Datasets:

---

ftopal

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huggingface-datasets

like 0

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

[]

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[ "## Description\n14,980 Images PPT OCR Data of 8 Languages. This dataset includes 8 languages, multiple scenes, different photographic angles, different photographic distances, different light conditions. For annotation, line-level quadrilateral bounding box annotation and transcription for the texts were annotated in the data. The dataset can be used for tasks such as OCR of multi-language.\n\nFor more details, please refer to the link: URL", "## Data size\n14,980 images, 8 languages", "## Data environment\nincluding meeting room, conference room", "## Language types\nFrench, Korean, Japanese, Spanish, German, Italian, Portuguese and Russian", "## Data diversity\nmultiple scenes, multiple languages, different photographic angles, different photographic distances, different light conditions", "## Device\ncellphone", "## Collecting angles\nfront, left, right, looking up angle", "## Data format\nthe image data format is .jpg, the annotation file data format is .json", "## Annotation content\nline-level quadrilateral bounding box annotation and transcription for the texts", "## Accuracy\nthe error bound of each vertex of quadrilateral bounding box is within 5 pixels, which is a qualified annotation, the accuracy of bounding boxes is not less than 95%; the texts transcription accuracy is not less than 95%", "# Licensing Information\nCommercial License" ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "## Description\n14,980 Images PPT OCR Data of 8 Languages. This dataset includes 8 languages, multiple scenes, different photographic angles, different photographic distances, different light conditions. For annotation, line-level quadrilateral bounding box annotation and transcription for the texts were annotated in the data. The dataset can be used for tasks such as OCR of multi-language.\n\nFor more details, please refer to the link: URL", "## Data size\n14,980 images, 8 languages", "## Data environment\nincluding meeting room, conference room", "## Language types\nFrench, Korean, Japanese, Spanish, German, Italian, Portuguese and Russian", "## Data diversity\nmultiple scenes, multiple languages, different photographic angles, different photographic distances, different light conditions", "## Device\ncellphone", "## Collecting angles\nfront, left, right, looking up angle", "## Data format\nthe image data format is .jpg, the annotation file data format is .json", "## Annotation content\nline-level quadrilateral bounding box annotation and transcription for the texts", "## Accuracy\nthe error bound of each vertex of quadrilateral bounding box is within 5 pixels, which is a qualified annotation, the accuracy of bounding boxes is not less than 95%; the texts transcription accuracy is not less than 95%", "# Licensing Information\nCommercial License" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Kquant03/Cognito-2x7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Cognito-2x7B-bf16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T09:26:22.410222(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 Kquant03/Cognito-2x7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Cognito-2x7B-bf16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T09:26:22.410222(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" ]

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

[]

[]

[ "## Description\n46,695 Images- Household Waste Data.The data includes multiple types of waste, multiple scenes. This data set can be used for tasks such as object detection, classification, and recognition.\n\nFor more details, please refer to the link: URL", "## Date size\n46,695 images, 46,695 bounding boxes", "## Collecting environment\nincluding indoor and outdoor scenes", "## Data diversity\nmultiple types of waste, multiple scenes", "## Device\ncellphone", "## Data format\nthe image data formats are .jpg, png and jpeg , the annotation file format is .json", "## Annotation content\nrectangular bounding boxes of waste", "## Accuracy\nthe accuracy of rectangular bounding boxes is not less than 97%", "# Licensing Information\nCommercial License" ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "## Description\n46,695 Images- Household Waste Data.The data includes multiple types of waste, multiple scenes. This data set can be used for tasks such as object detection, classification, and recognition.\n\nFor more details, please refer to the link: URL", "## Date size\n46,695 images, 46,695 bounding boxes", "## Collecting environment\nincluding indoor and outdoor scenes", "## Data diversity\nmultiple types of waste, multiple scenes", "## Device\ncellphone", "## Data format\nthe image data formats are .jpg, png and jpeg , the annotation file format is .json", "## Annotation content\nrectangular bounding boxes of waste", "## Accuracy\nthe accuracy of rectangular bounding boxes is not less than 97%", "# Licensing Information\nCommercial License" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of alchemonaut/QuartetAnemoi-70B-t0.0001\n\n\n\nDataset automatically created during the evaluation run of model alchemonaut/QuartetAnemoi-70B-t0.0001 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T09:33:24.428024(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 alchemonaut/QuartetAnemoi-70B-t0.0001\n\n\n\nDataset automatically created during the evaluation run of model alchemonaut/QuartetAnemoi-70B-t0.0001 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T09:33:24.428024(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" ]

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

[]

[]

[ "## Description\n180,718 Images - Sign Language Gestures Recognition Data. The data diversity includes multiple scenes, 41 static gestures, 95 dynamic gestures, multiple photographic angles, and multiple light conditions. In terms of data annotation, 21 landmarks, gesture types, and gesture attributes were annotated. This dataset can be used for tasks such as gesture recognition and sign language translation.\n\nFor more details, please refer to the link: URL", "## Data size\n180,718 images, including 83,013 images of static gestures, 97,705 images of dynamic gestures", "## Population distribution\nthe race distribution is Asian, the gender distribution is male and female, the age distribution is mainly young people and middle-aged people", "## Collection environment\nincluding indoor scenes and outdoor scenes", "## Collection diversity\nincluding multiple scenes, 41 static gestures, 95 dynamic gestures, multiple photographic angles, multiple light conditions", "## Device\ncellphone", "## Data forma\nthe image data format is .jpg, the annotation file format is .json", "## Collecting content\nsign language gestures were collected in different scenes", "## Annotation content\n21 landmarks annotation (each landmark includes the attribute of visible or invisible), gesture type annotation, gesture attributes annotation (left hand or right hand)", "## Accuracy\naccuracy requirement: the point location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; accuracy of landmark annotation: the annotation part (each landmark) is regarded as the unit, the accuracy rate shall be more than 95%", "# Licensing Information\nCommercial License" ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "## Description\n180,718 Images - Sign Language Gestures Recognition Data. The data diversity includes multiple scenes, 41 static gestures, 95 dynamic gestures, multiple photographic angles, and multiple light conditions. In terms of data annotation, 21 landmarks, gesture types, and gesture attributes were annotated. This dataset can be used for tasks such as gesture recognition and sign language translation.\n\nFor more details, please refer to the link: URL", "## Data size\n180,718 images, including 83,013 images of static gestures, 97,705 images of dynamic gestures", "## Population distribution\nthe race distribution is Asian, the gender distribution is male and female, the age distribution is mainly young people and middle-aged people", "## Collection environment\nincluding indoor scenes and outdoor scenes", "## Collection diversity\nincluding multiple scenes, 41 static gestures, 95 dynamic gestures, multiple photographic angles, multiple light conditions", "## Device\ncellphone", "## Data forma\nthe image data format is .jpg, the annotation file format is .json", "## Collecting content\nsign language gestures were collected in different scenes", "## Annotation content\n21 landmarks annotation (each landmark includes the attribute of visible or invisible), gesture type annotation, gesture attributes annotation (left hand or right hand)", "## Accuracy\naccuracy requirement: the point location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; accuracy of landmark annotation: the annotation part (each landmark) is regarded as the unit, the accuracy rate shall be more than 95%", "# Licensing Information\nCommercial License" ]

[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:other", "medical", "region:us" ]

[]

[ "en" ]

[ "# Dataset for \"Discharge Me!\"\n\nWelcome to the dataset for \"Discharge Me!\", the BioNLP ACL'24 Shared Task on Streamlining Discharge Documentation.\n\nFor information about the shared task, please visit the Codabench Challenge Website or the GitHub Page. \n\nThe dataset for this shared task is based off the MIMIC-IV dataset. Before accessing and utilizing the provided data split, all participants are required to have signed the data access agreements on PhysioNet.\n\nBy requesting access and using the provided Huggingface dataset split, the participants agree to the following:\n- Participants will comply with the *PhysioNet Credentialed Health Data License 1.5.0* pertaining to the use of the MIMIC-IV dataset and its modules (MIMIC-IV-Note and MIMIC-IV-ED).\n- Participants will use the granted access solely for the purpose of participating in \"Discharge Me!\" at BioNLP ACL'24. Unauthorized use or distribution of the data is prohibited.\n- Participants will ensure the confidentiality of the data and will not disclose any protected health information (PHI) if found.\n- Participants will acknowledge the use of the MIMIC-IV dataset in any publications, presentations, or reports resulting from their work in \"Discharge Me!\".\n\nWe hope you enjoy the shared task!\n\n-- Organizers of \"Discharge Me!\" @ BioNLP ACL'24" ]

[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-other #medical #region-us \n", "# Dataset for \"Discharge Me!\"\n\nWelcome to the dataset for \"Discharge Me!\", the BioNLP ACL'24 Shared Task on Streamlining Discharge Documentation.\n\nFor information about the shared task, please visit the Codabench Challenge Website or the GitHub Page. \n\nThe dataset for this shared task is based off the MIMIC-IV dataset. Before accessing and utilizing the provided data split, all participants are required to have signed the data access agreements on PhysioNet.\n\nBy requesting access and using the provided Huggingface dataset split, the participants agree to the following:\n- Participants will comply with the *PhysioNet Credentialed Health Data License 1.5.0* pertaining to the use of the MIMIC-IV dataset and its modules (MIMIC-IV-Note and MIMIC-IV-ED).\n- Participants will use the granted access solely for the purpose of participating in \"Discharge Me!\" at BioNLP ACL'24. Unauthorized use or distribution of the data is prohibited.\n- Participants will ensure the confidentiality of the data and will not disclose any protected health information (PHI) if found.\n- Participants will acknowledge the use of the MIMIC-IV dataset in any publications, presentations, or reports resulting from their work in \"Discharge Me!\".\n\nWe hope you enjoy the shared task!\n\n-- Organizers of \"Discharge Me!\" @ BioNLP ACL'24" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of kevin009/flyingllama-v2\n\n\n\nDataset automatically created during the evaluation run of model kevin009/flyingllama-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T09:40:33.484186(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 kevin009/flyingllama-v2\n\n\n\nDataset automatically created during the evaluation run of model kevin009/flyingllama-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T09:40:33.484186(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:conversational", "size_categories:1K<n<10K", "language:bn", "language:gu", "language:hi", "language:kn", "language:ml", "language:mr", "language:pa", "language:ta", "language:te", "language:ur", "license:cc", "region:us" ]

[]

[ "bn", "gu", "hi", "kn", "ml", "mr", "pa", "ta", "te", "ur" ]

[ "### Description\n\nThis dataset is derived from the already existing dataset made by AI4Bharat. We have used the IndicSentiment dataset of AI4Bharat to create an instruction style dataset. \n\nIndicSentiment is a multilingual parallel dataset for sentiment analysis. It encompasses product reviews, translations into Indic languages, sentiment labels, and more. \nThe original dataset(IndicSentiment) was made available under the cc-0 license. \n\nThis dataset contains 10 split with 1150+ rows each.Each split corresponds to a language.", "### Template\n\nThe following template was used for converting the original dataset:" ]

[ "TAGS\n#task_categories-conversational #size_categories-1K<n<10K #language-Bengali #language-Gujarati #language-Hindi #language-Kannada #language-Malayalam #language-Marathi #language-Panjabi #language-Tamil #language-Telugu #language-Urdu #license-cc #region-us \n", "### Description\n\nThis dataset is derived from the already existing dataset made by AI4Bharat. We have used the IndicSentiment dataset of AI4Bharat to create an instruction style dataset. \n\nIndicSentiment is a multilingual parallel dataset for sentiment analysis. It encompasses product reviews, translations into Indic languages, sentiment labels, and more. \nThe original dataset(IndicSentiment) was made available under the cc-0 license. \n\nThis dataset contains 10 split with 1150+ rows each.Each split corresponds to a language.", "### Template\n\nThe following template was used for converting the original dataset:" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of kevin009/TinyNaughtyLlama-v1.0\n\n\n\nDataset automatically created during the evaluation run of model kevin009/TinyNaughtyLlama-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T09:54:29.075197(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 kevin009/TinyNaughtyLlama-v1.0\n\n\n\nDataset automatically created during the evaluation run of model kevin009/TinyNaughtyLlama-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T09:54:29.075197(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 \"pokemon_caption_data_chatgpt\"\n\nMore Information needed" ]

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

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

[]

[ "en" ]

[ "# Evol-Instruct-Code-80k-v1\n\nThis is a cleansed version of nickrosh/Evol-Instruct-Code-80k-v1", "## Usage" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #code #region-us \n", "# Evol-Instruct-Code-80k-v1\n\nThis is a cleansed version of nickrosh/Evol-Instruct-Code-80k-v1", "## Usage" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ru", "license:cc0-1.0", "region:us" ]

[]

[ "ru" ]

