", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nThis dataset contains historical material, potentially including names, addresses etc., but these are not likely to refer to living individuals.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases\n\n\nAs with any historical material, the views and attitudes expressed in some texts will likely diverge from contemporary beliefs. One should consider carefully how this potential bias may become reflected in language models trained on this data.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nInitial data created by: Labusch, Kai; Zellhöfer, David", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International", "### Contributions\n\n\nThanks to @davanstrien for adding this dataset." ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "license:cc-by-nd-4.0", "quran", "qa", "region:us" ]

[]

[ "ar" ]

[ "### Dataset Summary\n\n\nThe QRCD (Qur'anic Reading Comprehension Dataset) is composed of 1,093 tuples of question-passage pairs that are\ncoupled with their extracted answers to constitute 1,337 question-passage-answer triplets.", "### Supported Tasks and Leaderboards\n\n\nThis task is evaluated as a ranking task.\nTo give credit to a QA system that may retrieve an answer (not necessarily at the first rank) that does not fully\nmatch one of the gold answers but partially matches it, we use partial Reciprocal Rank (pRR) measure.\nIt is a variant of the traditional Reciprocal Rank evaluation metric that considers partial matching.\npRR is the official evaluation measure of this shared task.\nWe will also report Exact Match (EM) and F1@1, which are evaluation metrics applied only on the top predicted answer.\nThe EM metric is a binary measure that rewards a system only if the top predicted answer exactly matches one of the\ngold answers.\nWhereas, the F1@1 metric measures the token overlap between the top predicted answer and the best matching gold answer.\nTo get an overall evaluation score, each of the above measures is averaged over all questions.", "### Languages\n\n\nQur'anic Arabic\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nTo simplify the structure of the dataset, each tuple contains one passage, one question and a list that may contain\none or more answers to that question, as shown below:\n\n\nEach Qur’anic passage in QRCD may have more than one occurrence; and each passage occurrence is paired with a different\nquestion.\nLikewise, each question in QRCD may have more than one occurrence; and each question occurrence is paired with a\ndifferent Qur’anic passage.\nThe source of the Qur'anic text in QRCD is the Tanzil project download page, which provides verified versions of the\nHoly Qur'an in several scripting styles.\nWe have chosen the simple-clean text style of Tanzil version 1.0.2.", "### Data Fields\n\n\n* 'pq\\_id': Sample ID\n* 'passage': Context text\n* 'surah': Surah number\n* 'verses': Verse range\n* 'question': Question text\n* 'answers': List of answers and their start character", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe QRCD v1.1 dataset is distributed under the CC-BY-ND 4.0 License URL\nFor a human-readable summary of (and not a substitute for) the above CC-BY-ND 4.0 License, please refer to URL", "### Contributions\n\n\nThanks to @piraka9011 for adding this dataset." ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-n<1K #size_categories-1K<n<10K #source_datasets-original #language-Arabic #license-cc-by-nd-4.0 #quran #qa #region-us \n", "### Dataset Summary\n\n\nThe QRCD (Qur'anic Reading Comprehension Dataset) is composed of 1,093 tuples of question-passage pairs that are\ncoupled with their extracted answers to constitute 1,337 question-passage-answer triplets.", "### Supported Tasks and Leaderboards\n\n\nThis task is evaluated as a ranking task.\nTo give credit to a QA system that may retrieve an answer (not necessarily at the first rank) that does not fully\nmatch one of the gold answers but partially matches it, we use partial Reciprocal Rank (pRR) measure.\nIt is a variant of the traditional Reciprocal Rank evaluation metric that considers partial matching.\npRR is the official evaluation measure of this shared task.\nWe will also report Exact Match (EM) and F1@1, which are evaluation metrics applied only on the top predicted answer.\nThe EM metric is a binary measure that rewards a system only if the top predicted answer exactly matches one of the\ngold answers.\nWhereas, the F1@1 metric measures the token overlap between the top predicted answer and the best matching gold answer.\nTo get an overall evaluation score, each of the above measures is averaged over all questions.", "### Languages\n\n\nQur'anic Arabic\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nTo simplify the structure of the dataset, each tuple contains one passage, one question and a list that may contain\none or more answers to that question, as shown below:\n\n\nEach Qur’anic passage in QRCD may have more than one occurrence; and each passage occurrence is paired with a different\nquestion.\nLikewise, each question in QRCD may have more than one occurrence; and each question occurrence is paired with a\ndifferent Qur’anic passage.\nThe source of the Qur'anic text in QRCD is the Tanzil project download page, which provides verified versions of the\nHoly Qur'an in several scripting styles.\nWe have chosen the simple-clean text style of Tanzil version 1.0.2.", "### Data Fields\n\n\n* 'pq\\_id': Sample ID\n* 'passage': Context text\n* 'surah': Surah number\n* 'verses': Verse range\n* 'question': Question text\n* 'answers': List of answers and their start character", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe QRCD v1.1 dataset is distributed under the CC-BY-ND 4.0 License URL\nFor a human-readable summary of (and not a substitute for) the above CC-BY-ND 4.0 License, please refer to URL", "### Contributions\n\n\nThanks to @piraka9011 for adding this dataset." ]

[ "task_categories:unconditional-image-generation", "size_categories:1K<n<10K", "license:other", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-unconditional-image-generation #size_categories-1K<n<10K #license-other #region-us \n" ]

[ "task_categories:text-classification", "task_ids:sentiment-scoring", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:nl", "license:cc-by-2.0", "newspapers", "historic", "dutch", "problematic", "ConConCor", "region:us" ]

[]

[ "nl" ]

[ "### Dataset Summary\n\n\nThis dataset contains extracts from historical Dutch newspapers containing keywords of potentially contentious words (according to present-day sensibilities). The dataset contains multiple annotations per instance, given the option to quantify agreement scores for annotations. This dataset can be used to track how words and their meanings have changed over time", "### Supported Tasks and Leaderboards\n\n\n* 'text-classification': This dataset can be used for tracking how the meanings of words in different contexts have changed and become contentious over time", "### Languages\n\n\nThe text in the dataset is in Dutch. The responses are available in both English and Dutch. Suggestions, where present, are only in Dutch. The associated BCP-47 code is 'nl'\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nTrain: 2720\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n\n> \n> Cultural heritage institutions recognise the problem of language use in their collections. The cultural objects in archives, libraries, and museums contain words and phrases that are inappropriate in modern society but were used broadly back in times. Such words can be offensive and discriminative. In our work, we use the term \"contentious\" to refer to all (potentially) inappropriate or otherwise sensitive words. For example, words suggestive of some (implicit or explicit) bias towards or against something. The National Archives of the Netherlands stated that they \"explore the possibility of explaining language that was acceptable and common in the past and providing it with contemporary alternatives\", meanwhile \"keeping the original descriptions [with contentious words], because they give an idea of the time in which they were made or included in the collection\". There is a page on the institution website where people can report \"offensive language\".\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\n\n> \n> The queries were run on OCR'd versions of the Europeana Newspaper collection, as provided by the KB National Library of the Netherlands. We limited our pool to text categorised as \"article\", thus excluding other types of texts such as advertisements and family notices. We then only focused our sample on the 6 decades between 1890-01-01 and 1941-12-31, as this is the period available in the Europeana newspaper corpus. The dataset represents a stratified sample set over target word, decade, and newspaper issue distribution metadata. For the final set of extracts for annotation, we gave extracts sampling weights proportional to their actual probabilities, as estimated from the initial set of extracts via trigram frequencies, rather than sampling uniformly.\n> \n> \n>", "#### Who are the source language producers?\n\n\n[N/A]", "### Annotations", "#### Annotation process\n\n\n\n> \n> The annotation process included 3 stages: pilot annotation, expert annotation, and crowdsourced annotation on the \"Prolific\" platform. All stages required the participation of Dutch speakers. The pilot stage was intended for testing the annotation layout, the instructions clarity, the number of sentences provided as context, the survey questions, and the difficulty of the task in general. The Dutch-speaking members of the Cultural AI Lab were asked to test the annotation process and give their feedback anonymously using Google Sheets. Six volunteers contributed to the pilot stage, each annotating the same 40 samples where either a context of 3 or 5 sentences surrounding the term were given. An individual annotation sheet had a table layout with 4 options to choose for every sample\n> \n> \n> * 'Omstreden'(Contentious)\n> * 'Niet omstreden'(Not contentious)\n> * 'Weet ik niet'(I don't know)\n> * 'Onleesbare OCR'(Illegible OCR)\n> 2 open fields\n> * 'Andere omstreden termen in de context'(Other contentious terms in the context)\n> * 'Notities'(Notes)\n> and the instructions in the header. The rows were the samples with the highlighted words, the tickboxes for every option, and 2 empty cells for the open questions. The obligatory part of the annotation was to select one of the 4 options for every sample. Finding other contentious terms in the given sample, leaving notes, and answering 4 additional open questions at the end of the task were optional. Based on the received feedback and the answers to the open questions in the pilot study, the following decisions were made regarding the next, experts' annotation stage:\n> * The annotation layout was built in Google Forms as a questionnaire instead of the table layout in Google Sheets to make the data collection and analysis faster as the number of participants would increase;\n> * The context window of 5 sentences per sample was found optimal;\n> * The number of samples per annotator was increased to 50;\n> * The option 'Omstreden' (Contentious) was changed to 'Omstreden naar huidige maatstaven' ('Contentious according to current standards') to clarify that annotators should judge contentiousness of the word's use in context from today's perspective;\n> * The annotation instruction was edited to clarify 2 points: (1) that annotators while judging contentiousness should take into account not only a bolded word but also the context surrounding it, and (2) if a word seems even slightly contentious to an annotator, they should choose the option 'Omstreden naar huidige maatstaven' (Contentious according to current standards);\n> * The non-required field for every sample 'Notities' (Notes) was removed as there was an open question at the end of the annotation, where participants could leave their comments;\n> * Another open question was added at the end of the annotation asking how much time it took to complete the annotation.\n> \n> \n>", "#### Who are the annotators?\n\n\nVolunteers and Expert annotators", "### Personal and Sensitive Information\n\n\n[N/A]\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nAccessing the annotations\n-------------------------\n\n\nEach example text has multiple annotations. These annotations may not always agree. There are various approaches one could take to calculate agreement, including a majority vote, rating some annotators more highly, or calculating a score based on the 'votes' of annotators. Since there are many ways of doing this, we have not implemented this as part of the dataset loading script.\n\n\nAn example of how one could generate an \"OCR quality rating\" based on the number of times an annotator labelled an example with 'Illegible OCR':\n\n\nTo take the majority vote (or return a tie) based on whether a example is labelled contentious or not:", "### Social Impact of Dataset\n\n\nThis dataset can be used to see how words change in meaning over time", "### Discussion of Biases\n\n\n\n> \n> Due to the nature of the project, some examples used in this documentation may be shocking or offensive. They are provided only as an illustration or explanation of the resulting dataset and do not reflect the opinions of the project team or their organisations.\n> \n> \n> \n\n\nSince this project was explicitly created to help assess bias, it should be used primarily in the context of assess bias, and methods for detecting bias.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nCultural AI", "### Licensing Information\n\n\nCC-BY" ]

