Datasets:

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ftopal

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huggingface-datasets

like 0

[ "vdf", "vector-io", "vector-dataset", "vector-embeddings", "region:us" ]

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[ "TAGS\n#vdf #vector-io #vector-dataset #vector-embeddings #region-us \n" ]

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"parallel-pt-nl-pl\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"parallel-pt-nl-pl\"\n\nMore Information needed" ]

[ "task_categories:text-generation", "size_categories:10M<n<100M", "language:pt", "license:cc-by-4.0", "legal", "arxiv:2306.02069", "arxiv:2305.06721", "arxiv:2010.11934", "arxiv:2107.06499", "region:us" ]

[ "2306.02069", "2305.06721", "2010.11934", "2107.06499" ]

[ "pt" ]

[ "### Dataset Description\n\n\n* Curated by:\n* Funded by:\n* Language(s) (NLP): Brazilian Portuguese (pt-BR)\n* License: Creative Commons Attribution 4.0 International Public License", "### Dataset Sources\n\n\n* Repository: URL\n* Paper:\n\n\nDataset Structure\n-----------------\n\n\nData Collection and Processing\n------------------------------\n\n\nLegalPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).\n\n\nWe used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.\n\n\nDuplicate rate found by the Minhash-LSH algorithm for the LegalPT corpus:\n\n\n\nAcknowledgment\n--------------\n\n\nThis work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG)." ]

[ "TAGS\n#task_categories-text-generation #size_categories-10M<n<100M #language-Portuguese #license-cc-by-4.0 #legal #arxiv-2306.02069 #arxiv-2305.06721 #arxiv-2010.11934 #arxiv-2107.06499 #region-us \n", "### Dataset Description\n\n\n* Curated by:\n* Funded by:\n* Language(s) (NLP): Brazilian Portuguese (pt-BR)\n* License: Creative Commons Attribution 4.0 International Public License", "### Dataset Sources\n\n\n* Repository: URL\n* Paper:\n\n\nDataset Structure\n-----------------\n\n\nData Collection and Processing\n------------------------------\n\n\nLegalPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).\n\n\nWe used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.\n\n\nDuplicate rate found by the Minhash-LSH algorithm for the LegalPT corpus:\n\n\n\nAcknowledgment\n--------------\n\n\nThis work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG)." ]

[ "arxiv:2401.05604", "region:us" ]

[ "2401.05604" ]

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[ "TAGS\n#arxiv-2401.05604 #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"module_pairwise_dataset_neg_from_pos_pool_v3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"module_pairwise_dataset_neg_from_pos_pool_v3\"\n\nMore Information needed" ]

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

[]

[ "code" ]

[ "### Overview\n\nDPO dataset meant to enhance python coding abilities.\n\nThis dataset uses the excellent URL dataset as the \"chosen\" responses, given this dataset was already tested and validated.\n\nThe \"rejected\" values were generated with a mix of airoboros-l2-13b-3.1 and bagel-7b-v0.1.\n\nThe rejected values may actually be perfectly fine, but the assumption here is that the values are generally a lower quality than the chosen counterpart. Items with duplicate code blocks were removed.", "### Contribute\n\nIf you're interested in new functionality/datasets, take a look at bagel repo and airoboros and either make a PR or open an issue with details.\n\nTo help me with the fine-tuning costs, dataset generation, etc., please use one of the following:\n\n- URL\n- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "TAGS\n#language-code #license-cc-by-4.0 #region-us \n", "### Overview\n\nDPO dataset meant to enhance python coding abilities.\n\nThis dataset uses the excellent URL dataset as the \"chosen\" responses, given this dataset was already tested and validated.\n\nThe \"rejected\" values were generated with a mix of airoboros-l2-13b-3.1 and bagel-7b-v0.1.\n\nThe rejected values may actually be perfectly fine, but the assumption here is that the values are generally a lower quality than the chosen counterpart. Items with duplicate code blocks were removed.", "### Contribute\n\nIf you're interested in new functionality/datasets, take a look at bagel repo and airoboros and either make a PR or open an issue with details.\n\nTo help me with the fine-tuning costs, dataset generation, etc., please use one of the following:\n\n- URL\n- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "region:us" ]

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[ "# Dataset Card for \"cai-conversation-dev1704834920\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cai-conversation-dev1704834920\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"parallel-9\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"parallel-9\"\n\nMore Information needed" ]

[ "task_categories:translation", "size_categories:1K<n<10K", "language:en", "language:el", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "en", "el" ]

[ "# FLORES-200 EN-EL with prompts for translation by LLMs\nBased on FLORES-200 dataset.\n\nPublication:\n@article{nllb2022,\n author = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang},\n title = {No Language Left Behind: Scaling Human-Centered Machine Translation},\n year = {2022}\n}\n\nNumber of examples : 1012", "## FLORES-200 for EN to EL with 0-shot prompts\nContains 2 prompt variants:\n- EN:\\n\\[English Sentence\\]\\nEL:\n- English:\\n\\[English Sentence\\]\\nΕλληνικά:", "## FLORES-200 for EL to EN with 0-shot prompts\nContains 2 prompt variants:\n- EL:\\n\\[Greek Sentence\\]\\nEL:\n- Ελληνικά:\\n\\[Greek Sentence\\]\\nEnglish:", "## How to load datasets", "## How to generate translation results with different configurations", "## How to push updated datasets to hub" ]

[ "TAGS\n#task_categories-translation #size_categories-1K<n<10K #language-English #language-Modern Greek (1453-) #license-cc-by-sa-4.0 #region-us \n", "# FLORES-200 EN-EL with prompts for translation by LLMs\nBased on FLORES-200 dataset.\n\nPublication:\n@article{nllb2022,\n author = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang},\n title = {No Language Left Behind: Scaling Human-Centered Machine Translation},\n year = {2022}\n}\n\nNumber of examples : 1012", "## FLORES-200 for EN to EL with 0-shot prompts\nContains 2 prompt variants:\n- EN:\\n\\[English Sentence\\]\\nEL:\n- English:\\n\\[English Sentence\\]\\nΕλληνικά:", "## FLORES-200 for EL to EN with 0-shot prompts\nContains 2 prompt variants:\n- EL:\\n\\[Greek Sentence\\]\\nEL:\n- Ελληνικά:\\n\\[Greek Sentence\\]\\nEnglish:", "## How to load datasets", "## How to generate translation results with different configurations", "## How to push updated datasets to hub" ]

[ "region:us" ]

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[ "# Dataset Card for \"cai-conversation-dev1704836562\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cai-conversation-dev1704836562\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"parallel-all\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"mmlu-abstract_algebra-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-abstract_algebra-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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

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

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

[]

[ "hi" ]

[ "# Dataset Card for Indian Sweets" ]

[ "TAGS\n#language-Hindi #license-apache-2.0 #region-us \n", "# Dataset Card for Indian Sweets" ]

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

[]

[ "en" ]

[ "# Open-Orca-FLAN-50K-Synthetic-5-Models Dataset Card", "### Dataset Summary\n\nThe Open-Orca-FLAN-50K-Synthetic-5-Models dataset is a large-scale, synthetic dataset based on 50K filtered examples from Open-Orca/Flan . It contains 50,000 examples, each consisting of a prompt, a completion, and the corresponding task. Additionally, it includes model-generated responses from five different models: ignos-Mistral-T5-7B-v1, cognAI-lil-c3po, viethq188-Rabbit-7B-DPO-Chat, cookinai-DonutLM-v1, and v1olet-v1olet-merged-dpo-7B. This dataset is particularly useful for research in natural language understanding, language model comparison, and AI-generated text analysis.", "### Supported Tasks\n\n- Natural Language Understanding: The dataset can be used to train models to understand and generate human-like text.\n- Model Comparison: Researchers can compare the performance of different language models using this dataset.\n- CoE Router Reward Modeling: The responses from the 5 models can be used to train the routing mechanism given a query\n- Text Generation: It's suitable for training and evaluating models on text generation tasks.", "### Languages\n\nThe dataset is primarily in English.", "## Dataset Structure", "### Data Instances\n\nA typical data instance comprises the following fields:\n- 'prompt': The input prompt (string).\n- 'completion': The expected completion of the prompt (string).\n- 'task': The specific task or category the example belongs to (string).\n- Model-generated responses from five different models, each in a separate field.", "### Data Fields\n\n- 'prompt': A string containing the input prompt.\n- 'completion': A string containing the expected response or completion to the prompt.\n- 'task': A string indicating the type of task.\n- 'ignos-Mistral-T5-7B-v1': Model-generated response from ignos-Mistral-T5-7B-v1.\n- 'cognAI-lil-c3po': Model-generated response from cognAI-lil-c3po.\n- 'viethq188-Rabbit-7B-DPO-Chat': Model-generated response from viethq188-Rabbit-7B-DPO-Chat.\n- 'cookinai-DonutLM-v1': Model-generated response from cookinai-DonutLM-v1.\n- 'v1olet-v1olet-merged-dpo-7B': Model-generated response from v1olet-v1olet-merged-dpo-7B.", "### Data Splits\n\nThe dataset is not split into traditional training, validation, and test sets. It contains 50,000 examples in a single batch, designed for evaluation and comparison purposes.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was curated to provide a diverse and extensive set of prompts and completions, along with responses from various state-of-the-art language models, for comprehensive evaluation and comparison in language understanding and generation tasks.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was synthetically generated, ensuring a wide variety of prompts, tasks, and model-generated responses.", "#### Who are the source language producers?\n\nThe prompts and completions are from a known dataset, and the responses are produced by the specified language models.", "### Annotations\n\nThe dataset does not include manual annotations. The responses are generated by the models listed.", "### Personal and Sensitive Information\n\nSince the dataset is synthetic, it does not contain any personal or sensitive information.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset contributes to the advancement of natural language processing by providing a rich source for model comparison and analysis.", "### Discussion of Biases\n\nAs the dataset is generated by AI models, it may inherit biases present in those models. Users should be aware of this when analyzing the data.", "### Other Known Limitations\n\nThe effectiveness of the dataset is contingent on the quality and diversity of the synthetic data and the responses generated by the models.", "### Licensing Information\n\nPlease refer to the repository for licensing information." ]

