Datasets:

cfahlgren1

/

hub-stats

like 11

63990f21cc50af73d29ecfa3

fka/awesome-chatgpt-prompts

fka

{"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]}

False

2024-09-03T21:28:41.000Z

459a66186f8f83020117b8acc5ff5af69fc95b45

🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0

2022-12-13T23:47:45.000Z

67181a27dfa0b095f0902d33

qq8933/OpenLongCoT-Pretrain

qq8933

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 269352240, "num_examples": 102906}], "download_size": 64709509, "dataset_size": 269352240}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-10-28T13:50:37.000Z

40562378be9f86728440a0fb44f07ba2bdc03646

Please cite me if this dataset is helpful for you!🥰 @article{zhang2024llama, title={LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning}, author={Zhang, Di and Wu, Jianbo and Lei, Jingdi and Che, Tong and Li, Jiatong and Xie, Tong and Huang, Xiaoshui and Zhang, Shufei and Pavone, Marco and Li, Yuqiang and others}, journal={arXiv preprint arXiv:2410.02884}, year={2024} } @article{zhang2024accessing, title={Accessing GPT-4 level Mathematical Olympiad… See the full description on the dataset page: https://huggingface.co/datasets/qq8933/OpenLongCoT-Pretrain.

2024-10-22T21:33:27.000Z

66f5a5d9763d438dab13f188

Spawning/PD12M

Spawning

{"language": ["en"], "pretty_name": "PD12M", "license": "cdla-permissive-2.0", "tags": ["image"]}

False

2024-10-31T15:25:49.000Z

4fd5d707a72aad71bd88c7e7bc5df2ae5e0d6c53

PD12M Summary At 12.4 million image-caption pairs, PD12M is the largest public domain image-text dataset to date, with sufficient size to train foundation models while minimizing copyright concerns. Through the Source.Plus platform, we also introduce novel, community-driven dataset governance mechanisms that reduce harm and support reproducibility over time. Jordan Meyer Nicholas Padgett Cullen Miller Laura Exline Paper Datasheet Project… See the full description on the dataset page: https://huggingface.co/datasets/Spawning/PD12M.

2024-09-26T18:20:09.000Z

67214aee41fba8f8b985b247

wyu1/Leopard-Instruct

wyu1

{"configs": [{"config_name": "arxiv", "data_files": [{"split": "train", "path": "arxiv/*"}]}, {"config_name": "chartgemma", "data_files": [{"split": "train", "path": "chartgemma/*"}]}, {"config_name": "chartqa", "data_files": [{"split": "train", "path": "chartqa/*"}]}, {"config_name": "dude", "data_files": [{"split": "train", "path": "dude/*"}]}, {"config_name": "dvqa", "data_files": [{"split": "train", "path": "dvqa/*"}]}, {"config_name": "figureqa", "data_files": [{"split": "train", "path": "figureqa/*"}]}, {"config_name": "iconqa", "data_files": [{"split": "train", "path": "iconqa/*"}]}, {"config_name": "infographics", "data_files": [{"split": "train", "path": "infographics/*"}]}, {"config_name": "llavar", "data_files": [{"split": "train", "path": "llavar/*"}]}, {"config_name": "mapqa", "data_files": [{"split": "train", "path": "mapqa/*"}]}, {"config_name": "mathv360k", "data_files": [{"split": "train", "path": "mathv360k/*"}]}, {"config_name": "mind2web", "data_files": [{"split": "train", "path": "mind2web/*"}]}, {"config_name": "monkey", "data_files": [{"split": "train", "path": "monkey/*"}]}, {"config_name": "mpdocvqa", "data_files": [{"split": "train", "path": "mpdocvqa/*"}]}, {"config_name": "mplugdocreason", "data_files": [{"split": "train", "path": "mplugdocreason/*"}]}, {"config_name": "multichartqa", "data_files": [{"split": "train", "path": "multi_chartqa/*"}]}, {"config_name": "multihiertt", "data_files": [{"split": "train", "path": "multihiertt/*"}]}, {"config_name": "multitab", "data_files": [{"split": "train", "path": "multitab/*"}]}, {"config_name": "omniact", "data_files": [{"split": "train", "path": "omniact/*"}]}, {"config_name": "pew_chart", "data_files": [{"split": "train", "path": "pew_chart/*"}]}, {"config_name": "rico", "data_files": [{"split": "train", "path": "rico/*"}]}, {"config_name": "slidesgeneration", "data_files": [{"split": "train", "path": "slidesgeneration/*"}]}, {"config_name": "slideshare", "data_files": [{"split": "train", "path": "slideshare/*"}]}, {"config_name": "slidevqa", "data_files": [{"split": "train", "path": "slidevqa/*"}]}, {"config_name": "docvqa", "data_files": [{"split": "train", "path": "spdocvqa/*"}]}, {"config_name": "tab_entity", "data_files": [{"split": "train", "path": "tab_entity/*"}]}, {"config_name": "tabmwp", "data_files": [{"split": "train", "path": "tabmwp/*"}]}, {"config_name": "tat_dqa", "data_files": [{"split": "train", "path": "tat_dqa/*"}]}, {"config_name": "website_screenshots", "data_files": [{"split": "train", "path": "website_screenshots/*"}]}, {"config_name": "webui", "data_files": [{"split": "train", "path": "webui/*"}]}, {"config_name": "webvision", "data_files": [{"split": "train", "path": "webvision/*"}]}], "license": "apache-2.0", "language": ["en"], "tags": ["multimodal", "instruction-following", "multi-image", "lmm", "vlm", "mllm"], "size_categories": ["100K<n<1M"]}

False

2024-11-08T00:12:25.000Z

93317b272c5a9d9c0417fa6ea6e2be89ac9215ea

Leopard-Instruct Paper | Github | Models-LLaVA | Models-Idefics2 Summaries Leopard-Instruct is a large instruction-tuning dataset, comprising 925K instances, with 739K specifically designed for text-rich, multiimage scenarios. It's been used to train Leopard-LLaVA [checkpoint] and Leopard-Idefics2 [checkpoint]. Loading dataset to load the dataset without automatically downloading and process the images (Please run the following codes with… See the full description on the dataset page: https://huggingface.co/datasets/wyu1/Leopard-Instruct.

2024-10-29T20:51:58.000Z

670d0cb9d905bbbc78d7a18a

neuralwork/arxiver

neuralwork

{"license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"]}

False

2024-11-01T21:18:04.000Z

698a6662e77fd5dd45dbbec988abc8123e5fa086

Arxiver Dataset Arxiver consists of 63,357 arXiv papers converted to multi-markdown (.mmd) format. Our dataset includes original arXiv article IDs, titles, abstracts, authors, publication dates, URLs and corresponding markdown files published between January 2023 and October 2023. We hope our dataset will be useful for various applications such as semantic search, domain specific language modeling, question answering and summarization. Curation The Arxiver dataset… See the full description on the dataset page: https://huggingface.co/datasets/neuralwork/arxiver.

2024-10-14T12:21:13.000Z

670e1f14c308791317666994

BAAI/Infinity-MM

BAAI

{"license": "cc-by-sa-4.0", "configs": [{"config_name": "stage1", "data_files": [{"split": "train", "path": "stage1/*/*"}]}, {"config_name": "stage2", "data_files": [{"split": "train", "path": "stage2/*/*/*"}]}, {"config_name": "stage3", "data_files": [{"split": "train", "path": "stage3/*/*"}]}, {"config_name": "stage4", "data_files": [{"split": "train", "path": "stage4/*/*/*"}]}], "language": ["en", "zh"], "size_categories": ["10M<n<100M"], "task_categories": ["image-to-text"], "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}}

auto

2024-11-05T06:57:13.000Z

79e444ad1cf4744630e75964b277944bbc44f837

Introduction Beijing Academy of Artificial Intelligence (BAAI) We collect, organize and open-source the large-scale multimodal instruction dataset, Infinity-MM, consisting of tens of millions of samples. Through quality filtering and deduplication, the dataset has high quality and diversity. We propose a synthetic data generation method based on open-source models and labeling system, using detailed image annotations and diverse question generation. News… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-MM.

2024-10-15T07:51:48.000Z

66c84764a47b2d6c582bbb02

amphion/Emilia-Dataset

amphion

{"license": "cc-by-nc-4.0", "task_categories": ["text-to-speech", "automatic-speech-recognition"], "language": ["zh", "en", "ja", "fr", "de", "ko"], "pretty_name": "Emilia", "size_categories": ["10M<n<100M"], "extra_gated_prompt": "Terms of Access: The researcher has requested permission to use the Emilia dataset and the Emilia-Pipe preprocessing pipeline. In exchange for such permission, the researcher hereby agrees to the following terms and conditions:\n1. The researcher shall use the dataset ONLY for non-commercial research and educational purposes.\n2. The authors make no representations or warranties regarding the dataset, \n including but not limited to warranties of non-infringement or fitness for a particular purpose.\n\n3. The researcher accepts full responsibility for their use of the dataset and shall defend and indemnify the authors of Emilia, \n including their employees, trustees, officers, and agents, against any and all claims arising from the researcher's use of the dataset, \n including but not limited to the researcher's use of any copies of copyrighted content that they may create from the dataset.\n\n4. The researcher may provide research associates and colleagues with access to the dataset,\n provided that they first agree to be bound by these terms and conditions.\n \n5. The authors reserve the right to terminate the researcher's access to the dataset at any time.\n6. If the researcher is employed by a for-profit, commercial entity, the researcher's employer shall also be bound by these terms and conditions, and the researcher hereby represents that they are fully authorized to enter into this agreement on behalf of such employer.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Position": "text", "Your Supervisor/manager/director": "text", "I agree to the Terms of Access": "checkbox"}}

auto

2024-09-06T13:29:55.000Z

bcaad00d13e7c101485990a46e88f5884ffed3fc

Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech data preprocessing pipeline. News 🔥 2024/08/28: Welcome to join Amphion's Discord channel to stay connected and engage with our community! 2024/08/27: The Emilia dataset is now publicly available! Discover the most extensive and diverse speech generation… See the full description on the dataset page: https://huggingface.co/datasets/amphion/Emilia-Dataset.

2024-08-23T08:25:08.000Z

670f08ae2e97b2afe4d2df9b

GAIR/o1-journey

GAIR

{"language": ["en"], "size_categories": ["n<1K"]}

False

2024-10-16T00:42:02.000Z

32deef4773fe1f9488ff2052daf64035c034c0ea

Dataset for O1 Replication Journey: A Strategic Progress Report Usage from datasets import load_dataset dataset = load_dataset("GAIR/o1-journey", split="train") Citation If you find our dataset useful, please cite: @misc{o1journey, author = {Yiwei Qin and Xuefeng Li and Haoyang Zou and Yixiu Liu and Shijie Xia and Zhen Huang and Yixin Ye and Weizhe Yuan and Zhengzhong Liu and Yuanzhi Li and Pengfei Liu}, title = {O1 Replication Journey: A Strategic Progress… See the full description on the dataset page: https://huggingface.co/datasets/GAIR/o1-journey.

2024-10-16T00:28:30.000Z

67261c706b966e02542c1743

beomi/KoAlpaca-RealQA

beomi

{"dataset_info": {"features": [{"name": "custom_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26211669, "num_examples": 18524}], "download_size": 13989391, "dataset_size": 26211669}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cc-by-sa-4.0"}

auto

2024-11-03T07:00:13.000Z

a7df38a0b2cc187b72b40330af81e7b9f28dd95b

KoAlpaca-RealQA: A Korean Instruction Dataset Reflecting Real User Scenarios Dataset Summary The KoAlpaca-RealQA dataset is a unique Korean instruction dataset designed to closely reflect real user interactions in the Korean language. Unlike conventional Korean instruction datasets that rely heavily on translated prompts, this dataset is composed of authentic Korean instructions derived from real-world use cases. Specifically, the dataset has been curated from… See the full description on the dataset page: https://huggingface.co/datasets/beomi/KoAlpaca-RealQA.

