Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "language:en", "license:agpl-3.0", "region:us" ]

[]

[ "en" ]

[ "# Gnosis\n\nThis dataset was provided by jeiku" ]

[ "TAGS\n#language-English #license-agpl-3.0 #region-us \n", "# Gnosis\n\nThis dataset was provided by jeiku" ]

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

[]

[]

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

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

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

[]

[]

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

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

[ "Lilac", "region:us" ]

[]

[]

[ "# lilac/lmsys-chat-1m\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "TAGS\n#Lilac #region-us \n", "# lilac/lmsys-chat-1m\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of chargoddard/internlm2-base-7b-llama\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/internlm2-base-7b-llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T22:11:28.111983(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 chargoddard/internlm2-base-7b-llama\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/internlm2-base-7b-llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T22:11:28.111983(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:10K<n<100K", "language:ja", "license:llama2", "region:us" ]

[]

[ "ja" ]

[ "# Model\nURL", "# Dataset\nURL", "# Compute by\nTsuginosuke AI SuperComputer\nFreeAI Ltd.\n\nURL" ]

[ "TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Japanese #license-llama2 #region-us \n", "# Model\nURL", "# Dataset\nURL", "# Compute by\nTsuginosuke AI SuperComputer\nFreeAI Ltd.\n\nURL" ]

[ "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:1M<n<10M", "language:en", "license:cc-by-sa-4.0", "license:gpl", "math", "region:us" ]

[]

[ "en" ]

[ "# Basic Math 1M\n\nA dataset of 1 million basic arithmetic problems with potential user prompts. See the numerical version for a version with only numbers.", "## License\n\nBasic Math 1M is dual-licensed under the GNU GPL license and the CC-BY-SA 4.0 license, you may choose either at your choice. If you are interested in including this dataset in another differently-licensed dataset, please contact me.", "## Credit\n\nBasic Math 1M was inspired by Simple Math but was created independently." ]

[ "TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-1M<n<10M #language-English #license-cc-by-sa-4.0 #license-gpl #math #region-us \n", "# Basic Math 1M\n\nA dataset of 1 million basic arithmetic problems with potential user prompts. See the numerical version for a version with only numbers.", "## License\n\nBasic Math 1M is dual-licensed under the GNU GPL license and the CC-BY-SA 4.0 license, you may choose either at your choice. If you are interested in including this dataset in another differently-licensed dataset, please contact me.", "## Credit\n\nBasic Math 1M was inspired by Simple Math but was created independently." ]

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

[]

[]

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

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

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

[]

[]

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

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

[ "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:1M<n<10M", "language:en", "license:cc-by-sa-4.0", "license:gpl", "math", "region:us" ]

[]

[ "en" ]

[ "# Basic Math 1M Numerical\n\nA dataset of 1 million basic arithmetic problems with only numbers. See the original version for a version with potential prompts as well.", "## License\n\nBasic Math 1M Numerical is dual-licensed under the GNU GPL license and the CC-BY-SA 4.0 license, you may choose either at your choice. If you are interested in including this dataset in another differently-licensed dataset, please contact me.", "## Credit\n\nBasic Math 1M was inspired by Simple Math but was created independently." ]

[ "TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-1M<n<10M #language-English #license-cc-by-sa-4.0 #license-gpl #math #region-us \n", "# Basic Math 1M Numerical\n\nA dataset of 1 million basic arithmetic problems with only numbers. See the original version for a version with potential prompts as well.", "## License\n\nBasic Math 1M Numerical is dual-licensed under the GNU GPL license and the CC-BY-SA 4.0 license, you may choose either at your choice. If you are interested in including this dataset in another differently-licensed dataset, please contact me.", "## Credit\n\nBasic Math 1M was inspired by Simple Math but was created independently." ]

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of abhishekchohan/mistral-7B-forest-merge\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-forest-merge 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 2024-01-21T23:23:15.649063(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 abhishekchohan/mistral-7B-forest-merge\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-forest-merge 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 2024-01-21T23:23:15.649063(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 \"hub-report-raw\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of abhishekchohan/mistral-7B-med-merge\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-med-merge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T23:55:53.444793(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 abhishekchohan/mistral-7B-med-merge\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-med-merge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-21T23:55:53.444793(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" ]

[]

[]

[ "# 한국어 모델 캘리브레이션용 데이터셋\n\n허깅페이스에 올라와 있는 다양한 한국어 데이터셋이 사용되었습니다." ]

[ "TAGS\n#region-us \n", "# 한국어 모델 캘리브레이션용 데이터셋\n\n허깅페이스에 올라와 있는 다양한 한국어 데이터셋이 사용되었습니다." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of lodrick-the-lafted/Grafted-Llama2-2x70B\n\n\n\nDataset automatically created during the evaluation run of model lodrick-the-lafted/Grafted-Llama2-2x70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T00:41:15.048922(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 lodrick-the-lafted/Grafted-Llama2-2x70B\n\n\n\nDataset automatically created during the evaluation run of model lodrick-the-lafted/Grafted-Llama2-2x70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T00:41:15.048922(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", "license:cc-by-4.0", "health", "region:us" ]

[]

[ "en" ]

[ "# PMData Dataset", "## About Dataset\n\nPaper: <URL\n\nIn this dataset, we present the PMData dataset that aims to combine traditional lifelogging with sports activity logging. Such a dataset enables the development of several interesting analysis applications, e.g., where additional sports data can be used to predict and analyze everyday developments like a person's weight and sleep patterns, and where traditional lifelog data can be used in a sports context to predict an athletes performance. In this respect, we have used the Fitbit Versa 2 smartwatch wristband, the PMSys sports logging app a and Google forms for the data collection, and PMData contains logging data for 5 months from 16 persons. Our initial experiments show that such analyzes are possible, but there are still large rooms for improvements.", "### Dataset Details\n\nThe structure of the main folder:\n\n\n\nEach participant's folder (pXX) contains:\n\n- 'fitbit' [folder]\n - 'URL': Shows how many calories the person have burned the last minute.\n - 'URL': Gives the distance moved per minute. Distance seems to be in centimeters.\n - 'URL': Describes each activity in more detail. It contains the date with start and stop time, time in different activity levels, type of activity and various performance metrics depending a bit on type of exercise, e.g., for running, it contains distance, time, steps, calories, speed and pace.\n - 'heart_rate.json': Shows the number of heart beats per minute (bpm) at a given time.\n - 'lightly_active_minutes.json': Sums up the number of lightly active minutes per day.\n - 'moderately_active_minutes.json': Sums up the number of moderately active minutes per day.\n - 'resting_heart_rate.json': Gives the resting heart rate per day.\n - 'sedentary_minutes.json': Sums up the number of sedentary minutes per day.\n - 'sleep_score.csv': Helps understand the sleep each night so you can see trends in the sleep patterns. It contains an overall 0-100 score made up from composition, revitalization and duration scores, the number of deep sleep minutes, the resting heart rate and a restlessness score.\n - 'URL': A per sleep breakdown of the sleep into periods of light, deep, rem sleeps and time awake.\n - 'URL': Displays the number of steps per minute.\n - 'time_in_heart_rate_zones.json': Gives the number of minutes in different heart rate zones. Using the common formula of 220 minus your age, Fitbit will calculate your maximum heart rate and then create three target heart rate zones fat burn (50 to 69 percent of your max heart rate), cardio (70 to 84 percent of your max heart rate), and peak (85 to 100 percent of your max heart rate) - based off that number.\n - 'very_active_minutes.json': Sums up the number of very active minutes per day.\n\n- 'googledocs' [folder]\n - 'URL': Contains one line per report including the date reported for, a timestamp of the report submission time, the eaten meals (breakfast, lunch, dinner and evening meal), the participants weight this day, the number of glasses drunk, and whether one has consumed alcohol.\n\n- 'pmsys' [folder]\n - 'URL': Shows injuries with a time and date and corresponding injury locations and a minor and major severity.\n - 'URL': Contains a training session’s end-time, type of activity, the perceived exertion (RPE), and the duration in the number of minutes. This is, for example, used to calculate the sessions training load or sRPE (RPE×duration).\n - 'URL': Includes parameters like time and date, fatigue, mood, readiness, sleep duration (number of hours), sleep quality, soreness (and soreness area), and stress. Fatigue, sleep qual-ity, soreness, stress, and mood all have a 1-5 scale. The score 3 is normal, and 1-2 are scores below normal and 4-5 are scores above normal. Sleep length is just a measure of how long the sleep was in hours, and readiness (scale 0-10) is an overall subjective measure of how ready are you to exercise, i.e., 0 means not ready at all and 10 indicates that you cannot feel any better and are ready for anything!\n\n- 'URL': Participants 1, 3 and 5 have taken pictures of everything they have eaten except water during 2 months (February and March). There are food images included in this .zip file, and information about day and time is given in the image header. The participants used their own mobile cameras to collect the images (Iphone 6s, Iphone X and Iphone XS). The standard export function of the MacOS Photos software with full quality was used to export the images.", "### Term of use\n\nThe license for the PMData dataset is Attribution-NonCommercial 4.0 International. More information can be found here: <URL" ]

