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

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

66fec09298f30194f8b8ac36

LLM360/TxT360

LLM360

{"license": "odc-by"}

False

2024-10-18T07:59:36.000Z

724939bf37fdb8e0851215cf5b87bea7235b4758

TxT360: A Top-Quality LLM Pre-training Dataset Requires the Perfect Blend We introduce TxT360 (Trillion eXtracted Text) the first dataset to globally deduplicate 99 CommonCrawl snapshots and 14 commonly used non-web data sources (e.g. FreeLaw, PG-19, etc.) providing pretraining teams with a recipe to easily adjust data weighting, obtain the largest high-quality open source dataset, and train the most performant models. TxT360 Compared to Common… See the full description on the dataset page: https://huggingface.co/datasets/LLM360/TxT360.

2024-10-03T16:04:34.000Z

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

66e4b270f5579b829f4c18eb

Zyphra/Zyda-2

Zyphra

{"license": "odc-by", "pretty_name": "Zyda-2", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*/*"}]}, {"config_name": "dclm_crossdeduped", "data_files": [{"split": "train", "path": "data/dclm_crossdeduped/*/*"}]}, {"config_name": "zyda_crossdeduped-filtered", "data_files": [{"split": "train", "path": "data/zyda_crossdeduped-filtered /*/*"}]}, {"config_name": "dolma-cc_crossdeduped-filtered", "data_files": [{"split": "train", "path": "data/dolma-cc_crossdeduped-filtered/*"}]}, {"config_name": "fwe3", "data_files": [{"split": "train", "path": "data/fwe3/*/*"}]}]}

False

2024-10-15T21:55:42.000Z

d3429a1d6532e98a739a8c6157894d8241d807e6

Zyda-2 Zyda-2 is a 5 trillion token language modeling dataset created by collecting open and high quality datasets and combining them and cross-deduplication and model-based quality filtering. Zyda-2 comprises diverse sources of web data, highly educational content, math, code, and scientific papers. To construct Zyda-2, we took the best open-source datasets available: Zyda, FineWeb, DCLM, and Dolma. Models trained on Zyda-2 significantly outperform identical models trained on… See the full description on the dataset page: https://huggingface.co/datasets/Zyphra/Zyda-2.

2024-09-13T21:45:20.000Z

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

2024-10-13T17:46:04.000Z

c3d3d1047d2a73664a3418e971cbc77c28d1edf9

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

670befa7623c91990f914eb6

mlabonne/open-perfectblend

mlabonne

{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2951380166, "num_examples": 1420909}], "download_size": 1483360321, "dataset_size": 2951380166}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0"}

False

2024-10-18T14:57:53.000Z

5aa6d8ba9d7f11bed65e3ac4a1455ec1d855ea8f

🎨 Open-PerfectBlend Open-PerfectBlend is an open-source reproduction of the instruction dataset introduced in the paper "The Perfect Blend: Redefining RLHF with Mixture of Judges". It's a solid general-purpose instruction dataset with chat, math, code, and instruction-following data. Data source Here is the list of the datasets used in this mix: Dataset # Samples meta-math/MetaMathQA 395,000 openbmb/UltraInteract_sft 288,579… See the full description on the dataset page: https://huggingface.co/datasets/mlabonne/open-perfectblend.

2024-10-13T16:04:55.000Z

670d316b46a7e8dd87882d2a

migtissera/Synthia-Coder-v1.5-I

migtissera

{"license": "apache-2.0"}

False

2024-10-14T14:58:40.000Z

caf7fdabcf4f6fdc7f36d1360b64124c6ca069a1

2024-10-14T14:57:47.000Z

6705437a76d4290e37a66c0d

rombodawg/Everything_Instruct

rombodawg

{"license": "apache-2.0", "language": ["en"], "tags": ["Num_Rows = 5,685,816", "Max_length = 8180"]}

False

2024-10-08T21:11:58.000Z

03a74ec104b664081df86f4ffa4f32e26a8aa35e

Everything you need... all in one place 💘 Everything instruct is a massive alpaca instruct formatted dataset consisting of a wide variety of topics meant to bring LLM's to the next level in open source AI. Note: This dataset is fully uncensored (No model will refuse any request trained on this dataset unless otherwise aligned) The data in this dataset features: Science: 12,580 rows Social media: 18,405 rows General Knowledge: 906,346 rows Cooking: 20,763 rows Writing: 414,646 rows Medicine:… See the full description on the dataset page: https://huggingface.co/datasets/rombodawg/Everything_Instruct.