2024-11-02T12:34:56.000Z

649f37af37bfb5202beabdf4

allenai/dolma

allenai

{"license": "odc-by", "viewer": false, "task_categories": ["text-generation"], "language": ["en"], "tags": ["language-modeling", "casual-lm", "llm"], "pretty_name": "Dolma", "size_categories": ["n>1T"]}

False

2024-04-17T02:57:00.000Z

7f48140530a023e9ea4c5cfb141160922727d4d3

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

2023-06-30T20:14:39.000Z

@article{dolma, title = {{Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}}, author = { Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Ian Magnusson and Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and Oyvind Tafjord and Evan Pete Walsh and Hannaneh Hajishirzi and Noah A. Smith and Luke Zettlemoyer and Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo }, year = {2024}, journal={arXiv preprint}, }

656d9c2bc497edf0a7be5959

tomytjandra/h-and-m-fashion-caption

tomytjandra

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 7843224039.084, "num_examples": 20491}], "download_size": 6302088359, "dataset_size": 7843224039.084}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2023-12-04T11:07:53.000Z

2083a7e30878af2993632b2fc3565ed4a2159534

Dataset Card for "h-and-m-fashion-caption" More Information needed

2023-12-04T09:30:19.000Z

66fc03bc2d7c7dffd1d95786

argilla/Synth-APIGen-v0.1

argilla

{"dataset_info": {"features": [{"name": "func_name", "dtype": "string"}, {"name": "func_desc", "dtype": "string"}, {"name": "tools", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answers", "dtype": "string"}, {"name": "model_name", "dtype": "string"}, {"name": "hash_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 77390022, "num_examples": 49402}], "download_size": 29656761, "dataset_size": 77390022}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "distilabel", "function-calling"], "size_categories": ["10K<n<100K"]}

False

2024-10-10T11:52:03.000Z

20107f6709aabd18c7f7b4afc96fe7bfe848b5bb

Dataset card for Synth-APIGen-v0.1 This dataset has been created with distilabel. Pipeline script: pipeline_apigen_train.py. Dataset creation It has been created with distilabel==1.4.0 version. This dataset is an implementation of APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets in distilabel, generated from synthetic functions. The process can be summarized as follows: Generate (or in this case modify)… See the full description on the dataset page: https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1.

2024-10-01T14:14:20.000Z

66f830e08d215c6331bec22a

nvidia/OpenMathInstruct-2

nvidia

{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10M<n<100M"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "OpenMathInstruct-2", "dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "generated_solution", "dtype": "string"}, {"name": "expected_answer", "dtype": "string"}, {"name": "problem_source", "dtype": "string"}], "splits": [{"name": "train_1M", "num_bytes": 1350383003, "num_examples": 1000000}, {"name": "train_2M", "num_bytes": 2760009675, "num_examples": 2000000}, {"name": "train_5M", "num_bytes": 6546496157, "num_examples": 5000000}, {"name": "train", "num_bytes": 15558412976, "num_examples": 13972791}], "download_size": 20208929853, "dataset_size": 26215301811}, "tags": ["math", "nvidia"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_1M", "path": "data/train_1M-*"}, {"split": "train_2M", "path": "data/train_2M-*"}, {"split": "train_5M", "path": "data/train_5M-*"}]}]}

False

2024-11-01T22:04:33.000Z

ac3d019aa67043f0f25cce7eed8f5926fe580c5a

OpenMathInstruct-2 OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs generated using the Llama3.1-405B-Instruct model. The training set problems of GSM8K and MATH are used for constructing the dataset in the following ways: Solution augmentation: Generating chain-of-thought solutions for training set problems in GSM8K and MATH. Problem-Solution augmentation: Generating new problems, followed by solutions for these new problems.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-2.

2024-09-28T16:37:52.000Z

672e4b6b741fa21478bd7bc3

OpenCoder-LLM/opencoder-sft-stage2

OpenCoder-LLM

{"license": "mit", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 782171831, "num_examples": 375029}], "download_size": 381524317, "dataset_size": 782171831}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-11-08T19:33:16.000Z

77dab434cdabd5ce60bdb2113720c0d3fc2ff501

This is the dataset used for OpenCoder Stage2 training. For time reasons, we are still in the process of further organizing it, and will provide more clearly labeled tags later :-)

2024-11-08T17:33:31.000Z

6644c76014331c74667fb214

TIGER-Lab/WebInstructSub

TIGER-Lab

{"language": ["en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering"], "pretty_name": "WebInstruct", "dataset_info": {"features": [{"name": "orig_question", "dtype": "string"}, {"name": "orig_answer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "index", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6215888891, "num_examples": 2335220}], "download_size": 3509803840, "dataset_size": 6215888891}, "tags": ["language model"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-10-27T03:19:23.000Z

559b33b6bcd34da3da047bb235532941026955a4

🦣 MAmmoTH2: Scaling Instructions from the Web Project Page: https://tiger-ai-lab.github.io/MAmmoTH2/ Paper: https://arxiv.org/pdf/2405.03548 Code: https://github.com/TIGER-AI-Lab/MAmmoTH2 WebInstruct (Subset) This repo contains the partial dataset used in "MAmmoTH2: Scaling Instructions from the Web". This partial data is coming mostly from the forums like stackexchange. This subset contains very high-quality data to boost LLM performance through instruction… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/WebInstructSub.

2024-05-15T14:32:00.000Z

6655eb19d17e141dcb546ed5

HuggingFaceFW/fineweb-edu

HuggingFaceFW

{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb-Edu", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-10/*"}]}, {"config_name": "CC-MAIN-2023-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-50/*"}]}, {"config_name": "CC-MAIN-2023-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-40/*"}]}, {"config_name": "CC-MAIN-2023-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-23/*"}]}, {"config_name": "CC-MAIN-2023-14", "data_files": 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{"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]}

False

2024-10-11T07:55:10.000Z

651a648da38bf545cc5487530dbf59d8168c8de3

📚 FineWeb-Edu 1.3 trillion tokens of the finest educational data the 🌐 web has to offer Paper: https://arxiv.org/abs/2406.17557 What is it? 📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version. To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu.

2024-05-28T14:32:57.000Z

672e43b562371d59e7202334

OpenCoder-LLM/opencoder-sft-stage1

OpenCoder-LLM

{"license": "mit", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10560942945, "num_examples": 4216321}], "download_size": 5296128053, "dataset_size": 10560942945}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-11-08T19:14:24.000Z

8a14240c34242f61c8b997343af1d696ff51e66a

This is the dataset used for OpenCoder Stage1 training. For time reasons, we are still in the process of further organizing it, and will provide more clearly labeled tags later :-)

2024-11-08T17:00:37.000Z

66952974b8a00bc24d6b112a

HuggingFaceTB/smollm-corpus

HuggingFaceTB

{"license": "odc-by", "dataset_info": [{"config_name": "cosmopedia-v2", "features": [{"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "token_length", "dtype": "int64"}, {"name": "audience", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 212503640747, "num_examples": 39134000}], "download_size": 122361137711, "dataset_size": 212503640747}, {"config_name": "fineweb-edu-dedup", "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "dump", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "timestamp[s]"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}, {"name": "token_count", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 957570164451, "num_examples": 190168005}], "download_size": 550069279849, "dataset_size": 957570164451}, {"config_name": "python-edu", "features": [{"name": "blob_id", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 989334135, "num_examples": 7678448}], "download_size": 643903049, "dataset_size": 989334135}], "configs": [{"config_name": "cosmopedia-v2", "data_files": [{"split": "train", "path": "cosmopedia-v2/train-*"}]}, {"config_name": "fineweb-edu-dedup", "data_files": [{"split": "train", "path": "fineweb-edu-dedup/train-*"}]}, {"config_name": "python-edu", "data_files": [{"split": "train", "path": "python-edu/train-*"}]}], "language": ["en"]}

False

2024-09-06T07:04:57.000Z

3ba9d605774198c5868892d7a8deda78031a781f

SmolLM-Corpus This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models. You can find more details about the models trained on this dataset in our SmolLM blog post. Dataset subsets Cosmopedia v2 Cosmopedia v2 is an enhanced version of Cosmopedia, the largest synthetic dataset for pre-training, consisting of over 39 million textbooks, blog posts, and stories generated by… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus.

2024-07-15T13:51:48.000Z

6703a9b1dfea46624547b361

Sterzhang/PVIT-3M

Sterzhang

{"configs": [{"config_name": "PVIT-3M", "data_files": [{"split": "all_data", "path": "PVIT-3M.json"}]}], "language": ["en"], "task_categories": ["visual-question-answering", "image-text-to-text"], "tags": ["multi-modal", "personalized"], "license": "apache-2.0", "pretty_name": "personalized visual instruction tuning", "size_categories": ["1M<n<10M"]}

False

2024-11-02T07:41:57.000Z

68c0ad34851b06e7e408b092c1f8ee1004f6c92b

PVIT-3M The paper titled "Personalized Visual Instruction Tuning" introduces a novel dataset called PVIT-3M. This dataset is specifically designed for tuning MLLMs in the context of personalized visual instruction tasks. The dataset consists of 3 million image-text pairs that aim to improve MLLMs' abilities to generate responses based on personalized visual inputs, making them more tailored and adaptable to individual user needs and preferences. Here’s the PVIT-3M statistics:… See the full description on the dataset page: https://huggingface.co/datasets/Sterzhang/PVIT-3M.

2024-10-07T09:28:17.000Z

671928371e52d113736171a4

ClimatePolicyRadar/all-document-text-data

ClimatePolicyRadar

{"license": "cc-by-4.0", "size_categories": ["10M<n<100M"]}

auto

2024-10-28T12:00:00.000Z

13d13430311b09d3f58676625a0e38c61f66355c

Climate Policy Radar Open Data This repo contains the full text data of all of the documents from the Climate Policy Radar database (CPR), which is also available at Climate Change Laws of the World (CCLW). Please note that this replaces the Global Stocktake open dataset: that data, including all NDCs and IPCC reports is now a subset of this dataset. What’s in this dataset This dataset contains two corpus types (groups of the same types or sources of documents)… See the full description on the dataset page: https://huggingface.co/datasets/ClimatePolicyRadar/all-document-text-data.

2024-10-23T16:45:43.000Z

625552d2b339bb03abe3432d

openai/gsm8k

openai

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]}

False

2024-01-04T12:05:15.000Z

e53f048856ff4f594e959d75785d2c2d37b678ee

Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.

2022-04-12T10:22:10.000Z

gsm8k

653785ff8e37b02865e64be0

HuggingFaceH4/ultrafeedback_binarized

HuggingFaceH4

{"language": ["en"], "license": "mit", "task_categories": ["text-generation"], "pretty_name": "UltraFeedback Binarized", "configs": [{"config_name": "default", "data_files": [{"split": "train_prefs", "path": "data/train_prefs-*"}, {"split": "train_sft", "path": "data/train_sft-*"}, {"split": "test_prefs", "path": "data/test_prefs-*"}, {"split": "test_sft", "path": "data/test_sft-*"}, {"split": "train_gen", "path": "data/train_gen-*"}, {"split": "test_gen", "path": "data/test_gen-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "score_chosen", "dtype": "float64"}, {"name": "score_rejected", "dtype": "float64"}], "splits": [{"name": "train_prefs", "num_bytes": 405688662, "num_examples": 61135}, {"name": "train_sft", "num_bytes": 405688662, "num_examples": 61135}, {"name": "test_prefs", "num_bytes": 13161585, "num_examples": 2000}, {"name": "test_sft", "num_bytes": 6697333, "num_examples": 1000}, {"name": "train_gen", "num_bytes": 325040536, "num_examples": 61135}, {"name": "test_gen", "num_bytes": 5337695, "num_examples": 1000}], "download_size": 649967196, "dataset_size": 1161614473}}

False

2024-10-16T11:49:06.000Z

3949bf5f8c17c394422ccfab0c31ea9c20bdeb85

Dataset Card for UltraFeedback Binarized Dataset Description This is a pre-processed version of the UltraFeedback dataset and was used to train Zephyr-7Β-β, a state of the art chat model at the 7B parameter scale. The original UltraFeedback dataset consists of 64k prompts, where each prompt is accompanied with four model completions from a wide variety of open and proprietary models. GPT-4 is then used to assign a score to each completion, along criteria like… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized.