[ "TAGS\n#language-English #license-cc-by-4.0 #health #region-us \n", "# PMData Dataset", "## About Dataset\n\nPaper: <URL\n\nIn this dataset, we present the PMData dataset that aims to combine traditional lifelogging with sports activity logging. Such a dataset enables the development of several interesting analysis applications, e.g., where additional sports data can be used to predict and analyze everyday developments like a person's weight and sleep patterns, and where traditional lifelog data can be used in a sports context to predict an athletes performance. In this respect, we have used the Fitbit Versa 2 smartwatch wristband, the PMSys sports logging app a and Google forms for the data collection, and PMData contains logging data for 5 months from 16 persons. Our initial experiments show that such analyzes are possible, but there are still large rooms for improvements.", "### Dataset Details\n\nThe structure of the main folder:\n\n\n\nEach participant's folder (pXX) contains:\n\n- 'fitbit' [folder]\n - 'URL': Shows how many calories the person have burned the last minute.\n - 'URL': Gives the distance moved per minute. Distance seems to be in centimeters.\n - 'URL': Describes each activity in more detail. It contains the date with start and stop time, time in different activity levels, type of activity and various performance metrics depending a bit on type of exercise, e.g., for running, it contains distance, time, steps, calories, speed and pace.\n - 'heart_rate.json': Shows the number of heart beats per minute (bpm) at a given time.\n - 'lightly_active_minutes.json': Sums up the number of lightly active minutes per day.\n - 'moderately_active_minutes.json': Sums up the number of moderately active minutes per day.\n - 'resting_heart_rate.json': Gives the resting heart rate per day.\n - 'sedentary_minutes.json': Sums up the number of sedentary minutes per day.\n - 'sleep_score.csv': Helps understand the sleep each night so you can see trends in the sleep patterns. It contains an overall 0-100 score made up from composition, revitalization and duration scores, the number of deep sleep minutes, the resting heart rate and a restlessness score.\n - 'URL': A per sleep breakdown of the sleep into periods of light, deep, rem sleeps and time awake.\n - 'URL': Displays the number of steps per minute.\n - 'time_in_heart_rate_zones.json': Gives the number of minutes in different heart rate zones. Using the common formula of 220 minus your age, Fitbit will calculate your maximum heart rate and then create three target heart rate zones fat burn (50 to 69 percent of your max heart rate), cardio (70 to 84 percent of your max heart rate), and peak (85 to 100 percent of your max heart rate) - based off that number.\n - 'very_active_minutes.json': Sums up the number of very active minutes per day.\n\n- 'googledocs' [folder]\n - 'URL': Contains one line per report including the date reported for, a timestamp of the report submission time, the eaten meals (breakfast, lunch, dinner and evening meal), the participants weight this day, the number of glasses drunk, and whether one has consumed alcohol.\n\n- 'pmsys' [folder]\n - 'URL': Shows injuries with a time and date and corresponding injury locations and a minor and major severity.\n - 'URL': Contains a training session’s end-time, type of activity, the perceived exertion (RPE), and the duration in the number of minutes. This is, for example, used to calculate the sessions training load or sRPE (RPE×duration).\n - 'URL': Includes parameters like time and date, fatigue, mood, readiness, sleep duration (number of hours), sleep quality, soreness (and soreness area), and stress. Fatigue, sleep qual-ity, soreness, stress, and mood all have a 1-5 scale. The score 3 is normal, and 1-2 are scores below normal and 4-5 are scores above normal. Sleep length is just a measure of how long the sleep was in hours, and readiness (scale 0-10) is an overall subjective measure of how ready are you to exercise, i.e., 0 means not ready at all and 10 indicates that you cannot feel any better and are ready for anything!\n\n- 'URL': Participants 1, 3 and 5 have taken pictures of everything they have eaten except water during 2 months (February and March). There are food images included in this .zip file, and information about day and time is given in the image header. The participants used their own mobile cameras to collect the images (Iphone 6s, Iphone X and Iphone XS). The standard export function of the MacOS Photos software with full quality was used to export the images.", "### Term of use\n\nThe license for the PMData dataset is Attribution-NonCommercial 4.0 International. More information can be found here: <URL" ]

[ "region:us" ]

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

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

[ "annotations_creators:expert-generated", "annotations_creators:found", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "chart", "plot", "chart-to-text", "vistext", "statista", "pew", "chart-understanding", "chart-captioning", "chart-summarization", "document-image", "arxiv:2312.10160", "region:us" ]

[ "2312.10160" ]

[ "en" ]

[ "# Dataset Card for CHOCOLATE\n\n- Dataset Description\n- Paper Information\n- Citation", "## Dataset Description\n\nCHOCOLATE is a benchmark for detecting and correcting factual inconsistency in generated chart captions. It consists of captions produced by six most advanced models, which are categorized into three subsets:\n\n- LVLM: GPT-4V, Bard (before Gemini)\n- LLM-based Pipeline: DePlot + GPT-4\n- Fine-tuned Model: ChartT5, MatCha, UniChart\n\n\nThe charts are from two datasets: VisText and the Pew split of Chart-to-Text. In total, CHOCOLATE consists of 1,187 examples. Each instance in CHOCOLATE consists of a caption generated by one of the model and the annotations of the factual errors for each caption sentence.", "## Paper Information\n\n- Paper: URL\n- Code: URL\n- Project: URL\n\n\nIf you use the CHOCOLATE dataset in your work, please kindly cite the paper using this BibTeX:" ]

[ "TAGS\n#annotations_creators-expert-generated #annotations_creators-found #language_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-apache-2.0 #chart #plot #chart-to-text #vistext #statista #pew #chart-understanding #chart-captioning #chart-summarization #document-image #arxiv-2312.10160 #region-us \n", "# Dataset Card for CHOCOLATE\n\n- Dataset Description\n- Paper Information\n- Citation", "## Dataset Description\n\nCHOCOLATE is a benchmark for detecting and correcting factual inconsistency in generated chart captions. It consists of captions produced by six most advanced models, which are categorized into three subsets:\n\n- LVLM: GPT-4V, Bard (before Gemini)\n- LLM-based Pipeline: DePlot + GPT-4\n- Fine-tuned Model: ChartT5, MatCha, UniChart\n\n\nThe charts are from two datasets: VisText and the Pew split of Chart-to-Text. In total, CHOCOLATE consists of 1,187 examples. Each instance in CHOCOLATE consists of a caption generated by one of the model and the annotations of the factual errors for each caption sentence.", "## Paper Information\n\n- Paper: URL\n- Code: URL\n- Project: URL\n\n\nIf you use the CHOCOLATE dataset in your work, please kindly cite the paper using this BibTeX:" ]

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

[]

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[]

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

[ "task_categories:text-generation", "size_categories:100K<n<1M", "license:bsd-3-clause", "dpo", "fine-tuning", "rlhf", "region:us" ]

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[ "# Pandora RLHF\n\nA Reinforcement Learning from Human Feedback (RLHF) dataset for Direct Preference Optimization (DPO) fine-tuning of the Pandora Large Language Model (LLM).\n\nThe dataset is based on the anthropic/hh-rlhf dataset.", "## Copyright and license\n\nCopyright (c) 2024, Danilo Peixoto Ferreira. All rights reserved.\n\nProject developed under a BSD-3-Clause license." ]

[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #license-bsd-3-clause #dpo #fine-tuning #rlhf #region-us \n", "# Pandora RLHF\n\nA Reinforcement Learning from Human Feedback (RLHF) dataset for Direct Preference Optimization (DPO) fine-tuning of the Pandora Large Language Model (LLM).\n\nThe dataset is based on the anthropic/hh-rlhf dataset.", "## Copyright and license\n\nCopyright (c) 2024, Danilo Peixoto Ferreira. All rights reserved.\n\nProject developed under a BSD-3-Clause license." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Vasanth/Beast-Soul\n\n\n\nDataset automatically created during the evaluation run of model Vasanth/Beast-Soul on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T01:37:25.115466(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 Vasanth/Beast-Soul\n\n\n\nDataset automatically created during the evaluation run of model Vasanth/Beast-Soul on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T01:37:25.115466(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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[]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Weyaxi/Stellaris-internlm2-20b-r128\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Stellaris-internlm2-20b-r128 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:02:49.417178(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/Stellaris-internlm2-20b-r128\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Stellaris-internlm2-20b-r128 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:02:49.417178(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 kimwooglae/AISquare-Instruct-SOLAR-10.7b-v0.5.31\n\n\n\nDataset automatically created during the evaluation run of model kimwooglae/AISquare-Instruct-SOLAR-10.7b-v0.5.31 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:13:58.257879(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 kimwooglae/AISquare-Instruct-SOLAR-10.7b-v0.5.31\n\n\n\nDataset automatically created during the evaluation run of model kimwooglae/AISquare-Instruct-SOLAR-10.7b-v0.5.31 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:13:58.257879(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 andysalerno/cloudymixtral7Bx2-nectar-0.2\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/cloudymixtral7Bx2-nectar-0.2 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 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 2024-01-22T02:17:36.925599(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 andysalerno/cloudymixtral7Bx2-nectar-0.2\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/cloudymixtral7Bx2-nectar-0.2 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 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 2024-01-22T02:17:36.925599(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:tabular-classification", "task_ids:multi-class-classification", "size_categories:1K<n<10K", "source_datasets:original", "language:fr", "license:cc-by-4.0", "automatic-diagnosis", "automatic-symptom-detection", "differential-diagnosis", "synthetic-patients", "diseases", "health-care", "arxiv:2205.09148", "region:us" ]

[ "2205.09148" ]

[ "fr" ]