[ "TAGS\n#task_categories-text-classification #task_ids-sentiment-scoring #task_ids-multi-label-classification #annotations_creators-expert-generated #annotations_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Dutch #license-cc-by-2.0 #newspapers #historic #dutch #problematic #ConConCor #region-us \n", "### Dataset Summary\n\n\nThis dataset contains extracts from historical Dutch newspapers containing keywords of potentially contentious words (according to present-day sensibilities). The dataset contains multiple annotations per instance, given the option to quantify agreement scores for annotations. This dataset can be used to track how words and their meanings have changed over time", "### Supported Tasks and Leaderboards\n\n\n* 'text-classification': This dataset can be used for tracking how the meanings of words in different contexts have changed and become contentious over time", "### Languages\n\n\nThe text in the dataset is in Dutch. The responses are available in both English and Dutch. Suggestions, where present, are only in Dutch. The associated BCP-47 code is 'nl'\n\n\nDataset Structure\n-----------------", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nTrain: 2720\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n\n> \n> Cultural heritage institutions recognise the problem of language use in their collections. The cultural objects in archives, libraries, and museums contain words and phrases that are inappropriate in modern society but were used broadly back in times. Such words can be offensive and discriminative. In our work, we use the term \"contentious\" to refer to all (potentially) inappropriate or otherwise sensitive words. For example, words suggestive of some (implicit or explicit) bias towards or against something. The National Archives of the Netherlands stated that they \"explore the possibility of explaining language that was acceptable and common in the past and providing it with contemporary alternatives\", meanwhile \"keeping the original descriptions [with contentious words], because they give an idea of the time in which they were made or included in the collection\". There is a page on the institution website where people can report \"offensive language\".\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\n\n> \n> The queries were run on OCR'd versions of the Europeana Newspaper collection, as provided by the KB National Library of the Netherlands. We limited our pool to text categorised as \"article\", thus excluding other types of texts such as advertisements and family notices. We then only focused our sample on the 6 decades between 1890-01-01 and 1941-12-31, as this is the period available in the Europeana newspaper corpus. The dataset represents a stratified sample set over target word, decade, and newspaper issue distribution metadata. For the final set of extracts for annotation, we gave extracts sampling weights proportional to their actual probabilities, as estimated from the initial set of extracts via trigram frequencies, rather than sampling uniformly.\n> \n> \n>", "#### Who are the source language producers?\n\n\n[N/A]", "### Annotations", "#### Annotation process\n\n\n\n> \n> The annotation process included 3 stages: pilot annotation, expert annotation, and crowdsourced annotation on the \"Prolific\" platform. All stages required the participation of Dutch speakers. The pilot stage was intended for testing the annotation layout, the instructions clarity, the number of sentences provided as context, the survey questions, and the difficulty of the task in general. The Dutch-speaking members of the Cultural AI Lab were asked to test the annotation process and give their feedback anonymously using Google Sheets. Six volunteers contributed to the pilot stage, each annotating the same 40 samples where either a context of 3 or 5 sentences surrounding the term were given. An individual annotation sheet had a table layout with 4 options to choose for every sample\n> \n> \n> * 'Omstreden'(Contentious)\n> * 'Niet omstreden'(Not contentious)\n> * 'Weet ik niet'(I don't know)\n> * 'Onleesbare OCR'(Illegible OCR)\n> 2 open fields\n> * 'Andere omstreden termen in de context'(Other contentious terms in the context)\n> * 'Notities'(Notes)\n> and the instructions in the header. The rows were the samples with the highlighted words, the tickboxes for every option, and 2 empty cells for the open questions. The obligatory part of the annotation was to select one of the 4 options for every sample. Finding other contentious terms in the given sample, leaving notes, and answering 4 additional open questions at the end of the task were optional. Based on the received feedback and the answers to the open questions in the pilot study, the following decisions were made regarding the next, experts' annotation stage:\n> * The annotation layout was built in Google Forms as a questionnaire instead of the table layout in Google Sheets to make the data collection and analysis faster as the number of participants would increase;\n> * The context window of 5 sentences per sample was found optimal;\n> * The number of samples per annotator was increased to 50;\n> * The option 'Omstreden' (Contentious) was changed to 'Omstreden naar huidige maatstaven' ('Contentious according to current standards') to clarify that annotators should judge contentiousness of the word's use in context from today's perspective;\n> * The annotation instruction was edited to clarify 2 points: (1) that annotators while judging contentiousness should take into account not only a bolded word but also the context surrounding it, and (2) if a word seems even slightly contentious to an annotator, they should choose the option 'Omstreden naar huidige maatstaven' (Contentious according to current standards);\n> * The non-required field for every sample 'Notities' (Notes) was removed as there was an open question at the end of the annotation, where participants could leave their comments;\n> * Another open question was added at the end of the annotation asking how much time it took to complete the annotation.\n> \n> \n>", "#### Who are the annotators?\n\n\nVolunteers and Expert annotators", "### Personal and Sensitive Information\n\n\n[N/A]\n\n\nConsiderations for Using the Data\n---------------------------------\n\n\nAccessing the annotations\n-------------------------\n\n\nEach example text has multiple annotations. These annotations may not always agree. There are various approaches one could take to calculate agreement, including a majority vote, rating some annotators more highly, or calculating a score based on the 'votes' of annotators. Since there are many ways of doing this, we have not implemented this as part of the dataset loading script.\n\n\nAn example of how one could generate an \"OCR quality rating\" based on the number of times an annotator labelled an example with 'Illegible OCR':\n\n\nTo take the majority vote (or return a tie) based on whether a example is labelled contentious or not:", "### Social Impact of Dataset\n\n\nThis dataset can be used to see how words change in meaning over time", "### Discussion of Biases\n\n\n\n> \n> Due to the nature of the project, some examples used in this documentation may be shocking or offensive. They are provided only as an illustration or explanation of the resulting dataset and do not reflect the opinions of the project team or their organisations.\n> \n> \n> \n\n\nSince this project was explicitly created to help assess bias, it should be used primarily in the context of assess bias, and methods for detecting bias.", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nCultural AI", "### Licensing Information\n\n\nCC-BY" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-book-summary\n* Dataset: billsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nlpconnect/roberta-base-squad2-nq\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ankur310794 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nlpconnect/roberta-base-squad2-nq\n* Dataset: squad_v2\n* Config: squad_v2\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ankur310794 for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nlpconnect/roberta-base-squad2-nq\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ankur310794 for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: nlpconnect/roberta-base-squad2-nq\n* Dataset: squad\n* Config: plain_text\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @ankur310794 for evaluating this model." ]

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

[]

[ "en" ]

[ "# Dataset Card for bc2gm_corpus", "## 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: Github\n- Repository: Github\n- Paper: NCBI\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'id': Sentence identifier. \n- 'tokens': Array of tokens composing a sentence. \n- 'ner_tags': Array of tags, where '0' indicates no disease mentioned, '1' signals the first token of a disease and '2' the subsequent disease tokens.", "### 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 @mahajandiwakar for adding this dataset." ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us \n", "# Dataset Card for bc2gm_corpus", "## 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: Github\n- Repository: Github\n- Paper: NCBI\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'id': Sentence identifier. \n- 'tokens': Array of tokens composing a sentence. \n- 'ner_tags': Array of tags, where '0' indicates no disease mentioned, '1' signals the first token of a disease and '2' the subsequent disease tokens.", "### 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 @mahajandiwakar for adding this dataset." ]

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

[]

[ "en" ]

[ "# Dataset Card for Legal Advice Reddit Dataset", "## Dataset Description\n\n- Paper: Parameter-Efficient Legal Domain Adaptation \n- Point of Contact: jxl@URL", "### Dataset Summary\n\nNew dataset introduced in Parameter-Efficient Legal Domain Adaptation (Li et al., NLLP 2022) from the Legal Advice Reddit community (known as \"/r/legaldvice\"), sourcing the Reddit posts from the Pushshift\nReddit dataset. The dataset maps the text and title of each legal question posted into one of eleven classes, based on the original Reddit\npost's \"flair\" (i.e., tag). Questions are typically informal and use non-legal-specific language. Per the Legal Advice Reddit rules, posts \nmust be about actual personal circumstances or situations. We limit the number of labels to the top eleven classes and remove the other \nsamples from the dataset." ]

[ "TAGS\n#language-English #reddit #law #region-us \n", "# Dataset Card for Legal Advice Reddit Dataset", "## Dataset Description\n\n- Paper: Parameter-Efficient Legal Domain Adaptation \n- Point of Contact: jxl@URL", "### Dataset Summary\n\nNew dataset introduced in Parameter-Efficient Legal Domain Adaptation (Li et al., NLLP 2022) from the Legal Advice Reddit community (known as \"/r/legaldvice\"), sourcing the Reddit posts from the Pushshift\nReddit dataset. The dataset maps the text and title of each legal question posted into one of eleven classes, based on the original Reddit\npost's \"flair\" (i.e., tag). Questions are typically informal and use non-legal-specific language. Per the Legal Advice Reddit rules, posts \nmust be about actual personal circumstances or situations. We limit the number of labels to the top eleven classes and remove the other \nsamples from the dataset." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2111.11431", "region:us" ]

[ "2111.11431" ]

[ "en" ]