2023-10-24T08:53:19.000Z

66a48190424f6ad0636bbd70

vikhyatk/lofi

vikhyatk

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": "audio"}, {"name": "prompt", "dtype": "string"}]}, "license": "cc-by-nc-4.0"}

False

2024-10-26T20:42:55.000Z

966a2d3065aac26c0385b4ef2d50983c0429a305

7,000+ hours of lofi music generated by MusicGen Large, with diverse prompts. The prompts were sampled from Llama 3.1 8B Base, starting with a seed set of 1,960 handwritten prompts of which a random 16 are used in a few-shot setting to generate additional diverse prompts. In addition to the CC-BY-NC license, by using this dataset you are agreeing to the fact that the Pleiades star system is a binary system and that any claim otherwise is a lie. What people are saying this… See the full description on the dataset page: https://huggingface.co/datasets/vikhyatk/lofi.

2024-07-27T05:11:44.000Z

6727611f89116e24a4fc40a8

selimc/InstructPapers-TR

selimc

{"license": "apache-2.0", "task_categories": ["text-generation", "text2text-generation", "question-answering"], "language": ["tr"], "tags": ["turkish", "academic-papers", "question-answering", "research", "dergipark"], "pretty_name": "InstructPapers-TR Dataset", "size_categories": ["1K<n<10K"]}

False

2024-11-04T15:01:27.000Z

d45417369abcc8853c39c79acdd83e8bd9314fdf

A specialized question-answering dataset derived from publicly available Turkish academic papers published on DergiPark. The dataset contains synthetic QA pairs generated using the gemini-1.5-flash-002 model. Each entry has metadata including the source paper's title, topic, and DergiPark URL. Dataset Info Number of Instances: ~11k Dataset Size: 9.89 MB Language: Turkish Dataset License: apache-2.0 Dataset Category: Text2Text Generation Data Fields… See the full description on the dataset page: https://huggingface.co/datasets/selimc/InstructPapers-TR.

2024-11-03T11:40:15.000Z

62581cc50efac682e4de7619

google-research-datasets/conceptual_captions

google-research-datasets

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False

2024-06-17T10:51:29.000Z

0bb028f274446e0b102c1253d087a98eeb4519a3

Dataset Card for Conceptual Captions Dataset Summary Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/conceptual_captions.

2022-04-14T13:08:21.000Z

conceptual-captions

639244f571c51c43091df168

Anthropic/hh-rlhf

Anthropic

{"license": "mit", "tags": ["human-feedback"]}

False

2023-05-26T18:47:34.000Z

09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa

Dataset Card for HH-RLHF Dataset Summary This repository provides access to two different kinds of data: Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf.

2022-12-08T20:11:33.000Z

66558cea3e96e1c5975420f6

OpenGVLab/ShareGPT-4o

OpenGVLab

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auto

2024-08-17T07:51:28.000Z

a69d5b4d2c5343146e27b46a22638d346f14f013

2024-05-28T07:51:06.000Z

670bd71d721603bf001c0399

opencsg/chinese-fineweb-edu-v2

opencsg

{"language": ["zh"], "pipeline_tag": "text-generation", "license": "apache-2.0", "task_categories": ["text-generation"], "size_categories": ["10B<n<100B"]}

False

2024-10-26T04:51:41.000Z

bd123e34c706a1b34274a79e1e1cd81b18cda5cc

Chinese Fineweb Edu Dataset V2 [中文] [English] [OpenCSG Community] [github] [wechat] [Twitter] Chinese Fineweb Edu Dataset V2 is a comprehensive upgrade of the original Chinese Fineweb Edu, designed and optimized for natural language processing (NLP) tasks in the education sector. This high-quality Chinese pretraining dataset has undergone significant improvements and expansions, aimed at providing researchers and developers with more diverse and broadly… See the full description on the dataset page: https://huggingface.co/datasets/opencsg/chinese-fineweb-edu-v2.

2024-10-13T14:20:13.000Z

6718c7eb95693d6c54671278

marcelbinz/Psych-101

marcelbinz

{"license": "apache-2.0", "language": ["en"], "tags": ["Psychology"], "pretty_name": "Psych-101", "size_categories": ["100B<n<1T"]}

False

2024-11-02T16:43:37.000Z

611565c66395e2787cd7e3305149bb75dc138024

Dataset Summary Psych-101 is a data set of natural language transcripts from human psychological experiments. It comprises trial-by-trial data from 160 psychological experiments and 60,092 participants, making 10,681,650 choices. Human choices are encapsuled in "<<" and ">>" tokens. Paper: Centaur: a foundation model of human cognition Point of Contact: Marcel Binz Example Prompt You will be presented with triplets of objects, which will be assigned to the… See the full description on the dataset page: https://huggingface.co/datasets/marcelbinz/Psych-101.

2024-10-23T09:54:51.000Z

621ffdd236468d709f18200d

Salesforce/wikitext

Salesforce

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False

2024-01-04T16:49:18.000Z

b08601e04326c79dfdd32d625aee71d232d685c3

Dataset Card for "wikitext" Dataset Summary The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext.

2022-03-02T23:29:22.000Z

wikitext-2

621ffdd236468d709f184284

wikimedia/wikipedia

wikimedia

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{"config_name": "20231101.zu", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7088246, "num_examples": 11561}], "download_size": 3792429, "dataset_size": 7088246}], "language_bcp47": ["be-tarask", "en-simple"]}

False

2024-01-09T09:40:51.000Z

b04c8d1ceb2f5cd4588862100d08de323dccfbaa

Dataset Card for Wikimedia Wikipedia Dataset Summary Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/) with one subset per language, each containing a single train split. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). All language subsets have already been processed for recent dump… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia.

2022-03-02T23:29:22.000Z

646b7ff2db697c798a3e4b00

shibing624/medical

shibing624

{"license": "apache-2.0", "language": ["zh"], "tags": ["text-generation"], "pretty_name": "medical", "task_categories": ["text-generation"], "size_categories": ["n<1K"]}

False

2024-10-12T12:11:32.000Z

6e219f1a14856833ee436063d3b73c5f1ab9cfb9

纯文本数据，中文医疗数据集，包含预训练数据的百科数据，指令微调数据和奖励模型数据。

2023-05-22T14:45:06.000Z

650a9248d26103b6eee3ea7b

lmsys/lmsys-chat-1m

lmsys

{"size_categories": ["1M<n<10M"], "task_categories": ["conversational"], "extra_gated_prompt": "You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text"}, "extra_gated_button_content": "I agree to the terms and conditions of the LMSYS-Chat-1M Dataset License Agreement.", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "turn", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "openai_moderation", "list": [{"name": "categories", "struct": [{"name": "harassment", "dtype": "bool"}, {"name": "harassment/threatening", "dtype": "bool"}, {"name": "hate", "dtype": "bool"}, {"name": "hate/threatening", "dtype": "bool"}, {"name": "self-harm", "dtype": "bool"}, {"name": "self-harm/instructions", "dtype": "bool"}, {"name": "self-harm/intent", "dtype": "bool"}, {"name": "sexual", "dtype": "bool"}, {"name": "sexual/minors", "dtype": "bool"}, {"name": "violence", "dtype": "bool"}, {"name": "violence/graphic", "dtype": "bool"}]}, {"name": "category_scores", "struct": [{"name": "harassment", "dtype": "float64"}, {"name": "harassment/threatening", "dtype": "float64"}, {"name": "hate", "dtype": "float64"}, {"name": "hate/threatening", "dtype": "float64"}, {"name": "self-harm", "dtype": "float64"}, {"name": "self-harm/instructions", "dtype": "float64"}, {"name": "self-harm/intent", "dtype": "float64"}, {"name": "sexual", "dtype": "float64"}, {"name": "sexual/minors", "dtype": "float64"}, {"name": "violence", "dtype": "float64"}, {"name": "violence/graphic", "dtype": "float64"}]}, {"name": "flagged", "dtype": "bool"}]}, {"name": "redacted", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2626438904, "num_examples": 1000000}], "download_size": 1488850250, "dataset_size": 2626438904}}

auto

2024-07-27T09:28:42.000Z

200748d9d3cddcc9d782887541057aca0b18c5da

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset This dataset contains one million real-world conversations with 25 state-of-the-art LLMs. It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023. Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag. User consent is obtained through the "Terms of… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m.

2023-09-20T06:33:44.000Z

650f0710b63668f448157b64

openbmb/UltraFeedback

openbmb

{"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["100K<n<1M"]}

False

2023-12-29T14:11:19.000Z

40b436560ca83a8dba36114c22ab3c66e43f6d5e

Introduction GitHub Repo UltraRM-13b UltraCM-13b UltraFeedback is a large-scale, fine-grained, diverse preference dataset, used for training powerful reward models and critic models. We collect about 64k prompts from diverse resources (including UltraChat, ShareGPT, Evol-Instruct, TruthfulQA, FalseQA, and FLAN). We then use these prompts to query multiple LLMs (see Table for model lists) and generate 4 different responses for each prompt, resulting in a total of 256k samples.… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraFeedback.

2023-09-23T15:41:04.000Z

663b7fd5a4152b77b637ba11

TIGER-Lab/MMLU-Pro

TIGER-Lab

{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "MMLU-Pro", "tags": ["evaluation"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_index", "dtype": "int64"}, {"name": "cot_content", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "src", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 61143, "num_examples": 70}, {"name": "test", "num_bytes": 8715484, "num_examples": 12032}], "download_size": 58734087, "dataset_size": 8776627}}

False

2024-10-18T12:22:50.000Z

3373e0b32277875b8db2aa555a333b78a08477ea

MMLU-Pro Dataset MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset contains 12K complex questions across various disciplines. |Github | 🏆Leaderboard | 📖Paper | 🚀 What's New [2024.10.16] We have added Gemini-1.5-Flash-002, Gemini-1.5-Pro-002, Jamba-1.5-Large, Llama-3.1-Nemotron-70B-Instruct-HF and Ministral-8B-Instruct-2410 to our… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro.

2024-05-08T13:36:21.000Z

666363ddacc86c4174f6b49a

evendrow/INQUIRE-Rerank

evendrow

{"license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "query", "dtype": "string"}, {"name": "relevant", "dtype": "int64"}, {"name": "clip_score", "dtype": "float64"}, {"name": "inat24_image_id", "dtype": "int64"}, {"name": "inat24_file_name", "dtype": "string"}, {"name": "supercategory", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "iconic_group", "dtype": "string"}, {"name": "inat24_species_id", "dtype": "int64"}, {"name": "inat24_species_name", "dtype": "string"}, {"name": "latitude", "dtype": "float64"}, {"name": "longitude", "dtype": "float64"}, {"name": "location_uncertainty", "dtype": "float64"}, {"name": "date", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "rights_holder", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 293789663, "num_examples": 4000}, {"name": "test", "num_bytes": 1694429058, "num_examples": 16000}], "download_size": 1879381267, "dataset_size": 1988218721}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-09-28T04:45:09.000Z

ff3cd84df075ef27d1bcc59f1018c651d4aa6ac5

INQUIRE-Rerank Please note that this is dataset is preliminary, and will be updated soon. INQUIRE is a text-to-image retrieval benchmark designed to challenge multimodal models with expert-level queries about the natural world. This dataset aims to emulate real world image retrieval and analysis problems faced by scientists working with large-scale image collections. Therefore, we hope that INQUIRE will both encourage and track advancements in the real scientific utility… See the full description on the dataset page: https://huggingface.co/datasets/evendrow/INQUIRE-Rerank.