[ "# Dataset Description\n\nWe are releasing under the CC-BY licence a new large-scale dataset for Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the medical domain. The dataset contains patients synthesized using a proprietary medical knowledge base and a commercial rule-based AD system. Patients in the dataset are characterized by their socio-demographic data, a pathology they are suffering from, a set of symptoms and antecedents related to this pathology, and a differential diagnosis. The symptoms and antecedents can be binary, categorical and multi-choice, with the potential of leading to more efficient and natural interactions between ASD/AD systems and patients. To the best of our knowledge, this is the first large-scale dataset that includes the differential diagnosis, and non-binary symptoms and antecedents.\n\nNote: We use evidence as a general term to refer to a symptom or an antecedent.\n\nThis directory contains the following files:\n - release_evidences.json: a JSON file describing all possible evidences considered in the dataset.\n - release_conditions.json: a JSON file describing all pathologies considered in the dataset.\n - release_train_patients.zip: a CSV file containing the patients of the training set.\n - release_validate_patients.zip: a CSV file containing the patients of the validation set.\n - release_test_patients.zip: a CSV file containing the patients of the test set.", "## Evidence Description\n\nEach evidence in the 'release_evidences.json' file is described using the following entries:\n - name: name of the evidence.\n - code_question: a code allowing to identify which evidences are related. Evidences having the same 'code_question' form a group of related symptoms. The value of the 'code_question' refers to the evidence that need to be simulated/activated for the other members of the group to be eventually simulated.\n - question_fr: the query, in French, associated to the evidence.\n - question_en: the query, in English, associated to the evidence.\n - is_antecedent: a flag indicating whether the evidence is an antecedent or a symptom.\n - data_type: the type of evidence. We use 'B' for binary, 'C' for categorical, and 'M' for multi-choice evidences.\n - default_value: the default value of the evidence. If this value is used to characterize the evidence, then it is as if the evidence was not synthesized.\n - possible-values: the possible values for the evidences. Only valid for categorical and multi-choice evidences.\n - value_meaning: The meaning, in French and English, of each code that is part of the 'possible-values' field. Only valid for categorical and multi-choice evidences.", "## Pathology Description\nThe file 'release_conditions.json' contains information about the pathologies that patients in the datasets may suffer from. Each pathology has the following attributes:\n - condition_name: name of the pathology.\n - cond-name-fr: name of the pathology in French.\n - cond-name-eng: name of the pathology in English.\n - icd10-id: ICD-10 code of the pathology.\n - severity: the severity associated with the pathology. The lower the more severe.\n - symptoms: data structure describing the set of symptoms characterizing the pathology. Each symptom is represented by its corresponding 'name' entry in the 'release_evidences.json' file.\n - antecedents: data structure describing the set of antecedents characterizing the pathology. Each antecedent is represented by its corresponding 'name' entry in the 'release_evidences.json' file.", "## Patient Description\n\nEach patient in each of the 3 sets has the following attributes:\n - AGE: the age of the synthesized patient.\n - SEX: the sex of the synthesized patient.\n - PATHOLOGY: name of the ground truth pathology ('condition_name' property in the 'release_conditions.json' file) that the synthesized patient is suffering from.\n - EVIDENCES: list of evidences experienced by the patient. An evidence can either be binary, categorical or multi-choice. A categorical or multi-choice evidence is represented in the format '[evidence-name]_@_[evidence-value]' where ['evidence-name'] is the name of the evidence ('name' entry in the 'release_evidences.json' file) and ['evidence-value'] is a value from the 'possible-values' entry. Note that for a multi-choice evidence, it is possible to have several '[evidence-name]_@_[evidence-value]' items in the evidence list, with each item being associated with a different evidence value. A binary evidence is represented as '[evidence-name]'.\n - INITIAL_EVIDENCE: the evidence provided by the patient to kick-start an interaction with an ASD/AD system. This is useful during model evaluation for a fair comparison of ASD/AD systems as they will all begin an interaction with a given patient from the same starting point. The initial evidence is randomly selected from the binary evidences found in the evidence list mentioned above (i.e., 'EVIDENCES') and it is part of this list.\n - DIFFERENTIAL_DIAGNOSIS: The ground truth differential diagnosis for the patient. It is represented as a list of pairs of the form '[[patho_1, proba_1], [patho_2, proba_2], ...]' where 'patho_i' is the pathology name ('condition_name' entry in the 'release_conditions.json' file) and 'proba_i' is its related probability.", "## Note:\n\nWe hope this dataset will encourage future works for ASD and AD systems that consider the differential diagnosis and the severity of pathologies. It is important to keep in mind that this dataset is formed of synthetic patients and is meant for research purposes. Given the assumptions made during the generation process of this dataset, we would like to emphasize that the dataset should not be used to train and deploy a model prior to performing rigorous evaluations of the model performance and verifying that the system has proper coverage and representation of the population that it will interact with.\n\nIt is important to understand that the level of specificity, sensitivity and confidence that a physician will seek when evaluating a patient will be influenced by the clinical setting. The dataset was built for acute care and biased toward high mortality and morbidity pathologies. Physicians will tend to consider negative evidences as equally important in such a clinical context in order to evaluate high acuity diseases.\n\nIn the creation of the DDXPlus dataset, a small subset of the diseases was chosen to establish a baseline. Medical professionals have to consider this very important point when reviewing the results of models trained with this dataset, as the differential is considerably smaller. A smaller differential means less potential evidences to collect. It is thus essential to understand this point when we look at the differential produced and the evidence collected by a model based on this dataset.\n\nFor more information, please check our paper." ]

[ "TAGS\n#task_categories-tabular-classification #task_ids-multi-class-classification #size_categories-1K<n<10K #source_datasets-original #language-French #license-cc-by-4.0 #automatic-diagnosis #automatic-symptom-detection #differential-diagnosis #synthetic-patients #diseases #health-care #arxiv-2205.09148 #region-us \n", "# Dataset Description\n\nWe are releasing under the CC-BY licence a new large-scale dataset for Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the medical domain. The dataset contains patients synthesized using a proprietary medical knowledge base and a commercial rule-based AD system. Patients in the dataset are characterized by their socio-demographic data, a pathology they are suffering from, a set of symptoms and antecedents related to this pathology, and a differential diagnosis. The symptoms and antecedents can be binary, categorical and multi-choice, with the potential of leading to more efficient and natural interactions between ASD/AD systems and patients. To the best of our knowledge, this is the first large-scale dataset that includes the differential diagnosis, and non-binary symptoms and antecedents.\n\nNote: We use evidence as a general term to refer to a symptom or an antecedent.\n\nThis directory contains the following files:\n - release_evidences.json: a JSON file describing all possible evidences considered in the dataset.\n - release_conditions.json: a JSON file describing all pathologies considered in the dataset.\n - release_train_patients.zip: a CSV file containing the patients of the training set.\n - release_validate_patients.zip: a CSV file containing the patients of the validation set.\n - release_test_patients.zip: a CSV file containing the patients of the test set.", "## Evidence Description\n\nEach evidence in the 'release_evidences.json' file is described using the following entries:\n - name: name of the evidence.\n - code_question: a code allowing to identify which evidences are related. Evidences having the same 'code_question' form a group of related symptoms. The value of the 'code_question' refers to the evidence that need to be simulated/activated for the other members of the group to be eventually simulated.\n - question_fr: the query, in French, associated to the evidence.\n - question_en: the query, in English, associated to the evidence.\n - is_antecedent: a flag indicating whether the evidence is an antecedent or a symptom.\n - data_type: the type of evidence. We use 'B' for binary, 'C' for categorical, and 'M' for multi-choice evidences.\n - default_value: the default value of the evidence. If this value is used to characterize the evidence, then it is as if the evidence was not synthesized.\n - possible-values: the possible values for the evidences. Only valid for categorical and multi-choice evidences.\n - value_meaning: The meaning, in French and English, of each code that is part of the 'possible-values' field. Only valid for categorical and multi-choice evidences.", "## Pathology Description\nThe file 'release_conditions.json' contains information about the pathologies that patients in the datasets may suffer from. Each pathology has the following attributes:\n - condition_name: name of the pathology.\n - cond-name-fr: name of the pathology in French.\n - cond-name-eng: name of the pathology in English.\n - icd10-id: ICD-10 code of the pathology.\n - severity: the severity associated with the pathology. The lower the more severe.\n - symptoms: data structure describing the set of symptoms characterizing the pathology. Each symptom is represented by its corresponding 'name' entry in the 'release_evidences.json' file.\n - antecedents: data structure describing the set of antecedents characterizing the pathology. Each antecedent is represented by its corresponding 'name' entry in the 'release_evidences.json' file.", "## Patient Description\n\nEach patient in each of the 3 sets has the following attributes:\n - AGE: the age of the synthesized patient.\n - SEX: the sex of the synthesized patient.\n - PATHOLOGY: name of the ground truth pathology ('condition_name' property in the 'release_conditions.json' file) that the synthesized patient is suffering from.\n - EVIDENCES: list of evidences experienced by the patient. An evidence can either be binary, categorical or multi-choice. A categorical or multi-choice evidence is represented in the format '[evidence-name]_@_[evidence-value]' where ['evidence-name'] is the name of the evidence ('name' entry in the 'release_evidences.json' file) and ['evidence-value'] is a value from the 'possible-values' entry. Note that for a multi-choice evidence, it is possible to have several '[evidence-name]_@_[evidence-value]' items in the evidence list, with each item being associated with a different evidence value. A binary evidence is represented as '[evidence-name]'.\n - INITIAL_EVIDENCE: the evidence provided by the patient to kick-start an interaction with an ASD/AD system. This is useful during model evaluation for a fair comparison of ASD/AD systems as they will all begin an interaction with a given patient from the same starting point. The initial evidence is randomly selected from the binary evidences found in the evidence list mentioned above (i.e., 'EVIDENCES') and it is part of this list.\n - DIFFERENTIAL_DIAGNOSIS: The ground truth differential diagnosis for the patient. It is represented as a list of pairs of the form '[[patho_1, proba_1], [patho_2, proba_2], ...]' where 'patho_i' is the pathology name ('condition_name' entry in the 'release_conditions.json' file) and 'proba_i' is its related probability.", "## Note:\n\nWe hope this dataset will encourage future works for ASD and AD systems that consider the differential diagnosis and the severity of pathologies. It is important to keep in mind that this dataset is formed of synthetic patients and is meant for research purposes. Given the assumptions made during the generation process of this dataset, we would like to emphasize that the dataset should not be used to train and deploy a model prior to performing rigorous evaluations of the model performance and verifying that the system has proper coverage and representation of the population that it will interact with.\n\nIt is important to understand that the level of specificity, sensitivity and confidence that a physician will seek when evaluating a patient will be influenced by the clinical setting. The dataset was built for acute care and biased toward high mortality and morbidity pathologies. Physicians will tend to consider negative evidences as equally important in such a clinical context in order to evaluate high acuity diseases.\n\nIn the creation of the DDXPlus dataset, a small subset of the diseases was chosen to establish a baseline. Medical professionals have to consider this very important point when reviewing the results of models trained with this dataset, as the differential is considerably smaller. A smaller differential means less potential evidences to collect. It is thus essential to understand this point when we look at the differential produced and the evidence collected by a model based on this dataset.\n\nFor more information, please check our paper." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of antiven0m/brugle-rp\n\n\n\nDataset automatically created during the evaluation run of model antiven0m/brugle-rp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:19:10.123124(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 antiven0m/brugle-rp\n\n\n\nDataset automatically created during the evaluation run of model antiven0m/brugle-rp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:19:10.123124(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 Weyaxi/Stellaris-internlm2-20b-r256\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Stellaris-internlm2-20b-r256 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:30:15.872651(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/Stellaris-internlm2-20b-r256\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Stellaris-internlm2-20b-r256 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:30:15.872651(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:de", "language:es", "language:ar", "language:zh", "language:vi", "language:hi", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "en", "de", "es", "ar", "zh", "vi", "hi" ]