2024-10-08T14:36:42.000Z

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

670dc5114968ab91d80e2258

Marqo/marqo-GS-10M

Marqo

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "query", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "position", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "pair_id", "dtype": "string"}, {"name": "score_linear", "dtype": "int64"}, {"name": "score_reciprocal", "dtype": "float64"}, {"name": "no_score", "dtype": "int64"}, {"name": "query_id", "dtype": "string"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "in_domain", "path": "data/in_domain-*"}, {"split": "novel_document", "path": "data/novel_document-*"}, {"split": "novel_query", "path": "data/novel_query-*"}, {"split": "zero_shot", "path": "data/zero_shot-*"}]}], "language": ["en"], "tags": ["multimodal", "GCL"], "pretty_name": "marqo-GS-10M", "size_categories": ["1M<n<10M"]}

False

2024-10-16T12:50:11.000Z

cf665f0a2fb39830a4ae6011c54beb7bbc7a39a5

Marqo-GS-10M This dataset is our multimodal, fine-grained, ranking Google Shopping dataset, Marqo-GS-10M, followed by our novel training framework: Generalized Contrastive Learning (GCL). GCL aims to improve and measure the ranking performance of information retrieval models, especially for retrieving relevant products given a search query. Blog post:… See the full description on the dataset page: https://huggingface.co/datasets/Marqo/marqo-GS-10M.

2024-10-15T01:27:45.000Z

6704e0e03421058a7329a358

upstage/dp-bench

upstage

{"license": "mit", "tags": ["nlp", "Image-to-Text"]}

False

2024-10-17T02:25:32.000Z

964dabef1c24c670bc33a6863ed8d13d5650ba92

DP-Bench: Document Parsing Benchmark Document parsing refers to the process of converting complex documents, such as PDFs and scanned images, into structured text formats like HTML and Markdown. It is especially useful as a preprocessor for RAG systems, as it preserves key structural information from visually rich documents. While various parsers are available on the market, there is currently no standard evaluation metric to assess their performance. To address this gap… See the full description on the dataset page: https://huggingface.co/datasets/upstage/dp-bench.