2024-06-07T19:47:41.000Z

666ae33f611afe17cd982829

BAAI/Infinity-Instruct

BAAI

{"configs": [{"config_name": "3M", "data_files": [{"split": "train", "path": "3M/*"}]}, {"config_name": "7M", "data_files": [{"split": "train", "path": "7M/*"}]}, {"config_name": "0625", "data_files": [{"split": "train", "path": "0625/*"}]}, {"config_name": "Gen", "data_files": [{"split": "train", "path": "Gen/*"}]}, {"config_name": "7M_domains", "data_files": [{"split": "train", "path": "7M_domains/*/*"}]}], "task_categories": ["text-generation"], "language": ["en", "zh"], "size_categories": ["1M<n<10M"], "license": "cc-by-sa-4.0", "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}}

auto

2024-10-31T15:06:59.000Z

05cd7e304312b9afc9c4cb5817927805554af437

Infinity Instruct Beijing Academy of Artificial Intelligence (BAAI) [Paper][Code][🤗] (would be released soon) The quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-Instruct.

2024-06-13T12:17:03.000Z

66e6268178f2c37966b02f97

BAAI/IndustryCorpus2

BAAI

{"license": "apache-2.0", "language": ["en", "zh"], "size_categories": ["n>1T"], "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}}

auto

2024-10-29T10:11:42.000Z

c4619decb5e73150a0961da0dcf828e1f9a7179c

Industry models play a vital role in promoting the intelligent transformation and innovative development of enterprises. High-quality industry data is the key to improving the performance of large models and realizing the implementation of industry applications. However, the data sets currently used for industry model training generally have problems such as small data volume, low quality, and lack of professionalism. In June, we released the IndustryCorpus dataset: We have further upgraded… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryCorpus2.

2024-09-15T00:12:49.000Z

670808f9672d9dcd311d155f

WenhaoWang/TIP-I2V

WenhaoWang

{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["image-to-video", "text-to-video"], "dataset_info": {"features": [{"name": "UUID", "dtype": "string"}, {"name": "Text_Prompt", "dtype": "string"}, {"name": "Image_Prompt", "dtype": "image"}, {"name": "Subject", "dtype": "string"}, {"name": "Timestamp", "dtype": "string"}, {"name": "Text_NSFW", "dtype": "float32"}, {"name": "Image_NSFW", "dtype": "string"}], "splits": [{"name": "Full", "num_bytes": 13440652664.125, "num_examples": 1701935}, {"name": "Subset", "num_bytes": 790710630, "num_examples": 100000}, {"name": "Eval", "num_bytes": 78258893, "num_examples": 10000}], "download_size": 27500759907, "dataset_size": 27750274851.25}, "configs": [{"config_name": "default", "data_files": [{"split": "Full", "path": "data/Full-*"}, {"split": "Subset", "path": "data/Subset-*"}, {"split": "Eval", "path": "data/Eval-*"}]}], "tags": ["prompt", "image-to-video", "text-to-video", "visual-generation", "video-generation"], "pretty_name": "TIP-I2V"}

False

2024-11-08T01:50:59.000Z

a4b4cf083eeaf3696b0e7fd7e7fe1b15ba6b72bf

Summary This is the dataset proposed in our paper TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation. TIP-I2V is the first dataset comprising over 1.70 million unique user-provided text and image prompts. Besides the prompts, TIP-I2V also includes videos generated by five state-of-the-art image-to-video models (Pika, Stable Video Diffusion, Open-Sora, I2VGen-XL, and CogVideoX-5B). The TIP-I2V contributes to the development of better and… See the full description on the dataset page: https://huggingface.co/datasets/WenhaoWang/TIP-I2V.

2024-10-10T17:03:53.000Z

627007d3becab9e2dcf15a40

ILSVRC/imagenet-1k

ILSVRC

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "license_details": "imagenet-agreement", "multilinguality": ["monolingual"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "test", "num_bytes": 13613661561, "num_examples": 100000}, {"name": "train", "num_bytes": 146956944242, "num_examples": 1281167}, {"name": "validation", "num_bytes": 6709003386, "num_examples": 50000}], "download_size": 166009941208, "dataset_size": 167279609189}}

auto

2024-07-16T13:30:57.000Z

4603483700ee984ea9debe3ddbfdeae86f6489eb

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, ImageNet hopes to offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. ImageNet 2012 is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images

2022-05-02T16:33:23.000Z

@article{imagenet15russakovsky, Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, Title = { {ImageNet Large Scale Visual Recognition Challenge} }, Year = {2015}, journal = {International Journal of Computer Vision (IJCV)}, doi = {10.1007/s11263-015-0816-y}, volume={115}, number={3}, pages={211-252} }

imagenet-1k-1

627a79e9c7f48ed9dc4eb531

facebook/voxpopuli

facebook

{"annotations_creators": [], "language": ["en", "de", "fr", "es", "pl", "it", "ro", "hu", "cs", "nl", "fi", "hr", "sk", "sl", "et", "lt"], "language_creators": [], "license": ["cc0-1.0", "other"], "multilinguality": ["multilingual"], "pretty_name": "VoxPopuli", "size_categories": [], "source_datasets": [], "tags": [], "task_categories": ["automatic-speech-recognition"], "task_ids": []}

False

2022-10-14T13:43:12.000Z

719aaef8225945c0d80b277de6c79aa42ab053d5

A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.

2022-05-10T14:42:49.000Z

@inproceedings{wang-etal-2021-voxpopuli, title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation", author = "Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.80", doi = "10.18653/v1/2021.acl-long.80", pages = "993--1003", }

643dda8f317127fb1e30b27b

liuhaotian/LLaVA-Instruct-150K

liuhaotian

{"license": "cc-by-4.0", "task_categories": ["visual-question-answering", "question-answering"], "language": ["en"], "pretty_name": "LLaVA Visual Instruct 150K", "size_categories": ["100K<n<1M"]}

False

2024-01-03T01:59:20.000Z

9d451dc7629cfe0469f6ae4432b765cd603d5fcb

LLaVA Visual Instruct 150K Dataset Card Dataset details Dataset type: LLaVA Visual Instruct 150K is a set of GPT-generated multimodal instruction-following data. It is constructed for visual instruction tuning and for building large multimodal towards GPT-4 vision/language capability. Dataset date: LLaVA Visual Instruct 150K was collected in April 2023, by prompting GPT-4-0314 API. Paper or resources for more information: https://llava-vl.github.io/ License:… See the full description on the dataset page: https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K.

2023-04-17T23:47:27.000Z

648b556b363cf923caddc497

Open-Orca/OpenOrca

Open-Orca

{"language": ["en"], "license": "mit", "task_categories": ["conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation"], "pretty_name": "OpenOrca", "size_categories": ["10M<n<100M"]}

False

2023-10-21T10:09:31.000Z

3e85783ecb0db83df8b30dbbd94107857b5ac830

🐋 The OpenOrca Dataset! 🐋 We are thrilled to announce the release of the OpenOrca dataset! This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper. It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers! Official Models Mistral-7B-OpenOrca Our latest model, the first 7B to score better overall than all… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/OpenOrca.

2023-06-15T18:16:11.000Z

654e20ba5ed9289072f5d523

HuggingFaceH4/no_robots

HuggingFaceH4

{"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "pretty_name": "No Robots", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16496867, "num_examples": 9500}, {"name": "test", "num_bytes": 887460, "num_examples": 500}], "download_size": 11045587, "dataset_size": 17384327}}

False

2024-04-18T08:40:39.000Z

e6f9a4ac5c37faeb744ba9ecf0473184d7f8105b

Dataset Card for No Robots 🙅‍♂️🤖 Look Ma, an instruction dataset that wasn't generated by GPTs! Dataset Summary No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/no_robots.

2023-11-10T12:23:22.000Z

65cf50a5f5a15aa42133ac44

ruslanmv/ai-medical-chatbot

ruslanmv

{"configs": [{"config_name": "default", "data_files": [{"path": "dialogues.*", "split": "train"}]}], "dataset_info": {"dataset_size": 141665910, "download_size": 141665910, "features": [{"dtype": "string", "name": "Description"}, {"dtype": "string", "name": "Patient"}, {"dtype": "string", "name": "Doctor"}], "splits": [{"name": "train", "num_bytes": 141665910, "num_examples": 256916}]}}

False

2024-03-23T20:45:11.000Z

138c99336a3afce0df88ffe6fd67bd231df25d36

AI Medical Chatbot Dataset This is an experimental Dataset designed to run a Medical Chatbot It contains at least 250k dialogues between a Patient and a Doctor. Playground ChatBot ruslanmv/AI-Medical-Chatbot For furter information visit the project here: https://github.com/ruslanmv/ai-medical-chatbot

2024-02-16T12:10:13.000Z

65d8078da3c18e931627f12d

m-a-p/Code-Feedback

m-a-p

{"language": ["en"], "pipeline_tag": "text-generation", "tags": ["code"], "license": "apache-2.0", "task_categories": ["question-answering"], "size_categories": ["10K<n<100K"]}

False

2024-02-26T05:45:12.000Z

f411b16a97c910ac9acf8b0d0948e340aa77cc34

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement [🏠Homepage] | [🛠️Code] Introduction OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. For further information and… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/Code-Feedback.

2024-02-23T02:48:45.000Z

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

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"CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/*"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]}

False

2024-07-16T16:04:38.000Z

cd850543a88ba055067841ce91d2669344ff7b7a

🍷 FineWeb 15 trillion tokens of the finest data the 🌐 web has to offer What is it? The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. 🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.

2024-04-18T14:33:13.000Z

666a59145c3bb7e4a6c8d180

Salesforce/xlam-function-calling-60k

Salesforce

{"extra_gated_heading": "Acknowledge to follow corresponding license and cite APIGen to access the repository", "extra_gated_button_content": "Agree and access repository", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Country": "country", "Affiliation": "text"}, "license": "cc-by-4.0", "task_categories": ["question-answering", "text-generation", "reinforcement-learning"], "language": ["en"], "tags": ["function-calling", "LLM Agent", "code", "synthetic"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "dataset", "data_files": [{"split": "train", "path": "xlam_function_calling_60k.json"}]}]}

auto

2024-07-19T20:37:48.000Z

1d5ae9b3285c9ab6ec147a2baba438a170ea7947

APIGen Function-Calling Datasets Paper | Website | Models This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness. We conducted human evaluation over 600 sampled data points… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k.

2024-06-13T02:27:32.000Z

66a53dc7d40a13036c5f2ebe

mlabonne/FineTome-100k

mlabonne

{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "source", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 239650960.7474458, "num_examples": 100000}], "download_size": 116531415, "dataset_size": 239650960.7474458}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-07-29T09:52:30.000Z

c2343c1372ff31f51aa21248db18bffa3193efdb

FineTome-100k The FineTome dataset is a subset of arcee-ai/The-Tome (without arcee-ai/qwen2-72b-magpie-en), re-filtered using HuggingFaceFW/fineweb-edu-classifier. It was made for my article "Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth".

2024-07-27T18:34:47.000Z

66c582fe30010c0f2bba4176

Team-ACE/ToolACE

Team-ACE

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "tools"], "size_categories": ["10K<n<100K"]}

False

2024-09-04T02:37:59.000Z

6bda777c88d21e5a204703c1ee45597a8fa4f734

ToolACE ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data. ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs. Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process. To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks. More… See the full description on the dataset page: https://huggingface.co/datasets/Team-ACE/ToolACE.

