Datasets:

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ftopal

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huggingface-datasets

like 0

[ "region:us" ]

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

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

[ "license:apache-2.0", "arxiv:2011.04163", "region:us" ]

[ "2011.04163" ]

[]

[]

[ "TAGS\n#license-apache-2.0 #arxiv-2011.04163 #region-us \n" ]

[ "task_categories:text-generation", "size_categories:n<1K", "language:en", "license:cc-by-nc-4.0", "medical", "simplification", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Medical texts simplification}}\n\nThe dataset consisting of 30 triples (around 800 sentences) of the original text, \nhuman- and ChatGPT-simplified texts was created from a subset Medical Notes Classification dataset. \nThe original dataset contains medical notes, which come from exactly one of the following five clinical domains: \nGastroenterology, Neurology, Orthopedics, Radiology, and Urology. There are 1239 texts in total in the original dataset.", "## Dataset Details", "### Dataset Description\n\n- Curated by: Liliya Makhmutova, Giancarlo Salton, Fernando Perez-Tellez, and Robert Ross\n- Funded by: Science Foundation Ireland under Grant number 18/CRT/6183 and the ADAPT SFI Research Centre for AI-Driven Digital Content Technology under Grant No. 13/RC/2106/P2\n- Language(s) (NLP): English\n- License: CC-BY-NC-4.0", "### Dataset Sources [optional]\n\n\n\n- Repository: GitHub", "## Uses\n\nShould not be used for any kind of patient treatment without medical professional supervision.", "## Dataset Structure\n\n\n\nColumns in URL:\n- file_number (int): a filename (numeric) in the original dataset\n- line_number (int): the number of a sentence in the original dataset\n- original (str): sentence of the original text (preprocessed)\n- human_simplification (str): sentence of the text simplified by a human\n- chatgpt_simplification (str): sentence of the text simplified by the ChatGPT\n- images (list[str]): list of image file names related to a sentence that helps to understand a medical text", "## Dataset Creation", "### Curation Rationale\n\n\n\nMedical texts can be very difficult to understand for patients, which may lead to health problems. \nMore importantly, patients often don’t have access to their medical records, and where they have, \nthe patients often cannot understand the meaning due to the very different mental models and background \nknowledge that patients and clinicians have. \nThis leads to patients’ partial exclusion from the recovery process and sub-optimal outcomes. \nMedical texts usually contain lots of special terminology, many abbreviations, \nlack of coordination, subordination, and explanations in sentences making it harder to understand causal relationships. \nMoreover, medical texts usually consist of short ungrammatical sentences. \nThis makes their understanding difficult not only for laymen but also for healthcare professionals from other fields. \nGiven these challenges, a machine learning model for medical text simplifications \nmay be very beneficial both in terms of democratising information access and improving outcomes. \nAlthough a model under no circumstances should add irrelevant information (making up some facts), \nit may incorporate true knowledge that is not mentioned in a report to make a medical text clearer and more understandable for a patient. \nSo, for example, a model might add \"Your mother or father are likely to have similar conditions too\" \nexplaining \"genetic\" reasons, but should not judge whether the blood sugar level in a patient is normal or not.", "### Source Data\n\nBased on the Medical Notes Classification dataset.", "#### Data Collection and Processing\n\n\n\nThe texts from the Medical Notes Classification dataset \nwere preprocessed in the following way to create the original column:\n\n- HTML tags were removed\n- Minor typos were fixed\n- The texts were divided by sentences, each of which was enumerated\n\nSimplified text was created out of complex text under congruence (preserve the original information and not add extra information), \nfluency (readability), and simplicity principles.\nby a non-native English speaker with no medical or healthcare professional background. \nThe dataset hasn't been reviewed by a medical professional, the simplifications were created manually with the help of open resources \nincluding medical papers, surgerical videos explaining the procedures, and medical articles with simplified explanations. \nFor some sentences, pictorial explanations were added for a better understanding.\n\nAutomatically created simplifications were obtained through the use of the OpenAI chat interface where the following prompt was used: \n\"Please simplify the text so that non-professionals could understand it\". \nChatGPT tends to produce summarization rather than simplification on longer texts, so, for long texts (typically more than 20 sentences), \nthe text was inputted by parts (with the following prompt after the main one within the same chat context: \n\"Could you also simplify one more follow-up text so that non-professional could understand it: \\<NEXT PART OF THE COMPLEX TEXT\\>''). \nIt was decided not to add any examples or special prompting techniques for clarity reasons.", "### Recommendations\n\n\n\n\n- Should not be used for any kind of patient treatment without medical professional supervision.\n- The dataset is not for commercial use.\n- If a model is trained on the dataset to solve a simplification task, it may produce hallucinations.\n\nThe outputs of ChatGPT from the chatgpt_simplification column were analysed and it was found that:\n\n1. ChatGPT can disclose abbreviations depending on the context.\n2. ChatGPT has a very good rewriting ability (this is related to both general language skills and the ability to understand and simplify medical terms).\n\nand \n\n1. ChatGPT tends to produce abstracts or summarizations rather than simplification on long texts.\n This may be explained by the limited length of the context.\n2. ChatGPT sometimes makes up some facts, which may be very dangerous in such a sensitive field as medicine. ChatGPT may even contradict its output.\n3. ChatGPT somehow lacks commonsense reasoning or medical \"knowledge\".\n4. ChatGPT may omit important facts or oversimplify.\n5. ChatGPT is biased towards rewriting a text by any means, even if it has been already quite simple. Sometimes the rewriting may change the meaning.\n ChatGPT also tends to produce more personal sentences.\n6. ChatGPT sometimes uses words such as \"a\", \"about\", \"some\", \"called\", rather than properly simplify a concept or explain.\n It also frequently outputs undersimplifications.\n\nExamples and analysis may be found on GitHub.", "## Dataset Card Contact\n\nLiliya Makhmutova" ]

