Datasets:

---

ftopal

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huggingface-datasets

like 0

[ "task_categories:image-classification", "size_categories:n<1K", "license:unknown", "region:us" ]

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[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#task_categories-image-classification #size_categories-n<1K #license-unknown #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

[ "# ColorSwap: A Color and Word Order Dataset for Multimodal Evaluation", "## Dataset Description\n\nColorSwap is a dataset designed to assess and improve the proficiency of multimodal models in matching objects with their colors. The dataset is comprised of 2,000 unique image-caption pairs, grouped into 1,000 examples. Each example includes a caption-image pair, along with a \"color-swapped\" pair. Crucially, the two captions in an example have the same words, but the color words have been rearranged to modify different objects. The dataset was created through a novel blend of automated caption and image generation with humans in the loop. \n\nPaper: Coming soon!", "## Usage\n\nYou can download the dataset directly from the Hugging Face API with the following code:\n\n\n\nPlease make sure to install the 'datasets' library and use the 'use_auth_token' parameter to authenticate with the Hugging Face API.\n\nAn example of the dataset is as follows:", "## Evaluations\n\nThis Google Colab showcases our ITM model evaluations.\n\nPlease refer to our Github repository for the VLM evaluations: ColorSwap.\n\nIf you find our work useful, please cite the following paper:" ]

[ "TAGS\n#license-mit #region-us \n", "# ColorSwap: A Color and Word Order Dataset for Multimodal Evaluation", "## Dataset Description\n\nColorSwap is a dataset designed to assess and improve the proficiency of multimodal models in matching objects with their colors. The dataset is comprised of 2,000 unique image-caption pairs, grouped into 1,000 examples. Each example includes a caption-image pair, along with a \"color-swapped\" pair. Crucially, the two captions in an example have the same words, but the color words have been rearranged to modify different objects. The dataset was created through a novel blend of automated caption and image generation with humans in the loop. \n\nPaper: Coming soon!", "## Usage\n\nYou can download the dataset directly from the Hugging Face API with the following code:\n\n\n\nPlease make sure to install the 'datasets' library and use the 'use_auth_token' parameter to authenticate with the Hugging Face API.\n\nAn example of the dataset is as follows:", "## Evaluations\n\nThis Google Colab showcases our ITM model evaluations.\n\nPlease refer to our Github repository for the VLM evaluations: ColorSwap.\n\nIf you find our work useful, please cite the following paper:" ]

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

[]

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

[ "region:us" ]

[]

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[]

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

[ "task_categories:object-detection", "size_categories:100K<n<1M", "license:cc-by-4.0", "coco", "region:us" ]

[]

[]

[ "# Dataset Card for \"coco-2014-instance\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-object-detection #size_categories-100K<n<1M #license-cc-by-4.0 #coco #region-us \n", "# Dataset Card for \"coco-2014-instance\"\n\nMore Information needed" ]

[ "language:en", "license:apache-2.0", "knowledge", "cc", "Retrieval", "Reasoning", "arxiv:2401.14624", "region:us" ]

[ "2401.14624" ]

[ "en" ]

[]

[ "TAGS\n#language-English #license-apache-2.0 #knowledge #cc #Retrieval #Reasoning #arxiv-2401.14624 #region-us \n" ]

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

[]

[ "en" ]

[ "# Model Library DB", "## Table of Contents\n\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- Point of Contact: AIRES at PUCRS", "### Dataset Summary\n\nThe Model Library is a project that maps the risks associated with modern machine learning systems. Here, we assess some of the most recent and capable AI systems ever created. This is the database for the Model Library.", "### Supported Tasks and Leaderboards\n\nThis dataset serves as a catalog of machine learning models, all displayed in the Model Library.", "### Languages\n\nEnglish.", "## Dataset Structure", "### Data Instances\n\nFeatures available are: 'model_name_string, model_name_url, model_size_string, dataset, data_type, research_field, risks_and_limitations, risk_types,publication_date, organization_and_url, institution_type, country, license, paper_name_url, model_description, organization_info'.", "### Data Fields\n\nRead Data Instances.", "### Data Splits\n\n\"main\" slipt is the current version displayed in the Model Library.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset is maintained as part of a research project to catalog risks related to ML models.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll data was collected manually.", "#### Who are the source language producers?\n\nMore information can be found here.", "### Annotations", "#### Annotation process\n\nMore information can be found here.", "#### Who are the annotators?\n\nMembers of the AI Robotics Ethics Society (AIRES).", "### Personal and Sensitive Information\n\nNo personal or sensitive information is part of this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNo considerations.", "### Discussion of Biases\n\nNo considerations.", "### Other Known Limitations\n\nNo considerations.", "## Additional Information", "### Dataset Curators\n\nMembers of the AI Robotics Ethics Society (AIRES).", "### Licensing Information\n\nThis dataset is licensed under the Apache License, version 2.0.", "### Contributions\n\nIf you would like to add a model, read our documentation and submit a PR on GitHub!" ]

[ "TAGS\n#language-English #license-apache-2.0 #region-us \n", "# Model Library DB", "## Table of Contents\n\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- Point of Contact: AIRES at PUCRS", "### Dataset Summary\n\nThe Model Library is a project that maps the risks associated with modern machine learning systems. Here, we assess some of the most recent and capable AI systems ever created. This is the database for the Model Library.", "### Supported Tasks and Leaderboards\n\nThis dataset serves as a catalog of machine learning models, all displayed in the Model Library.", "### Languages\n\nEnglish.", "## Dataset Structure", "### Data Instances\n\nFeatures available are: 'model_name_string, model_name_url, model_size_string, dataset, data_type, research_field, risks_and_limitations, risk_types,publication_date, organization_and_url, institution_type, country, license, paper_name_url, model_description, organization_info'.", "### Data Fields\n\nRead Data Instances.", "### Data Splits\n\n\"main\" slipt is the current version displayed in the Model Library.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset is maintained as part of a research project to catalog risks related to ML models.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll data was collected manually.", "#### Who are the source language producers?\n\nMore information can be found here.", "### Annotations", "#### Annotation process\n\nMore information can be found here.", "#### Who are the annotators?\n\nMembers of the AI Robotics Ethics Society (AIRES).", "### Personal and Sensitive Information\n\nNo personal or sensitive information is part of this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNo considerations.", "### Discussion of Biases\n\nNo considerations.", "### Other Known Limitations\n\nNo considerations.", "## Additional Information", "### Dataset Curators\n\nMembers of the AI Robotics Ethics Society (AIRES).", "### Licensing Information\n\nThis dataset is licensed under the Apache License, version 2.0.", "### Contributions\n\nIf you would like to add a model, read our documentation and submit a PR on GitHub!" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# VMMFC-30K Dataset\n\nVMMFC-30K is a dataset available on Hugging Face that focuses on vision and multi-modal function calling samples. This dataset is specifically designed to train Language Learning Models (LLMs) for tool usage, API calls, and parallel tool usage.", "## Datasets Included\n-----------------\n\nThe VMMFC-30K dataset consists of the following datasets:\n\n1. AgoraX\n2. VMMFC-3OK", "## Dataset Structure\n-----------------\n\nThe dataset contains the following key features:\n\n- 'image_bytes': This feature represents the image data in the form of bytes.\n- 'image_path': This feature contains the path of the image file.\n- 'caption': This feature includes the caption associated with the image.\n- 'fn_call': This feature contains the function call information.", "## Files and Versions\n------------------\n\n- The dataset is available in Parquet format.\n- Version: Auto-converted", "## Dataset Viewer and API\n----------------------\n\nThe dataset can be accessed and explored using the following methods:\n\n- Dataset Viewer: Go to dataset viewer\n- API: API documentation", "## Community\n---------\n\nFor any questions, discussions, or contributions related to the VMMFC-30K dataset, please visit the Hugging Face community page.", "## Settings\n--------\n\nThe dataset settings and configurations can be modified and adjusted based on specific requirements.", "## Dataset Card\n------------\n\nAvailable dataset card information:\n\n- Dataset card" ]

[ "TAGS\n#region-us \n", "# VMMFC-30K Dataset\n\nVMMFC-30K is a dataset available on Hugging Face that focuses on vision and multi-modal function calling samples. This dataset is specifically designed to train Language Learning Models (LLMs) for tool usage, API calls, and parallel tool usage.", "## Datasets Included\n-----------------\n\nThe VMMFC-30K dataset consists of the following datasets:\n\n1. AgoraX\n2. VMMFC-3OK", "## Dataset Structure\n-----------------\n\nThe dataset contains the following key features:\n\n- 'image_bytes': This feature represents the image data in the form of bytes.\n- 'image_path': This feature contains the path of the image file.\n- 'caption': This feature includes the caption associated with the image.\n- 'fn_call': This feature contains the function call information.", "## Files and Versions\n------------------\n\n- The dataset is available in Parquet format.\n- Version: Auto-converted", "## Dataset Viewer and API\n----------------------\n\nThe dataset can be accessed and explored using the following methods:\n\n- Dataset Viewer: Go to dataset viewer\n- API: API documentation", "## Community\n---------\n\nFor any questions, discussions, or contributions related to the VMMFC-30K dataset, please visit the Hugging Face community page.", "## Settings\n--------\n\nThe dataset settings and configurations can be modified and adjusted based on specific requirements.", "## Dataset Card\n------------\n\nAvailable dataset card information:\n\n- Dataset card" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"coco-2017-instance\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"coco-2017-instance\"\n\nMore Information needed" ]

[ "task_categories:sentence-similarity", "task_categories:feature-extraction", "language:en", "license:odc-by", "region:us" ]

[]

[ "en" ]

[ "# angle UAE pairs\n\n\nloaded the four datasets containing pairs for Universal AnglE Embeddings\n- note that 'qrecc' is not included in this dataset" ]

[ "TAGS\n#task_categories-sentence-similarity #task_categories-feature-extraction #language-English #license-odc-by #region-us \n", "# angle UAE pairs\n\n\nloaded the four datasets containing pairs for Universal AnglE Embeddings\n- note that 'qrecc' is not included in this dataset" ]