[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "# Open-Orca-FLAN-50K-Synthetic-5-Models Dataset Card", "### Dataset Summary\n\nThe Open-Orca-FLAN-50K-Synthetic-5-Models dataset is a large-scale, synthetic dataset based on 50K filtered examples from Open-Orca/Flan . It contains 50,000 examples, each consisting of a prompt, a completion, and the corresponding task. Additionally, it includes model-generated responses from five different models: ignos-Mistral-T5-7B-v1, cognAI-lil-c3po, viethq188-Rabbit-7B-DPO-Chat, cookinai-DonutLM-v1, and v1olet-v1olet-merged-dpo-7B. This dataset is particularly useful for research in natural language understanding, language model comparison, and AI-generated text analysis.", "### Supported Tasks\n\n- Natural Language Understanding: The dataset can be used to train models to understand and generate human-like text.\n- Model Comparison: Researchers can compare the performance of different language models using this dataset.\n- CoE Router Reward Modeling: The responses from the 5 models can be used to train the routing mechanism given a query\n- Text Generation: It's suitable for training and evaluating models on text generation tasks.", "### Languages\n\nThe dataset is primarily in English.", "## Dataset Structure", "### Data Instances\n\nA typical data instance comprises the following fields:\n- 'prompt': The input prompt (string).\n- 'completion': The expected completion of the prompt (string).\n- 'task': The specific task or category the example belongs to (string).\n- Model-generated responses from five different models, each in a separate field.", "### Data Fields\n\n- 'prompt': A string containing the input prompt.\n- 'completion': A string containing the expected response or completion to the prompt.\n- 'task': A string indicating the type of task.\n- 'ignos-Mistral-T5-7B-v1': Model-generated response from ignos-Mistral-T5-7B-v1.\n- 'cognAI-lil-c3po': Model-generated response from cognAI-lil-c3po.\n- 'viethq188-Rabbit-7B-DPO-Chat': Model-generated response from viethq188-Rabbit-7B-DPO-Chat.\n- 'cookinai-DonutLM-v1': Model-generated response from cookinai-DonutLM-v1.\n- 'v1olet-v1olet-merged-dpo-7B': Model-generated response from v1olet-v1olet-merged-dpo-7B.", "### Data Splits\n\nThe dataset is not split into traditional training, validation, and test sets. It contains 50,000 examples in a single batch, designed for evaluation and comparison purposes.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was curated to provide a diverse and extensive set of prompts and completions, along with responses from various state-of-the-art language models, for comprehensive evaluation and comparison in language understanding and generation tasks.", "### Source Data", "#### Initial Data Collection and Normalization\n\nData was synthetically generated, ensuring a wide variety of prompts, tasks, and model-generated responses.", "#### Who are the source language producers?\n\nThe prompts and completions are from a known dataset, and the responses are produced by the specified language models.", "### Annotations\n\nThe dataset does not include manual annotations. The responses are generated by the models listed.", "### Personal and Sensitive Information\n\nSince the dataset is synthetic, it does not contain any personal or sensitive information.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThis dataset contributes to the advancement of natural language processing by providing a rich source for model comparison and analysis.", "### Discussion of Biases\n\nAs the dataset is generated by AI models, it may inherit biases present in those models. Users should be aware of this when analyzing the data.", "### Other Known Limitations\n\nThe effectiveness of the dataset is contingent on the quality and diversity of the synthetic data and the responses generated by the models.", "### Licensing Information\n\nPlease refer to the repository for licensing information." ]

[ "region:us" ]

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

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

[ "region:us" ]

[]

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[ "# tokenspace directory\n\nThis directory contains utilities for the purpose of browsing the\n\"token space\" of CLIP ViT-L/14\n\nPrimary tools are:\n\n* \"URL\": allows command-line browsing of words and their neighbours\n* \"URL\": plots graph of full values of two embeddings", "## (clipmodel,cliptextmodel)-URL\n\nLoads the generated embeddings, reads in a word, calculates \"distance\" to every\nembedding, and then shows the closest \"neighbours\".\n\nTo run this requires the files \"embeddings.safetensors\" and \"dictionary\",\nin matching format\n\nYou will need to rename or copy appropriate files for this as mentioned below.\n\nNote that SD models use cliptextmodel, NOT clipmodel", "## URL\n\nShows the difference between the same word, embedded by CLIPTextModel\nvs CLIPModel", "## URL\n\nRun the script. It will ask you for two text strings. \nOnce you enter both, it will plot the graph and display it for you\n\nNote that this tool does not require any of the other files; just that you \nhave the requisite python modules installed. (pip install -r URL)", "### embeddings.safetensors\n\nYou can either copy one of the provided files, or generate your own.\nSee URL for that. \n\nNote that you muist always use the \"dictionary\" file that matchnes your embeddings file", "### URL.safetensors\n\nDO NOT USE THIS ONE for programs that expect a matching dictionary.\nThis one is purely numeric based. \nIts intention is more for research datamining, but it does have a matching\ngraph front end, URL", "### dictionary\n\nMake sure to always use the dictionary file that matches your embeddings file.\n\nThe \"dictionary.fullword\" file is pulled from URL, which is distilled from \"full words\"\npresent in the ViT-L/14 CLIP model's provided token dictionary, called \"URL\".\nThus there are only around 30,000 words in it\n\nIf you want to use the provided \"URL\" file, you will want to use the matching\n\"URL\" file, which has over 300,000 words\n\nThis huge file comes from the linux \"wamerican-huge\" package, which delivers it under\n/usr/share/dict/american-english-huge\n\nThere also exists a \"american-insane\" package", "## URL\n\nGenerates the \"embeddings.safetensor\" file, based on the \"dictionary\" file present.\nTakes a few minutes to run, depending on size of the dictionary\n\nThe shape of the embeddings tensor, is\n [number-of-words][768]\n\nNote that yes, it is possible to directly pull a tensor from the CLIP model,\nusing keyname of text_model.embeddings.token_embedding.weight\n\nThis will NOT GIVE YOU THE RIGHT DISTANCES!\nHence why we are calculating and then storing the embedding weights actually\ngenerated by the CLIP process", "## URL\n\nThis file contains a collection of \"one word, one CLIP token id\" pairings.\nThe file was taken from URL, which is part of multiple SD models in URL\n\nThe file was optimized for what people are actually going to type as words.\nFirst all the non-(/w) entries were stripped out.\nThen all the garbage punctuation and foreign characters were stripped out.\nFinally, the actual (/w) was stripped out, for ease of use." ]

[ "TAGS\n#region-us \n", "# tokenspace directory\n\nThis directory contains utilities for the purpose of browsing the\n\"token space\" of CLIP ViT-L/14\n\nPrimary tools are:\n\n* \"URL\": allows command-line browsing of words and their neighbours\n* \"URL\": plots graph of full values of two embeddings", "## (clipmodel,cliptextmodel)-URL\n\nLoads the generated embeddings, reads in a word, calculates \"distance\" to every\nembedding, and then shows the closest \"neighbours\".\n\nTo run this requires the files \"embeddings.safetensors\" and \"dictionary\",\nin matching format\n\nYou will need to rename or copy appropriate files for this as mentioned below.\n\nNote that SD models use cliptextmodel, NOT clipmodel", "## URL\n\nShows the difference between the same word, embedded by CLIPTextModel\nvs CLIPModel", "## URL\n\nRun the script. It will ask you for two text strings. \nOnce you enter both, it will plot the graph and display it for you\n\nNote that this tool does not require any of the other files; just that you \nhave the requisite python modules installed. (pip install -r URL)", "### embeddings.safetensors\n\nYou can either copy one of the provided files, or generate your own.\nSee URL for that. \n\nNote that you muist always use the \"dictionary\" file that matchnes your embeddings file", "### URL.safetensors\n\nDO NOT USE THIS ONE for programs that expect a matching dictionary.\nThis one is purely numeric based. \nIts intention is more for research datamining, but it does have a matching\ngraph front end, URL", "### dictionary\n\nMake sure to always use the dictionary file that matches your embeddings file.\n\nThe \"dictionary.fullword\" file is pulled from URL, which is distilled from \"full words\"\npresent in the ViT-L/14 CLIP model's provided token dictionary, called \"URL\".\nThus there are only around 30,000 words in it\n\nIf you want to use the provided \"URL\" file, you will want to use the matching\n\"URL\" file, which has over 300,000 words\n\nThis huge file comes from the linux \"wamerican-huge\" package, which delivers it under\n/usr/share/dict/american-english-huge\n\nThere also exists a \"american-insane\" package", "## URL\n\nGenerates the \"embeddings.safetensor\" file, based on the \"dictionary\" file present.\nTakes a few minutes to run, depending on size of the dictionary\n\nThe shape of the embeddings tensor, is\n [number-of-words][768]\n\nNote that yes, it is possible to directly pull a tensor from the CLIP model,\nusing keyname of text_model.embeddings.token_embedding.weight\n\nThis will NOT GIVE YOU THE RIGHT DISTANCES!\nHence why we are calculating and then storing the embedding weights actually\ngenerated by the CLIP process", "## URL\n\nThis file contains a collection of \"one word, one CLIP token id\" pairings.\nThe file was taken from URL, which is part of multiple SD models in URL\n\nThe file was optimized for what people are actually going to type as words.\nFirst all the non-(/w) entries were stripped out.\nThen all the garbage punctuation and foreign characters were stripped out.\nFinally, the actual (/w) was stripped out, for ease of use." ]