[ "TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #license-cc-by-nc-4.0 #medical #simplification #region-us \n", "# Dataset Card for Medical texts simplification}}\n\nThe dataset consisting of 30 triples (around 800 sentences) of the original text, \nhuman- and ChatGPT-simplified texts was created from a subset Medical Notes Classification dataset. \nThe original dataset contains medical notes, which come from exactly one of the following five clinical domains: \nGastroenterology, Neurology, Orthopedics, Radiology, and Urology. There are 1239 texts in total in the original dataset.", "## Dataset Details", "### Dataset Description\n\n- Curated by: Liliya Makhmutova, Giancarlo Salton, Fernando Perez-Tellez, and Robert Ross\n- Funded by: Science Foundation Ireland under Grant number 18/CRT/6183 and the ADAPT SFI Research Centre for AI-Driven Digital Content Technology under Grant No. 13/RC/2106/P2\n- Language(s) (NLP): English\n- License: CC-BY-NC-4.0", "### Dataset Sources [optional]\n\n\n\n- Repository: GitHub", "## Uses\n\nShould not be used for any kind of patient treatment without medical professional supervision.", "## Dataset Structure\n\n\n\nColumns in URL:\n- file_number (int): a filename (numeric) in the original dataset\n- line_number (int): the number of a sentence in the original dataset\n- original (str): sentence of the original text (preprocessed)\n- human_simplification (str): sentence of the text simplified by a human\n- chatgpt_simplification (str): sentence of the text simplified by the ChatGPT\n- images (list[str]): list of image file names related to a sentence that helps to understand a medical text", "## Dataset Creation", "### Curation Rationale\n\n\n\nMedical texts can be very difficult to understand for patients, which may lead to health problems. \nMore importantly, patients often don’t have access to their medical records, and where they have, \nthe patients often cannot understand the meaning due to the very different mental models and background \nknowledge that patients and clinicians have. \nThis leads to patients’ partial exclusion from the recovery process and sub-optimal outcomes. \nMedical texts usually contain lots of special terminology, many abbreviations, \nlack of coordination, subordination, and explanations in sentences making it harder to understand causal relationships. \nMoreover, medical texts usually consist of short ungrammatical sentences. \nThis makes their understanding difficult not only for laymen but also for healthcare professionals from other fields. \nGiven these challenges, a machine learning model for medical text simplifications \nmay be very beneficial both in terms of democratising information access and improving outcomes. \nAlthough a model under no circumstances should add irrelevant information (making up some facts), \nit may incorporate true knowledge that is not mentioned in a report to make a medical text clearer and more understandable for a patient. \nSo, for example, a model might add \"Your mother or father are likely to have similar conditions too\" \nexplaining \"genetic\" reasons, but should not judge whether the blood sugar level in a patient is normal or not.", "### Source Data\n\nBased on the Medical Notes Classification dataset.", "#### Data Collection and Processing\n\n\n\nThe texts from the Medical Notes Classification dataset \nwere preprocessed in the following way to create the original column:\n\n- HTML tags were removed\n- Minor typos were fixed\n- The texts were divided by sentences, each of which was enumerated\n\nSimplified text was created out of complex text under congruence (preserve the original information and not add extra information), \nfluency (readability), and simplicity principles.\nby a non-native English speaker with no medical or healthcare professional background. \nThe dataset hasn't been reviewed by a medical professional, the simplifications were created manually with the help of open resources \nincluding medical papers, surgerical videos explaining the procedures, and medical articles with simplified explanations. \nFor some sentences, pictorial explanations were added for a better understanding.\n\nAutomatically created simplifications were obtained through the use of the OpenAI chat interface where the following prompt was used: \n\"Please simplify the text so that non-professionals could understand it\". \nChatGPT tends to produce summarization rather than simplification on longer texts, so, for long texts (typically more than 20 sentences), \nthe text was inputted by parts (with the following prompt after the main one within the same chat context: \n\"Could you also simplify one more follow-up text so that non-professional could understand it: \\<NEXT PART OF THE COMPLEX TEXT\\>''). \nIt was decided not to add any examples or special prompting techniques for clarity reasons.", "### Recommendations\n\n\n\n\n- Should not be used for any kind of patient treatment without medical professional supervision.\n- The dataset is not for commercial use.\n- If a model is trained on the dataset to solve a simplification task, it may produce hallucinations.\n\nThe outputs of ChatGPT from the chatgpt_simplification column were analysed and it was found that:\n\n1. ChatGPT can disclose abbreviations depending on the context.\n2. ChatGPT has a very good rewriting ability (this is related to both general language skills and the ability to understand and simplify medical terms).\n\nand \n\n1. ChatGPT tends to produce abstracts or summarizations rather than simplification on long texts.\n This may be explained by the limited length of the context.\n2. ChatGPT sometimes makes up some facts, which may be very dangerous in such a sensitive field as medicine. ChatGPT may even contradict its output.\n3. ChatGPT somehow lacks commonsense reasoning or medical \"knowledge\".\n4. ChatGPT may omit important facts or oversimplify.\n5. ChatGPT is biased towards rewriting a text by any means, even if it has been already quite simple. Sometimes the rewriting may change the meaning.\n ChatGPT also tends to produce more personal sentences.\n6. ChatGPT sometimes uses words such as \"a\", \"about\", \"some\", \"called\", rather than properly simplify a concept or explain.\n It also frequently outputs undersimplifications.\n\nExamples and analysis may be found on GitHub.", "## Dataset Card Contact\n\nLiliya Makhmutova" ]

[ "language:en", "language:es", "machine-translation", "English", "Spanish", "region:us" ]

[]

[ "en", "es" ]

[ "# Dataset Card for Dataset Name\nThis dataset was culled from the English-Spanish plain-text section of the United Nations Parallel Corpus.", "## Dataset Sources\nURL", "## Uses\nThis dataset can be used for various tasks in NLP, including but not limited to: Machine Translation, Cross-lingual Transfer Learning, Linguistic Research, etc.", "## Dataset Card Contact\nFor any queries or contributions, please contact Okezie OKOYE at okezieowen@URL." ]