2024-10-08T07:36:00.000Z

66cd7bbefc6f503213a054e7

lmms-lab/LLaVA-Video-178K

lmms-lab

{"configs": [{"config_name": "0_30_s_academic_v0_1", "data_files": [{"split": "caption", "path": "0_30_s_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "0_30_s_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_academic_v0_1/*mc*.json"}]}, {"config_name": "0_30_s_youtube_v0_1", "data_files": [{"split": "caption", "path": "0_30_s_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "0_30_s_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_youtube_v0_1/*mc*.json"}]}, {"config_name": "0_30_s_activitynet", "data_files": [{"split": "open_ended", "path": "0_30_s_activitynet/*oe*.json"}]}, {"config_name": "0_30_s_perceptiontest", "data_files": [{"split": "multi_choice", "path": "0_30_s_perceptiontest/*mc*.json"}]}, {"config_name": "0_30_s_nextqa", "data_files": [{"split": "open_ended", "path": "0_30_s_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "0_30_s_nextqa/*mc*.json"}]}, {"config_name": "30_60_s_academic_v0_1", "data_files": [{"split": "caption", "path": "30_60_s_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "30_60_s_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_academic_v0_1/*mc*.json"}]}, {"config_name": "30_60_s_youtube_v0_1", "data_files": [{"split": "caption", "path": "30_60_s_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "30_60_s_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_youtube_v0_1/*mc*.json"}]}, {"config_name": "30_60_s_activitynet", "data_files": [{"split": "open_ended", "path": "30_60_s_activitynet/*oe*.json"}]}, {"config_name": "30_60_s_perceptiontest", "data_files": [{"split": "multi_choice", "path": "30_60_s_perceptiontest/*mc*.json"}]}, {"config_name": "30_60_s_nextqa", "data_files": [{"split": "open_ended", "path": "30_60_s_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "30_60_s_nextqa/*mc*.json"}]}, {"config_name": "1_2_m_youtube_v0_1", "data_files": [{"split": "caption", "path": "1_2_m_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "1_2_m_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_youtube_v0_1/*mc*.json"}]}, {"config_name": "1_2_m_academic_v0_1", "data_files": [{"split": "caption", "path": "1_2_m_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "1_2_m_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_academic_v0_1/*mc*.json"}]}, {"config_name": "1_2_m_activitynet", "data_files": [{"split": "open_ended", "path": "1_2_m_activitynet/*oe*.json"}]}, {"config_name": "1_2_m_nextqa", "data_files": [{"split": "open_ended", "path": "1_2_m_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "1_2_m_nextqa/*mc*.json"}]}, {"config_name": "2_3_m_youtube_v0_1", "data_files": [{"split": "caption", "path": "2_3_m_youtube_v0_1/*cap*.json"}, {"split": "open_ended", "path": "2_3_m_youtube_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_youtube_v0_1/*mc*.json"}]}, {"config_name": "2_3_m_academic_v0_1", "data_files": [{"split": "caption", "path": "2_3_m_academic_v0_1/*cap*.json"}, {"split": "open_ended", "path": "2_3_m_academic_v0_1/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_academic_v0_1/*mc*.json"}]}, {"config_name": "2_3_m_activitynet", "data_files": [{"split": "open_ended", "path": "2_3_m_activitynet/*oe*.json"}]}, {"config_name": "2_3_m_nextqa", "data_files": [{"split": "open_ended", "path": "2_3_m_nextqa/*oe*.json"}, {"split": "multi_choice", "path": "2_3_m_nextqa/*mc*.json"}]}, {"config_name": "llava_hound", "data_files": [{"split": "open_ended", "path": "llava_hound/sharegptvideo_qa_255k_processed.json"}]}], "language": ["en"], "task_categories": ["visual-question-answering", "video-text-to-text"], "tags": ["video"]}

False

2024-10-11T04:59:25.000Z

6d8c562dc26d70042a0d9704d1cae58c94b89098

Dataset Card for LLaVA-Video-178K Uses This dataset is used for the training of the LLaVA-Video model. We only allow the use of this dataset for academic research and education purpose. For OpenAI GPT-4 generated data, we recommend the users to check the OpenAI Usage Policy. Data Sources For the training of LLaVA-Video, we utilized video-language data from five primary sources: LLaVA-Video-178K: This dataset includes 178,510 caption entries, 960… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K.

2024-08-27T07:09:50.000Z

6709054a623c91990fbef46d

NerdyRodent/NR-Flux-ComfyUI-Workflows

NerdyRodent

{"license": "mit"}

False

2024-10-19T18:04:55.000Z

16d5ff0700d77d00aa8864249e3580bce97636ce

Overview A collection of various workflows for using Flux.1 in ComfyUI. Workflows will require custom nodes, best installed with ComfyUI Manager - https://github.com/ltdrdata/ComfyUI-Manager If you need to manually install (rather than via the usual "install missing nodes"), start by installing these: Impact Pack Extra Models for ComfyUI ComfyUI Extra Samplers ComfyUI-GGUF ComfyUI_SUNoise Comfyroll Studio ComfyUI-ppm stability-ComfyUI-nodes rgthree's ComfyUI Nodes Use… See the full description on the dataset page: https://huggingface.co/datasets/NerdyRodent/NR-Flux-ComfyUI-Workflows.