[ "### Dataset Summary\n\n\n\n```\nMLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.\nMLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,\nGerman, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between\n4 different languages on average.\n\n```", "### Supported Tasks and Leaderboards", "### Languages\n\n\nMLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 5.48 MB\n* Total amount of disk used: 15.56 MB\n\n\nAn example of 'test' looks as follows.", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 3.88 MB\n* Total amount of disk used: 13.96 MB\n\n\nAn example of 'test' looks as follows.", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 3.92 MB\n* Total amount of disk used: 13.99 MB\n\n\nAn example of 'test' looks as follows.", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 4.61 MB\n* Total amount of disk used: 14.68 MB\n\n\nAn example of 'test' looks as follows.", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 6.00 MB\n* Total amount of disk used: 16.07 MB\n\n\nAn example of 'test' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.\n\n\n\n\n---\n\n\nlicense: apache-2.0\n-------------------" ]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-German #language-Spanish #language-Arabic #language-Chinese #language-Vietnamese #language-Hindi #license-cc-by-sa-3.0 #region-us \n", "### Dataset Summary\n\n\n\n```\nMLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.\nMLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,\nGerman, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between\n4 different languages on average.\n\n```", "### Supported Tasks and Leaderboards", "### Languages\n\n\nMLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 5.48 MB\n* Total amount of disk used: 15.56 MB\n\n\nAn example of 'test' looks as follows.", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 3.88 MB\n* Total amount of disk used: 13.96 MB\n\n\nAn example of 'test' looks as follows.", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 3.92 MB\n* Total amount of disk used: 13.99 MB\n\n\nAn example of 'test' looks as follows.", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 4.61 MB\n* Total amount of disk used: 14.68 MB\n\n\nAn example of 'test' looks as follows.", "#### URL\n\n\n* Size of downloaded dataset files: 10.08 MB\n* Size of the generated dataset: 6.00 MB\n* Total amount of disk used: 16.07 MB\n\n\nAn example of 'test' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "#### URL\n\n\n* 'context': a 'string' feature.\n* 'question': a 'string' feature.\n* 'answers': a dictionary feature containing:\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'text': a 'string' feature.\n* 'id': a 'string' feature.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions\n\n\nThanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.\n\n\n\n\n---\n\n\nlicense: apache-2.0\n-------------------" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Weyaxi/Stellaris-internlm2-20b-r512\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Stellaris-internlm2-20b-r512 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:33:44.720538(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/Stellaris-internlm2-20b-r512\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Stellaris-internlm2-20b-r512 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T02:33:44.720538(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" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:sentence-similarity", "size_categories:100M<n<1B", "language:en", "license:odc-by", "region:us" ]

[]

[ "en" ]

[ "### default", "### triplets" ]

[ "TAGS\n#task_categories-sentence-similarity #size_categories-100M<n<1B #language-English #license-odc-by #region-us \n", "### default", "### triplets" ]

[ "task_categories:tabular-classification", "task_ids:multi-class-classification", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "automatic-diagnosis", "automatic-symptom-detection", "differential-diagnosis", "synthetic-patients", "diseases", "health-care", "arxiv:2205.09148", "region:us" ]

[ "2205.09148" ]

[ "en" ]

[ "# Dataset Description\n\nWe are releasing under the CC-BY licence a new large-scale dataset for Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the medical domain. The dataset contains patients synthesized using a proprietary medical knowledge base and a commercial rule-based AD system. Patients in the dataset are characterized by their socio-demographic data, a pathology they are suffering from, a set of symptoms and antecedents related to this pathology, and a differential diagnosis. The symptoms and antecedents can be binary, categorical and multi-choice, with the potential of leading to more efficient and natural interactions between ASD/AD systems and patients. To the best of our knowledge, this is the first large-scale dataset that includes the differential diagnosis, and non-binary symptoms and antecedents.\n\nNote: We use evidence as a general term to refer to a symptom or an antecedent.\n\nThis directory contains the following files:\n - release_evidences.json: a JSON file describing all possible evidences considered in the dataset.\n - release_conditions.json: a JSON file describing all pathologies considered in the dataset.\n - release_train_patients.zip: a CSV file containing the patients of the training set.\n - release_validate_patients.zip: a CSV file containing the patients of the validation set.\n - release_test_patients.zip: a CSV file containing the patients of the test set.", "## Evidence Description\n\nEach evidence in the 'release_evidences.json' file is described using the following entries:\n - name: name of the evidence.\n - code_question: a code allowing to identify which evidences are related. Evidences having the same 'code_question' form a group of related symptoms. The value of the 'code_question' refers to the evidence that need to be simulated/activated for the other members of the group to be eventually simulated.\n - question_fr: the query, in French, associated to the evidence.\n - question_en: the query, in English, associated to the evidence.\n - is_antecedent: a flag indicating whether the evidence is an antecedent or a symptom.\n - data_type: the type of evidence. We use 'B' for binary, 'C' for categorical, and 'M' for multi-choice evidences.\n - default_value: the default value of the evidence. If this value is used to characterize the evidence, then it is as if the evidence was not synthesized.\n - possible-values: the possible values for the evidences. Only valid for categorical and multi-choice evidences.\n - value_meaning: The meaning, in French and English, of each code that is part of the 'possible-values' field. Only valid for categorical and multi-choice evidences.", "## Pathology Description\nThe file 'release_conditions.json' contains information about the pathologies that patients in the datasets may suffer from. Each pathology has the following attributes:\n - condition_name: name of the pathology.\n - cond-name-fr: name of the pathology in French.\n - cond-name-eng: name of the pathology in English.\n - icd10-id: ICD-10 code of the pathology.\n - severity: the severity associated with the pathology. The lower the more severe.\n - symptoms: data structure describing the set of symptoms characterizing the pathology. Each symptom is represented by its corresponding 'name' entry in the 'release_evidences.json' file.\n - antecedents: data structure describing the set of antecedents characterizing the pathology. Each antecedent is represented by its corresponding 'name' entry in the 'release_evidences.json' file.", "## Patient Description\n\nEach patient in each of the 3 sets has the following attributes:\n - AGE: the age of the synthesized patient.\n - SEX: the sex of the synthesized patient.\n - PATHOLOGY: name of the ground truth pathology ('condition_name' property in the 'release_conditions.json' file) that the synthesized patient is suffering from.\n - EVIDENCES: list of evidences experienced by the patient. An evidence can either be binary, categorical or multi-choice. A categorical or multi-choice evidence is represented in the format '[evidence-name]_@_[evidence-value]' where ['evidence-name'] is the name of the evidence ('name' entry in the 'release_evidences.json' file) and ['evidence-value'] is a value from the 'possible-values' entry. Note that for a multi-choice evidence, it is possible to have several '[evidence-name]_@_[evidence-value]' items in the evidence list, with each item being associated with a different evidence value. A binary evidence is represented as '[evidence-name]'.\n - INITIAL_EVIDENCE: the evidence provided by the patient to kick-start an interaction with an ASD/AD system. This is useful during model evaluation for a fair comparison of ASD/AD systems as they will all begin an interaction with a given patient from the same starting point. The initial evidence is randomly selected from the binary evidences found in the evidence list mentioned above (i.e., 'EVIDENCES') and it is part of this list.\n - DIFFERENTIAL_DIAGNOSIS: The ground truth differential diagnosis for the patient. It is represented as a list of pairs of the form '[[patho_1, proba_1], [patho_2, proba_2], ...]' where 'patho_i' is the pathology name ('condition_name' entry in the 'release_conditions.json' file) and 'proba_i' is its related probability.", "## Note:\n\nWe hope this dataset will encourage future works for ASD and AD systems that consider the differential diagnosis and the severity of pathologies. It is important to keep in mind that this dataset is formed of synthetic patients and is meant for research purposes. Given the assumptions made during the generation process of this dataset, we would like to emphasize that the dataset should not be used to train and deploy a model prior to performing rigorous evaluations of the model performance and verifying that the system has proper coverage and representation of the population that it will interact with.\n\nIt is important to understand that the level of specificity, sensitivity and confidence that a physician will seek when evaluating a patient will be influenced by the clinical setting. The dataset was built for acute care and biased toward high mortality and morbidity pathologies. Physicians will tend to consider negative evidences as equally important in such a clinical context in order to evaluate high acuity diseases.\n\nIn the creation of the DDXPlus dataset, a small subset of the diseases was chosen to establish a baseline. Medical professionals have to consider this very important point when reviewing the results of models trained with this dataset, as the differential is considerably smaller. A smaller differential means less potential evidences to collect. It is thus essential to understand this point when we look at the differential produced and the evidence collected by a model based on this dataset.\n\nFor more information, please check our paper." ]