[ "# Dataset Card for PochtaMarket products", "### Dataset Summary\n\nThis dataset was scraped from product pages on the Russian marketplace PochtaMarket. It includes all information from the product card. The dataset was collected by processing around 500 thousand, starting from the first one. At the time the dataset was collected, it is assumed that these were all the products available on this marketplace. Some fields may be empty, but the string is expected to contain some data, empty responses have been sorted.", "### Languages\n\nThe dataset is mostly in Russian, but there may be other languages present.", "## Dataset Structure", "### Data Fields\n\nThis dataset includes the following fields:\n\n- 'id': Identifier for the product (integer)\n- 'name': Name of the product (string)\n- 'description': Short description of the product (string)\n- 'longDescription': Detailed description of the product (string)\n- 'seoKeywords': Search engine optimization keywords for the product (string)\n- 'brand': Brand name associated with the product (string)\n- 'providerName': Name of the provider or seller (string)", "### Data Splits\n\nAll examples are in the train split, there is no validation split.", "## Additional Information", "### License\n\nThis dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can:\n\n* Use it for any purpose, including commercial projects.\n* Modify it however you like.\n* Distribute it without asking permission.\n\nNo attribution is required, but it's always appreciated!\n\nCC0 license: URL\n\nTo learn more about CC0, visit the Creative Commons website: URL", "### Dataset Curators\n\n- nyuuzyou" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Russian #license-cc0-1.0 #region-us \n", "# Dataset Card for PochtaMarket products", "### Dataset Summary\n\nThis dataset was scraped from product pages on the Russian marketplace PochtaMarket. It includes all information from the product card. The dataset was collected by processing around 500 thousand, starting from the first one. At the time the dataset was collected, it is assumed that these were all the products available on this marketplace. Some fields may be empty, but the string is expected to contain some data, empty responses have been sorted.", "### Languages\n\nThe dataset is mostly in Russian, but there may be other languages present.", "## Dataset Structure", "### Data Fields\n\nThis dataset includes the following fields:\n\n- 'id': Identifier for the product (integer)\n- 'name': Name of the product (string)\n- 'description': Short description of the product (string)\n- 'longDescription': Detailed description of the product (string)\n- 'seoKeywords': Search engine optimization keywords for the product (string)\n- 'brand': Brand name associated with the product (string)\n- 'providerName': Name of the provider or seller (string)", "### Data Splits\n\nAll examples are in the train split, there is no validation split.", "## Additional Information", "### License\n\nThis dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can:\n\n* Use it for any purpose, including commercial projects.\n* Modify it however you like.\n* Distribute it without asking permission.\n\nNo attribution is required, but it's always appreciated!\n\nCC0 license: URL\n\nTo learn more about CC0, visit the Creative Commons website: URL", "### Dataset Curators\n\n- nyuuzyou" ]

[ "task_categories:conversational", "task_categories:text-generation", "size_categories:100K<n<1M", "license:mit", "region:us" ]

[]

[]

[ "# janitorai-cards\n\nThis dataset contains 190k cards that I received from janitorai, from a source that wished to remain anonymous.\n\nMy addition to this data is conversion of cards to v2 character card format, and a local webpage that can be used to explore the dataset.", "### Webpage\n\n\n\nTher webpage lets you browse cards, search by text, fitler by tags and order by date/name/popularity.\n\nTo use the webpage, put URL into a directory, and download and extract archives into same directory: URL, URL, URL, URL, and URL.\n\nAfter that, just open URL in the browser.\n\nThe directory structure should look like this:\n\n\n\nFor performance reasons, the webpage only loads 10000 most popular cards when you open it. To view all, click the \"Load all\" button in the top row.\n\nCaveat: instead of downloading the card, it opens it in a new page—you have to save it yourself. I can't figure out how to get the download to work.", "### Files\n\n- URL, URL, URL, URL - archives with v2 character cards, tested to work with SillyTavern.\n- cards-js.7z - all v2 character cards in json format, without images, tested to work with SillyTavern.\n- URL - webpage for browsing cards.\n- URL - files with information about cards - it's needed for the webpage to function.\n- orig.7z - original json files with cards from janitorai - not compatible with any software." ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-100K<n<1M #license-mit #region-us \n", "# janitorai-cards\n\nThis dataset contains 190k cards that I received from janitorai, from a source that wished to remain anonymous.\n\nMy addition to this data is conversion of cards to v2 character card format, and a local webpage that can be used to explore the dataset.", "### Webpage\n\n\n\nTher webpage lets you browse cards, search by text, fitler by tags and order by date/name/popularity.\n\nTo use the webpage, put URL into a directory, and download and extract archives into same directory: URL, URL, URL, URL, and URL.\n\nAfter that, just open URL in the browser.\n\nThe directory structure should look like this:\n\n\n\nFor performance reasons, the webpage only loads 10000 most popular cards when you open it. To view all, click the \"Load all\" button in the top row.\n\nCaveat: instead of downloading the card, it opens it in a new page—you have to save it yourself. I can't figure out how to get the download to work.", "### Files\n\n- URL, URL, URL, URL - archives with v2 character cards, tested to work with SillyTavern.\n- cards-js.7z - all v2 character cards in json format, without images, tested to work with SillyTavern.\n- URL - webpage for browsing cards.\n- URL - files with information about cards - it's needed for the webpage to function.\n- orig.7z - original json files with cards from janitorai - not compatible with any software." ]

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

[]

[ "en" ]

[ "# SQL Code\n\nThis is a cleansed version of b-mc2/sql-create-context", "## Usage" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #code #region-us \n", "# SQL Code\n\nThis is a cleansed version of b-mc2/sql-create-context", "## Usage" ]

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

[]

[]

[ "# Dataset Card for Dataset Name\n\nThis dataset was created to train a model that generates diverse image generation prompts from simple Japanese descriptions.", "## Dataset Details", "### Dataset Description\n\nSTAIR Captions + Microsoft COCO Caption + Microsoft COCO Images caption interpreted by UForm \n\nThis dataset was created by having UForm explain Microsoft COCO Images of source Microsoft COCO based on the pair of translation inforThis dataset was created to train a model that generates diverse image generation prompts from simple Japanese URL STAIR Captions and source information Microsoft COCO Caption." ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "# Dataset Card for Dataset Name\n\nThis dataset was created to train a model that generates diverse image generation prompts from simple Japanese descriptions.", "## Dataset Details", "### Dataset Description\n\nSTAIR Captions + Microsoft COCO Caption + Microsoft COCO Images caption interpreted by UForm \n\nThis dataset was created by having UForm explain Microsoft COCO Images of source Microsoft COCO based on the pair of translation inforThis dataset was created to train a model that generates diverse image generation prompts from simple Japanese URL STAIR Captions and source information Microsoft COCO Caption." ]

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

[]

[ "en" ]

[ "# Open Platypus Code\n\nThis is a cleansed version of garage-bAInd/Open-Platypus", "## Usage" ]

[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #math #region-us \n", "# Open Platypus Code\n\nThis is a cleansed version of garage-bAInd/Open-Platypus", "## Usage" ]

[ "size_categories:1K<n<10K", "license:apache-2.0", "Golang", "Code", "Go", "QA", "region:us" ]

[]

[]

[ "# Code-Golang-QA-2k\n\nThis (small) dataset comprises 2,000 question-and-answer entries related to the Go programming language. It is designed to serve as a resource for individuals looking to enhance machine learning models, create chatbots, or simply to provide a comprehensive knowledge base for developers working with Go.", "## Data Format\n\nbash\\ngo get -u URL main\\n\\nimport \\\"URL main() {\\n router := gin.Default()\\n}\\ngo\\nfunc main() {\\n router := gin.Default()\\n \\n router.GET(\\\"/message\\\", func(c *gin.Context) {\\n c.JSON(200, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n}\\ngo\\nfunc main() {\\n router := gin.Default()\\n\\n router.GET(\\\"/message\\\", func(c *gin.Context) {\\n c.JSON(200, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n\\n router.Run(\\\":8080\\\") // Listen and serve on 0.0.0.0:8080\\n}\\nbash\\ncurl http://localhost:8080/message\\njson\\n{\\\"message\\\": \\\"Hello, World!\\\"}\\n" ]

[ "TAGS\n#size_categories-1K<n<10K #license-apache-2.0 #Golang #Code #Go #QA #region-us \n", "# Code-Golang-QA-2k\n\nThis (small) dataset comprises 2,000 question-and-answer entries related to the Go programming language. It is designed to serve as a resource for individuals looking to enhance machine learning models, create chatbots, or simply to provide a comprehensive knowledge base for developers working with Go.", "## Data Format\n\nbash\\ngo get -u URL main\\n\\nimport \\\"URL main() {\\n router := gin.Default()\\n}\\ngo\\nfunc main() {\\n router := gin.Default()\\n \\n router.GET(\\\"/message\\\", func(c *gin.Context) {\\n c.JSON(200, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n}\\ngo\\nfunc main() {\\n router := gin.Default()\\n\\n router.GET(\\\"/message\\\", func(c *gin.Context) {\\n c.JSON(200, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n\\n router.Run(\\\":8080\\\") // Listen and serve on 0.0.0.0:8080\\n}\\nbash\\ncurl http://localhost:8080/message\\njson\\n{\\\"message\\\": \\\"Hello, World!\\\"}\\n" ]

[ "language:en", "license:apache-2.0", "Python", "Code Generation", "Algorithm", "Pseudo-code", "Source Code", "Programmming", "Python Programming", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-apache-2.0 #Python #Code Generation #Algorithm #Pseudo-code #Source Code #Programmming #Python Programming #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"target-elements-0.2split\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"target-elements-0.2split\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"target-elements-0.3split\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"target-elements-0.3split\"\n\nMore Information needed" ]

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

[]

[ "en" ]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train1000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train1000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train5000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train5000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train10000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train10000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train50000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train50000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train100000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train100000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train500000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train500000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train1000000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train1000000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train5000000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train5000000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train10000000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train10000000_eval1000_rare\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"random25eof_find_passage_train50000000_eval1000_rare\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"random25eof_find_passage_train50000000_eval1000_rare\"\n\nMore Information needed" ]

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

[]

[ "en" ]