2024-10-11T11:00:26.000Z

66df36ce81d0833b80539504

HuggingFaceFV/finevideo

HuggingFaceFV

{"dataset_info": {"features": [{"name": "mp4", "dtype": "binary"}, {"name": "json", "struct": [{"name": "content_fine_category", "dtype": "string"}, {"name": "content_metadata", "struct": [{"name": "characterList", "list": [{"name": "characterId", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "name", "dtype": "string"}]}, {"name": "description", "dtype": "string"}, {"name": "fps", "dtype": "float64"}, {"name": "qAndA", "list": [{"name": "answer", "dtype": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "scenes", "list": [{"name": "activities", "list": [{"name": "description", "dtype": "string"}, {"name": "timestamp", "struct": [{"name": "end_timestamp", "dtype": "string"}, {"name": "start_timestamp", "dtype": "string"}]}]}, {"name": "audioVisualCorrelation", "dtype": "float64"}, {"name": "cast", "sequence": "string"}, {"name": "characterInteraction", "list": [{"name": "characters", "sequence": "string"}, {"name": "description", "dtype": "string"}]}, {"name": "contextualRelevance", "dtype": "string"}, {"name": "dynamismScore", "dtype": "float64"}, {"name": "mood", "struct": [{"name": "description", "dtype": "string"}, {"name": "keyMoments", "list": [{"name": "changeDescription", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}]}]}, {"name": "narrativeProgression", "list": [{"name": "description", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}]}, {"name": "props", "list": [{"name": "name", "dtype": "string"}, {"name": "timestamp", "struct": [{"name": "end_timestamp", "dtype": "string"}, {"name": "start_timestamp", "dtype": "string"}]}]}, {"name": "sceneId", "dtype": "int64"}, {"name": "thematicElements", "dtype": "string"}, {"name": "timestamps", "struct": [{"name": "end_timestamp", "dtype": "string"}, {"name": "start_timestamp", "dtype": "string"}]}, {"name": "title", "dtype": "string"}, {"name": "videoEditingDetails", "list": [{"name": "description", "dtype": "string"}, {"name": "timestamps", "struct": [{"name": "end_timestamp", "dtype": "string"}, {"name": "start_timestamp", "dtype": "string"}]}]}]}, {"name": "storylines", "struct": [{"name": "climax", "struct": [{"name": "description", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}]}, {"name": "description", "dtype": "string"}, {"name": "scenes", "sequence": "int64"}]}, {"name": "title", "dtype": "string"}, {"name": "trimmingSuggestions", "list": [{"name": "description", "dtype": "string"}, {"name": "timestamps", "struct": [{"name": "end_timestamp", "dtype": "string"}, {"name": "start_timestamp", "dtype": "string"}]}]}]}, {"name": "content_parent_category", "dtype": "string"}, {"name": "duration_seconds", "dtype": "int64"}, {"name": "original_json_filename", "dtype": "string"}, {"name": "original_video_filename", "dtype": "string"}, {"name": "resolution", "dtype": "string"}, {"name": "text_to_speech", "dtype": "string"}, {"name": "text_to_speech_word_count", "dtype": "int64"}, {"name": "youtube_age_limit", "dtype": "int64"}, {"name": "youtube_categories", "sequence": "string"}, {"name": "youtube_channel", "dtype": "string"}, {"name": "youtube_channel_follower_count", "dtype": "int64"}, {"name": "youtube_comment_count", "dtype": "int64"}, {"name": "youtube_description", "dtype": "string"}, {"name": "youtube_like_count", "dtype": "int64"}, {"name": "youtube_tags", "sequence": "string"}, {"name": "youtube_title", "dtype": "string"}, {"name": "youtube_upload_date", "dtype": "string"}, {"name": "youtube_view_count", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 677741879467, "num_examples": 43751}], "download_size": 673422781186, "dataset_size": 677741879467}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cc", "task_categories": ["visual-question-answering", "video-text-to-text"], "language": ["en"], "size_categories": ["10K<n<100K"], "extra_gated_prompt": "## Terms of Use for FineVideo\nFineVideo dataset is a collection of over 43.000 YouTube videos. We ask that you read and acknowledge the following points before using the dataset:\n1. FineVideo is a collection of Creative Commons videos. Any use of all or part of the videos must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n2. FineVideo is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of FineVideo to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/HuggingFaceFV/finevideo/discussions/2). If you have questions about dataset versions and allowed uses, please also ask them in the dataset's [community discussions](https://huggingface.co/datasets/HuggingFaceFV/finevideo/discussions/3). We will also notify users via email when the latest usable version changes.\n3. To host, share, or otherwise provide access to FineVideo, you must include [these Terms of Use](https://huggingface.co/datasets/HuggingFaceFV/finevideo#terms-of-use-for-finevideo) and require users to agree to it.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}, "tags": ["video"]}

auto

2024-09-23T14:30:45.000Z

1985691839d66504a398976754e9c5aaaa16401c

FineVideo FineVideo Description Dataset Explorer Dataset Distribution How to download and use FineVideo Using datasets Using huggingface_hub Load a subset of the dataset Dataset Structure Data Instances Data Fields Dataset Creation License CC-By Considerations for Using the Data Social Impact of Dataset Discussion of Biases Additional Information Credits Future Work Opting out of FineVideo Citation Information Terms of use for FineVideo… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFV/finevideo.

