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

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

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

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

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

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

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

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

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

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

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

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

653785ff8e37b02865e64be0

HuggingFaceH4/ultrafeedback_binarized

HuggingFaceH4

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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

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

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

625552d2b339bb03abe3432d

openai/gsm8k

openai

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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

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

6655eb19d17e141dcb546ed5

HuggingFaceFW/fineweb-edu

HuggingFaceFW

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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

666ae33f611afe17cd982829

BAAI/Infinity-Instruct

BAAI

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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

6727611f89116e24a4fc40a8

selimc/InstructPapers-TR

selimc

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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