[ "task_categories:feature-extraction", "size_categories:1K<n<10K", "language:en", "license:cc-by-nc-4.0", "biology", "cancer", "images", "United States", "Healthcare", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\nLGG Segmentation Dataset \nThis dataset contains brain MR images together with manual FLAIR abnormality segmentation masks. \nThe images were obtained from The Cancer Imaging Archive (TCIA). \nThis dataset is the subset of llg-mri-segmentation <a href =\"URL\n\nThey correspond to 61 patients (16 CS: Case Western Reserve University & 45 DU: Duke University) included in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with at least fluid-attenuated inversion recovery (FLAIR) sequence and genomic cluster data available. \nTumor genomic clusters and patient data are provided in the <code>URL</code> file.\n\nAll images are provided in <code>.tif</code> format with 3 channels per image. For 61 cases (16 CS: Case Western Reserve University & 45 DU: Duke University), 3 sequences are available, i.e. pre-contrast, FLAIR, and post-contrast (in this order of channels). Post-contrast sequence and pre-contrast sequence are missing in some cases. Missing sequences are replaced with FLAIR sequences to make all images 3-channel. Masks are binary, 1-channel images. They segment FLAIR abnormality in the FLAIR sequence (available for all cases).\n\nThe dataset is organized into 61 folders named after case ID containing source institution information.\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: [URL\n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure\n\n\n.tif images and a csv file", "## Dataset Creation", "### Curation Rationale", "### Source Data\n\n\n\n - Source: [URL", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [URL", "## Dataset Card Contact" ]

[ "TAGS\n#task_categories-feature-extraction #size_categories-1K<n<10K #language-English #license-cc-by-nc-4.0 #biology #cancer #images #United States #Healthcare #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\nLGG Segmentation Dataset \nThis dataset contains brain MR images together with manual FLAIR abnormality segmentation masks. \nThe images were obtained from The Cancer Imaging Archive (TCIA). \nThis dataset is the subset of llg-mri-segmentation <a href =\"URL\n\nThey correspond to 61 patients (16 CS: Case Western Reserve University & 45 DU: Duke University) included in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with at least fluid-attenuated inversion recovery (FLAIR) sequence and genomic cluster data available. \nTumor genomic clusters and patient data are provided in the <code>URL</code> file.\n\nAll images are provided in <code>.tif</code> format with 3 channels per image. For 61 cases (16 CS: Case Western Reserve University & 45 DU: Duke University), 3 sequences are available, i.e. pre-contrast, FLAIR, and post-contrast (in this order of channels). Post-contrast sequence and pre-contrast sequence are missing in some cases. Missing sequences are replaced with FLAIR sequences to make all images 3-channel. Masks are binary, 1-channel images. They segment FLAIR abnormality in the FLAIR sequence (available for all cases).\n\nThe dataset is organized into 61 folders named after case ID containing source institution information.\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: [URL\n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure\n\n\n.tif images and a csv file", "## Dataset Creation", "### Curation Rationale", "### Source Data\n\n\n\n - Source: [URL", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [URL", "## Dataset Card Contact" ]

[ "task_categories:summarization", "size_categories:n<1K", "language:en", "license:mit", "natural-language-understanding", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for wbfns 2018 \n\n42 publicly-available document texts downloaded from the World Bank Documents and Report API.", "## Dataset Details", "### Dataset Description\n\n42 World Bank document texts, related to Nutrition and food security, published in 2018. All documents are publicly available from the World Bank Project API, here: URL\n\n- License: mit", "## Uses\n\nIntended to be used in very short text summarisation task.", "### Out-of-Scope Use\n\nNot intended to be used for any other purposes.", "## Dataset Structure\n\n\"id\" = World Bank document ID number.\n\n\"admreg\" = Administrative region.\n\n\"count\" = The country or countries covered by the document.\n\n\"docty\" = The type of document, such as 'Project Paper' or 'Working Paper'.\n\n\"theme\" = Comma-separated list of themes which the document pertains to.\n\n\"docdt\" = Date on which the document was published.\n\n\"majdocty\" = Document type according to main usage e.g. 'Project Documents'.\n\n\"pdfurl\" = Public URL from which the PDF version of the document can be accessed. \n\n\"txturl\" = Public URL from which the TXT version of the document can be accessed. \n\n\"url_friendly_title\" = Public parent URL at which the document is hosted. \n\n\"projectid\" = World Bank Project ID.\n\n\"url\" = Alternate parent URL at the document is hosted.\n\n\"doc-text\" = Contents of the 'txturl', above.", "## Dataset Creation", "### Curation Rationale\n\nServes as material for short sample exercise in text summarisation.", "## Dataset Card Contact\n\nlodea@URL" ]

[ "TAGS\n#task_categories-summarization #size_categories-n<1K #language-English #license-mit #natural-language-understanding #region-us \n", "# Dataset Card for wbfns 2018 \n\n42 publicly-available document texts downloaded from the World Bank Documents and Report API.", "## Dataset Details", "### Dataset Description\n\n42 World Bank document texts, related to Nutrition and food security, published in 2018. All documents are publicly available from the World Bank Project API, here: URL\n\n- License: mit", "## Uses\n\nIntended to be used in very short text summarisation task.", "### Out-of-Scope Use\n\nNot intended to be used for any other purposes.", "## Dataset Structure\n\n\"id\" = World Bank document ID number.\n\n\"admreg\" = Administrative region.\n\n\"count\" = The country or countries covered by the document.\n\n\"docty\" = The type of document, such as 'Project Paper' or 'Working Paper'.\n\n\"theme\" = Comma-separated list of themes which the document pertains to.\n\n\"docdt\" = Date on which the document was published.\n\n\"majdocty\" = Document type according to main usage e.g. 'Project Documents'.\n\n\"pdfurl\" = Public URL from which the PDF version of the document can be accessed. \n\n\"txturl\" = Public URL from which the TXT version of the document can be accessed. \n\n\"url_friendly_title\" = Public parent URL at which the document is hosted. \n\n\"projectid\" = World Bank Project ID.\n\n\"url\" = Alternate parent URL at the document is hosted.\n\n\"doc-text\" = Contents of the 'txturl', above.", "## Dataset Creation", "### Curation Rationale\n\nServes as material for short sample exercise in text summarisation.", "## Dataset Card Contact\n\nlodea@URL" ]

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

[]

[]

[]

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

[ "task_categories:object-detection", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0", "agriculture", "environment", "doi:10.57967/hf/1733", "region:us" ]

[]

[ "en" ]

[ "# Land application field trial data", "### Intro\nThis dataset is a repository of results from our Land Application Detection Model trial with two organizations. \nLand application is the process of disposing of agricultural animal waste by spraying it onto fields. We developed a model to detect these practices. \nThis dataset represents the results of a real world trial to verify and label these detected spreads.", "### Data description", "#### Structured data\n- sent_to_wdnr.csv\n - Each row is a detected spread that we forwarded to our partners at WDNR\n- sent_to_elpc.csv\n - Each row is a detected spread that we forwarded to our partners at ELPC\n- wdnr_responses.csv\n - Each row is a response to a detection from sent_to_wdnr.csv which contains a preliminary determination by WDNR staff as to whether the image looks like a spread and if it was determined to be likely spreading, the results of an investigation into said spread.\n- elpc_responses_raw.csv\n - Each row is a response to a detection from sent_to_elpc.csv which is the results of the ELPC investigation into that detection through the use of citizen volunteers verifiying in person.\n- elpc_responses_clean.csv\n - Same as the raw file but with corrected detection ids to deal with a data entry error.", "#### Image data\n- images/\n - This directory contains .jpeg images of satellite data fed into the model that were sent to either of the partners. Images were captured by Planet using the PlanetScope sensor, visual spectrum 3m images.\n\n\n'@misc {stanford_regulation,_evaluation,_and_governance_lab_2024,\n\tauthor = { {Stanford Regulation, Evaluation, and Governance Lab} },\n\ttitle = { land-app-trial (Revision b3d0e11) },\n\tyear = 2024,\n\turl = { URL },\n\tdoi = { 10.57967/hf/1733 },\n\tpublisher = { Hugging Face }\n}'" ]

[ "TAGS\n#task_categories-object-detection #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #agriculture #environment #doi-10.57967/hf/1733 #region-us \n", "# Land application field trial data", "### Intro\nThis dataset is a repository of results from our Land Application Detection Model trial with two organizations. \nLand application is the process of disposing of agricultural animal waste by spraying it onto fields. We developed a model to detect these practices. \nThis dataset represents the results of a real world trial to verify and label these detected spreads.", "### Data description", "#### Structured data\n- sent_to_wdnr.csv\n - Each row is a detected spread that we forwarded to our partners at WDNR\n- sent_to_elpc.csv\n - Each row is a detected spread that we forwarded to our partners at ELPC\n- wdnr_responses.csv\n - Each row is a response to a detection from sent_to_wdnr.csv which contains a preliminary determination by WDNR staff as to whether the image looks like a spread and if it was determined to be likely spreading, the results of an investigation into said spread.\n- elpc_responses_raw.csv\n - Each row is a response to a detection from sent_to_elpc.csv which is the results of the ELPC investigation into that detection through the use of citizen volunteers verifiying in person.\n- elpc_responses_clean.csv\n - Same as the raw file but with corrected detection ids to deal with a data entry error.", "#### Image data\n- images/\n - This directory contains .jpeg images of satellite data fed into the model that were sent to either of the partners. Images were captured by Planet using the PlanetScope sensor, visual spectrum 3m images.\n\n\n'@misc {stanford_regulation,_evaluation,_and_governance_lab_2024,\n\tauthor = { {Stanford Regulation, Evaluation, and Governance Lab} },\n\ttitle = { land-app-trial (Revision b3d0e11) },\n\tyear = 2024,\n\turl = { URL },\n\tdoi = { 10.57967/hf/1733 },\n\tpublisher = { Hugging Face }\n}'" ]

[ "license:cc-by-nc-2.0", "region:us" ]

[]

[]