2024-09-09T17:56:30.000Z

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

6690566cd7741cade02b8fe2

Magpie-Align/Magpie-Reasoning-150K

Magpie-Align

{"dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "gen_input_configs", "struct": [{"name": "temperature", "dtype": "float64"}, {"name": "top_p", "dtype": "float64"}, {"name": "input_generator", "dtype": "string"}, {"name": "seed", "dtype": "null"}, {"name": "extract_input", "dtype": "string"}]}, {"name": "gen_response_configs", "struct": [{"name": "prompt", "dtype": "string"}, {"name": "temperature", "dtype": "int64"}, {"name": "top_p", "dtype": "float64"}, {"name": "repetition_penalty", "dtype": "float64"}, {"name": "max_tokens", "dtype": "int64"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "output_generator", "dtype": "string"}]}, {"name": "intent", "dtype": "string"}, {"name": "knowledge", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "difficulty_generator", "dtype": "string"}, {"name": "input_quality", "dtype": "string"}, {"name": "quality_explanation", "dtype": "string"}, {"name": "quality_generator", "dtype": "string"}, {"name": "task_category", "dtype": "string"}, {"name": "other_task_category", "sequence": "string"}, {"name": "task_category_generator", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 833223418, "num_examples": 150000}], "download_size": 368443556, "dataset_size": 833223418}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "llama3", "language": ["en"], "size_categories": ["100K<n<1M"]}

False

2024-07-22T01:08:44.000Z

b35ad8226bdadbf4c1829bbeb3fc06274db90dde

Project Web: https://magpie-align.github.io/ Arxiv Technical Report: https://arxiv.org/abs/2406.08464 Codes: https://github.com/magpie-align/magpie Abstract Click Here High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Reasoning-150K.

2024-07-11T22:02:20.000Z

66e53119ad382452bb404275

KbsdJames/Omni-MATH

KbsdJames

{"license": "apache-2.0", "language": ["en"], "tags": ["math", "olympiads"], "size_categories": ["1K<n<10K"]}

False

2024-10-12T09:02:05.000Z

40ba231d8f16e29ecd40e6407e2c8640145a8f62

Dataset Card for Omni-MATH Recent advancements in AI, particularly in large language models (LLMs), have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g., OpenAI o1 achieves 94.8% on MATH dataset), indicating their inadequacy for truly challenging these models. To mitigate this limitation, we propose a comprehensive and challenging benchmark specifically… See the full description on the dataset page: https://huggingface.co/datasets/KbsdJames/Omni-MATH.

2024-09-14T06:45:45.000Z

66e6268178f2c37966b02f97

BAAI/IndustryCorpus2

BAAI

{"license": "apache-2.0", "language": ["en", "zh"], "size_categories": ["n>1T"]}

False

2024-10-17T08:51:54.000Z

8ba80b26a2620732ba82c7ea6cafdef5537f5dcb

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

66eb894483591125987548f7

google/frames-benchmark

google

{"license": "apache-2.0", "language": ["en"], "tags": ["rag", "long-context", "llm-search", "reasoning", "factuality", "retrieval", "question-answering", "iterative-search"], "task_categories": ["text-classification", "token-classification", "table-question-answering", "question-answering"], "pretty_name": "Who are I or you", "size_categories": ["n>1T"]}

False

2024-10-15T18:18:24.000Z

58d9fb6330f3ab1316d1eca12e5e8ef23dcc22ef

FRAMES: Factuality, Retrieval, And reasoning MEasurement Set FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning. Our paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941. Dataset Overview 824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles Questions span diverse… See the full description on the dataset page: https://huggingface.co/datasets/google/frames-benchmark.