[ "TAGS\n#language-English #language-Spanish #machine-translation #English #Spanish #region-us \n", "# Dataset Card for Dataset Name\nThis dataset was culled from the English-Spanish plain-text section of the United Nations Parallel Corpus.", "## Dataset Sources\nURL", "## Uses\nThis dataset can be used for various tasks in NLP, including but not limited to: Machine Translation, Cross-lingual Transfer Learning, Linguistic Research, etc.", "## Dataset Card Contact\nFor any queries or contributions, please contact Okezie OKOYE at okezieowen@URL." ]

[ "task_categories:feature-extraction", "language:en", "license:cc-by-nc-nd-4.0", "region:us" ]

[]

[ "en" ]

[ "# RadioModRec-1\n\n\n\nRadioModRec-1 is an Automatic Modulation Recognition (AMR) simulated dataset carefully curated for fifteen digital modulation schemes consisting of 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK whose usefulness is predominantly found in modern wireless communication systems. RadioModRec-1 dataset caters for the Rayleigh and the Rician channel models under the Additive White Gaussian Noise (AWGN) from -20dB to +20dB at a step of +5dB.", "### Dataset Description\n\n\n\n- Curated by: [Emmanuel Adetiba and Jamiu R. Olasina]\n- Funded by: [Part Funding by Google Award for TensorFlow Outreaches in Colleges]\n- Language(s) (AMC): [Automatic Modulation Recognition]\n- License: [cc-by-nc-nd-4.0]", "## Uses\n\nRadioModRec-1 is a vital resource for state-of-the-art Automatic Modulation Recognition (AMR) research in Software Defined and Cognitive Radio Systems.\n\n[optional]\n\nEmmanuel Adetiba and Jamiu R. Olasina, RadioModRec: A Dataset for Automatic Modulation Recognition in Software Defined and Cognitive Radio Research.", "## Dataset Card Authors [optional]\n\nEmmanuel Adetiba", "## Dataset Card Contact\n\naspmirlab@URL\nemmanuel.adetiba@URL" ]

[ "TAGS\n#task_categories-feature-extraction #language-English #license-cc-by-nc-nd-4.0 #region-us \n", "# RadioModRec-1\n\n\n\nRadioModRec-1 is an Automatic Modulation Recognition (AMR) simulated dataset carefully curated for fifteen digital modulation schemes consisting of 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK whose usefulness is predominantly found in modern wireless communication systems. RadioModRec-1 dataset caters for the Rayleigh and the Rician channel models under the Additive White Gaussian Noise (AWGN) from -20dB to +20dB at a step of +5dB.", "### Dataset Description\n\n\n\n- Curated by: [Emmanuel Adetiba and Jamiu R. Olasina]\n- Funded by: [Part Funding by Google Award for TensorFlow Outreaches in Colleges]\n- Language(s) (AMC): [Automatic Modulation Recognition]\n- License: [cc-by-nc-nd-4.0]", "## Uses\n\nRadioModRec-1 is a vital resource for state-of-the-art Automatic Modulation Recognition (AMR) research in Software Defined and Cognitive Radio Systems.\n\n[optional]\n\nEmmanuel Adetiba and Jamiu R. Olasina, RadioModRec: A Dataset for Automatic Modulation Recognition in Software Defined and Cognitive Radio Research.", "## Dataset Card Authors [optional]\n\nEmmanuel Adetiba", "## Dataset Card Contact\n\naspmirlab@URL\nemmanuel.adetiba@URL" ]

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

[]

[]

[]

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

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

[]

[ "en" ]

[ "# Dataset Card for kz919/flan-50k-synthetic-reward-pretrained-mistral-7b-open-orca", "## Dataset Description\n\n- License: Apache-2.0\n- Pretty Name: Ranking is generated by normalized inverse perplexity on each of the responses (Open-Orca/Mistral-7B-OpenOrca)", "### Dataset Info\n\nThe dataset includes features essential for tasks related to response generation and ranking:\n\n1. prompt: (string) - The original text prompt.\n2. completion: (string) - The corresponding completion for each prompt.\n3. task: (string) - Categorization or description of the task.\n4. ignos-Mistral-T5-7B-v1: (string) - Responses from the ignos-Mistral-T5-7B-v1 model.\n5. cognAI-lil-c3po: (string) - Responses from the cognAI-lil-c3po model.\n6. viethq188-Rabbit-7B-DPO-Chat: (string) - Responses from the viethq188-Rabbit-7B-DPO-Chat model.\n7. cookinai-DonutLM-v1: (string) - Responses from the cookinai-DonutLM-v1 model.\n8. v1olet-v1olet-merged-dpo-7B: (string) - Responses from the v1olet-v1olet-merged-dpo-7B model.\n9. normalized_rewards: (sequence of float32) - Normalized reward scores based on the inverse perplexity, calculated and ranked by Open-Orca/Mistral-7B-OpenOrca.\n10. router_label: (int64) - Labels for routing the query to the most appropriate model.", "### Ranking Methodology\n\n- Ranking Model: Open-Orca/Mistral-7B-OpenOrca\n- Criteria: The ranking is based on normalized inverse perplexity, a measure that assesses the fluency and relevance of the model responses in relation to the prompts.", "### Splits\n\n- Train Split: \n - num_bytes: 105,157,970\n - num_examples: 50,000", "### Size\n\n- Download Size: 48,848,643 bytes\n- Dataset Size: 105,157,970 bytes", "## Configurations\n\n- Config Name: default\n- Data Files: \n - Train Split:\n - Path: data/train-*", "## Task Categories\n\n- Text Classification\n- Response Generation and Evaluation", "## Language\n\n- English (en)", "## Size Category\n\n- Medium (10K < n < 100K)\n\n---\n\nThis dataset is particularly useful for developing and testing models in response generation tasks, offering a robust framework for comparing different AI models' performance. The unique ranking system based on Open-Orca/Mistral-7B-OpenOrca's normalized inverse perplexity provides an insightful metric for evaluating the fluency and relevance of responses in a wide range of conversational contexts." ]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "# Dataset Card for kz919/flan-50k-synthetic-reward-pretrained-mistral-7b-open-orca", "## Dataset Description\n\n- License: Apache-2.0\n- Pretty Name: Ranking is generated by normalized inverse perplexity on each of the responses (Open-Orca/Mistral-7B-OpenOrca)", "### Dataset Info\n\nThe dataset includes features essential for tasks related to response generation and ranking:\n\n1. prompt: (string) - The original text prompt.\n2. completion: (string) - The corresponding completion for each prompt.\n3. task: (string) - Categorization or description of the task.\n4. ignos-Mistral-T5-7B-v1: (string) - Responses from the ignos-Mistral-T5-7B-v1 model.\n5. cognAI-lil-c3po: (string) - Responses from the cognAI-lil-c3po model.\n6. viethq188-Rabbit-7B-DPO-Chat: (string) - Responses from the viethq188-Rabbit-7B-DPO-Chat model.\n7. cookinai-DonutLM-v1: (string) - Responses from the cookinai-DonutLM-v1 model.\n8. v1olet-v1olet-merged-dpo-7B: (string) - Responses from the v1olet-v1olet-merged-dpo-7B model.\n9. normalized_rewards: (sequence of float32) - Normalized reward scores based on the inverse perplexity, calculated and ranked by Open-Orca/Mistral-7B-OpenOrca.\n10. router_label: (int64) - Labels for routing the query to the most appropriate model.", "### Ranking Methodology\n\n- Ranking Model: Open-Orca/Mistral-7B-OpenOrca\n- Criteria: The ranking is based on normalized inverse perplexity, a measure that assesses the fluency and relevance of the model responses in relation to the prompts.", "### Splits\n\n- Train Split: \n - num_bytes: 105,157,970\n - num_examples: 50,000", "### Size\n\n- Download Size: 48,848,643 bytes\n- Dataset Size: 105,157,970 bytes", "## Configurations\n\n- Config Name: default\n- Data Files: \n - Train Split:\n - Path: data/train-*", "## Task Categories\n\n- Text Classification\n- Response Generation and Evaluation", "## Language\n\n- English (en)", "## Size Category\n\n- Medium (10K < n < 100K)\n\n---\n\nThis dataset is particularly useful for developing and testing models in response generation tasks, offering a robust framework for comparing different AI models' performance. The unique ranking system based on Open-Orca/Mistral-7B-OpenOrca's normalized inverse perplexity provides an insightful metric for evaluating the fluency and relevance of responses in a wide range of conversational contexts." ]