[ "# Atget Paris Collection\n\nWelcome to the Atget Paris Collection, a carefully assembled dataset of public domain images by the esteemed photographer Eugène Atget, depicting Paris in the 1900s. This dataset, enhanced with captions generated by GPT-Vision, is designed for training AI models in recognizing and interpreting historical urban imagery.\n\n![Discord](URL", "## Dataset Overview\n\n- Content: This collection features 31 meticulously chosen images that capture the essence of Parisian life, architecture, and streetscapes as seen through Atget's lens over a century ago. The range of photographs includes storefronts, cobbled streets, and ornate doorways, offering a rich variety of scenes.\n- Source: The original photographs are part of the public domain, accessed from the National Gallery of Art. The dataset has been curated with an added value of descriptive captions, making it a unique resource for AI training.\n- Usage: This dataset is intended for use in training AI models for tasks such as historical photo analysis, pattern recognition in urban settings, and artistic image synthesis.", "## Licensing\n\n- The original images by Eugène Atget, sourced from the National Gallery of Art, are in the public domain. However, this curated dataset, including the accompanying GPT-Vision generated captions, is provided under the Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) license. This licensing facilitates non-commercial use with appropriate credit and prohibits commercial usage.\n- For more details on this license, please visit CC BY-NC 2.0 License details.", "## Dataset Composition\n\nEach image in the collection is paired with a caption that has been optimized for AI training, including token shuffling, to enhance learning efficiency. This thoughtful combination of historic images and modern AI technology creates a valuable tool for developers and researchers.", "## How to Use the Collection\n\n1. Download the Collection: Access and download the collection via the provided link for non-commercial AI model training purposes.\n2. Review Images and Captions: Engage with the collection to appreciate the diverse urban scenarios and detailed captions.\n3. Implement in AI Training: Employ the dataset in training your AI models, taking advantage of the nuanced captions for advanced historical image comprehension.", "## Contributions and Feedback\n\nWe value your insights and contributions. Should you have any feedback or desire to contribute additional images or captions to the collection, please reach out to us. Your participation aids in the continual enhancement of this dataset for the AI and historical research communities.", "## Related\n\nURL\n\n---\n\nThe Atget Paris Collection is a distinctive asset for advancing AI capabilities in historical image understanding. We trust it will serve as a significant resource in your AI endeavors." ]

[ "TAGS\n#license-cc-by-nc-2.0 #region-us \n", "# Atget Paris Collection\n\nWelcome to the Atget Paris Collection, a carefully assembled dataset of public domain images by the esteemed photographer Eugène Atget, depicting Paris in the 1900s. This dataset, enhanced with captions generated by GPT-Vision, is designed for training AI models in recognizing and interpreting historical urban imagery.\n\n![Discord](URL", "## Dataset Overview\n\n- Content: This collection features 31 meticulously chosen images that capture the essence of Parisian life, architecture, and streetscapes as seen through Atget's lens over a century ago. The range of photographs includes storefronts, cobbled streets, and ornate doorways, offering a rich variety of scenes.\n- Source: The original photographs are part of the public domain, accessed from the National Gallery of Art. The dataset has been curated with an added value of descriptive captions, making it a unique resource for AI training.\n- Usage: This dataset is intended for use in training AI models for tasks such as historical photo analysis, pattern recognition in urban settings, and artistic image synthesis.", "## Licensing\n\n- The original images by Eugène Atget, sourced from the National Gallery of Art, are in the public domain. However, this curated dataset, including the accompanying GPT-Vision generated captions, is provided under the Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) license. This licensing facilitates non-commercial use with appropriate credit and prohibits commercial usage.\n- For more details on this license, please visit CC BY-NC 2.0 License details.", "## Dataset Composition\n\nEach image in the collection is paired with a caption that has been optimized for AI training, including token shuffling, to enhance learning efficiency. This thoughtful combination of historic images and modern AI technology creates a valuable tool for developers and researchers.", "## How to Use the Collection\n\n1. Download the Collection: Access and download the collection via the provided link for non-commercial AI model training purposes.\n2. Review Images and Captions: Engage with the collection to appreciate the diverse urban scenarios and detailed captions.\n3. Implement in AI Training: Employ the dataset in training your AI models, taking advantage of the nuanced captions for advanced historical image comprehension.", "## Contributions and Feedback\n\nWe value your insights and contributions. Should you have any feedback or desire to contribute additional images or captions to the collection, please reach out to us. Your participation aids in the continual enhancement of this dataset for the AI and historical research communities.", "## Related\n\nURL\n\n---\n\nThe Atget Paris Collection is a distinctive asset for advancing AI capabilities in historical image understanding. We trust it will serve as a significant resource in your AI endeavors." ]

[ "region:us" ]

[]

[]

[ "## Description\nThis dataset is a combined version of two separate datasets: yahma_alpaca_cleaned_telugu_filtered_and_romanized and teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized. Both datasets contain Telugu text that has been filtered and romanized.", "## Credits\n- The dataset yahma_alpaca_cleaned_telugu_filtered_and_romanized is provided by Telugu-LLM-Labs.\n- The dataset teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized is provided by Telugu-LLM-Labs." ]

[ "TAGS\n#region-us \n", "## Description\nThis dataset is a combined version of two separate datasets: yahma_alpaca_cleaned_telugu_filtered_and_romanized and teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized. Both datasets contain Telugu text that has been filtered and romanized.", "## Credits\n- The dataset yahma_alpaca_cleaned_telugu_filtered_and_romanized is provided by Telugu-LLM-Labs.\n- The dataset teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized is provided by Telugu-LLM-Labs." ]

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

[]

[]

[]

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

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

[]

[ "zh" ]

[ "# 中文錯字糾正資料集\n\n由規則與字典自維基百科產生的錯誤糾正資料集。\n\n包含錯誤類型：隨機錯字、近似音錯字、缺字錯誤、冗字錯誤。\n\n- alpha: 95%錯誤，5%不變。單句中可能有多個錯誤。\n- beta: 50%錯誤，50%不變。僅有一個錯誤。\n- gamma: 100%錯誤。單句中可能有多個錯誤。" ]

[ "TAGS\n#language-Chinese #region-us \n", "# 中文錯字糾正資料集\n\n由規則與字典自維基百科產生的錯誤糾正資料集。\n\n包含錯誤類型：隨機錯字、近似音錯字、缺字錯誤、冗字錯誤。\n\n- alpha: 95%錯誤，5%不變。單句中可能有多個錯誤。\n- beta: 50%錯誤，50%不變。僅有一個錯誤。\n- gamma: 100%錯誤。單句中可能有多個錯誤。" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "task_categories:image-to-image", "size_categories:1K<n<10K", "license:apache-2.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-image-to-image #size_categories-1K<n<10K #license-apache-2.0 #region-us \n" ]

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

[]

[ "tr" ]

[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#language-Turkish #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[ "en" ]

[ "# ddpm-butterflies-128", "## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training data\n\n[TODO: describe the data used to train the model]", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 16\n- gradient_accumulation_steps: 1\n- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None\n- lr_scheduler: None\n- lr_warmup_steps: 500\n- ema_inv_gamma: None\n- ema_inv_gamma: None\n- ema_inv_gamma: None\n- mixed_precision: fp16", "### Training results\n\n TensorBoard logs" ]

[ "TAGS\n#language-English #license-apache-2.0 #region-us \n", "# ddpm-butterflies-128", "## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training data\n\n[TODO: describe the data used to train the model]", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 16\n- gradient_accumulation_steps: 1\n- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None\n- lr_scheduler: None\n- lr_warmup_steps: 500\n- ema_inv_gamma: None\n- ema_inv_gamma: None\n- ema_inv_gamma: None\n- mixed_precision: fp16", "### Training results\n\n TensorBoard logs" ]

[ "task_categories:conversational", "task_categories:text-generation", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language:zh", "license:cc-by-nc-4.0", "not-for-all-audiences", "region:us" ]

[]

[ "zh" ]

[ "## ️注意\n\n- 请注意，数据来自 R18 的视觉小说，并且包含可能被认为是不适当、令人震惊、令人不安、令人反感和极端的主题。如果您不确定在您的国家拥有任何形式的虚构文字内容的法律后果，请不要下载。\n- 本项目内的所有数据及基于这些数据的衍生作品禁止用作商业性目的。 我不拥有 'scenario-raw' 里的 krkr2 脚本源文件，而其余的数据处理方法按照 CC BY-NC 4.0 协议开放。\n- 按照数据预处理的先后次序，依次是：'scenario-raw' 里是 krkr2 脚本源文件，'scenario' 里是清理后的结构化脚本，'conversation' 里是我主观分段制作的对话格式数据。\n- 对于主观分段，一部分是手动的，其余是基于文本相似度的不太靠谱自动分段（我还没推的那部分，我不想被剧透啊啊啊）。手动分段道且阻且长，慢慢做吧，进度记录在 manual_seg-URL。\n- 2015-2017 的前四作是单女主，后面的作品都是双女主的，脚本格式也略微不同。\n- 压缩包已加密，解压密码是 yorunohitsuji", "## 感谢数据源背后的汉化组们\n\n- 与小萝莉相思相爱：脸肿汉化组\n- 勾指婚约洛丽塔：守夜人汉化组&7/9工作室\n- 与小萝莉相思相爱的生活：脸肿汉化组\n- 同居恋人洛丽塔：守夜人汉化组\n- 双子洛丽塔后宫：靴下汉化组x仓库汉化组\n- 爱欲姐妹洛丽塔：守夜人汉化组\n- 诱惑自大洛丽塔：守夜人汉化组\n- 每日亲吻洛丽塔：比喵个人汉化", "## 给我自己看的预处理流程\n\n0. 各作的脚本提取出来放在 'scenario-raw/' 里，用 'script/URL' 转成 UTF-8，'2015-sssa' 额外需要 'script/URL' 转成 LF\n1. 修复格式小问题 'cd scenario-raw && bash URL'\n2. 运行 'python URL' 得到 'scenario/'\n3. 分段，再转成 'conversation/'\n a. 自动分段：'python -m URL path/to/URL'\n b. 手动分段后，'python -m URL path/to/scenario-manual_seg.jsonl'\n\n添加新卷：\n\n0. 脚本放在 'scenario-raw/' 里\n1. 在 'URL' 里添加新卷的元数据" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #annotations_creators-expert-generated #annotations_creators-machine-generated #language-Chinese #license-cc-by-nc-4.0 #not-for-all-audiences #region-us \n", "## ️注意\n\n- 请注意，数据来自 R18 的视觉小说，并且包含可能被认为是不适当、令人震惊、令人不安、令人反感和极端的主题。如果您不确定在您的国家拥有任何形式的虚构文字内容的法律后果，请不要下载。\n- 本项目内的所有数据及基于这些数据的衍生作品禁止用作商业性目的。 我不拥有 'scenario-raw' 里的 krkr2 脚本源文件，而其余的数据处理方法按照 CC BY-NC 4.0 协议开放。\n- 按照数据预处理的先后次序，依次是：'scenario-raw' 里是 krkr2 脚本源文件，'scenario' 里是清理后的结构化脚本，'conversation' 里是我主观分段制作的对话格式数据。\n- 对于主观分段，一部分是手动的，其余是基于文本相似度的不太靠谱自动分段（我还没推的那部分，我不想被剧透啊啊啊）。手动分段道且阻且长，慢慢做吧，进度记录在 manual_seg-URL。\n- 2015-2017 的前四作是单女主，后面的作品都是双女主的，脚本格式也略微不同。\n- 压缩包已加密，解压密码是 yorunohitsuji", "## 感谢数据源背后的汉化组们\n\n- 与小萝莉相思相爱：脸肿汉化组\n- 勾指婚约洛丽塔：守夜人汉化组&7/9工作室\n- 与小萝莉相思相爱的生活：脸肿汉化组\n- 同居恋人洛丽塔：守夜人汉化组\n- 双子洛丽塔后宫：靴下汉化组x仓库汉化组\n- 爱欲姐妹洛丽塔：守夜人汉化组\n- 诱惑自大洛丽塔：守夜人汉化组\n- 每日亲吻洛丽塔：比喵个人汉化", "## 给我自己看的预处理流程\n\n0. 各作的脚本提取出来放在 'scenario-raw/' 里，用 'script/URL' 转成 UTF-8，'2015-sssa' 额外需要 'script/URL' 转成 LF\n1. 修复格式小问题 'cd scenario-raw && bash URL'\n2. 运行 'python URL' 得到 'scenario/'\n3. 分段，再转成 'conversation/'\n a. 自动分段：'python -m URL path/to/URL'\n b. 手动分段后，'python -m URL path/to/scenario-manual_seg.jsonl'\n\n添加新卷：\n\n0. 脚本放在 'scenario-raw/' 里\n1. 在 'URL' 里添加新卷的元数据" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Celebrity 1000\n\nTop 1000 celebrities. 18,184 images. 256x256. Square cropped to face." ]