2024-09-19T02:15:32.000Z

6711382513d4904b64d4f43b

meta-ai-for-media-research/movie_gen_video_bench

meta-ai-for-media-research

{"language": ["en"], "pretty_name": "Movie Gen Video Benchmark", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "video", "dtype": "binary"}], "splits": [{"name": "test_with_generations", "num_bytes": 16029316444, "num_examples": 1003}, {"name": "test", "num_bytes": 113706, "num_examples": 1003}], "download_size": 16029724908, "dataset_size": 16029430150}, "configs": [{"config_name": "default", "data_files": [{"split": "test_with_generations", "path": "data/test_with_generations-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-10-17T17:39:08.000Z

42b45cb11a57ffd3cd2a65e551911351d214cb9e

Dataset Card for the Movie Gen Benchmark Movie Gen is a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. Here, we introduce our evaluation benchmark "Movie Gen Bench Video Bench", as detailed in the Movie Gen technical report (Section 3.5.2). To enable fair and easy comparison to Movie Gen for future works on these evaluation benchmarks, we additionally release the non cherry-picked generated videos from… See the full description on the dataset page: https://huggingface.co/datasets/meta-ai-for-media-research/movie_gen_video_bench.

2024-10-17T16:15:33.000Z

66507e3f27e2ff751883bf2b

glaiveai/RAG-v1

glaiveai

{"license": "apache-2.0", "size_categories": ["10K<n<100K"], "tags": ["code", "synthetic", "rag"], "language": ["en"]}

False

2024-06-25T22:46:06.000Z

eb050fc6592502122f2b3775a5627b5b79ffd626

Glaive-RAG-v1 Glaive-RAG-v1 is a dataset with ~50k samples built using the Glaive platform, for finetuning models for RAG use cases. Each row has: List of documents for context Question Answer Mode Answer The answer mode is to define if the model should output only grounded responses or if it should combine it's internal information as well. The answers have Cited documents at the beginning and also <co: 1> tags in the text to mark citations. To report any problems or… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/RAG-v1.

2024-05-24T11:47:11.000Z

67105fb3e506c35ff594baab

TIGER-Lab/MEGA-Bench

TIGER-Lab

{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "dataset_info": [{"config_name": "core", "features": [{"name": "id", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "task_description", "dtype": "string"}, {"name": "global_media", "dtype": "string"}, {"name": "example_text", "dtype": "string"}, {"name": "example_media", "dtype": "string"}, {"name": "query_text", "dtype": "string"}, {"name": "query_media", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "metric_info", "dtype": "string"}, {"name": "eval_context", "dtype": "string"}, {"name": "taxonomy_tree_path", "dtype": "string"}, {"name": "application", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 15494017, "num_examples": 6531}], "download_size": 1316006, "dataset_size": 15494017}, {"config_name": "core_single_image", "features": [{"name": "id", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "task_description", "dtype": "string"}, {"name": "global_media", "dtype": "string"}, {"name": "example_text", "dtype": "string"}, {"name": "example_media", "dtype": "string"}, {"name": "query_text", "dtype": "string"}, {"name": "query_media", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "metric_info", "dtype": "string"}, {"name": "eval_context", "dtype": "string"}, {"name": "taxonomy_tree_path", "dtype": "string"}, {"name": "application", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 7878892, "num_examples": 4108}], "download_size": 701355, "dataset_size": 7878892}, {"config_name": "open", "features": [{"name": "id", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "task_description", "dtype": "string"}, {"name": "global_media", "dtype": "string"}, {"name": "example_text", "dtype": "string"}, {"name": "example_media", "dtype": "string"}, {"name": "query_text", "dtype": "string"}, {"name": "query_media", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "metric_info", "dtype": "string"}, {"name": "eval_context", "dtype": "string"}, {"name": "taxonomy_tree_path", "dtype": "string"}, {"name": "application", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 7088336, "num_examples": 1158}], "download_size": 851329, "dataset_size": 7088336}, {"config_name": "open_single_image", "features": [{"name": "id", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "task_description", "dtype": "string"}, {"name": "global_media", "dtype": "string"}, {"name": "example_text", "dtype": "string"}, {"name": "example_media", "dtype": "string"}, {"name": "query_text", "dtype": "string"}, {"name": "query_media", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "metric_info", "dtype": "string"}, {"name": "eval_context", "dtype": "string"}, {"name": "taxonomy_tree_path", "dtype": "string"}, {"name": "application", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 5023562, "num_examples": 808}], "download_size": 638672, "dataset_size": 5023562}], "configs": [{"config_name": "core", "data_files": [{"split": "test", "path": "core/test-*"}]}, {"config_name": "core_single_image", "data_files": [{"split": "test", "path": "core_single_image/test-*"}]}, {"config_name": "open", "data_files": [{"split": "test", "path": "open/test-*"}]}, {"config_name": "open_single_image", "data_files": [{"split": "test", "path": "open_single_image/test-*"}]}]}