2024-08-21T06:02:38.000Z

66e2a9050abc319acedb372c

AI4Industry/MolParser-7M

AI4Industry

{"dataset_info": [{"config_name": "pretrain_synthetic_7M", "features": [{"name": "image", "dtype": "image"}, {"name": "SMILES", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 115375911760.028, "num_examples": 7720468}], "download_size": 122046202421, "dataset_size": 115375911760.028}, {"config_name": "test_markush_10k", "features": [{"name": "image", "dtype": "image"}, {"name": "SMILES", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 228019568, "num_examples": 10000}], "download_size": 233407872, "dataset_size": 228019568}, {"config_name": "test_simple_10k", "features": [{"name": "image", "dtype": "image"}, {"name": "SMILES", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 291640094, "num_examples": 10000}], "download_size": 292074581, "dataset_size": 291640094}, {"config_name": "valid", "features": [{"name": "image", "dtype": "image"}, {"name": "SMILES", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13538058, "num_examples": 403}], "download_size": 13451383, "dataset_size": 13538058}], "configs": [{"config_name": "pretrain_synthetic_7M", "data_files": [{"split": "train", "path": "pretrain_synthetic_7M/train-*"}]}, {"config_name": "valid", "data_files": [{"split": "train", "path": "valid/train-*"}]}, {"config_name": "test_simple_10k", "data_files": [{"split": "train", "path": "test_simple_10k/train-*"}]}, {"config_name": "test_markush_10k", "data_files": [{"split": "train", "path": "test_markush_10k/train-*"}]}], "license": "mit", "tags": ["chemistry"]}

False

2024-11-05T06:33:51.000Z

6eb011af5988ac468932bd0db31ea71c20e11044

MolParser-7M Anonymous Open Source now This repo provids the training data and evaluation data for MolParser, proposed in paper “MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild“ MolParser-7M contains nearly 8 million paired image-SMILES data. It should be noted that the caption of image is our extended-SMILES format, which suggested in our paper. Training Dataset: More than 7.7M training data in pretrain_synthetic_7M subset; Validation Dataset: A… See the full description on the dataset page: https://huggingface.co/datasets/AI4Industry/MolParser-7M.

2024-09-12T08:40:37.000Z

66f7bcb5cc934de072affc99

k-mktr/improved-flux-prompts-photoreal-portrait

k-mktr

{"license": "mit", "task_categories": ["text-classification"], "language": ["en"], "tags": ["art"], "pretty_name": "Improved FLUX.1 Prompts - Photo Portraits", "size_categories": ["10K<n<100K"]}

False

2024-10-03T10:55:26.000Z

36cf6aac4216523e41c831517bc93ca42624cd58

Photo Portrait Prompt Dataset for FLUX Overview This dataset contains a curated collection of prompts specifically designed for generating photo portraits using FLUX.1, an advanced text-to-image model. These prompts are crafted to produce high-quality, lifelike portraits by leveraging sophisticated prompting techniques and best practices. Latest Version Improved on October 3, 2024. This version has undergone curation and improvement. What is new?… See the full description on the dataset page: https://huggingface.co/datasets/k-mktr/improved-flux-prompts-photoreal-portrait.

2024-09-28T08:22:13.000Z

6718d840f899b4feea110c34

OpenFace-CQUPT/HumanCaption-HQ-311K

OpenFace-CQUPT

{"license": "cc-by-4.0", "language": ["en"], "task_categories": ["image-to-text", "text-to-image"], "tags": ["Human Caption", "Face Caption", "Multimodal", "Computer Vision", "datasets"], "size_categories": ["10K<n<100K"]}

False

2024-11-06T03:08:33.000Z

fcedf2f0c0f703f604176cf2180a0b8fc0c6e486

HumanCaption-HQ-311K HumanCaption-HQ-311K: Approximately 311,000 human-related images and their corresponding natural language descriptions. Compared to HumanCaption-10M, this dataset not only includes associated facial language descriptions but also filters out images with higher resolution and employs the powerful visual understanding capabilities of GPT-4V to generate more detailed and accurate text descriptions. This dataset is used for the second phase of training HumanVLM… See the full description on the dataset page: https://huggingface.co/datasets/OpenFace-CQUPT/HumanCaption-HQ-311K.

2024-10-23T11:04:32.000Z

67236c72d4839ad80e73892c

ManzhenWei/MusicSet

ManzhenWei

{"license": "mit"}

False

2024-11-05T10:32:48.000Z

ec42f753ed5226f51b65cfa83ecf13db1691167f

MusicSet The MusicSet dataset is built upon the MTG-Jamendo Dataset, where music audio is filtered and expanded with descriptive text. We selected music audio with at least 5 tags, loaded the audio files, extracted the middle 80% of the content for segmentation, and obtained 10-second clips to remove non-melodic segments from the beginning and end. The segmented clips were then selected based on the corresponding number of tags, saved as individual WAV files, and their… See the full description on the dataset page: https://huggingface.co/datasets/ManzhenWei/MusicSet.

2024-10-31T11:39:30.000Z

672858fdc5e9db9a8c063387

iapp/thai_handwriting_dataset

iapp

{"license": "apache-2.0", "task_categories": ["text-to-image", "image-to-text"], "language": ["th"], "tags": ["handwriting-recognition", "ocr"], "pretty_name": "Thai Handwriting Dataset", "size_categories": ["10K<n<100K"], "maintainer": "Kobkrit Viriyayudhakorn ([email protected])", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "label_file", "dtype": "string"}]}}

False

2024-11-06T06:54:22.000Z

00ef1056799dfcf179927831acb4fb6ffd73a788

Thai Handwriting Dataset This dataset combines two major Thai handwriting datasets: BEST 2019 Thai Handwriting Recognition dataset (train-0000.parquet) Thai Handwritten Free Dataset by Wang (train-0001.parquet onwards) Maintainer [email protected] Dataset Description BEST 2019 Dataset Contains handwritten Thai text images along with their ground truth transcriptions. The images have been processed and standardized for machine… See the full description on the dataset page: https://huggingface.co/datasets/iapp/thai_handwriting_dataset.

2024-11-04T05:17:49.000Z

672e4097987efdc25b010447

ChicagoHAI/CaseSumm

ChicagoHAI

{"license": "cc-by-nc-3.0", "task_categories": ["summarization"], "language": ["en"], "tags": ["legal"]}

False

2024-11-08T21:28:45.000Z

d121a154587f61a56c0f794217cc7ea72e04b157

The CaseSumm dataset consists of U.S. Supreme Court cases and their official summaries, called syllabuses, from the period 1815-2019. Syllabuses are written by an attorney employed by the Court and approved by the Justices. The syllabus is therefore the gold standard for summarizing majority opinions, and ideal for evaluating other summaries of the opinion. We obtain the opinions from Public Resource Org's archive and extract syllabuses from the official opinions published in the U.S. Reporter… See the full description on the dataset page: https://huggingface.co/datasets/ChicagoHAI/CaseSumm.

2024-11-08T16:47:19.000Z

621ffdd236468d709f181d5e

allenai/ai2_arc

allenai

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False

2023-12-21T15:09:48.000Z

210d026faf9955653af8916fad021475a3f00453

Dataset Card for "ai2_arc" Dataset Summary A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences… See the full description on the dataset page: https://huggingface.co/datasets/allenai/ai2_arc.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181e3f

nyu-mll/glue

nyu-mll

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"col_mapping": {"question": "text1", "sentence": "text2", "label": "target"}}, {"config": "rte", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"sentence1": "text1", "sentence2": "text2", "label": "target"}}, {"config": "wnli", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"sentence1": "text1", "sentence2": "text2", "label": "target"}}]}

False

2024-01-30T07:41:18.000Z

bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c

Dataset Card for GLUE Dataset Summary GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems. Supported Tasks and Leaderboards The leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks: ax A manually-curated evaluation dataset for fine-grained… See the full description on the dataset page: https://huggingface.co/datasets/nyu-mll/glue.

2022-03-02T23:29:22.000Z

glue

621ffdd236468d709f181e5e

cais/mmlu

cais

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False

2024-03-08T20:36:26.000Z

c30699e8356da336a370243923dbaf21066bb9fe

Dataset Card for MMLU Dataset Summary Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu.

2022-03-02T23:29:22.000Z

mmlu

62be6afc1e22ec8427aac2c7

zh-plus/tiny-imagenet

zh-plus

{"annotations_creators": ["crowdsourced"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "language": ["en"], "language_creators": ["crowdsourced"], "license": [], "multilinguality": ["monolingual"], "paperswithcode_id": "imagenet", "pretty_name": "Tiny-ImageNet", "size_categories": ["100K<n<1M"], "source_datasets": ["extended|imagenet-1k"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"]}

False

2022-07-12T09:04:30.000Z

5a77092c28e51558c5586e9c5eb71a7e17a5e43f

Dataset Card for tiny-imagenet Dataset Summary Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images. Languages The class labels in the dataset are in English. Dataset Structure Data Instances { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190… See the full description on the dataset page: https://huggingface.co/datasets/zh-plus/tiny-imagenet.

2022-07-01T03:33:16.000Z

imagenet

640f5b2fb63b6f18522d6d44

tatsu-lab/alpaca

tatsu-lab

{"license": "cc-by-nc-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca", "task_categories": ["text-generation"]}

False

2023-05-22T20:33:36.000Z

dce01c9b08f87459cf36a430d809084718273017

Dataset Card for Alpaca Dataset Summary Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications: The text-davinci-003 engine to generate the instruction data… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca.

2023-03-13T17:19:43.000Z

64382440c212a363c3ac15c8

OpenAssistant/oasst1

OpenAssistant

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 100367999, "num_examples": 84437}, {"name": "validation", "num_bytes": 5243405, "num_examples": 4401}], "download_size": 41596430, "dataset_size": 105611404}, "language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "tags": ["human-feedback"], "size_categories": ["100K<n<1M"], "pretty_name": "OpenAssistant Conversations"}

False

2023-05-02T13:21:21.000Z

fdf72ae0827c1cda404aff25b6603abec9e3399b

OpenAssistant Conversations Dataset (OASST1) Dataset Summary In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst1.

2023-04-13T15:48:16.000Z

6447d3903e498d66918fa2a5

zhengyun21/PMC-Patients

zhengyun21

{"license": "cc-by-nc-sa-4.0", "language": ["en"], "tags": ["patient summary", "medical", "biology"], "size_categories": ["100K<n<1M"]}

False

2024-01-06T01:01:34.000Z

5d31975519603541d4bec7e1f4013cc4490ed997

Dataset Card for PMC-Patients Dataset Summary PMC-Patients is a first-of-its-kind dataset consisting of 167k patient summaries extracted from case reports in PubMed Central (PMC), 3.1M patient-article relevance and 293k patient-patient similarity annotations defined by PubMed citation graph. Supported Tasks and Leaderboards This is purely the patient summary dataset with relational annotations. For ReCDS benchmark, refer to this dataset Based on… See the full description on the dataset page: https://huggingface.co/datasets/zhengyun21/PMC-Patients.

2023-04-25T13:20:16.000Z

64ba8be81d0a5a576089edcd

Open-Orca/FLAN

Open-Orca

{"license": "cc-by-4.0", "language": ["en"], "library_name": "transformers", "pipeline_tag": "text-generation", "datasets": ["Open-Orca/OpenOrca"], "size_categories": ["1B<n<10B"]}

False

2023-08-02T15:08:01.000Z

6845b1b3b53c6d5c5b1e49767ed759df3fc246cc

🍮 The WHOLE FLAN Collection! 🍮 Overview This repository includes the full dataset from the FLAN Collection, totalling ~300GB as parquets. Generated using the official seqio templating from the Google FLAN Collection GitHub repo. The data is subject to all the same licensing of the component datasets. To keep up with our continued work on OpenOrca and other exciting research, find our Discord here: https://AlignmentLab.ai Motivation This work was done as part… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/FLAN.