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

[]

[]

[]

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

[ "task_categories:text-generation", "language:zh", "license:cc-by-4.0", "region:us" ]

[]

[ "zh" ]

[ "## Haruhi-Zero的Conversation训练数据\n\n我们计划拓展ChatHaruhi，从Few-shot到Zero-shot，这个数据集记录使用各个（中文）角色扮演api进行Baize式相互聊天后得到的数据结果\n\nids代表聊天的时候两张bot的角色卡片， 角色卡片的信息可以在https://URL 中找到\n\n并且对于第一次出现的id0，也会在prompt字段中进行记录。\n\n聊天的时候id和ids的卡片进行对应\n\n- openai 代表两个聊天的bot都使用openai\n- GLM 代表两个聊天的bot都使用CharacterGLM\n- Claude 代表两个聊天的bot都使用Claude\n- Claude_openai 代表id0的使用Claude， id1的使用openai\n- Baichuan 代表两个聊天的bot都使用Character-Baichuan-Turbo\n\n目前百川有很严重的访问限制，如果谁有并发更大的百川的接口，可以联系我们借用一下(邮箱chengli.thu@URL)\n\n或者跑下面的代码，(start_id = 10590 end_id = 12708)反馈给我\n\nURL\n\n目前平衡生成时间、成本和效果来看，最终训练准备先采用openai和Claude_openai， 这两者已经采集了15000/2000的数据，正在进一步生成更多数据\n\n主项目链接\n\nURL", "## API和服务器\n\n如果你有OpenAI、Claude或者Character-Baichuan的api资源 可以参与进来的话，方便联系我一下 发邮件或者在知乎 URL 留一下您的微信\n\n如果你有足够的训练资源去tuning 13B以及Yi 34B规模的模型（2000长度），也可以联系我加入到项目后面的训练中。\n\n如果你能够组织Human Feedback的比较标注，也可以联系我们\n\n整体计划\n\nURL\n\n后期如果有空的话可以进一步拿各家api的结果做一下RLHF或者DPO。" ]

[ "TAGS\n#task_categories-text-generation #language-Chinese #license-cc-by-4.0 #region-us \n", "## Haruhi-Zero的Conversation训练数据\n\n我们计划拓展ChatHaruhi，从Few-shot到Zero-shot，这个数据集记录使用各个（中文）角色扮演api进行Baize式相互聊天后得到的数据结果\n\nids代表聊天的时候两张bot的角色卡片， 角色卡片的信息可以在https://URL 中找到\n\n并且对于第一次出现的id0，也会在prompt字段中进行记录。\n\n聊天的时候id和ids的卡片进行对应\n\n- openai 代表两个聊天的bot都使用openai\n- GLM 代表两个聊天的bot都使用CharacterGLM\n- Claude 代表两个聊天的bot都使用Claude\n- Claude_openai 代表id0的使用Claude， id1的使用openai\n- Baichuan 代表两个聊天的bot都使用Character-Baichuan-Turbo\n\n目前百川有很严重的访问限制，如果谁有并发更大的百川的接口，可以联系我们借用一下(邮箱chengli.thu@URL)\n\n或者跑下面的代码，(start_id = 10590 end_id = 12708)反馈给我\n\nURL\n\n目前平衡生成时间、成本和效果来看，最终训练准备先采用openai和Claude_openai， 这两者已经采集了15000/2000的数据，正在进一步生成更多数据\n\n主项目链接\n\nURL", "## API和服务器\n\n如果你有OpenAI、Claude或者Character-Baichuan的api资源 可以参与进来的话，方便联系我一下 发邮件或者在知乎 URL 留一下您的微信\n\n如果你有足够的训练资源去tuning 13B以及Yi 34B规模的模型（2000长度），也可以联系我加入到项目后面的训练中。\n\n如果你能够组织Human Feedback的比较标注，也可以联系我们\n\n整体计划\n\nURL\n\n后期如果有空的话可以进一步拿各家api的结果做一下RLHF或者DPO。" ]