[ "### Model\n\n\nURL", "### Quantization\n\n\n1. Used URL:URL to convert the model to an f16 GGUF\n2. Calculated an importance matrix using wikitext-2-raw/URL\n3. Used URL:quantize to quantize the f16 model to to the various quants with the calculated importance matrix.", "### Input\n\n\nI created a file: prompts/logic\\_precidence\\_inference.txt\n\n\nThis input was run many times to attempt to see how the answers vary. Different classifications of answers were noted while functionally equivalent answers were not.\n\n\nA secondary case with 10 runs includes an additional instruction: \"Furthermore, the AI is to answer tersely, not explaining it's rationale. Single word answers are optimal.\" *Note: 10 runs may not be enough*", "### Command\n\n\n\n\n---\n\n\nf16\n---\n\n\nNon-Quanted Model\n\n\nSize: 24G (25759553440)", "### INCORRECT\n\n\n\n> \n> Considering the typical generational order between a father and his child, we can infer that Charlie was most likely born before Alex became a father. Therefore, Alex was probably born after Charlie. So, Alex was born later in comparison to the given context.\n> \n> \n>", "### CORRECT\n\n\n\n> \n> Considering the usual family structure and age dynamics between parents and their children, it is more likely that Charlie was born later than Alex. This assumption holds true unless there is any specific context given to suggest otherwise.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ8\\_0\n-----\n\n\nPerforms roughly as well as the non-quantized model\n\n\nSize: 13G (13685856224)", "### CORRECT\n\n\n\n> \n> Considering the traditional family structure where parents are older than their children, it is likely that Charlie was born later than Alex.\n> \n> \n>", "### RIGHT ANSWER - BAD LOGIC (partially)\n\n\n\n> \n> Considering the given information, it's logical to assume that Charlie was born after Alex since Alex would have been Charlie's age when Charlie was born. So in most cases, Alex would have been older when Charlie was born, implying Charlie was born later relatively in comparison. But it's important to note that this assumption does not hold for all circumstances (e.g., if Alex himself was born later in life), but it's the majority case based on typical generational gaps.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ6\\_K\n-----\n\n\nBest performing, even ahead of the non-quanted model, oddly. No wrong answers or refusals were generated in long-form.\n\n\nSize: 9.9G (10566817760)", "### CORRECT\n\n\n\n> \n> Considering the usual family dynamics and age progression, it is most likely that Charlie was born later than his father Alex. This assumption is made due to limited context and the general understanding that parents are older than their children. However, there may be exceptions to this rule in certain circumstances.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ5\\_K\\_M\n--------\n\n\nGave refusal and incorrect long-form answers. Bare-word performance was perfect.\n\n\nSize: 8.6G (9132259296)", "### REFUSAL\n\n\n\n> \n> Considering the given information only states a parent-child relationship between Alex and Charlie, it cannot be definitively concluded who was born later. Typically, parents are older than their children but without a specific age mentioned, we cannot make that assumption. So, in this context, it is ambiguous.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the normal course of events where parents give birth before having children themselves, it is likely that Charlie was born earlier than Alex. Therefore, Alex was born later compared to Charlie.\n> \n> \n>", "### CORRECT\n\n\n\n> \n> Considering the common order of events in life, it's usually assumed that parents are older than their children. So in this case, it can be inferred that Charlie was born later than when Alex became a father, making Charlie the one born after a later point in time compared to Alex. Hence, Charlie was likely born later.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ5\\_1\n-----\n\n\nLong form writing is slightly weakened. Bare-word results are normal.\n\n\nSize: 9.1G (9670506464)", "### CORRECT\n\n\n\n> \n> Considering the given information, it's logical to assume that when Alex is Charlie's father, Alex was born before Charlie. Fatherhood typically occurs after the child's birth. Therefore, in most cases, Charlie was born later compared to Alex.\n> However, it's important to note that this assumption relies on typical life events and might not hold in every scenario.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the typical generational order, it's generally assumed that parents are older than their children. Therefore, in this case, Charlie was likely born before Alex became a father, implying Charlie was born earlier but Alex started his parental role later. So, Alex was born later relatively when compared to being Charlie's age at conception.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ5\\_K\\_S\n--------\n\n\nThe long-form writing has become unreliable and gives predominantly non-correct answers, but is fine when correct. Bare-word results are good.\n\n\nSize: 8.3G (8873785312)", "### CORRECT\n\n\n\n> \n> Considering the typical generational relationship between a father and child, it is likely that Alex was born earlier than Charlie. So, Charlie would have been born later relatively when compared to his father Alex.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the typical parent-child relationship where parents are older than their children, it is most likely that Charlie was born earlier than Alex became a father. Hence, Alex was born later relatively compared to Charlie.\n> \n> \n>", "### ANSWERING A PROXIMAL QUESTION\n\n\n\n> \n> Considering the given information where Alex is Charlie's father, it's generally assumed that a parent is older than their child. Thus, we can infer that Alex was probably born earlier than Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ4\\_K\\_M\n--------\n\n\nPredominantly correct long-form answers. Perfect bare-word results. No Refusals.\n\n\nSize: 7.3G (7782086624)", "### CORRECT\n\n\n\n> \n> Considering the given information where Alex is Charlie's father, it is generally assumed that a father is older than his child. Therefore, it can be inferred that Alex was born earlier than Charlie. Hence, Charlie would have been born later compared to Alex.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the given information, it is generally assumed that parents are older than their children when they have them. Therefore, in most cases, Charlie would have been born before Alex if Alex is Charlie's father. Hence, Alex (as the father) was born later relatively compared to Charlie.\"\n> \n> \n>", "### ANSWERING\\_A\\_PROXIMAL\\_QUESTION\n\n\n\n> \n> Considering the typical order of events in a family structure, when Alex is Charlie's father, it is generally assumed that Alex was born earlier than Charlie.\"\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ5\\_0\n-----\n\n\nBoth long-form and bare-word have lost a little reliability.\n\n\nSize: 8.3G (8903145440)", "### CORRECT\n\n\n\n> \n> Considering the typical generational order where parents are older than their children, it is more likely that Charlie was born later than Alex.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the typical order of events where a child is born after their parents, it is likely that Charlie was born before Alex became his father, implying Charlie was born later relatively compared to Alex. However, without specific birth dates, this answer is subject to change if unusual circumstances exist. But generally speaking, Charlie would have been born later than when Alex became his father.\n> \n> \n>", "### CONFUSED\n\n\n\n> \n> Considering the typical family structure where a parent is older than their child, it is most likely that Charlie was born earlier than Alex became a father, implying Charlie was born later relatively but Alex was born before. So in this scenario, Alex was born earlier but Charlie was born comparatively later in their lives.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ4\\_K\\_S\n--------\n\n\nGood long-form behaviour. Perfect bare-word performance. Low variability observed.\n\n\nSize: 6.9G (7341160416)", "### CORRECT\n\n\n\n> \n> Considering the given context where Alex is Charlie's father, it is generally assumed that an older person becomes a parent to a younger one. Thus, we can infer that Charlie was born later than Alex.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the typical family dynamic where parents are older than their children, it is more likely that Charlie was born earlier than Alex. Hence, Alex was born later compared to Charlie in most scenarios.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ4\\_1\n-----\n\n\nIn long-form, the model frequently answered a proximal question (who was born earlier) rather than the exact question. No incorrect answers observed in long-form.\n\n\nSize: 7.6G (8077064160)", "### CORRECT\n\n\n\n> \n> Considering the typical generation gap between a parent and their child, it's more likely that Alex (Charlie's father) was born earlier than Charlie. Therefore, Charlie was born later in this scenario.\n> \n> \n>", "### REFUSAL\n\n\n\n> \n> Considering the given information only states the relationship between Alex and Charlie as father and son, it does not provide enough context to determine when they were born or who was born later. Generally speaking, parents are born before their children but without a specific timeline, we cannot assume the exact order of birth here.\n> \n> \n>", "### RIGHT\\_ANSWER\\_BAD\\_LOGIC\n\n\n\n> \n> Considering the given information, it's most likely that Charlie was born after Alex since Alex would have been Charlie's age when Charlie was born. However, direct birth order cannot be definitively concluded without more context.\n> \n> \n>", "### ANSWERING\\_A\\_PROXIMAL\\_QUESTION\n\n\n\n> \n> Considering the given information where Alex is Charlie's father, it would typically imply a generational relationship with Alex being older than Charlie. Therefore, Alex was most likely born before Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ3\\_K\\_L\n--------\n\n\nNo incorrect or refusal long-form answers. Bare-word performance was degraded somewhat.\n\n\nSize: 6.3G (6729209824)", "### CORRECT\n\n\n\n> \n> In general, when discussing parent-child relationships, it's assumed that the parent was born earlier than the child. Therefore, Charlie was likely born later than his father Alex.\\n\\nHowever, to provide a definitive answer, we would need more context or information about their birth dates. But given the typical parent-child relationship dynamic, it's safe to infer this scenario.\n> \n> \n>", "### ANSWERING\\_A\\_PROXIMAL\\_QUESTION\n\n\n\n> \n> In the majority case when considering age differences between a father and child, it's assumed that the parent (Alex in this scenario) is older than their offspring (Charlie). Therefore, Alex was most likely born earlier than Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ4\\_0\n-----\n\n\nLong-form answers were evenly distributed among classifications, meaning it did not reliably give correct answers. Bare-word performance was perfect.\n\n\nSize: 6.9G (7309703136)", "### CORRECT\n\n\n\n> \n> Considering the typical order of events, when a person becomes a parent (as Alex is to Charlie), it usually implies that Alex was born earlier. Therefore, it's more likely that Charlie was born later in this context.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the normal life progression where parents are older than their children, it can be inferred that Charlie was born earlier, thus making Alex, his father, born later. So, Alex was born later compared to Charlie.\n> \n> \n>", "### ANSWERING\\_A\\_PROXIMAL\\_QUESTION\n\n\n\n> \n> Considering the general progression of life, parents are typically older than their children when they give birth. Hence, it is more likely that Alex was born earlier than Charlie in this scenario.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ3\\_K\\_M\n--------\n\n\nGood bare-word performance, even if it wasn't all correct answers.\n\n\n5.8G (6205970400)", "### CORRECT\n\n\n\n> \n> In most cases, when a parent gives birth to a child, it's assumed that the parent was born earlier than their child. So, in this scenario where Alex is Charlie's father, it's likely that Alex was born before Charlie. Hence, Charlie was born later compared to Alex.\n> \n> \n>", "### ANSWERING A PROXIMAL QUESTION\n\n\n\n> \n> In most cases, when a person becomes a parent, it means they have already lived through their own childhood and adolescence before having a child. Therefore, it can be inferred that Alex (Charlie's father) was born earlier than Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ3\\_K\\_S\n--------\n\n\nMostly correct output in long form. Mostly incorrect output in bare-word.\n\n\nSize: 5.3G (5587310560)", "### CORRECT\n\n\n\n> \n> In most cases, when a person is someone's father, it implies that the father was born before the child. Therefore, Charlie would have been born later than Alex in the majority of situations.\n> However, it's important to note that exceptions can exist (for example, in cases of assisted reproduction, where the age difference is compressed, or if they are related but not biologically father and son). In general discourse and without further context, we would assume the majority case.\n> So, in the assumed majority case, Charlie was born later than Alex.\n> \n> \n>", "### ANSWERING A PROXIMAL QUESTION\n\n\n\n> \n> In this scenario, it is generally assumed that when someone is referred to as a father, they are the older parent. Therefore, it is likely that Alex, being Charlie's father, was born earlier than Charlie.\n> However, without further context or information, it is impossible to know for certain if there are any exceptional circumstances such as age reversal treatments or other factors that may have changed their relative ages. So, in the majority case, Alex was born earlier than Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ3\\_K\\_XS\n---------\n\n\nNo wrong answers or refusals were generated in long-form. One case where the AI attempted to answer both ways for a single individual. Bad bare-word performance.\n\n\nSize: 4.9G (5230532576)", "### CORRECT\n\n\n\n> \n> Considering the typical generational order between a parent and child, it is more likely that Alex, being Charlie's father, was born earlier than Charlie. Thus, Charlie was born later in comparison to Alex.\n> \n> \n>", "### SLIGHTLY HEDGED\n\n\n\n> \n> It is more likely that Alex was born earlier since he is Charlie's father. Generally speaking, a parent is older than their child. Therefore, we can assume that Alex was born later as a child but earlier as a parent when compared to Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nIQ3\\_XXS\n--------\n\n\nNo incorrect or refusal long-form answers. Good Bare-word performance.\n\n\nSize: 4.8G (5062604768)", "### CORRECT\n\n\n\n> \n> In this scenario, since Alex is Charlie's father, it's more likely that Alex was born earlier. Therefore, Charlie would have been born later than his father Alex.\n> \n> \n>", "### PROXIMAL\\_QUESTION\n\n\n\n> \n> Considering the given information only states the relationship between Alex and Charlie as father and son respectively, it is assumed that Alex is older since he is the parent of Charlie. Typically, parents are older than their children. However, this assumption depends on other factors, like if Charlie had Alex late in life or if Alex had a late start to fatherhood due to infertility treatments, that might change the scenario. But in a general case, Alex would have been born earlier than Charlie.\n> \n> \n>", "### bare\\_word\\_results\n\n\n\n\n---\n\n\nQ2\\_K\n-----\n\n\nNo wrong answers or refusals were generated in long-form. Bad bare-word performance.\n\n\nSize: 4.5G (4760303584)", "### CORRECT\n\n\n\n> \n> Considering that parenthood generally happens before a child's birth, it's more likely that Charlie was born later than Alex.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ2\\_K\\_S\n--------\n\n\nAnswers were typically mixed-language (French, code, no spaces) and refusal. Never gave a correct response.\n\n\nSize: 4.2G (4434196448)", "### REFUSAL (AND FRENCH)\n\n\n\n> \n> même si Alex est le père de Charlie, cela ne nous donne pas d'information sur quand ils sont nés pour déterminer qui est né plus tard. On devrait savoir leur âge ou la période de leur naissance pour résoudre ce riddles.\n> \n> \n> Translation\n> ===========\n> \n> \n> Even though Alex is Charlie's father, this does not provide us with information about when they were born to determine who was born later. In order to solve this riddle, we need to know their age or the period of their birth.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> opponentially but considering the given information but assuming Charlie is an adult with age comparable to or older than Alex's age range as parents, we can infer that Charlie was born earlier than Alex since Alex is Charlie's father.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nIQ2\\_XS\n-------\n\n\nThis is much better than the incoherent results of IQ2\\_XXS.\n\n\nSize: 3.6G (3837450208)", "### INCORRECT\n\n\n\n> \n> In this case, it is more likely assumed that Charlie was born earlier than Alex since Alex is his father, implying an age relationship where a father is usually older than his child.\n> \n> \n>", "### CORRECT\n\n\n\n> \n> In this scenario, it is generally assumed that since Alex is Charlie's father, Charlie would be Alex's younger counterpart as father-child relationships inherently involve an older parent and a younger offspring.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nIQ2\\_XXS\n--------\n\n\nNot Recommended. There were many possible answer classifications, and it was not predictable which the quant would output.\n\n\nSize: 3.3G (3462060000)", "### INCORRECT\n\n\n\n> \n> To infer the answer correctly, we need more information about their ages, as having only the relationship between them doesn't provide enough context. However, if it is the majority case that older people tend to have children younger than themselves, we can assume that Charlie was born earlier and Alex was born later. But without more contextual details, this remains uncertain.\n> Glastring ambiguity without clear ages.\n> \n> \n>", "### CORRECT\n\n\n\n> \n> To solve this riddle, we need more specific information about the age gap or birth dates between Alex and Charlie. However, by default assuming equality between their age differences as average across society today, it's more common that Charlie, being younger than Alex who is his father, was born later.\n> \n> \n>", "### INCOHERENT\n\n\n\n> \n> To assume age equality between Alex and Charlie initially remploys ambiguity but considering age limitations and generational shifts Alex (born older) would presumably be older still whereas Charlie being his son would have been born later relatively recently compared to Alex's age range.\n> \n> \n>", "### RIGHT ANSWER - BAD LOGIC\n\n\n\n> \n> To solve this riddle efficiently, we should consider the age difference and the most common scenario of generations in families. Typically, younger generations tend to have children rather than the opposite. Therefore, it's more likely that Charlie was born later than Alex. However, without specific birth dates or ages available, we cannot definitively confirm this assumption.\n> Answer Late: Charlie\n> \n> \n>", "### ANSWERING A PROXIMAL QUESTION\n\n\n\n> \n> To assume age equality between Alex and Charlie when they first became father and son would make Alex older when they reunite for this query. ⠀\n> conde: However capabilities of AI limiting precise knowledge of generation dates make assumptions necessary when extracting age relations between Alex and Charlie from context. ⠀\n> condeconde: ₂ Peroften when a father acknowleges their child as mentioned here with \"Alex is Charlie's father\", it implies Alex is older as typically younger Charlie would not have gotten pregnantouver nor given birth yet. ⠀\n> condecondefinal: ₂ENDORSED\\_STRONG\\_Most likely, Alex (the father) was born earlier than Charlie./code>\n> \n> \n> \n\n\n\n> \n> In summary: Most likely Alex (the father) was born earlier than Charlie.\n> \n> \n>", "### NONSENSE\n\n\n\n> \n> TDMerstHOUTSmixedup=%removedmix%)typen=% havetypes=% havetypes=% havetypes=% havetypes=% havetypes=% notenostmix%)typen=% notenostmix%)typen=% notenestonnievey=%typenoestonniestr=%typen=% bekanfact=% bekanfact=% bekanfact=ThinkingaboutCharliesagebrained-%LMVRevertleader=%thinkwas=% thinkwas=% thinkwas=% thinking=% thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%finalanswer=% thinkingaboutCharliesagebeingolderwhenhewasbornafterCharliesagehadapositive=% finalanswermostlikelyCharliesagehadapositiveyearswhenhehimself=% finalanswermostlikelyAlexwasbornafterCharliesagehadapositive=% finalanswermostlikelyAlexgotolderwhenhewasbornafterthatyear:%RM\n> \n> \n> \n\n\n...and so on", "### Bare-word Results\n\n\nInterpretation\n--------------\n\n\n* Q6\\_K is an excellent quant choice to make.\n* IQ3\\_XXS is the smallest quant that has very low degradation.\n* Down to Q3\\_K\\_M, the model does not become particularly broken.\n* Q3\\_K\\_S and below, the quants make the model unreliable.\n* K-means quants are usually superior to non-K-means quants" ]