[ "TAGS\n#region-us \n", "# Celebrity 1000\n\nTop 1000 celebrities. 18,184 images. 256x256. Square cropped to face." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"6M_Alyaum\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"6M_Alyaum\"\n\nMore Information needed" ]

[ "license:cc-by-4.0", "arxiv:2306.08625", "region:us" ]

[ "2306.08625" ]

[]

[ "# RRSIS: Referring Remote Sensing Image Segmentation\n\n\nThe RefSegRS dataset that is used in RRSIS: Referring Remote Sensing Image Segmentation.\n\n\nPlease kindly cite our paper if you find our dataset useful.\n\n~~~\n@article{yuan2023rrsis,\n title={RRSIS: Referring Remote Sensing Image Segmentation},\n author={Yuan, Zhenghang and Mou, Lichao and Hua, Yuansheng and Zhu, Xiao Xiang},\n journal={arXiv preprint arXiv:2306.08625},\n year={2023}\n}\n~~~" ]

[ "TAGS\n#license-cc-by-4.0 #arxiv-2306.08625 #region-us \n", "# RRSIS: Referring Remote Sensing Image Segmentation\n\n\nThe RefSegRS dataset that is used in RRSIS: Referring Remote Sensing Image Segmentation.\n\n\nPlease kindly cite our paper if you find our dataset useful.\n\n~~~\n@article{yuan2023rrsis,\n title={RRSIS: Referring Remote Sensing Image Segmentation},\n author={Yuan, Zhenghang and Mou, Lichao and Hua, Yuansheng and Zhu, Xiao Xiang},\n journal={arXiv preprint arXiv:2306.08625},\n year={2023}\n}\n~~~" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"14_24_Alriyadh\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"14_24_Alriyadh\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "size_categories:n<1K", "language:es", "license:cc-by-sa-4.0", "code", "region:us" ]

[]

[ "es" ]

[ "# SentiMP-Sp Dataset\n\nThe SentiMP-Sp Dataset is a spanish sentiment analysis dataset based on tweets written by members of parliament in Spain in 2021. It has been developed collaboratively by the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) research group from the University of Granada, the SINAI research group from the University of Jaén and the Cardiff NLP research group from the University of Cardiff.\n\n<div align=\"center\", style=\"text-align:center; display:block\">\n<img style=\"float:left; padding-right:10px\" src=\"URL alt=\"DaSCI\" width=\"150\"/>\n<img style=\"float:left; padding-right:10px\" src=\"URL alt=\"UJAEN\" width=\"175\"/>\n<img style=\"float:left;\" src=\"URL alt=\"Cardiff\" width=\"125\"/>\n</div>\n<div style=\"clear:both\"></div>", "## Dataset details\n\nThe dataset containst 500 tweets in Spanish. For each tweet we provide the following information:\n* full_text: Which containts the content of the tweet.\n* fold: Proposed partitions \\{0,1,2,3,4\\} in 5 folds for 5 fold cross-validation for the sake of reproducibility.\n* label_i : Annotator's i label (i in \\{1,2,3,4,5\\}). It takes values in \\{-1,0,1\\}.\n* majority_vote: The result after applying the majority vote strategy to the annotators' partial labelling. When there is a tie we use the label \"TIE\". It takes values in \\{-1,0,1,TIE\\}.\n* tie_break: We use this column to break ties in cases where there is a tie. Therefore, it is only completed when TIE appears in the *majority_vote* column. It takes values in \\{-1,0,1\\}.\n* gold_label: It represents the final label. It is a combination between the *majority_vote* abd the *tie_break* columns. It takes values in \\{-1,0,1\\}.\n\n\nIf you use this dataset, please cite:", "## Contact\nNuria Rodríguez Barroso - rbnuria@URL", "## Acknowledgements\n\nThis work was partly supported by the grants PID2020-119478GB-I00, PID2020-116118GA-I00 and TED2021-130145B-I00 funded by MCIN/AEI/10.13039/501100011033 of the Spanish Government. \n\nShield: [![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]\n\nThis work is licensed under a\n[Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa].\n\n[![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa]\n\n[cc-by-sa]: URL\n[cc-by-sa-image]: URL\n[cc-by-sa-shield]: URL" ]

[ "TAGS\n#task_categories-text-classification #size_categories-n<1K #language-Spanish #license-cc-by-sa-4.0 #code #region-us \n", "# SentiMP-Sp Dataset\n\nThe SentiMP-Sp Dataset is a spanish sentiment analysis dataset based on tweets written by members of parliament in Spain in 2021. It has been developed collaboratively by the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) research group from the University of Granada, the SINAI research group from the University of Jaén and the Cardiff NLP research group from the University of Cardiff.\n\n<div align=\"center\", style=\"text-align:center; display:block\">\n<img style=\"float:left; padding-right:10px\" src=\"URL alt=\"DaSCI\" width=\"150\"/>\n<img style=\"float:left; padding-right:10px\" src=\"URL alt=\"UJAEN\" width=\"175\"/>\n<img style=\"float:left;\" src=\"URL alt=\"Cardiff\" width=\"125\"/>\n</div>\n<div style=\"clear:both\"></div>", "## Dataset details\n\nThe dataset containst 500 tweets in Spanish. For each tweet we provide the following information:\n* full_text: Which containts the content of the tweet.\n* fold: Proposed partitions \\{0,1,2,3,4\\} in 5 folds for 5 fold cross-validation for the sake of reproducibility.\n* label_i : Annotator's i label (i in \\{1,2,3,4,5\\}). It takes values in \\{-1,0,1\\}.\n* majority_vote: The result after applying the majority vote strategy to the annotators' partial labelling. When there is a tie we use the label \"TIE\". It takes values in \\{-1,0,1,TIE\\}.\n* tie_break: We use this column to break ties in cases where there is a tie. Therefore, it is only completed when TIE appears in the *majority_vote* column. It takes values in \\{-1,0,1\\}.\n* gold_label: It represents the final label. It is a combination between the *majority_vote* abd the *tie_break* columns. It takes values in \\{-1,0,1\\}.\n\n\nIf you use this dataset, please cite:", "## Contact\nNuria Rodríguez Barroso - rbnuria@URL", "## Acknowledgements\n\nThis work was partly supported by the grants PID2020-119478GB-I00, PID2020-116118GA-I00 and TED2021-130145B-I00 funded by MCIN/AEI/10.13039/501100011033 of the Spanish Government. \n\nShield: [![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]\n\nThis work is licensed under a\n[Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa].\n\n[![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa]\n\n[cc-by-sa]: URL\n[cc-by-sa-image]: URL\n[cc-by-sa-shield]: URL" ]

[ "language:vi", "medical", "region:us" ]

[]

[ "vi" ]

[ "# Vietnamese Multiple Choice Medical Dataset", "## Overview\nThe Vietnamese Multiple Choice Medical Dataset is a collection of multiple-choice medical questions in Vietnamese language. This dataset was crawled from the Vietjack website, which is a popular online resource for educational materials in Vietnam. The dataset is intended to serve as a benchmark or an evaluation dataset for assessing the performance of Language Model (LLM) models in understanding medical knowledge", "## Contents\nThe dataset is provided in a structured format: JSON (JavaScript Object Notation) format. Each entry in the dataset represents a single multiple-choice question along with its answer choices and correct answer as the following.\n- ID of the question\n- Multiple-choice medical questions in Vietnamese language\n- Corresponding answer choices\n- Correct answers" ]

[ "TAGS\n#language-Vietnamese #medical #region-us \n", "# Vietnamese Multiple Choice Medical Dataset", "## Overview\nThe Vietnamese Multiple Choice Medical Dataset is a collection of multiple-choice medical questions in Vietnamese language. This dataset was crawled from the Vietjack website, which is a popular online resource for educational materials in Vietnam. The dataset is intended to serve as a benchmark or an evaluation dataset for assessing the performance of Language Model (LLM) models in understanding medical knowledge", "## Contents\nThe dataset is provided in a structured format: JSON (JavaScript Object Notation) format. Each entry in the dataset represents a single multiple-choice question along with its answer choices and correct answer as the following.\n- ID of the question\n- Multiple-choice medical questions in Vietnamese language\n- Corresponding answer choices\n- Correct answers" ]

[ "task_categories:text-classification", "size_categories:n<1K", "language:el", "license:cc-by-sa-4.0", "code", "region:us" ]

