Datasets:

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ftopal

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huggingface-datasets

like 0

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

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[ "en" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"VNTL-v3.1-1k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"VNTL-v3.1-1k\"\n\nMore Information needed" ]

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

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[ "en" ]

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

[ "task_categories:question-answering", "license:apache-2.0", "region:us" ]

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[ "# Description\nThis dataset contains csv's from the Lex Fridman podcast transcripts provided by Whispering-GPT.\nI split the episode transcripts into parent and child chunks for use with RAG.\nThe parent chunks is size 500 and the child is size 50. The children come with embeddings using OpenAI 'text-embedding-3-small' with 1024 dimensionality.", "# Motivation\nThis was designed for use with ParentDocumentRetriever or LlamaIndex.\nIt should provide better retrievals for queries on specific details. Embedding all the chunks took a while so hopefully this saves someone time.\nThe size of the csv's allow for uploading with Supabase \"Table Editor\" because I didn't want to use postgres COPY." ]

[ "TAGS\n#task_categories-question-answering #license-apache-2.0 #region-us \n", "# Description\nThis dataset contains csv's from the Lex Fridman podcast transcripts provided by Whispering-GPT.\nI split the episode transcripts into parent and child chunks for use with RAG.\nThe parent chunks is size 500 and the child is size 50. The children come with embeddings using OpenAI 'text-embedding-3-small' with 1024 dimensionality.", "# Motivation\nThis was designed for use with ParentDocumentRetriever or LlamaIndex.\nIt should provide better retrievals for queries on specific details. Embedding all the chunks took a while so hopefully this saves someone time.\nThe size of the csv's allow for uploading with Supabase \"Table Editor\" because I didn't want to use postgres COPY." ]

[ "region:us" ]

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

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

[ "task_categories:summarization", "task_ids:news-articles-summarization", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for short_summary", "## Table of Contents\n- Dataset Description\n- Languages\n- Dataset Structure\n- Source Data", "## Dataset Description\n\n50000 News Articles with corresponding short summary", "## Languages\n\nThe text in the dataset is in English", "## Dataset Structure\n\nThe dataset consists of two columns namely Excerpt and Summary.\nThe Excerpt column consists of short text from the news article and the Summary column consists of the few words summary of the excerpt", "## Source Data\n\nThe dataset is scrapped from Otherweb database" ]

[ "TAGS\n#task_categories-summarization #task_ids-news-articles-summarization #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-other #region-us \n", "# Dataset Card for short_summary", "## Table of Contents\n- Dataset Description\n- Languages\n- Dataset Structure\n- Source Data", "## Dataset Description\n\n50000 News Articles with corresponding short summary", "## Languages\n\nThe text in the dataset is in English", "## Dataset Structure\n\nThe dataset consists of two columns namely Excerpt and Summary.\nThe Excerpt column consists of short text from the news article and the Summary column consists of the few words summary of the excerpt", "## Source Data\n\nThe dataset is scrapped from Otherweb database" ]

[ "task_categories:visual-question-answering", "task_categories:question-answering", "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]

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[ "en" ]

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[ "TAGS\n#task_categories-visual-question-answering #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-mit #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"A-OKVQA\"\n\nMore Information needed" ]

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

[ "task_categories:text-to-speech", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "arxiv:1904.02882", "region:us" ]

[ "1904.02882" ]

[ "en" ]

[ "# Dataset Card for LibriTTS\n\n\n\nLibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, \nprepared by Heiga Zen with the assistance of Google Speech and Google Brain team members. The LibriTTS corpus is \ndesigned for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files \nfrom Project Gutenberg) of the LibriSpeech corpus.", "## Overview\n\nThis is the LibriTTS dataset, adapted for the 'datasets' library.", "## Usage", "### Splits\n\nThere are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):\n\n- URL\n- URL\n- URL\n- URL\n- URL.100\n- URL.360\n- URL.500", "### Configurations\n\nThere are 3 configurations, each which limits the splits the 'load_dataset()' function will download.\n\nThe default configuration is \"all\".\n\n- \"dev\": only the \"URL\" split (good for testing the dataset quickly)\n- \"clean\": contains only \"clean\" splits\n- \"other\": contains only \"other\" splits\n- \"all\": contains only \"all\" splits", "### Example\n\nLoading the 'clean' config with only the 'URL.360' split.\n\n\nStreaming is also supported.", "### Columns", "### Example Row", "## Dataset Details", "### Dataset Description\n\n- License: CC BY 4.0", "### Dataset Sources [optional]\n\n\n\n- Homepage: URL\n- Paper: URL" ]

[ "TAGS\n#task_categories-text-to-speech #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #arxiv-1904.02882 #region-us \n", "# Dataset Card for LibriTTS\n\n\n\nLibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, \nprepared by Heiga Zen with the assistance of Google Speech and Google Brain team members. The LibriTTS corpus is \ndesigned for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files \nfrom Project Gutenberg) of the LibriSpeech corpus.", "## Overview\n\nThis is the LibriTTS dataset, adapted for the 'datasets' library.", "## Usage", "### Splits\n\nThere are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):\n\n- URL\n- URL\n- URL\n- URL\n- URL.100\n- URL.360\n- URL.500", "### Configurations\n\nThere are 3 configurations, each which limits the splits the 'load_dataset()' function will download.\n\nThe default configuration is \"all\".\n\n- \"dev\": only the \"URL\" split (good for testing the dataset quickly)\n- \"clean\": contains only \"clean\" splits\n- \"other\": contains only \"other\" splits\n- \"all\": contains only \"all\" splits", "### Example\n\nLoading the 'clean' config with only the 'URL.360' split.\n\n\nStreaming is also supported.", "### Columns", "### Example Row", "## Dataset Details", "### Dataset Description\n\n- License: CC BY 4.0", "### Dataset Sources [optional]\n\n\n\n- Homepage: URL\n- Paper: URL" ]

[ "task_categories:summarization", "task_categories:text-generation", "size_categories:n<1K", "language:en", "license:apache-2.0", "legal", "region:us" ]

[]

[ "en" ]

[ "# Canadian Case Law Summaries\nA database of (currently, still growing) >600 case law summaries generated by GPT 4 for random case law in Ontario or Canada" ]

[ "TAGS\n#task_categories-summarization #task_categories-text-generation #size_categories-n<1K #language-English #license-apache-2.0 #legal #region-us \n", "# Canadian Case Law Summaries\nA database of (currently, still growing) >600 case law summaries generated by GPT 4 for random case law in Ontario or Canada" ]

[ "size_categories:1K<n<10K", "license:llama2", "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name\n\na copy of huggingface samsum\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]\n\nLe Minh Long Nguyen", "## Dataset Card Contact" ]

[ "TAGS\n#size_categories-1K<n<10K #license-llama2 #region-us \n", "# Dataset Card for Dataset Name\n\na copy of huggingface samsum\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]\n\nLe Minh Long Nguyen", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:1M<n<10M", "language:en", "license:cc0-1.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #size_categories-1M<n<10M #language-English #license-cc0-1.0 #region-us \n" ]

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

[]

[ "en" ]

[]

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

[ "task_categories:conversational", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:n<1K", "language:en", "license:mit", "arxiv:2306.02707", "arxiv:2301.13688", "region:us" ]

[ "2306.02707", "2301.13688" ]

[ "en" ]

[ "# Overview\n\nThis is a new curated subset of the SlimOpenOrca data." ]

[ "TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-English #license-mit #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n", "# Overview\n\nThis is a new curated subset of the SlimOpenOrca data." ]

[ "region:us" ]

[]

[]

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

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

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

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

[]

[]

[]

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

[ "task_categories:image-to-text", "task_categories:text-generation", "task_categories:conversational", "task_categories:text2text-generation", "task_categories:sentence-similarity", "size_categories:10K<n<100K", "language:en", "conversational", "image-to-text", "vision", "convAI", "arxiv:2402.05930", "region:us" ]

[ "2402.05930" ]

[ "en" ]

[ "## Quickstart\n\nTo get started, simply install 'datasets' with 'pip install datasets' and load the chat data splits:\n\n\n\nYou can now use 'turn_text' as an input to LLaMA-style models. For example, you can use Sheared-LLaMA:", "## Raw Data\n\nTo use the raw data, you will need to use the 'huggingface_hub':\n\n\n\nFor more information on how to use this data using our official library, please refer to the WebLINX documentation.\n\nIf you use our dataset, please cite our work as follows:" ]

[ "TAGS\n#task_categories-image-to-text #task_categories-text-generation #task_categories-conversational #task_categories-text2text-generation #task_categories-sentence-similarity #size_categories-10K<n<100K #language-English #conversational #image-to-text #vision #convAI #arxiv-2402.05930 #region-us \n", "## Quickstart\n\nTo get started, simply install 'datasets' with 'pip install datasets' and load the chat data splits:\n\n\n\nYou can now use 'turn_text' as an input to LLaMA-style models. For example, you can use Sheared-LLaMA:", "## Raw Data\n\nTo use the raw data, you will need to use the 'huggingface_hub':\n\n\n\nFor more information on how to use this data using our official library, please refer to the WebLINX documentation.\n\nIf you use our dataset, please cite our work as follows:" ]

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

[]

[ "en" ]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "task_categories:translation", "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:fill-mask", "size_categories:100B<n<1T", "language:fr", "language:en", "arxiv:2402.00786", "region:us" ]

[ "2402.00786" ]

[ "fr", "en" ]

[ "# CroissantLLM: A Truly Bilingual French-English Language Model", "## Dataset\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.", "## Note\n\nOnly the 'english_660B_11' split is kept hidden for the moment (until release of the Canary paper) but is available upon request !" ]