[ "TAGS\n#task_categories-tabular-classification #task_ids-multi-class-classification #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #automatic-diagnosis #automatic-symptom-detection #differential-diagnosis #synthetic-patients #diseases #health-care #arxiv-2205.09148 #region-us \n", "# Dataset Description\n\nWe are releasing under the CC-BY licence a new large-scale dataset for Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the medical domain. The dataset contains patients synthesized using a proprietary medical knowledge base and a commercial rule-based AD system. Patients in the dataset are characterized by their socio-demographic data, a pathology they are suffering from, a set of symptoms and antecedents related to this pathology, and a differential diagnosis. The symptoms and antecedents can be binary, categorical and multi-choice, with the potential of leading to more efficient and natural interactions between ASD/AD systems and patients. To the best of our knowledge, this is the first large-scale dataset that includes the differential diagnosis, and non-binary symptoms and antecedents.\n\nNote: We use evidence as a general term to refer to a symptom or an antecedent.\n\nThis directory contains the following files:\n - release_evidences.json: a JSON file describing all possible evidences considered in the dataset.\n - release_conditions.json: a JSON file describing all pathologies considered in the dataset.\n - release_train_patients.zip: a CSV file containing the patients of the training set.\n - release_validate_patients.zip: a CSV file containing the patients of the validation set.\n - release_test_patients.zip: a CSV file containing the patients of the test set.", "## Evidence Description\n\nEach evidence in the 'release_evidences.json' file is described using the following entries:\n - name: name of the evidence.\n - code_question: a code allowing to identify which evidences are related. Evidences having the same 'code_question' form a group of related symptoms. The value of the 'code_question' refers to the evidence that need to be simulated/activated for the other members of the group to be eventually simulated.\n - question_fr: the query, in French, associated to the evidence.\n - question_en: the query, in English, associated to the evidence.\n - is_antecedent: a flag indicating whether the evidence is an antecedent or a symptom.\n - data_type: the type of evidence. We use 'B' for binary, 'C' for categorical, and 'M' for multi-choice evidences.\n - default_value: the default value of the evidence. If this value is used to characterize the evidence, then it is as if the evidence was not synthesized.\n - possible-values: the possible values for the evidences. Only valid for categorical and multi-choice evidences.\n - value_meaning: The meaning, in French and English, of each code that is part of the 'possible-values' field. Only valid for categorical and multi-choice evidences.", "## Pathology Description\nThe file 'release_conditions.json' contains information about the pathologies that patients in the datasets may suffer from. Each pathology has the following attributes:\n - condition_name: name of the pathology.\n - cond-name-fr: name of the pathology in French.\n - cond-name-eng: name of the pathology in English.\n - icd10-id: ICD-10 code of the pathology.\n - severity: the severity associated with the pathology. The lower the more severe.\n - symptoms: data structure describing the set of symptoms characterizing the pathology. Each symptom is represented by its corresponding 'name' entry in the 'release_evidences.json' file.\n - antecedents: data structure describing the set of antecedents characterizing the pathology. Each antecedent is represented by its corresponding 'name' entry in the 'release_evidences.json' file.", "## Patient Description\n\nEach patient in each of the 3 sets has the following attributes:\n - AGE: the age of the synthesized patient.\n - SEX: the sex of the synthesized patient.\n - PATHOLOGY: name of the ground truth pathology ('condition_name' property in the 'release_conditions.json' file) that the synthesized patient is suffering from.\n - EVIDENCES: list of evidences experienced by the patient. An evidence can either be binary, categorical or multi-choice. A categorical or multi-choice evidence is represented in the format '[evidence-name]_@_[evidence-value]' where ['evidence-name'] is the name of the evidence ('name' entry in the 'release_evidences.json' file) and ['evidence-value'] is a value from the 'possible-values' entry. Note that for a multi-choice evidence, it is possible to have several '[evidence-name]_@_[evidence-value]' items in the evidence list, with each item being associated with a different evidence value. A binary evidence is represented as '[evidence-name]'.\n - INITIAL_EVIDENCE: the evidence provided by the patient to kick-start an interaction with an ASD/AD system. This is useful during model evaluation for a fair comparison of ASD/AD systems as they will all begin an interaction with a given patient from the same starting point. The initial evidence is randomly selected from the binary evidences found in the evidence list mentioned above (i.e., 'EVIDENCES') and it is part of this list.\n - DIFFERENTIAL_DIAGNOSIS: The ground truth differential diagnosis for the patient. It is represented as a list of pairs of the form '[[patho_1, proba_1], [patho_2, proba_2], ...]' where 'patho_i' is the pathology name ('condition_name' entry in the 'release_conditions.json' file) and 'proba_i' is its related probability.", "## Note:\n\nWe hope this dataset will encourage future works for ASD and AD systems that consider the differential diagnosis and the severity of pathologies. It is important to keep in mind that this dataset is formed of synthetic patients and is meant for research purposes. Given the assumptions made during the generation process of this dataset, we would like to emphasize that the dataset should not be used to train and deploy a model prior to performing rigorous evaluations of the model performance and verifying that the system has proper coverage and representation of the population that it will interact with.\n\nIt is important to understand that the level of specificity, sensitivity and confidence that a physician will seek when evaluating a patient will be influenced by the clinical setting. The dataset was built for acute care and biased toward high mortality and morbidity pathologies. Physicians will tend to consider negative evidences as equally important in such a clinical context in order to evaluate high acuity diseases.\n\nIn the creation of the DDXPlus dataset, a small subset of the diseases was chosen to establish a baseline. Medical professionals have to consider this very important point when reviewing the results of models trained with this dataset, as the differential is considerably smaller. A smaller differential means less potential evidences to collect. It is thus essential to understand this point when we look at the differential produced and the evidence collected by a model based on this dataset.\n\nFor more information, please check our paper." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

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

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

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

[]

[]

[ "## Generating Chunks\n\nChunking the sizes helps depending if you want to modify the llm to something with a larger token window. I tried to keep mine within the\ntoken limit of ChatGPT 3.5 Turbo 1106 for the larger context window.", "## Generating text (questions and answers) and formating\n\nUsing the chatgpt to generate the context of the chunk, then having it create a historical question from that text. Then having it create\nthe answer. (At time it wanted to put the answers into a summarized list, so I add to the instruction NOT to include it in a list. This \nhas seemed to work a little better.)\n\nThe last part is to bring it all into a csv file from the format generated from the code above. Make sure that you have made backup to the\nchunks gereated by the first python script. If you wish to keep them. The last part of this code deletes the last chunk that it created\nan answer and question for. This helped me figure out where I need to stop and start it next time." ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "## Generating Chunks\n\nChunking the sizes helps depending if you want to modify the llm to something with a larger token window. I tried to keep mine within the\ntoken limit of ChatGPT 3.5 Turbo 1106 for the larger context window.", "## Generating text (questions and answers) and formating\n\nUsing the chatgpt to generate the context of the chunk, then having it create a historical question from that text. Then having it create\nthe answer. (At time it wanted to put the answers into a summarized list, so I add to the instruction NOT to include it in a list. This \nhas seemed to work a little better.)\n\nThe last part is to bring it all into a csv file from the format generated from the code above. Make sure that you have made backup to the\nchunks gereated by the first python script. If you wish to keep them. The last part of this code deletes the last chunk that it created\nan answer and question for. This helped me figure out where I need to stop and start it next time." ]

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

[]

[ "en" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of inswave/AISquare-Instruct-llama2-koen-13b-v0.9.24\n\n\n\nDataset automatically created during the evaluation run of model inswave/AISquare-Instruct-llama2-koen-13b-v0.9.24 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T04:44:53.381027(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 inswave/AISquare-Instruct-llama2-koen-13b-v0.9.24\n\n\n\nDataset automatically created during the evaluation run of model inswave/AISquare-Instruct-llama2-koen-13b-v0.9.24 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T04:44:53.381027(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 kimwooglae/AISquare-Instruct-SOLAR-10.7b-v0.5.32\n\n\n\nDataset automatically created during the evaluation run of model kimwooglae/AISquare-Instruct-SOLAR-10.7b-v0.5.32 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T04:57:39.972792(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 kimwooglae/AISquare-Instruct-SOLAR-10.7b-v0.5.32\n\n\n\nDataset automatically created during the evaluation run of model kimwooglae/AISquare-Instruct-SOLAR-10.7b-v0.5.32 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T04:57:39.972792(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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[ "TAGS\n#region-us \n" ]

[ "region:us" ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of LordNoah/Alpaca-tuned-gpt2\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca-tuned-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 2024-01-22T05:37:05.003524(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 LordNoah/Alpaca-tuned-gpt2\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca-tuned-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 2024-01-22T05:37:05.003524(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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[ "TAGS\n#region-us \n" ]

[ "region:us" ]

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[ "# MMCBench Dataset: Benchmarking Dataset for Multimodal Model Evaluation", "## Overview\n\nThe MMCBench Dataset is a curated collection of data designed for the comprehensive evaluation of Large Multimodal Models (LMMs) under common corruption scenarios. This dataset supports the MMCBench framework, focusing on cross-modal interactions involving text, image, and speech. It provides essential data for generative tasks such as text-to-image, image-to-text, text-to-speech, and speech-to-text, enabling robustness and self-consistency assessments of LMMs.", "## Dataset Composition \n\nThe MMCBench Dataset is structured to facilitate the evaluation across four key generative tasks:\n\n- Text-to-Image: A collection of text descriptions with their corresponding corrupted versions and associated images.\n- Image-to-Text: A set of images with clean and corrupted captions.\n- Text-to-Speech: Text inputs with their clean and corrupted audio outputs.\n- Speech-to-Text: Audio files with transcriptions before and after audio corruptions.\n\nEach subset of the dataset has been meticulously selected and processed to represent challenging scenarios for LMMs.", "## Using the Dataset ️\n\nTo use the MMCBench Dataset for model evaluation:\n\n1. Access the Data: The dataset is hosted on Hugging Face and can be accessed using their dataset library or direct download.\n2. Select the Task: Choose from text-to-image, image-to-text, text-to-speech, or speech-to-text tasks based on your model's capabilities.\n3. Apply the Benchmark: Utilize the data for each task to test your model's performance against various corruptions. Follow the MMCBench framework for a consistent and standardized evaluation.", "### Dataset Structure \n\nThe dataset is organized into four main directories, each corresponding to one of the generative tasks:\n\n- 'text2image/': Contains text inputs and associated images.\n- 'image2text/': Comprises images and their descriptive captions.\n- 'text2speech/': Includes text inputs and generated speech outputs.\n- 'speech2text/': Contains audio files and their transcriptions.", "## Contributing to the Dataset \n\nContributions to the MMCBench Dataset are welcome. If you have suggestions for additional data or improvements, please reach out through the Hugging Face platform or directly contribute via GitHub.", "## License \n\nThe MMCBench Dataset is made available under the Apache 2.0 License, ensuring open and ethical use for research and development.", "## Acknowledgments and Citations \n\nWhen using the MMCBench Dataset in your research, please cite it appropriately. We extend our gratitude to all contributors and collaborators who have enriched this dataset, making it a valuable resource for the AI and ML community." ]

[ "TAGS\n#region-us \n", "# MMCBench Dataset: Benchmarking Dataset for Multimodal Model Evaluation", "## Overview\n\nThe MMCBench Dataset is a curated collection of data designed for the comprehensive evaluation of Large Multimodal Models (LMMs) under common corruption scenarios. This dataset supports the MMCBench framework, focusing on cross-modal interactions involving text, image, and speech. It provides essential data for generative tasks such as text-to-image, image-to-text, text-to-speech, and speech-to-text, enabling robustness and self-consistency assessments of LMMs.", "## Dataset Composition \n\nThe MMCBench Dataset is structured to facilitate the evaluation across four key generative tasks:\n\n- Text-to-Image: A collection of text descriptions with their corresponding corrupted versions and associated images.\n- Image-to-Text: A set of images with clean and corrupted captions.\n- Text-to-Speech: Text inputs with their clean and corrupted audio outputs.\n- Speech-to-Text: Audio files with transcriptions before and after audio corruptions.\n\nEach subset of the dataset has been meticulously selected and processed to represent challenging scenarios for LMMs.", "## Using the Dataset ️\n\nTo use the MMCBench Dataset for model evaluation:\n\n1. Access the Data: The dataset is hosted on Hugging Face and can be accessed using their dataset library or direct download.\n2. Select the Task: Choose from text-to-image, image-to-text, text-to-speech, or speech-to-text tasks based on your model's capabilities.\n3. Apply the Benchmark: Utilize the data for each task to test your model's performance against various corruptions. Follow the MMCBench framework for a consistent and standardized evaluation.", "### Dataset Structure \n\nThe dataset is organized into four main directories, each corresponding to one of the generative tasks:\n\n- 'text2image/': Contains text inputs and associated images.\n- 'image2text/': Comprises images and their descriptive captions.\n- 'text2speech/': Includes text inputs and generated speech outputs.\n- 'speech2text/': Contains audio files and their transcriptions.", "## Contributing to the Dataset \n\nContributions to the MMCBench Dataset are welcome. If you have suggestions for additional data or improvements, please reach out through the Hugging Face platform or directly contribute via GitHub.", "## License \n\nThe MMCBench Dataset is made available under the Apache 2.0 License, ensuring open and ethical use for research and development.", "## Acknowledgments and Citations \n\nWhen using the MMCBench Dataset in your research, please cite it appropriately. We extend our gratitude to all contributors and collaborators who have enriched this dataset, making it a valuable resource for the AI and ML community." ]