[ "# Dataset Card for RedCaps", "## Table of Contents\n- Table of Contents\n- Dataset Information\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 Information\n- Path /home/URL\n- Content type image\n- Tag sensor, common, ai, dataset\n- Description \n - Homepage: RedCaps homepage\n - Repository: RedCaps repository\n - Paper: RedCaps: web-curated image-text data created by the people, for the people\n - Leaderboard:\n - Point of Contact: Karan Desai", "### Dataset Summary\n\nRedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.\nImages and captions from Reddit depict and describe a wide variety of objects and scenes.\nThe data is collected from a manually curated set of subreddits (350 total),\nwhich give coarse image labels and allow steering of the dataset composition\nwithout labeling individual instances. RedCaps data is created *by the people, for the people* – it contains everyday things that users like to share on social media, for example hobbies (r/crafts) and pets (r/shiba). Captions often contain specific and\nfine-grained descriptions (northern cardinal, taj mahal). Subreddit names provide relevant image\nlabels (r/shiba) even when captions may not (mlem!), and sometimes may group many visually\nunrelated images through a common semantic meaning (r/perfectfit).", "### 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:\n\n\n\nSome image links point to more than one image. You can process and downloaded those as follows:\n\n\n\nNote that in the above code, we use the 'datasets.Sequence' feature to represent a list of images for the multi-image links.", "### Supported Tasks and Leaderboards\n\nFrom the paper:\n> We have used our dataset to train deep neural networks that perform image captioning, and\nthat learn transferable visual representations for a variety of downstream visual recognition tasks\n(image classification, object detection, instance segmentation).\n\n> We anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks,\nsuch as image or text retrieval or text-to-image synthesis.", "### Languages\n\nAll of the subreddits in RedCaps use English as their primary language.", "## Dataset Structure", "### Data Instances\n\nEach instance in RedCaps represents a single Reddit image post:", "### Data Fields\n\n- 'image_id': Unique alphanumeric ID of the image post (assigned by Reddit).\n- 'author': Reddit username of the image post author.\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'raw_caption': Textual description of the image, written by the post author.\n- 'caption': Cleaned version of \"raw_caption\" by us (see Q35).\n- 'subreddit': Name of subreddit where the post was submitted.\n- 'score': Net upvotes (discounting downvotes) received by the image post. This field is equal to 'None' if the image post is a crosspost.\n- 'created_utc': Integer time epoch (in UTC) when the post was submitted to Reddit.\n- 'permalink': Partial URL of the Reddit post (URL\n- 'crosspost_parents': List of parent posts. This field is optional.", "### Data Splits\n\nAll the data is contained in training set. The training set has nearly 12M (12,011,111) instances. \n\nFrom the paper:\n> We intend our dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Hence, all instances in our dataset would be used for training while\nthe validation split is derived from downstream task(s). If users require a validation split, we\nrecommend sampling it such that it follows the same subreddit distribution as entire dataset.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> Large datasets of image-text pairs are widely used for pre-training generic representations\nthat transfer to a variety of downstream vision and vision-and-language tasks. Existing public\ndatasets of this kind were curated from search engine results (SBU Captions [1]) or HTML\nalt-text from arbitrary web pages (Conceptual Captions [2, 31]). They performed complex\ndata filtering to deal with noisy web data. Due to aggressive filtering, their data collection is\ninefficient and diversity is artificially supressed. We argue that the quality of data depends on\nits source, and the human intent behind its creation. In this work, we explore Reddit – a social\nmedia platform, for curating high quality data. We introduce RedCaps – a large dataset of\n12M image-text pairs from Reddit. While we expect the use-cases of RedCaps to be similar to\nexisting datasets, we discuss how Reddit as a data source leads to fast and lightweight collection,\nbetter data quality, lets us easily steer the data distribution, and facilitates ethically responsible data curation.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper:\n> Data Collection Pipeline\nReddit’s uniform structure allows us to parallelize data collection as independent tasks – each task\ninvolves collecting posts submitted to a single subreddit in one year. Our collection pipeline has three steps: (1) subreddit selection, (2) image post filtering, and (3) caption cleaning.\nStep 1. Subreddit selection: We collect data from a manually curated set of subreddits. Subreddits\nhave their own rules, community norms, and moderators so curating subreddits allows us to steer the\ndataset’s composition without annotating individual instances. We select subreddits with a high volume of images posts, where images tend to be photographs (rather than memes, drawings, screenshots,\netc) and post titles tend to describe image content (rather than making jokes, political commentary,\netc). We do not select any NSFW, banned, or quarantined subreddits. We want to minimize the\nnumber of people that appear in RedCaps, so we omit subreddits whose primary purpose is to share or\ncomment on images of people (such as celebrity pics or user selfies). We choose subreddits focused on\ngeneral photography (r/pics, r/itookapicture), animals (r/axolotls, r/birdsofprey, r/dachshund),\nplants (r/roses, r/succulents), objects (r/classiccars, r/trains, r/mechanicalkeyboards), food\n(r/steak, r/macarons), scenery (r/cityporn1\n, r/desertporn), or activities (r/carpentry, r/kayaking).\nIn total we collect data from 350 subreddits; the full list can be found in Appendix A.\nStep 2. Image post filtering: We use Pushshift [41] and Reddit [42, 43] APIs to download all image\nposts submitted to our selected subreddits from 2008–2020. Posts are collected at least six months\nafter their creation to let upvotes stabilize. We only collect posts with images hosted on three domains:\nReddit (i.URL), Imgur (i.URL), and Flickr (URL). Some image posts contain\nmultiple images (gallery posts) – in this case we only collect the first image and associate it with\nthe caption. We discard posts with < 2 upvotes to avoid unappealing content, and we discard posts\nmarked NSFW (by their authors or subreddit moderators) to avoid pornographic or disturbing content.\nStep 3. Caption cleaning: We expect Reddit post titles to be less noisy than other large-scale\nsources of image captions such as alt-text [2, 31], so we apply minimal text cleaning. We lowercase\ncaptions and use ftfy [44] to remove character accents, emojis, and non-latin characters, following\n[29, 35, 36]. Then we apply simple pattern matching to discard all sub-strings enclosed in brackets\n((.*), [.*]). These sub-strings usually give non-semantic information: original content tags [oc],\nimage resolutions (800x600 px), camera specs (shot with iPhone), self-promotion [Instagram:\n@user], and other references (link in comments). Finally, like [31] we replace social media\nhandles (words starting with ‘@’) with a [USR] token to protect user privacy and reduce redundancy.\nDue to such filtering, ≈12K (0.1%) captions in our dataset are empty strings. We do not discard them,\nas subreddit names alone provide meaningful supervision. Unlike CC-3M or CC-12M that discard\ncaptions without nouns or that don’t overlap image tags, we do not discard any instances in this step.\nThrough this pipeline, we collect 13.4M instances from 350 subreddits. Our collection pipeline is\nless resource-intensive than existing datasets – we do not require webpage crawlers, search engines,\nor large databases of indexed webpages. RedCaps is easily extensible in the future by selecting more\nsubreddits and collecting posts from future years. Next, we perform additional filtering to mitigate\nuser privacy risks and harmful stereotypes in RedCaps, resulting in final size of 12M instances.", "#### Who are the source language producers?\n\nReddit is the singular data source for RedCaps.", "### Annotations", "#### Annotation process\n\nThe dataset is built using fully automatic data collection pipeline which doesn't require any human annotators.", "#### Who are the annotators?\n\nThe annotation process doesn't require any human annotators.", "### Personal and Sensitive Information\n\nFrom the paper:\n> Does the dataset relate to people?\nThe dataset pertains to people in that people wrote the captions and posted images to Reddit\nthat we curate in RedCaps. We made specific design choices while curating RedCaps to avoid\nlarge quantities of images containing people:\n(a) We collect data from manually curated subreddits in which most contain primarily pertains\nto animals, objects, places, or activities. We exclude all subreddits whose primary purpose\nis to share and describe images of people (such as celebrity photos or user selfies).\n(b) We use an off-the-shelf face detector to find and remove images with potential presence of\nhuman faces. We manually checked 50K random images in RedCaps (Q16) and found 79\nimages with identifiable human faces – the entire dataset may have ≈19K (0.15%) images\nwith identifiable people. Refer Section 2.2 in the main paper.\n\n> Is it possible to identify one or more natural persons, either directly or indirectly (i.e., in\ncombination with other data) from the dataset? \nYes, all instances in RedCaps include Reddit usernames of their post authors. This could be\nused to look up the Reddit user profile, and some Reddit users may have identifying information\nin their profiles. Some images may contain human faces which could be identified by\nappearance. However, note that all this information is already public on Reddit, and searching it\nin RedCaps is no easier than searching directly on Reddit.\n\n> Were the individuals in question notified about the data collection?\nNo. Reddit users are anonymous by default, and are not required to share their personal contact\ninformation (email, phone numbers, etc.). Hence, the only way to notify the authors of RedCaps\nimage posts is by sending them private messages on Reddit. This is practically difficult to do\nmanually, and will be classified as spam and blocked by Reddit if attempted to programmatically\nsend a templated message to millions of users.\n\n> Did the individuals in question consent to the collection and use of their data?\nUsers did not explicitly consent to the use of their data in our dataset. However, by uploading\ntheir data on Reddit, they consent that it would appear on the Reddit plaform and will be\naccessible via the official Reddit API (which we use to collect RedCaps).\n\n> If consent was obtained, were the consenting individuals provided with a mechanism to\nrevoke their consent in the future or for certain uses?\nUsers have full control over the presence of their data in our dataset. If users wish to revoke\ntheir consent, they can delete the underlying Reddit post – it will be automatically removed\ndfrom RedCaps since we distributed images as URLs. Moreover, we provide an opt-out request\nform on our dataset website for anybody to request removal of an individual instance if it is\npotentially harmful (e.g. NSFW, violates privacy, harmful stereotypes, etc.).", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nFrom the paper:\n> Has an analysis of the potential impact of the dataset and its use on data subjects (e.g.,\na data protection impact analysis) been conducted?\nNo.", "### Discussion of Biases\n\nFrom the paper:\n> Harmful Stereotypes: Another concern with\nReddit data is that images or language may represent harmful stereotypes about gender, race, or other\ncharacteristics of people [48, 49, 51]. We select only non-NSFW subreddits with active moderation\nfor collecting data. This stands in contrast to less curated uses of Reddit data, such as GPT-2 [35]\nwhose training data includes at least 63K documents from banned or quarantined subreddits which\nmay contain toxic language [53]. We attempt to further reduce harmful stereotypes in two ways:\n> * NSFW images: We use the InceptionV3 [54] model from [55] to filter images detected as porn or hentai with confidence ≥ 0.9. Similar to face filtering, we estimated precision of our filtering and estimated amount of missed detections, shown in Table 1. The model detects 87K images with low\nprecision (∼1%) – most detections are non-NSFW images with pink and beige hues.\n> * Potentially derogatory language: We filter instances whose captions contain words or phrases from a common blocklist [56]. It is important to note that such coarse filtering might suppress language from marginalized groups reclaiming slurs [51]; however, as RedCaps is not intended to describe people, we believe this is a pragmatic tradeoff to avoid propagating harmful labels.\n\n> Reddit demographics: Reddit’s user demographics are not representative of the population at large.\nCompared to US adults, Reddit users skew male (69% vs 49%), young (58% 18-29 years old vs\n22%), college educated (36% vs 28%), and politically liberal (41% vs 25%) [57]. Reddit users\nare predominantly white (63%) [57], and 49% of desktop traffic to Reddit comes from the United\nStates [58]. All of the subreddits in RedCaps use English as their primary language. Taken together,\nthese demographic biases likely also bias the types of objects and places that appear in images on\nReddit, and the language used to describe these images. We do not offer explicit countermeasures to\nthese biases, but users of RedCaps should keep in mind that size doesn’t guarantee diversity [51].\nSubtler issues may also exist, such as imbalanced representation of demographic groups [59] or\ngender bias in object co-occurrence [60] or language [61]. These are hard to control in internet\ndata, so we release RedCaps with explicit instructions on suitable use-cases; specifically requesting models not be trained to identify people, or make decisions that impact people. We document these instructions and other terms-of-use in a datasheet [45], provided in Appendix G.\n\n> Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?\nThe scale of RedCaps means that we are unable to verify the contents of all images and\ncaptions. However we have tried to minimize the possibility that RedCaps contains data that\nmight be offensive, insulting, threatening, or might cause anxiety via the following mitigations:\n(a) We manually curate the set of subreddits from which to collect data; we only chose\nsubreddits that are not marked NSFW and which generally contain non-offensive content.\n(b) Within our curated subreddits, we did not include any posts marked NSFW.\n(c) We removed all instances whose captions contained any of the 400 potentially offensive\nwords or phrases. Refer Section 2.2 in the main paper.\n(d) We remove all instances whose images were flagged NSFW by an off-the-shelf detector.\nWe manually checked 50K random images in RedCaps and found one image containing\nnudity (exposed buttocks; no identifiable face). Refer Section 2.2 in the main paper\n\n> Does the dataset identify any subpopulations (e.g., by age, gender)?\nRedCaps does not explicitly identify any subpopulations. Since some images contain people\nand captions are free-form natural language written by Reddit users, it is possible that some\ncaptions may identify people appearing in individual images as part of a subpopulation.\n\n> Were any ethical review processes conducted (e.g., by an institutional review board)?\nWe did not conduct a formal ethical review process via institutional review boards. However,\nas described in Section 2.2 of the main paper and Q16 we employed several filtering mechanisms\nto try and remove instances that could be problematic.", "### Other Known Limitations\n\nFrom the paper:\n> Are there any errors, sources of noise, or redundancies in the dataset?\nRedCaps is noisy by design since image-text pairs on the internet are noisy and unstructured.\nSome instances may also have duplicate images and captions – Reddit users may have shared\nthe same image post in multiple subreddits. Such redundancies constitute a very small fraction\nof the dataset, and should have almost no effect in training large-scale models.\n\n> Does the dataset contain data that might be considered confidential (e.g., data that is\nprotected by legal privilege or by doctor-patient confidentiality, data that includes the\ncontent of individuals non-public communications)?\nNo, the subreddits included in RedCaps do not cover topics that may be considered confidential. All posts were publicly shared on Reddit prior to inclusion in RedCaps.", "## Additional Information", "### Dataset Curators\n\nFrom the paper:\n> Four researchers at the University of Michigan (affiliated as of 2021) have created RedCaps:\nKaran Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson.", "### Licensing Information\n\nThe image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (URL\napi-terms) and users must comply with Reddit User Agreeement, Content Policy,\nand Privacy Policy – all accessible at URL\n\nFrom the paper:\n> RedCaps should only be used for non-commercial research. RedCaps should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of RedCaps are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.", "### Contributions\n\nThanks to @mariosasko 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-10M<n<100M #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2111.11431 #region-us \n", "# Dataset Card for RedCaps", "## Table of Contents\n- Table of Contents\n- Dataset Information\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 Information\n- Path /home/URL\n- Content type image\n- Tag sensor, common, ai, dataset\n- Description \n - Homepage: RedCaps homepage\n - Repository: RedCaps repository\n - Paper: RedCaps: web-curated image-text data created by the people, for the people\n - Leaderboard:\n - Point of Contact: Karan Desai", "### Dataset Summary\n\nRedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.