[ "size_categories:n<1K", "language:en", "license:mit", "arxiv:2401.03601", "region:us" ]

[ "2401.03601" ]

[ "en" ]

[ "# Dataset Card for InFoBench Dataset", "## Table of Contents\n- Dataset Description\n- Dataset Usage\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: InFoBench Repository\n- Paper: InFoBench: Evaluating Instruction Following Ability in Large Language Models\n\nThe InFoBench Dataset is an evaluation benchmark dataset containing 500 instructions and corresponding 2250 decomposed requirements.", "## Dataset Usage\nYou can directly download it with huggingface datasets.", "## Dataset Structure", "### Data Instances\nFor each instance, there is an instruction string, an input string (optional), a list of decomposed questions, and a list of the labels for each decomposed question.", "### Data Fields\n- 'id': a string.\n- 'subset': 'Hard_Set' or 'Easy_Set'.\n- 'category': a string containing categorical information.\n- 'instruction': a string containing instructions.\n- 'input': a string, containing the context information, could be an empty string.\n- 'decomposed_questions': a list of strings, each corresponding to a decomposed requirement.\n- 'question_label': a list of list of strings, each list of strings containing a series of labels for the corresponding decomposed questions.", "## Additional Information", "### Licensing Information\nThe InFoBench Dataset version 1.0.0 is released under the MIT LISENCE" ]

[ "TAGS\n#size_categories-n<1K #language-English #license-mit #arxiv-2401.03601 #region-us \n", "# Dataset Card for InFoBench Dataset", "## Table of Contents\n- Dataset Description\n- Dataset Usage\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information", "## Dataset Description\n\n- Repository: InFoBench Repository\n- Paper: InFoBench: Evaluating Instruction Following Ability in Large Language Models\n\nThe InFoBench Dataset is an evaluation benchmark dataset containing 500 instructions and corresponding 2250 decomposed requirements.", "## Dataset Usage\nYou can directly download it with huggingface datasets.", "## Dataset Structure", "### Data Instances\nFor each instance, there is an instruction string, an input string (optional), a list of decomposed questions, and a list of the labels for each decomposed question.", "### Data Fields\n- 'id': a string.\n- 'subset': 'Hard_Set' or 'Easy_Set'.\n- 'category': a string containing categorical information.\n- 'instruction': a string containing instructions.\n- 'input': a string, containing the context information, could be an empty string.\n- 'decomposed_questions': a list of strings, each corresponding to a decomposed requirement.\n- 'question_label': a list of list of strings, each list of strings containing a series of labels for the corresponding decomposed questions.", "## Additional Information", "### Licensing Information\nThe InFoBench Dataset version 1.0.0 is released under the MIT LISENCE" ]

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

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[]

[ "# Jobstreet Webscraping\n!alt text\n\n\nThe data was scraped from jobstreet malaysia site with search keyword *data scientist* using beautifulsoup4 object." ]

[ "TAGS\n#size_categories-n<1K #license-apache-2.0 #region-us \n", "# Jobstreet Webscraping\n!alt text\n\n\nThe data was scraped from jobstreet malaysia site with search keyword *data scientist* using beautifulsoup4 object." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dolphin-coder-templated-ia-flat\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dolphin-coder-templated-ia-flat\"\n\nMore Information needed" ]

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

[]

[]

[ "# Korean Effective Crowdsourcing of Multiple Tasks (ECMT) for Comprehensive Knowledge Extraction\n\n- Project: URL\n- Data source: URL", "## Details\n\nAnnotated text from Korean Wikipedia and KBox (Korean DBpedia). Includes a crowd sourced training set and expert annotated (reviewed by four experts) test set.\n\nThe dataset was annotated by crowdworks in multiple stages.\n* Phase I: entity mention detection annotation; candidate entity mentions are selected in a text\n* Phase II: entity linking annotation; candidate mentions can be linked to a knowledge base\n* Phase III: coreference annotation; entities can be linked to pronouns, demonstrative determiners, and antecedent mentions\n* Phase IV: relation extraction annotation; relations between entities are annotated", "### Annotation Notes", "#### Phase I\n* For each mention, the annotator selects a category from one of 16 options: person, study field, theory, artifact, organization, location, civilization, event, year, time, quantity, job, animal, plant, material, and term.\n* Entities can be things, concepts, ideas, or events:\n\n* Compound nouns are tagged with the largest span:\n\n* Final result is created by merging annotations from two separate annotators.", "#### Phase II\n* For each mention, a list of candidates from the knowledge base are shown. The annotator can select a candidate, not in candidate list, or not an entity.\n* Each document was annotated by a single annotator.", "#### Phase III\n* For each mention, the annotator can select a preceding mention, no antecedent, or error. Noun phrases and pronouns are extracted using the parse information.\n* \"We scaled down the coreference resolution by limiting the scope of the target mentions to a named entity, pronoun, and definite noun phrase.\"\n* Postfixes particles (조사) are not included in the antecedent:" ]

[ "TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n", "# Korean Effective Crowdsourcing of Multiple Tasks (ECMT) for Comprehensive Knowledge Extraction\n\n- Project: URL\n- Data source: URL", "## Details\n\nAnnotated text from Korean Wikipedia and KBox (Korean DBpedia). Includes a crowd sourced training set and expert annotated (reviewed by four experts) test set.\n\nThe dataset was annotated by crowdworks in multiple stages.\n* Phase I: entity mention detection annotation; candidate entity mentions are selected in a text\n* Phase II: entity linking annotation; candidate mentions can be linked to a knowledge base\n* Phase III: coreference annotation; entities can be linked to pronouns, demonstrative determiners, and antecedent mentions\n* Phase IV: relation extraction annotation; relations between entities are annotated", "### Annotation Notes", "#### Phase I\n* For each mention, the annotator selects a category from one of 16 options: person, study field, theory, artifact, organization, location, civilization, event, year, time, quantity, job, animal, plant, material, and term.\n* Entities can be things, concepts, ideas, or events:\n\n* Compound nouns are tagged with the largest span:\n\n* Final result is created by merging annotations from two separate annotators.", "#### Phase II\n* For each mention, a list of candidates from the knowledge base are shown. The annotator can select a candidate, not in candidate list, or not an entity.\n* Each document was annotated by a single annotator.", "#### Phase III\n* For each mention, the annotator can select a preceding mention, no antecedent, or error. Noun phrases and pronouns are extracted using the parse information.\n* \"We scaled down the coreference resolution by limiting the scope of the target mentions to a named entity, pronoun, and definite noun phrase.\"\n* Postfixes particles (조사) are not included in the antecedent:" ]

[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:en", "medical", "music", "biology", "chemistry", "art", "climate", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #medical #music #biology #chemistry #art #climate #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"VA_test1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"VA_test1\"\n\nMore Information needed" ]

[ "task_categories:question-answering", "size_categories:100M<n<1B", "language:en", "language:zh", "license:apache-2.0", "code", "arxiv:2312.14033", "region:us" ]

[ "2312.14033" ]

[ "en", "zh" ]