[ "region:us" ]

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

[ "license:mit", "arxiv:2401.13311", "region:us" ]

[ "2401.13311" ]

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[ "TAGS\n#license-mit #arxiv-2401.13311 #region-us \n" ]

[ "region:us" ]

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

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

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

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

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

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of FelixChao/Sirius-10B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/Sirius-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 2024-01-22T07:26:29.480473(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/Sirius-10B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/Sirius-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 2024-01-22T07:26:29.480473(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 Eurdem/Voltran-1.0-MoE-2x7B\n\n\n\nDataset automatically created during the evaluation run of model Eurdem/Voltran-1.0-MoE-2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T07:49:54.062079(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 Eurdem/Voltran-1.0-MoE-2x7B\n\n\n\nDataset automatically created during the evaluation run of model Eurdem/Voltran-1.0-MoE-2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T07:49:54.062079(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 \"one-summary-test\"\n\nMore Information needed" ]

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

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

[]

[]

[]

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

[ "region:eu" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

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

[]

[]

[]

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

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

[]

[]

[]

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

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

[]

[]

[ "# Animagine XL 3.0 Character\n\nEasySdxlWebUi による Animagine XL 3.0 の 公式 Character ワイルドカード の立ち絵データセットです。\n\nデータセットのダウンロードは こちら（2880枚、497MB）。 \n表情(278MB) と 画風(115MB) も用意しました。\n\n!face\n\n画像の類似度や Tagger の結果比較で正常動作するワイルドカードリストを用意できないかな？と思って始めてみました。 \nが、衣装違いなどの不正解画像でも作品名やキャラ名の影響を大きく受けるため、他のソースなしの正否分類は難しそうです。\n\n- 各 webp 画像を Stable Diffusion web UI の 'PNG内の情報を表示' にドラッグ＆ドロップすると生成情報を確認できます。\n- プロンプトは '__animagine/character__, solo, full body, standing, no background, simple background, masterpiece, best quality <lora:lcm-animagine-3:1>' です。\n- ネガティブプロンプト Animagine XL のデフォルトネガティブの先頭に NSFW 対策付与で 'nsfw, rating: sensitive, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name' です。\n- アップスケール前の生成サイズは '832' x '1216' です。\n- Seed は '1234567' です。\n\t- 他のシードで正否が変わる可能性があります。\n- 他は EasySdxlWebUi のデフォルト設定です。\n\ngrid0, \ngrid1, \ngrid2, \ngrid3" ]

[ "TAGS\n#license-unknown #region-us \n", "# Animagine XL 3.0 Character\n\nEasySdxlWebUi による Animagine XL 3.0 の 公式 Character ワイルドカード の立ち絵データセットです。\n\nデータセットのダウンロードは こちら（2880枚、497MB）。 \n表情(278MB) と 画風(115MB) も用意しました。\n\n!face\n\n画像の類似度や Tagger の結果比較で正常動作するワイルドカードリストを用意できないかな？と思って始めてみました。 \nが、衣装違いなどの不正解画像でも作品名やキャラ名の影響を大きく受けるため、他のソースなしの正否分類は難しそうです。\n\n- 各 webp 画像を Stable Diffusion web UI の 'PNG内の情報を表示' にドラッグ＆ドロップすると生成情報を確認できます。\n- プロンプトは '__animagine/character__, solo, full body, standing, no background, simple background, masterpiece, best quality <lora:lcm-animagine-3:1>' です。\n- ネガティブプロンプト Animagine XL のデフォルトネガティブの先頭に NSFW 対策付与で 'nsfw, rating: sensitive, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name' です。\n- アップスケール前の生成サイズは '832' x '1216' です。\n- Seed は '1234567' です。\n\t- 他のシードで正否が変わる可能性があります。\n- 他は EasySdxlWebUi のデフォルト設定です。\n\ngrid0, \ngrid1, \ngrid2, \ngrid3" ]

[ "arxiv:2305.09249", "region:eu" ]

[ "2305.09249" ]

[]

[]

[ "TAGS\n#arxiv-2305.09249 #region-eu \n" ]

[ "language:en", "language:es", "language:ru", "language:zh", "language:de", "language:fr", "language:th", "language:ca", "language:it", "language:ja", "language:pl", "language:eo", "language:eu", "language:vi", "language:fi", "language:hu", "language:ar", "language:nl", "language:da", "language:tr", "language:ko", "language:he", "language:id", "language:cs", "language:bn", "language:sv", "region:us" ]

[]

[ "en", "es", "ru", "zh", "de", "fr", "th", "ca", "it", "ja", "pl", "eo", "eu", "vi", "fi", "hu", "ar", "nl", "da", "tr", "ko", "he", "id", "cs", "bn", "sv" ]

[]

[ "TAGS\n#language-English #language-Spanish #language-Russian #language-Chinese #language-German #language-French #language-Thai #language-Catalan #language-Italian #language-Japanese #language-Polish #language-Esperanto #language-Basque #language-Vietnamese #language-Finnish #language-Hungarian #language-Arabic #language-Dutch #language-Danish #language-Turkish #language-Korean #language-Hebrew #language-Indonesian #language-Czech #language-Bengali #language-Swedish #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of senseable/WestLake-7B-v2\n\n\n\nDataset automatically created during the evaluation run of model senseable/WestLake-7B-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 2024-01-22T09:52:08.185697(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 senseable/WestLake-7B-v2\n\n\n\nDataset automatically created during the evaluation run of model senseable/WestLake-7B-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 2024-01-22T09:52:08.185697(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 silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-dpo-e1\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-dpo-e1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T09:52:31.786673(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 silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-dpo-e1\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-dpo-e1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T09:52:31.786673(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 silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e1\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T09:52:53.024904(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 silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e1\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T09:52:53.024904(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 moreh/MoMo-72B-lora-1.8.7-DPO\n\n\n\nDataset automatically created during the evaluation run of model moreh/MoMo-72B-lora-1.8.7-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 2024-01-22T10:33:58.465501(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 moreh/MoMo-72B-lora-1.8.7-DPO\n\n\n\nDataset automatically created during the evaluation run of model moreh/MoMo-72B-lora-1.8.7-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 2024-01-22T10:33:58.465501(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", "task_categories:fill-mask", "size_categories:100K<n<1M", "language:cs", "license:cc-by-sa-3.0", "license:gfdl", "region:us" ]

[]

[ "cs" ]

[ "## Usage\n\nLoad the dataset:\n\n\n\n\n\nThe structure makes it easy to use the dataset for implementing semantic search.\n\n<details>\n<summary>Load the data in Elasticsearch</summary>\n\n\n</details>\n\n<details>\n<summary>Use <code>sentence_transformers.util.semantic_search</code></summary>\n\n\n</details>\n\nThe embeddings generation took about 15 minutes on an NVIDIA A100 80GB GPU.", "## License\n\nSee license of the original dataset: <URL" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-100K<n<1M #language-Czech #license-cc-by-sa-3.0 #license-gfdl #region-us \n", "## Usage\n\nLoad the dataset:\n\n\n\n\n\nThe structure makes it easy to use the dataset for implementing semantic search.\n\n<details>\n<summary>Load the data in Elasticsearch</summary>\n\n\n</details>\n\n<details>\n<summary>Use <code>sentence_transformers.util.semantic_search</code></summary>\n\n\n</details>\n\nThe embeddings generation took about 15 minutes on an NVIDIA A100 80GB GPU.", "## License\n\nSee license of the original dataset: <URL" ]

[ "arxiv:2402.00786", "region:us" ]

[ "2402.00786" ]

[]

[ "# CroissantChat SFT data" ]

[ "TAGS\n#arxiv-2402.00786 #region-us \n", "# CroissantChat SFT data" ]

[ "task_categories:image-classification", "size_categories:10K<n<100K", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"testpapercomments-ds\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of TomGrc/FusionNet_34Bx2_MoE\n\n\n\nDataset automatically created during the evaluation run of model TomGrc/FusionNet_34Bx2_MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T11:29:51.974520(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 TomGrc/FusionNet_34Bx2_MoE\n\n\n\nDataset automatically created during the evaluation run of model TomGrc/FusionNet_34Bx2_MoE on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T11:29:51.974520(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "arxiv:2401.11944", "region:us" ]

[ "2401.11944" ]

[]

[]

[ "TAGS\n#arxiv-2401.11944 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-Instruct-v4.0\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-Instruct-v4.0 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 2024-01-22T11:42:12.340351(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 zhengr/MixTAO-7Bx2-MoE-Instruct-v4.0\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-Instruct-v4.0 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 2024-01-22T11:42:12.340351(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:visual-question-answering", "task_categories:question-answering", "size_categories:1M<n<10M", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# ViP-LLaVA Instruct Dataset Card", "## Dataset details\n\nDataset type:\nViP-LLaVA Instruct is composed of a mixture of LLaVA-1.5 instruction data and the region-level visual prompting data. \nIt is constructed for visual instruction tuning and for building large multimodal towards GPT-4 level regional understanding capability.\n\nSpecifically, we use 1.2M data for stage 2 finetuning, and use 26K data for the optional stage 3 finetuning. \n\nDataset date:\nViP-LLaVA Instruct was collected in November 2023, by using a mixture of academic dataset and GPT-4/GPT-4V instructed dataset.\n\nPaper or resources for more information:\nURL\n\nLicense:\nApache-2.0; and it should abide by the policy of OpenAI: URL\n\nWhere to send questions or comments about the model:\nURL", "## Intended use\nPrimary intended uses:\nThe primary use of ViP-LLaVA is research on large multimodal models and chatbots.\n\nPrimary intended users:\nThe primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence." ]