[ "TAGS\n#task_categories-translation #task_categories-text-generation #task_categories-text2text-generation #task_categories-fill-mask #size_categories-100B<n<1T #language-French #language-English #arxiv-2402.00786 #region-us \n", "# CroissantLLM: A Truly Bilingual French-English Language Model", "## Dataset\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.", "## Note\n\nOnly the 'english_660B_11' split is kept hidden for the moment (until release of the Canary paper) but is available upon request !" ]

[ "license:apache-2.0", "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- 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#license-apache-2.0 #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- 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-to-speech", "task_categories:automatic-speech-recognition", "size_categories:100K<n<1M", "language:hi", "license:mit", "region:us" ]

[]

[ "hi" ]

[ "# Rajasthani Hindi Speech Dataset\n\n\nThis dataset consists of audio recordings of participants reading out stories in Rajasthani Hindi, one sentence at a time. We had 98 participants from Soda, Rajasthan. Each participant read 30 stories. In total, we have 426873 recordings in this dataset. We had roughly 58 male participants and 40 female participants.\n\n> Point to Note:\n> While random sampling suggests that most users have to their best effort tried to accurately read out the sentences, we have not performed any quality analysis on the data. There could be errors in some of the recordings.", "### Dataset Sources\n\n\n\n- Link: Download\n- Curated By: Kalika Bali", "## Dataset Structure\n\n\nContains two headers: audio and sentence containing the Audio file and sentence respectively." ]

[ "TAGS\n#task_categories-text-to-speech #task_categories-automatic-speech-recognition #size_categories-100K<n<1M #language-Hindi #license-mit #region-us \n", "# Rajasthani Hindi Speech Dataset\n\n\nThis dataset consists of audio recordings of participants reading out stories in Rajasthani Hindi, one sentence at a time. We had 98 participants from Soda, Rajasthan. Each participant read 30 stories. In total, we have 426873 recordings in this dataset. We had roughly 58 male participants and 40 female participants.\n\n> Point to Note:\n> While random sampling suggests that most users have to their best effort tried to accurately read out the sentences, we have not performed any quality analysis on the data. There could be errors in some of the recordings.", "### Dataset Sources\n\n\n\n- Link: Download\n- Curated By: Kalika Bali", "## Dataset Structure\n\n\nContains two headers: audio and sentence containing the Audio file and sentence respectively." ]

[ "region:us" ]

[]

[]

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

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

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

[]

[ "en" ]

[]

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

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

[]

[]

[ "# AI Project Data Card\n ## Overview \n - Value of State and Country NPI Dataset \n - Provides comprehensive insights into healthcare providers across various regions.\n - Enhances data accuracy and availability for medical research and policy making.\n - NUCC Specialty File \n - Delivers detailed descriptions of physician specialties.\n - Includes unique codes for over 100 specialties, facilitating standardized data analysis.\n\n # This study into AI discovers cross references from provider data and poses search technology to make it easier to build perrsonalized information on physicians for AI scientists and patients to begin to undderstand how we can assist in building relationships.\n\n There are a number of assets here:\n 1. URL and URL for pre processor which curates state and country files with high fidelity and distilledd knowledge using the NPI registry and NUCC taxonomy at scale compiling over 9GB data into a curated data strategy.\n 2. URL sample state - This provides a snapshot of just Minnesota which is used to Test AI.\n 3. Zip file with all. To reuse the state and country datasets a zip file is provided to get all the ccurated data using one file. My thoughts are this later becauses the standard for contextual 'brain' files which represent data context around a subject.\n 4. NUCC specialty file which is the guide to medical and various provider specialties. The description of these is a good keyword prompt data source for understanding specialties.\n\n # Pain / Joy / Superpower\n 1. Pain - When looking for a medical procedure provider it is not easy to know what to look for or who in your area may match.\n 2. Joy - A new use might be able to consider search needs of patient in keywords, then find personalized physician relationships available in an area.\n 3. Superpower - This will help patients and referral specialists find personalized optimal physician sets for condition sets '\n - an obstetrician, surgeon, or physician that might be optimal for a given patient given that they align to multiple patient requirements. \n - a test case for this is analyzing the treatments available for cancer which might require multi specialty PCPs. \n - like a dermatologist that also can perform surgery for cheap since one physician might match the two specialties.\n\n # Sources:\n 1. NUCC Taxonomy: URL\n 2. NPI Registry updated monthly: URL\n \n ## Physician Lists Library \n - Features over 100 state and country-based lists.\n - Offers contact details and provider specialty references, aiding in network expansion and collaboration.\n ## Licensing Boards and Certification Exams \n - Covers all types of physicians and healthcare professionals.\n - Medical (USMLE) \n - Dental (NBDE) \n - Nursing (NCLEX) \n - Pharmacy (NAPLEX) \n - Plus more for comprehensive coverage.\n ## Applications and Use Cases \n - Building Libraries of Specialists for MoE Information ️\n - Supports the creation of specialized knowledge bases.\n - Facilitates targeted outreach and collaboration among healthcare professionals.\n - Enhancing Healthcare Directories and Networks \n - Improves the accuracy and reach of healthcare directories.\n - Enables patients and providers to connect more efficiently.\n ## Conclusion and Next Steps ️\n - This dataset and its associated libraries offer vast potential for healthcare improvement.\n - Encourages further exploration and development of AI-driven applications in healthcare.\n\n _For more details on accessing and utilizing these resources, please refer to the project documentation and code repositories._" ]

[ "TAGS\n#license-mit #region-us \n", "# AI Project Data Card\n ## Overview \n - Value of State and Country NPI Dataset \n - Provides comprehensive insights into healthcare providers across various regions.\n - Enhances data accuracy and availability for medical research and policy making.\n - NUCC Specialty File \n - Delivers detailed descriptions of physician specialties.\n - Includes unique codes for over 100 specialties, facilitating standardized data analysis.\n\n # This study into AI discovers cross references from provider data and poses search technology to make it easier to build perrsonalized information on physicians for AI scientists and patients to begin to undderstand how we can assist in building relationships.\n\n There are a number of assets here:\n 1. URL and URL for pre processor which curates state and country files with high fidelity and distilledd knowledge using the NPI registry and NUCC taxonomy at scale compiling over 9GB data into a curated data strategy.\n 2. URL sample state - This provides a snapshot of just Minnesota which is used to Test AI.\n 3. Zip file with all. To reuse the state and country datasets a zip file is provided to get all the ccurated data using one file. My thoughts are this later becauses the standard for contextual 'brain' files which represent data context around a subject.\n 4. NUCC specialty file which is the guide to medical and various provider specialties. The description of these is a good keyword prompt data source for understanding specialties.\n\n # Pain / Joy / Superpower\n 1. Pain - When looking for a medical procedure provider it is not easy to know what to look for or who in your area may match.\n 2. Joy - A new use might be able to consider search needs of patient in keywords, then find personalized physician relationships available in an area.\n 3. Superpower - This will help patients and referral specialists find personalized optimal physician sets for condition sets '\n - an obstetrician, surgeon, or physician that might be optimal for a given patient given that they align to multiple patient requirements. \n - a test case for this is analyzing the treatments available for cancer which might require multi specialty PCPs. \n - like a dermatologist that also can perform surgery for cheap since one physician might match the two specialties.\n\n # Sources:\n 1. NUCC Taxonomy: URL\n 2. NPI Registry updated monthly: URL\n \n ## Physician Lists Library \n - Features over 100 state and country-based lists.\n - Offers contact details and provider specialty references, aiding in network expansion and collaboration.\n ## Licensing Boards and Certification Exams \n - Covers all types of physicians and healthcare professionals.\n - Medical (USMLE) \n - Dental (NBDE) \n - Nursing (NCLEX) \n - Pharmacy (NAPLEX) \n - Plus more for comprehensive coverage.\n ## Applications and Use Cases \n - Building Libraries of Specialists for MoE Information ️\n - Supports the creation of specialized knowledge bases.\n - Facilitates targeted outreach and collaboration among healthcare professionals.\n - Enhancing Healthcare Directories and Networks \n - Improves the accuracy and reach of healthcare directories.\n - Enables patients and providers to connect more efficiently.\n ## Conclusion and Next Steps ️\n - This dataset and its associated libraries offer vast potential for healthcare improvement.\n - Encourages further exploration and development of AI-driven applications in healthcare.\n\n _For more details on accessing and utilizing these resources, please refer to the project documentation and code repositories._" ]

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:multilingual", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "size_categories:10M<n<100M", "size_categories:100M<n<1B", "size_categories:1B<n<10B", "source_datasets:original", "language:af", "language:ar", "language:az", "language:be", "language:bg", "language:bn", "language:ca", "language:cs", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:ga", "language:gl", "language:gu", "language:hbs", "language:he", "language:hi", "language:hu", "language:hy", "language:id", "language:is", "language:it", "language:ja", "language:ka", "language:kk", "language:kn", "language:ko", "language:ky", "language:la", "language:lt", "language:lv", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:my", "language:nb", "language:ne", "language:nl", "language:nn", "language:pa", "language:pl", "language:ps", "language:pt", "language:ro", "language:ru", "language:si", "language:sk", "language:sl", "language:so", "language:sq", "language:sv", "language:sw", "language:ta", "language:te", "language:th", "language:tl", "language:tr", "language:tt", "language:uk", "language:ur", "language:uz", "language:vi", "language:zh", "license:cc-by-4.0", "region:us" ]

[]