[ "# T-Eval: Evaluating the Tool Utilization Capability Step by Step\n\n", "## Introduction \n\nThis is an evaluation harness for the benchmark described in T-Eval: Evaluating the Tool Utilization Capability Step by Step. \n\n[Paper]\n[Project Page]\n[LeaderBoard]\n\n> Large language models (LLM) have achieved remarkable performance on various NLP tasks and are augmented by tools for broader applications. Yet, how to evaluate and analyze the tool utilization capability of LLMs is still under-explored. In contrast to previous works that evaluate models holistically, we comprehensively decompose the tool utilization into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. Based on that, we further introduce T-Eval to evaluate the tool-utilization capability step by step. T-Eval disentangles the tool utilization evaluation into several sub-domains along model capabilities, facilitating the inner understanding of both holistic and isolated competency of LLMs. We conduct extensive experiments on T-Eval and in-depth analysis of various LLMs. T-Eval not only exhibits consistency with the outcome-oriented evaluation but also provides a more fine-grained analysis of the capabilities of LLMs, providing a new perspective in LLM evaluation on tool-utilization ability.", "## What's New\n\n- [2024.01.08] Release ZH Leaderboard and ZH data, where the questions and answer formats are in Chinese. （公布了中文评测数据集和榜单）\n- [2023.12.22] Paper available on ArXiv. \n- [2023.12.21] Release the test scripts and [data]() for T-Eval.", "## ️ Preparations", "## ️ Get Started\n\nWe support both API-based models and HuggingFace models via Lagent.", "### Test Data\n\nYou can use the following link to access to the test data:\n\n[EN data] (English format) [ZH data] (Chinese format)\n\nAfter downloading, please put the data in the 'data' folder directly:", "### API Models\n\n1. Set your OPENAI key in your environment.\n\n2. Run the model with the following scripts", "### HuggingFace Models\n\n1. Download the huggingface model to your local path.\n2. Modify the 'meta_template' json according to your tested model.\n3. Run the model with the following scripts", "### Final Results\nOnce you finish all tested samples, a detailed evluation results will be logged at '$out_dir/$model_display_name/$model_display_name_-1.json' (For ZH dataset, there is a '_zh' suffix). To obtain your final score, please run the following command:", "## Protocols\n\nT-Eval adopts multi-conversation style evaluation to gauge the model. The format of our saved prompt is as follows:\n\nwhere 'role' can be ['system', 'user', 'assistant'], and 'content' must be in string format. Before infering it by a LLM, we need to construct it into a raw string format via 'meta_template'. A 'meta_template' sample for InternLM is provided at meta_template.py:\n\nYou need to specify the 'begin' and 'end' token based on your tested huggingface model at meta_template.py and specify the 'meta_template' args in 'URL', same as the name you set in the 'meta_template.py'. As for OpenAI model, we will handle that for you.", "## Benchmark Results\n\nMore detailed and comprehensive benchmark results can refer to T-Eval official leaderboard !", "### ️ Submit Your Results\n\nYou can submit your inference results (via running URL) to this email. We will run your predictions and update the results in our leaderboard. Please also provide the scale of your tested model. A sample structure of your submission should be like:", "## ️ Acknowledgements\n\nT-Eval is built with Lagent and OpenCompass. Thanks for their awesome work!", "## ️ Citation\n\nIf you find this project useful in your research, please consider cite:", "## License\n\nThis project is released under the Apache 2.0 license." ]

[ "TAGS\n#task_categories-question-answering #size_categories-100M<n<1B #language-English #language-Chinese #license-apache-2.0 #code #arxiv-2312.14033 #region-us \n", "# T-Eval: Evaluating the Tool Utilization Capability Step by Step\n\n", "## Introduction \n\nThis is an evaluation harness for the benchmark described in T-Eval: Evaluating the Tool Utilization Capability Step by Step. \n\n[Paper]\n[Project Page]\n[LeaderBoard]\n\n> Large language models (LLM) have achieved remarkable performance on various NLP tasks and are augmented by tools for broader applications. Yet, how to evaluate and analyze the tool utilization capability of LLMs is still under-explored. In contrast to previous works that evaluate models holistically, we comprehensively decompose the tool utilization into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. Based on that, we further introduce T-Eval to evaluate the tool-utilization capability step by step. T-Eval disentangles the tool utilization evaluation into several sub-domains along model capabilities, facilitating the inner understanding of both holistic and isolated competency of LLMs. We conduct extensive experiments on T-Eval and in-depth analysis of various LLMs. T-Eval not only exhibits consistency with the outcome-oriented evaluation but also provides a more fine-grained analysis of the capabilities of LLMs, providing a new perspective in LLM evaluation on tool-utilization ability.", "## What's New\n\n- [2024.01.08] Release ZH Leaderboard and ZH data, where the questions and answer formats are in Chinese. （公布了中文评测数据集和榜单）\n- [2023.12.22] Paper available on ArXiv. \n- [2023.12.21] Release the test scripts and [data]() for T-Eval.", "## ️ Preparations", "## ️ Get Started\n\nWe support both API-based models and HuggingFace models via Lagent.", "### Test Data\n\nYou can use the following link to access to the test data:\n\n[EN data] (English format) [ZH data] (Chinese format)\n\nAfter downloading, please put the data in the 'data' folder directly:", "### API Models\n\n1. Set your OPENAI key in your environment.\n\n2. Run the model with the following scripts", "### HuggingFace Models\n\n1. Download the huggingface model to your local path.\n2. Modify the 'meta_template' json according to your tested model.\n3. Run the model with the following scripts", "### Final Results\nOnce you finish all tested samples, a detailed evluation results will be logged at '$out_dir/$model_display_name/$model_display_name_-1.json' (For ZH dataset, there is a '_zh' suffix). To obtain your final score, please run the following command:", "## Protocols\n\nT-Eval adopts multi-conversation style evaluation to gauge the model. The format of our saved prompt is as follows:\n\nwhere 'role' can be ['system', 'user', 'assistant'], and 'content' must be in string format. Before infering it by a LLM, we need to construct it into a raw string format via 'meta_template'. A 'meta_template' sample for InternLM is provided at meta_template.py:\n\nYou need to specify the 'begin' and 'end' token based on your tested huggingface model at meta_template.py and specify the 'meta_template' args in 'URL', same as the name you set in the 'meta_template.py'. As for OpenAI model, we will handle that for you.", "## Benchmark Results\n\nMore detailed and comprehensive benchmark results can refer to T-Eval official leaderboard !", "### ️ Submit Your Results\n\nYou can submit your inference results (via running URL) to this email. We will run your predictions and update the results in our leaderboard. Please also provide the scale of your tested model. A sample structure of your submission should be like:", "## ️ Acknowledgements\n\nT-Eval is built with Lagent and OpenCompass. Thanks for their awesome work!", "## ️ Citation\n\nIf you find this project useful in your research, please consider cite:", "## License\n\nThis project is released under the Apache 2.0 license." ]

[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:mit", "asd", "autism", "esl", "english_second_language", "NLP", "second_language", "phi-2", "openassistant_reward", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"Contextual Response Evaluation for ESL and ASD Support\"\"", "## Dataset Description", "### Dataset Summary \n\nCurated by Eric Soderquist, this dataset is a collection of English prompts and responses generated by the Phi-2 model, designed to evaluate and improve NLP models for supporting ESL (English as a Second Language) and ASD (Autism Spectrum Disorder) user bases. Each prompt is paired with multiple AI-generated responses and evaluated using a reward model to assess their relevance and quality.", "### Supported Tasks and Leaderboards \n\n- 'text-generation': This dataset is intended to train and refine language models for generating sensitive and context-aware responses.\n- 'language-modeling': It can also be used for scoring the quality of language model responses to support ESL and ASD individuals.", "### Languages \n\nThe dataset is monolingual and written in English.", "## Dataset Structure", "### Data Instances \n\nEach data instance contains a prompt, multiple AI-generated responses to that prompt, and scores reflecting the quality of each response.", "### Data Fields \n\n- 'prompt': a string containing the original English prompt.\n- 'responses': an array of strings containing responses generated by the language model.\n- 'scores': an array of floats representing the reward model's evaluation of each response.", "### Data Splits \n\nThis dataset is not divided into traditional splits and consists of one complete set for evaluation purposes.", "## Dataset Creation", "### Curation Rationale \n\nThe dataset was curated with the goal of advancing NLP technologies to better serve ESL and ASD communities, offering a resource to evaluate and enhance the sensitivity of language models in understanding and generating responses that cater to the unique needs of these groups.", "### Source Data", "#### Initial Data Collection and Normalization \n\nData was generated using the Phi-2 model in response to carefully crafted prompts, aiming to cover a range of contexts and challenges faced by ESL and ASD individuals.", "#### Annotations \n\nThe dataset includes scores from a reward model, providing an evaluation based on the model's perceived quality and appropriateness of the responses.", "### Personal and Sensitive Information \n\nResponses are generated and do not contain any real personal or sensitive information.", "## Considerations for Using the Data ️", "### Social Impact of the Dataset \n\nThis dataset has the potential to impact the development of inclusive language models that are attuned to the nuances of communication required by ESL and ASD individuals.", "### Discussion of Biases \n\nAs with any language model, biases present in the training data of the Phi-2 model may be reflected in the responses.", "### Other Known Limitations \n\nThe reward model's scores are based on its own training data and may not cover the full scope of human evaluative diversity.", "## Additional Information", "### Dataset Curator \n\nThis dataset was curated by Eric Soderquist with the intent to foster developments in NLP that can adapt to and support the diverse linguistic and communicative needs of ESL and ASD communities.", "### Licensing Information ©️\n\nThe dataset is made available under the MIT license.\n\n \n\nIf you use this dataset in your research, please cite it as follows:", "### Contributions \nContributions to further develop and expand this dataset are welcome." ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #asd #autism #esl #english_second_language #NLP #second_language #phi-2 #openassistant_reward #region-us \n", "# Dataset Card for \"Contextual Response Evaluation for ESL and ASD Support\"\"", "## Dataset Description", "### Dataset Summary \n\nCurated by Eric Soderquist, this dataset is a collection of English prompts and responses generated by the Phi-2 model, designed to evaluate and improve NLP models for supporting ESL (English as a Second Language) and ASD (Autism Spectrum Disorder) user bases. Each prompt is paired with multiple AI-generated responses and evaluated using a reward model to assess their relevance and quality.", "### Supported Tasks and Leaderboards \n\n- 'text-generation': This dataset is intended to train and refine language models for generating sensitive and context-aware responses.\n- 'language-modeling': It can also be used for scoring the quality of language model responses to support ESL and ASD individuals.", "### Languages \n\nThe dataset is monolingual and written in English.", "## Dataset Structure", "### Data Instances \n\nEach data instance contains a prompt, multiple AI-generated responses to that prompt, and scores reflecting the quality of each response.", "### Data Fields \n\n- 'prompt': a string containing the original English prompt.\n- 'responses': an array of strings containing responses generated by the language model.\n- 'scores': an array of floats representing the reward model's evaluation of each response.", "### Data Splits \n\nThis dataset is not divided into traditional splits and consists of one complete set for evaluation purposes.", "## Dataset Creation", "### Curation Rationale \n\nThe dataset was curated with the goal of advancing NLP technologies to better serve ESL and ASD communities, offering a resource to evaluate and enhance the sensitivity of language models in understanding and generating responses that cater to the unique needs of these groups.", "### Source Data", "#### Initial Data Collection and Normalization \n\nData was generated using the Phi-2 model in response to carefully crafted prompts, aiming to cover a range of contexts and challenges faced by ESL and ASD individuals.", "#### Annotations \n\nThe dataset includes scores from a reward model, providing an evaluation based on the model's perceived quality and appropriateness of the responses.", "### Personal and Sensitive Information \n\nResponses are generated and do not contain any real personal or sensitive information.", "## Considerations for Using the Data ️", "### Social Impact of the Dataset \n\nThis dataset has the potential to impact the development of inclusive language models that are attuned to the nuances of communication required by ESL and ASD individuals.", "### Discussion of Biases \n\nAs with any language model, biases present in the training data of the Phi-2 model may be reflected in the responses.", "### Other Known Limitations \n\nThe reward model's scores are based on its own training data and may not cover the full scope of human evaluative diversity.", "## Additional Information", "### Dataset Curator \n\nThis dataset was curated by Eric Soderquist with the intent to foster developments in NLP that can adapt to and support the diverse linguistic and communicative needs of ESL and ASD communities.", "### Licensing Information ©️\n\nThe dataset is made available under the MIT license.\n\n \n\nIf you use this dataset in your research, please cite it as follows:", "### Contributions \nContributions to further develop and expand this dataset are welcome." ]