\nImages and captions from Reddit depict and describe a wide variety of objects and scenes.\nThe data is collected from a manually curated set of subreddits (350 total),\nwhich give coarse image labels and allow steering of the dataset composition\nwithout labeling individual instances. RedCaps data is created *by the people, for the people* – it contains everyday things that users like to share on social media, for example hobbies (r/crafts) and pets (r/shiba). Captions often contain specific and\nfine-grained descriptions (northern cardinal, taj mahal). Subreddit names provide relevant image\nlabels (r/shiba) even when captions may not (mlem!), and sometimes may group many visually\nunrelated images through a common semantic meaning (r/perfectfit).", "### 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:\n\n\n\nSome image links point to more than one image. You can process and downloaded those as follows:\n\n\n\nNote that in the above code, we use the 'datasets.Sequence' feature to represent a list of images for the multi-image links.", "### Supported Tasks and Leaderboards\n\nFrom the paper:\n> We have used our dataset to train deep neural networks that perform image captioning, and\nthat learn transferable visual representations for a variety of downstream visual recognition tasks\n(image classification, object detection, instance segmentation).\n\n> We anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks,\nsuch as image or text retrieval or text-to-image synthesis.", "### Languages\n\nAll of the subreddits in RedCaps use English as their primary language.", "## Dataset Structure", "### Data Instances\n\nEach instance in RedCaps represents a single Reddit image post:", "### Data Fields\n\n- 'image_id': Unique alphanumeric ID of the image post (assigned by Reddit).\n- 'author': Reddit username of the image post author.\n- 'image_url': Static URL for downloading the image associated with the post.\n- 'raw_caption': Textual description of the image, written by the post author.\n- 'caption': Cleaned version of \"raw_caption\" by us (see Q35).\n- 'subreddit': Name of subreddit where the post was submitted.\n- 'score': Net upvotes (discounting downvotes) received by the image post. This field is equal to 'None' if the image post is a crosspost.\n- 'created_utc': Integer time epoch (in UTC) when the post was submitted to Reddit.\n- 'permalink': Partial URL of the Reddit post (URL\n- 'crosspost_parents': List of parent posts. This field is optional.", "### Data Splits\n\nAll the data is contained in training set. The training set has nearly 12M (12,011,111) instances. \n\nFrom the paper:\n> We intend our dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Hence, all instances in our dataset would be used for training while\nthe validation split is derived from downstream task(s). If users require a validation split, we\nrecommend sampling it such that it follows the same subreddit distribution as entire dataset.", "## Dataset Creation", "### Curation Rationale\n\nFrom the paper:\n> Large datasets of image-text pairs are widely used for pre-training generic representations\nthat transfer to a variety of downstream vision and vision-and-language tasks. Existing public\ndatasets of this kind were curated from search engine results (SBU Captions [1]) or HTML\nalt-text from arbitrary web pages (Conceptual Captions [2, 31]). They performed complex\ndata filtering to deal with noisy web data. Due to aggressive filtering, their data collection is\ninefficient and diversity is artificially supressed. We argue that the quality of data depends on\nits source, and the human intent behind its creation. In this work, we explore Reddit – a social\nmedia platform, for curating high quality data. We introduce RedCaps – a large dataset of\n12M image-text pairs from Reddit. While we expect the use-cases of RedCaps to be similar to\nexisting datasets, we discuss how Reddit as a data source leads to fast and lightweight collection,\nbetter data quality, lets us easily steer the data distribution, and facilitates ethically responsible data curation.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFrom the paper:\n> Data Collection Pipeline\nReddit’s uniform structure allows us to parallelize data collection as independent tasks – each task\ninvolves collecting posts submitted to a single subreddit in one year. Our collection pipeline has three steps: (1) subreddit selection, (2) image post filtering, and (3) caption cleaning.\nStep 1. Subreddit selection: We collect data from a manually curated set of subreddits. Subreddits\nhave their own rules, community norms, and moderators so curating subreddits allows us to steer the\ndataset’s composition without annotating individual instances. We select subreddits with a high volume of images posts, where images tend to be photographs (rather than memes, drawings, screenshots,\netc) and post titles tend to describe image content (rather than making jokes, political commentary,\netc). We do not select any NSFW, banned, or quarantined subreddits. We want to minimize the\nnumber of people that appear in RedCaps, so we omit subreddits whose primary purpose is to share or\ncomment on images of people (such as celebrity pics or user selfies). We choose subreddits focused on\ngeneral photography (r/pics, r/itookapicture), animals (r/axolotls, r/birdsofprey, r/dachshund),\nplants (r/roses, r/succulents), objects (r/classiccars, r/trains, r/mechanicalkeyboards), food\n(r/steak, r/macarons), scenery (r/cityporn1\n, r/desertporn), or activities (r/carpentry, r/kayaking).\nIn total we collect data from 350 subreddits; the full list can be found in Appendix A.\nStep 2. Image post filtering: We use Pushshift [41] and Reddit [42, 43] APIs to download all image\nposts submitted to our selected subreddits from 2008–2020. Posts are collected at least six months\nafter their creation to let upvotes stabilize. We only collect posts with images hosted on three domains:\nReddit (i.URL), Imgur (i.URL), and Flickr (URL). Some image posts contain\nmultiple images (gallery posts) – in this case we only collect the first image and associate it with\nthe caption. We discard posts with < 2 upvotes to avoid unappealing content, and we discard posts\nmarked NSFW (by their authors or subreddit moderators) to avoid pornographic or disturbing content.\nStep 3. Caption cleaning: We expect Reddit post titles to be less noisy than other large-scale\nsources of image captions such as alt-text [2, 31], so we apply minimal text cleaning. We lowercase\ncaptions and use ftfy [44] to remove character accents, emojis, and non-latin characters, following\n[29, 35, 36]. Then we apply simple pattern matching to discard all sub-strings enclosed in brackets\n((.*), [.*]). These sub-strings usually give non-semantic information: original content tags [oc],\nimage resolutions (800x600 px), camera specs (shot with iPhone), self-promotion [Instagram:\n@user], and other references (link in comments). Finally, like [31] we replace social media\nhandles (words starting with ‘@’) with a [USR] token to protect user privacy and reduce redundancy.\nDue to such filtering, ≈12K (0.1%) captions in our dataset are empty strings. We do not discard them,\nas subreddit names alone provide meaningful supervision. Unlike CC-3M or CC-12M that discard\ncaptions without nouns or that don’t overlap image tags, we do not discard any instances in this step.\nThrough this pipeline, we collect 13.4M instances from 350 subreddits. Our collection pipeline is\nless resource-intensive than existing datasets – we do not require webpage crawlers, search engines,\nor large databases of indexed webpages. RedCaps is easily extensible in the future by selecting more\nsubreddits and collecting posts from future years. Next, we perform additional filtering to mitigate\nuser privacy risks and harmful stereotypes in RedCaps, resulting in final size of 12M instances.", "#### Who are the source language producers?\n\nReddit is the singular data source for RedCaps.", "### Annotations", "#### Annotation process\n\nThe dataset is built using fully automatic data collection pipeline which doesn't require any human annotators.", "#### Who are the annotators?\n\nThe annotation process doesn't require any human annotators.", "### Personal and Sensitive Information\n\nFrom the paper:\n> Does the dataset relate to people?\nThe dataset pertains to people in that people wrote the captions and posted images to Reddit\nthat we curate in RedCaps. We made specific design choices while curating RedCaps to avoid\nlarge quantities of images containing people:\n(a) We collect data from manually curated subreddits in which most contain primarily pertains\nto animals, objects, places, or activities. We exclude all subreddits whose primary purpose\nis to share and describe images of people (such as celebrity photos or user selfies).\n(b) We use an off-the-shelf face detector to find and remove images with potential presence of\nhuman faces. We manually checked 50K random images in RedCaps (Q16) and found 79\nimages with identifiable human faces – the entire dataset may have ≈19K (0.15%) images\nwith identifiable people. Refer Section 2.2 in the main paper.\n\n> Is it possible to identify one or more natural persons, either directly or indirectly (i.e., in\ncombination with other data) from the dataset? \nYes, all instances in RedCaps include Reddit usernames of their post authors. This could be\nused to look up the Reddit user profile, and some Reddit users may have identifying information\nin their profiles. Some images may contain human faces which could be identified by\nappearance. However, note that all this information is already public on Reddit, and searching it\nin RedCaps is no easier than searching directly on Reddit.\n\n> Were the individuals in question notified about the data collection?\nNo. Reddit users are anonymous by default, and are not required to share their personal contact\ninformation (email, phone numbers, etc.). Hence, the only way to notify the authors of RedCaps\nimage posts is by sending them private messages on Reddit. This is practically difficult to do\nmanually, and will be classified as spam and blocked by Reddit if attempted to programmatically\nsend a templated message to millions of users.\n\n> Did the individuals in question consent to the collection and use of their data?\nUsers did not explicitly consent to the use of their data in our dataset. However, by uploading\ntheir data on Reddit, they consent that it would appear on the Reddit plaform and will be\naccessible via the official Reddit API (which we use to collect RedCaps).\n\n> If consent was obtained, were the consenting individuals provided with a mechanism to\nrevoke their consent in the future or for certain uses?\nUsers have full control over the presence of their data in our dataset. If users wish to revoke\ntheir consent, they can delete the underlying Reddit post – it will be automatically removed\ndfrom RedCaps since we distributed images as URLs. Moreover, we provide an opt-out request\nform on our dataset website for anybody to request removal of an individual instance if it is\npotentially harmful (e.g. NSFW, violates privacy, harmful stereotypes, etc.).", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nFrom the paper:\n> Has an analysis of the potential impact of the dataset and its use on data subjects (e.g.,\na data protection impact analysis) been conducted?\nNo.", "### Discussion of Biases\n\nFrom the paper:\n> Harmful Stereotypes: Another concern with\nReddit data is that images or language may represent harmful stereotypes about gender, race, or other\ncharacteristics of people [48, 49, 51]. We select only non-NSFW subreddits with active moderation\nfor collecting data. This stands in contrast to less curated uses of Reddit data, such as GPT-2 [35]\nwhose training data includes at least 63K documents from banned or quarantined subreddits which\nmay contain toxic language [53]. We attempt to further reduce harmful stereotypes in two ways:\n> * NSFW images: We use the InceptionV3 [54] model from [55] to filter images detected as porn or hentai with confidence ≥ 0.9. Similar to face filtering, we estimated precision of our filtering and estimated amount of missed detections, shown in Table 1. The model detects 87K images with low\nprecision (∼1%) – most detections are non-NSFW images with pink and beige hues.\n> * Potentially derogatory language: We filter instances whose captions contain words or phrases from a common blocklist [56]. It is important to note that such coarse filtering might suppress language from marginalized groups reclaiming slurs [51]; however, as RedCaps is not intended to describe people, we believe this is a pragmatic tradeoff to avoid propagating harmful labels.\n\n> Reddit demographics: Reddit’s user demographics are not representative of the population at large.\nCompared to US adults, Reddit users skew male (69% vs 49%), young (58% 18-29 years old vs\n22%), college educated (36% vs 28%), and politically liberal (41% vs 25%) [57]. Reddit users\nare predominantly white (63%) [57], and 49% of desktop traffic to Reddit comes from the United\nStates [58]. All of the subreddits in RedCaps use English as their primary language. Taken together,\nthese demographic biases likely also bias the types of objects and places that appear in images on\nReddit, and the language used to describe these images. We do not offer explicit countermeasures to\nthese biases, but users of RedCaps should keep in mind that size doesn’t guarantee diversity [51].\nSubtler issues may also exist, such as imbalanced representation of demographic groups [59] or\ngender bias in object co-occurrence [60] or language [61]. These are hard to control in internet\ndata, so we release RedCaps with explicit instructions on suitable use-cases; specifically requesting models not be trained to identify people, or make decisions that impact people. We document these instructions and other terms-of-use in a datasheet [45], provided in Appendix G.\n\n> Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?\nThe scale of RedCaps means that we are unable to verify the contents of all images and\ncaptions. However we have tried to minimize the possibility that RedCaps contains data that\nmight be offensive, insulting, threatening, or might cause anxiety via the following mitigations:\n(a) We manually curate the set of subreddits from which to collect data; we only chose\nsubreddits that are not marked NSFW and which generally contain non-offensive content.\n(b) Within our curated subreddits, we did not include any posts marked NSFW.\n(c) We removed all instances whose captions contained any of the 400 potentially offensive\nwords or phrases. Refer Section 2.2 in the main paper.\n(d) We remove all instances whose images were flagged NSFW by an off-the-shelf detector.\nWe manually checked 50K random images in RedCaps and found one image containing\nnudity (exposed buttocks; no identifiable face). Refer Section 2.2 in the main paper\n\n> Does the dataset identify any subpopulations (e.g., by age, gender)?\nRedCaps does not explicitly identify any subpopulations. Since some images contain people\nand captions are free-form natural language written by Reddit users, it is possible that some\ncaptions may identify people appearing in individual images as part of a subpopulation.\n\n> Were any ethical review processes conducted (e.g., by an institutional review board)?\nWe did not conduct a formal ethical review process via institutional review boards. However,\nas described in Section 2.2 of the main paper and Q16 we employed several filtering mechanisms\nto try and remove instances that could be problematic.", "### Other Known Limitations\n\nFrom the paper:\n> Are there any errors, sources of noise, or redundancies in the dataset?\nRedCaps is noisy by design since image-text pairs on the internet are noisy and unstructured.\nSome instances may also have duplicate images and captions – Reddit users may have shared\nthe same image post in multiple subreddits. Such redundancies constitute a very small fraction\nof the dataset, and should have almost no effect in training large-scale models.\n\n> Does the dataset contain data that might be considered confidential (e.g., data that is\nprotected by legal privilege or by doctor-patient confidentiality, data that includes the\ncontent of individuals non-public communications)?\nNo, the subreddits included in RedCaps do not cover topics that may be considered confidential. All posts were publicly shared on Reddit prior to inclusion in RedCaps.", "## Additional Information", "### Dataset Curators\n\nFrom the paper:\n> Four researchers at the University of Michigan (affiliated as of 2021) have created RedCaps:\nKaran Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson.", "### Licensing Information\n\nThe image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (URL\napi-terms) and users must comply with Reddit User Agreeement, Content Policy,\nand Privacy Policy – all accessible at URL\n\nFrom the paper:\n> RedCaps should only be used for non-commercial research. RedCaps should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of RedCaps are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.", "### Contributions\n\nThanks to @mariosasko for adding this dataset." ]