[ "af", "ar", "az", "be", "bg", "bn", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "ga", "gl", "gu", "hbs", "he", "hi", "hu", "hy", "id", "is", "it", "ja", "ka", "kk", "kn", "ko", "ky", "la", "lt", "lv", "mk", "ml", "mn", "mr", "ms", "mt", "my", "nb", "ne", "nl", "nn", "pa", "pl", "ps", "pt", "ro", "ru", "si", "sk", "sl", "so", "sq", "sv", "sw", "ta", "te", "th", "tl", "tr", "tt", "uk", "ur", "uz", "vi", "zh" ]

[ "### Dataset Summary\n\n\nFrom the team that brought you CulutraX, we present CulturaY, another substantial multilingual dataset that applies the same dataset cleaning methodology to the HPLT v1.1 dataset.\nPlease note that HPLT v1.2 has also been released and is an alternative verison with different cleaning methodolgies.\nThis data was used in part to train our SOTA Vietnamese model: Vistral-7B-Chat.\n\n\nOur annotations and arrangements are licensed under CC-BY-4.0, and we make the data available for fair use machine learning research. \n\nBut we make no claims as to the underlying copyrights of the work. This data was copied from the HPLT project, which in turn used the data from Common Crawl and the Internet Archive.", "### Acknowledgement\n\n\nWe thank our collaborators at UONLP - The Natural Language Processing Group at the University of Oregon, and the computing resources of the managers of the Karolina Supercomputers.\nWe also thank our friends at TurkuNLP and URL for their support.", "### Data Breakdown:\n\n\nThere are 75 langauges, with the following breakdown:", "### Dataset structure\n\n\nThe dataset has a total of 6 columns, including:\n\n\n* 2 columns 'text, url' will be the two main columns in this dataset.\n* the remaining columns 'id, document\\_lang, scores, langs' belong to the original document in the HPLT V1.1 dataset, retained for debugging purposes. and will be removed in the future.\n\n\nTherefore, when using, please only utilize the two columns text and url.\n\n\nTo cite CulturaY, please use:" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #size_categories-10M<n<100M #size_categories-100M<n<1B #size_categories-1B<n<10B #source_datasets-original #language-Afrikaans #language-Arabic #language-Azerbaijani #language-Belarusian #language-Bulgarian #language-Bengali #language-Catalan #language-Czech #language-Welsh #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Irish #language-Galician #language-Gujarati #language-Serbo-Croatian #language-Hebrew #language-Hindi #language-Hungarian #language-Armenian #language-Indonesian #language-Icelandic #language-Italian #language-Japanese #language-Georgian #language-Kazakh #language-Kannada #language-Korean #language-Kirghiz #language-Latin #language-Lithuanian #language-Latvian #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Burmese #language-Norwegian Bokmål #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Panjabi #language-Polish #language-Pushto #language-Portuguese #language-Romanian #language-Russian #language-Sinhala #language-Slovak #language-Slovenian #language-Somali #language-Albanian #language-Swedish #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Thai #language-Tagalog #language-Turkish #language-Tatar #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Chinese #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nFrom the team that brought you CulutraX, we present CulturaY, another substantial multilingual dataset that applies the same dataset cleaning methodology to the HPLT v1.1 dataset.\nPlease note that HPLT v1.2 has also been released and is an alternative verison with different cleaning methodolgies.\nThis data was used in part to train our SOTA Vietnamese model: Vistral-7B-Chat.\n\n\nOur annotations and arrangements are licensed under CC-BY-4.0, and we make the data available for fair use machine learning research. \n\nBut we make no claims as to the underlying copyrights of the work. This data was copied from the HPLT project, which in turn used the data from Common Crawl and the Internet Archive.", "### Acknowledgement\n\n\nWe thank our collaborators at UONLP - The Natural Language Processing Group at the University of Oregon, and the computing resources of the managers of the Karolina Supercomputers.\nWe also thank our friends at TurkuNLP and URL for their support.", "### Data Breakdown:\n\n\nThere are 75 langauges, with the following breakdown:", "### Dataset structure\n\n\nThe dataset has a total of 6 columns, including:\n\n\n* 2 columns 'text, url' will be the two main columns in this dataset.\n* the remaining columns 'id, document\\_lang, scores, langs' belong to the original document in the HPLT V1.1 dataset, retained for debugging purposes. and will be removed in the future.\n\n\nTherefore, when using, please only utilize the two columns text and url.\n\n\nTo cite CulturaY, please use:" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

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[ "# Dataset Card for \"ExeBench-Eval-small-gpt3.5-zeroshot-result\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ExeBench-Eval-small-gpt3.5-zeroshot-result\"\n\nMore Information needed" ]

[ "task_categories:feature-extraction", "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-feature-extraction #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n" ]

[ "task_categories:question-answering", "task_categories:text-generation", "size_categories:10K<n<100K", "language:km", "license:mit", "region:us" ]

[]

[ "km" ]

[ "# 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-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-Khmer #license-mit #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 Dataset Name\n\n\n\n\n Tokenizer: mbert\n\n Dataset: TASTEset\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/EW-TASTE_en-it_DEEPL_localized_uom.json", "## 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\n\n Tokenizer: mbert\n\n Dataset: TASTEset\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/EW-TASTE_en-it_DEEPL_localized_uom.json", "## 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 Dataset Name\n\n\n\n\n Tokenizer: mbert\n\n Dataset: TASTEset\n\n Unshuffled ratio: 0\n\n Shuffled ratio: 1\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/EW-TASTE_en-it_DEEPL_localized_uom.json", "## 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\n\n Tokenizer: mbert\n\n Dataset: TASTEset\n\n Unshuffled ratio: 0\n\n Shuffled ratio: 1\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/EW-TASTE_en-it_DEEPL_localized_uom.json", "## 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 Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: TASTEset\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/EW-TASTE_en-it_DEEPL_localized_uom.json", "## 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\n\n Tokenizer: mdeberta\n\n Dataset: TASTEset\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/EW-TASTE_en-it_DEEPL_localized_uom.json", "## 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 Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: TASTEset\n\n Unshuffled ratio: 0\n\n Shuffled ratio: 1\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/EW-TASTE_en-it_DEEPL_localized_uom.json", "## 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\n\n Tokenizer: mdeberta\n\n Dataset: TASTEset\n\n Unshuffled ratio: 0\n\n Shuffled ratio: 1\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/EW-TASTE_en-it_DEEPL_localized_uom.json", "## 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 \"UT\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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[]

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

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

[ "region:us" ]

[]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

[]

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[ "# Dataset Card for \"financial_sentiment_analysis_train_compilation_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"financial_sentiment_analysis_train_compilation_v2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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

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

[ "region:us" ]

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[]

[ "# Dataset Card for Dataset Name\n\nOpen source articles about Psoriatic arthritis which have been published in reputed journals\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\nspecifically uses articles which were referenced by international treatment guidelines\n\n\n\n- Curated by: [Devika Dua]]\n- Funded by [optional]: [None]\n- Shared by [optional]: [test case with no public sharing]\n- Language(s) (NLP): [NLP]\n- License: [Private]", "### Dataset Sources [optional]\n\n\n\n- Repository: [None]\n- Paper [optional]: \n- Demo [optional]:", "## Uses\n\ntext generation about psoriatic arthritis treatment recommendations based on information provided", "### Direct Use\n\nuse it to train llm about Psoriatic arthrtis treatment", "### Out-of-Scope Use\n\ndo not use it for basing treatment of patients. it is only a theoretical exercise", "## Dataset Structure\n\nDataset structure is divided into input and output fields. input fields contain labels 'Psoriatic arthritis', 'GRAPPA' etc whereas the output field contains detailed information about the input prompt", "## Dataset Creation", "### Curation Rationale\n\nto study llm uses in article generation", "### Source Data\n\nopen access articles sourced from reputed medicla journals", "#### 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\nOpen source articles about Psoriatic arthritis which have been published in reputed journals\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\nspecifically uses articles which were referenced by international treatment guidelines\n\n\n\n- Curated by: [Devika Dua]]\n- Funded by [optional]: [None]\n- Shared by [optional]: [test case with no public sharing]\n- Language(s) (NLP): [NLP]\n- License: [Private]", "### Dataset Sources [optional]\n\n\n\n- Repository: [None]\n- Paper [optional]: \n- Demo [optional]:", "## Uses\n\ntext generation about psoriatic arthritis treatment recommendations based on information provided", "### Direct Use\n\nuse it to train llm about Psoriatic arthrtis treatment", "### Out-of-Scope Use\n\ndo not use it for basing treatment of patients. it is only a theoretical exercise", "## Dataset Structure\n\nDataset structure is divided into input and output fields. input fields contain labels 'Psoriatic arthritis', 'GRAPPA' etc whereas the output field contains detailed information about the input prompt", "## Dataset Creation", "### Curation Rationale\n\nto study llm uses in article generation", "### Source Data\n\nopen access articles sourced from reputed medicla journals", "#### 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 \"test\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Function to scrape data from the website\ndef scrape_website(url):\n # Send a GET request to the URL\n response = URL(url)\n\n # Check if the request was successful\n if response.status_code == 200:\n # Parse the HTML content\n soup = BeautifulSoup(response.content, 'URL')\n\n # Find the press release content\n press_release_content = URL('div', {'id': 'divPressRelease'})\n\n # Extract the title and content\n title = press_release_content.find('h1').URL()\n content = press_release_content.find('div', {'class': 'pressreldetail'}).URL()\n\n return title, content\n else:\n print(\"Failed to retrieve data from the website.\")\n return None, None", "# Main function\ndef main():\n # URL of the website to scrape\n url = 'URL\n\n # Scrape data from the website\n title, content = scrape_website(url)\n\n # Write the scraped data to a CSV file\n if title and content:\n with open('scraped_data.csv', 'w', newline='', encoding='utf-8') as csvfile:\n writer = URL(csvfile)\n writer.writerow(['Title', 'Content'])\n writer.writerow([title, content])\n\n print(\"Scraped data has been saved to 'scraped_data.csv'.\")\n else:\n print(\"No data was scraped.\")" ]