[ "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:image-classification", "size_categories:1M<n<10M", "language:en", "art", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #task_categories-image-classification #size_categories-1M<n<10M #language-English #art #region-us \n" ]

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

[]

[]

[]

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

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

[]

[]

[ "# File: wikipedia_word_sub.json\n\nInput: 150 Wikipedia articles + A request to substitute one word for another (usually a synonym)\n\nOutput: The same article, with the word substituted as requested\n\nFormat: Fastchat", "# File: wikipedia_err_correct.json\n\nInput: 224 Wikipedia articles with typos and other errors introduced randomly using the python typo library + A request to fix errors\n\nOutput: The original article (presumably, without errors)\n\nFormat: Fastchat" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# File: wikipedia_word_sub.json\n\nInput: 150 Wikipedia articles + A request to substitute one word for another (usually a synonym)\n\nOutput: The same article, with the word substituted as requested\n\nFormat: Fastchat", "# File: wikipedia_err_correct.json\n\nInput: 224 Wikipedia articles with typos and other errors introduced randomly using the python typo library + A request to fix errors\n\nOutput: The original article (presumably, without errors)\n\nFormat: Fastchat" ]

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

[]

[]

[]

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

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

[]

[]

[]

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

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

[]

[]

[]

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

[ "license:apache-2.0", "not-for-all-audiences", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-apache-2.0 #not-for-all-audiences #region-us \n" ]

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

[]

[]

[]

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

[]

[]

[]

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

[ "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-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-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" ]

[ "region:us" ]

[]

[]

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

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

[ "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-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-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 \"cpm_test\"\n\nMore Information needed" ]

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

[ "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-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-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 \"audioset_synth\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"c_x86_simd_extension\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"c_x86_simd_extension\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"c_x86_simd_extension_filtered\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"c_x86_simd_extension_filtered\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"medical-dpo-small\"\n\nMore Information needed" ]

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

[ "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-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" ]

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

[]

[ "en" ]

[ "# This example will start serving the 1.3B model.\nexport CUDA_DEVICE_MAX_CONNECTIONS=1\n\nDISTRIBUTED_ARGS=\"--nproc_per_node 1 \\\n --nnodes 1 \\\n --node_rank 0 \\\n --master_addr localhost \\\n --master_port 6000\"\n\nCHECKPOINT=/path/to/model/checkpoint/folder\nVOCAB_FILE=/path/to/vocab/file\nMERGE_FILE=/path/to/merge/file\n\nMODEL_SIZE=\"1.3B\"\nif [[ ${MODEL_SIZE} == \"110M\" ]]; then HIDDEN_SIZE=768; NUM_HEAD=12; NUM_QUERY_GROUP=12; NUM_LAYERS=12; FFN_HIDDEN_SIZE=3072; NORM_EPS=1e-5;\nelif [[ ${MODEL_SIZE} == \"345M\" ]]; then HIDDEN_SIZE=1024; NUM_HEAD=16; NUM_QUERY_GROUP=16; NUM_LAYERS=24; FFN_HIDDEN_SIZE=4096; NORM_EPS=1e-5;\nelif [[ ${MODEL_SIZE} == \"770M\" ]]; then HIDDEN_SIZE=1280; NUM_HEAD=20; NUM_QUERY_GROUP=20; NUM_LAYERS=36; FFN_HIDDEN_SIZE=5120; NORM_EPS=1e-5;\nelif [[ ${MODEL_SIZE} == \"1.3B\" ]]; then HIDDEN_SIZE=1536; NUM_HEAD=24; NUM_QUERY_GROUP=24; NUM_LAYERS=48; FFN_HIDDEN_SIZE=6144; NORM_EPS=1e-5;\nelse echo \"invalid MODEL_SIZE: ${MODEL_SIZE}\"; exit 1\nfi\nMAX_SEQ_LEN=8192\nMAX_POSITION_EMBEDDINGS=8192\n\npip install flask-restful\n\ntorchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py \\\n --tensor-model-parallel-size 1 \\\n --pipeline-model-parallel-size 1 \\\n --num-layers ${NUM_LAYERS} \\\n --hidden-size ${HIDDEN_SIZE} \\\n --ffn-hidden-size ${FFN_HIDDEN_SIZE} \\\n --load ${CHECKPOINT} \\\n --group-query-attention \\\n --num-query-groups ${NUM_QUERY_GROUP} \\\n --position-embedding-type rope \\\n --num-attention-heads ${NUM_HEAD} \\\n --max-position-embeddings ${MAX_POSITION_EMBEDDINGS} \\\n --tokenizer-type GPT2BPETokenizer \\\n --normalization RMSNorm \\\n --norm-epsilon ${NORM_EPS} \\\n --make-vocab-size-divisible-by 1 \\\n --swiglu \\\n --use-flash-attn \\\n --bf16 \\\n --micro-batch-size 1 \\\n --disable-bias-linear \\\n --no-bias-gelu-fusion \\\n --untie-embeddings-and-output-weights \\\n --seq-length ${MAX_SEQ_LEN} \\\n --vocab-file $VOCAB_FILE \\\n --merge-file $MERGE_FILE \\\n --attention-dropout 0.0 \\\n --hidden-dropout 0.0 \\\n --weight-decay 1e-1 \\\n --clip-grad 1.0 \\\n --adam-beta1 0.9 \\\n --adam-beta2 0.95 \\\n --adam-eps 1e-8 \\\n --seed 42\n\n```\n\n\nUse CURL to query the server directly, note that the newline token '\\n' is represented by '' in the vocabulary, so we need to replace the newline token with '' in both the prompt and the generated tokens.\n\n\nProcessed Output:\n\n\nOnce you encode the generated tokens into audio, you will hear the following music.\n\n\n<audio controls src=\"URL" ]