[ "task_categories:text-to-image", "task_categories:image-to-text", "task_categories:image-to-image", "task_ids:image-captioning", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "language:fr", "language:de", "license:cc-by-nc-4.0", "lam", "1800-1900", "region:us" ]

[]

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

[ "# Dataset Card for Old Book Illustrations", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Discussion of Biases\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage", "### Dataset Summary\n\nThe Old Book Illustrations contains 4172 illustrations scanned from old books, this collection was collected & curated by the team of the website Old Book Illustrations.\nThe webmaster of Old Book Illustrations kindly allowed us to scrap these information in order to create this dataset for the BigLAM initiative.", "### Languages\n\nThe captions and descriptions are mostly in English but can contain some sentences from other languages such as French or German.\nFor instance you can find this description that contains a French sentence:\n\n>The caption reads in the original French: Vue de l’aqueduc de Salones qui conduisait l’eau à Spalatro.", "## Dataset Structure\n\nEach row contains information gathered from the page of an illustration on the website Old Book Illustrations. As of July 2022, there are 4172 illustrations in this dataset.", "### Data Fields\n\n* 'rawscan': the image as originally scanned from the book, without further processing\n* '1600px': the cleaned image, resized to a width of 1600 pixels (height can vary)\n* 'info_url': URL to the illustration page on URL\n* 'ìnfo_src': URL to an icon-sized version of the image\n* 'info_alt': short description of the image\n* 'artist_name': artist name\n* 'artist_date': birth date of the artist\n* 'artist_countries': list of the countries the artist is from\n* 'book_title': original title of the book the illustration is extracted from\n* 'book_authors': list of the authors of the book\n* 'book_publishers': list of the publishers of the book\n* 'openlibrary-url': URL to the openlibrary entry for the book\n* 'tags': list of keywords for this illustration on URL\n* 'illustration_source_name': list of the sources for this illustration\n* 'illustration_source_url': list of the URL for these sources\n* 'illustration_subject': category of the subject represented in the illustration\n* 'illustration_format': category of the format of the illustration\n* 'image_title': title of the image\n* 'image_caption': caption of the image. Seems to be the caption that appears next to the image in the book, translated to English if in another language\n* 'image_description': longer description of the image. If there is one, it also quotes the caption in the original language\n* 'rawscan_url': URL to the rawscan image on URL\n* '1600px_url': URL to the cleaned image on URL", "## Dataset Creation", "### Curation Rationale\n\nThis collection was collected & curated by the team of the website Old Book Illustrations.\nThis version contains all the data that was available on the website as of July 2022, but the website is being actively maintained so if you want more old book illustrations, make sure to check Old Book Illustrations.", "### Source Data", "#### Initial Data Collection and Normalization\n\nInitial data is gathered from the website Old Book Illustrations. The sources of the illustration scans are specified for each entry in the columns 'illustration_source_name' and 'illustration_source_url'.", "### Personal and Sensitive Information\n\nThe Old Book Illustrations' Terms and conditions reads:\n>OBI [Old Book Illustrations] explores the art of book illustrations within boundaries defined by time and age, not by subject, treatment, or intent. This means that some illustrations might be deemed offensive, disturbing, misleading, or otherwise objectionable. We do not endorse views or opinions the Illustrations may express, neither do we guarantee that the information conveyed by any Illustration is accurate.", "## Considerations for Using the Data", "### Discussion of Biases\n\nThe Old Book Illustrations' Terms and conditions reads:\n>OBI [Old Book Illustrations] explores the art of book illustrations within boundaries defined by time and age, not by subject, treatment, or intent. This means that some illustrations might be deemed offensive, disturbing, misleading, or otherwise objectionable. We do not endorse views or opinions the Illustrations may express, neither do we guarantee that the information conveyed by any Illustration is accurate.", "## Additional Information", "### Dataset Curators\n\nThe Old Book Illustrations collection is curated and maintained by the team of the Old Book Illustrations website.", "### Licensing Information\n\nOld Book Illustrations website reads:\n>We don’t limit the use of the illustrations available on our site, but we accept no responsibility regarding any problem, legal or otherwise, which might result from such use. More specifically, we leave it up to users to make sure that their project complies with the copyright laws of their country of residence. Text content (descriptions, translations, etc.) is published under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n\nThe Old Book Illustrations webmaster mentioned that most images are public domain in the US and Europe, but there can be some exceptions. An example are the illustrations from *Early poems of William Morris* as the illustrator died 1955, so her work is not public domain in Europe as of 2022, or *Under the hill* which was published in the US in 1928 and therefore is not public domain there.", "### Contributions\n\nThanks to @gigant (@giganttheo) for adding this dataset." ]

[ "TAGS\n#task_categories-text-to-image #task_categories-image-to-text #task_categories-image-to-image #task_ids-image-captioning #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-original #language-English #language-French #language-German #license-cc-by-nc-4.0 #lam #1800-1900 #region-us \n", "# Dataset Card for Old Book Illustrations", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Discussion of Biases\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage", "### Dataset Summary\n\nThe Old Book Illustrations contains 4172 illustrations scanned from old books, this collection was collected & curated by the team of the website Old Book Illustrations.\nThe webmaster of Old Book Illustrations kindly allowed us to scrap these information in order to create this dataset for the BigLAM initiative.", "### Languages\n\nThe captions and descriptions are mostly in English but can contain some sentences from other languages such as French or German.\nFor instance you can find this description that contains a French sentence:\n\n>The caption reads in the original French: Vue de l’aqueduc de Salones qui conduisait l’eau à Spalatro.", "## Dataset Structure\n\nEach row contains information gathered from the page of an illustration on the website Old Book Illustrations. As of July 2022, there are 4172 illustrations in this dataset.", "### Data Fields\n\n* 'rawscan': the image as originally scanned from the book, without further processing\n* '1600px': the cleaned image, resized to a width of 1600 pixels (height can vary)\n* 'info_url': URL to the illustration page on URL\n* 'ìnfo_src': URL to an icon-sized version of the image\n* 'info_alt': short description of the image\n* 'artist_name': artist name\n* 'artist_date': birth date of the artist\n* 'artist_countries': list of the countries the artist is from\n* 'book_title': original title of the book the illustration is extracted from\n* 'book_authors': list of the authors of the book\n* 'book_publishers': list of the publishers of the book\n* 'openlibrary-url': URL to the openlibrary entry for the book\n* 'tags': list of keywords for this illustration on URL\n* 'illustration_source_name': list of the sources for this illustration\n* 'illustration_source_url': list of the URL for these sources\n* 'illustration_subject': category of the subject represented in the illustration\n* 'illustration_format': category of the format of the illustration\n* 'image_title': title of the image\n* 'image_caption': caption of the image. Seems to be the caption that appears next to the image in the book, translated to English if in another language\n* 'image_description': longer description of the image. If there is one, it also quotes the caption in the original language\n* 'rawscan_url': URL to the rawscan image on URL\n* '1600px_url': URL to the cleaned image on URL", "## Dataset Creation", "### Curation Rationale\n\nThis collection was collected & curated by the team of the website Old Book Illustrations.\nThis version contains all the data that was available on the website as of July 2022, but the website is being actively maintained so if you want more old book illustrations, make sure to check Old Book Illustrations.", "### Source Data", "#### Initial Data Collection and Normalization\n\nInitial data is gathered from the website Old Book Illustrations. The sources of the illustration scans are specified for each entry in the columns 'illustration_source_name' and 'illustration_source_url'.", "### Personal and Sensitive Information\n\nThe Old Book Illustrations' Terms and conditions reads:\n>OBI [Old Book Illustrations] explores the art of book illustrations within boundaries defined by time and age, not by subject, treatment, or intent. This means that some illustrations might be deemed offensive, disturbing, misleading, or otherwise objectionable. We do not endorse views or opinions the Illustrations may express, neither do we guarantee that the information conveyed by any Illustration is accurate.", "## Considerations for Using the Data", "### Discussion of Biases\n\nThe Old Book Illustrations' Terms and conditions reads:\n>OBI [Old Book Illustrations] explores the art of book illustrations within boundaries defined by time and age, not by subject, treatment, or intent. This means that some illustrations might be deemed offensive, disturbing, misleading, or otherwise objectionable. We do not endorse views or opinions the Illustrations may express, neither do we guarantee that the information conveyed by any Illustration is accurate.", "## Additional Information", "### Dataset Curators\n\nThe Old Book Illustrations collection is curated and maintained by the team of the Old Book Illustrations website.", "### Licensing Information\n\nOld Book Illustrations website reads:\n>We don’t limit the use of the illustrations available on our site, but we accept no responsibility regarding any problem, legal or otherwise, which might result from such use. More specifically, we leave it up to users to make sure that their project complies with the copyright laws of their country of residence. Text content (descriptions, translations, etc.) is published under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n\nThe Old Book Illustrations webmaster mentioned that most images are public domain in the US and Europe, but there can be some exceptions. An example are the illustrations from *Early poems of William Morris* as the illustrator died 1955, so her work is not public domain in Europe as of 2022, or *Under the hill* which was published in the US in 1928 and therefore is not public domain there.", "### Contributions\n\nThanks to @gigant (@giganttheo) for adding this dataset." ]

[ "task_categories:text-classification", "task_ids:topic-classification", "task_ids:multi-class-classification", "annotations_creators:other", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:afl-3.0", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe News Dataset is an English-language dataset containing just over 4k unique news articles scrapped from AriseTv- One of the most popular news television in Nigeria.", "### Supported Tasks and Leaderboards\n\n\nIt supports news article classification into different categories.", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n'''\n{'Title': 'Nigeria: APC Yet to Zone Party Positions Ahead of Convention'\n'Excerpt': 'The leadership of the All Progressives Congress (APC), has denied reports that it had zoned some party positions ahead of'\n'Category': 'politics'\n'labels': 2}\n'''", "### Data Fields\n\n\n* Title: a string containing the title of a news title as shown\n* Excerpt: a string containing a short extract from the body of the news\n* Category: a string that tells the category of an example (string label)\n* labels: integer telling the class of an example (label)", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe code for the dataset creation at \\*URL The examples were scrapped from\n<URL", "### Annotations", "#### Annotation process\n\n\nThe annotation is based on the news category in the arisetv website", "#### Who are the annotators?\n\n\nJournalists at arisetv\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to help develop models that can classify news articles into categories.\n\n\nThis task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.", "### Discussion of Biases\n\n\nThis data is biased towards news happenings in Nigeria but the model built using it can as well classify news from other parts of the world\nwith a slight degradation in performance.", "### Dataset Curators\n\n\nThe dataset is created by people at arise but was scrapped by @github-chimaobi-okite" ]

[ "TAGS\n#task_categories-text-classification #task_ids-topic-classification #task_ids-multi-class-classification #annotations_creators-other #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-afl-3.0 #region-us \n", "### Dataset Summary\n\n\nThe News Dataset is an English-language dataset containing just over 4k unique news articles scrapped from AriseTv- One of the most popular news television in Nigeria.", "### Supported Tasks and Leaderboards\n\n\nIt supports news article classification into different categories.", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n'''\n{'Title': 'Nigeria: APC Yet to Zone Party Positions Ahead of Convention'\n'Excerpt': 'The leadership of the All Progressives Congress (APC), has denied reports that it had zoned some party positions ahead of'\n'Category': 'politics'\n'labels': 2}\n'''", "### Data Fields\n\n\n* Title: a string containing the title of a news title as shown\n* Excerpt: a string containing a short extract from the body of the news\n* Category: a string that tells the category of an example (string label)\n* labels: integer telling the class of an example (label)", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe code for the dataset creation at \\*URL The examples were scrapped from\n<URL", "### Annotations", "#### Annotation process\n\n\nThe annotation is based on the news category in the arisetv website", "#### Who are the annotators?\n\n\nJournalists at arisetv\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to help develop models that can classify news articles into categories.\n\n\nThis task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.", "### Discussion of Biases\n\n\nThis data is biased towards news happenings in Nigeria but the model built using it can as well classify news from other parts of the world\nwith a slight degradation in performance.", "### Dataset Curators\n\n\nThe dataset is created by people at arise but was scrapped by @github-chimaobi-okite" ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:tr", "license:cc-by-2.0", "offensive-language-classification", "region:us" ]

[]

[ "tr" ]

[ "### Task and Labels\n\n\nOffensive language identification:\n\n\n* (0) Not Offensive - Tweet does not contain offense or profanity.\n* (1) Offensive - Tweet contains offensive language or a targeted (veiled or direct) offense", "### Data Splits", "### Paper codes\n\n\nURL\n\n\nReferences\n==========\n\n\nWe merged open-source offensive language dataset studies in Turkish to increase contextuality with existing data even more, before our method is applied.\n\n\n* URL\n* URL\n* URL" ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #language-Turkish #license-cc-by-2.0 #offensive-language-classification #region-us \n", "### Task and Labels\n\n\nOffensive language identification:\n\n\n* (0) Not Offensive - Tweet does not contain offense or profanity.\n* (1) Offensive - Tweet contains offensive language or a targeted (veiled or direct) offense", "### Data Splits", "### Paper codes\n\n\nURL\n\n\nReferences\n==========\n\n\nWe merged open-source offensive language dataset studies in Turkish to increase contextuality with existing data even more, before our method is applied.\n\n\n* URL\n* URL\n* URL" ]

[ "task_categories:token-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:dr inventor corpus", "language:en", "argument mining", "scientific text", "relation extraction", "argumentative discourse unit recognition", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"sciarg\"", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: An argument-annotated corpus of scientific publications\n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThe SciArg dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \nscientific writing.", "### Supported Tasks and Leaderboards", "### Languages\n\nThe language in the dataset is English.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'document_id': the base file name, e.g. \"A28\"\n- 'text': the parsed text of the scientific publication in the XML format\n- 'text_bound_annotations': span annotations that mark argumentative discourse units (ADUs). Each entry has the following fields: 'offsets', 'text', 'type', and 'id'.\n- 'relations': binary relation annotations that mark the argumentative relations that hold between a head and a tail ADU. Each entry has the following fields: 'id', 'head', 'tail', and 'type' where 'head' and 'tail' each have the fields: 'ref_id' and 'role'.", "### Data Splits\n\nThe dataset consists of a single 'train' split that has 40 documents.", "## 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-token-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-dr inventor corpus #language-English #argument mining #scientific text #relation extraction #argumentative discourse unit recognition #region-us \n", "# Dataset Card for \"sciarg\"", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: An argument-annotated corpus of scientific publications\n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThe SciArg dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \nscientific writing.", "### Supported Tasks and Leaderboards", "### Languages\n\nThe language in the dataset is English.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'document_id': the base file name, e.g. \"A28\"\n- 'text': the parsed text of the scientific publication in the XML format\n- 'text_bound_annotations': span annotations that mark argumentative discourse units (ADUs). Each entry has the following fields: 'offsets', 'text', 'type', and 'id'.\n- 'relations': binary relation annotations that mark the argumentative relations that hold between a head and a tail ADU. Each entry has the following fields: 'id', 'head', 'tail', and 'type' where 'head' and 'tail' each have the fields: 'ref_id' and 'role'.", "### Data Splits\n\nThe dataset consists of a single 'train' split that has 40 documents.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Emoji Predictor\n\nDataset consists of raw tweets as text and an emoji as the label.\noriginal dataset: URL\n\n- Fine-tuned model: URL\n- Try the model here: URL" ]