[]

[ "el" ]

[ "# SentiMP-Gr Dataset\n\nThe SentiMP-Gr Dataset is a greek sentiment analysis dataset based on tweets written by members of parliament in United Kingdom in 2021. It has been developed collaboratively by the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) research group from the University of Granada, the SINAI research group from the University of Jaén and the Cardiff NLP research group from the University of Cardiff.\n\n<div align=\"center\", style=\"text-align:center; display:block\">\n<img style=\"float:left; padding-right:10px\" src=\"URL alt=\"DaSCI\" width=\"150\"/>\n<img style=\"float:left; padding-right:10px\" src=\"URL alt=\"UJAEN\" width=\"175\"/>\n<img style=\"float:left;\" src=\"URL alt=\"Cardiff\" width=\"125\"/>\n</div>\n<div style=\"clear:both\"></div>", "## Dataset details\n\nThe dataset containst 500 tweets in Greek. For each tweet we provide the following information:\n* full_text: Which containts the content of the tweet.\n* fold: Proposed partitions \\{0,1,2,3,4\\} in 5 folds for 5 fold cross-validation for the sake of reproducibility.\n* label_i : Annotator's i label (i in \\{1,2,3\\}). It takes values in \\{-1,0,1\\}.\n* majority_vote: The result after applying the majority vote strategy to the annotators' partial labelling. When there is a tie we use the label \"TIE\". It takes values in \\{-1,0,1,TIE\\}.\n* tie_break: We use this column to break ties in cases where there is a tie. Therefore, it is only completed when TIE appears in the *majority_vote* column. It takes values in \\{-1,0,1\\}.\n* gold_label: It represents the final label. It is a combination between the *majority_vote* abd the *tie_break* columns. It takes values in \\{-1,0,1\\}.\n\n\nIf you use this dataset, please cite:", "## Contact\nNuria Rodríguez Barroso - rbnuria@URL", "## Acknowledgements\n\nThis work was partly supported by the grants PID2020-119478GB-I00, PID2020-116118GA-I00 and TED2021-130145B-I00 funded by MCIN/AEI/10.13039/501100011033 of the Spanish Government. \n\nShield: [![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]\n\nThis work is licensed under a\n[Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa].\n\n[![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa]\n\n[cc-by-sa]: URL\n[cc-by-sa-image]: URL\n[cc-by-sa-shield]: URL" ]

[ "TAGS\n#task_categories-text-classification #size_categories-n<1K #language-Modern Greek (1453-) #license-cc-by-sa-4.0 #code #region-us \n", "# SentiMP-Gr Dataset\n\nThe SentiMP-Gr Dataset is a greek sentiment analysis dataset based on tweets written by members of parliament in United Kingdom in 2021. It has been developed collaboratively by the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) research group from the University of Granada, the SINAI research group from the University of Jaén and the Cardiff NLP research group from the University of Cardiff.\n\n<div align=\"center\", style=\"text-align:center; display:block\">\n<img style=\"float:left; padding-right:10px\" src=\"URL alt=\"DaSCI\" width=\"150\"/>\n<img style=\"float:left; padding-right:10px\" src=\"URL alt=\"UJAEN\" width=\"175\"/>\n<img style=\"float:left;\" src=\"URL alt=\"Cardiff\" width=\"125\"/>\n</div>\n<div style=\"clear:both\"></div>", "## Dataset details\n\nThe dataset containst 500 tweets in Greek. For each tweet we provide the following information:\n* full_text: Which containts the content of the tweet.\n* fold: Proposed partitions \\{0,1,2,3,4\\} in 5 folds for 5 fold cross-validation for the sake of reproducibility.\n* label_i : Annotator's i label (i in \\{1,2,3\\}). It takes values in \\{-1,0,1\\}.\n* majority_vote: The result after applying the majority vote strategy to the annotators' partial labelling. When there is a tie we use the label \"TIE\". It takes values in \\{-1,0,1,TIE\\}.\n* tie_break: We use this column to break ties in cases where there is a tie. Therefore, it is only completed when TIE appears in the *majority_vote* column. It takes values in \\{-1,0,1\\}.\n* gold_label: It represents the final label. It is a combination between the *majority_vote* abd the *tie_break* columns. It takes values in \\{-1,0,1\\}.\n\n\nIf you use this dataset, please cite:", "## Contact\nNuria Rodríguez Barroso - rbnuria@URL", "## Acknowledgements\n\nThis work was partly supported by the grants PID2020-119478GB-I00, PID2020-116118GA-I00 and TED2021-130145B-I00 funded by MCIN/AEI/10.13039/501100011033 of the Spanish Government. \n\nShield: [![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]\n\nThis work is licensed under a\n[Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa].\n\n[![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa]\n\n[cc-by-sa]: URL\n[cc-by-sa-image]: URL\n[cc-by-sa-shield]: URL" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_doc\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_doc\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_doc_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_doc_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_recite_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_recite_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "task_categories:object-detection", "language:en", "license:mit", "roboflow", "roboflow2huggingface", "Volcanoes", "Plumes", "UAVs", "Drone", "doi:10.57967/hf/1728", "region:us" ]

[]

[ "en" ]

[ "# Dataset Description\n\nThe dataset presents labelled pictures of plumes and of the Fuego Summit in Guatemala. The data was collected by the University of Bristol Flight Lab in Guatemala from March 22 to April 3, 2019. The drone used for this purpose was a Skywalker X8, equipped with a Pixhawk onboard computer running ArduPlane 3.7.1 and a Raspberry Pi 3B+ for mission management and communication with the ground station. The drone was also equipped of a GoPro Hero 9.", "# Acknowledgement\n\nThis work is supported by the WildDrone MSCA Doctoral Network funded by EU Horizon Europe under grant agreement no. 101071224, the Innovation Fund Denmark for the project DIREC (9142-00001B), and by the Engineering & Physical Sciences Research Council (UK) through the CASCADE (Complex Autonomous aircraft Systems Configuration, Analysis and Design Exploratory) programme grant (EP/R009953/1).", "# Dataset Labels", "# Example of Labelled Images\n\n<div align=\"center\">\n <<img width=50% alt=\"edouard-rolland/volcanic-plumes\" src=\"URL\n</div>", "# Number of Images", "# Example of Application\n\nThe dataset was used to train a YOLOv8 neural network. More details can be found in the paper mentioned in the citation section. The following <a href=\"URL presents the model output for an entire flight.", "# How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "# License\nMIT" ]

[ "TAGS\n#task_categories-object-detection #language-English #license-mit #roboflow #roboflow2huggingface #Volcanoes #Plumes #UAVs #Drone #doi-10.57967/hf/1728 #region-us \n", "# Dataset Description\n\nThe dataset presents labelled pictures of plumes and of the Fuego Summit in Guatemala. The data was collected by the University of Bristol Flight Lab in Guatemala from March 22 to April 3, 2019. The drone used for this purpose was a Skywalker X8, equipped with a Pixhawk onboard computer running ArduPlane 3.7.1 and a Raspberry Pi 3B+ for mission management and communication with the ground station. The drone was also equipped of a GoPro Hero 9.", "# Acknowledgement\n\nThis work is supported by the WildDrone MSCA Doctoral Network funded by EU Horizon Europe under grant agreement no. 101071224, the Innovation Fund Denmark for the project DIREC (9142-00001B), and by the Engineering & Physical Sciences Research Council (UK) through the CASCADE (Complex Autonomous aircraft Systems Configuration, Analysis and Design Exploratory) programme grant (EP/R009953/1).", "# Dataset Labels", "# Example of Labelled Images\n\n<div align=\"center\">\n <<img width=50% alt=\"edouard-rolland/volcanic-plumes\" src=\"URL\n</div>", "# Number of Images", "# Example of Application\n\nThe dataset was used to train a YOLOv8 neural network. More details can be found in the paper mentioned in the citation section. The following <a href=\"URL presents the model output for an entire flight.", "# How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "# License\nMIT" ]

[ "task_categories:feature-extraction", "size_categories:1M<n<10M", "language:en", "region:us" ]

[]

[ "en" ]

[ "# wolt-food-clip-ViT-B-32-embeddings\n\nQdrant's Food Discovery demo relies on the dataset of food images from the Wolt \napp. Each point in the collection represents a dish with a single image. The image is represented as a vector of 512 \nfloat numbers.", "## Generation process\n\nThe embeddings generated with clip-ViT-B-32 model have been generated using the following code snippet:" ]

[ "TAGS\n#task_categories-feature-extraction #size_categories-1M<n<10M #language-English #region-us \n", "# wolt-food-clip-ViT-B-32-embeddings\n\nQdrant's Food Discovery demo relies on the dataset of food images from the Wolt \napp. Each point in the collection represents a dish with a single image. The image is represented as a vector of 512 \nfloat numbers.", "## Generation process\n\nThe embeddings generated with clip-ViT-B-32 model have been generated using the following code snippet:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_docidx\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train300_eval100_v1_docidx\"\n\nMore Information needed" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "language:it", "law", "region:us" ]

[]

[ "it" ]