[ "TAGS\n#language-English #license-apache-2.0 #music #art #region-us \n", "# This example will start serving the 1.3B model.\nexport CUDA_DEVICE_MAX_CONNECTIONS=1\n\nDISTRIBUTED_ARGS=\"--nproc_per_node 1 \\\n --nnodes 1 \\\n --node_rank 0 \\\n --master_addr localhost \\\n --master_port 6000\"\n\nCHECKPOINT=/path/to/model/checkpoint/folder\nVOCAB_FILE=/path/to/vocab/file\nMERGE_FILE=/path/to/merge/file\n\nMODEL_SIZE=\"1.3B\"\nif [[ ${MODEL_SIZE} == \"110M\" ]]; then HIDDEN_SIZE=768; NUM_HEAD=12; NUM_QUERY_GROUP=12; NUM_LAYERS=12; FFN_HIDDEN_SIZE=3072; NORM_EPS=1e-5;\nelif [[ ${MODEL_SIZE} == \"345M\" ]]; then HIDDEN_SIZE=1024; NUM_HEAD=16; NUM_QUERY_GROUP=16; NUM_LAYERS=24; FFN_HIDDEN_SIZE=4096; NORM_EPS=1e-5;\nelif [[ ${MODEL_SIZE} == \"770M\" ]]; then HIDDEN_SIZE=1280; NUM_HEAD=20; NUM_QUERY_GROUP=20; NUM_LAYERS=36; FFN_HIDDEN_SIZE=5120; NORM_EPS=1e-5;\nelif [[ ${MODEL_SIZE} == \"1.3B\" ]]; then HIDDEN_SIZE=1536; NUM_HEAD=24; NUM_QUERY_GROUP=24; NUM_LAYERS=48; FFN_HIDDEN_SIZE=6144; NORM_EPS=1e-5;\nelse echo \"invalid MODEL_SIZE: ${MODEL_SIZE}\"; exit 1\nfi\nMAX_SEQ_LEN=8192\nMAX_POSITION_EMBEDDINGS=8192\n\npip install flask-restful\n\ntorchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py \\\n --tensor-model-parallel-size 1 \\\n --pipeline-model-parallel-size 1 \\\n --num-layers ${NUM_LAYERS} \\\n --hidden-size ${HIDDEN_SIZE} \\\n --ffn-hidden-size ${FFN_HIDDEN_SIZE} \\\n --load ${CHECKPOINT} \\\n --group-query-attention \\\n --num-query-groups ${NUM_QUERY_GROUP} \\\n --position-embedding-type rope \\\n --num-attention-heads ${NUM_HEAD} \\\n --max-position-embeddings ${MAX_POSITION_EMBEDDINGS} \\\n --tokenizer-type GPT2BPETokenizer \\\n --normalization RMSNorm \\\n --norm-epsilon ${NORM_EPS} \\\n --make-vocab-size-divisible-by 1 \\\n --swiglu \\\n --use-flash-attn \\\n --bf16 \\\n --micro-batch-size 1 \\\n --disable-bias-linear \\\n --no-bias-gelu-fusion \\\n --untie-embeddings-and-output-weights \\\n --seq-length ${MAX_SEQ_LEN} \\\n --vocab-file $VOCAB_FILE \\\n --merge-file $MERGE_FILE \\\n --attention-dropout 0.0 \\\n --hidden-dropout 0.0 \\\n --weight-decay 1e-1 \\\n --clip-grad 1.0 \\\n --adam-beta1 0.9 \\\n --adam-beta2 0.95 \\\n --adam-eps 1e-8 \\\n --seed 42\n\n```\n\n\nUse CURL to query the server directly, note that the newline token '\\n' is represented by '' in the vocabulary, so we need to replace the newline token with '' in both the prompt and the generated tokens.\n\n\nProcessed Output:\n\n\nOnce you encode the generated tokens into audio, you will hear the following music.\n\n\n<audio controls src=\"URL" ]

[ "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 \"vietnamese-retrieval\"\n\nMore Information needed" ]

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

[ "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-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" ]

[]

[]

[]

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

[ "task_categories:audio-classification", "size_categories:1K<n<10K", "language:ja", "license:other", "region:us" ]

[]

[ "ja" ]

[]

[ "TAGS\n#task_categories-audio-classification #size_categories-1K<n<10K #language-Japanese #license-other #region-us \n" ]

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

[]

[ "hu" ]

[]

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

[ "task_categories:question-answering", "task_ids:open-domain-qa", "task_ids:multiple-choice-qa", "size_categories:n<1K", "language:en", "license:mit", "logical reasoning", "reading comprehension", "common sense", "astrophysics", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #task_ids-open-domain-qa #task_ids-multiple-choice-qa #size_categories-n<1K #language-English #license-mit #logical reasoning #reading comprehension #common sense #astrophysics #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:apache-2.0", "region:us" ]

[]

[]

[ "# Understanding Tagalog Questions and tasks (~2023)\n\"Raws\".\n\nUTaQk is a small (informal) collection of questions and writing tasks meant to test the capability of language models, but in Tagalog.\n\nThe questions are loosely categorized into:\n* 0 - Puzzles/riddles\n* 1 - Languages/translation\n* 2 - Writing tasks\n* 3 - Creative writing\n* 4 - Facts\n* 5 - Coding\n* 6 - Logic\n\nFeel free to add/expand and/or correct any mistranslations.\n\nWIP" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Understanding Tagalog Questions and tasks (~2023)\n\"Raws\".\n\nUTaQk is a small (informal) collection of questions and writing tasks meant to test the capability of language models, but in Tagalog.\n\nThe questions are loosely categorized into:\n* 0 - Puzzles/riddles\n* 1 - Languages/translation\n* 2 - Writing tasks\n* 3 - Creative writing\n* 4 - Facts\n* 5 - Coding\n* 6 - Logic\n\nFeel free to add/expand and/or correct any mistranslations.\n\nWIP" ]

[ "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:question-answering", "task_ids:open-domain-qa", "task_ids:multiple-choice-qa", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:hi", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "hi" ]