2023-07-21T13:45:12.000Z

64de1c5d39ac80b71fb96a3b

ds4sd/DocLayNet-v1.1

ds4sd

{"annotations_creators": ["crowdsourced"], "license": "other", "pretty_name": "DocLayNet", "size_categories": ["10K<n<100K"], "tags": ["layout-segmentation", "COCO", "document-understanding", "PDF"], "task_categories": ["object-detection", "image-segmentation"], "task_ids": ["instance-segmentation"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "bboxes", "sequence": {"sequence": "float64"}}, {"name": "category_id", "sequence": "int64"}, {"name": "segmentation", "sequence": {"sequence": {"sequence": "float64"}}}, {"name": "area", "sequence": "float64"}, {"name": "pdf_cells", "list": {"list": [{"name": "bbox", "sequence": "float64"}, {"name": "font", "struct": [{"name": "color", "sequence": "int64"}, {"name": "name", "dtype": "string"}, {"name": "size", "dtype": "float64"}]}, {"name": "text", "dtype": "string"}]}}, {"name": "metadata", "struct": [{"name": "coco_height", "dtype": "int64"}, {"name": "coco_width", "dtype": "int64"}, {"name": "collection", "dtype": "string"}, {"name": "doc_category", "dtype": "string"}, {"name": "image_id", "dtype": "int64"}, {"name": "num_pages", "dtype": "int64"}, {"name": "original_filename", "dtype": "string"}, {"name": "original_height", "dtype": "float64"}, {"name": "original_width", "dtype": "float64"}, {"name": "page_hash", "dtype": "string"}, {"name": "page_no", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 28172005254.125, "num_examples": 69375}, {"name": "test", "num_bytes": 1996179229.125, "num_examples": 4999}, {"name": "val", "num_bytes": 2493896901.875, "num_examples": 6489}], "download_size": 7766115331, "dataset_size": 32662081385.125}}

False

2023-09-01T09:58:52.000Z

5e89392376049f4d589ea339ed64468310ed5c3f

Dataset Card for DocLayNet v1.1 Dataset Summary DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank: Human Annotation: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and… See the full description on the dataset page: https://huggingface.co/datasets/ds4sd/DocLayNet-v1.1.

2023-08-17T13:10:53.000Z

64fc177dd268b2f1adb97ec9

lavita/ChatDoctor-HealthCareMagic-100k

lavita

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 126454896, "num_examples": 112165}], "download_size": 70518148, "dataset_size": 126454896}}

False

2023-09-09T07:40:38.000Z

505443eac4e99ccedeffbb6f640061223d1d4bb3

Dataset Card for "ChatDoctor-HealthCareMagic-100k" More Information needed

2023-09-09T06:58:05.000Z

65377f5989dd48faca8f7cf1

HuggingFaceH4/ultrachat_200k

HuggingFaceH4

{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "UltraChat 200k", "configs": [{"config_name": "default", "data_files": [{"split": "train_sft", "path": "data/train_sft-*"}, {"split": "test_sft", "path": "data/test_sft-*"}, {"split": "train_gen", "path": "data/train_gen-*"}, {"split": "test_gen", "path": "data/test_gen-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train_sft", "num_bytes": 1397058554, "num_examples": 207865}, {"name": "test_sft", "num_bytes": 154695659, "num_examples": 23110}, {"name": "train_gen", "num_bytes": 1347396812, "num_examples": 256032}, {"name": "test_gen", "num_bytes": 148276089, "num_examples": 28304}], "download_size": 1624049723, "dataset_size": 3047427114}}

False

2024-10-16T11:52:27.000Z

8049631c405ae6576f93f445c6b8166f76f5505a

Dataset Card for UltraChat 200k Dataset Description This is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create UltraChat 200k, we applied the following logic: Selection of a subset of data for faster supervised fine tuning. Truecasing of the dataset, as we observed around 5% of… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k.

2023-10-24T08:24:57.000Z

65529b923e99765b039b71bb

allenai/tulu-v2-sft-mixture

allenai

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "dataset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1239293363, "num_examples": 326154}], "download_size": 554561769, "dataset_size": 1239293363}, "license": "odc-by", "task_categories": ["question-answering", "conversational", "text-generation"], "language": ["en"], "size_categories": ["100K<n<1M"]}

False

2024-05-24T21:29:24.000Z

6248b175d2ccb5ec7c4aeb22e6d8ee3b21b2c752

Dataset Card for Tulu V2 Mix Note the ODC-BY license, indicating that different licenses apply to subsets of the data. This means that some portions of the dataset are non-commercial. We present the mixture as a research artifact. Tulu is a series of language models that are trained to act as helpful assistants. The dataset consists of a mix of : FLAN (Apache 2.0): We use 50,000 examples sampled from FLAN v2. To emphasize CoT-style reasoning, we sample another 50,000… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture.

2023-11-13T21:56:34.000Z

656d7a05d848a6683a0c5c75

m-a-p/COIG-CQIA

m-a-p

{"configs": [{"config_name": "chinese_traditional", "data_files": [{"split": "train", "path": "chinese_traditional/*"}]}, {"config_name": "coig_pc", "data_files": [{"split": "train", "path": "coig_pc/*"}]}, {"config_name": "exam", "data_files": [{"split": "train", "path": "exam/*"}]}, {"config_name": "finance"}, {"config_name": "douban", "data_files": [{"split": "train", "path": "douban/*"}]}, {"config_name": "finance", "data_files": [{"split": "train", "path": "finance/*"}]}, {"config_name": "human_value", "data_files": [{"split": "train", "path": "human_value/*"}]}, {"config_name": "logi_qa", "data_files": [{"split": "train", "path": "logi_qa/*"}]}, {"config_name": "ruozhiba", "data_files": [{"split": "train", "path": "ruozhiba/*"}]}, {"config_name": "segmentfault", "data_files": [{"split": "train", "path": "segmentfault/*"}]}, {"config_name": "wiki", "data_files": [{"split": "train", "path": "wiki/*"}]}, {"config_name": "wikihow", "data_files": [{"split": "train", "path": "wikihow/*"}]}, {"config_name": "xhs", "data_files": [{"split": "train", "path": "xhs/*"}]}, {"config_name": "zhihu", "data_files": [{"split": "train", "path": "zhihu/*"}]}], "task_categories": ["question-answering", "text-classification", "text-generation", "text2text-generation"], "language": ["zh"], "size_categories": ["10K<n<100K"]}

False

2024-04-18T12:10:58.000Z

8b55868c6168adf86c30e7ca0f782cca1c514297

COIG-CQIA：Quality is All you need for Chinese Instruction Fine-tuning Dataset Details Dataset Description 欢迎来到COIG-CQIA，COIG-CQIA全称为Chinese Open Instruction Generalist - Quality is All You Need， 是一个开源的高质量指令微调数据集，旨在为中文NLP社区提供高质量且符合人类交互行为的指令微调数据。COIG-CQIA以中文互联网获取到的问答及文章作为原始数据，经过深度清洗、重构及人工审核构建而成。本项目受LIMA: Less Is More for Alignment等研究启发，使用少量高质量的数据即可让大语言模型学习到人类交互行为，因此在数据构建中我们十分注重数据的来源、质量与多样性，数据集详情请见数据介绍以及我们接下来的论文。 Welcome to the… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/COIG-CQIA.

2023-12-04T07:04:37.000Z

6596b26db31c349cd75eb40e

nyanko7/danbooru2023

nyanko7

{"license": "mit", "task_categories": ["image-classification", "image-to-image", "text-to-image"], "language": ["en", "ja"], "pretty_name": "danbooru2023", "size_categories": ["1M<n<10M"], "viewer": false}

False

2024-05-22T18:43:24.000Z

4ddd8c6504b1381716bbeb2cb3f502eeb14e48d2

Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset Danbooru2023 is a large-scale anime image dataset with over 5 million images contributed and annotated in detail by an enthusiast community. Image tags cover aspects like characters, scenes, copyrights, artists, etc with an average of 30 tags per image. Danbooru is a veteran anime image board with high-quality images and extensive tag metadata. The dataset can be used to train image classification… See the full description on the dataset page: https://huggingface.co/datasets/nyanko7/danbooru2023.

2024-01-04T13:28:13.000Z

660e7b9b4636ce2b0e77b699

mozilla-foundation/common_voice_17_0

mozilla-foundation

{"pretty_name": "Common Voice Corpus 17.0", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "af", "am", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "gn", "ha", "he", "hi", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lij", "lo", "lt", "ltg", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan", "ne", "nhi", "nl", "nn", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quy", "rm", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yi", "yo", "yue", "zgh", "zh", "zu", "zza"], "language_bcp47": ["zh-CN", "zh-HK", "zh-TW", "sv-SE", "rm-sursilv", "rm-vallader", "pa-IN", "nn-NO", "ne-NP", "nan-tw", "hy-AM", "ga-IE", "fy-NL"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "source_datasets": ["extended|common_voice"], "paperswithcode_id": "common-voice", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."}

auto

2024-06-16T13:50:23.000Z

b10d53980ef166bc24ce3358471c1970d7e6b5ec

Dataset Card for Common Voice Corpus 17.0 Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 31175 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 20408 validated hours in 124 languages, but more voices and languages are always added. Take a look at the Languages… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0.

2024-04-04T10:06:19.000Z

@inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 }

common-voice

66150427cf3fef4fa8656274

LooksJuicy/ruozhiba

LooksJuicy

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["zh"]}

False

2024-04-09T09:10:55.000Z

2a39d86721e0109a7c598a25a1338e297c639d2f

受COIG-CQIA启发，构建类似数据集，但答案风格相对更简洁。 弱智吧精选问题数据来自github提供的疑问句，调用GPT-4获取答案，并过滤掉明显拒答的回复。

2024-04-09T09:02:31.000Z

66299f1f4f9d8e75f2a8a6b0

simon3000/genshin-voice

simon3000

{"language": ["zh", "en", "ja", "ko"], "task_categories": ["audio-classification", "automatic-speech-recognition", "text-to-speech"], "pretty_name": "Genshin Voice", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "speaker", "dtype": "string"}, {"name": "speaker_type", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "inGameFilename", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 264598217401.752, "num_examples": 463383}], "download_size": 227704444125, "dataset_size": 264598217401.752}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-08-30T08:36:05.000Z

ffe17e2e7938508a73255ec294b0ded17ed071f1

Genshin Voice Genshin Voice is a dataset of voice lines from the popular game Genshin Impact. Hugging Face 🤗 Genshin-Voice Last update at 2024-08-30 463383 wavs 20231 without speaker (4%) 24819 without transcription (5%) 602 without inGameFilename (0%) Dataset Details Dataset Description The dataset contains voice lines from the game's characters in multiple languages, including Chinese, English, Japanese, and Korean. The voice lines are spoken… See the full description on the dataset page: https://huggingface.co/datasets/simon3000/genshin-voice.