[ "task_categories:text2text-generation", "size_categories:n<1K", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text2text-generation #size_categories-n<1K #language-English #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"ee5\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ee5\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"ee10\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ee10\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"ee15\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ee15\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-anatomy-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-anatomy-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-astronomy-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-astronomy-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-business_ethics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-business_ethics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-clinical_knowledge-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-clinical_knowledge-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-college_biology-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-college_biology-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-college_chemistry-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-college_chemistry-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-college_computer_science-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-college_computer_science-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-college_mathematics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-college_mathematics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-college_medicine-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-college_medicine-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-college_physics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-college_physics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-computer_security-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-computer_security-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-conceptual_physics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-conceptual_physics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-econometrics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-econometrics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-electrical_engineering-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-electrical_engineering-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-elementary_mathematics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-elementary_mathematics-neg-prepend-verbal\"\n\nMore Information needed" ]

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

[]

[ "en" ]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-global_facts-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-global_facts-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-high_school_biology-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-high_school_biology-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-high_school_geography-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-high_school_geography-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-high_school_government_and_politics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-high_school_government_and_politics-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"mmlu-high_school_psychology-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-high_school_psychology-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# GolaifVirus-llama2-60\n60 QA entries on a made-up Golaif Virus with fictional symptoms" ]

[ "TAGS\n#region-us \n", "# GolaifVirus-llama2-60\n60 QA entries on a made-up Golaif Virus with fictional symptoms" ]

[ "region:us" ]

[]

[]

[ "# Demo Datasets for WordCamp Asia 2024\n\nDemo datasets for example finetuning and embedding.\n\nPaired with Colab Notebook: URL\n\nRecipes coming from the website: robs.kitchen as well as Kaggle's URL Recipe list open source dataset.\n\nFinetuned model: iamchum/URL (based on TinyLlama/TinyLlama-1.1B-Chat-v1.0)\nInference endpoint: URL\n\n---\nlicense: mit\n---" ]

[ "TAGS\n#region-us \n", "# Demo Datasets for WordCamp Asia 2024\n\nDemo datasets for example finetuning and embedding.\n\nPaired with Colab Notebook: URL\n\nRecipes coming from the website: robs.kitchen as well as Kaggle's URL Recipe list open source dataset.\n\nFinetuned model: iamchum/URL (based on TinyLlama/TinyLlama-1.1B-Chat-v1.0)\nInference endpoint: URL\n\n---\nlicense: mit\n---" ]

[ "language:no", "license:apache-2.0", "region:us" ]

[]

[ "no" ]

[]

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

[ "region:us" ]

[]

[]

[ "# LCA Project Level Code Completion", "## How to load the dataset", "## Data Point Structure\n* 'repo' -- repository name in format '{GitHub_user_name}__{repository_name}'\n* 'commit_hash' -- commit hash\n* 'completion_file' -- dictionary with the completion file content in the following format:\n * 'filename' -- filepath to the completion file\n * 'content' -- content of the completion file\n* 'completion_lines' -- dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:\n * 'committed' -- line contains at least one function or class that was declared in the committed files\n * 'inproject' -- line contains at least one function and class that was declared in the project (excluding previous)\n * 'infile' -- line contains at least one function and class that was declared in the completion file (excluding previous)\n * 'common' -- line contains at least one function and class that was classified to be common, e.g. 'main', 'get', etc (excluding previous)\n * 'non_informative' -- line that was classified to be non-informative, e.g. too short, contains comments, etc\n * 'random' -- randomly sampled from the rest of the lines\n* 'repo_snapshot' -- dictionary with a snapshot of the repository before the commit. Has the same structure as 'completion_file', but filenames and contents are orginized as lists.\n* 'completion_lines_raw' -- the same as 'completion_lines', but before sampling.", "## How we collected the data\n* TBA" ]

[ "TAGS\n#region-us \n", "# LCA Project Level Code Completion", "## How to load the dataset", "## Data Point Structure\n* 'repo' -- repository name in format '{GitHub_user_name}__{repository_name}'\n* 'commit_hash' -- commit hash\n* 'completion_file' -- dictionary with the completion file content in the following format:\n * 'filename' -- filepath to the completion file\n * 'content' -- content of the completion file\n* 'completion_lines' -- dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:\n * 'committed' -- line contains at least one function or class that was declared in the committed files\n * 'inproject' -- line contains at least one function and class that was declared in the project (excluding previous)\n * 'infile' -- line contains at least one function and class that was declared in the completion file (excluding previous)\n * 'common' -- line contains at least one function and class that was classified to be common, e.g. 'main', 'get', etc (excluding previous)\n * 'non_informative' -- line that was classified to be non-informative, e.g. too short, contains comments, etc\n * 'random' -- randomly sampled from the rest of the lines\n* 'repo_snapshot' -- dictionary with a snapshot of the repository before the commit. Has the same structure as 'completion_file', but filenames and contents are orginized as lists.\n* 'completion_lines_raw' -- the same as 'completion_lines', but before sampling.", "## How we collected the data\n* TBA" ]

[ "region:us" ]

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[ "# LCA Project Level Code Completion", "## How to load the dataset", "## Data Point Structure\n* 'repo' -- repository name in format '{GitHub_user_name}__{repository_name}'\n* 'commit_hash' -- commit hash\n* 'completion_file' -- dictionary with the completion file content in the following format:\n * 'filename' -- filepath to the completion file\n * 'content' -- content of the completion file\n* 'completion_lines' -- dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:\n * 'committed' -- line contains at least one function or class that was declared in the committed files\n * 'inproject' -- line contains at least one function and class that was declared in the project (excluding previous)\n * 'infile' -- line contains at least one function and class that was declared in the completion file (excluding previous)\n * 'common' -- line contains at least one function and class that was classified to be common, e.g. 'main', 'get', etc (excluding previous)\n * 'non_informative' -- line that was classified to be non-informative, e.g. too short, contains comments, etc\n * 'random' -- randomly sampled from the rest of the lines\n* 'repo_snapshot' -- dictionary with a snapshot of the repository before the commit. Has the same structure as 'completion_file', but filenames and contents are orginized as lists.\n* 'completion_lines_raw' -- the same as 'completion_lines', but before sampling.", "## How we collected the data\n* TBA" ]

[ "TAGS\n#region-us \n", "# LCA Project Level Code Completion", "## How to load the dataset", "## Data Point Structure\n* 'repo' -- repository name in format '{GitHub_user_name}__{repository_name}'\n* 'commit_hash' -- commit hash\n* 'completion_file' -- dictionary with the completion file content in the following format:\n * 'filename' -- filepath to the completion file\n * 'content' -- content of the completion file\n* 'completion_lines' -- dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:\n * 'committed' -- line contains at least one function or class that was declared in the committed files\n * 'inproject' -- line contains at least one function and class that was declared in the project (excluding previous)\n * 'infile' -- line contains at least one function and class that was declared in the completion file (excluding previous)\n * 'common' -- line contains at least one function and class that was classified to be common, e.g. 'main', 'get', etc (excluding previous)\n * 'non_informative' -- line that was classified to be non-informative, e.g. too short, contains comments, etc\n * 'random' -- randomly sampled from the rest of the lines\n* 'repo_snapshot' -- dictionary with a snapshot of the repository before the commit. Has the same structure as 'completion_file', but filenames and contents are orginized as lists.\n* 'completion_lines_raw' -- the same as 'completion_lines', but before sampling.", "## How we collected the data\n* TBA" ]

[ "region:us" ]