[ "TAGS\n#region-us \n", "# Function to scrape data from the website\ndef scrape_website(url):\n # Send a GET request to the URL\n response = URL(url)\n\n # Check if the request was successful\n if response.status_code == 200:\n # Parse the HTML content\n soup = BeautifulSoup(response.content, 'URL')\n\n # Find the press release content\n press_release_content = URL('div', {'id': 'divPressRelease'})\n\n # Extract the title and content\n title = press_release_content.find('h1').URL()\n content = press_release_content.find('div', {'class': 'pressreldetail'}).URL()\n\n return title, content\n else:\n print(\"Failed to retrieve data from the website.\")\n return None, None", "# Main function\ndef main():\n # URL of the website to scrape\n url = 'URL\n\n # Scrape data from the website\n title, content = scrape_website(url)\n\n # Write the scraped data to a CSV file\n if title and content:\n with open('scraped_data.csv', 'w', newline='', encoding='utf-8') as csvfile:\n writer = URL(csvfile)\n writer.writerow(['Title', 'Content'])\n writer.writerow([title, content])\n\n print(\"Scraped data has been saved to 'scraped_data.csv'.\")\n else:\n print(\"No data was scraped.\")" ]

[ "task_categories:text-generation", "task_categories:summarization", "size_categories:1K<n<10K", "language:fr", "language:en", "license:apache-2.0", "CTF", "pentesting", "region:us" ]

[]

[ "fr", "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\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\n\n\nENG: This only me that provide my own data. \nFR : C'est juste moi qui vais remplir le dataset avec mes infos et ma manière de faire.", "#### 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-text-generation #task_categories-summarization #size_categories-1K<n<10K #language-French #language-English #license-apache-2.0 #CTF #pentesting #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\n\n\nENG: This only me that provide my own data. \nFR : C'est juste moi qui vais remplir le dataset avec mes infos et ma manière de faire.", "#### 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", "task_categories:text2text-generation", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "text classification", "relation extraction", "region:us" ]

[]

[ "en" ]

[ "### SentenceRex\nThis is a dataset for training zero-shot and few-shot sentence level relation extraction models.\n\nThe dataset was created with a distant supervision technique from Wikipedia. \n\nAfter that, labels were manually checked to be logically consistent with a sentence. Overall, it consists of 847 unique relations. \n\nEach entity between which there is a relation is tagged in the following way: <e1></e1> for the source entity and <e2></e2> for the target entity.\n\n'labels' column indicates the relation name.", "### Feedback\nWe value your input! Share your feedback and suggestions to help us improve our models and datasets.\nFill out the feedback form", "### Join Our Discord\nConnect with our community on Discord for news, support, and discussion about our models and datasets.\nJoin Discord" ]

[ "TAGS\n#task_categories-text-classification #task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #text classification #relation extraction #region-us \n", "### SentenceRex\nThis is a dataset for training zero-shot and few-shot sentence level relation extraction models.\n\nThe dataset was created with a distant supervision technique from Wikipedia. \n\nAfter that, labels were manually checked to be logically consistent with a sentence. Overall, it consists of 847 unique relations. \n\nEach entity between which there is a relation is tagged in the following way: <e1></e1> for the source entity and <e2></e2> for the target entity.\n\n'labels' column indicates the relation name.", "### Feedback\nWe value your input! Share your feedback and suggestions to help us improve our models and datasets.\nFill out the feedback form", "### Join Our Discord\nConnect with our community on Discord for news, support, and discussion about our models and datasets.\nJoin Discord" ]

[ "task_categories:question-answering", "task_categories:text-classification", "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "arxiv:2402.06044", "region:us" ]

[ "2402.06044" ]

[ "en" ]

[ "## Dataset Details\n\nThe OpenToM benchmark contains 696 narratives, 596 of which are narratives of normal length (average word count: 194.3 words) and 100 of which are long narratives (average word count: 491.6 words).\nEach of the narrative is followed with 23 ToM questions, making a total of 16008 questions.\nThe OpenToM benchmark pose first-order and second-order questions in the following genres:\n1. Location: this is a prevelant type of question seen in many ToM benchmarks. We break location questions into *coarse* and *fine*, differ by granularity. *Coarse* questions ask if a character thinks that an entity is in its initial location where as *fine* questions ask the precise location of an entity.\n2. Multihop: we compose questions that demand an additional reasoning hop on top of the *Location* questions. Specifically, we inquire characters' perception of the *fullness* and the *accessibility* of an entity. We incoporate social commonsense in the *accessibility* questions. For instance, if an entity is moved into someone's bag, then it beomces *less accessible* to others since people shall not access other's bag without asking for permission.\n3. Attitude: LLMs' capability of understanding character's perception of the psychological world has been overlooked by many established N-ToM benchmarks. We propose the *attitude* question to test LLMs' capabilities in understanding character's attitude towards some events. For instance, if my favorite rubber duck is taken away from me without asking, I would hold a *negative* attitude towards this event.\n\nAll the OpenToM questions are designed to be a binary or ternary classification task. We recommend using *macro-averaged F1 score* to evaluate LLMs' performance as the labels are not uniformly distributed.", "### Dataset Description\n\n- Curated by: KclNLP\n- Funded by [optional]: KclNLP\n- Language(s) (NLP): English\n- License:", "### Dataset Generating Process\n\n\n\n- Repository: URL\n- Paper: URL", "## Uses\n\nThe OpenToM dataset is designed to benchmark the performance of LLMs. It shall not be used for training or fine-tuning. Therefore, <span style=\"color:red\">please avoid testing OpenToM questions in OpenAI playground or places where the data might be used for LLM training.</span>", "## Dataset Structure\n\n\n\n'URL' contains the QA data with 13,708 questions derived from 596 OpenToM stories of normal length\n\n'opentom_long.json' contains the QA data with 2,300 questions derived from 100 OpenToM long stories\n\nTo access individual question types, nevigate to the 'opentom_data' folder, in which there is a 'URL / metadata_long.json' file containing the metadata of OpenToM. The other JSON files store OpenToM questions of each genre asked for either first-order (fo) or second-order (so) ToM. \n- 'location_cg_fo': Coarse location questions asking about characters' belief of whether an entity is in its initial location (First-Order).\n- 'location_cg_so': Coarse location questions asking about characters' belief of whether another character believes that an entity is in its initial location (Second-Order)\n- 'location_fg_fo': Fine location questions asking about characters' belief of the precise location of an entity (First-Order).\n- 'location_fg_so': Fine location questions asking about characters' belief of another character's belief of the precise location of an entity (Second-Order).\n- 'multihop_fo': Multihop questions that requesting additional reasoning hops based on location questions (First-Order).\n- 'multihop_so': Multihop questions that requesting additional reasoning hops based on location questions (Second-Order).\n- 'attitude': Questions inquire about characters' attitude towards others' actions.\n\nEach metadata contains the following information:\n- 'plot': stores the OpenToM plot used to produce an OpenToM story.\n- 'plot_info': stores the key information in OpenToM plot, which include the two protangonists, the entity-of-interest, and the two containers.\n- 'preferences': stores the first-order and second-order preference belief of the characters.\n- 'personality': stores the presonality trait of the *mover*.\n- 'sentiment_statement': stores the *mover*'s latent sentiment towards the entity-of-interest.\n- 'true_sentiment': stores the *mover*'s latent sentiment towards the entity-of-interest.\n- 'intention': stores the *mover*'s latent intention towards the entity-of-interest.\n- 'new_location': the new location (fine-grained) of the entity.\n- 'observed': documents whether the *observer* witnessed the *mover*'s action.\n- 'narrative': the OpenToM narrative.", "## Dataset Creation\n\n!alt text", "## Acknowledgement\nPart of the contents of our story generation plots are derived from the ToMi dataset. We wish to thank them for generously making the ToMi dataset publicaly available.", "## Bias, Risks, and Limitations\n\n\n\nThe drafts of OpenToM stories are composed using LLMs. Although some of the stories went through human revision, we acknowledge that the texts generated by LLMs could contain biases and lack lexical diversity.\n\nIf you find our benchmark useful, please cite our work:\n\nBibTeX:", "## Dataset Card Contact\n\nFor any question or inquiry about the OpenToM benchmark, please email URL@URL\n\n<p align=\"center\">\n <img src=\"assets/figures/URL\" width=\"256\">\n</p>" ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #size_categories-10K<n<100K #language-English #arxiv-2402.06044 #region-us \n", "## Dataset Details\n\nThe OpenToM benchmark contains 696 narratives, 596 of which are narratives of normal length (average word count: 194.3 words) and 100 of which are long narratives (average word count: 491.6 words).\nEach of the narrative is followed with 23 ToM questions, making a total of 16008 questions.\nThe OpenToM benchmark pose first-order and second-order questions in the following genres:\n1. Location: this is a prevelant type of question seen in many ToM benchmarks. We break location questions into *coarse* and *fine*, differ by granularity. *Coarse* questions ask if a character thinks that an entity is in its initial location where as *fine* questions ask the precise location of an entity.\n2. Multihop: we compose questions that demand an additional reasoning hop on top of the *Location* questions. Specifically, we inquire characters' perception of the *fullness* and the *accessibility* of an entity. We incoporate social commonsense in the *accessibility* questions. For instance, if an entity is moved into someone's bag, then it beomces *less accessible* to others since people shall not access other's bag without asking for permission.\n3. Attitude: LLMs' capability of understanding character's perception of the psychological world has been overlooked by many established N-ToM benchmarks. We propose the *attitude* question to test LLMs' capabilities in understanding character's attitude towards some events. For instance, if my favorite rubber duck is taken away from me without asking, I would hold a *negative* attitude towards this event.\n\nAll the OpenToM questions are designed to be a binary or ternary classification task. We recommend using *macro-averaged F1 score* to evaluate LLMs' performance as the labels are not uniformly distributed.", "### Dataset Description\n\n- Curated by: KclNLP\n- Funded by [optional]: KclNLP\n- Language(s) (NLP): English\n- License:", "### Dataset Generating Process\n\n\n\n- Repository: URL\n- Paper: URL", "## Uses\n\nThe OpenToM dataset is designed to benchmark the performance of LLMs. It shall not be used for training or fine-tuning. Therefore, <span style=\"color:red\">please avoid testing OpenToM questions in OpenAI playground or places where the data might be used for LLM training.</span>", "## Dataset Structure\n\n\n\n'URL' contains the QA data with 13,708 questions derived from 596 OpenToM stories of normal length\n\n'opentom_long.json' contains the QA data with 2,300 questions derived from 100 OpenToM long stories\n\nTo access individual question types, nevigate to the 'opentom_data' folder, in which there is a 'URL / metadata_long.json' file containing the metadata of OpenToM. The other JSON files store OpenToM questions of each genre asked for either first-order (fo) or second-order (so) ToM. \n- 'location_cg_fo': Coarse location questions asking about characters' belief of whether an entity is in its initial location (First-Order).\n- 'location_cg_so': Coarse location questions asking about characters' belief of whether another character believes that an entity is in its initial location (Second-Order)\n- 'location_fg_fo': Fine location questions asking about characters' belief of the precise location of an entity (First-Order).\n- 'location_fg_so': Fine location questions asking about characters' belief of another character's belief of the precise location of an entity (Second-Order).\n- 'multihop_fo': Multihop questions that requesting additional reasoning hops based on location questions (First-Order).\n- 'multihop_so': Multihop questions that requesting additional reasoning hops based on location questions (Second-Order).\n- 'attitude': Questions inquire about characters' attitude towards others' actions.\n\nEach metadata contains the following information:\n- 'plot': stores the OpenToM plot used to produce an OpenToM story.\n- 'plot_info': stores the key information in OpenToM plot, which include the two protangonists, the entity-of-interest, and the two containers.\n- 'preferences': stores the first-order and second-order preference belief of the characters.\n- 'personality': stores the presonality trait of the *mover*.\n- 'sentiment_statement': stores the *mover*'s latent sentiment towards the entity-of-interest.\n- 'true_sentiment': stores the *mover*'s latent sentiment towards the entity-of-interest.\n- 'intention': stores the *mover*'s latent intention towards the entity-of-interest.\n- 'new_location': the new location (fine-grained) of the entity.\n- 'observed': documents whether the *observer* witnessed the *mover*'s action.\n- 'narrative': the OpenToM narrative.", "## Dataset Creation\n\n!alt text", "## Acknowledgement\nPart of the contents of our story generation plots are derived from the ToMi dataset. We wish to thank them for generously making the ToMi dataset publicaly available.", "## Bias, Risks, and Limitations\n\n\n\nThe drafts of OpenToM stories are composed using LLMs. Although some of the stories went through human revision, we acknowledge that the texts generated by LLMs could contain biases and lack lexical diversity.\n\nIf you find our benchmark useful, please cite our work:\n\nBibTeX:", "## Dataset Card Contact\n\nFor any question or inquiry about the OpenToM benchmark, please email URL@URL\n\n<p align=\"center\">\n <img src=\"assets/figures/URL\" width=\"256\">\n</p>" ]