False

2024-10-18T10:32:31.000Z

19dd94a01451db7092e2f641ee5ca418b31c158b

MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks 🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub ❗❗ Data Information We put the file path of images/videos in HF datasets. Please download the zipped data here. We chose not to directly include images in the Parquet files because the viewer of Hugging Face Datasets cannot display rows beyond a size limit, causing visualization failure on some of our tasks. We will… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MEGA-Bench.

2024-10-17T00:52:03.000Z

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

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

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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

667ee649a7d8b1deba8d4f4c

proj-persona/PersonaHub

proj-persona

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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

66a26e6812bd8c8ee66f5676

lmms-lab/LLaVA-OneVision-Data

lmms-lab

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False

2024-09-10T03:06:06.000Z

2da58d4455b1040d1288ffac8aa56b190767b1f0

Dataset Card for LLaVA-OneVision [2024-09-01]: Uploaded VisualWebInstruct(filtered), it's used in OneVision Stage almost all subsets are uploaded with HF's required format and you can use the recommended interface to download them and follow our code below to convert them. the subset of ureader_kg and ureader_qa are uploaded with the processed jsons and tar.gz of image folders. You may directly download them from the following url.… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data.

2024-07-25T15:25:28.000Z

66a53dc7d40a13036c5f2ebe

mlabonne/FineTome-100k

mlabonne

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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

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

66fa17d4096cf50ff9453e69

MathGenie/MathCode-Pile

MathGenie

{"license": "apache-2.0"}

False

2024-10-16T03:01:09.000Z

df9a4417658cdbe8875e851fa908a50c98c8e247

MathCode-Pile MathCode-Pile is a dataset for continue pretraining large language models to enhance their mathematical reasoning abilities. It is introduced in the paper MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code. It contains 19.2B tokens, with math-related data covering web pages, textbooks, model-synthesized text, and math related code. Currently, filtered-OpenWebMath, filtered-CC-En-math, and translated mathematical code… See the full description on the dataset page: https://huggingface.co/datasets/MathGenie/MathCode-Pile.

2024-09-30T03:15:32.000Z

6702c7ce7d7577ffe5789856

migtissera/Synthia-v1.5-II

migtissera

{"license": "apache-2.0"}

False

2024-10-06T17:25:05.000Z

af6fc48cadf254962dd90e9aae1f4c445406abee

2024-10-06T17:24:30.000Z

621ffdd236468d709f181e16

dair-ai/emotion

dair-ai

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False

2024-08-08T06:10:47.000Z

cab853a1dbdf4c42c2b3ef2173804746df8825fe

Dataset Card for "emotion" Dataset Summary Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. Supported Tasks and Leaderboards More Information Needed Languages More Information Needed Dataset Structure Data Instances An example looks as follows. { "text": "im feeling quite sad… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion.

2022-03-02T23:29:22.000Z

emotion

621ffdd236468d709f182a80

allenai/c4

allenai

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False

2024-01-09T19:14:03.000Z

1588ec454efa1a09f29cd18ddd04fe05fc8653a2

C4 Dataset Summary A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's C4 dataset We prepared five variants of the data: en, en.noclean, en.noblocklist, realnewslike, and multilingual (mC4). For reference, these are the sizes of the variants: en: 305GB en.noclean: 2.3TB en.noblocklist: 380GB realnewslike: 15GB multilingual (mC4): 9.7TB (108 subsets, one… See the full description on the dataset page: https://huggingface.co/datasets/allenai/c4.

2022-03-02T23:29:22.000Z

c4

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

645e8da96320b0efe40ade7a

roneneldan/TinyStories

roneneldan

{"license": "cdla-sharing-1.0", "task_categories": ["text-generation"], "language": ["en"]}

False

2024-08-12T13:27:26.000Z

f54c09fd23315a6f9c86f9dc80f725de7d8f9c64

Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary. Described in the following paper: https://arxiv.org/abs/2305.07759. The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M. Additional resources: tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories.