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[ "# Answer Reformulation\n\nThe \"Answer Reformulation\" dataset is designed for a task that involves providing a detailed, comprehensive answer to a given question, supported by a set of related documents. The unique aspect of this dataset is the specific format required for the answers, emphasizing thorough exploration and understanding of the material in a specified language.\n\n---", "## Dataset Details\nEach data point in the dataset has three main elements:\n - Query: A question that needs to be addressed\n - Input Documents (input_docs): A collection of documents related (or not) to the query. These documents may provide the information required to formulate the answer.\n - Answer: A comprehensive response to the query, incorporating information from the input documents and following strict citation guidelines, referencing specific input documents using citation IDs (e.g., [@1], [@2]). If the input documents do not provide sufficient information to answer the query, the response should be \"Answer not found.\"\n\n---", "## Data Generation (with GPT)", "### Local Question Answers\n\nQuestion Answer Generation: \nThe idea is to generate based on a complete and long document (a section from a textbook): \n - A question that could be answered based on the given document\n - An answer citing specific parts of the document using citation IDs\n\nTo achieve this: \n 1. Breaking down the complete document into smaller, numbered segments to facilitate the citation process.\n 2. Employing GPT-4 to generate both the question and its corresponding answer, which includes citations referring to the numbered segments of the text.\n\nPostprocess Enhancements:\nTo enrich the dataset, each data point is supplemented with irrelevant texts (selected E5 model) as part of the input documents. This addition is intended to increase the complexity and diversity of the dataset, thereby making it more challenging and realistic for training purposes." ]

[ "TAGS\n#region-us \n", "# Answer Reformulation\n\nThe \"Answer Reformulation\" dataset is designed for a task that involves providing a detailed, comprehensive answer to a given question, supported by a set of related documents. The unique aspect of this dataset is the specific format required for the answers, emphasizing thorough exploration and understanding of the material in a specified language.\n\n---", "## Dataset Details\nEach data point in the dataset has three main elements:\n - Query: A question that needs to be addressed\n - Input Documents (input_docs): A collection of documents related (or not) to the query. These documents may provide the information required to formulate the answer.\n - Answer: A comprehensive response to the query, incorporating information from the input documents and following strict citation guidelines, referencing specific input documents using citation IDs (e.g., [@1], [@2]). If the input documents do not provide sufficient information to answer the query, the response should be \"Answer not found.\"\n\n---", "## Data Generation (with GPT)", "### Local Question Answers\n\nQuestion Answer Generation: \nThe idea is to generate based on a complete and long document (a section from a textbook): \n - A question that could be answered based on the given document\n - An answer citing specific parts of the document using citation IDs\n\nTo achieve this: \n 1. Breaking down the complete document into smaller, numbered segments to facilitate the citation process.\n 2. Employing GPT-4 to generate both the question and its corresponding answer, which includes citations referring to the numbered segments of the text.\n\nPostprocess Enhancements:\nTo enrich the dataset, each data point is supplemented with irrelevant texts (selected E5 model) as part of the input documents. This addition is intended to increase the complexity and diversity of the dataset, thereby making it more challenging and realistic for training purposes." ]

[ "region:us" ]

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[ "# Dataset Card for \"alpaca_farm-alpaca_gpt4_preference-re-preference_eval\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"alpaca_farm-alpaca_gpt4_preference-re-preference_eval\"\n\nMore Information needed" ]

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

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[ "# MMVP Benchmark Datacard", "## Basic Information\n\nTitle: MMVP Benchmark\n\nDescription: The MMVP (Multimodal Visual Patterns) Benchmark focuses on identifying “CLIP-blind pairs” – images that are perceived as similar by CLIP despite having clear visual differences. MMVP benchmarks the performance of state-of-the-art systems, including GPT-4V, across nine basic visual patterns. It highlights the challenges these systems face in answering straightforward questions, often leading to incorrect responses and hallucinated explanations.", "## Dataset Details\n\n- Content Types: Images (CLIP-blind pairs)\n- Volume: 300 images\n- Source of Data: Derived from ImageNet-1k and LAION-Aesthetics\n- Data Collection Method: Identification of CLIP-blind pairs through comparative analysis" ]

[ "TAGS\n#task_categories-question-answering #size_categories-n<1K #license-mit #region-us \n", "# MMVP Benchmark Datacard", "## Basic Information\n\nTitle: MMVP Benchmark\n\nDescription: The MMVP (Multimodal Visual Patterns) Benchmark focuses on identifying “CLIP-blind pairs” – images that are perceived as similar by CLIP despite having clear visual differences. MMVP benchmarks the performance of state-of-the-art systems, including GPT-4V, across nine basic visual patterns. It highlights the challenges these systems face in answering straightforward questions, often leading to incorrect responses and hallucinated explanations.", "## Dataset Details\n\n- Content Types: Images (CLIP-blind pairs)\n- Volume: 300 images\n- Source of Data: Derived from ImageNet-1k and LAION-Aesthetics\n- Data Collection Method: Identification of CLIP-blind pairs through comparative analysis" ]

[ "region:us" ]

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[ "# Dataset Card for \"death_se42-type2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"death_se42-type2\"\n\nMore Information needed" ]

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

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

[ "task_categories:zero-shot-classification", "size_categories:n<1K", "license:mit", "region:us" ]

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[ "# MMVP-VLM Benchmark Datacard", "## Basic Information\n\nTitle: MMVP-VLM Benchmark\n\nDescription: The MMVP-VLM (Multimodal Visual Patterns - Visual Language Models) Benchmark is designed to systematically evaluate the performance of recent CLIP-based models in understanding and processing visual patterns. It distills a subset of questions from the original MMVP benchmark into simpler language descriptions, categorizing them into distinct visual patterns. Each visual pattern is represented by 15 text-image pairs. The benchmark assesses whether CLIP models can accurately match these image-text combinations, providing insights into the capabilities and limitations of these models.", "## Dataset Details\n\n- Content Types: Text-Image Pairs\n- Volume: Balanced number of questions for each visual pattern, with each pattern represented by 15 pairs. \n- Source of Data: Subset from MMVP benchmark, supplemented with additional questions for balance\n- Data Collection Method: Distillation and categorization of questions from MMVP benchmark into simpler language", "## Usage", "### Intended Use\n\n- Evaluation of CLIP models' ability to understand and process various visual patterns." ]

[ "TAGS\n#task_categories-zero-shot-classification #size_categories-n<1K #license-mit #region-us \n", "# MMVP-VLM Benchmark Datacard", "## Basic Information\n\nTitle: MMVP-VLM Benchmark\n\nDescription: The MMVP-VLM (Multimodal Visual Patterns - Visual Language Models) Benchmark is designed to systematically evaluate the performance of recent CLIP-based models in understanding and processing visual patterns. It distills a subset of questions from the original MMVP benchmark into simpler language descriptions, categorizing them into distinct visual patterns. Each visual pattern is represented by 15 text-image pairs. The benchmark assesses whether CLIP models can accurately match these image-text combinations, providing insights into the capabilities and limitations of these models.", "## Dataset Details\n\n- Content Types: Text-Image Pairs\n- Volume: Balanced number of questions for each visual pattern, with each pattern represented by 15 pairs. \n- Source of Data: Subset from MMVP benchmark, supplemented with additional questions for balance\n- Data Collection Method: Distillation and categorization of questions from MMVP benchmark into simpler language", "## Usage", "### Intended Use\n\n- Evaluation of CLIP models' ability to understand and process various visual patterns." ]

[ "region:us" ]

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

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

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

[ "size_categories:1M<n<10M", "region:us" ]

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[ "TAGS\n#size_categories-1M<n<10M #region-us \n" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"myriade_noun_aligned_with_wordnet_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"myriade_noun_aligned_with_wordnet_v2\"\n\nMore Information needed" ]

[ "task_categories:object-detection", "size_categories:1K<n<10K", "language:en", "Table", "Unstructured Document", "YOLOv8", "Object Detection", "Table Detection", "region:us" ]

[]

[ "en" ]

[ "# Table Detection in Document Images using YOLOv8\n\nThe Table Detection YOLO dataset is a collection of document images annotated with table bounding boxes suitable for \\\ntraining object detection models, specifically using the YOLOv8 (You Only Look Once) architecture. The dataset is intended \\\nfor developing and evaluating table detection algorithms within the field of document analysis and recognition. The \\\nannotations define the locations of tables within a variety of document images, which can range from scanned documents to \\\ndigital PDFs.", "### Dataset Labels", "### Number of Images", "### Getting Started" ]

[ "TAGS\n#task_categories-object-detection #size_categories-1K<n<10K #language-English #Table #Unstructured Document #YOLOv8 #Object Detection #Table Detection #region-us \n", "# Table Detection in Document Images using YOLOv8\n\nThe Table Detection YOLO dataset is a collection of document images annotated with table bounding boxes suitable for \\\ntraining object detection models, specifically using the YOLOv8 (You Only Look Once) architecture. The dataset is intended \\\nfor developing and evaluating table detection algorithms within the field of document analysis and recognition. The \\\nannotations define the locations of tables within a variety of document images, which can range from scanned documents to \\\ndigital PDFs.", "### Dataset Labels", "### Number of Images", "### Getting Started" ]

[ "region:us" ]

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[ "# Dataset Card for \"vsums_synthetic_gpt4_deduped\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"vsums_synthetic_gpt4_deduped\"\n\nMore Information needed" ]