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

[]

[]

[ "### What is this?\nThis is a subset of the aurora-m training data. The data has been clustered into samples by languages and for code. \nThe license to the underlying data is based on the original data source, which is the HPLT dataset, the Pile, Refined Web, Red Pajama 1, and the Stack.\n\nWe make no claim to any copyrights under the original works, and have only compiled these into clusters for fair use machine learning research.\n\nPLEASE USE AT YOUR OWN RISK. WE MAKE NO WARRANTIES AS TO NON-INFRINGEMENT.\n\n\nPlease note that some multi-lingual wikipedia articles from red pajama 1 are in the \"en\" folder. We will need to move those out to their individual language folders.", "### Usage:\n\nWe will write a more full fledged data card, but the user can use the files under each language as a cluster of data for training in context or otherwise." ]

[ "TAGS\n#license-cc-by-nc-2.0 #region-us \n", "### What is this?\nThis is a subset of the aurora-m training data. The data has been clustered into samples by languages and for code. \nThe license to the underlying data is based on the original data source, which is the HPLT dataset, the Pile, Refined Web, Red Pajama 1, and the Stack.\n\nWe make no claim to any copyrights under the original works, and have only compiled these into clusters for fair use machine learning research.\n\nPLEASE USE AT YOUR OWN RISK. WE MAKE NO WARRANTIES AS TO NON-INFRINGEMENT.\n\n\nPlease note that some multi-lingual wikipedia articles from red pajama 1 are in the \"en\" folder. We will need to move those out to their individual language folders.", "### Usage:\n\nWe will write a more full fledged data card, but the user can use the files under each language as a cluster of data for training in context or otherwise." ]

[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:pt", "region:us" ]

[]

[ "pt" ]

[]

[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Portuguese #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

[ "arxiv:2310.02743", "region:us" ]

[ "2310.02743" ]

[]

[]

[ "TAGS\n#arxiv-2310.02743 #region-us \n" ]

[ "arxiv:2310.02743", "region:us" ]

[ "2310.02743" ]

[]

[]

[ "TAGS\n#arxiv-2310.02743 #region-us \n" ]

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

[]

[]

[ "# Cantonese School Math 0.25M\n\nThis dataset is Cantonese translation of the Simplified Chinese dataset BelleGroup/school_math_0.25M, please check the original dataset for more information.\n\nThis dataset is translated by indiejoseph/bart-translation-zh-yue and has not undergone any manual verification. The content may be inaccurate or misleading. please keep this in mind when using this dataset.", "## Sample", "## Licensing Information\n\nThis dataset is provided under the same license as the original dataset: gpl-3.0", "## Limitation and Usage Limits\n\nPlease check the original dataset for more information." ]

[ "TAGS\n#license-gpl-3.0 #region-us \n", "# Cantonese School Math 0.25M\n\nThis dataset is Cantonese translation of the Simplified Chinese dataset BelleGroup/school_math_0.25M, please check the original dataset for more information.\n\nThis dataset is translated by indiejoseph/bart-translation-zh-yue and has not undergone any manual verification. The content may be inaccurate or misleading. please keep this in mind when using this dataset.", "## Sample", "## Licensing Information\n\nThis dataset is provided under the same license as the original dataset: gpl-3.0", "## Limitation and Usage Limits\n\nPlease check the original dataset for more information." ]

[ "language:pl", "wikipedia", "region:us" ]

[]

[ "pl" ]

[]

[ "TAGS\n#language-Polish #wikipedia #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"iv4-msg\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"iv4-msg\"\n\nMore Information needed" ]

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

[]

[ "en" ]

[ "## How to use\n\nSee notebook Wikipedia Q&A Retrieval-Semantic Search", "## Installing the package", "## The converting process" ]

[ "TAGS\n#language-English #license-mit #region-us \n", "## How to use\n\nSee notebook Wikipedia Q&A Retrieval-Semantic Search", "## Installing the package", "## The converting process" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"VNTL-v3.1-1k-q\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"VNTL-v3.1-1k-q\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"safety-utcustom-TRAIN-30\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"safety-utcustom-TRAIN-30\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[]

[]

[ "# The Serbian Parliamentary Spoken Dataset ParlaSpeech-RS 1.0\n\nURL\n\nThe ParlaSpeech-RS dataset is built from the transcripts of parliamentary proceedings available in the Serbian part of the ParlaMint corpus, and the parliamentary recordings available from the Serbian Parliament's YouTube channel.\n\nThe corpus consists of audio segments that correspond to specific sentences in the transcripts. The transcript contains word-level alignments to the recordings, each instance consisting of character and millisecond start and end offsets, allowing for simple further segmentation of long sentences into shorter segments for ASR and other memory-sensitive applications. Sequences longer than 30 seconds have already been removed from this dataset, which should allow for a simple usage on most modern GPUs.\n\nEach segment has an identifier reference to the ParlaMint 4.0 corpus (URL via the utterance ID and character offsets.\n\nWhile in the original dataset all the speaker information from the ParlaMint corpus is available via the 'speaker_info' attribute, in the HuggingFace version only a subset of metadata is available, namely: the date, the name of the speaker, their gender, year of birth, party affiliation at that point in time, status of the party at that point in time (coalition or opposition), and party orientation (left, right, centre etc.).\n\nDifferent to the original dataset, this version has also a 'text_normalised' attribute, which contains the text with parliamentary comments ('[[Applause]]' and similar) removed. Also, different to the other ParlaSpeech corpora on HuggingFace, this dataset has two additional text columns, 'text_cyrillic' and 'text_cyrillic_normalised', with Cyrillic transliteration of the corresponding columns, for simpler downstream usage, given that Serbian is a digraphic language.\n\nIf you use the dataset, please cite the following paper:" ]