[ "# Gazzetta Ufficiale ‍️️️\n\n<div style=\"width: auto; margin-left: auto; margin-right: auto\">\n<img src=\"URL alt=\"Mii-LLM\" style=\"width: 50%; min-width: 400px; display: block; margin: auto;\">\n</div>\n<hr style=\"margin-top: 1.0em; margin-bottom: 1.0em;\">\n\n(wip )\n\nLa Gazzetta Ufficiale della Repubblica Italiana, quale fonte ufficiale di conoscenza delle norme in vigore in Italia e strumento di diffusione, informazione e ufficializzazione di testi legislativi, atti pubblici e privati, è edita dall’Istituto Poligrafico e Zecca dello Stato e pubblicata in collaborazione con il Ministero della Giustizia, il quale provvede alla direzione e redazione della stessa.\n\nL'Istituto Poligrafico e Zecca dello Stato S.p.A. promuove la più ampia fruibilità della Gazzetta Ufficiale della Repubblica Italiana in formato digitale.\n\nSi segnala che l'unico testo definitivo è quello pubblicato sulla Gazzetta Ufficiale a mezzo stampa, che prevale in caso di discordanza. La riproduzione dei testi forniti nel formato elettronico è consentita purché venga menzionata la fonte, il carattere non autentico e gratuito." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #language-Italian #law #region-us \n", "# Gazzetta Ufficiale ‍️️️\n\n<div style=\"width: auto; margin-left: auto; margin-right: auto\">\n<img src=\"URL alt=\"Mii-LLM\" style=\"width: 50%; min-width: 400px; display: block; margin: auto;\">\n</div>\n<hr style=\"margin-top: 1.0em; margin-bottom: 1.0em;\">\n\n(wip )\n\nLa Gazzetta Ufficiale della Repubblica Italiana, quale fonte ufficiale di conoscenza delle norme in vigore in Italia e strumento di diffusione, informazione e ufficializzazione di testi legislativi, atti pubblici e privati, è edita dall’Istituto Poligrafico e Zecca dello Stato e pubblicata in collaborazione con il Ministero della Giustizia, il quale provvede alla direzione e redazione della stessa.\n\nL'Istituto Poligrafico e Zecca dello Stato S.p.A. promuove la più ampia fruibilità della Gazzetta Ufficiale della Repubblica Italiana in formato digitale.\n\nSi segnala che l'unico testo definitivo è quello pubblicato sulla Gazzetta Ufficiale a mezzo stampa, che prevale in caso di discordanza. La riproduzione dei testi forniti nel formato elettronico è consentita purché venga menzionata la fonte, il carattere non autentico e gratuito." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train300_eval100\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train300_eval100\"\n\nMore Information needed" ]

[ "size_categories:10M<n<100M", "language:hi", "license:apache-2.0", "mC4", "Common Crawl", "region:us" ]

[]

[ "hi" ]

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

[ "TAGS\n#size_categories-10M<n<100M #language-Hindi #license-apache-2.0 #mC4 #Common Crawl #region-us \n", "# Dataset Card for \"test\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train300_eval100_v1_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train300_eval100_v1_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train300_eval100_v1_doc\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train300_eval100_v1_doc\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train300_eval100_v1_docidx\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train300_eval100_v1_docidx\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train300_eval100_v1_doc_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train300_eval100_v1_doc_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train300_eval100_v1_recite_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train300_eval100_v1_recite_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train300_eval100_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train300_eval100_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dataset_0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset_0\"\n\nMore Information needed" ]

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

[]

[]

[]

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

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-generation", "task_categories:question-answering", "size_categories:1B<n<10B", "language:en", "license:cc-by-4.0", "mathematical-reasoning", "reasoning", "finetuning", "pretraining", "llm", "region:us" ]

[]

[ "en" ]

[ "# TemplateGSM Dataset\n\nThe TemplateGSM dataset is a novel and extensive collection designed for advancing the study and application of mathematical reasoning within the realm of artificial intelligence. This dataset is crafted to challenge and evaluate the capabilities of language models in understanding and generating solutions to mathematical problems derived from a set of predefined problem templates using examples from the GSM8K dataset as prototypes. Each template encapsulates a unique mathematical problem structure, offering a diverse array of challenges that span various domains of mathematics.\n\nGitHub Homepage: [[link]](URL", "## Objective\n\nTemplateGSM aims to serve as a benchmark for:\n- Assessing language models' proficiency in mathematical reasoning and symbolic computation.\n- Training and fine-tuning language models to improve their performance in generating accurate and logically sound mathematical solutions.\n- Encouraging the development of models capable of understanding and solving complex mathematical problems, thereby bridging the gap between natural language processing and mathematical reasoning.", "## Dataset Structure\n\nTemplateGSM is organized into configurations based on the volume of problems generated from each template:", "### Configurations\n\n- templategsm-32-10k: Contains 10,000 problems generated from each of the 32 templates, totaling over 320,000 individual problems.\n- templategsm-32-100k: Expands each template's reach by generating 100,000 problems, culminating in a dataset exceeding 3.2 million problems.", "### Data Fields\n\nEach problem in the dataset includes the following fields:\n- 'problem': The problem statement.\n- 'solution_code': A commented solution code that solves the problem in Python.\n- 'solution_wocode': The solution in natural language without the use of code.\n- 'result': The final answer to the problem.\n- 'template_name': This field indicates the template from which the problem was generated, e.g., 'gsm0001-1'.\n- 'idx': An index unique to each problem within its template.", "## How to Use\n\n\n\nTo access the TemplateGSM dataset, you can use the Huggingface 'datasets' library:", "## License\n\nThis dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.\n\nIf you utilize the TemplateGSM dataset in your research or application, please consider citing it (GitHub Homepage: [[link]](URL\n\n'''bibtex\n@misc{templatemath2024,\n title={TemplateMath: Syntactic Data Generation for Mathematical Problems},\n author={Zhang, Yifan and Luo, Yifan and Yuan, Yang and Yao, Andrew Chi-Chih},\n year={2024},\n}" ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-1B<n<10B #language-English #license-cc-by-4.0 #mathematical-reasoning #reasoning #finetuning #pretraining #llm #region-us \n", "# TemplateGSM Dataset\n\nThe TemplateGSM dataset is a novel and extensive collection designed for advancing the study and application of mathematical reasoning within the realm of artificial intelligence. This dataset is crafted to challenge and evaluate the capabilities of language models in understanding and generating solutions to mathematical problems derived from a set of predefined problem templates using examples from the GSM8K dataset as prototypes. Each template encapsulates a unique mathematical problem structure, offering a diverse array of challenges that span various domains of mathematics.\n\nGitHub Homepage: [[link]](URL", "## Objective\n\nTemplateGSM aims to serve as a benchmark for:\n- Assessing language models' proficiency in mathematical reasoning and symbolic computation.\n- Training and fine-tuning language models to improve their performance in generating accurate and logically sound mathematical solutions.\n- Encouraging the development of models capable of understanding and solving complex mathematical problems, thereby bridging the gap between natural language processing and mathematical reasoning.", "## Dataset Structure\n\nTemplateGSM is organized into configurations based on the volume of problems generated from each template:", "### Configurations\n\n- templategsm-32-10k: Contains 10,000 problems generated from each of the 32 templates, totaling over 320,000 individual problems.\n- templategsm-32-100k: Expands each template's reach by generating 100,000 problems, culminating in a dataset exceeding 3.2 million problems.", "### Data Fields\n\nEach problem in the dataset includes the following fields:\n- 'problem': The problem statement.\n- 'solution_code': A commented solution code that solves the problem in Python.\n- 'solution_wocode': The solution in natural language without the use of code.\n- 'result': The final answer to the problem.\n- 'template_name': This field indicates the template from which the problem was generated, e.g., 'gsm0001-1'.\n- 'idx': An index unique to each problem within its template.", "## How to Use\n\n\n\nTo access the TemplateGSM dataset, you can use the Huggingface 'datasets' library:", "## License\n\nThis dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.\n\nIf you utilize the TemplateGSM dataset in your research or application, please consider citing it (GitHub Homepage: [[link]](URL\n\n'''bibtex\n@misc{templatemath2024,\n title={TemplateMath: Syntactic Data Generation for Mathematical Problems},\n author={Zhang, Yifan and Luo, Yifan and Yuan, Yang and Yao, Andrew Chi-Chih},\n year={2024},\n}" ]

[ "size_categories:100K<n<1M", "language:hi", "license:apache-2.0", "Common Crawl", "region:us" ]

[]

[ "hi" ]

[ "# Dataset Card for \"OSCAR-2301-Hindi-Cleaned\"\n\nMore Information needed" ]

[ "TAGS\n#size_categories-100K<n<1M #language-Hindi #license-apache-2.0 #Common Crawl #region-us \n", "# Dataset Card for \"OSCAR-2301-Hindi-Cleaned\"\n\nMore Information needed" ]

[ "task_categories:text2text-generation", "task_categories:text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "size_categories:100K<n<1M", "language:en", "license:other", "dpo", "text-to-sql", "sql", "arxiv:2305.18290", "region:us" ]

[ "2305.18290" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-text2text-generation #task_categories-text-generation #language_creators-crowdsourced #language_creators-expert-generated #size_categories-100K<n<1M #language-English #license-other #dpo #text-to-sql #sql #arxiv-2305.18290 #region-us \n" ]

[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:hi", "license:apache-2.0", " OSCAR-2301", "region:us" ]

[]

[ "hi" ]

[ "# Dataset Card for \"OSCAR-2301-Hindi-Cleaned-2.0\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Hindi #license-apache-2.0 # OSCAR-2301 #region-us \n", "# Dataset Card for \"OSCAR-2301-Hindi-Cleaned-2.0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of rufjdk5480/WestLake-dpo-train-sft-v1\n\n\n\nDataset automatically created during the evaluation run of model rufjdk5480/WestLake-dpo-train-sft-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T14:19:45.427052(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of rufjdk5480/WestLake-dpo-train-sft-v1\n\n\n\nDataset automatically created during the evaluation run of model rufjdk5480/WestLake-dpo-train-sft-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T14:19:45.427052(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Technoculture/MT7Bi-sft\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/MT7Bi-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T14:25:40.116952(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Technoculture/MT7Bi-sft\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/MT7Bi-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T14:25:40.116952(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-classification", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #language-English #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "language:ru", "language:uk", "language:de", "language:es", "language:am", "language:zh", "language:ar", "language:hi", "license:openrail++", "region:us" ]

[]

[ "en", "ru", "uk", "de", "es", "am", "zh", "ar", "hi" ]

[]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #language-Russian #language-Ukrainian #language-German #language-Spanish #language-Amharic #language-Chinese #language-Arabic #language-Hindi #license-openrail++ #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train600_eval300_v1_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train600_eval300_v1_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train600_eval300_v1_doc\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train600_eval300_v1_doc\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train600_eval300_v1_docidx\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train600_eval300_v1_docidx\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_doc\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_doc\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train600_eval300_v1_doc_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train600_eval300_v1_doc_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_docidx\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_docidx\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train600_eval300_v1_recite_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train600_eval300_v1_recite_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_doc_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_doc_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_nq_train600_eval300_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_nq_train600_eval300_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_recite_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_recite_qa\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lmind_hotpot_train500_eval300_v1_reciteonly_qa\"\n\nMore Information needed" ]

[ "task_categories:translation", "language:en", "language:ja", "dpo", "region:us" ]

[]

[ "en", "ja" ]