[ "TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #license-gpl-2.0 #region-us \n", "### Model\n\n\nURL", "### Quantization\n\n\n1. Used URL:URL to convert the model to an f16 GGUF\n2. Calculated an importance matrix using wikitext-2-raw/URL\n3. Used URL:quantize to quantize the f16 model to to the various quants with the calculated importance matrix.", "### Input\n\n\nI created a file: prompts/logic\\_precidence\\_inference.txt\n\n\nThis input was run many times to attempt to see how the answers vary. Different classifications of answers were noted while functionally equivalent answers were not.\n\n\nA secondary case with 10 runs includes an additional instruction: \"Furthermore, the AI is to answer tersely, not explaining it's rationale. Single word answers are optimal.\" *Note: 10 runs may not be enough*", "### Command\n\n\n\n\n---\n\n\nf16\n---\n\n\nNon-Quanted Model\n\n\nSize: 24G (25759553440)", "### INCORRECT\n\n\n\n> \n> Considering the typical generational order between a father and his child, we can infer that Charlie was most likely born before Alex became a father. Therefore, Alex was probably born after Charlie. So, Alex was born later in comparison to the given context.\n> \n> \n>", "### CORRECT\n\n\n\n> \n> Considering the usual family structure and age dynamics between parents and their children, it is more likely that Charlie was born later than Alex. This assumption holds true unless there is any specific context given to suggest otherwise.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ8\\_0\n-----\n\n\nPerforms roughly as well as the non-quantized model\n\n\nSize: 13G (13685856224)", "### CORRECT\n\n\n\n> \n> Considering the traditional family structure where parents are older than their children, it is likely that Charlie was born later than Alex.\n> \n> \n>", "### RIGHT ANSWER - BAD LOGIC (partially)\n\n\n\n> \n> Considering the given information, it's logical to assume that Charlie was born after Alex since Alex would have been Charlie's age when Charlie was born. So in most cases, Alex would have been older when Charlie was born, implying Charlie was born later relatively in comparison. But it's important to note that this assumption does not hold for all circumstances (e.g., if Alex himself was born later in life), but it's the majority case based on typical generational gaps.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ6\\_K\n-----\n\n\nBest performing, even ahead of the non-quanted model, oddly. No wrong answers or refusals were generated in long-form.\n\n\nSize: 9.9G (10566817760)", "### CORRECT\n\n\n\n> \n> Considering the usual family dynamics and age progression, it is most likely that Charlie was born later than his father Alex. This assumption is made due to limited context and the general understanding that parents are older than their children. However, there may be exceptions to this rule in certain circumstances.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ5\\_K\\_M\n--------\n\n\nGave refusal and incorrect long-form answers. Bare-word performance was perfect.\n\n\nSize: 8.6G (9132259296)", "### REFUSAL\n\n\n\n> \n> Considering the given information only states a parent-child relationship between Alex and Charlie, it cannot be definitively concluded who was born later. Typically, parents are older than their children but without a specific age mentioned, we cannot make that assumption. So, in this context, it is ambiguous.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the normal course of events where parents give birth before having children themselves, it is likely that Charlie was born earlier than Alex. Therefore, Alex was born later compared to Charlie.\n> \n> \n>", "### CORRECT\n\n\n\n> \n> Considering the common order of events in life, it's usually assumed that parents are older than their children. So in this case, it can be inferred that Charlie was born later than when Alex became a father, making Charlie the one born after a later point in time compared to Alex. Hence, Charlie was likely born later.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ5\\_1\n-----\n\n\nLong form writing is slightly weakened. Bare-word results are normal.\n\n\nSize: 9.1G (9670506464)", "### CORRECT\n\n\n\n> \n> Considering the given information, it's logical to assume that when Alex is Charlie's father, Alex was born before Charlie. Fatherhood typically occurs after the child's birth. Therefore, in most cases, Charlie was born later compared to Alex.\n> However, it's important to note that this assumption relies on typical life events and might not hold in every scenario.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the typical generational order, it's generally assumed that parents are older than their children. Therefore, in this case, Charlie was likely born before Alex became a father, implying Charlie was born earlier but Alex started his parental role later. So, Alex was born later relatively when compared to being Charlie's age at conception.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ5\\_K\\_S\n--------\n\n\nThe long-form writing has become unreliable and gives predominantly non-correct answers, but is fine when correct. Bare-word results are good.\n\n\nSize: 8.3G (8873785312)", "### CORRECT\n\n\n\n> \n> Considering the typical generational relationship between a father and child, it is likely that Alex was born earlier than Charlie. So, Charlie would have been born later relatively when compared to his father Alex.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the typical parent-child relationship where parents are older than their children, it is most likely that Charlie was born earlier than Alex became a father. Hence, Alex was born later relatively compared to Charlie.\n> \n> \n>", "### ANSWERING A PROXIMAL QUESTION\n\n\n\n> \n> Considering the given information where Alex is Charlie's father, it's generally assumed that a parent is older than their child. Thus, we can infer that Alex was probably born earlier than Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ4\\_K\\_M\n--------\n\n\nPredominantly correct long-form answers. Perfect bare-word results. No Refusals.\n\n\nSize: 7.3G (7782086624)", "### CORRECT\n\n\n\n> \n> Considering the given information where Alex is Charlie's father, it is generally assumed that a father is older than his child. Therefore, it can be inferred that Alex was born earlier than Charlie. Hence, Charlie would have been born later compared to Alex.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the given information, it is generally assumed that parents are older than their children when they have them. Therefore, in most cases, Charlie would have been born before Alex if Alex is Charlie's father. Hence, Alex (as the father) was born later relatively compared to Charlie.\"\n> \n> \n>", "### ANSWERING\\_A\\_PROXIMAL\\_QUESTION\n\n\n\n> \n> Considering the typical order of events in a family structure, when Alex is Charlie's father, it is generally assumed that Alex was born earlier than Charlie.\"\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ5\\_0\n-----\n\n\nBoth long-form and bare-word have lost a little reliability.\n\n\nSize: 8.3G (8903145440)", "### CORRECT\n\n\n\n> \n> Considering the typical generational order where parents are older than their children, it is more likely that Charlie was born later than Alex.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the typical order of events where a child is born after their parents, it is likely that Charlie was born before Alex became his father, implying Charlie was born later relatively compared to Alex. However, without specific birth dates, this answer is subject to change if unusual circumstances exist. But generally speaking, Charlie would have been born later than when Alex became his father.\n> \n> \n>", "### CONFUSED\n\n\n\n> \n> Considering the typical family structure where a parent is older than their child, it is most likely that Charlie was born earlier than Alex became a father, implying Charlie was born later relatively but Alex was born before. So in this scenario, Alex was born earlier but Charlie was born comparatively later in their lives.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ4\\_K\\_S\n--------\n\n\nGood long-form behaviour. Perfect bare-word performance. Low variability observed.\n\n\nSize: 6.9G (7341160416)", "### CORRECT\n\n\n\n> \n> Considering the given context where Alex is Charlie's father, it is generally assumed that an older person becomes a parent to a younger one. Thus, we can infer that Charlie was born later than Alex.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the typical family dynamic where parents are older than their children, it is more likely that Charlie was born earlier than Alex. Hence, Alex was born later compared to Charlie in most scenarios.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ4\\_1\n-----\n\n\nIn long-form, the model frequently answered a proximal question (who was born earlier) rather than the exact question. No incorrect answers observed in long-form.\n\n\nSize: 7.6G (8077064160)", "### CORRECT\n\n\n\n> \n> Considering the typical generation gap between a parent and their child, it's more likely that Alex (Charlie's father) was born earlier than Charlie. Therefore, Charlie was born later in this scenario.\n> \n> \n>", "### REFUSAL\n\n\n\n> \n> Considering the given information only states the relationship between Alex and Charlie as father and son, it does not provide enough context to determine when they were born or who was born later. Generally speaking, parents are born before their children but without a specific timeline, we cannot assume the exact order of birth here.\n> \n> \n>", "### RIGHT\\_ANSWER\\_BAD\\_LOGIC\n\n\n\n> \n> Considering the given information, it's most likely that Charlie was born after Alex since Alex would have been Charlie's age when Charlie was born. However, direct birth order cannot be definitively concluded without more context.\n> \n> \n>", "### ANSWERING\\_A\\_PROXIMAL\\_QUESTION\n\n\n\n> \n> Considering the given information where Alex is Charlie's father, it would typically imply a generational relationship with Alex being older than Charlie. Therefore, Alex was most likely born before Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ3\\_K\\_L\n--------\n\n\nNo incorrect or refusal long-form answers. Bare-word performance was degraded somewhat.\n\n\nSize: 6.3G (6729209824)", "### CORRECT\n\n\n\n> \n> In general, when discussing parent-child relationships, it's assumed that the parent was born earlier than the child. Therefore, Charlie was likely born later than his father Alex.\\n\\nHowever, to provide a definitive answer, we would need more context or information about their birth dates. But given the typical parent-child relationship dynamic, it's safe to infer this scenario.\n> \n> \n>", "### ANSWERING\\_A\\_PROXIMAL\\_QUESTION\n\n\n\n> \n> In the majority case when considering age differences between a father and child, it's assumed that the parent (Alex in this scenario) is older than their offspring (Charlie). Therefore, Alex was most likely born earlier than Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ4\\_0\n-----\n\n\nLong-form answers were evenly distributed among classifications, meaning it did not reliably give correct answers. Bare-word performance was perfect.\n\n\nSize: 6.9G (7309703136)", "### CORRECT\n\n\n\n> \n> Considering the typical order of events, when a person becomes a parent (as Alex is to Charlie), it usually implies that Alex was born earlier. Therefore, it's more likely that Charlie was born later in this context.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> Considering the normal life progression where parents are older than their children, it can be inferred that Charlie was born earlier, thus making Alex, his father, born later. So, Alex was born later compared to Charlie.\n> \n> \n>", "### ANSWERING\\_A\\_PROXIMAL\\_QUESTION\n\n\n\n> \n> Considering the general progression of life, parents are typically older than their children when they give birth. Hence, it is more likely that Alex was born earlier than Charlie in this scenario.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ3\\_K\\_M\n--------\n\n\nGood bare-word performance, even if it wasn't all correct answers.\n\n\n5.8G (6205970400)", "### CORRECT\n\n\n\n> \n> In most cases, when a parent gives birth to a child, it's assumed that the parent was born earlier than their child. So, in this scenario where Alex is Charlie's father, it's likely that Alex was born before Charlie. Hence, Charlie was born later compared to Alex.\n> \n> \n>", "### ANSWERING A PROXIMAL QUESTION\n\n\n\n> \n> In most cases, when a person becomes a parent, it means they have already lived through their own childhood and adolescence before having a child. Therefore, it can be inferred that Alex (Charlie's father) was born earlier than Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ3\\_K\\_S\n--------\n\n\nMostly correct output in long form. Mostly incorrect output in bare-word.\n\n\nSize: 5.3G (5587310560)", "### CORRECT\n\n\n\n> \n> In most cases, when a person is someone's father, it implies that the father was born before the child. Therefore, Charlie would have been born later than Alex in the majority of situations.\n> However, it's important to note that exceptions can exist (for example, in cases of assisted reproduction, where the age difference is compressed, or if they are related but not biologically father and son). In general discourse and without further context, we would assume the majority case.\n> So, in the assumed majority case, Charlie was born later than Alex.\n> \n> \n>", "### ANSWERING A PROXIMAL QUESTION\n\n\n\n> \n> In this scenario, it is generally assumed that when someone is referred to as a father, they are the older parent. Therefore, it is likely that Alex, being Charlie's father, was born earlier than Charlie.\n> However, without further context or information, it is impossible to know for certain if there are any exceptional circumstances such as age reversal treatments or other factors that may have changed their relative ages. So, in the majority case, Alex was born earlier than Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ3\\_K\\_XS\n---------\n\n\nNo wrong answers or refusals were generated in long-form. One case where the AI attempted to answer both ways for a single individual. Bad bare-word performance.\n\n\nSize: 4.9G (5230532576)", "### CORRECT\n\n\n\n> \n> Considering the typical generational order between a parent and child, it is more likely that Alex, being Charlie's father, was born earlier than Charlie. Thus, Charlie was born later in comparison to Alex.\n> \n> \n>", "### SLIGHTLY HEDGED\n\n\n\n> \n> It is more likely that Alex was born earlier since he is Charlie's father. Generally speaking, a parent is older than their child. Therefore, we can assume that Alex was born later as a child but earlier as a parent when compared to Charlie.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nIQ3\\_XXS\n--------\n\n\nNo incorrect or refusal long-form answers. Good Bare-word performance.\n\n\nSize: 4.8G (5062604768)", "### CORRECT\n\n\n\n> \n> In this scenario, since Alex is Charlie's father, it's more likely that Alex was born earlier. Therefore, Charlie would have been born later than his father Alex.\n> \n> \n>", "### PROXIMAL\\_QUESTION\n\n\n\n> \n> Considering the given information only states the relationship between Alex and Charlie as father and son respectively, it is assumed that Alex is older since he is the parent of Charlie. Typically, parents are older than their children. However, this assumption depends on other factors, like if Charlie had Alex late in life or if Alex had a late start to fatherhood due to infertility treatments, that might change the scenario. But in a general case, Alex would have been born earlier than Charlie.\n> \n> \n>", "### bare\\_word\\_results\n\n\n\n\n---\n\n\nQ2\\_K\n-----\n\n\nNo wrong answers or refusals were generated in long-form. Bad bare-word performance.\n\n\nSize: 4.5G (4760303584)", "### CORRECT\n\n\n\n> \n> Considering that parenthood generally happens before a child's birth, it's more likely that Charlie was born later than Alex.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nQ2\\_K\\_S\n--------\n\n\nAnswers were typically mixed-language (French, code, no spaces) and refusal. Never gave a correct response.\n\n\nSize: 4.2G (4434196448)", "### REFUSAL (AND FRENCH)\n\n\n\n> \n> même si Alex est le père de Charlie, cela ne nous donne pas d'information sur quand ils sont nés pour déterminer qui est né plus tard. On devrait savoir leur âge ou la période de leur naissance pour résoudre ce riddles.\n> \n> \n> Translation\n> ===========\n> \n> \n> Even though Alex is Charlie's father, this does not provide us with information about when they were born to determine who was born later. In order to solve this riddle, we need to know their age or the period of their birth.\n> \n> \n>", "### INCORRECT\n\n\n\n> \n> opponentially but considering the given information but assuming Charlie is an adult with age comparable to or older than Alex's age range as parents, we can infer that Charlie was born earlier than Alex since Alex is Charlie's father.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nIQ2\\_XS\n-------\n\n\nThis is much better than the incoherent results of IQ2\\_XXS.\n\n\nSize: 3.6G (3837450208)", "### INCORRECT\n\n\n\n> \n> In this case, it is more likely assumed that Charlie was born earlier than Alex since Alex is his father, implying an age relationship where a father is usually older than his child.\n> \n> \n>", "### CORRECT\n\n\n\n> \n> In this scenario, it is generally assumed that since Alex is Charlie's father, Charlie would be Alex's younger counterpart as father-child relationships inherently involve an older parent and a younger offspring.\n> \n> \n>", "### Bare-word Results\n\n\n\n\n---\n\n\nIQ2\\_XXS\n--------\n\n\nNot Recommended. There were many possible answer classifications, and it was not predictable which the quant would output.\n\n\nSize: 3.3G (3462060000)", "### INCORRECT\n\n\n\n> \n> To infer the answer correctly, we need more information about their ages, as having only the relationship between them doesn't provide enough context. However, if it is the majority case that older people tend to have children younger than themselves, we can assume that Charlie was born earlier and Alex was born later. But without more contextual details, this remains uncertain.\n> Glastring ambiguity without clear ages.\n> \n> \n>", "### CORRECT\n\n\n\n> \n> To solve this riddle, we need more specific information about the age gap or birth dates between Alex and Charlie. However, by default assuming equality between their age differences as average across society today, it's more common that Charlie, being younger than Alex who is his father, was born later.\n> \n> \n>", "### INCOHERENT\n\n\n\n> \n> To assume age equality between Alex and Charlie initially remploys ambiguity but considering age limitations and generational shifts Alex (born older) would presumably be older still whereas Charlie being his son would have been born later relatively recently compared to Alex's age range.\n> \n> \n>", "### RIGHT ANSWER - BAD LOGIC\n\n\n\n> \n> To solve this riddle efficiently, we should consider the age difference and the most common scenario of generations in families. Typically, younger generations tend to have children rather than the opposite. Therefore, it's more likely that Charlie was born later than Alex. However, without specific birth dates or ages available, we cannot definitively confirm this assumption.\n> Answer Late: Charlie\n> \n> \n>", "### ANSWERING A PROXIMAL QUESTION\n\n\n\n> \n> To assume age equality between Alex and Charlie when they first became father and son would make Alex older when they reunite for this query. ⠀\n> conde: However capabilities of AI limiting precise knowledge of generation dates make assumptions necessary when extracting age relations between Alex and Charlie from context. ⠀\n> condeconde: ₂ Peroften when a father acknowleges their child as mentioned here with \"Alex is Charlie's father\", it implies Alex is older as typically younger Charlie would not have gotten pregnantouver nor given birth yet. ⠀\n> condecondefinal: ₂ENDORSED\\_STRONG\\_Most likely, Alex (the father) was born earlier than Charlie./code>\n> \n> \n> \n\n\n\n> \n> In summary: Most likely Alex (the father) was born earlier than Charlie.\n> \n> \n>", "### NONSENSE\n\n\n\n> \n> TDMerstHOUTSmixedup=%removedmix%)typen=% havetypes=% havetypes=% havetypes=% havetypes=% havetypes=% notenostmix%)typen=% notenostmix%)typen=% notenestonnievey=%typenoestonniestr=%typen=% bekanfact=% bekanfact=% bekanfact=ThinkingaboutCharliesagebrained-%LMVRevertleader=%thinkwas=% thinkwas=% thinkwas=% thinking=% thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%thinkwas>%finalanswer=% thinkingaboutCharliesagebeingolderwhenhewasbornafterCharliesagehadapositive=% finalanswermostlikelyCharliesagehadapositiveyearswhenhehimself=% finalanswermostlikelyAlexwasbornafterCharliesagehadapositive=% finalanswermostlikelyAlexgotolderwhenhewasbornafterthatyear:%RM\n> \n> \n> \n\n\n...and so on", "### Bare-word Results\n\n\nInterpretation\n--------------\n\n\n* Q6\\_K is an excellent quant choice to make.\n* IQ3\\_XXS is the smallest quant that has very low degradation.\n* Down to Q3\\_K\\_M, the model does not become particularly broken.\n* Q3\\_K\\_S and below, the quants make the model unreliable.\n* K-means quants are usually superior to non-K-means quants" ]