[ "TAGS\n#region-us \n", "# Emoji Predictor\n\nDataset consists of raw tweets as text and an emoji as the label.\noriginal dataset: URL\n\n- Fine-tuned model: URL\n- Try the model here: URL" ]

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

[]

[]

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

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

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: nbroad/longt5-base-global-mediasum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: nbroad/longt5-base-global-mediasum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: nbroad/longt5-base-global-mediasum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: nbroad/longt5-base-global-mediasum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @nbroad for evaluating this model." ]

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:found", "size_categories:1K<n<10K", "source_datasets:original", "license:apache-2.0", "mgt", "magic-card-game", "creature-dataset", "region:us" ]

[]

[]

[ "## Dataset Description\n\n- Homepage: the Gatherer\n- Repository: URL", "### Dataset Summary\n\nA card images dataset of 4 types of creatures from Magic the Gathering card game: elf, goblin, knight, and zombie.", "## Dataset Creation\n\nAll card information from Magic the Gathering card game is public available from the \nGatherer website, the official Magic Card Database. The dataset is just\na subset selection of 4 kind of creatures from the game." ]

[ "TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #annotations_creators-found #size_categories-1K<n<10K #source_datasets-original #license-apache-2.0 #mgt #magic-card-game #creature-dataset #region-us \n", "## Dataset Description\n\n- Homepage: the Gatherer\n- Repository: URL", "### Dataset Summary\n\nA card images dataset of 4 types of creatures from Magic the Gathering card game: elf, goblin, knight, and zombie.", "## Dataset Creation\n\nAll card information from Magic the Gathering card game is public available from the \nGatherer website, the official Magic Card Database. The dataset is just\na subset selection of 4 kind of creatures from the game." ]

[ "arxiv:2205.13760", "region:us" ]

[ "2205.13760" ]

[]

[ "## ProteinGym benchmarks overview\nProteinGym is an extensive set of Deep Mutational Scanning (DMS) assays curated to enable thorough comparisons of various mutation effect predictors indifferent regimes. It is comprised of two benchmarks: 1) a substitution benchmark which consists of the experimental characterisation of ∼1.5M missense variants across 87 DMS assays 2) an indel benchmark that includes ∼300k mutants across 7 DMS assays.\n\nEach processed file in each benchmark corresponds to a single DMS assay, and contains the following three variables:\n\n1) mutant (str):\n- for the substitution benchmark, it describes the set of substitutions to apply on the reference sequence to obtain the mutated sequence (eg., A1P:D2N implies the amino acid 'A' at position 1 should be replaced by 'P', and 'D' at position 2 should be replaced by 'N')\n- for the indel benchmark, it corresponds to the full mutated sequence\n2) DMS_score (float): corresponds to the experimental measurement in the DMS assay. Across all assays, the higher the DMS_score value, the higher the fitness of the mutated protein\n3) DMS_score_bin (int): indicates whether the DMS_score is above the fitness cutoff (1 is fit, 0 is not fit)\n\nAdditionally, we provide two reference files (ProteinGym_reference_file_substitutions.csv and ProteinGym_reference_file_indels.csv) that give further details on each assay and contain in particular:\n- The UniProt_ID of the corresponding protein, along with taxon and MSA depth category\n- The target sequence (target_seq) used in the assay\n- Details on how the DMS_score was created from the raw files and how it was binarized", "## Reference\nIf you use ProteinGym in your work, please cite the following paper:", "## Links\n- Pre-print: URL\n- Code: URL" ]

[ "TAGS\n#arxiv-2205.13760 #region-us \n", "## ProteinGym benchmarks overview\nProteinGym is an extensive set of Deep Mutational Scanning (DMS) assays curated to enable thorough comparisons of various mutation effect predictors indifferent regimes. It is comprised of two benchmarks: 1) a substitution benchmark which consists of the experimental characterisation of ∼1.5M missense variants across 87 DMS assays 2) an indel benchmark that includes ∼300k mutants across 7 DMS assays.\n\nEach processed file in each benchmark corresponds to a single DMS assay, and contains the following three variables:\n\n1) mutant (str):\n- for the substitution benchmark, it describes the set of substitutions to apply on the reference sequence to obtain the mutated sequence (eg., A1P:D2N implies the amino acid 'A' at position 1 should be replaced by 'P', and 'D' at position 2 should be replaced by 'N')\n- for the indel benchmark, it corresponds to the full mutated sequence\n2) DMS_score (float): corresponds to the experimental measurement in the DMS assay. Across all assays, the higher the DMS_score value, the higher the fitness of the mutated protein\n3) DMS_score_bin (int): indicates whether the DMS_score is above the fitness cutoff (1 is fit, 0 is not fit)\n\nAdditionally, we provide two reference files (ProteinGym_reference_file_substitutions.csv and ProteinGym_reference_file_indels.csv) that give further details on each assay and contain in particular:\n- The UniProt_ID of the corresponding protein, along with taxon and MSA depth category\n- The target sequence (target_seq) used in the assay\n- Details on how the DMS_score was created from the raw files and how it was binarized", "## Reference\nIf you use ProteinGym in your work, please cite the following paper:", "## Links\n- Pre-print: URL\n- Code: URL" ]

[ "arxiv:2205.13760", "region:us" ]

[ "2205.13760" ]

[]

[ "## ProteinGym benchmarks overview\nProteinGym is an extensive set of Deep Mutational Scanning (DMS) assays curated to enable thorough comparisons of various mutation effect predictors indifferent regimes. It is comprised of two benchmarks: 1) a substitution benchmark which consists of the experimental characterisation of ∼1.5M missense variants across 87 DMS assays 2) an indel benchmark that includes ∼300k mutants across 7 DMS assays.\n\nEach processed file in each benchmark corresponds to a single DMS assay, and contains the following three variables:\n\n1) mutant (str):\n- for the substitution benchmark, it describes the set of substitutions to apply on the reference sequence to obtain the mutated sequence (eg., A1P:D2N implies the amino acid 'A' at position 1 should be replaced by 'P', and 'D' at position 2 should be replaced by 'N')\n- for the indel benchmark, it corresponds to the full mutated sequence\n2) DMS_score (float): corresponds to the experimental measurement in the DMS assay. Across all assays, the higher the DMS_score value, the higher the fitness of the mutated protein\n3) DMS_score_bin (int): indicates whether the DMS_score is above the fitness cutoff (1 is fit, 0 is not fit)\nAdditionally, we provide two reference files (ProteinGym_reference_file_substitutions.csv and ProteinGym_reference_file_indels.csv) that give further details on each assay and contain in particular:\n- The UniProt_ID of the corresponding protein, along with taxon and MSA depth category\n- The target sequence (target_seq) used in the assay\n- Details on how the DMS_score was created from the raw files and how it was binarized", "## Reference\nIf you use ProteinGym in your work, please cite the following paper:", "## Links\n- Pre-print: URL\n- Code: URL" ]

[ "TAGS\n#arxiv-2205.13760 #region-us \n", "## ProteinGym benchmarks overview\nProteinGym is an extensive set of Deep Mutational Scanning (DMS) assays curated to enable thorough comparisons of various mutation effect predictors indifferent regimes. It is comprised of two benchmarks: 1) a substitution benchmark which consists of the experimental characterisation of ∼1.5M missense variants across 87 DMS assays 2) an indel benchmark that includes ∼300k mutants across 7 DMS assays.\n\nEach processed file in each benchmark corresponds to a single DMS assay, and contains the following three variables:\n\n1) mutant (str):\n- for the substitution benchmark, it describes the set of substitutions to apply on the reference sequence to obtain the mutated sequence (eg., A1P:D2N implies the amino acid 'A' at position 1 should be replaced by 'P', and 'D' at position 2 should be replaced by 'N')\n- for the indel benchmark, it corresponds to the full mutated sequence\n2) DMS_score (float): corresponds to the experimental measurement in the DMS assay. Across all assays, the higher the DMS_score value, the higher the fitness of the mutated protein\n3) DMS_score_bin (int): indicates whether the DMS_score is above the fitness cutoff (1 is fit, 0 is not fit)\nAdditionally, we provide two reference files (ProteinGym_reference_file_substitutions.csv and ProteinGym_reference_file_indels.csv) that give further details on each assay and contain in particular:\n- The UniProt_ID of the corresponding protein, along with taxon and MSA depth category\n- The target sequence (target_seq) used in the assay\n- Details on how the DMS_score was created from the raw files and how it was binarized", "## Reference\nIf you use ProteinGym in your work, please cite the following paper:", "## Links\n- Pre-print: URL\n- Code: URL" ]

[ "task_categories:object-detection", "annotations_creators:expert-generated", "language_creators:expert-generated", "size_categories:n<1K", "license:cc-by-4.0", "manuscripts", "LAM", "arxiv:2207.11230", "region:us" ]

[ "2207.11230" ]

[]

[ "### Dataset Summary\n\n\nThis dataset contains a subset of data used in the paper You Actually Look Twice At it (YALTAi): using an object detectionapproach instead of region segmentation within the Kraken engine. This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset covers pages with tabular information with the following objects \"Header\", \"Col\", \"Marginal\", \"text\".", "### Supported Tasks and Leaderboards\n\n\n* 'object-detection': This dataset can be used to train a model for object-detection on historic document images.\n\n\nDataset Structure\n-----------------\n\n\nThis dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.\n\n\n* The first configuration, 'YOLO', uses the data's original format.\n* The second configuration converts the YOLO format into a format which is closer to the 'COCO' annotation format. This is done to make it easier to work with the 'feature\\_extractor's from the 'Transformers' models for object detection, which expect data to be in a COCO style format.", "### Data Instances\n\n\nAn example instance from the COCO config:\n\n\nAn example instance from the YOLO config:", "### Data Fields\n\n\nThe fields for the YOLO config:\n\n\n* 'image': the image\n* 'objects': the annotations which consist of:\n\t+ 'bbox': a list of bounding boxes for the image\n\t+ 'label': a list of labels for this image\n\n\nThe fields for the COCO config:\n\n\n* 'height': height of the image\n* 'width': width of the image\n* 'image': image\n* 'image\\_id': id for the image\n* 'objects': annotations in COCO format, consisting of a list containing dictionaries with the following keys:\n\t+ 'bbox': bounding boxes for the images\n\t+ 'category\\_id': a label for the image\n\t+ 'image\\_id': id for the image\n\t+ 'iscrowd': COCO 'iscrowd' flag\n\t+ 'segmentation': COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)", "### Data Splits\n\n\nThe dataset contains a train, validation and test split with the following numbers per split:\n\n\n\nDataset Creation\n----------------\n\n\n\n> \n> [this] dataset was produced using a single source, the Lectaurep Repertoires dataset [Rostaing et al., 2021], which served as a basis for only the training and development split. The testset is composed of original data, from various documents, from the 17th century up to the early 20th with a single soldier war report. The test set is voluntarily very different and out of domain with column borders that are not drawn nor printed in certain cases, layout in some kind of masonry layout. p.8\n> .\n> \n> \n>", "### Curation Rationale\n\n\nThis dataset was created to produce a simplified version of the Lectaurep Repertoires dataset, which was found to contain:\n\n\n\n> \n> around 16 different ways to describe columns, from Col1 to Col7, the case-different col1-col7 and finally ColPair and ColOdd, which we all reduced to Col p.8\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe LECTAUREP (LECTure Automatique de REPertoires) project, which began in 2018, is a joint initiative of the Minutier central des notaires de Paris, the National Archives and the\nMinutier central des notaires de Paris of the National Archives, the ALMAnaCH (Automatic Language Modeling and Analysis & Computational Humanities) team at Inria and the EPHE (Ecole Pratique des Hautes Etudes), in partnership with the Ministry of Culture.\n\n\n\n> \n> The lectaurep-bronod corpus brings together 100 pages from the repertoire of Maître Louis Bronod (1719-1765), notary in Paris from December 13, 1719 to July 23, 1765. The pages concerned were written during the years 1742 to 1745.\n> \n> \n>", "#### Who are the source language producers?\n\n\n[More information needed]", "### Annotations", "#### Annotation process\n\n\n[More information needed]", "#### Who are the annotators?\n\n\n[More information needed]", "### Personal and Sensitive Information\n\n\nThis data does not contain information relating to living individuals.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nA growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.", "### Discussion of Biases\n\n\nHistorical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed.", "### Other Known Limitations\n\n\n[More information needed]\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International\n\n\n![DOI](URL", "### Contributions\n\n\nThanks to @davanstrien for adding this dataset." ]