[ "TAGS\n#region-us \n", "# The Serbian Parliamentary Spoken Dataset ParlaSpeech-RS 1.0\n\nURL\n\nThe ParlaSpeech-RS dataset is built from the transcripts of parliamentary proceedings available in the Serbian part of the ParlaMint corpus, and the parliamentary recordings available from the Serbian Parliament's YouTube channel.\n\nThe corpus consists of audio segments that correspond to specific sentences in the transcripts. The transcript contains word-level alignments to the recordings, each instance consisting of character and millisecond start and end offsets, allowing for simple further segmentation of long sentences into shorter segments for ASR and other memory-sensitive applications. Sequences longer than 30 seconds have already been removed from this dataset, which should allow for a simple usage on most modern GPUs.\n\nEach segment has an identifier reference to the ParlaMint 4.0 corpus (URL via the utterance ID and character offsets.\n\nWhile in the original dataset all the speaker information from the ParlaMint corpus is available via the 'speaker_info' attribute, in the HuggingFace version only a subset of metadata is available, namely: the date, the name of the speaker, their gender, year of birth, party affiliation at that point in time, status of the party at that point in time (coalition or opposition), and party orientation (left, right, centre etc.).\n\nDifferent to the original dataset, this version has also a 'text_normalised' attribute, which contains the text with parliamentary comments ('[[Applause]]' and similar) removed. Also, different to the other ParlaSpeech corpora on HuggingFace, this dataset has two additional text columns, 'text_cyrillic' and 'text_cyrillic_normalised', with Cyrillic transliteration of the corresponding columns, for simpler downstream usage, given that Serbian is a digraphic language.\n\nIf you use the dataset, please cite the following paper:" ]

[ "size_categories:n<1K", "language:pt", "license:mit", "biology", "climate", "region:us" ]

[]

[ "pt" ]

[]

[ "TAGS\n#size_categories-n<1K #language-Portuguese #license-mit #biology #climate #region-us \n" ]

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

[]

[ "en" ]

[ "# Arxiv Math\n\nThis is a cleansed version of ArtifactAI/arxiv-math-instruct-50k", "## Usage" ]

[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "# Arxiv Math\n\nThis is a cleansed version of ArtifactAI/arxiv-math-instruct-50k", "## Usage" ]

[ "task_categories:token-classification", "size_categories:n<1K", "language:en", "license:mit", "workout", "motivation", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThis is a WIP synthetic dataset generated by AI for the Workout Domain.\n\n\n* Entity Types: 'workout', 'duration', 'frequency', 'number'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.", "### Data Splits" ]

[ "TAGS\n#task_categories-token-classification #size_categories-n<1K #language-English #license-mit #workout #motivation #region-us \n", "### Dataset Summary\n\n\nThis is a WIP synthetic dataset generated by AI for the Workout Domain.\n\n\n* Entity Types: 'workout', 'duration', 'frequency', 'number'\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of 'train' looks as follows.", "### Label ID\n\n\nThe label2id dictionary can be found at here.", "### Data Splits" ]

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

[]

[ "en" ]

[ "## The converting process below." ]

[ "TAGS\n#language-English #license-apache-2.0 #region-us \n", "## The converting process below." ]

[ "size_categories:n<1K", "rlfh", "argilla", "human-feedback", "region:us" ]

[]

[]

[ "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ prompt is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ quality is of type 'label\\_selection' with the following allowed values ['0', '1', '2', '3', '4'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) quality-suggestion is of type 'label\\_selection' with the following allowed values ['0', '1', '2', '3', '4'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nTask\n====\n\n\nWe are collaboratively creating a database of prompts in English. Our aim is to identify effective prompts and understand the interaction between AI-generated and human-generated prompts. The focus is on functionality and precision.\n\n\nGuidelines\n----------\n\n\nYou need to assign a rating to each prompt thinking about the complexity for an assistant and if the intent is clear. A very good prompt is one that is challenging but also very clear in the intent of the user.\n\n\nYou can use keyboard shortcuts (the numbers) to quickly rate the examples.\n\n\nIf you find some pattern, you can also use the search box and filters as well as the bulk labelling mode, please use this with care and only when you find a clear pattern (e.g., prompts that are completely incorrect and share a common issue).\n\n\nIf you are unsure about your answer, you can click on the tag and then “Save as a draft” to save if for later. In the case that you feel unequipped of rating a specific prompt, you can use the “Discard” button.\n\n\nRatings\n-------", "### 1. Very Bad:\n\n\nThe prompt doesn't communicate its purpose, is non-sensical or is in a language other than English.\n\n\nThe prompt assumes the usage of tools or capabilities that don’t apply to this model, like generating an image or scraping a website.\n\n\n*Examples:*\n\n\n\n> \n> \"Do the thing.\"\n> “Hello!”\n> \"asdajflajfada”\n> “Quiero que redactes una entrada de blog.”\n> \"Extract data from a website.”\n> “Tell me how you feel when someone insults you.”\n> \n> \n>", "### 2. Bad:\n\n\nSuggests a goal but lacks clarity and coherence.\n\n\n*Examples:*\n\n\n\n> \n> \"Find me stuff about that thing, you know?\"\n> “Write something.”\n> \"Tell me about this thing.\"\n> \"Can you help with this?\"\n> \"I need to know more.\"\n> \n> \n>", "### 3. Ok:\n\n\nThe intent is understandable, but it's missing information to complete the task.\n\n\n*Examples:*\n\n\n\n> \n> \"I need information on something important.\"\n> “Write a blogpost.”\n> \n> \n>", "### 4. Good:\n\n\nPresents a clear goal and necessary information, effectively directing the AI, but the prompt could be more specific.\n\n\n*Examples:*\n\n\n\n> \n> \"Provide a summary of renewable energy sources.\"\n> “Tell me about Sean Connery.”\n> \"Explain global warming.\"\n> \n> \n>", "### 5. Very Good:\n\n\nComprehensive and explicit, leaving no room for ambiguity. Perfectly guides the AI and includes details.\n\n\n*Examples:*\n\n\n\n> \n> \"Compare the efficiency and environmental impact of solar and wind energy, including recent advancements and case studies from 2023.\"\n> “Make a list of 5 plant-based recipes that I can try that don’t have red peppers as an ingredient.”\n> \n> \n>", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n", "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ prompt is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ quality is of type 'label\\_selection' with the following allowed values ['0', '1', '2', '3', '4'].\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) quality-suggestion is of type 'label\\_selection' with the following allowed values ['0', '1', '2', '3', '4'].\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines\n\n\nTask\n====\n\n\nWe are collaboratively creating a database of prompts in English. Our aim is to identify effective prompts and understand the interaction between AI-generated and human-generated prompts. The focus is on functionality and precision.\n\n\nGuidelines\n----------\n\n\nYou need to assign a rating to each prompt thinking about the complexity for an assistant and if the intent is clear. A very good prompt is one that is challenging but also very clear in the intent of the user.\n\n\nYou can use keyboard shortcuts (the numbers) to quickly rate the examples.\n\n\nIf you find some pattern, you can also use the search box and filters as well as the bulk labelling mode, please use this with care and only when you find a clear pattern (e.g., prompts that are completely incorrect and share a common issue).\n\n\nIf you are unsure about your answer, you can click on the tag and then “Save as a draft” to save if for later. In the case that you feel unequipped of rating a specific prompt, you can use the “Discard” button.\n\n\nRatings\n-------", "### 1. Very Bad:\n\n\nThe prompt doesn't communicate its purpose, is non-sensical or is in a language other than English.\n\n\nThe prompt assumes the usage of tools or capabilities that don’t apply to this model, like generating an image or scraping a website.\n\n\n*Examples:*\n\n\n\n> \n> \"Do the thing.\"\n> “Hello!”\n> \"asdajflajfada”\n> “Quiero que redactes una entrada de blog.”\n> \"Extract data from a website.”\n> “Tell me how you feel when someone insults you.”\n> \n> \n>", "### 2. Bad:\n\n\nSuggests a goal but lacks clarity and coherence.\n\n\n*Examples:*\n\n\n\n> \n> \"Find me stuff about that thing, you know?\"\n> “Write something.”\n> \"Tell me about this thing.\"\n> \"Can you help with this?\"\n> \"I need to know more.\"\n> \n> \n>", "### 3. Ok:\n\n\nThe intent is understandable, but it's missing information to complete the task.\n\n\n*Examples:*\n\n\n\n> \n> \"I need information on something important.\"\n> “Write a blogpost.”\n> \n> \n>", "### 4. Good:\n\n\nPresents a clear goal and necessary information, effectively directing the AI, but the prompt could be more specific.\n\n\n*Examples:*\n\n\n\n> \n> \"Provide a summary of renewable energy sources.\"\n> “Tell me about Sean Connery.”\n> \"Explain global warming.\"\n> \n> \n>", "### 5. Very Good:\n\n\nComprehensive and explicit, leaving no room for ambiguity. Perfectly guides the AI and includes details.\n\n\n*Examples:*\n\n\n\n> \n> \"Compare the efficiency and environmental impact of solar and wind energy, including recent advancements and case studies from 2023.\"\n> “Make a list of 5 plant-based recipes that I can try that don’t have red peppers as an ingredient.”\n> \n> \n>", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "biomed NER", "PubMed NER", "biology", "medicine", "NER", "entity extraction", "region:us" ]

[]

[ "en" ]