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

[]

[ "en" ]

[]

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

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

[]

[]

[ "# 945 rows of Alpaca \n\nsource URL" ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "# 945 rows of Alpaca \n\nsource URL" ]

[ "task_categories:translation", "language:ar", "language:en", "license:cc", "region:us" ]

[]

[ "ar", "en" ]

[]

[ "TAGS\n#task_categories-translation #language-Arabic #language-English #license-cc #region-us \n" ]

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

[]

[]

[ "# Dataset Card for Dataset Name\n\nDataset Summary (Taken from Piqa)\n\nTo apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA.\n\nPhysical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language.\n\nPIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by URL, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials.\n\n\n- Curated by: Samrat Saha\n- Language(s) (NLP): ISO 639-2 Code - ben, hin, kan\n- License: Apache-2.0", "### Dataset Sources [optional]\n\n- Demo [optional]:\n- goal\tsol1\tsol2\tlabel\n ಬೆಣ್ಣೆಯನ್ನು ಕುದಿಸುವಾಗ, ಅದು ಸಿದ್ಧವಾದಾಗ, ನೀವು ಮಾ...\tಅದನ್ನು ತಟ್ಟೆಯಲ್ಲಿ ಸುರಿಯಿರಿ.\tಅದನ್ನು ಬಾಟಲಿಯಲ್ಲಿ ಸುರಿಯಿರಿ.\t1", "## Dataset Structure\n\nPlease Refer to Piqa", "## Dataset Creation\nThis dataset is created from Piqa using 1B High quality Indic Transformer(Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin\n).\nCurrently the Train and validation dataset provided for Bengali, Hindi, Kannada Languages. \nThe Translation is done using beam search with a beam width of 3.", "### Curation Rationale\n\nThe goal of the dataset is to convert the Piqa data into Indic Languages for the Development of Indic LLM.", "### Source Data\nPiqa", "### Annotations \nManual Annotation not done, this is completley high quality machine translation dataset.\n\n\n@inproceedings{Bisk2020,\n author = {Yonatan Bisk and Rowan Zellers and\n Ronan Le Bras and Jianfeng Gao\n and Yejin Choi},\n title = {PIQA: Reasoning about Physical Commonsense in\n Natural Language},\n booktitle = {Thirty-Fourth AAAI Conference on\n Artificial Intelligence},\n year = {2020},\n}\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]\n{\nSamrat Saha \nURL@URL\n}", "## Dataset Card Contact\n\n{\n author = {Samrat Saha},\n title = {PIQA_indic: Reasoning about Physical Commonsense in\n Natural Language For Indic Languages},\n year = {2024},\n\n}" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Dataset Card for Dataset Name\n\nDataset Summary (Taken from Piqa)\n\nTo apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA.\n\nPhysical commonsense knowledge is a major challenge on the road to true AI-completeness, including robots that interact with the world and understand natural language.\n\nPIQA focuses on everyday situations with a preference for atypical solutions. The dataset is inspired by URL, which provides users with instructions on how to build, craft, bake, or manipulate objects using everyday materials.\n\n\n- Curated by: Samrat Saha\n- Language(s) (NLP): ISO 639-2 Code - ben, hin, kan\n- License: Apache-2.0", "### Dataset Sources [optional]\n\n- Demo [optional]:\n- goal\tsol1\tsol2\tlabel\n ಬೆಣ್ಣೆಯನ್ನು ಕುದಿಸುವಾಗ, ಅದು ಸಿದ್ಧವಾದಾಗ, ನೀವು ಮಾ...\tಅದನ್ನು ತಟ್ಟೆಯಲ್ಲಿ ಸುರಿಯಿರಿ.\tಅದನ್ನು ಬಾಟಲಿಯಲ್ಲಿ ಸುರಿಯಿರಿ.\t1", "## Dataset Structure\n\nPlease Refer to Piqa", "## Dataset Creation\nThis dataset is created from Piqa using 1B High quality Indic Transformer(Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin\n).\nCurrently the Train and validation dataset provided for Bengali, Hindi, Kannada Languages. \nThe Translation is done using beam search with a beam width of 3.", "### Curation Rationale\n\nThe goal of the dataset is to convert the Piqa data into Indic Languages for the Development of Indic LLM.", "### Source Data\nPiqa", "### Annotations \nManual Annotation not done, this is completley high quality machine translation dataset.\n\n\n@inproceedings{Bisk2020,\n author = {Yonatan Bisk and Rowan Zellers and\n Ronan Le Bras and Jianfeng Gao\n and Yejin Choi},\n title = {PIQA: Reasoning about Physical Commonsense in\n Natural Language},\n booktitle = {Thirty-Fourth AAAI Conference on\n Artificial Intelligence},\n year = {2020},\n}\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]\n{\nSamrat Saha \nURL@URL\n}", "## Dataset Card Contact\n\n{\n author = {Samrat Saha},\n title = {PIQA_indic: Reasoning about Physical Commonsense in\n Natural Language For Indic Languages},\n year = {2024},\n\n}" ]

[ "size_categories:1K<n<10K", "license:apache-2.0", "Golang", "Code", "Go", "QA", "region:us" ]

[]

[]

[ "# Code-Golang-QA-2k\n\nThis (small) dataset comprises ~1.8k dpo entries related to the Go programming language. It is designed to serve as a resource for individuals looking to enhance machine learning models, create chatbots, or simply to provide a comprehensive knowledge base for developers working with Go.", "## Data Format\n\nbash\\ngo get -u URL main\\n\\nimport \\\"URL main() {\\n router := gin.Default()\\n}\\ngo\\nfunc main() {\\n router := gin.Default()\\n \\n router.GET(\\\"/message\\\", func(c *gin.Context) {\\n c.JSON(200, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n}\\ngo\\nfunc main() {\\n router := gin.Default()\\n\\n router.GET(\\\"/message\\\", func(c *gin.Context) {\\n c.JSON(200, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n\\n router.Run(\\\":8080\\\") // Listen and serve on 0.0.0.0:8080\\n}\\nbash\\ncurl http://localhost:8080/message\\njson\\n{\\\"message\\\": \\\"Hello, World!\\\"}\\ngo\\ngo get -u URL \\\"URL := gin.Default()\\ngo\\nr.GET(\\\"/api\\\", func(c *gin.Context) {\\n // Your API logic goes here\\n})\\ngo\\nr.GET(\\\"/api\\\", func(c *gin.Context) {\\n c.JSON(http.StatusOK, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n})\\ngo\\nr.Run(\\\":8080\\\")\\ngo\\npackage main\\n\\nimport \\\"URL main() {\\n r := gin.Default()\\n\\n r.GET(\\\"/api\\\", func(c *gin.Context) {\\n c.JSON(http.StatusOK, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n\\n r.Run(\\\":8080\\\")\\n}\\n" ]

[ "TAGS\n#size_categories-1K<n<10K #license-apache-2.0 #Golang #Code #Go #QA #region-us \n", "# Code-Golang-QA-2k\n\nThis (small) dataset comprises ~1.8k dpo entries related to the Go programming language. It is designed to serve as a resource for individuals looking to enhance machine learning models, create chatbots, or simply to provide a comprehensive knowledge base for developers working with Go.", "## Data Format\n\nbash\\ngo get -u URL main\\n\\nimport \\\"URL main() {\\n router := gin.Default()\\n}\\ngo\\nfunc main() {\\n router := gin.Default()\\n \\n router.GET(\\\"/message\\\", func(c *gin.Context) {\\n c.JSON(200, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n}\\ngo\\nfunc main() {\\n router := gin.Default()\\n\\n router.GET(\\\"/message\\\", func(c *gin.Context) {\\n c.JSON(200, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n\\n router.Run(\\\":8080\\\") // Listen and serve on 0.0.0.0:8080\\n}\\nbash\\ncurl http://localhost:8080/message\\njson\\n{\\\"message\\\": \\\"Hello, World!\\\"}\\ngo\\ngo get -u URL \\\"URL := gin.Default()\\ngo\\nr.GET(\\\"/api\\\", func(c *gin.Context) {\\n // Your API logic goes here\\n})\\ngo\\nr.GET(\\\"/api\\\", func(c *gin.Context) {\\n c.JSON(http.StatusOK, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n})\\ngo\\nr.Run(\\\":8080\\\")\\ngo\\npackage main\\n\\nimport \\\"URL main() {\\n r := gin.Default()\\n\\n r.GET(\\\"/api\\\", func(c *gin.Context) {\\n c.JSON(http.StatusOK, gin.H{\\n \\\"message\\\": \\\"Hello, World!\\\",\\n })\\n })\\n\\n r.Run(\\\":8080\\\")\\n}\\n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Sharathhebbar24/code_gpt2\n\n\n\nDataset automatically created during the evaluation run of model Sharathhebbar24/code_gpt2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T16:58:30.724457(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 Sharathhebbar24/code_gpt2\n\n\n\nDataset automatically created during the evaluation run of model Sharathhebbar24/code_gpt2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T16:58:30.724457(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" ]

[ "license:mit", "csgo", "counter strike", "cs2", "counter-strike", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #csgo #counter strike #cs2 #counter-strike #region-us \n" ]