2023-05-12T19:04:09.000Z

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

6706152975454343dfebfe77

worldcuisines/vqa

worldcuisines

{"license": "cc-by-sa-4.0", "language": ["eng", "ind", "ind", "zho", "kor", "kor", "jpn", "jpn", "sun", "jav", "jav", "ces", "spa", "fra", "ara", "hin", "ben", "mar", "sin", "yor", "yue", "nan", "nan", "tgl", "tha", "aze", "rus", "rus", "ita", "srd"], "multilinguality": ["multilingual"], "language_details": "en, id_formal, id_casual, zh_cn, ko_formal, ko_casual, ja_formal, ja_casual, su_loma, jv_krama, jv_ngoko, cs, es, fr, ar, hi, bn, mr, si_formal_spoken, yo, yue, nan, nan_spoken, tl, th, az, ru_formal, ru_casual, it, sc", "configs": [{"config_name": "task1", "data_files": [{"split": "test_large", "path": "hf_prompt/large_eval_task1/*"}, {"split": "test_small", "path": "hf_prompt/small_eval_task1/*"}, {"split": "train", "path": "hf_prompt/train_task1/*"}]}, {"config_name": "task2", "data_files": [{"split": "test_large", "path": "hf_prompt/large_eval_task2/*"}, {"split": "test_small", "path": "hf_prompt/small_eval_task2/*"}, {"split": "train", "path": "hf_prompt/train_task2/*"}]}]}

False

2024-10-18T14:32:59.000Z

de41a4a0e328fc7e4729fa7973671e071bacde38

WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines WorldCuisines is a massive-scale visual question answering (VQA) benchmark for multilingual and multicultural understanding through global cuisines. The dataset contains text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark as of 17 October… See the full description on the dataset page: https://huggingface.co/datasets/worldcuisines/vqa.

2024-10-09T05:31:21.000Z

67094a36d0d7527b66c56266

ai-safety-institute/AgentHarm

ai-safety-institute

{"license": "other", "configs": [{"config_name": "harmless_benign", "data_files": [{"split": "test_public", "path": "benchmark/benign_behaviors_test_public.json"}, {"split": "validation", "path": "benchmark/benign_behaviors_validation.json"}], "field": "behaviors"}, {"config_name": "harmful", "data_files": [{"split": "test_public", "path": "benchmark/harmful_behaviors_test_public.json"}, {"split": "validation", "path": "benchmark/harmful_behaviors_validation.json"}], "field": "behaviors"}]}

False

2024-10-14T07:58:31.000Z

a82cbefaf77e194849d7bc829a92bf502894c31a

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents Maksym Andriushchenko1,†,*, Alexandra Souly2,* Mateusz Dziemian1, Derek Duenas1, Maxwell Lin1, Justin Wang1, Dan Hendrycks1,§, Andy Zou1,¶,§, Zico Kolter1,¶, Matt Fredrikson1,¶,* Eric Winsor2, Jerome Wynne2, Yarin Gal2,♯, Xander Davies2,♯,* 1Gray Swan AI, 2UK AI Safety Institute, *Core Contributor †EPFL, §Center for AI Safety, ¶Carnegie Mellon University, ♯University of Oxford Paper:… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/AgentHarm.

2024-10-11T15:54:30.000Z

670d7aa55c92b2893293425c

DEVAI-benchmark/DEVAI

DEVAI-benchmark

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False

2024-10-16T19:02:25.000Z

15168a163edab543963da1c36b4268024c58a65e

DEVAI dataset DEVAI is a benchmark of 55 realistic AI development tasks. It consists of plentiful manual annotations, including a total of 365 hierarchical user requirements. This dataset enables rich reinforcement signals for better automated AI software development. Here is an example of our tasks. We apply three state-of-the-art automatic software development systems to DEVAI, namely MetaGPT, GPT-Piolt, and OpenHands. We suggest expanding the task queries with… See the full description on the dataset page: https://huggingface.co/datasets/DEVAI-benchmark/DEVAI.

2024-10-14T20:10:13.000Z