[ "# Dataset Card for \"ai2_arc\" translated into Hindi\n\nThis is Hindi translated version of \"ai2_arc\" using the IndicTrans2 model (Gala et al., 2023).\n\nWe recommend you to visit the \"ai2_arc\" huggingface dataset card (link) for the details." ]

[ "TAGS\n#task_categories-question-answering #task_ids-open-domain-qa #task_ids-multiple-choice-qa #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Hindi #license-cc-by-sa-4.0 #region-us \n", "# Dataset Card for \"ai2_arc\" translated into Hindi\n\nThis is Hindi translated version of \"ai2_arc\" using the IndicTrans2 model (Gala et al., 2023).\n\nWe recommend you to visit the \"ai2_arc\" huggingface dataset card (link) for the details." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"12-01-2024-last-2000-row-QA\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"12-01-2024-last-2000-row-QA\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"12-01-2024-last-2000-row-QA-segmentation\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"12-01-2024-last-2000-row-QA-segmentation\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of tuantran1632001/Psyfighter2-Orca2-13B-ties\n\n\n\nDataset automatically created during the evaluation run of model tuantran1632001/Psyfighter2-Orca2-13B-ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T15:05:15.491956(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 tuantran1632001/Psyfighter2-Orca2-13B-ties\n\n\n\nDataset automatically created during the evaluation run of model tuantran1632001/Psyfighter2-Orca2-13B-ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T15:05:15.491956(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 damerajee/Oot-v2_lll\n\n\n\nDataset automatically created during the evaluation run of model damerajee/Oot-v2_lll 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 2024-01-13T15:05:46.112716(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 damerajee/Oot-v2_lll\n\n\n\nDataset automatically created during the evaluation run of model damerajee/Oot-v2_lll 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 2024-01-13T15:05:46.112716(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 timpal0l/Mistral-7B-v0.1-flashback-v2\n\n\n\nDataset automatically created during the evaluation run of model timpal0l/Mistral-7B-v0.1-flashback-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 2024-01-13T15:10:27.393635(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 timpal0l/Mistral-7B-v0.1-flashback-v2\n\n\n\nDataset automatically created during the evaluation run of model timpal0l/Mistral-7B-v0.1-flashback-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 2024-01-13T15:10:27.393635(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of argilla/distilabeled-Marcoro14-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model argilla/distilabeled-Marcoro14-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T15:19:50.264268(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of argilla/distilabeled-Marcoro14-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model argilla/distilabeled-Marcoro14-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T15:19:50.264268(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 abideen/NexoNimbus-7B\n\n\n\nDataset automatically created during the evaluation run of model abideen/NexoNimbus-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 2024-01-13T15:21:36.768833(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 abideen/NexoNimbus-7B\n\n\n\nDataset automatically created during the evaluation run of model abideen/NexoNimbus-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 2024-01-13T15:21:36.768833(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 Evaluation run of UCLA-AGI/zephyr-7b-sft-full-SPIN-iter2\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/zephyr-7b-sft-full-SPIN-iter2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T15:36:50.763352(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 UCLA-AGI/zephyr-7b-sft-full-SPIN-iter2\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/zephyr-7b-sft-full-SPIN-iter2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T15:36:50.763352(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:no", "language:en", "region:us" ]

[]

[ "no", "en" ]

[ "# Norwegian Translated SST-2 Dataset", "## Dataset", "### Overview\n\nThe dataset is a Norwegian machine-translation of the Stanford Sentiment Treebank (SST-2). The original dataset comprises sentences extracted from movie reviews, accompanied by human annotations indicating their sentiment.", "### Dataset Structure\n\nThe dataset has the following structure:", "### Data Fields\n\n- 'idx': Monotonically increasing index ID.\n- 'sentence': Complete sentence expressing an opinion about a film.\n- 'label': Sentiment of the opinion, either \"negative\" (0) or positive (1). The test set labels are hidden (-1).\n- 'sentence_nob': Norwegian translation of the sentence." ]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Norwegian #language-English #region-us \n", "# Norwegian Translated SST-2 Dataset", "## Dataset", "### Overview\n\nThe dataset is a Norwegian machine-translation of the Stanford Sentiment Treebank (SST-2). The original dataset comprises sentences extracted from movie reviews, accompanied by human annotations indicating their sentiment.", "### Dataset Structure\n\nThe dataset has the following structure:", "### Data Fields\n\n- 'idx': Monotonically increasing index ID.\n- 'sentence': Complete sentence expressing an opinion about a film.\n- 'label': Sentiment of the opinion, either \"negative\" (0) or positive (1). The test set labels are hidden (-1).\n- 'sentence_nob': Norwegian translation of the sentence." ]

[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:sq", "language:en", "region:us" ]

[]

[ "sq", "en" ]

[ "# Albanian Translated SST-2 Dataset", "## Dataset", "### Overview\n\nThe dataset is a Albanian machine-translation of the Stanford Sentiment Treebank (SST-2). The original dataset comprises sentences extracted from movie reviews, accompanied by human annotations indicating their sentiment.", "### Dataset Structure\n\nThe dataset has the following structure:", "### Data Fields\n\n- 'idx': Monotonically increasing index ID.\n- 'sentence': Complete sentence expressing an opinion about a film.\n- 'label': Sentiment of the opinion, either \"negative\" (0) or positive (1). The test set labels are hidden (-1).\n- 'sentence_alb': Albanian translation of the sentence." ]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Albanian #language-English #region-us \n", "# Albanian Translated SST-2 Dataset", "## Dataset", "### Overview\n\nThe dataset is a Albanian machine-translation of the Stanford Sentiment Treebank (SST-2). The original dataset comprises sentences extracted from movie reviews, accompanied by human annotations indicating their sentiment.", "### Dataset Structure\n\nThe dataset has the following structure:", "### Data Fields\n\n- 'idx': Monotonically increasing index ID.\n- 'sentence': Complete sentence expressing an opinion about a film.\n- 'label': Sentiment of the opinion, either \"negative\" (0) or positive (1). The test set labels are hidden (-1).\n- 'sentence_alb': Albanian translation of the sentence." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-13T15:47:16.037619(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 UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-13T15:47:16.037619(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 davanstrien/TinyLlama-1.1B-Chat-v1.0-intel-dpo\n\n\n\nDataset automatically created during the evaluation run of model davanstrien/TinyLlama-1.1B-Chat-v1.0-intel-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 2024-01-13T15:50:10.127087(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 davanstrien/TinyLlama-1.1B-Chat-v1.0-intel-dpo\n\n\n\nDataset automatically created during the evaluation run of model davanstrien/TinyLlama-1.1B-Chat-v1.0-intel-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 2024-01-13T15:50:10.127087(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/Medusa-7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Medusa-7B-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 2024-01-13T15:51:28.685005(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/Medusa-7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Medusa-7B-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 2024-01-13T15:51:28.685005(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 UCLA-AGI/test_final\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/test_final 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 2024-01-13T15:52:58.260309(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 UCLA-AGI/test_final\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/test_final 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 2024-01-13T15:52:58.260309(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "license:apache-2.0", "arxiv:2401.06080", "region:us" ]