[ "license:mit", "quake3", "ioquake3", "quake3arena", "teamarena", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #quake3 #ioquake3 #quake3arena #teamarena #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T17:23:23.398012(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T17:23:23.398012(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" ]

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

[]

[]

[]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted\n\n\n\nDataset automatically created during the evaluation run of model sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T17:31:09.507616(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 sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted\n\n\n\nDataset automatically created during the evaluation run of model sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-corrupted on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T17:31:09.507616(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[ "# Dataset Card for Evaluation run of kevin009/llamaRAGdrama\n\n\n\nDataset automatically created during the evaluation run of model kevin009/llamaRAGdrama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T17:32:11.811282(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 kevin009/llamaRAGdrama\n\n\n\nDataset automatically created during the evaluation run of model kevin009/llamaRAGdrama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T17:32:11.811282(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of FelixChao/Faraday-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/Faraday-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T08:14:19.602946(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of FelixChao/Faraday-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/Faraday-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T08:14:19.602946(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of gmonsoon/MiaAffogato-Indo-Mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model gmonsoon/MiaAffogato-Indo-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 2024-02-04T17:34:06.201391(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 gmonsoon/MiaAffogato-Indo-Mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model gmonsoon/MiaAffogato-Indo-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 2024-02-04T17:34:06.201391(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of gmonsoon/OpenMia-Indo-Mistral-7b-v3-refined\n\n\n\nDataset automatically created during the evaluation run of model gmonsoon/OpenMia-Indo-Mistral-7b-v3-refined 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T19:41:28.521258(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 gmonsoon/OpenMia-Indo-Mistral-7b-v3-refined\n\n\n\nDataset automatically created during the evaluation run of model gmonsoon/OpenMia-Indo-Mistral-7b-v3-refined 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T19:41:28.521258(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of sethuiyer/Eida_10.7B\n\n\n\nDataset automatically created during the evaluation run of model sethuiyer/Eida_10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T17:53:38.566336(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 sethuiyer/Eida_10.7B\n\n\n\nDataset automatically created during the evaluation run of model sethuiyer/Eida_10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T17:53:38.566336(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[ "# Dataset Card for Evaluation run of paulml/OmniBeagleMBX-v3-7B\n\n\n\nDataset automatically created during the evaluation run of model paulml/OmniBeagleMBX-v3-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T18:02:06.576942(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of paulml/OmniBeagleMBX-v3-7B\n\n\n\nDataset automatically created during the evaluation run of model paulml/OmniBeagleMBX-v3-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T18:02:06.576942(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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[]

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[ "# Dataset Card for Evaluation run of Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T20:26:53.463273(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T20:26:53.463273(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" ]

[]

[]

[ "## id2label = {", "## 0: 'binary visual question answering',", "## 1:'search by image',", "## 2:'image search by text',", "## 3:'geospatial question answering',", "## 4:'count objects in image' ,", "## 5:'object extraction in image',", "## 6:'image segmentation'", "## }\n\n---\nsize_categories:\n- 1K<n<10K\ntask_categories:\n- text-classification\ndataset_info:\n features:\n - name: request\n dtype: string\n - name: label\n dtype: int64\n splits:\n - name: train\n num_bytes: 99611\n num_examples: 1120\n - name: validation\n num_bytes: 24896\n num_examples: 280\n download_size: 27907\n dataset_size: 124507\nconfigs:\n- config_name: default\n data_files:\n - split: train\n path: data/train-*\n - split: validation\n path: data/validation-*\n---" ]

[ "TAGS\n#region-us \n", "## id2label = {", "## 0: 'binary visual question answering',", "## 1:'search by image',", "## 2:'image search by text',", "## 3:'geospatial question answering',", "## 4:'count objects in image' ,", "## 5:'object extraction in image',", "## 6:'image segmentation'", "## }\n\n---\nsize_categories:\n- 1K<n<10K\ntask_categories:\n- text-classification\ndataset_info:\n features:\n - name: request\n dtype: string\n - name: label\n dtype: int64\n splits:\n - name: train\n num_bytes: 99611\n num_examples: 1120\n - name: validation\n num_bytes: 24896\n num_examples: 280\n download_size: 27907\n dataset_size: 124507\nconfigs:\n- config_name: default\n data_files:\n - split: train\n path: data/train-*\n - split: validation\n path: data/validation-*\n---" ]

[ "task_categories:question-answering", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "size_categories:10K<n<100K", "language:ar", "license:mit", "region:us" ]

[]

[ "ar" ]

[]

[ "TAGS\n#task_categories-question-answering #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #size_categories-10K<n<100K #language-Arabic #license-mit #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of vikash06/doctorMistralLLM10k\n\n\n\nDataset automatically created during the evaluation run of model vikash06/doctorMistralLLM10k 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 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T06:54:54.227795(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 vikash06/doctorMistralLLM10k\n\n\n\nDataset automatically created during the evaluation run of model vikash06/doctorMistralLLM10k 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 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T06:54:54.227795(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 EleutherAI/llemma_7b\n\n\n\nDataset automatically created during the evaluation run of model EleutherAI/llemma_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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T21:06:01.286568(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 EleutherAI/llemma_7b\n\n\n\nDataset automatically created during the evaluation run of model EleutherAI/llemma_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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T21:06:01.286568(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[ "hi" ]

[ "# Dataset Card for \"ai4bharat-hi-subset\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-text-generation #size_categories-100M<n<1B #language-Hindi #license-apache-2.0 #region-us \n", "# Dataset Card for \"ai4bharat-hi-subset\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of nlpguy/Westgate\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/Westgate on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T19:13:46.734414(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 nlpguy/Westgate\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/Westgate on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T19:13:46.734414(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 \"hanna-pairwise\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of gmonsoon/OpenMia-Indo-Engineering\n\n\n\nDataset automatically created during the evaluation run of model gmonsoon/OpenMia-Indo-Engineering on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T20:44:41.527501(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 gmonsoon/OpenMia-Indo-Engineering\n\n\n\nDataset automatically created during the evaluation run of model gmonsoon/OpenMia-Indo-Engineering on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T20:44:41.527501(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" ]

[ "size_categories:10K<n<100K", "language:ja", "license:mit", "region:us" ]

[]

[ "ja" ]

[ "# hh-rlhf-12k-ja\n\nThis repository provides a human preference dataset developed by LLM-jp, a collaborative project launched in Japan.\n\nThis dataset is a Japanese translation of a subset of hh-rlhf using DeepL.\n\nThis dataset consists of 12,000 entries randomly sampled from hh-rlhf. Specifically, it includes a random selection of 3,000 entries from the training splits of the four groups: harmless-base, helpful-base, helpful-online, and helpful-rejection-sampled. For more information on each group, please refer to the original dataset documentation.", "## Send Questions to\nllm-jp(at)URL", "## Model Card Authors\nThe names are listed in alphabetical order.\n\nHirokazu Kiyomaru, Hiroshi Matsuda, Jun Suzuki, Namgi Han, Saku Sugawara, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takashi Kodama, Takumi Okamoto." ]

[ "TAGS\n#size_categories-10K<n<100K #language-Japanese #license-mit #region-us \n", "# hh-rlhf-12k-ja\n\nThis repository provides a human preference dataset developed by LLM-jp, a collaborative project launched in Japan.\n\nThis dataset is a Japanese translation of a subset of hh-rlhf using DeepL.\n\nThis dataset consists of 12,000 entries randomly sampled from hh-rlhf. Specifically, it includes a random selection of 3,000 entries from the training splits of the four groups: harmless-base, helpful-base, helpful-online, and helpful-rejection-sampled. For more information on each group, please refer to the original dataset documentation.", "## Send Questions to\nllm-jp(at)URL", "## Model Card Authors\nThe names are listed in alphabetical order.\n\nHirokazu Kiyomaru, Hiroshi Matsuda, Jun Suzuki, Namgi Han, Saku Sugawara, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takashi Kodama, Takumi Okamoto." ]

[ "task_categories:text-generation", "task_categories:question-answering", "language:fi", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "fi" ]

[ "# Dataset Card for Finnish-NLP/benebele", "## Creation process\n - Finnish subset loaded from facebook/belebele" ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #language-Finnish #license-cc-by-sa-4.0 #region-us \n", "# Dataset Card for Finnish-NLP/benebele", "## Creation process\n - Finnish subset loaded from facebook/belebele" ]

[ "language:eng", "license:mit", "Scrapped Code", "region:us" ]

[]

[ "eng" ]

[ "# Restful Booker Platform Data Set\n\nScrapped code from the Restful Booker Platform code base found here:\n\nURL" ]

[ "TAGS\n#language-English #license-mit #Scrapped Code #region-us \n", "# Restful Booker Platform Data Set\n\nScrapped code from the Restful Booker Platform code base found here:\n\nURL" ]

[ "size_categories:10K<n<100K", "language:ka", "license:cc-by-4.0", "region:us" ]

[]

[ "ka" ]

[ "# Alpaca-kartuli-0.1\n\n\n\nalpaca Dataset-ის ქართულად გადმოთრგმნილი ვერსია.\n\n- წყარო: alpaca-cleaned" ]

[ "TAGS\n#size_categories-10K<n<100K #language-Georgian #license-cc-by-4.0 #region-us \n", "# Alpaca-kartuli-0.1\n\n\n\nalpaca Dataset-ის ქართულად გადმოთრგმნილი ვერსია.\n\n- წყარო: alpaca-cleaned" ]

[ "task_categories:text-classification", "task_categories:question-answering", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "size_categories:n<1K", "language:fr", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "fr", "en" ]

[ "### Dataset Summary\n\n\nThis repository contains the French version of the FraCaS Test Suite introduced in this paper, as well as the original English one, in a TSV format (as opposed to the XML format provided with the original paper).\n\n\nFraCaS stands for \"Framework for Computational Semantics\".", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nIt can also be used for the task of Question Answering (QA) (when using the columns 'question' and 'answer' instead of 'hypothesis' and 'label').\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'id': Index number.\n* 'premises': All premises provided for this particular example, concatenated, in French.\n* 'hypothesis': The translated hypothesis in the target language (French).\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction'), or undef (for undefined).\n* 'question': The hypothesis in the form of question, in French.\n* 'answer': The answer to the question, with possible values 'Yes' (0), 'Don't know' / 'Unknown' (1), 'No' (2), 'undef', or a longer phrase containing qualifications or elaborations such as 'Yes, on one reading'.\n* 'premises\\_original': All premises provided for this particular example, concatenated, in their language of origin (English).\n* 'premise1': The first premise in English.\n* 'premise1\\_original': The first premise in English.\n* 'premise2': When available, the second premise in French.\n* 'premise2\\_original': When available, the second premise in English.\n* 'premise3': When available, the third premise in French.\n* 'premise3\\_original': When available, the third premise in English.\n* 'premise4': When available, the fourth premise in French.\n* 'premise4\\_original': When available, the fourth premise in English.\n* 'premise5': When available, the fifth premise in French.\n* 'premise5\\_original': When available, the fifth premise in English.\n* 'hypothesis\\_original': The hypothesis in English.\n* 'question\\_original': The hypothesis in the form of question, in English.\n* 'note': Text from the source document intended to explain or justify the answer, or notes added to a number of problems in order to explain issues which arose during translation.\n* 'topic': Problem set / topic.", "### Data Splits\n\n\nThe premise counts are distributed as follows:\n\n\n\nThe answer distribution is roughly as follows:\n\n\n\nHere's the breakdown by topic:\n\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nMaxime Amblard, Clément Beysson, Philippe de Groote, Bruno Guillaume, and Sylvain Pogodalla. 2020. A French Version of the FraCaS Test Suite. In *Proceedings of the Twelfth Language Resources and Evaluation Conference*, pages 5887–5895, Marseille, France. European Language Resources Association." ]

[ "TAGS\n#task_categories-text-classification #task_categories-question-answering #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-n<1K #language-French #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nThis repository contains the French version of the FraCaS Test Suite introduced in this paper, as well as the original English one, in a TSV format (as opposed to the XML format provided with the original paper).\n\n\nFraCaS stands for \"Framework for Computational Semantics\".", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nIt can also be used for the task of Question Answering (QA) (when using the columns 'question' and 'answer' instead of 'hypothesis' and 'label').\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'id': Index number.\n* 'premises': All premises provided for this particular example, concatenated, in French.\n* 'hypothesis': The translated hypothesis in the target language (French).\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction'), or undef (for undefined).\n* 'question': The hypothesis in the form of question, in French.\n* 'answer': The answer to the question, with possible values 'Yes' (0), 'Don't know' / 'Unknown' (1), 'No' (2), 'undef', or a longer phrase containing qualifications or elaborations such as 'Yes, on one reading'.\n* 'premises\\_original': All premises provided for this particular example, concatenated, in their language of origin (English).\n* 'premise1': The first premise in English.\n* 'premise1\\_original': The first premise in English.\n* 'premise2': When available, the second premise in French.\n* 'premise2\\_original': When available, the second premise in English.\n* 'premise3': When available, the third premise in French.\n* 'premise3\\_original': When available, the third premise in English.\n* 'premise4': When available, the fourth premise in French.\n* 'premise4\\_original': When available, the fourth premise in English.\n* 'premise5': When available, the fifth premise in French.\n* 'premise5\\_original': When available, the fifth premise in English.\n* 'hypothesis\\_original': The hypothesis in English.\n* 'question\\_original': The hypothesis in the form of question, in English.\n* 'note': Text from the source document intended to explain or justify the answer, or notes added to a number of problems in order to explain issues which arose during translation.\n* 'topic': Problem set / topic.", "### Data Splits\n\n\nThe premise counts are distributed as follows:\n\n\n\nThe answer distribution is roughly as follows:\n\n\n\nHere's the breakdown by topic:\n\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nMaxime Amblard, Clément Beysson, Philippe de Groote, Bruno Guillaume, and Sylvain Pogodalla. 2020. A French Version of the FraCaS Test Suite. In *Proceedings of the Twelfth Language Resources and Evaluation Conference*, pages 5887–5895, Marseille, France. European Language Resources Association." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of AA051615/A0204\n\n\n\nDataset automatically created during the evaluation run of model AA051615/A0204 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T22:24:08.088490(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 AA051615/A0204\n\n\n\nDataset automatically created during the evaluation run of model AA051615/A0204 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T22:24:08.088490(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of s3nh/SeverusWestLake-7B-DPO\n\n\n\nDataset automatically created during the evaluation run of model s3nh/SeverusWestLake-7B-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T22:42:18.214091(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 s3nh/SeverusWestLake-7B-DPO\n\n\n\nDataset automatically created during the evaluation run of model s3nh/SeverusWestLake-7B-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T22:42:18.214091(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of minghaowu/phi-2-OpenHermes-2.5\n\n\n\nDataset automatically created during the evaluation run of model minghaowu/phi-2-OpenHermes-2.5 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T03:58:10.319056(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 minghaowu/phi-2-OpenHermes-2.5\n\n\n\nDataset automatically created during the evaluation run of model minghaowu/phi-2-OpenHermes-2.5 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T03:58:10.319056(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Eric111/NeuralBeagleOpenChat\n\n\n\nDataset automatically created during the evaluation run of model Eric111/NeuralBeagleOpenChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:07:18.461587(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 Eric111/NeuralBeagleOpenChat\n\n\n\nDataset automatically created during the evaluation run of model Eric111/NeuralBeagleOpenChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:07:18.461587(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for \"Wikipedia-Hindi\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling\n\n\n\nDataset automatically created during the evaluation run of model AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:43:15.860527(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 AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling\n\n\n\nDataset automatically created during the evaluation run of model AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:43:15.860527(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[]