[ "# Dataset Card for \"VNTL-v2.5-1.6k-dpo-pairs\"\n\nThis is a very experimental DPO dataset for VNTL, I have no idea if DPO will work well to improve translation, but I guess it's worth a shot!\n\nThis dataset was generated using the model vntl-7b-v0.3.1 using prompts from the dataset VNTL-v2.5-1k. All rejected sequences were generated using temperature 0.7, and they were chosen using a cosine similarity threshold.\n\nThings to consider afterwards:\n- Distilation: This dataset wasn't filtered in anyway, so there may be pairs that are actually ties or where the chosen sequence is bad.\n - URL\n- Avoid human data: According to the paper, DPO performs better with sequences sampled directly from the model. Therefore, the dataset could be enhanced by trying to extract the chosen sequences from the model itself.\n - URL\n- CPO: CPO may be a better fit than DPO, it is supposedly more forgiving for accuracy, which is better for translation tasks since the translation being correct is better than it being 100% accurate to the chosen sequence.\n - URL" ]

[ "TAGS\n#task_categories-translation #language-English #language-Japanese #dpo #region-us \n", "# Dataset Card for \"VNTL-v2.5-1.6k-dpo-pairs\"\n\nThis is a very experimental DPO dataset for VNTL, I have no idea if DPO will work well to improve translation, but I guess it's worth a shot!\n\nThis dataset was generated using the model vntl-7b-v0.3.1 using prompts from the dataset VNTL-v2.5-1k. All rejected sequences were generated using temperature 0.7, and they were chosen using a cosine similarity threshold.\n\nThings to consider afterwards:\n- Distilation: This dataset wasn't filtered in anyway, so there may be pairs that are actually ties or where the chosen sequence is bad.\n - URL\n- Avoid human data: According to the paper, DPO performs better with sequences sampled directly from the model. Therefore, the dataset could be enhanced by trying to extract the chosen sequences from the model itself.\n - URL\n- CPO: CPO may be a better fit than DPO, it is supposedly more forgiving for accuracy, which is better for translation tasks since the translation being correct is better than it being 100% accurate to the chosen sequence.\n - URL" ]

[ "task_categories:feature-extraction", "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Napoleon Bonaparte\nThe Napoleon Bonaparte dataset is a collection of information and data related to the life and reign of Napoleon Bonaparte. It includes details on his military campaigns, battles, and conquests, as well as information on his political career as Emperor of France. The dataset also contains information on the social and economic reforms he implemented in France, such as the establishment of the Napoleonic Code. The data is gathered from various sources, including historical records, biographies, and academic research." ]

[ "TAGS\n#task_categories-feature-extraction #task_categories-text-generation #size_categories-10K<n<100K #language-English #region-us \n", "# Napoleon Bonaparte\nThe Napoleon Bonaparte dataset is a collection of information and data related to the life and reign of Napoleon Bonaparte. It includes details on his military campaigns, battles, and conquests, as well as information on his political career as Emperor of France. The dataset also contains information on the social and economic reforms he implemented in France, such as the establishment of the Napoleonic Code. The data is gathered from various sources, including historical records, biographies, and academic research." ]

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:translation", "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:fill-mask", "size_categories:10B<n<100B", "language:fr", "language:en", "arxiv:2402.00786", "region:us" ]

[ "2402.00786" ]

[ "fr", "en" ]

[ "# CroissantLLM: A Truly Bilingual French-English Language Model", "## Dataset\n\nRessources are currently being uploaded !\n\nURL", "## Licenses\n\nData redistributed here is subject to the original license under which it was collected. All license information is detailed in the 'Data' section of the Technical report." ]

[ "TAGS\n#task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #task_categories-fill-mask #size_categories-10B<n<100B #language-French #language-English #arxiv-2402.00786 #region-us \n", "# CroissantLLM: A Truly Bilingual French-English Language Model", "## Dataset\n\nRessources are currently being uploaded !\n\nURL", "## Licenses\n\nData redistributed here is subject to the original license under which it was collected. All license information is detailed in the 'Data' section of the Technical report." ]

[ "task_categories:image-classification", "size_categories:1K<n<10K", "language:en", "license:mit", "arxiv:2106.08254", "region:us" ]

[ "2106.08254" ]

[ "en" ]

[ "## <div align=\"center\">Documentation</div>\n\n<details open>\n<summary>\nInstall\n</summary>\n\nBoth a docker installation and a more conventional installation are available. If you're not very familiar with all the code, \ndocker installation is recommended. Otherwise, opt for the classic installation.", "#### Docker installation\n\nIf you are familiar with docker, the docker image is available here.\n\nOtherwise, I recommend you to download DockerDesktop if you are on Windows.\nIf you are on Linux, you can refer to the documentation here.\n\nOnce it is done, you simply have to execute the following command,\n\nYour installation is now completed. You can press 'Ctrl+C' and continue to Usage section.", "#### Classic installation\n\nYou need python to be installed. Python installation isn't going to be detailed here, you can refer to the documentation.\n\nWe first need to install pytorch. It is recommended to use a package manager such as miniconda. \nPlease refer to the documentation.\n\nWhen everything is set up you can run the following command to install pytorch:\n\nIf you want to use you gpus to make everything run faster, please refer the documentation\n\nThen you just have to clone the repo and install 'requirements':\n\n\nYour installation is now completed.\n\n</details>\n\n<details open>\n<summary>Usage</summary>\n\nNow to use it you need to download the models and the data, in section Models and Data.\n\nOnce you have it, follow instruction depending on you have docker or classic installation.\nPut all the model in the same folder, and keep the dataset as it is", "#### Docker installation\n\nYou have to copy the data and models in the container. Execute the following command:\n\n\n\nOnce it is done you just have to run the command:\n\nopen the adress 'localhost:5000', and enjoy the maximum. Refer bellow for details about parameters", "#### Classic installation\n\nYou need to modify the 'URL' file by putting the paths of you dataset folder in '\"data_path\"' parameter \nand the path to model folder in '\"trained_models\"' parameter.\n\nOnce done, just run:\n\nopen the adress 'localhost:5000', and enjoy the maximum. Refer bellow for details about parameters", "#### Both\n\n* In the first parameter, the one with gears, put the 'URL' file\n* In the second parameter, the one with a camera, put the video you want to process (leave it empty to use your webcam)\n* In the last one, put your deck list in the format 'ydk'\n\nThen you can press the button and start the process !\n\n</details>\n\n---", "## <div align=\"center\">️Models and Data</div>\n\n<details open>\n<summary>Models</summary>\n\nIn this project, the tasks were divided so that one model would locate the card and another model would classify them. \nSimilarly, to classify the cards, I divided the task so that there is one model for each type of card,\nand the model to be used was determined by the color of the card.\n\nModels can be downloaded in <a href=\"URL Face</a>. \nModels starting with 'beit' stands for classification and the one starting with 'yolo' for localization.\n\n![Model on HF](URL\n\nFor now only models for \"retro\" gameplay are available but the ones for classic format play will be added soon.\nI considered \"retro\" format all cards before the first _syncro_ set, so all the cards edited until Light of Destruction set (LODT - 05/13/2008) set and all speed duel cards. \n\n</details>\n\n<details open>\n<summary>Data</summary>\n\nTo create a dataset, the <a href=\"URL api was used. Two datasets were thus created, \none for \"retro\" play and the other for classic format play. Just as there is a model for each type of card,\nthere is a dataset for each type of card.\n\nDataset can be downloaded in <a href=\"\">Hugging Face</a>.\n\n![Dataset on HF](URL\n\nFor now only \"retro\" dataset is available, but the one for classic format play will be added soon.\n\n\n</details>\n\n\n---", "## <div align=\"center\">Inspiration</div>\n\nThis project is inspired by content creator SuperZouloux's idea of a hologram bringing _Yu-Gi-Oh!_ cards to life. \nHis project uses chips inserted under the sleeves of each card, \nwhich are read by the play mat, enabling the cards to be recognized.\n\nInserting the chips into the sleeves is not only laborious, but also poses another problem: \nface-down cards are read in the same way as face-up ones. \nSo an automatic detector is a really suitable solution.\n\nAlthough this project was discouraged by _KONAMI_ ^®, the game's publisher (which is quite understandable),\nwe can nevertheless imagine such a system being used to display the cards played during a live duel, \nto allow spectators to read the cards.\n\n---", "## <div align=\"center\">Related Works</div>\n\nAlthough to my knowledge 'draw' is the first detector capable of locating and detecting _Yu-Gi-Oh!_ cards in a dueling environment, \nother works exist and were a source of inspiration for this project. It's worth mentioning them here.\n\nYu-Gi-Oh! NEURON is an official application developed by _KONAMI_ ^®.\nIt's packed with features, including cards recognition. The application is capable of recognizing a total of 20 cards at a time, which is very decent. \nThe drawback is that the cards must be of good quality to be recognized, which is not necessarily the case in a duel context. \nWhat's more, it can't be integrated, so the only way to use it is to use the application.\n\nyugioh one shot learning made by 'vanstorm9' is a \nYu-Gi-Oh! cards classification program that allow you to recognize cards. It uses siamese network to train its classification\nmodel. It gives very impressive results on images with a good quality but not that good on low quality images, and it \ncan't localize cards.\n\nYolov8 is the last version of the very famous 'yolo' family of object detector models.\nI think it doesn't need to be presented today, it represents state-of-the-art real time object detection model.\n\nBEiT is a pre-trained model for image classification. It uses image transofrmers \nwhich are based on attention mechanism. It suits our problem because authors also propose a pre-trained model in 'Imagenet-22K'.\nIt is a dataset with 22k classes (more than most classifiers) which is interesting for our case since there is mode than 11k cards in _Yu-Gi-Oh!_. \n\n---", "## <div align=\"center\">Method Overview</div>\n\nA medium blog will soon be written and published, explaining the main process from data collection to final prediction.\nIf you have any questions, don't hesitate to open an issue.\n\n---", "## <div align=\"center\">Contact</div>\n\nYou can reach me on Twitter @tiazden or by email at URL@URL." ]