[ "2401.06080" ]

[]

[]

[ "TAGS\n#license-apache-2.0 #arxiv-2401.06080 #region-us \n" ]

[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:zh", "license:gpl-3.0", "medical", "region:us" ]

[]

[ "zh" ]

[]

[ "TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #medical #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Praneeth/StarMix-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Praneeth/StarMix-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T16:15:20.775949(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 Praneeth/StarMix-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Praneeth/StarMix-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T16:15:20.775949(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/Hippolyta-7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Hippolyta-7B-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 2024-01-13T16:20:49.648715(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/Hippolyta-7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Hippolyta-7B-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 2024-01-13T16:20:49.648715(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-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 superlazycoder/NeuralPipe-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model superlazycoder/NeuralPipe-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T16:47:37.959217(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 superlazycoder/NeuralPipe-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model superlazycoder/NeuralPipe-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T16:47:37.959217(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 fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser\n\n\n\nDataset automatically created during the evaluation run of model fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser 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 2024-01-13T16:50:26.517326(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 fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser\n\n\n\nDataset automatically created during the evaluation run of model fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser 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 2024-01-13T16:50:26.517326(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 SanjiWatsuki/Kunoichi-DPO-7B\n\n\n\nDataset automatically created during the evaluation run of model SanjiWatsuki/Kunoichi-DPO-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 2024-01-13T16:55:23.006879(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 SanjiWatsuki/Kunoichi-DPO-7B\n\n\n\nDataset automatically created during the evaluation run of model SanjiWatsuki/Kunoichi-DPO-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 2024-01-13T16:55:23.006879(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 alnrg2arg/test\n\n\n\nDataset automatically created during the evaluation run of model alnrg2arg/test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T17:13:28.432807(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of alnrg2arg/test\n\n\n\nDataset automatically created during the evaluation run of model alnrg2arg/test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-13T17:13:28.432807(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-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:question-answering", "size_categories:10K<n<100K", "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #license-mit #region-us \n" ]

[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:mt", "license:cc-by-sa-4.0", "nlp", "nli", "rte", "maltese", "malti", "region:us" ]

[]

[ "mt" ]

[ "# Dataset Card for the SMNLI-MT\n\nThe SMNLI-MT datasets are machine-translated versions of the Stanford NLI and MultiNLI datasets in Maltese.", "## Dataset Details", "### Dataset Description\n\nThe datasets were translated using the Google Cloud Translate as part of the initial exploration of NLI in the Maltese language.\n\n\n- Curated by: Matthew Darmanin\n- Language(s) (NLP): Maltese\n- License: CC 4.0", "## Dataset Structure\n\nThe datasets are in the form of CSV files, delimited by semi-colons (;) and encoded in UTF-8.\n\nEach dataset contains the following columns:\n - id - the index of the sentence pair\n - premise - the premise sentence\n - hypothesis - the hypothesis sentence\n - gold_label - the consensus label assigned in the original datasets; either _entailment_, _contradiction_ or _neutral_", "## Dataset Card Contact\n\nE-mail: <URL@URL>" ]

[ "TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-Maltese #license-cc-by-sa-4.0 #nlp #nli #rte #maltese #malti #region-us \n", "# Dataset Card for the SMNLI-MT\n\nThe SMNLI-MT datasets are machine-translated versions of the Stanford NLI and MultiNLI datasets in Maltese.", "## Dataset Details", "### Dataset Description\n\nThe datasets were translated using the Google Cloud Translate as part of the initial exploration of NLI in the Maltese language.\n\n\n- Curated by: Matthew Darmanin\n- Language(s) (NLP): Maltese\n- License: CC 4.0", "## Dataset Structure\n\nThe datasets are in the form of CSV files, delimited by semi-colons (;) and encoded in UTF-8.\n\nEach dataset contains the following columns:\n - id - the index of the sentence pair\n - premise - the premise sentence\n - hypothesis - the hypothesis sentence\n - gold_label - the consensus label assigned in the original datasets; either _entailment_, _contradiction_ or _neutral_", "## Dataset Card Contact\n\nE-mail: <URL@URL>" ]

[ "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-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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name", "## 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", "## 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 \"ForwardScreeningFS\"\n\nMore Information needed" ]

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

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

[]

[]

[ "# Terra 4M Training Log\nTerra 4M is a 4.2 million parameter, purely convolutional diffusion model for terrain generation. \nThis is a log of all the checkpoints and images generated throughout training. \nNote that the checkpoint corresponding to step 3,982,014 was selected as the final model.\nThe training code can be found here.\n\n!image/png\n!image/png" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Terra 4M Training Log\nTerra 4M is a 4.2 million parameter, purely convolutional diffusion model for terrain generation. \nThis is a log of all the checkpoints and images generated throughout training. \nNote that the checkpoint corresponding to step 3,982,014 was selected as the final model.\nThe training code can be found here.\n\n!image/png\n!image/png" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name", "## 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", "## 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-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" ]