[ "# Dataset Card for Evaluation run of Josephgflowers/distillgpt2Cinder\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/distillgpt2Cinder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:46:27.372796(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Josephgflowers/distillgpt2Cinder\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/distillgpt2Cinder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:46:27.372796(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", "size_categories:1K<n<10K", "language:nb", "license:cc0-1.0", "region:us" ]

[]

[ "nb" ]

[ "# Dataset Card for Dataset Name\n\n\n\nNorQuAD is the first Norwegian question answering dataset for machine reading comprehension, created from scratch in Norwegian. The dataset consists of 4,752 manually created question-answer pairs.", "## Dataset Details", "### Dataset Description\n\n\nThe dataset provides Norwegian question-answer pairs taken from two data sources: Wikipedia and news.\n\n\n- Curated by: Human annotators.\n- Funded by: The UiO Teksthub initiative\n- Shared by: The Language Technology Group, University of Oslo\n- Language(s) (NLP): Norwegian Bokmål\n- License: CC0-1.0", "### Dataset Sources\n\n\n\n- Repository: URL\n- Paper: Ivanova et. al., 2023", "## Uses\n\n\n\nThe dataset is intended to be used for NLP model development and benchmarking.", "## Dataset Structure\n\n\n\nData Instances\n\n\n\nData Fields\n\n\n\nDataset Splits\n\nNorQuAD consists of training (3808 examples), validation (472), and public test (472) sets.", "## Dataset Creation", "### Curation Rationale\n\n\nMachine reading comprehension is one of the key problems in natural language understanding. The question answering (QA) task requires a machine to read and comprehend a given text passage, and then answer questions about the passage. There is progress in reading comprehension and question answering for English and a few other languages. We would like to fill in the lack of annotated data for question answering for Norwegian. This project aims at compiling human-created training, validation, and test sets for the task for Norwegian.", "### Source Data\n\n\n\nWikipedia: 872 articles were sampled from Norwegian Bokmal Wikipedia. \n\nNews: For the news category, articles were sampled from Norsk Aviskorpus, an openly available dataset of Norwegian news.", "#### Data Collection and Processing\n\n\n\nWikipedia:In order to include high-quality articles, 130 articles from the\n‘Recommended‘ section and 139 from the ‘Featured‘ section were sampled. The remaining 603 articles were randomly sampled from the remaining Wikipedia\ncorpus. From the sampled articles, we chose only the “Introduction“ sections to be selected as passages for annotation.\n\nNews: 1000 articles were sampled from the Norsk Aviskorpus (NAK)—a collection of Norwegian news texts\nfor the year 2019. As was the case with Wikipedia articles, we chose\nonly news articles which consisted of at least 300\nwords.", "#### Who are the source data producers?\n\n\n\nThe data is sourced from Norwegian Wikipedia dumps as well as the openly available Norwegian News Corpus, available from the Språkbanken repository.", "### Annotations\n\n\nIn total, the annotators processed 353 passages from Wikipedia and 403 passages from news, creating a\ntotal of 4,752 question-answer pairs.", "#### Annotation process\n\n\n\nThe dataset was created in three stages: (i) selecting text passages, (ii) collecting question-answer\npairs for those passages, and (iii) human validation of (a subset of) created question-answer pairs.", "#### Text selection\n\nData was selected from openly available sources from Wikipedia and News data, as described above.", "#### Question-Answer Pairs\n\nThe annotators were provided with a set of initial instructions, largely based on those for similar datasets, in particular, the English SQuAD\ndataset (Rajpurkar et al., 2016) and the GermanQuAD data (Moller et al., 2021). These instructions were subsequently refined following regular\nmeetings with the annotation team.\nThe annotation guidelines provided to the annotators are available (here)[URL\nFor annotation, we used the Haystack annotation tool, which was designed for QA collection.", "#### Human validation\n\nIn a separate stage, the annotators validated a subset of the NorQuAD dataset. In this phase, each\nannotator replied to the questions created by the\nother annotator. We chose the question-answer\npairs for validation at random. In total, 1378 questions from the set of question-answer pairs were\nanswered by validators.", "#### Who are the annotators?\n\n\n\nTwo students of the Master’s program in Natural Language Processing at the University of Oslo,\nboth native Norwegian speakers, created question-answer pairs from the collected passages. Each\nstudent received a separate set of passages for annotation. The students received financial remuneration for their efforts and are co-authors of the\npaper describing the resource. \n\n\n\nBibTeX:\n\nAPA:", "## Dataset Card Authors\n\nVladislav Mikhailov and Lilja Øvrelid", "## Dataset Card Contact\n\nvladism@URL and liljao@URL" ]

[ "TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-Norwegian Bokmål #license-cc0-1.0 #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nNorQuAD is the first Norwegian question answering dataset for machine reading comprehension, created from scratch in Norwegian. The dataset consists of 4,752 manually created question-answer pairs.", "## Dataset Details", "### Dataset Description\n\n\nThe dataset provides Norwegian question-answer pairs taken from two data sources: Wikipedia and news.\n\n\n- Curated by: Human annotators.\n- Funded by: The UiO Teksthub initiative\n- Shared by: The Language Technology Group, University of Oslo\n- Language(s) (NLP): Norwegian Bokmål\n- License: CC0-1.0", "### Dataset Sources\n\n\n\n- Repository: URL\n- Paper: Ivanova et. al., 2023", "## Uses\n\n\n\nThe dataset is intended to be used for NLP model development and benchmarking.", "## Dataset Structure\n\n\n\nData Instances\n\n\n\nData Fields\n\n\n\nDataset Splits\n\nNorQuAD consists of training (3808 examples), validation (472), and public test (472) sets.", "## Dataset Creation", "### Curation Rationale\n\n\nMachine reading comprehension is one of the key problems in natural language understanding. The question answering (QA) task requires a machine to read and comprehend a given text passage, and then answer questions about the passage. There is progress in reading comprehension and question answering for English and a few other languages. We would like to fill in the lack of annotated data for question answering for Norwegian. This project aims at compiling human-created training, validation, and test sets for the task for Norwegian.", "### Source Data\n\n\n\nWikipedia: 872 articles were sampled from Norwegian Bokmal Wikipedia. \n\nNews: For the news category, articles were sampled from Norsk Aviskorpus, an openly available dataset of Norwegian news.", "#### Data Collection and Processing\n\n\n\nWikipedia:In order to include high-quality articles, 130 articles from the\n‘Recommended‘ section and 139 from the ‘Featured‘ section were sampled. The remaining 603 articles were randomly sampled from the remaining Wikipedia\ncorpus. From the sampled articles, we chose only the “Introduction“ sections to be selected as passages for annotation.\n\nNews: 1000 articles were sampled from the Norsk Aviskorpus (NAK)—a collection of Norwegian news texts\nfor the year 2019. As was the case with Wikipedia articles, we chose\nonly news articles which consisted of at least 300\nwords.", "#### Who are the source data producers?\n\n\n\nThe data is sourced from Norwegian Wikipedia dumps as well as the openly available Norwegian News Corpus, available from the Språkbanken repository.", "### Annotations\n\n\nIn total, the annotators processed 353 passages from Wikipedia and 403 passages from news, creating a\ntotal of 4,752 question-answer pairs.", "#### Annotation process\n\n\n\nThe dataset was created in three stages: (i) selecting text passages, (ii) collecting question-answer\npairs for those passages, and (iii) human validation of (a subset of) created question-answer pairs.", "#### Text selection\n\nData was selected from openly available sources from Wikipedia and News data, as described above.", "#### Question-Answer Pairs\n\nThe annotators were provided with a set of initial instructions, largely based on those for similar datasets, in particular, the English SQuAD\ndataset (Rajpurkar et al., 2016) and the GermanQuAD data (Moller et al., 2021). These instructions were subsequently refined following regular\nmeetings with the annotation team.\nThe annotation guidelines provided to the annotators are available (here)[URL\nFor annotation, we used the Haystack annotation tool, which was designed for QA collection.", "#### Human validation\n\nIn a separate stage, the annotators validated a subset of the NorQuAD dataset. In this phase, each\nannotator replied to the questions created by the\nother annotator. We chose the question-answer\npairs for validation at random. In total, 1378 questions from the set of question-answer pairs were\nanswered by validators.", "#### Who are the annotators?\n\n\n\nTwo students of the Master’s program in Natural Language Processing at the University of Oslo,\nboth native Norwegian speakers, created question-answer pairs from the collected passages. Each\nstudent received a separate set of passages for annotation. The students received financial remuneration for their efforts and are co-authors of the\npaper describing the resource. \n\n\n\nBibTeX:\n\nAPA:", "## Dataset Card Authors\n\nVladislav Mikhailov and Lilja Øvrelid", "## Dataset Card Contact\n\nvladism@URL and liljao@URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AIGym/deepseek-coder-1.3b-chat-and-function-calling\n\n\n\nDataset automatically created during the evaluation run of model AIGym/deepseek-coder-1.3b-chat-and-function-calling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:54:42.230347(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 AIGym/deepseek-coder-1.3b-chat-and-function-calling\n\n\n\nDataset automatically created during the evaluation run of model AIGym/deepseek-coder-1.3b-chat-and-function-calling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:54:42.230347(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 AIGym/deepseek-coder-6.7b-chat-and-function-calling\n\n\n\nDataset automatically created during the evaluation run of model AIGym/deepseek-coder-6.7b-chat-and-function-calling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:57:59.059131(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 AIGym/deepseek-coder-6.7b-chat-and-function-calling\n\n\n\nDataset automatically created during the evaluation run of model AIGym/deepseek-coder-6.7b-chat-and-function-calling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-04T23:57:59.059131(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" ]

[ "license:apache-2.0", "arxiv:2203.14371", "region:us" ]

[ "2203.14371" ]

[]

[ "# medmcqa-rephrased-20k-v0.1 \n\n(Rephrased Sample) MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering", "## Dataset Details", "### Dataset Description\n\nMedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.\nMedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity.\n\n'medmcqa-rephrased-20k-v0.1' contains 20,000 random samples of MedMCQA questions rephrased into different multiple-choice formats using 'mistralai/Mixtral-8x7B-Instruct-v0.1'.", "### Dataset Sources\n\n\n\n- Repository: URL\n- Paper: Pal et al. (2022) MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering\n." ]

[ "TAGS\n#license-apache-2.0 #arxiv-2203.14371 #region-us \n", "# medmcqa-rephrased-20k-v0.1 \n\n(Rephrased Sample) MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering", "## Dataset Details", "### Dataset Description\n\nMedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.\nMedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity.\n\n'medmcqa-rephrased-20k-v0.1' contains 20,000 random samples of MedMCQA questions rephrased into different multiple-choice formats using 'mistralai/Mixtral-8x7B-Instruct-v0.1'.", "### Dataset Sources\n\n\n\n- Repository: URL\n- Paper: Pal et al. (2022) MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering\n." ]

[ "region:us" ]

[]

[]

[ "## Segmented ImageNet-1K Subset\n\nA subset of ImageNet-1K that has instance segmentation annotations (classes, boxes, and masks), originally intended for use by the ViT Prisma Library.\n\nThe annotations were autogenerated by Grounded Segment Anything.\n\n\n\nThe total size of the dataset is 12,000 images: 10,000 from ImageNet-1K train and 1,000 each from test and val.", "### Organization\n\nImages are organized in the same structure as ImageNet-1K:\n\n\n\nThe train and val ImageNet classes can be identified from the filenames. See imagenet-1k classes.\n\nMasks are stored in a similar manner:\n\n\n\nFinally 'URL', 'URL', 'URL' store box, label, score and path information:\n\nPlease consider citing this dataset if used in your research:\n\n\n\nGrounded Segment Anything and Imagenet can be cited as follows:" ]

[ "TAGS\n#region-us \n", "## Segmented ImageNet-1K Subset\n\nA subset of ImageNet-1K that has instance segmentation annotations (classes, boxes, and masks), originally intended for use by the ViT Prisma Library.\n\nThe annotations were autogenerated by Grounded Segment Anything.\n\n\n\nThe total size of the dataset is 12,000 images: 10,000 from ImageNet-1K train and 1,000 each from test and val.", "### Organization\n\nImages are organized in the same structure as ImageNet-1K:\n\n\n\nThe train and val ImageNet classes can be identified from the filenames. See imagenet-1k classes.\n\nMasks are stored in a similar manner:\n\n\n\nFinally 'URL', 'URL', 'URL' store box, label, score and path information:\n\nPlease consider citing this dataset if used in your research:\n\n\n\nGrounded Segment Anything and Imagenet can be cited as follows:" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