[ "TAGS\n#task_categories-object-detection #annotations_creators-expert-generated #language_creators-expert-generated #size_categories-n<1K #license-cc-by-4.0 #manuscripts #LAM #arxiv-2207.11230 #region-us \n", "### Dataset Summary\n\n\nThis dataset contains a subset of data used in the paper You Actually Look Twice At it (YALTAi): using an object detectionapproach instead of region segmentation within the Kraken engine. This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset covers pages with tabular information with the following objects \"Header\", \"Col\", \"Marginal\", \"text\".", "### Supported Tasks and Leaderboards\n\n\n* 'object-detection': This dataset can be used to train a model for object-detection on historic document images.\n\n\nDataset Structure\n-----------------\n\n\nThis dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.\n\n\n* The first configuration, 'YOLO', uses the data's original format.\n* The second configuration converts the YOLO format into a format which is closer to the 'COCO' annotation format. This is done to make it easier to work with the 'feature\\_extractor's from the 'Transformers' models for object detection, which expect data to be in a COCO style format.", "### Data Instances\n\n\nAn example instance from the COCO config:\n\n\nAn example instance from the YOLO config:", "### Data Fields\n\n\nThe fields for the YOLO config:\n\n\n* 'image': the image\n* 'objects': the annotations which consist of:\n\t+ 'bbox': a list of bounding boxes for the image\n\t+ 'label': a list of labels for this image\n\n\nThe fields for the COCO config:\n\n\n* 'height': height of the image\n* 'width': width of the image\n* 'image': image\n* 'image\\_id': id for the image\n* 'objects': annotations in COCO format, consisting of a list containing dictionaries with the following keys:\n\t+ 'bbox': bounding boxes for the images\n\t+ 'category\\_id': a label for the image\n\t+ 'image\\_id': id for the image\n\t+ 'iscrowd': COCO 'iscrowd' flag\n\t+ 'segmentation': COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)", "### Data Splits\n\n\nThe dataset contains a train, validation and test split with the following numbers per split:\n\n\n\nDataset Creation\n----------------\n\n\n\n> \n> [this] dataset was produced using a single source, the Lectaurep Repertoires dataset [Rostaing et al., 2021], which served as a basis for only the training and development split. The testset is composed of original data, from various documents, from the 17th century up to the early 20th with a single soldier war report. The test set is voluntarily very different and out of domain with column borders that are not drawn nor printed in certain cases, layout in some kind of masonry layout. p.8\n> .\n> \n> \n>", "### Curation Rationale\n\n\nThis dataset was created to produce a simplified version of the Lectaurep Repertoires dataset, which was found to contain:\n\n\n\n> \n> around 16 different ways to describe columns, from Col1 to Col7, the case-different col1-col7 and finally ColPair and ColOdd, which we all reduced to Col p.8\n> \n> \n>", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe LECTAUREP (LECTure Automatique de REPertoires) project, which began in 2018, is a joint initiative of the Minutier central des notaires de Paris, the National Archives and the\nMinutier central des notaires de Paris of the National Archives, the ALMAnaCH (Automatic Language Modeling and Analysis & Computational Humanities) team at Inria and the EPHE (Ecole Pratique des Hautes Etudes), in partnership with the Ministry of Culture.\n\n\n\n> \n> The lectaurep-bronod corpus brings together 100 pages from the repertoire of Maître Louis Bronod (1719-1765), notary in Paris from December 13, 1719 to July 23, 1765. The pages concerned were written during the years 1742 to 1745.\n> \n> \n>", "#### Who are the source language producers?\n\n\n[More information needed]", "### Annotations", "#### Annotation process\n\n\n[More information needed]", "#### Who are the annotators?\n\n\n[More information needed]", "### Personal and Sensitive Information\n\n\nThis data does not contain information relating to living individuals.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nA growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.", "### Discussion of Biases\n\n\nHistorical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed.", "### Other Known Limitations\n\n\n[More information needed]\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nCreative Commons Attribution 4.0 International\n\n\n![DOI](URL", "### Contributions\n\n\nThanks to @davanstrien for adding this dataset." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[ "uk" ]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# ExeBench: an ML-scale dataset of executable C functions\n\nExeBench is a dataset of millions of C functions paired with dependencies and metadatada such that at least a subset of it can be executed with IO pairs. It is mainly inteded for machine learning applications but it is application-agnostic enough to have other usages.\nPlease read the paper for more information: URL\nPlease see 'examples/' in URL for examples.", "## Usage", "### Option 1: Using the helpers in this repo", "### Option 2: Directly using the Hugginface Datasets library", "### Option 3: Directly download the dataset\n\nTake a look at the files at: URL\nThe dataset consist of directories compressed with TAR. Inside each TAR, there is a series of jsonline files compressed with zstandard.", "## Statistics and versions\n\nThis release corresponds to ExeBench v1.01, a version with some improvements with respect to the original one presented in the paper. The statistics and studies presented in the paper remain consistent with respect to the new ones. The final splits of the new version consist of the following functions:\n\n\n\n\nThe original dataset (v1.00) with the exact same data studied in the paper can be accessed on request at: URL (please reach out for access)", "## License\n\nAll C functions keep the original license as per their original Github repository (available in the metadata). All ExeBench contributions (I/O examples, boilerplate to run functions, etc) are released with an MIT license.", "## Credits\n\nWe thank Anghabench authors for their type inference-based synthetic dependencies generation for C functions. This software, Psyche-C, can be found at: URL", "## Contact" ]

[ "TAGS\n#region-us \n", "# ExeBench: an ML-scale dataset of executable C functions\n\nExeBench is a dataset of millions of C functions paired with dependencies and metadatada such that at least a subset of it can be executed with IO pairs. It is mainly inteded for machine learning applications but it is application-agnostic enough to have other usages.\nPlease read the paper for more information: URL\nPlease see 'examples/' in URL for examples.", "## Usage", "### Option 1: Using the helpers in this repo", "### Option 2: Directly using the Hugginface Datasets library", "### Option 3: Directly download the dataset\n\nTake a look at the files at: URL\nThe dataset consist of directories compressed with TAR. Inside each TAR, there is a series of jsonline files compressed with zstandard.", "## Statistics and versions\n\nThis release corresponds to ExeBench v1.01, a version with some improvements with respect to the original one presented in the paper. The statistics and studies presented in the paper remain consistent with respect to the new ones. The final splits of the new version consist of the following functions:\n\n\n\n\nThe original dataset (v1.00) with the exact same data studied in the paper can be accessed on request at: URL (please reach out for access)", "## License\n\nAll C functions keep the original license as per their original Github repository (available in the metadata). All ExeBench contributions (I/O examples, boilerplate to run functions, etc) are released with an MIT license.", "## Credits\n\nWe thank Anghabench authors for their type inference-based synthetic dependencies generation for C functions. This software, Psyche-C, can be found at: URL", "## Contact" ]

[ "task_categories:other", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100M<n<1B", "language:ak", "language:ar", "language:as", "language:bm", "language:bn", "language:ca", "language:code", "language:en", "language:es", "language:eu", "language:fon", "language:fr", "language:gu", "language:hi", "language:id", "language:ig", "language:ki", "language:kn", "language:lg", "language:ln", "language:ml", "language:mr", "language:ne", "language:nso", "language:ny", "language:or", "language:pa", "language:pt", "language:rn", "language:rw", "language:sn", "language:st", "language:sw", "language:ta", "language:te", "language:tn", "language:ts", "language:tum", "language:tw", "language:ur", "language:vi", "language:wo", "language:xh", "language:yo", "language:zh", "language:zu", "license:apache-2.0", "arxiv:2211.01786", "region:us" ]

[ "2211.01786" ]

[ "ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zu" ]

[ "### Dataset Summary\n\n\n\n> \n> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.\n> \n> \n> \n\n\n* Creation: The dataset can be recreated using instructions available here. We provide this version to save processing time and ease reproducibility.\n* Languages: 46 (Can be extended by recreating with more splits)\n* xP3 Dataset Family:\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of \"train\" looks as follows:", "### Data Fields\n\n\nThe data fields are the same among all splits:\n\n\n* 'inputs': the natural language input fed to the model\n* 'targets': the natural language target that the model has to generate", "### Data Splits\n\n\nThe below table summarizes sizes per language (computed from the 'merged\\_{lang}.jsonl' files). Due to languages like 'tw' only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage.\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Training datasets\n\n\n* Code Miscellaneous\n\t+ CodeComplex\n\t+ Docstring Corpus\n\t+ GreatCode\n\t+ State Changes\n* Closed-book QA\n\t+ Hotpot QA\n\t+ Trivia QA\n\t+ Web Questions\n\t+ Wiki QA\n* Extractive QA\n\t+ Adversarial QA\n\t+ CMRC2018\n\t+ DRCD\n\t+ DuoRC\n\t+ MLQA\n\t+ Quoref\n\t+ ReCoRD\n\t+ ROPES\n\t+ SQuAD v2\n\t+ xQuAD\n\t+ TyDI QA\n\t\t- Primary\n\t\t- Goldp\n* Multiple-Choice QA\n\t+ ARC\n\t+ C3\n\t+ CoS-E\n\t+ Cosmos\n\t+ DREAM\n\t+ MultiRC\n\t+ OpenBookQA\n\t+ PiQA\n\t+ QUAIL\n\t+ QuaRel\n\t+ QuaRTz\n\t+ QASC\n\t+ RACE\n\t+ SciQ\n\t+ Social IQA\n\t+ Wiki Hop\n\t+ WiQA\n* Paraphrase Identification\n\t+ MRPC\n\t+ PAWS\n\t+ PAWS-X\n\t+ QQP\n* Program Synthesis\n\t+ APPS\n\t+ CodeContests\n\t+ JupyterCodePairs\n\t+ MBPP\n\t+ NeuralCodeSearch\n\t+ XLCoST\n* Structure-to-text\n\t+ Common Gen\n\t+ Wiki Bio\n* Sentiment\n\t+ Amazon\n\t+ App Reviews\n\t+ IMDB\n\t+ Rotten Tomatoes\n\t+ Yelp\n* Simplification\n\t+ BiSECT\n* Summarization\n\t+ CNN Daily Mail\n\t+ Gigaword\n\t+ MultiNews\n\t+ SamSum\n\t+ Wiki-Lingua\n\t+ XLSum\n\t+ XSum\n* Topic Classification\n\t+ AG News\n\t+ DBPedia\n\t+ TNEWS\n\t+ TREC\n\t+ CSL\n* Translation\n\t+ Flores-200\n\t+ Tatoeba\n* Word Sense disambiguation\n\t+ WiC\n\t+ XL-WiC", "#### Evaluation datasets (included in xP3all except for HumanEval)\n\n\n* Natural Language Inference\n\t+ ANLI\n\t+ CB\n\t+ RTE\n\t+ XNLI\n* Coreference Resolution\n\t+ Winogrande\n\t+ XWinograd\n* Program Synthesis\n\t+ HumanEval\n* Sentence Completion\n\t+ COPA\n\t+ Story Cloze\n\t+ XCOPA\n\t+ XStoryCloze", "#### Additional xP3all datasets\n\n\n* Coreference Resolution\n\t+ WSC (Fixed)\n* Sentence Completion\n\t+ HellaSwag\n* Translation\n\t+ MultiEurlex\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset is released under Apache 2.0.", "### Contributions\n\n\nThanks to the contributors of promptsource for adding many prompts used in this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-expert-generated #annotations_creators-crowdsourced #multilinguality-multilingual #size_categories-100M<n<1B #language-Akan #language-Arabic #language-Assamese #language-Bambara #language-Bengali #language-Catalan #language-code #language-English #language-Spanish #language-Basque #language-Fon #language-French #language-Gujarati #language-Hindi #language-Indonesian #language-Igbo #language-Kikuyu #language-Kannada #language-Ganda #language-Lingala #language-Malayalam #language-Marathi #language-Nepali (macrolanguage) #language-Pedi #language-Nyanja #language-Oriya (macrolanguage) #language-Panjabi #language-Portuguese #language-Rundi #language-Kinyarwanda #language-Shona #language-Southern Sotho #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Tswana #language-Tsonga #language-Tumbuka #language-Twi #language-Urdu #language-Vietnamese #language-Wolof #language-Xhosa #language-Yoruba #language-Chinese #language-Zulu #license-apache-2.0 #arxiv-2211.01786 #region-us \n", "### Dataset Summary\n\n\n\n> \n> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.\n> \n> \n> \n\n\n* Creation: The dataset can be recreated using instructions available here. We provide this version to save processing time and ease reproducibility.\n* Languages: 46 (Can be extended by recreating with more splits)\n* xP3 Dataset Family:\n\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of \"train\" looks as follows:", "### Data Fields\n\n\nThe data fields are the same among all splits:\n\n\n* 'inputs': the natural language input fed to the model\n* 'targets': the natural language target that the model has to generate", "### Data Splits\n\n\nThe below table summarizes sizes per language (computed from the 'merged\\_{lang}.jsonl' files). Due to languages like 'tw' only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage.\n\n\n\nDataset Creation\n----------------", "### Source Data", "#### Training datasets\n\n\n* Code Miscellaneous\n\t+ CodeComplex\n\t+ Docstring Corpus\n\t+ GreatCode\n\t+ State Changes\n* Closed-book QA\n\t+ Hotpot QA\n\t+ Trivia QA\n\t+ Web Questions\n\t+ Wiki QA\n* Extractive QA\n\t+ Adversarial QA\n\t+ CMRC2018\n\t+ DRCD\n\t+ DuoRC\n\t+ MLQA\n\t+ Quoref\n\t+ ReCoRD\n\t+ ROPES\n\t+ SQuAD v2\n\t+ xQuAD\n\t+ TyDI QA\n\t\t- Primary\n\t\t- Goldp\n* Multiple-Choice QA\n\t+ ARC\n\t+ C3\n\t+ CoS-E\n\t+ Cosmos\n\t+ DREAM\n\t+ MultiRC\n\t+ OpenBookQA\n\t+ PiQA\n\t+ QUAIL\n\t+ QuaRel\n\t+ QuaRTz\n\t+ QASC\n\t+ RACE\n\t+ SciQ\n\t+ Social IQA\n\t+ Wiki Hop\n\t+ WiQA\n* Paraphrase Identification\n\t+ MRPC\n\t+ PAWS\n\t+ PAWS-X\n\t+ QQP\n* Program Synthesis\n\t+ APPS\n\t+ CodeContests\n\t+ JupyterCodePairs\n\t+ MBPP\n\t+ NeuralCodeSearch\n\t+ XLCoST\n* Structure-to-text\n\t+ Common Gen\n\t+ Wiki Bio\n* Sentiment\n\t+ Amazon\n\t+ App Reviews\n\t+ IMDB\n\t+ Rotten Tomatoes\n\t+ Yelp\n* Simplification\n\t+ BiSECT\n* Summarization\n\t+ CNN Daily Mail\n\t+ Gigaword\n\t+ MultiNews\n\t+ SamSum\n\t+ Wiki-Lingua\n\t+ XLSum\n\t+ XSum\n* Topic Classification\n\t+ AG News\n\t+ DBPedia\n\t+ TNEWS\n\t+ TREC\n\t+ CSL\n* Translation\n\t+ Flores-200\n\t+ Tatoeba\n* Word Sense disambiguation\n\t+ WiC\n\t+ XL-WiC", "#### Evaluation datasets (included in xP3all except for HumanEval)\n\n\n* Natural Language Inference\n\t+ ANLI\n\t+ CB\n\t+ RTE\n\t+ XNLI\n* Coreference Resolution\n\t+ Winogrande\n\t+ XWinograd\n* Program Synthesis\n\t+ HumanEval\n* Sentence Completion\n\t+ COPA\n\t+ Story Cloze\n\t+ XCOPA\n\t+ XStoryCloze", "#### Additional xP3all datasets\n\n\n* Coreference Resolution\n\t+ WSC (Fixed)\n* Sentence Completion\n\t+ HellaSwag\n* Translation\n\t+ MultiEurlex\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe dataset is released under Apache 2.0.", "### Contributions\n\n\nThanks to the contributors of promptsource for adding many prompts used in this dataset." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:ko", "license:cc-by-2.0", "korean", "proverb", "region:us" ]