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

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[ "# 3.5k lengthy ShareGPT conversations used to train EABF Models\n\nFollowing the data cleaning pipeline in FastChat, we processed raw ShareGPT conversations by keeping English conversations only, excluding those with less than 10,000 tokens, and splitting long conversations that exceed 16,384 tokens. \n\nWe find multi-round long conversations efficient for extending LLMs' context window.", "# Dataset Overview\nOur released dataset follows the conventional ShareGPT multi-round conversation JSON format:\n\n- id: The unique identifier for each conversation in the dataset.\n- model: The model used for generating the response. (Can be left empty if not applicable)\n- conversations: Object containing the dialogue between human and AI assistants.\n - from: Indicates whether the message is from the \"human\" or the \"AI\".\n - value: The actual content of the message.\n\nExample JSON Object:" ]

[ "TAGS\n#license-mit #region-us \n", "# 3.5k lengthy ShareGPT conversations used to train EABF Models\n\nFollowing the data cleaning pipeline in FastChat, we processed raw ShareGPT conversations by keeping English conversations only, excluding those with less than 10,000 tokens, and splitting long conversations that exceed 16,384 tokens. \n\nWe find multi-round long conversations efficient for extending LLMs' context window.", "# Dataset Overview\nOur released dataset follows the conventional ShareGPT multi-round conversation JSON format:\n\n- id: The unique identifier for each conversation in the dataset.\n- model: The model used for generating the response. (Can be left empty if not applicable)\n- conversations: Object containing the dialogue between human and AI assistants.\n - from: Indicates whether the message is from the \"human\" or the \"AI\".\n - value: The actual content of the message.\n\nExample JSON Object:" ]

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

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[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

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

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[ "en" ]

[ "# StackMathQA\nStackMathQA is a meticulously curated collection of 2 million mathematical questions and answers, sourced from various Stack Exchange sites. This repository is designed to serve as a comprehensive resource for researchers, educators, and enthusiasts in the field of mathematics and AI research.", "## Configs\n\n\n\nHow to load data:", "## Preprocessed Data\nIn the './preprocessed/stackexchange-math' directory and './preprocessed/stackexchange-math--1q1a' directory, you will find the data structured in two formats:\n\n1. Question and List of Answers Format:\n Each entry is structured as {\"Q\": \"question\", \"A_List\": [\"answer1\", \"answer2\", ...]}.\n - 'URL': 827,439 lines\n - 'URL': 90,645 lines\n - 'URL': 103,024 lines\n - 'URL': 117,318 lines\n - In total: 1,138,426 questions\n\n\n\n2. Question and Single Answer Format:\n Each line contains a question and one corresponding answer, structured as {\"Q\": \"question\", \"A\": \"answer\"}. Multiple answers for the same question are separated into different lines.\n - 'URL': 1,407,739 lines\n - 'URL': 166,592 lines\n - 'URL': 156,143 lines\n - 'URL': 226,532 lines\n - In total: 1,957,006 answers", "## Selected Data\nThe dataset has been carefully curated using importance sampling. We offer selected subsets of the dataset ('./preprocessed/stackexchange-math--1q1a') with different sizes to cater to varied needs:", "### StackMathQA1600K\n- Location: './data/stackmathqa1600k'\n- Contents:\n - 'URL': Containing 1.6 million entries.\n - 'URL': Metadata and additional information.\n\n\n\nSimilar structures are available for StackMathQA800K, StackMathQA400K, StackMathQA200K, and StackMathQA100K subsets.", "### StackMathQA800K\n- Location: './data/stackmathqa800k'\n- Contents:\n - 'URL': Containing 800k entries.\n - 'URL': Metadata and additional information.", "### StackMathQA400K\n\n- Location: './data/stackmathqa400k'\n- Contents:\n - 'URL': Containing 400k entries.\n - 'URL': Metadata and additional information.", "### StackMathQA200K\n\n- Location: './data/stackmathqa200k'\n- Contents:\n - 'URL': Containing 200k entries.\n - 'URL': Metadata and additional information.", "### StackMathQA100K\n\n- Location: './data/stackmathqa100k'\n- Contents:\n - 'URL': Containing 100k entries.\n - 'URL': Metadata and additional information.\n\n\n\nWe appreciate your use of StackMathQA in your work. If you find this repository helpful, please consider citing it and star this repo. Feel free to contact zhangyif21@URL or open an issue if you have any questions." ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-1B<n<10B #language-English #license-cc-by-4.0 #mathematical-reasoning #reasoning #finetuning #pretraining #llm #region-us \n", "# StackMathQA\nStackMathQA is a meticulously curated collection of 2 million mathematical questions and answers, sourced from various Stack Exchange sites. This repository is designed to serve as a comprehensive resource for researchers, educators, and enthusiasts in the field of mathematics and AI research.", "## Configs\n\n\n\nHow to load data:", "## Preprocessed Data\nIn the './preprocessed/stackexchange-math' directory and './preprocessed/stackexchange-math--1q1a' directory, you will find the data structured in two formats:\n\n1. Question and List of Answers Format:\n Each entry is structured as {\"Q\": \"question\", \"A_List\": [\"answer1\", \"answer2\", ...]}.\n - 'URL': 827,439 lines\n - 'URL': 90,645 lines\n - 'URL': 103,024 lines\n - 'URL': 117,318 lines\n - In total: 1,138,426 questions\n\n\n\n2. Question and Single Answer Format:\n Each line contains a question and one corresponding answer, structured as {\"Q\": \"question\", \"A\": \"answer\"}. Multiple answers for the same question are separated into different lines.\n - 'URL': 1,407,739 lines\n - 'URL': 166,592 lines\n - 'URL': 156,143 lines\n - 'URL': 226,532 lines\n - In total: 1,957,006 answers", "## Selected Data\nThe dataset has been carefully curated using importance sampling. We offer selected subsets of the dataset ('./preprocessed/stackexchange-math--1q1a') with different sizes to cater to varied needs:", "### StackMathQA1600K\n- Location: './data/stackmathqa1600k'\n- Contents:\n - 'URL': Containing 1.6 million entries.\n - 'URL': Metadata and additional information.\n\n\n\nSimilar structures are available for StackMathQA800K, StackMathQA400K, StackMathQA200K, and StackMathQA100K subsets.", "### StackMathQA800K\n- Location: './data/stackmathqa800k'\n- Contents:\n - 'URL': Containing 800k entries.\n - 'URL': Metadata and additional information.", "### StackMathQA400K\n\n- Location: './data/stackmathqa400k'\n- Contents:\n - 'URL': Containing 400k entries.\n - 'URL': Metadata and additional information.", "### StackMathQA200K\n\n- Location: './data/stackmathqa200k'\n- Contents:\n - 'URL': Containing 200k entries.\n - 'URL': Metadata and additional information.", "### StackMathQA100K\n\n- Location: './data/stackmathqa100k'\n- Contents:\n - 'URL': Containing 100k entries.\n - 'URL': Metadata and additional information.\n\n\n\nWe appreciate your use of StackMathQA in your work. If you find this repository helpful, please consider citing it and star this repo. Feel free to contact zhangyif21@URL or open an issue if you have any questions." ]

[ "region:us" ]

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[ "# Dataset Card for \"arxiv-test-2048\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"arxiv-test-2048\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"pubmed-test-2048\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pubmed-test-2048\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel\n\n\n\nDataset automatically created during the evaluation run of model dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel 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 2024-01-10T14:17:38.745392(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 dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel\n\n\n\nDataset automatically created during the evaluation run of model dvilasuero/NeuralHermes-2.5-Mistral-7B-distilabel 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 2024-01-10T14:17:38.745392(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" ]

[]

[]

[ "# Medical-Calgary-Cambridge-multi-turn-llama2-37\n37 multi-turn conversational entries between a patient and doctor, using the Calgary-Cambridge model" ]

[ "TAGS\n#region-us \n", "# Medical-Calgary-Cambridge-multi-turn-llama2-37\n37 multi-turn conversational entries between a patient and doctor, using the Calgary-Cambridge model" ]

[ "task_categories:conversational", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "not-for-all-audiences", "region:us" ]

[]

[ "en" ]

[ "# these are the transcript files from the 09 01 2024 Tonic AI Community Discord", "## Summary", "# technology\n- recorded by clyde\n- transcribed by gladia", "# Open Tasks :\n- create a summary of the transcription\n- automate summary of the transcriptions for tonic ai" ]

[ "TAGS\n#task_categories-conversational #size_categories-1K<n<10K #language-English #license-apache-2.0 #not-for-all-audiences #region-us \n", "# these are the transcript files from the 09 01 2024 Tonic AI Community Discord", "## Summary", "# technology\n- recorded by clyde\n- transcribed by gladia", "# Open Tasks :\n- create a summary of the transcription\n- automate summary of the transcriptions for tonic ai" ]

[ "task_categories:image-to-text", "language:ca", "license:mit", "region:us" ]

[]

[ "ca" ]

[ "# Esposalles Dataset", "## Table of Contents\n- Esposalles Dataset\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields", "## Dataset Description\n\n- Homepage: The Esposalles Database\n- Source: IEHHR\n- Paper: The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition\n- Point of Contact: TEKLIA", "## Dataset Summary\n\nThe Marriage Licenses ground-truth is compiled from the Marriage Licenses Books conserved at the Archives of the Cathedral of Barcelona.", "### Languages\n\nAll the documents in the dataset are written in Catalan.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n\n- 'image': A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0][\"image\"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the \"image\" column, i.e. dataset[0][\"image\"] should always be preferred over dataset[\"image\"][0].\n- 'text': the label transcription of the image." ]