[ "TAGS\n#task_categories-visual-question-answering #task_categories-question-answering #size_categories-1M<n<10M #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "# ViP-LLaVA Instruct Dataset Card", "## Dataset details\n\nDataset type:\nViP-LLaVA Instruct is composed of a mixture of LLaVA-1.5 instruction data and the region-level visual prompting data. \nIt is constructed for visual instruction tuning and for building large multimodal towards GPT-4 level regional understanding capability.\n\nSpecifically, we use 1.2M data for stage 2 finetuning, and use 26K data for the optional stage 3 finetuning. \n\nDataset date:\nViP-LLaVA Instruct was collected in November 2023, by using a mixture of academic dataset and GPT-4/GPT-4V instructed dataset.\n\nPaper or resources for more information:\nURL\n\nLicense:\nApache-2.0; and it should abide by the policy of OpenAI: URL\n\nWhere to send questions or comments about the model:\nURL", "## Intended use\nPrimary intended uses:\nThe primary use of ViP-LLaVA is research on large multimodal models and chatbots.\n\nPrimary intended users:\nThe primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence." ]

[ "task_categories:text-generation", "task_categories:fill-mask", "size_categories:100K<n<1M", "language:cs", "license:cc-by-sa-3.0", "license:gfdl", "region:us" ]

[]

[ "cs" ]

[ "## Usage\n\nLoad the dataset:\n\n\n\n\n\nThe structure makes it easy to use the dataset for implementing semantic search.\n\n<details>\n<summary>Load the data in Elasticsearch</summary>\n\n\n</details>\n\n<details>\n<summary>Use <code>sentence_transformers.util.semantic_search</code></summary>\n\n\n</details>\n\nThe embeddings generation took about 2 hours on an NVIDIA A100 80GB GPU.", "## License\n\nSee license of the original dataset: <URL" ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-100K<n<1M #language-Czech #license-cc-by-sa-3.0 #license-gfdl #region-us \n", "## Usage\n\nLoad the dataset:\n\n\n\n\n\nThe structure makes it easy to use the dataset for implementing semantic search.\n\n<details>\n<summary>Load the data in Elasticsearch</summary>\n\n\n</details>\n\n<details>\n<summary>Use <code>sentence_transformers.util.semantic_search</code></summary>\n\n\n</details>\n\nThe embeddings generation took about 2 hours on an NVIDIA A100 80GB GPU.", "## License\n\nSee license of the original dataset: <URL" ]

[ "task_categories:translation", "language:id", "language:jv", "language:su", "language:ban", "language:min", "arxiv:2311.00998", "region:us" ]

[ "2311.00998" ]

[ "id", "jv", "su", "ban", "min" ]

[ "## License\n\nThis dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).\n\nYou are free to:\n- Share: Copy and redistribute the material in any medium or format.\n- Adapt: Remix, transform, and build upon the material for any purpose, even commercially.\n\nUnder the following terms:\n- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nSee the full text of the license for more details." ]

[ "TAGS\n#task_categories-translation #language-Indonesian #language-Javanese #language-Sundanese #language-Balinese #language-Minangkabau #arxiv-2311.00998 #region-us \n", "## License\n\nThis dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).\n\nYou are free to:\n- Share: Copy and redistribute the material in any medium or format.\n- Adapt: Remix, transform, and build upon the material for any purpose, even commercially.\n\nUnder the following terms:\n- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nSee the full text of the license for more details." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of LordNoah/Alpaca_spin_tuned_gpt2_large\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_spin_tuned_gpt2_large on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T13:42:11.763277(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 LordNoah/Alpaca_spin_tuned_gpt2_large\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_spin_tuned_gpt2_large on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T13:42:11.763277(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 LordNoah/Alpaca_refine_tuned_gpt2_large\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_refine_tuned_gpt2_large on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T13:48:41.144585(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 LordNoah/Alpaca_refine_tuned_gpt2_large\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_refine_tuned_gpt2_large on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T13:48:41.144585(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 Kquant03/Buttercup-4x7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Buttercup-4x7B-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 2024-01-22T13:49:42.612013(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/Buttercup-4x7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Buttercup-4x7B-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 2024-01-22T13:49:42.612013(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", "region:us" ]

[]

[]

[ "# Dataset Card for stanford-enigma-philosophy-chat dataset\n\nRoughly 27k questions and answers inspired by articles from Stanford Encyclopedia of Philosophy.\nThe questions range all the way from Zombies to the concept of Abduction, from Metaphysics to Neuroethics and thus cover some of the essence of mathematics, logic and philosophy.", "## Dataset Details\nThe dataset is simple and is ready for use by eg. Mixtral 8x7B in LLama-Factory with its three entries: input, instruction and output.", "### Dataset Description/How to use", "#### Format\n\nThe dataset is a json file with the following format:", "#### How to use with transformers dataset", "#### How to use with LLama-Factory\nAlter the dataset_info.json at LLaMa-Factory/data with an extra entry like below:\n\nThen use the flag \nLike this for example if you want to qlora train mixtral with flash attention:", "### Dataset Sources [optional]\n\n\n\n- Repository: -\n- Paper [optional]: Coming\n- Demo [optional]: Coming", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#license-cc #region-us \n", "# Dataset Card for stanford-enigma-philosophy-chat dataset\n\nRoughly 27k questions and answers inspired by articles from Stanford Encyclopedia of Philosophy.\nThe questions range all the way from Zombies to the concept of Abduction, from Metaphysics to Neuroethics and thus cover some of the essence of mathematics, logic and philosophy.", "## Dataset Details\nThe dataset is simple and is ready for use by eg. Mixtral 8x7B in LLama-Factory with its three entries: input, instruction and output.", "### Dataset Description/How to use", "#### Format\n\nThe dataset is a json file with the following format:", "#### How to use with transformers dataset", "#### How to use with LLama-Factory\nAlter the dataset_info.json at LLaMa-Factory/data with an extra entry like below:\n\nThen use the flag \nLike this for example if you want to qlora train mixtral with flash attention:", "### Dataset Sources [optional]\n\n\n\n- Repository: -\n- Paper [optional]: Coming\n- Demo [optional]: Coming", "## 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 Eurdem/megatron_1.1_MoE_2x7B\n\n\n\nDataset automatically created during the evaluation run of model Eurdem/megatron_1.1_MoE_2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T14:00:29.163053(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 Eurdem/megatron_1.1_MoE_2x7B\n\n\n\nDataset automatically created during the evaluation run of model Eurdem/megatron_1.1_MoE_2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T14:00:29.163053(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-to-audio", "task_categories:audio-to-audio", "task_categories:question-answering", "task_ids:parsing", "size_categories:10K<n<100K", "license:other", "python-copilot", "python-coding", "python-architecture", "knowledge-graphs", "multimodal", "text-image-audio", "fine-tuning", "training", "question-answering", "image-knowledge-graph", "alpaca", "mp3", "png", "text", "instruct", "functions", "global-functions", "region:us" ]

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[ "## Python Copilot Audio Training using Global Functions with Knowledge Graphs\n\nThis dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach global function has a question and answer mp3 where one voice reads the question and another voice reads the answer. Both mp3s are stored in the parquet dbytes column and the associated source code file_path identifier.\n\n- Rows: 49910\n- Size: 62.8 GB\n- Data type: mp3\n- Format: narrated alpaca question and answers using two voices", "### Schema", "### How to use the dataset" ]

[ "TAGS\n#task_categories-text-to-audio #task_categories-audio-to-audio #task_categories-question-answering #task_ids-parsing #size_categories-10K<n<100K #license-other #python-copilot #python-coding #python-architecture #knowledge-graphs #multimodal #text-image-audio #fine-tuning #training #question-answering #image-knowledge-graph #alpaca #mp3 #png #text #instruct #functions #global-functions #region-us \n", "## Python Copilot Audio Training using Global Functions with Knowledge Graphs\n\nThis dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach global function has a question and answer mp3 where one voice reads the question and another voice reads the answer. Both mp3s are stored in the parquet dbytes column and the associated source code file_path identifier.\n\n- Rows: 49910\n- Size: 62.8 GB\n- Data type: mp3\n- Format: narrated alpaca question and answers using two voices", "### Schema", "### How to use the dataset" ]

[ "task_categories:text-to-audio", "task_categories:audio-to-audio", "task_categories:question-answering", "task_ids:parsing", "size_categories:10K<n<100K", "license:other", "python-copilot", "python-coding", "python-architecture", "knowledge-graphs", "multimodal", "text-image-audio", "fine-tuning", "training", "question-answering", "image-knowledge-graph", "alpaca", "mp3", "png", "text", "instruct", "inheritance", "region:us" ]

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[ "## Python Copilot Audio Training using Inheritance and Polymorphism Knowledge Graphs\n\nThis dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach base class for each unique class in each module file has a question and answer mp3 where one voice reads the question and another voice reads the answer. Both mp3s are stored in the parquet dbytes column and the associated source code file_path identifier.\n\n- Rows: 96874\n- Size: 29.9 GB\n- Data type: mp3\n- Format: narrated alpaca question and answers using two voices", "### Schema", "### How to use the dataset" ]

[ "TAGS\n#task_categories-text-to-audio #task_categories-audio-to-audio #task_categories-question-answering #task_ids-parsing #size_categories-10K<n<100K #license-other #python-copilot #python-coding #python-architecture #knowledge-graphs #multimodal #text-image-audio #fine-tuning #training #question-answering #image-knowledge-graph #alpaca #mp3 #png #text #instruct #inheritance #region-us \n", "## Python Copilot Audio Training using Inheritance and Polymorphism Knowledge Graphs\n\nThis dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach base class for each unique class in each module file has a question and answer mp3 where one voice reads the question and another voice reads the answer. Both mp3s are stored in the parquet dbytes column and the associated source code file_path identifier.\n\n- Rows: 96874\n- Size: 29.9 GB\n- Data type: mp3\n- Format: narrated alpaca question and answers using two voices", "### Schema", "### How to use the dataset" ]

[ "task_categories:text-to-audio", "task_categories:audio-to-audio", "task_categories:question-answering", "task_ids:parsing", "size_categories:10K<n<100K", "license:other", "python-copilot", "python-coding", "python-architecture", "knowledge-graphs", "multimodal", "text-image-audio", "fine-tuning", "training", "question-answering", "image-knowledge-graph", "alpaca", "mp3", "png", "text", "instruct", "imports", "region:us" ]