[ "### BioMed_general_NER\n\nThis dataset consists of manually annotated biomedical abstracts from PubMed, drug descriptions from FDA and abstracts from patents. \n\nIt was extracted 24 different entity types, including those specific to medicine and biology and general such as location and organization as well. \n\nThis is one of the biggest datasets of such kind, which consists of 4840 annotated abstracts.", "### Classes\nHere's a description for each of the labels:\n\n1. CHEMICALS - Represents substances with distinct molecular composition, often involved in various biological or industrial processes.\n\n2. CLINICAL DRUG - Refers to pharmaceutical substances developed for medical use, aimed at preventing, treating, or managing diseases.\n\n3. BODY SUBSTANCE - Denotes materials or substances within the human body, including fluids, tissues, and other biological components.\n\n4. ANATOMICAL STRUCTURE - Describes specific parts or structures within an organism's body, often related to anatomy and physiology.\n\n5. CELLS AND THEIR COMPONENTS - Encompasses the basic structural and functional units of living organisms, along with their constituent elements.\n\n6. GENE AND GENE PRODUCTS - Involves genetic information and the resultant products, such as proteins, that play a crucial role in biological processes.\n\n7. INTELLECTUAL PROPERTY - Pertains to legal rights associated with creations of the mind, including inventions, literary and artistic works, and trademarks.\n\n8. LANGUAGE - Relates to linguistic elements, including words, phrases, and language constructs, often in the context of communication or analysis.\n\n9. REGULATION OR LAW - Represents rules, guidelines, or legal frameworks established by authorities to govern behavior, practices, or procedures.\n\n10. GEOGRAPHICAL AREAS - Refers to specific regions, locations, or places on the Earth's surface, often associated with particular characteristics or significance.\n\n11. ORGANISM - Denotes a living being, typically a plant, animal, or microorganism, as a distinct biological entity.\n\n12. GROUP - Encompasses collections of individuals with shared characteristics, interests, or affiliations.\n\n13. PERSON - Represents an individual human being, often considered as a distinct entity with personal attributes.\n\n14. ORGANIZATION - Refers to structured entities, institutions, or companies formed for specific purposes or activities.\n\n15. PRODUCT - Encompasses tangible or intangible items resulting from a process, often associated with manufacturing or creation.\n\n16. LOCATION - Describes a specific place or position, whether physical or abstract, with potential relevance to various contexts.\n\n17. PHENOTYPE - Represents the observable characteristics or traits of an organism, resulting from the interaction of its genotype with the environment.\n\n18. DISORDER - Denotes abnormal conditions or disruptions in the normal functioning of a biological organism, often associated with diseases or medical conditions.\n\n19. SIGNALING MOLECULES - Involves molecules that transmit signals within and between cells, playing a crucial role in various physiological processes.\n\n20. EVENT - Describes occurrences or happenings at a specific time and place, often with significance or impact.\n\n21. MEDICAL PROCEDURE - Involves specific actions or interventions conducted for medical purposes, such as surgeries, diagnostic tests, or therapeutic treatments.\n\n22. ACTIVITY - Encompasses actions, behaviors, or processes undertaken by individuals, groups, or entities.\n\n23. FUNCTION - Describes the purpose or role of a biological or mechanical entity, focusing on its intended or inherent activities.\n\n24. MONEY - Represents currency or financial assets used as a medium of exchange, often in the context of economic transactions.", "### Datasources\n* PubMed - biomedical articles abstracts;\n* FDA - drugs descriptions;\n* Patents - patents abstracts;" ]

[ "TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-English #license-apache-2.0 #biomed NER #PubMed NER #biology #medicine #NER #entity extraction #region-us \n", "### BioMed_general_NER\n\nThis dataset consists of manually annotated biomedical abstracts from PubMed, drug descriptions from FDA and abstracts from patents. \n\nIt was extracted 24 different entity types, including those specific to medicine and biology and general such as location and organization as well. \n\nThis is one of the biggest datasets of such kind, which consists of 4840 annotated abstracts.", "### Classes\nHere's a description for each of the labels:\n\n1. CHEMICALS - Represents substances with distinct molecular composition, often involved in various biological or industrial processes.\n\n2. CLINICAL DRUG - Refers to pharmaceutical substances developed for medical use, aimed at preventing, treating, or managing diseases.\n\n3. BODY SUBSTANCE - Denotes materials or substances within the human body, including fluids, tissues, and other biological components.\n\n4. ANATOMICAL STRUCTURE - Describes specific parts or structures within an organism's body, often related to anatomy and physiology.\n\n5. CELLS AND THEIR COMPONENTS - Encompasses the basic structural and functional units of living organisms, along with their constituent elements.\n\n6. GENE AND GENE PRODUCTS - Involves genetic information and the resultant products, such as proteins, that play a crucial role in biological processes.\n\n7. INTELLECTUAL PROPERTY - Pertains to legal rights associated with creations of the mind, including inventions, literary and artistic works, and trademarks.\n\n8. LANGUAGE - Relates to linguistic elements, including words, phrases, and language constructs, often in the context of communication or analysis.\n\n9. REGULATION OR LAW - Represents rules, guidelines, or legal frameworks established by authorities to govern behavior, practices, or procedures.\n\n10. GEOGRAPHICAL AREAS - Refers to specific regions, locations, or places on the Earth's surface, often associated with particular characteristics or significance.\n\n11. ORGANISM - Denotes a living being, typically a plant, animal, or microorganism, as a distinct biological entity.\n\n12. GROUP - Encompasses collections of individuals with shared characteristics, interests, or affiliations.\n\n13. PERSON - Represents an individual human being, often considered as a distinct entity with personal attributes.\n\n14. ORGANIZATION - Refers to structured entities, institutions, or companies formed for specific purposes or activities.\n\n15. PRODUCT - Encompasses tangible or intangible items resulting from a process, often associated with manufacturing or creation.\n\n16. LOCATION - Describes a specific place or position, whether physical or abstract, with potential relevance to various contexts.\n\n17. PHENOTYPE - Represents the observable characteristics or traits of an organism, resulting from the interaction of its genotype with the environment.\n\n18. DISORDER - Denotes abnormal conditions or disruptions in the normal functioning of a biological organism, often associated with diseases or medical conditions.\n\n19. SIGNALING MOLECULES - Involves molecules that transmit signals within and between cells, playing a crucial role in various physiological processes.\n\n20. EVENT - Describes occurrences or happenings at a specific time and place, often with significance or impact.\n\n21. MEDICAL PROCEDURE - Involves specific actions or interventions conducted for medical purposes, such as surgeries, diagnostic tests, or therapeutic treatments.\n\n22. ACTIVITY - Encompasses actions, behaviors, or processes undertaken by individuals, groups, or entities.\n\n23. FUNCTION - Describes the purpose or role of a biological or mechanical entity, focusing on its intended or inherent activities.\n\n24. MONEY - Represents currency or financial assets used as a medium of exchange, often in the context of economic transactions.", "### Datasources\n* PubMed - biomedical articles abstracts;\n* FDA - drugs descriptions;\n* Patents - patents abstracts;" ]

[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "math", "region:us" ]

[]

[ "en" ]

[ "# Meta Math Filtered\n\nThis is a combined and filtered (removed all the redundant rows) version of meta-math/MetaMathQA and meta-math/MetaMathQA-40K", "## Usage" ]

[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #math #region-us \n", "# Meta Math Filtered\n\nThis is a combined and filtered (removed all the redundant rows) version of meta-math/MetaMathQA and meta-math/MetaMathQA-40K", "## Usage" ]

[ "region:us" ]

[]

[]

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

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

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

[]

[]

[ "## Description\n\nA channel generating latent splatting scenes (of single objects for now)", "## Model\n\nSVD", "## Gaussian Model\n\nLGM", "## Voice\n\nMuted", "# Tags\n\n- Latent Splatting", "# Style\n\nclose-up, single, pro photoshoot, neutral background, empty room", "## Prompt\n\nA channel generating animated videos of various people, animal and items (eg. robots) in an empty, bare room.\nThose videos will usually just perform simple actions, like dancing, talking etc," ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "## Description\n\nA channel generating latent splatting scenes (of single objects for now)", "## Model\n\nSVD", "## Gaussian Model\n\nLGM", "## Voice\n\nMuted", "# Tags\n\n- Latent Splatting", "# Style\n\nclose-up, single, pro photoshoot, neutral background, empty room", "## Prompt\n\nA channel generating animated videos of various people, animal and items (eg. robots) in an empty, bare room.\nThose videos will usually just perform simple actions, like dancing, talking etc," ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of paulml/NeuralOmniBeagleMBX-v3-7B\n\n\n\nDataset automatically created during the evaluation run of model paulml/NeuralOmniBeagleMBX-v3-7B 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 2024-02-09T12:11:50.137118(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 paulml/NeuralOmniBeagleMBX-v3-7B\n\n\n\nDataset automatically created during the evaluation run of model paulml/NeuralOmniBeagleMBX-v3-7B 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 2024-02-09T12:11:50.137118(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" ]

[ "arxiv:2009.03300", "region:us" ]

[ "2009.03300" ]

[]

[]

[ "TAGS\n#arxiv-2009.03300 #region-us \n" ]

[ "task_categories:text-to-image", "task_categories:image-to-image", "size_categories:10K<n<100K", "license:mit", "midjourney-v6", "midjourney", "midjourney-images", "images", "croissant", "region:us" ]

[]

[]