2024-04-25T00:09:03.000Z

66347aa61500e67c72dedeb0

allenai/WildChat-1M

allenai

{"license": "odc-by", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation", "question-answering", "text2text-generation"], "pretty_name": "WildChat-1M", "dataset_info": {"features": [{"name": "conversation_hash", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[us, tz=UTC]"}, {"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "hashed_ip", "dtype": "string"}, {"name": "header", "struct": [{"name": "accept-language", "dtype": "string"}, {"name": "user-agent", "dtype": "string"}]}, {"name": "language", "dtype": "string"}, {"name": "redacted", "dtype": "bool"}, {"name": "role", "dtype": "string"}, {"name": "state", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[us, tz=UTC]"}, {"name": "toxic", "dtype": "bool"}, {"name": "turn_identifier", "dtype": "int64"}]}, {"name": "turn", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "openai_moderation", "list": [{"name": "categories", "struct": [{"name": "harassment", "dtype": "bool"}, {"name": "harassment/threatening", "dtype": "bool"}, {"name": "harassment_threatening", "dtype": "bool"}, {"name": "hate", "dtype": "bool"}, {"name": "hate/threatening", "dtype": "bool"}, {"name": "hate_threatening", "dtype": "bool"}, {"name": "self-harm", "dtype": "bool"}, {"name": "self-harm/instructions", "dtype": "bool"}, {"name": "self-harm/intent", "dtype": "bool"}, {"name": "self_harm", "dtype": "bool"}, {"name": "self_harm_instructions", "dtype": "bool"}, {"name": "self_harm_intent", "dtype": "bool"}, {"name": "sexual", "dtype": "bool"}, {"name": "sexual/minors", "dtype": "bool"}, {"name": "sexual_minors", "dtype": "bool"}, {"name": "violence", "dtype": "bool"}, {"name": "violence/graphic", "dtype": "bool"}, {"name": "violence_graphic", "dtype": "bool"}]}, {"name": "category_scores", "struct": [{"name": "harassment", "dtype": "float64"}, {"name": "harassment/threatening", "dtype": "float64"}, {"name": "harassment_threatening", "dtype": "float64"}, {"name": "hate", "dtype": "float64"}, {"name": "hate/threatening", "dtype": "float64"}, {"name": "hate_threatening", "dtype": "float64"}, {"name": "self-harm", "dtype": "float64"}, {"name": "self-harm/instructions", "dtype": "float64"}, {"name": "self-harm/intent", "dtype": "float64"}, {"name": "self_harm", "dtype": "float64"}, {"name": "self_harm_instructions", "dtype": "float64"}, {"name": "self_harm_intent", "dtype": "float64"}, {"name": "sexual", "dtype": "float64"}, {"name": "sexual/minors", "dtype": "float64"}, {"name": "sexual_minors", "dtype": "float64"}, {"name": "violence", "dtype": "float64"}, {"name": "violence/graphic", "dtype": "float64"}, {"name": "violence_graphic", "dtype": "float64"}]}, {"name": "flagged", "dtype": "bool"}]}, {"name": "detoxify_moderation", "list": [{"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "toxicity", "dtype": "float64"}]}, {"name": "toxic", "dtype": "bool"}, {"name": "redacted", "dtype": "bool"}, {"name": "state", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "hashed_ip", "dtype": "string"}, {"name": "header", "struct": [{"name": "accept-language", "dtype": "string"}, {"name": "user-agent", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 6844366367.030628, "num_examples": 837989}], "download_size": 3360836020, "dataset_size": 6844366367.030628}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["instruction-finetuning"]}

False

2024-10-17T18:04:41.000Z

7d6490e462285cf85d91eabea0f9a954fbddcd1f

Dataset Card for WildChat Dataset Description Paper: https://arxiv.org/abs/2405.01470 Interactive Search Tool: https://wildvisualizer.com (paper) License: ODC-BY Language(s) (NLP): multi-lingual Point of Contact: Yuntian Deng Dataset Summary WildChat is a collection of 1 million conversations between human users and ChatGPT, alongside demographic data, including state, country, hashed IP addresses, and request headers. We collected WildChat… See the full description on the dataset page: https://huggingface.co/datasets/allenai/WildChat-1M.

2024-05-03T05:48:22.000Z

665c1855221dda498772b8b5

nvidia/HelpSteer2

nvidia

{"license": "cc-by-4.0", "language": ["en"], "pretty_name": "HelpSteer2", "size_categories": ["10K<n<100K"], "tags": ["human-feedback"]}

False

2024-10-15T16:07:56.000Z

c459751b0b10466341949a26998f4537c9abc755

HelpSteer2: Open-source dataset for training top-performing reward models HelpSteer2 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses. This dataset has been created in partnership with Scale AI. When used to tune a Llama 3.1 70B Instruct Model, we achieve 94.1% on RewardBench, which makes it the best Reward… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer2.

2024-06-02T06:59:33.000Z

667ee649a7d8b1deba8d4f4c

proj-persona/PersonaHub

proj-persona

{"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "text-classification", "token-classification", "fill-mask", "table-question-answering", "text2text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "text", "math", "reasoning", "instruction", "tool"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "math", "data_files": "math.jsonl"}, {"config_name": "instruction", "data_files": "instruction.jsonl"}, {"config_name": "reasoning", "data_files": "reasoning.jsonl"}, {"config_name": "knowledge", "data_files": "knowledge.jsonl"}, {"config_name": "npc", "data_files": "npc.jsonl"}, {"config_name": "tool", "data_files": "tool.jsonl"}, {"config_name": "persona", "data_files": "persona.jsonl"}]}

False

2024-10-05T04:04:28.000Z

c91f99f3efd4d0977e338f3b77abd251653cd405

Scaling Synthetic Data Creation with 1,000,000,000 Personas This repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas: We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce PERSONA HUB – a collection of 1 billion diverse personas automatically curated from web… See the full description on the dataset page: https://huggingface.co/datasets/proj-persona/PersonaHub.

2024-06-28T16:35:21.000Z

6683a0393517f04dc6d22a65

walledai/AdvBench

walledai

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 84165, "num_examples": 520}], "download_size": 35101, "dataset_size": 84165}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "mit", "task_categories": ["text2text-generation"], "language": ["en"]}

False

2024-07-04T18:13:32.000Z

9d4730540082fa4017450b65ca1c0e1d8d30446e

Dataset Card for AdvBench Paper: Universal and Transferable Adversarial Attacks on Aligned Language Models Data: AdvBench Dataset About AdvBench is a set of 500 harmful behaviors formulated as instructions. These behaviors range over the same themes as the harmful strings setting, but the adversary’s goal is instead to find a single attack string that will cause the model to generate any response that attempts to comply with the instruction, and to do so over as… See the full description on the dataset page: https://huggingface.co/datasets/walledai/AdvBench.

2024-07-02T06:37:45.000Z

66bc06dc6da7aec8413d35ba

NousResearch/hermes-function-calling-v1

NousResearch

{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering", "feature-extraction"], "language": ["en"], "configs": [{"config_name": "func_calling_singleturn", "data_files": "func-calling-singleturn.json", "default": true}, {"config_name": "func_calling", "data_files": "func-calling.json"}, {"config_name": "glaive_func_calling", "data_files": "glaive-function-calling-5k.json"}, {"config_name": "json_mode_agentic", "data_files": "json-mode-agentic.json"}, {"config_name": "json_mode_singleturn", "data_files": "json-mode-singleturn.json"}]}

False

2024-08-30T06:07:08.000Z

8f025148382537ba84cd325e1834b706e1461692

Hermes Function-Calling V1 This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models. This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational… See the full description on the dataset page: https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1.

2024-08-14T01:22:36.000Z

66d858ab4eb2eb8dc0721c06

vidore/colpali_train_set

vidore

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_filename", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "options", "dtype": "string"}, {"name": "page", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40887661837.62469, "num_examples": 118195}, {"name": "test", "num_bytes": 172966846.15108374, "num_examples": 500}], "download_size": 52705427788, "dataset_size": 41060628683.77577}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-09-04T17:16:45.000Z

543db706a025c401ba9e020412da3fb1744a5146

Dataset Description This dataset is the training set of ColPali it includes 127,460 query-image pairs from both openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%). Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages. Dataset #examples (query-page pairs) Language DocVQA… See the full description on the dataset page: https://huggingface.co/datasets/vidore/colpali_train_set.

2024-09-04T12:55:07.000Z

66e46a3f6e6ce3af7295dde6

openai/MMMLU

openai

{"task_categories": ["question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "test/*.csv"}]}, {"config_name": "AR_XY", "data_files": [{"split": "test", "path": "test/mmlu_AR-XY.csv"}]}, {"config_name": "BN_BD", "data_files": [{"split": "test", "path": "test/mmlu_BN-BD.csv"}]}, {"config_name": "DE_DE", "data_files": [{"split": "test", "path": "test/mmlu_DE-DE.csv"}]}, {"config_name": "ES_LA", "data_files": [{"split": "test", "path": "test/mmlu_ES-LA.csv"}]}, {"config_name": "FR_FR", "data_files": [{"split": "test", "path": "test/mmlu_FR-FR.csv"}]}, {"config_name": "HI_IN", "data_files": [{"split": "test", "path": "test/mmlu_HI-IN.csv"}]}, {"config_name": "ID_ID", "data_files": [{"split": "test", "path": "test/mmlu_ID-ID.csv"}]}, {"config_name": "IT_IT", "data_files": [{"split": "test", "path": "test/mmlu_IT-IT.csv"}]}, {"config_name": "JA_JP", "data_files": [{"split": "test", "path": "test/mmlu_JA-JP.csv"}]}, {"config_name": "KO_KR", "data_files": [{"split": "test", "path": "test/mmlu_KO-KR.csv"}]}, {"config_name": "PT_BR", "data_files": [{"split": "test", "path": "test/mmlu_PT-BR.csv"}]}, {"config_name": "SW_KE", "data_files": [{"split": "test", "path": "test/mmlu_SW-KE.csv"}]}, {"config_name": "YO_NG", "data_files": [{"split": "test", "path": "test/mmlu_YO-NG.csv"}]}, {"config_name": "ZH_CN", "data_files": [{"split": "test", "path": "test/mmlu_ZH-CN.csv"}]}], "language": ["ar", "bn", "de", "es", "fr", "hi", "id", "it", "ja", "ko", "pt", "sw", "yo", "zh"], "license": "mit"}

False

2024-10-16T18:39:00.000Z

325a01dc3e173cac1578df94120499aaca2e2504

Multilingual Massive Multitask Language Understanding (MMMLU) The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science. We translated the MMLU’s test set into 14 languages using professional human translators. Relying on human translators for this evaluation increases… See the full description on the dataset page: https://huggingface.co/datasets/openai/MMMLU.

2024-09-13T16:37:19.000Z

66ebb7af703a567feca77e83

BAAI/CCI3-HQ

BAAI

{"task_categories": ["text-generation"], "language": ["zh"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "score", "dtype": "float"}], "splits": [{"name": "train"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/part_*"}]}]}

False

2024-10-29T08:26:21.000Z

892f0db8742fcc233e6208c8f15f36e8b196415e

Data Description To address the scarcity of high-quality safety datasets in the Chinese, we open-sourced the CCI (Chinese Corpora Internet) dataset on November 29, 2023. Building on this foundation, we continue to expand the data source, adopt stricter data cleaning methods, and complete the construction of the CCI 3.0 dataset. This dataset is composed of high-quality, reliable Internet data from trusted sources. And then with more stricter filtering, The CCI 3.0 HQ corpus… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/CCI3-HQ.

2024-09-19T05:33:35.000Z

66fd6222d935294087b8513e

KingNish/reasoning-base-20k

KingNish

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["reasoning", "synthetic"], "pretty_name": "Reasoning 20k Data", "size_categories": ["10K<n<100K"]}

False

2024-10-05T14:19:30.000Z

ae93576e3b315cf876e7429b7fa1fd041df72d29

Dataset Card for Reasoning Base 20k Dataset Details Dataset Description This dataset is designed to train a reasoning model. That can think through complex problems before providing a response, similar to how a human would. The dataset includes a wide range of problems from various domains (science, coding, math, etc.), each with a detailed chain of thought (COT) and the correct answer. The goal is to enable the model to learn and refine its… See the full description on the dataset page: https://huggingface.co/datasets/KingNish/reasoning-base-20k.

2024-10-02T15:09:22.000Z

670d4bb8207a1458e88ab1f6

gretelai/gretel-pii-masking-en-v1

gretelai

{"license": "apache-2.0", "task_categories": ["text-classification", "text-generation"], "language": ["en"], "tags": ["synthetic", "domain-specific", "text", "NER"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-10-24T18:14:21.000Z

e24ff6132034133cb7d43f72c4ed82c30da2ec9f

Gretel Synthetic Domain-Specific Documents Dataset (English) This dataset is a synthetically generated collection of documents enriched with Personally Identifiable Information (PII) and Protected Health Information (PHI) entities spanning multiple domains. Created using Gretel Navigator with mistral-nemo-2407 as the backend model, it is specifically designed for fine-tuning Gliner models. The dataset contains document passages featuring PII/PHI entities from a wide range of… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/gretel-pii-masking-en-v1.