[ "size_categories:n<1K", "language:en", "license:mit", "India, Economics, GDP", "region:us" ]

[]

[ "en" ]

[ "# Indian States GDP numbers\n\nI wanted to find out how Indian states were faring since independence and if government policies and regimes have made any difference. Since I could not find any aggregate data I wanted to use a list of government documents and compile it myself by state.\n\nFor the first dataset I will complile only the southern and richer northern states for comparison:\n- Andhra Pradesh\n- Telangana\n- Karnataka\n- Kerala\n- Maharashtra\n- Tamil Nadu\n- Gujrat\n- Punjab\n- West Bengal", "## Columns in the dataset\n- state: State codes\n- start_year: Start of the assessment year\n- end_year: End of the assessment year\n- value: Value in INR Lacs - 1 Lac is 100000 INR", "### Sources:\n- 1961 - 1984 : URL\n- 1993-94 to 05-06: URL\n- 1999-00 to 09-10: URL\n- 2011-12 to 22-23: URL", "## Notes\n- Sometimes when there are overlapping timelines like the second source and third source have overlap from 1999-00 to 05-06 - they often have different numbers. In such a case I have considered the calculation done at a later stage considering that is has been updated recently." ]

[ "TAGS\n#size_categories-n<1K #language-English #license-mit #India, Economics, GDP #region-us \n", "# Indian States GDP numbers\n\nI wanted to find out how Indian states were faring since independence and if government policies and regimes have made any difference. Since I could not find any aggregate data I wanted to use a list of government documents and compile it myself by state.\n\nFor the first dataset I will complile only the southern and richer northern states for comparison:\n- Andhra Pradesh\n- Telangana\n- Karnataka\n- Kerala\n- Maharashtra\n- Tamil Nadu\n- Gujrat\n- Punjab\n- West Bengal", "## Columns in the dataset\n- state: State codes\n- start_year: Start of the assessment year\n- end_year: End of the assessment year\n- value: Value in INR Lacs - 1 Lac is 100000 INR", "### Sources:\n- 1961 - 1984 : URL\n- 1993-94 to 05-06: URL\n- 1999-00 to 09-10: URL\n- 2011-12 to 22-23: URL", "## Notes\n- Sometimes when there are overlapping timelines like the second source and third source have overlap from 1999-00 to 05-06 - they often have different numbers. In such a case I have considered the calculation done at a later stage considering that is has been updated recently." ]

[ "region:us" ]

[]

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[ "# Dataset Card for Evaluation run of alnrg2arg/blockchainlabs_joe_bez_seminar\n\n\n\nDataset automatically created during the evaluation run of model alnrg2arg/blockchainlabs_joe_bez_seminar 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T07:09:56.959755(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 alnrg2arg/blockchainlabs_joe_bez_seminar\n\n\n\nDataset automatically created during the evaluation run of model alnrg2arg/blockchainlabs_joe_bez_seminar 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T07:09:56.959755(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[]

[ "# Dataset Card for Evaluation run of adonlee/Mistral_7B_SFT_DPO_v0\n\n\n\nDataset automatically created during the evaluation run of model adonlee/Mistral_7B_SFT_DPO_v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T04:12:04.142911(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 adonlee/Mistral_7B_SFT_DPO_v0\n\n\n\nDataset automatically created during the evaluation run of model adonlee/Mistral_7B_SFT_DPO_v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T04:12:04.142911(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" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"alpaca_farm-reward-model-deberta-v3-large-v2-re-eval-preference\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"alpaca_farm-reward-model-deberta-v3-large-v2-re-eval-preference\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of ZoidBB/Kory-0.1-11b-pre1\n\n\n\nDataset automatically created during the evaluation run of model ZoidBB/Kory-0.1-11b-pre1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T04:36:04.521362(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 ZoidBB/Kory-0.1-11b-pre1\n\n\n\nDataset automatically created during the evaluation run of model ZoidBB/Kory-0.1-11b-pre1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T04:36:04.521362(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 argilla/CapybaraHermes-2.5-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model argilla/CapybaraHermes-2.5-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 2024-02-05T04:37:41.932157(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 argilla/CapybaraHermes-2.5-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model argilla/CapybaraHermes-2.5-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 2024-02-05T04:37:41.932157(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", "size_categories:1K<n<10K", "language:ar", "license:apache-2.0", "qna", "questioning-answering", "questions-generation", "region:us" ]

[]

[ "ar" ]

[ "# Sadeem QnA: An Arabic QnA Dataset \n\nWelcome to the Sadeem QnA dataset, a vibrant collection designed for the advancement of Arabic natural language processing, specifically tailored for Question Answering (QnA) systems. Sourced from the rich and diverse content of Arabic Wikipedia, this dataset is a gateway to exploring the depths of Arabic language understanding, offering a unique challenge to both researchers and AI enthusiasts alike.", "## Table of Contents\n\n- About Sadeem QnA\n- Dataset Structure\n- Getting Started\n- Usage\n- Contributing\n- License\n- Citation", "## About Sadeem QnA\n\nThe Sadeem QnA dataset is crafted with the intent to foster research and development in Arabic Question Answering systems. It encompasses a broad range of topics, reflecting the rich tapestry of Arabic culture, history, and science, making it an ideal resource for training and evaluating AI models.", "### Why Sadeem QnA?\n\n- Rich Content: Over 6,000 QnA pairs across diverse subjects.\n- Real-World Questions: Derived from actual queries people might ask, providing practical value for real-world applications.\n- Dual Splits: Carefully partitioned into training (5,000 rows) and testing (1,030 rows) sets to facilitate effective model evaluation.", "## Dataset Structure\n\nEach record in the dataset follows a structured format, containing the following fields:\n\n- 'title': The title of the Wikipedia article.\n- 'text': A snippet from the article related to the question.\n- 'source': The URL of the Wikipedia page.\n- 'question': A question related to the text snippet.\n- 'answer': The answer to the question.\n- 'has_answer': A boolean indicating whether the answer is present in the text snippet.", "### Example Record", "## Getting Started\n\nTo get started with the Sadeem QnA dataset, you can download it directly from our Huggingface repository.\nFollow the instructions there to load the dataset into your environment and begin exploring.", "## Usage\n\nThis dataset is perfect for:\n- Training machine learning models for Arabic question answering.\n- Evaluating the performance of NLP models on Arabic text.\n- Enhancing language understanding systems with a focus on Arabic.", "## Contributing\n\nWe welcome contributions from the community! Whether it's improving the documentation, adding more questions, or reporting issues, your help makes Sadeem QnA better for everyone.", "## License\n\nThe Sadeem QnA dataset is available under the Apache License 2.0. We encourage its use for academic research, commercial applications, and beyond, provided proper attribution is given.\n\nIf you use the Sadeem QnA dataset in your research, please cite it using the following format:\n\n\n\nEmbark on your journey through the Arabic language with Sadeem QnA and unlock the potential of AI in understanding the complexity and beauty of Arabic text." ]

[ "TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-Arabic #license-apache-2.0 #qna #questioning-answering #questions-generation #region-us \n", "# Sadeem QnA: An Arabic QnA Dataset \n\nWelcome to the Sadeem QnA dataset, a vibrant collection designed for the advancement of Arabic natural language processing, specifically tailored for Question Answering (QnA) systems. Sourced from the rich and diverse content of Arabic Wikipedia, this dataset is a gateway to exploring the depths of Arabic language understanding, offering a unique challenge to both researchers and AI enthusiasts alike.", "## Table of Contents\n\n- About Sadeem QnA\n- Dataset Structure\n- Getting Started\n- Usage\n- Contributing\n- License\n- Citation", "## About Sadeem QnA\n\nThe Sadeem QnA dataset is crafted with the intent to foster research and development in Arabic Question Answering systems. It encompasses a broad range of topics, reflecting the rich tapestry of Arabic culture, history, and science, making it an ideal resource for training and evaluating AI models.", "### Why Sadeem QnA?\n\n- Rich Content: Over 6,000 QnA pairs across diverse subjects.\n- Real-World Questions: Derived from actual queries people might ask, providing practical value for real-world applications.\n- Dual Splits: Carefully partitioned into training (5,000 rows) and testing (1,030 rows) sets to facilitate effective model evaluation.", "## Dataset Structure\n\nEach record in the dataset follows a structured format, containing the following fields:\n\n- 'title': The title of the Wikipedia article.\n- 'text': A snippet from the article related to the question.\n- 'source': The URL of the Wikipedia page.\n- 'question': A question related to the text snippet.\n- 'answer': The answer to the question.\n- 'has_answer': A boolean indicating whether the answer is present in the text snippet.", "### Example Record", "## Getting Started\n\nTo get started with the Sadeem QnA dataset, you can download it directly from our Huggingface repository.\nFollow the instructions there to load the dataset into your environment and begin exploring.", "## Usage\n\nThis dataset is perfect for:\n- Training machine learning models for Arabic question answering.\n- Evaluating the performance of NLP models on Arabic text.\n- Enhancing language understanding systems with a focus on Arabic.", "## Contributing\n\nWe welcome contributions from the community! Whether it's improving the documentation, adding more questions, or reporting issues, your help makes Sadeem QnA better for everyone.", "## License\n\nThe Sadeem QnA dataset is available under the Apache License 2.0. We encourage its use for academic research, commercial applications, and beyond, provided proper attribution is given.\n\nIf you use the Sadeem QnA dataset in your research, please cite it using the following format:\n\n\n\nEmbark on your journey through the Arabic language with Sadeem QnA and unlock the potential of AI in understanding the complexity and beauty of Arabic text." ]

[ "region:us" ]

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[]

[ "# Dataset Card for Evaluation run of abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B\n\n\n\nDataset automatically created during the evaluation run of model abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T04:53:01.217298(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-10B\n\n\n\nDataset automatically created during the evaluation run of model abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T04:53:01.217298(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 \"sn18-all-20240204\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"sn18-all-20240204\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "language:en", "license:unlicense", "prompt-classification", "region:us" ]

[]

[ "en" ]

[ "# Prompt Task Clasification\n\nCategory prompt into categories and results into the most probably task", "## Current Supported Categories", "## Categories Data Composition\n!Categories Composition" ]

[ "TAGS\n#task_categories-text-classification #language-English #license-unlicense #prompt-classification #region-us \n", "# Prompt Task Clasification\n\nCategory prompt into categories and results into the most probably task", "## Current Supported Categories", "## Categories Data Composition\n!Categories Composition" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of xaviviro/OpenHermes-2.5-FLOR-6.3B\n\n\n\nDataset automatically created during the evaluation run of model xaviviro/OpenHermes-2.5-FLOR-6.3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T06:45:07.884886(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 xaviviro/OpenHermes-2.5-FLOR-6.3B\n\n\n\nDataset automatically created during the evaluation run of model xaviviro/OpenHermes-2.5-FLOR-6.3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T06:45:07.884886(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 indischepartij/OpenMia-Indo-Mistral-7b-v3-refined\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/OpenMia-Indo-Mistral-7b-v3-refined on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T07:17:24.697764(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 indischepartij/OpenMia-Indo-Mistral-7b-v3-refined\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/OpenMia-Indo-Mistral-7b-v3-refined on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T07:17:24.697764(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 indischepartij/OpenMia-Indo-Engineering-7b\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/OpenMia-Indo-Engineering-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T07:22:15.500441(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 indischepartij/OpenMia-Indo-Engineering-7b\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/OpenMia-Indo-Engineering-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T07:22:15.500441(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" ]

[ "language:en", "arxiv:2305.14233", "arxiv:2310.16944", "region:us" ]

[ "2305.14233", "2310.16944" ]

[ "en" ]

[]

[ "TAGS\n#language-English #arxiv-2305.14233 #arxiv-2310.16944 #region-us \n" ]

[ "task_categories:automatic-speech-recognition", "size_categories:10K<n<100K", "language:fa", "hezar", "region:us" ]

[]

[ "fa" ]

[ "#### Load" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #size_categories-10K<n<100K #language-Persian #hezar #region-us \n", "#### Load" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ZoidBB/MultiKory-0.1-4x11b-pre1\n\n\n\nDataset automatically created during the evaluation run of model ZoidBB/MultiKory-0.1-4x11b-pre1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T08:07:31.524035(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 ZoidBB/MultiKory-0.1-4x11b-pre1\n\n\n\nDataset automatically created during the evaluation run of model ZoidBB/MultiKory-0.1-4x11b-pre1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T08:07:31.524035(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:summarization", "task_categories:text2text-generation", "language:ne", "license:wtfpl", "nepali", "np", "nepali-text-summerizatio", "article-title", "region:us" ]

[]

[ "ne" ]

[]

[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #language-Nepali (macrolanguage) #license-wtfpl #nepali #np #nepali-text-summerizatio #article-title #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of dddsaty/SOLAR_Merge_Adapter_DPO_Orca\n\n\n\nDataset automatically created during the evaluation run of model dddsaty/SOLAR_Merge_Adapter_DPO_Orca on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T08:48:15.938281(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 dddsaty/SOLAR_Merge_Adapter_DPO_Orca\n\n\n\nDataset automatically created during the evaluation run of model dddsaty/SOLAR_Merge_Adapter_DPO_Orca on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T08:48:15.938281(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 sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-recovered\n\n\n\nDataset automatically created during the evaluation run of model sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-recovered on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T08:48:44.538072(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 sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-recovered\n\n\n\nDataset automatically created during the evaluation run of model sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-recovered on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-05T08:48:44.538072(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" ]