[]

[ "ko" ]

[ "# 한국어 속담 모음 v1.0\n국립국어원 우리말샘의 속담을 정제해 만든 데이터입니다.\n- 현대에 맞지 않는 단어가 포함된 속담 삭제\n- 괄호로 표현된 변형 삭제\n- 중복내용 통합", "## 원본 데이터 받기\n우리말샘에서 속담의 해설을 포함한 원본데이터를 다운받을 수 있습니다.\n\n> 국립국어원 누리집 사전에 실려 있는 속담을 '자세히 찾기' 기능을 활용하여 보실 수 있습니다. 속담이 더 많이 실려 있는 사전-우리말샘의 '자세히 찾기'로 들어가셔서 '속담'을 선택하시면 사전에 실려 있는 모든 속담의 목록이 나옵니다.\nURL\n\n우리말샘의 서비스 이용 약관에 따르면\n\n- ‘크리에이티브 커먼즈 저작자 표시-동일조건변경허락2.0 대한민국 라이선스’를 적용합니다.\n- 상업적 용도까지 포함하여 누구나 자유롭게 이용할 수 있으며 저작자의 특별한 허가가 필요하지 않습니다.\n- 저작물을 이용하기 위해서는 다음의 조건을 지켜야 합니다.\n 1. 저작자 표시: 자료를 사용할 때 저작자를 필수로 표시해야 합니다.\n 2. 동일조건변경허락: 자료를 변경하여 새로운 저작물을 만들 때, 그 저작물도 동일한 라이선스로 배포해야 합니다." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Korean #license-cc-by-2.0 #korean #proverb #region-us \n", "# 한국어 속담 모음 v1.0\n국립국어원 우리말샘의 속담을 정제해 만든 데이터입니다.\n- 현대에 맞지 않는 단어가 포함된 속담 삭제\n- 괄호로 표현된 변형 삭제\n- 중복내용 통합", "## 원본 데이터 받기\n우리말샘에서 속담의 해설을 포함한 원본데이터를 다운받을 수 있습니다.\n\n> 국립국어원 누리집 사전에 실려 있는 속담을 '자세히 찾기' 기능을 활용하여 보실 수 있습니다. 속담이 더 많이 실려 있는 사전-우리말샘의 '자세히 찾기'로 들어가셔서 '속담'을 선택하시면 사전에 실려 있는 모든 속담의 목록이 나옵니다.\nURL\n\n우리말샘의 서비스 이용 약관에 따르면\n\n- ‘크리에이티브 커먼즈 저작자 표시-동일조건변경허락2.0 대한민국 라이선스’를 적용합니다.\n- 상업적 용도까지 포함하여 누구나 자유롭게 이용할 수 있으며 저작자의 특별한 허가가 필요하지 않습니다.\n- 저작물을 이용하기 위해서는 다음의 조건을 지켜야 합니다.\n 1. 저작자 표시: 자료를 사용할 때 저작자를 필수로 표시해야 합니다.\n 2. 동일조건변경허락: 자료를 변경하여 새로운 저작물을 만들 때, 그 저작물도 동일한 라이선스로 배포해야 합니다." ]

[ "task_categories:text-generation", "task_categories:text-classification", "task_ids:language-modeling", "task_ids:text-scoring", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:tr", "license:unknown", "region:us" ]

[]

[ "tr" ]

[]

[ "TAGS\n#task_categories-text-generation #task_categories-text-classification #task_ids-language-modeling #task_ids-text-scoring #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #language-Turkish #license-unknown #region-us \n" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2\n* Dataset: kmfoda/booksum\n* Config: kmfoda--booksum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "license:apache-2.0", "arxiv:2104.08691", "region:us" ]

[ "2104.08691" ]

[]

[]

[ "TAGS\n#license-apache-2.0 #arxiv-2104.08691 #region-us \n" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "task_categories:image-classification", "task_categories:unconditional-image-generation", "size_categories:1K<n<10K", "license:other", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-image-classification #task_categories-unconditional-image-generation #size_categories-1K<n<10K #license-other #region-us \n" ]

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

[]

[]

[ "### Data summary\n- This repository contains small synthetic data for Image datasets; MNIST, SVHN, and CIFAR-10.\n- Each torch file contains the images and corresponding labels of sizes ranging from 1,10,50 images per class (IPC). \n- For more details, please refer to our GitHub page and paper below.", "### Reference\nURL" ]

[ "TAGS\n#license-mit #region-us \n", "### Data summary\n- This repository contains small synthetic data for Image datasets; MNIST, SVHN, and CIFAR-10.\n- Each torch file contains the images and corresponding labels of sizes ranging from 1,10,50 images per class (IPC). \n- For more details, please refer to our GitHub page and paper below.", "### Reference\nURL" ]

[ "task_categories:text-classification", "task_categories:token-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "task_ids:topic-classification", "task_ids:named-entity-recognition", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:extended", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:cc-by-4.0", "arxiv:2301.13126", "region:us" ]

[ "2301.13126" ]

[ "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv" ]

[ "### Dataset Summary\n\n\nThe dataset consists of 11 diverse multilingual legal NLU datasets. 6 datasets have one single configuration and 5 datasets have two or three configurations. This leads to a total of 18 tasks (8 single-label text classification tasks, 5 multi-label text classification tasks and 5 token-classification tasks).\n\n\nUse the dataset like this:", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports the tasks of text classification and token classification.\nIn detail, we support the folliwing tasks and configurations:", "### Languages\n\n\nThe following languages are supported: bg , cs , da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe file format is jsonl and three data splits are present for each configuration (train, validation and test).", "### Data Fields", "### Data Splits\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----------------------\n\n\nHow can I contribute a dataset to lextreme?\nPlease follow the following steps:\n\n\n1. Make sure your dataset is available on the huggingface hub and has a train, validation and test split.\n2. Create a pull request to the lextreme repository by adding the following to the URL file:\n\t* Create a dict \\_{YOUR\\_DATASET\\_NAME} (similar to \\_BRAZILIAN\\_COURT\\_DECISIONS\\_JUDGMENT) containing all the necessary information about your dataset (task\\_type, input\\_col, label\\_col, etc.)\n\t* Add your dataset to the BUILDER\\_CONFIGS list: 'LextremeConfig(name=\"{your\\_dataset\\_name}\", \\_{YOUR\\_DATASET\\_NAME})'\n\t* Test that it works correctly by loading your subset with 'load\\_dataset(\"lextreme\", \"{your\\_dataset\\_name}\")' and inspecting a few examples.", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @JoelNiklaus for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #task_categories-token-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-topic-classification #task_ids-named-entity-recognition #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-extended #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-4.0 #arxiv-2301.13126 #region-us \n", "### Dataset Summary\n\n\nThe dataset consists of 11 diverse multilingual legal NLU datasets. 6 datasets have one single configuration and 5 datasets have two or three configurations. This leads to a total of 18 tasks (8 single-label text classification tasks, 5 multi-label text classification tasks and 5 token-classification tasks).\n\n\nUse the dataset like this:", "### Supported Tasks and Leaderboards\n\n\nThe dataset supports the tasks of text classification and token classification.\nIn detail, we support the folliwing tasks and configurations:", "### Languages\n\n\nThe following languages are supported: bg , cs , da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe file format is jsonl and three data splits are present for each configuration (train, validation and test).", "### Data Fields", "### Data Splits\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----------------------\n\n\nHow can I contribute a dataset to lextreme?\nPlease follow the following steps:\n\n\n1. Make sure your dataset is available on the huggingface hub and has a train, validation and test split.\n2. Create a pull request to the lextreme repository by adding the following to the URL file:\n\t* Create a dict \\_{YOUR\\_DATASET\\_NAME} (similar to \\_BRAZILIAN\\_COURT\\_DECISIONS\\_JUDGMENT) containing all the necessary information about your dataset (task\\_type, input\\_col, label\\_col, etc.)\n\t* Add your dataset to the BUILDER\\_CONFIGS list: 'LextremeConfig(name=\"{your\\_dataset\\_name}\", \\_{YOUR\\_DATASET\\_NAME})'\n\t* Test that it works correctly by loading your subset with 'load\\_dataset(\"lextreme\", \"{your\\_dataset\\_name}\")' and inspecting a few examples.", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @JoelNiklaus for adding this dataset." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

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

[]

[]

[ "# statistics\n\ncpp-java: 627 pairs\n\npython-java: 616 pairs\n\ncpp-python: 545 pairs" ]

[ "TAGS\n#license-mit #region-us \n", "# statistics\n\ncpp-java: 627 pairs\n\npython-java: 616 pairs\n\ncpp-python: 545 pairs" ]

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

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pszemraj for evaluating this model." ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "size_categories:1K<n<10K", "language:fo", "license:cc-by-4.0", "region:us" ]

[]

[ "fo" ]

[]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #size_categories-1K<n<10K #language-Faroese #license-cc-by-4.0 #region-us \n" ]

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

[]

[]

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

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

[ "task_categories:summarization", "size_categories:10K<n<100K", "language:ko", "license:apache-2.0", "region:us" ]

[]

[ "ko" ]

[]

[ "TAGS\n#task_categories-summarization #size_categories-10K<n<100K #language-Korean #license-apache-2.0 #region-us \n" ]