2024-10-14T16:50:00.000Z

6710cd0aeac19807267b35cf

qq8933/OpenLongCoT-150K

qq8933

{"license": "mit", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "critic", "num_bytes": 274630587, "num_examples": 93101}, {"name": "expansion", "num_bytes": 500063315, "num_examples": 141332}, {"name": "expansionwithcritic", "num_bytes": 246096435, "num_examples": 84694}, {"name": "refinewithoutcritic", "num_bytes": 1071027885, "num_examples": 484395}], "download_size": 259618341, "dataset_size": 2091818222}, "configs": [{"config_name": "default", "data_files": [{"split": "critic", "path": "data/critic-*"}, {"split": "expansion", "path": "data/expansion-*"}, {"split": "expansionwithcritic", "path": "data/expansionwithcritic-*"}, {"split": "refinewithoutcritic", "path": "data/refinewithoutcritic-*"}]}]}

False

2024-10-21T01:48:36.000Z

424a72dcc5bef2d115390a56f4ec62f2c986bf75

Citation @article{zhang2024llama, title={LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning}, author={Zhang, Di and Wu, Jianbo and Lei, Jingdi and Che, Tong and Li, Jiatong and Xie, Tong and Huang, Xiaoshui and Zhang, Shufei and Pavone, Marco and Li, Yuqiang and others}, journal={arXiv preprint arXiv:2410.02884}, year={2024} } @article{zhang2024accessing, title={Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo… See the full description on the dataset page: https://huggingface.co/datasets/qq8933/OpenLongCoT-150K.

2024-10-17T08:38:34.000Z

6716146cfc14a25260d39431

openfoodfacts/product-database

openfoodfacts

{"language": ["en", "fr", "de", "es", "it", "nl", "pl", "pt", "sv", "bg", "ro", "fi", "ru", "nb", "cs", "th", "da", "hr", "hu", "ar", "el", "ja", "ca", "sr", "sl", "sk", "tr", "lt", "zh", "et", "lv", "xx", "uk", "id", "he", "vi", "is", "la", "in", "ko", "sq", "iw", "ka", "ms", "bs", "fa", "bn", "gl", "kk", "mk", "nn", "hi", "aa", "uz", "so", "af", "eu"], "license": ["agpl-3.0", "odbl"], "size_categories": ["1M<n<10M"], "pretty_name": "Open Food Facts Product Database", "dataset_info": {"config_name": "default"}, "configs": [{"config_name": "default", "data_files": [{"split": "main", "path": "products.parquet"}]}]}

False

2024-11-08T16:01:57.000Z

10bd57ec971430b9075f061afef3437d27a35d71

Open Food Facts Database What is 🍊 Open Food Facts? A food products database Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels. Made by everyone Open Food Facts is a non-profit association of volunteers. 25.000+ contributors like you have added 1.7 million + products from 150 countries using our Android, iPhone or Windows Phone… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/product-database.

2024-10-21T08:44:28.000Z

6718261a6878c3c7eb83619f

qq8933/OpenLongCoT-SFT

qq8933

{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82070367, "num_examples": 33765}], "download_size": 21886320, "dataset_size": 82070367}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-10-22T22:24:39.000Z

e312c19ea6956718831956337b2b1f11c0874b28

2024-10-22T22:24:26.000Z

6719eb4a483e4fa6a04b80fd

arcee-ai/EvolKit-20k-vi

arcee-ai

{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 37976541, "num_examples": 15378}], "download_size": 17873646, "dataset_size": 37976541}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-11-07T14:43:04.000Z

ade213f9c324b8aa5a56482c5291ffd9ed4f557b

This is a Vietnamese subset of a larger dataset generated for the purpose of training our Llama-3.1-SuperNova model. It utilized our EvolKit repository: https://github.com/arcee-ai/EvolKit.

2024-10-24T06:38:02.000Z

671ba02e89ecd4fe0b84a19b

prithivMLmods/Healthcare-Analysis-Rx

prithivMLmods

{"license": "creativeml-openrail-m", "language": ["en"]}

False

2024-10-26T15:59:58.000Z

7ba70037415c78b253946d8abf6c601f1d0cccc7

2024-10-25T13:42:06.000Z

671bdd9d3cf85ea3a513f1df

barc0/200k_HEAVY_gpt4o-description-gpt4omini-code_generated_problems

barc0

{"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["ARC"], "size_categories": ["100K<n<1M"]}

False

2024-11-02T13:45:57.000Z

4264798428e3c48cd67ec7a3402e0847f645da52

Here is the dataset of ~100k synthetic data generated by 162 seeds. We generate the dataset with the following steps and two approaches: Generate ~110k descriptions by GPT4o. Approach 1: Generate ~110k codes follow each description by GPT4o-mini. Approach 2: Generate ~110k codes follow each description by GPT4o-mini and suggest it to use specific library functions. Run the ~220k codes and do auto-filtering. Get the final ~200k legitimate ARC-like tasks with examples.

2024-10-25T18:04:13.000Z

671f3f4000b654a3640f09ee

ajibawa-2023/Software-Architecture

ajibawa-2023

{"license": "apache-2.0", "language": ["en"], "tags": ["Software", "Architecture", "Frameworks", "Architectural Patterns for Reliability", "Architectural Patterns for Scalability", "Architectural Patterns", "Architectural Quality Attributes", "Architectural Testing", "Architectural Views", "Architectural Decision-Making", "Cloud-Based Architectures", "Data Architecture", "Microservices", "Software Design Principles", "Security Architecture", "Component-Based Architecture"], "size_categories": ["100K<n<1M"]}

False

2024-10-28T12:32:56.000Z

c0ba59d7fce0c51071eefac70afdac5cf813e54d

Software-Architecture I am releasing a Large Dataset covering topics related to Software-Architecture. This dataset consists of around 450,000 lines of data in jsonl. I have included following topics: Architectural Frameworks Architectural Patterns for Reliability Architectural Patterns for Scalability Architectural Patterns Architectural Quality Attributes Architectural Testing Architectural Views Architectural Decision-Making Advanced Research Cloud-Based Architectures Component-Based… See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/Software-Architecture.

2024-10-28T07:37:36.000Z

6723044b5abaf4d115ca1b32

ariya2357/CORAL

ariya2357

{"license": "cc-by-sa-4.0", "task_categories": ["question-answering"], "language": ["en"]}

False

2024-10-31T11:17:54.000Z

9f6e2d756d94e925f52674cbb38cce764dab1407

CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation CORAL is a a large-scale multi-turn conversational RAG benchmark that fulfills the critical features mentioned in our paper to systematically evaluate and advance conversational RAG systems.In CORAL, we evaluate conversational RAG systems across three essential tasks:(1) Conversational Passage Retrieval: assessing the system’s ability to retrieve the relevant information from a large document set… See the full description on the dataset page: https://huggingface.co/datasets/ariya2357/CORAL.

2024-10-31T04:15:07.000Z

6723bcef8ff91d25f6330395

5CD-AI/Viet-Table-Markdown

5CD-AI

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "markdown", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20282993355.77, "num_examples": 65030}], "download_size": 16051778065, "dataset_size": 20282993355.77}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

auto

2024-10-31T17:39:50.000Z

e99545de26fa11d07f9cb550154ef5ffe0edaf16

2024-10-31T17:22:55.000Z

6729f458752f2ab1c8ce6843

reducto/rd-tablebench

reducto

{"license": "cc-by-nc-nd-4.0"}

False

2024-11-05T10:33:35.000Z

7748503e2bd5f210d27aa2ef5fdf4b8aa13099bb

2024-11-05T10:32:56.000Z

672c1ea1a3cac338ea27e674

ZTE-AIM/Telecom-Function-Calling-Evaluation

ZTE-AIM

False

2024-11-07T02:00:11.000Z

66c52aba31ba568f86e333599b44909d01836b34

TFCE(Telecom Function-Calling Evaluation)数据集 数据集摘要 TFCE是一个评估通信领域函数调用能力的数据集，由1800余个函数构成917道Python题目，并应用于通信领域的Simple（简单函数）、Multiple（多函数）、Parallel（并行函数）、Parallel-Multiple（并行多函数）等场景，涉及4G LTE、5G技术与6G探索、无线通信与网络优化、物联网（IoT）与M2M通信、移动通信系统与实施、网络安全与协议等方面的内容。 语言 数据集中question的文本是中文；其他部分的文本是英文。 数据集结构 TECE数据集中的数据按照“question-function-required”的结构；其中，“function”由“name”、“description”、“parameters”组成；“parameters”由“type”和“properties”组成。… See the full description on the dataset page: https://huggingface.co/datasets/ZTE-AIM/Telecom-Function-Calling-Evaluation.

2024-11-07T01:57:53.000Z

672f08e9940bb84d10bdf0ee

prithivMLmods/Mixed-Conversations-Chat-Split

prithivMLmods

{"license": "creativeml-openrail-m", "language": ["en"], "tags": ["mathinstruct", "mixed-conversation", "150K", "Chat-Split"], "size_categories": ["100K<n<1M"]}

False

2024-11-09T07:05:51.000Z

1b9ae219beabeb76abefc3cf1a393905524aa020

2024-11-09T07:02:01.000Z

621ffdd236468d709f181dba

abisee/cnn_dailymail

abisee

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False

2024-01-18T15:31:34.000Z

96df5e686bee6baa90b8bee7c28b81fa3fa6223d

Dataset Card for CNN Dailymail Dataset Dataset Summary The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering. Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/abisee/cnn_dailymail.

2022-03-02T23:29:22.000Z

cnn-daily-mail-1

621ffdd236468d709f181e51

tdavidson/hate_speech_offensive

tdavidson

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False

2024-01-04T12:06:17.000Z

adc5fb774614827695774f2dbe0ea8122f6a92b4

Dataset Card for [Dataset Name] Dataset Summary An annotated dataset for hate speech and offensive language detection on tweets. Supported Tasks and Leaderboards [More Information Needed] Languages English (en) Dataset Structure Data Instances { "count": 3, "hate_speech_annotation": 0, "offensive_language_annotation": 0, "neither_annotation": 3, "label": 2, # "neither" "tweet": "!!! RT… See the full description on the dataset page: https://huggingface.co/datasets/tdavidson/hate_speech_offensive.

2022-03-02T23:29:22.000Z

hate-speech-and-offensive-language

621ffdd236468d709f181e77

stanfordnlp/imdb

stanfordnlp

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False

2024-01-04T12:09:45.000Z

e6281661ce1c48d982bc483cf8a173c1bbeb5d31

Dataset Card for "imdb" Dataset Summary Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.

2022-03-02T23:29:22.000Z

imdb-movie-reviews

621ffdd236468d709f181f06

openai/openai_humaneval

openai

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False

2024-01-04T16:08:05.000Z

7dce6050a7d6d172f3cc5c32aa97f52fa1a2e544

Dataset Card for OpenAI HumanEval Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models. Supported Tasks and Leaderboards Languages The programming problems are written in Python and contain English natural text in comments and… See the full description on the dataset page: https://huggingface.co/datasets/openai/openai_humaneval.

2022-03-02T23:29:22.000Z

humaneval

621ffdd236468d709f181f0b

Helsinki-NLP/opus-100

Helsinki-NLP

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False

2024-02-28T09:17:34.000Z

805090dc28bf78897da9641cdf08b61287580df9

Dataset Card for OPUS-100 Dataset Summary OPUS-100 is an English-centric multilingual corpus covering 100 languages. OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English). The languages were selected based on the volume of parallel data available in OPUS. Supported Tasks and Leaderboards Translation. Languages OPUS-100… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/opus-100.

2022-03-02T23:29:22.000Z

opus-100