[ "TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #language-English #license-mit #arxiv-2106.08254 #region-us \n", "## <div align=\"center\">Documentation</div>\n\n<details open>\n<summary>\nInstall\n</summary>\n\nBoth a docker installation and a more conventional installation are available. If you're not very familiar with all the code, \ndocker installation is recommended. Otherwise, opt for the classic installation.", "#### Docker installation\n\nIf you are familiar with docker, the docker image is available here.\n\nOtherwise, I recommend you to download DockerDesktop if you are on Windows.\nIf you are on Linux, you can refer to the documentation here.\n\nOnce it is done, you simply have to execute the following command,\n\nYour installation is now completed. You can press 'Ctrl+C' and continue to Usage section.", "#### Classic installation\n\nYou need python to be installed. Python installation isn't going to be detailed here, you can refer to the documentation.\n\nWe first need to install pytorch. It is recommended to use a package manager such as miniconda. \nPlease refer to the documentation.\n\nWhen everything is set up you can run the following command to install pytorch:\n\nIf you want to use you gpus to make everything run faster, please refer the documentation\n\nThen you just have to clone the repo and install 'requirements':\n\n\nYour installation is now completed.\n\n</details>\n\n<details open>\n<summary>Usage</summary>\n\nNow to use it you need to download the models and the data, in section Models and Data.\n\nOnce you have it, follow instruction depending on you have docker or classic installation.\nPut all the model in the same folder, and keep the dataset as it is", "#### Docker installation\n\nYou have to copy the data and models in the container. Execute the following command:\n\n\n\nOnce it is done you just have to run the command:\n\nopen the adress 'localhost:5000', and enjoy the maximum. Refer bellow for details about parameters", "#### Classic installation\n\nYou need to modify the 'URL' file by putting the paths of you dataset folder in '\"data_path\"' parameter \nand the path to model folder in '\"trained_models\"' parameter.\n\nOnce done, just run:\n\nopen the adress 'localhost:5000', and enjoy the maximum. Refer bellow for details about parameters", "#### Both\n\n* In the first parameter, the one with gears, put the 'URL' file\n* In the second parameter, the one with a camera, put the video you want to process (leave it empty to use your webcam)\n* In the last one, put your deck list in the format 'ydk'\n\nThen you can press the button and start the process !\n\n</details>\n\n---", "## <div align=\"center\">️Models and Data</div>\n\n<details open>\n<summary>Models</summary>\n\nIn this project, the tasks were divided so that one model would locate the card and another model would classify them. \nSimilarly, to classify the cards, I divided the task so that there is one model for each type of card,\nand the model to be used was determined by the color of the card.\n\nModels can be downloaded in <a href=\"URL Face</a>. \nModels starting with 'beit' stands for classification and the one starting with 'yolo' for localization.\n\n![Model on HF](URL\n\nFor now only models for \"retro\" gameplay are available but the ones for classic format play will be added soon.\nI considered \"retro\" format all cards before the first _syncro_ set, so all the cards edited until Light of Destruction set (LODT - 05/13/2008) set and all speed duel cards. \n\n</details>\n\n<details open>\n<summary>Data</summary>\n\nTo create a dataset, the <a href=\"URL api was used. Two datasets were thus created, \none for \"retro\" play and the other for classic format play. Just as there is a model for each type of card,\nthere is a dataset for each type of card.\n\nDataset can be downloaded in <a href=\"\">Hugging Face</a>.\n\n![Dataset on HF](URL\n\nFor now only \"retro\" dataset is available, but the one for classic format play will be added soon.\n\n\n</details>\n\n\n---", "## <div align=\"center\">Inspiration</div>\n\nThis project is inspired by content creator SuperZouloux's idea of a hologram bringing _Yu-Gi-Oh!_ cards to life. \nHis project uses chips inserted under the sleeves of each card, \nwhich are read by the play mat, enabling the cards to be recognized.\n\nInserting the chips into the sleeves is not only laborious, but also poses another problem: \nface-down cards are read in the same way as face-up ones. \nSo an automatic detector is a really suitable solution.\n\nAlthough this project was discouraged by _KONAMI_ ^®, the game's publisher (which is quite understandable),\nwe can nevertheless imagine such a system being used to display the cards played during a live duel, \nto allow spectators to read the cards.\n\n---", "## <div align=\"center\">Related Works</div>\n\nAlthough to my knowledge 'draw' is the first detector capable of locating and detecting _Yu-Gi-Oh!_ cards in a dueling environment, \nother works exist and were a source of inspiration for this project. It's worth mentioning them here.\n\nYu-Gi-Oh! NEURON is an official application developed by _KONAMI_ ^®.\nIt's packed with features, including cards recognition. The application is capable of recognizing a total of 20 cards at a time, which is very decent. \nThe drawback is that the cards must be of good quality to be recognized, which is not necessarily the case in a duel context. \nWhat's more, it can't be integrated, so the only way to use it is to use the application.\n\nyugioh one shot learning made by 'vanstorm9' is a \nYu-Gi-Oh! cards classification program that allow you to recognize cards. It uses siamese network to train its classification\nmodel. It gives very impressive results on images with a good quality but not that good on low quality images, and it \ncan't localize cards.\n\nYolov8 is the last version of the very famous 'yolo' family of object detector models.\nI think it doesn't need to be presented today, it represents state-of-the-art real time object detection model.\n\nBEiT is a pre-trained model for image classification. It uses image transofrmers \nwhich are based on attention mechanism. It suits our problem because authors also propose a pre-trained model in 'Imagenet-22K'.\nIt is a dataset with 22k classes (more than most classifiers) which is interesting for our case since there is mode than 11k cards in _Yu-Gi-Oh!_. \n\n---", "## <div align=\"center\">Method Overview</div>\n\nA medium blog will soon be written and published, explaining the main process from data collection to final prediction.\nIf you have any questions, don't hesitate to open an issue.\n\n---", "## <div align=\"center\">Contact</div>\n\nYou can reach me on Twitter @tiazden or by email at URL@URL." ]

[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "language:uk", "language:ru", "language:de", "language:zh", "language:am", "language:ar", "language:hi", "language:es", "license:openrail++", "region:us" ]

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[ "en", "uk", "ru", "de", "zh", "am", "ar", "hi", "es" ]

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[ "TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-English #language-Ukrainian #language-Russian #language-German #language-Chinese #language-Amharic #language-Arabic #language-Hindi #language-Spanish #license-openrail++ #region-us \n" ]

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[ "# Dataset Card for Evaluation run of Gille/StrangeMerges_9-7B-dare_ties\n\n\n\nDataset automatically created during the evaluation run of model Gille/StrangeMerges_9-7B-dare_ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:46:42.889745(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Gille/StrangeMerges_9-7B-dare_ties\n\n\n\nDataset automatically created during the evaluation run of model Gille/StrangeMerges_9-7B-dare_ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:46:42.889745(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-10-v0.1\n\n\n\nDataset automatically created during the evaluation run of model notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-10-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:47:43.870919(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-10-v0.1\n\n\n\nDataset automatically created during the evaluation run of model notadib/Mistral-7B-Instruct-v0.2-attention-sparsity-10-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:47:43.870919(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Wanfq/FuseLLM-7B\n\n\n\nDataset automatically created during the evaluation run of model Wanfq/FuseLLM-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:48:44.963342(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Wanfq/FuseLLM-7B\n\n\n\nDataset automatically created during the evaluation run of model Wanfq/FuseLLM-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:48:44.963342(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of DreadPoor/Bageluccine-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model DreadPoor/Bageluccine-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:48:28.503122(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of DreadPoor/Bageluccine-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model DreadPoor/Bageluccine-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:48:28.503122(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[ "# Dataset Card for Evaluation run of YKM12/Mistral-7B-summ-privatev1\n\n\n\nDataset automatically created during the evaluation run of model YKM12/Mistral-7B-summ-privatev1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:51:47.124175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of YKM12/Mistral-7B-summ-privatev1\n\n\n\nDataset automatically created during the evaluation run of model YKM12/Mistral-7B-summ-privatev1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T16:51:47.124175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of BlouseJury/Mistral-7B-Discord-0.1-DPO\n\n\n\nDataset automatically created during the evaluation run of model BlouseJury/Mistral-7B-Discord-0.1-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:00:23.691484(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of BlouseJury/Mistral-7B-Discord-0.1-DPO\n\n\n\nDataset automatically created during the evaluation run of model BlouseJury/Mistral-7B-Discord-0.1-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:00:23.691484(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

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

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[ "# Dataset Card for Evaluation run of FelixChao/WestSeverus-10.7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/WestSeverus-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:05:37.081553(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of FelixChao/WestSeverus-10.7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/WestSeverus-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:05:37.081553(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[ "# Dataset Card for Evaluation run of NovoCode/Phi-2-DPO\n\n\n\nDataset automatically created during the evaluation run of model NovoCode/Phi-2-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:08:08.454430(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NovoCode/Phi-2-DPO\n\n\n\nDataset automatically created during the evaluation run of model NovoCode/Phi-2-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:08:08.454430(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[ "# Dataset Card for Evaluation run of yanolja/KoSOLAR-10.7B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model yanolja/KoSOLAR-10.7B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:12:29.578851(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of yanolja/KoSOLAR-10.7B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model yanolja/KoSOLAR-10.7B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:12:29.578851(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[ "# Dataset Card for Evaluation run of NovoCode/Metabird-7b-DPO\n\n\n\nDataset automatically created during the evaluation run of model NovoCode/Metabird-7b-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:13:18.108149(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NovoCode/Metabird-7b-DPO\n\n\n\nDataset automatically created during the evaluation run of model NovoCode/Metabird-7b-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:13:18.108149(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of mlabonne/NeuralDarewin-7B\n\n\n\nDataset automatically created during the evaluation run of model mlabonne/NeuralDarewin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:48:56.790250(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of mlabonne/NeuralDarewin-7B\n\n\n\nDataset automatically created during the evaluation run of model mlabonne/NeuralDarewin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:48:56.790250(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.3\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-V0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:49:35.277472(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.3\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-V0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:49:35.277472(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[ "# Dataset Card for Evaluation run of lamhieu/ghost-7b-v0.9.0\n\n\n\nDataset automatically created during the evaluation run of model lamhieu/ghost-7b-v0.9.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:50:44.669359(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of lamhieu/ghost-7b-v0.9.0\n\n\n\nDataset automatically created during the evaluation run of model lamhieu/ghost-7b-v0.9.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T17:50:44.669359(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of openagi-project/OpenAGI-7B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model openagi-project/OpenAGI-7B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T18:03:01.560923(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of openagi-project/OpenAGI-7B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model openagi-project/OpenAGI-7B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-01T18:03:01.560923(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]