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[ "## Python Copilot Audio Training using Imports with Knowledge Graphs\n\nThis dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach imported module for each unique class in each module file has a question and answer mp3 where one voice reads the question and another voice reads the answer. Both mp3s are stored in the parquet dbytes column and the associated source code file_path identifier.\n\n- Rows: 52086\n- Size: 17.3 GB\n- Data type: mp3\n- Format: narrated alpaca question and answers using two voices", "### Schema", "### How to use the dataset" ]

[ "TAGS\n#task_categories-text-to-audio #task_categories-audio-to-audio #task_categories-question-answering #task_ids-parsing #size_categories-10K<n<100K #license-other #python-copilot #python-coding #python-architecture #knowledge-graphs #multimodal #text-image-audio #fine-tuning #training #question-answering #image-knowledge-graph #alpaca #mp3 #png #text #instruct #imports #region-us \n", "## Python Copilot Audio Training using Imports with Knowledge Graphs\n\nThis dataset is a subset of the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach imported module for each unique class in each module file has a question and answer mp3 where one voice reads the question and another voice reads the answer. Both mp3s are stored in the parquet dbytes column and the associated source code file_path identifier.\n\n- Rows: 52086\n- Size: 17.3 GB\n- Data type: mp3\n- Format: narrated alpaca question and answers using two voices", "### Schema", "### How to use the dataset" ]

[ "task_categories:text-classification", "task_ids:multi-label-classification", "region:us" ]

[]

[]

[ "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: URL\n- Point of Contact: NAME" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #region-us \n", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: URL\n- Point of Contact: NAME" ]

[ "task_categories:text-classification", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:sentence-similarity", "size_categories:1K<n<10K", "language:en", "license:cc", "security", "ttp mapping", "mitre att&ck", "extreme multilabel ", "multilabel classification", "arxiv:2401.10337", "region:us" ]

[ "2401.10337" ]

[ "en" ]

[ "# The Security Attack Pattern (TTP) Recognition or Mapping Task\n![License](URL\n![arXiv](URL\n\nWe share in this repo the MITRE ATT&amp;CK mapping datasets, with 'training', 'validation' and 'test' splits. \nThe datasets can be considered as an emerging and challenging 'multilabel classification' NLP task, with over 600 hierarchical classes.\n\nNOTE: due to their security nature, these datasets contain textual information about 'malware' and other security aspects.", "## Datasets", "### TRAM\n\nThis dataset belongs to CTID, is originally provided in this github link. \n\nWe processed the original files (i.e., gather from all sources, remove duplicates, resolve noisy / too short text and noisy labels, remap to MITRE ATTACK 12.0) and split into training, dev and test splits.", "### Procedure+\n\nThe dataset consists of two sub- datasets:\n- Procedures: belong to MITRE. All procedure examples from v12.0 are gathered and processed (i.e., remove markups) and split into training, dev and test splits.\n- Derived procedures: we crawled the URL references for each procedure example, and extract original text from the articles that are determined to be relevant to the procedure examples. The text are processed and split into training, dev and test splits.", "### Expert\n\nThe dataset is constructed from a large pool of high-quality threat reports. \nThe rich textual paragraphs are carefully selected and then annotated by seasoned security experts.\n\nThe dataset is also pre-split into 'training', 'dev' and 'test' splits. There are ~4 labels per text in the 'test' split, on average.\n\ns\nIf you use the datasets in your research or want to refer to our work, please cite:", "## License\nThis project is licensed under the Creative Commons CC BY License, version 4.0." ]

[ "TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-sentence-similarity #size_categories-1K<n<10K #language-English #license-cc #security #ttp mapping #mitre att&ck #extreme multilabel #multilabel classification #arxiv-2401.10337 #region-us \n", "# The Security Attack Pattern (TTP) Recognition or Mapping Task\n![License](URL\n![arXiv](URL\n\nWe share in this repo the MITRE ATT&amp;CK mapping datasets, with 'training', 'validation' and 'test' splits. \nThe datasets can be considered as an emerging and challenging 'multilabel classification' NLP task, with over 600 hierarchical classes.\n\nNOTE: due to their security nature, these datasets contain textual information about 'malware' and other security aspects.", "## Datasets", "### TRAM\n\nThis dataset belongs to CTID, is originally provided in this github link. \n\nWe processed the original files (i.e., gather from all sources, remove duplicates, resolve noisy / too short text and noisy labels, remap to MITRE ATTACK 12.0) and split into training, dev and test splits.", "### Procedure+\n\nThe dataset consists of two sub- datasets:\n- Procedures: belong to MITRE. All procedure examples from v12.0 are gathered and processed (i.e., remove markups) and split into training, dev and test splits.\n- Derived procedures: we crawled the URL references for each procedure example, and extract original text from the articles that are determined to be relevant to the procedure examples. The text are processed and split into training, dev and test splits.", "### Expert\n\nThe dataset is constructed from a large pool of high-quality threat reports. \nThe rich textual paragraphs are carefully selected and then annotated by seasoned security experts.\n\nThe dataset is also pre-split into 'training', 'dev' and 'test' splits. There are ~4 labels per text in the 'test' split, on average.\n\ns\nIf you use the datasets in your research or want to refer to our work, please cite:", "## License\nThis project is licensed under the Creative Commons CC BY License, version 4.0." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseek-10b-v17.1-4k\n\n\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseek-10b-v17.1-4k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T15:20:55.890442(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 OpenBuddy/openbuddy-deepseek-10b-v17.1-4k\n\n\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseek-10b-v17.1-4k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T15:20:55.890442(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 LordNoah/Alpaca_spin_gpt2_e0_se1\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_spin_gpt2_e0_se1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T15:39:18.329884(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 LordNoah/Alpaca_spin_gpt2_e0_se1\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_spin_gpt2_e0_se1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T15:39:18.329884(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" ]

[ "20k", "random", "region:us" ]

[]

[]

[]

[ "TAGS\n#20k #random #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of abdulrahman-nuzha/finetuned-Mistral-7B-Instruct-v0.2-5000-v2.0\n\n\n\nDataset automatically created during the evaluation run of model abdulrahman-nuzha/finetuned-Mistral-7B-Instruct-v0.2-5000-v2.0 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 2024-01-22T15:53:57.776809(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 abdulrahman-nuzha/finetuned-Mistral-7B-Instruct-v0.2-5000-v2.0\n\n\n\nDataset automatically created during the evaluation run of model abdulrahman-nuzha/finetuned-Mistral-7B-Instruct-v0.2-5000-v2.0 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 2024-01-22T15:53:57.776809(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 LordNoah/Alpaca_refine_gpt2_e0_se1\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_refine_gpt2_e0_se1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T16:13:20.086955(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 LordNoah/Alpaca_refine_gpt2_e0_se1\n\n\n\nDataset automatically created during the evaluation run of model LordNoah/Alpaca_refine_gpt2_e0_se1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T16:13:20.086955(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", "license:apache-2.0", "region:us" ]

[]

[]

[ "#### Dataset:\nThis is the data used for training Snorkel model\n\nWe use ONLY the prompts from UltraFeedback; no external LLM responses used.", "#### Methodology:\n 1. Generate 5 response variations for each prompt from a subset of 20,000 using the LLM - to start, we used Mistral-7B-Instruct-v0.2.\n 2. Apply PairRM for response reranking.\n 3. Update the LLM by applying Direct Preference Optimization (DPO) on the top (chosen) and bottom (rejected) responses.\n 4. Use this LLM as the base model for the next iteration and use a different set of 20,000 prompts, repeating three times in total.\n\nPlease see the model page for more details on the methodology.\n\nColumns:\n- prompt: the current prompt\n- chosen: the list of messages for the chosen response\n- rejected: the list of messages for the rejected response\n- all_generated_responses: The 5 generated responses\n- all_rm_scores: The 5 corresponding reward model scores\n\nSplits:\n- train/test_iteration_{n}: The dataset used at the n_th iteration. We did 3 iterations in total.\n\nTraining recipe: This data is formatted to be compatible with the Hugging Face's Zephyr recipe.\nWe executed the n_th DPO iteration using the \"train/test_iteration_{n}\"." ]

[ "TAGS\n#task_categories-text-generation #license-apache-2.0 #region-us \n", "#### Dataset:\nThis is the data used for training Snorkel model\n\nWe use ONLY the prompts from UltraFeedback; no external LLM responses used.", "#### Methodology:\n 1. Generate 5 response variations for each prompt from a subset of 20,000 using the LLM - to start, we used Mistral-7B-Instruct-v0.2.\n 2. Apply PairRM for response reranking.\n 3. Update the LLM by applying Direct Preference Optimization (DPO) on the top (chosen) and bottom (rejected) responses.\n 4. Use this LLM as the base model for the next iteration and use a different set of 20,000 prompts, repeating three times in total.\n\nPlease see the model page for more details on the methodology.\n\nColumns:\n- prompt: the current prompt\n- chosen: the list of messages for the chosen response\n- rejected: the list of messages for the rejected response\n- all_generated_responses: The 5 generated responses\n- all_rm_scores: The 5 corresponding reward model scores\n\nSplits:\n- train/test_iteration_{n}: The dataset used at the n_th iteration. We did 3 iterations in total.\n\nTraining recipe: This data is formatted to be compatible with the Hugging Face's Zephyr recipe.\nWe executed the n_th DPO iteration using the \"train/test_iteration_{n}\"." ]

[ "Lilac", "region:us" ]

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[]

[ "# lilac/OpenOrca\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "TAGS\n#Lilac #region-us \n", "# lilac/OpenOrca\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "region:us" ]

[]

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[]

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

[ "region:us" ]

[]

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[ "# Dataset Card for Evaluation run of BarryFutureman/NeuralTurdusVariant1-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/NeuralTurdusVariant1-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T17:04:26.015836(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 BarryFutureman/NeuralTurdusVariant1-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/NeuralTurdusVariant1-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T17:04:26.015836(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 silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e2\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T17:11:06.460582(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 silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e2\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T17:11:06.460582(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 \"Calc-ape210k_selftrain\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Calc-ape210k_selftrain\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e3\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T17:17:10.255551(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 silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e3\n\n\n\nDataset automatically created during the evaluation run of model silvercoder45/Mistral-7b-instruct-v0.2-summ-dpo-e3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-22T17:17:10.255551(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" ]