[ "TAGS\n#task_categories-image-to-text #language-Catalan #license-mit #region-us \n", "# Esposalles Dataset", "## Table of Contents\n- Esposalles Dataset\n - Table of Contents\n - Dataset Description\n - Dataset Summary\n - Languages\n - Dataset Structure\n - Data Instances\n - Data Fields", "## Dataset Description\n\n- Homepage: The Esposalles Database\n- Source: IEHHR\n- Paper: The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition\n- Point of Contact: TEKLIA", "## Dataset Summary\n\nThe Marriage Licenses ground-truth is compiled from the Marriage Licenses Books conserved at the Archives of the Cathedral of Barcelona.", "### Languages\n\nAll the documents in the dataset are written in Catalan.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n\n- 'image': A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0][\"image\"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the \"image\" column, i.e. dataset[0][\"image\"] should always be preferred over dataset[\"image\"][0].\n- 'text': the label transcription of the image." ]

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

[]

[ "pl" ]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ewqr2130/mistral-7b-raw-sft\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/mistral-7b-raw-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-10T15:14:57.972449(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 ewqr2130/mistral-7b-raw-sft\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/mistral-7b-raw-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-10T15:14:57.972449(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 ewqr2130/llama2-7b-raw-sft\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/llama2-7b-raw-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-10T15:15:16.030532(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 ewqr2130/llama2-7b-raw-sft\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/llama2-7b-raw-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-10T15:15:16.030532(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 euclaise/crow-1b\n\n\n\nDataset automatically created during the evaluation run of model euclaise/crow-1b 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 2024-01-10T15:21:15.168348(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 euclaise/crow-1b\n\n\n\nDataset automatically created during the evaluation run of model euclaise/crow-1b 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 2024-01-10T15:21:15.168348(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 \"llava-pretrain\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of shitshow123/tinylamma-20000\n\n\n\nDataset automatically created during the evaluation run of model shitshow123/tinylamma-20000 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 2024-01-10T15:30:35.451394(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 shitshow123/tinylamma-20000\n\n\n\nDataset automatically created during the evaluation run of model shitshow123/tinylamma-20000 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 2024-01-10T15:30:35.451394(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:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-aqua-rat\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the AquA-RAT subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-aqua-rat\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the AquA-RAT subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of abacusai/Fewshot-Metamath-Mistral\n\n\n\nDataset automatically created during the evaluation run of model abacusai/Fewshot-Metamath-Mistral 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 2024-01-10T15:33:00.629548(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 abacusai/Fewshot-Metamath-Mistral\n\n\n\nDataset automatically created during the evaluation run of model abacusai/Fewshot-Metamath-Mistral 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 2024-01-10T15:33:00.629548(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 abacusai/Fewshot-Metamath-OrcaVicuna-Mistral\n\n\n\nDataset automatically created during the evaluation run of model abacusai/Fewshot-Metamath-OrcaVicuna-Mistral 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 2024-01-10T15:33:11.471365(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 abacusai/Fewshot-Metamath-OrcaVicuna-Mistral\n\n\n\nDataset automatically created during the evaluation run of model abacusai/Fewshot-Metamath-OrcaVicuna-Mistral 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 2024-01-10T15:33:11.471365(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 bn22/DolphinMini-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model bn22/DolphinMini-Mistral-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 2024-01-10T15:33:59.144282(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 bn22/DolphinMini-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model bn22/DolphinMini-Mistral-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 2024-01-10T15:33:59.144282(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:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-gaokao-biology\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao Biology subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-gaokao-biology\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao Biology subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of quantumaikr/quantum-v0.01\n\n\n\nDataset automatically created during the evaluation run of model quantumaikr/quantum-v0.01 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 2024-01-10T15:38:18.408039(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 quantumaikr/quantum-v0.01\n\n\n\nDataset automatically created during the evaluation run of model quantumaikr/quantum-v0.01 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 2024-01-10T15:38:18.408039(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:question-answering", "task_categories:text-generation", "task_categories:conversational", "size_categories:n<1K", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for the Alignement Internship Exercise", "## Dataset Description\n\n\n\nThis dataset provides a list of questions accompanied by Phi-2's best answer to them, as ranked by OpenAssitant's reward model.", "## Dataset Creation\n\nThe questions were handpicked from the LDJnr/Capybara, Open-Orca/OpenOrca and truthful_qa datasets, the coding exercise is from LeetCode's top 100 liked questions and I found the last prompt on a blog and modified it. I have chosen these prompts specifically to evaluate the model on different domains of knowledge (STEM, coding, humanities), different tasks (reasoning, writing, summarization, question-answering), different levels of complexity, different lengths of prompts as well as its safety and alignment with human values and ability to defend itself against adversarial prompts.\n\n\nThen each prompt was generated using the following logic:\n\n\"\"\"\\<USER>: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\\n that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not \\\ncorrect. If you don't know the answer to a question, please don't share false information.\n\nHere is my question: {question}\n\n\\<ASSISTANT>:\"\"\"\n\n\nAfter that, for each question we generate K=8 answers with Phi-2 by setting the maximum number of new tokens to 300, stopping if the end of text token is generated, doing sampling, and setting the temperature to some predefined value.\n\nWe then rank each answer using OpenAssitant's reward model and take the best one.\n\nFinally, we perform a small temperature hyperparameter scan and found that the best answers according to the reward model were given using a temperature value of 0.4. So these are the answers that are in the dataset.", "## Dataset Sources\n\n\n\n- Capybara Dataset: link\n- OpenOrca Dataset: link\n- Truthful QA Dataset: link\n- LeetCode's \"Subsets\" problem: link\n- DAN prompt: link\n- Llama's system prompt: link\n- Micrososft's Phi-2: link\n- OpenAssistant's reward model: link" ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-conversational #size_categories-n<1K #language-English #license-apache-2.0 #region-us \n", "# Dataset Card for the Alignement Internship Exercise", "## Dataset Description\n\n\n\nThis dataset provides a list of questions accompanied by Phi-2's best answer to them, as ranked by OpenAssitant's reward model.", "## Dataset Creation\n\nThe questions were handpicked from the LDJnr/Capybara, Open-Orca/OpenOrca and truthful_qa datasets, the coding exercise is from LeetCode's top 100 liked questions and I found the last prompt on a blog and modified it. I have chosen these prompts specifically to evaluate the model on different domains of knowledge (STEM, coding, humanities), different tasks (reasoning, writing, summarization, question-answering), different levels of complexity, different lengths of prompts as well as its safety and alignment with human values and ability to defend itself against adversarial prompts.\n\n\nThen each prompt was generated using the following logic:\n\n\"\"\"\\<USER>: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\\n that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not \\\ncorrect. If you don't know the answer to a question, please don't share false information.\n\nHere is my question: {question}\n\n\\<ASSISTANT>:\"\"\"\n\n\nAfter that, for each question we generate K=8 answers with Phi-2 by setting the maximum number of new tokens to 300, stopping if the end of text token is generated, doing sampling, and setting the temperature to some predefined value.\n\nWe then rank each answer using OpenAssitant's reward model and take the best one.\n\nFinally, we perform a small temperature hyperparameter scan and found that the best answers according to the reward model were given using a temperature value of 0.4. So these are the answers that are in the dataset.", "## Dataset Sources\n\n\n\n- Capybara Dataset: link\n- OpenOrca Dataset: link\n- Truthful QA Dataset: link\n- LeetCode's \"Subsets\" problem: link\n- DAN prompt: link\n- Llama's system prompt: link\n- Micrososft's Phi-2: link\n- OpenAssistant's reward model: link" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of cookinai/OpenCM-14\n\n\n\nDataset automatically created during the evaluation run of model cookinai/OpenCM-14 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 2024-01-10T15:40:02.112197(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 cookinai/OpenCM-14\n\n\n\nDataset automatically created during the evaluation run of model cookinai/OpenCM-14 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 2024-01-10T15:40:02.112197(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 cookinai/CM-14\n\n\n\nDataset automatically created during the evaluation run of model cookinai/CM-14 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 2024-01-10T15:39:56.317779(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 cookinai/CM-14\n\n\n\nDataset automatically created during the evaluation run of model cookinai/CM-14 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 2024-01-10T15:39:56.317779(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:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-gaokao-chemistry\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao Chemistry subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-gaokao-chemistry\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao Chemistry subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "arxiv:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-gaokao-chinese\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao Chinese subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-gaokao-chinese\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao Chinese subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "arxiv:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-gaokao-english\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao-English subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-gaokao-english\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao-English subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "arxiv:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-gaokao-geography\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao Geography subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-gaokao-geography\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao Geography subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "arxiv:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-gaokao-history\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao History subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-gaokao-history\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao History subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "arxiv:2304.06364", "region:us" ]

[ "2304.06364" ]

[]

[ "# Dataset Card for \"agieval-gaokao-mathqa\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao MathQA subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]

[ "TAGS\n#arxiv-2304.06364 #region-us \n", "# Dataset Card for \"agieval-gaokao-mathqa\"\n\n\nDataset taken from URL and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.\n\nThis dataset contains the contents of the Gaokao MathQA subtask of AGIEval, as accessed in URL .\n\n\nCitation:\n\n\nPlease make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:" ]