[ "# Midjourney Images Dataset\n\nThis is datase with images made by Midjourney V5/V6.", "## Dataset parameters\n1. Count of images: ~10.000\n2. Zip file with dataset: True\n3. Captions with images: False", "## License\n\nLicense for this dataset: MIT", "## Use in *datasets*\n\n1. \n2.", "#### *Enjoy with this dataset!*" ]

[ "TAGS\n#task_categories-text-to-image #task_categories-image-to-image #size_categories-10K<n<100K #license-mit #midjourney-v6 #midjourney #midjourney-images #images #croissant #region-us \n", "# Midjourney Images Dataset\n\nThis is datase with images made by Midjourney V5/V6.", "## Dataset parameters\n1. Count of images: ~10.000\n2. Zip file with dataset: True\n3. Captions with images: False", "## License\n\nLicense for this dataset: MIT", "## Use in *datasets*\n\n1. \n2.", "#### *Enjoy with this dataset!*" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of jondurbin/bagel-7b-v0.4\n\n\n\nDataset automatically created during the evaluation run of model jondurbin/bagel-7b-v0.4 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 2024-02-09T12:18:51.743149(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 jondurbin/bagel-7b-v0.4\n\n\n\nDataset automatically created during the evaluation run of model jondurbin/bagel-7b-v0.4 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 2024-02-09T12:18:51.743149(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", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "size_categories:100K<n<1M", "source_datasets:Kaggle 1 - jannesklaas/disasters-on-social-media", "source_datasets:Kaggle 2 - vstepanenko/disaster-tweets", "source_datasets:Kaggle 3 - sidharth178/disaster-response-messages", "source_datasets:Zahra et al. - doi: 10.1016/j.ipm.2019.102107", "source_datasets:CrisisMMD - arxiv: 1805.00713", "source_datasets:Alam et al. - arxiv: 1805.05151", "source_datasets:CrisisLexT26 - doi: 10.1145/2675133.2675242", "source_datasets:Imran et al. - aclanthology: L16-1259", "source_datasets:CrisisLexT6 - doi: 10.1609/icwsm.v8i1.14538", "source_datasets:HumAID - doi: 10.1609/icwsm.v15i1.18116", "source_datasets:CrisisBench - doi: 10.1609/icwsm.v15i1.18115", "language:en", "natural disasters", "tweets", "classification", "catastrophic events", "arxiv:1805.00713", "arxiv:1805.05151", "region:us" ]

[ "1805.00713", "1805.05151" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #size_categories-100K<n<1M #source_datasets-Kaggle 1 - jannesklaas/disasters-on-social-media #source_datasets-Kaggle 2 - vstepanenko/disaster-tweets #source_datasets-Kaggle 3 - sidharth178/disaster-response-messages #source_datasets-Zahra et al. - doi- 10.1016/j.ipm.2019.102107 #source_datasets-CrisisMMD - arxiv- 1805.00713 #source_datasets-Alam et al. - arxiv- 1805.05151 #source_datasets-CrisisLexT26 - doi- 10.1145/2675133.2675242 #source_datasets-Imran et al. - aclanthology- L16-1259 #source_datasets-CrisisLexT6 - doi- 10.1609/icwsm.v8i1.14538 #source_datasets-HumAID - doi- 10.1609/icwsm.v15i1.18116 #source_datasets-CrisisBench - doi- 10.1609/icwsm.v15i1.18115 #language-English #natural disasters #tweets #classification #catastrophic events #arxiv-1805.00713 #arxiv-1805.05151 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of jondurbin/bagel-dpo-7b-v0.4\n\n\n\nDataset automatically created during the evaluation run of model jondurbin/bagel-dpo-7b-v0.4 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 2024-02-09T12:26:08.289563(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 jondurbin/bagel-dpo-7b-v0.4\n\n\n\nDataset automatically created during the evaluation run of model jondurbin/bagel-dpo-7b-v0.4 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 2024-02-09T12:26:08.289563(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 ericpolewski/Palworld-SME-13b\n\n\n\nDataset automatically created during the evaluation run of model ericpolewski/Palworld-SME-13b 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 2024-02-09T12:30:34.834503(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 ericpolewski/Palworld-SME-13b\n\n\n\nDataset automatically created during the evaluation run of model ericpolewski/Palworld-SME-13b 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 2024-02-09T12:30:34.834503(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 manishiitg/open-aditi-hi-v2\n\n\n\nDataset automatically created during the evaluation run of model manishiitg/open-aditi-hi-v2 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 2024-02-09T12:31:28.234042(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 manishiitg/open-aditi-hi-v2\n\n\n\nDataset automatically created during the evaluation run of model manishiitg/open-aditi-hi-v2 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 2024-02-09T12:31:28.234042(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 Josephgflowers/3BigReasonCinder\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/3BigReasonCinder 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 2024-02-09T12:31:38.090504(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 Josephgflowers/3BigReasonCinder\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/3BigReasonCinder 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 2024-02-09T12:31:38.090504(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-alpha-dpo-v0.2\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/MT7Bi-alpha-dpo-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 2024-02-09T12:50:13.790724(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-alpha-dpo-v0.2\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/MT7Bi-alpha-dpo-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 2024-02-09T12:50:13.790724(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-generation", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Beaver Tails\n\nThis is a cleansed version of PKU-Alignment/BeaverTails\n\nIt has two version based on the sensitivity", "## Filtered", "### Usage", "## Unfiltered", "### Usage" ]

[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "# Beaver Tails\n\nThis is a cleansed version of PKU-Alignment/BeaverTails\n\nIt has two version based on the sensitivity", "## Filtered", "### Usage", "## Unfiltered", "### Usage" ]

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

[]

[ "en" ]

[ "# Beaver Tails\n\nThis is a cleansed version of PKU-Alignment/BeaverTails\n\nIt has two version based on the sensitivity", "## Filtered", "### Usage", "## Unfiltered", "### Usage" ]

[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "# Beaver Tails\n\nThis is a cleansed version of PKU-Alignment/BeaverTails\n\nIt has two version based on the sensitivity", "## Filtered", "### Usage", "## Unfiltered", "### Usage" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Novocoders/Lotus-7B\n\n\n\nDataset automatically created during the evaluation run of model Novocoders/Lotus-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 2024-02-09T13:03:53.418083(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 Novocoders/Lotus-7B\n\n\n\nDataset automatically created during the evaluation run of model Novocoders/Lotus-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 2024-02-09T13:03:53.418083(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 BarraHome/rezephyr-dpo\n\n\n\nDataset automatically created during the evaluation run of model BarraHome/rezephyr-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 2024-02-09T13:18:07.445187(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 BarraHome/rezephyr-dpo\n\n\n\nDataset automatically created during the evaluation run of model BarraHome/rezephyr-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 2024-02-09T13:18:07.445187(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 kenhktsui/nano-phi-115M-v0.1\n\n\n\nDataset automatically created during the evaluation run of model kenhktsui/nano-phi-115M-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 2024-02-09T13:21:05.777292(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 kenhktsui/nano-phi-115M-v0.1\n\n\n\nDataset automatically created during the evaluation run of model kenhktsui/nano-phi-115M-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 2024-02-09T13:21:05.777292(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:feature-extraction", "size_categories:1M<n<10M", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-feature-extraction #size_categories-1M<n<10M #language-English #license-mit #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of splm/zephyr-7b-sft-full-spin-peft-iter0\n\n\n\nDataset automatically created during the evaluation run of model splm/zephyr-7b-sft-full-spin-peft-iter0 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 2024-02-09T13:37:48.031125(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 splm/zephyr-7b-sft-full-spin-peft-iter0\n\n\n\nDataset automatically created during the evaluation run of model splm/zephyr-7b-sft-full-spin-peft-iter0 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 2024-02-09T13:37:48.031125(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 splm/zephyr-7b-sft-full-spin-peft-iter1\n\n\n\nDataset automatically created during the evaluation run of model splm/zephyr-7b-sft-full-spin-peft-iter1 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 2024-02-09T13:43:40.129900(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 splm/zephyr-7b-sft-full-spin-peft-iter1\n\n\n\nDataset automatically created during the evaluation run of model splm/zephyr-7b-sft-full-spin-peft-iter1 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 2024-02-09T13:43:40.129900(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 splm/zephyr-7b-sft-full-spin-peft-iter2\n\n\n\nDataset automatically created during the evaluation run of model splm/zephyr-7b-sft-full-spin-peft-iter2 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 2024-02-09T13:50:16.813938(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 splm/zephyr-7b-sft-full-spin-peft-iter2\n\n\n\nDataset automatically created during the evaluation run of model splm/zephyr-7b-sft-full-spin-peft-iter2 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 2024-02-09T13:50:16.813938(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:summarization", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:10K<n<100K", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "## DialogSumm\nDialogSumm is a mixture of the following dialog datasets:\n- dialogsum\n- samsum\n- MocktaiLEngineer/qmsum-processed\n- npc-engine/light-batch-summarize-dialogue", "## Usage\n\n Next: DialogSumm + cnn_dailymail + mediasum + EdinburghNLP/xsum" ]

[ "TAGS\n#task_categories-summarization #task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "## DialogSumm\nDialogSumm is a mixture of the following dialog datasets:\n- dialogsum\n- samsum\n- MocktaiLEngineer/qmsum-processed\n- npc-engine/light-batch-summarize-dialogue", "## Usage\n\n Next: DialogSumm + cnn_dailymail + mediasum + EdinburghNLP/xsum" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Weyaxi/Einstein-v3-7B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Einstein-v3-7B 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 2024-02-09T14:20:50.060350(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 Weyaxi/Einstein-v3-7B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Einstein-v3-7B 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 2024-02-09T14:20:50.060350(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" ]