Datasets:

cfahlgren1

/

hub-stats

like 9

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

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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

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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

621ffdd236468d709f183300

facebook/multilingual_librispeech

facebook

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"polish/1_hours-*"}]}, {"config_name": "portuguese", "data_files": [{"split": "dev", "path": "portuguese/dev-*"}, {"split": "test", "path": "portuguese/test-*"}, {"split": "train", "path": "portuguese/train-*"}, {"split": "9_hours", "path": "portuguese/9_hours-*"}, {"split": "1_hours", "path": "portuguese/1_hours-*"}]}, {"config_name": "spanish", "data_files": [{"split": "dev", "path": "spanish/dev-*"}, {"split": "test", "path": "spanish/test-*"}, {"split": "train", "path": "spanish/train-*"}, {"split": "9_hours", "path": "spanish/9_hours-*"}, {"split": "1_hours", "path": "spanish/1_hours-*"}]}]}

False

2024-08-12T16:50:57.000Z

2e83e61823b4c47dcbcb1980bb88601274127609

Dataset Card for MultiLingual LibriSpeech Dataset Summary This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from OpenSLR to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/multilingual_librispeech.

2022-03-02T23:29:22.000Z

multilingual-librispeech

65af4645d0a5cc99d51642da

McAuley-Lab/Amazon-Reviews-2023

McAuley-Lab

{"language": ["en"], "tags": ["recommendation", "reviews"], "size_categories": ["10B<n<100B"]}

False

2024-04-08T06:17:07.000Z

e85a0c5c5d8621a5b54003931ac233d3f764cf42

Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset. This dataset mainly includes reviews (ratings, text) and item metadata (desc- riptions, category information, price, brand, and images). Compared to the pre- vious versions, the 2023 version features larger size, newer reviews (up to Sep 2023), richer and cleaner meta data, and finer-grained timestamps (from day to milli-second).

2024-01-23T04:53:25.000Z

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

common-voice

@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 }

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"]}

auto

2024-09-19T09:48:10.000Z

8f98969e561502ca58bc8b58112e785209b4a583

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

6695831f2d25bd04e969b0a2

AI-MO/NuminaMath-CoT

AI-MO

{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2495457595.0398345, "num_examples": 859494}, {"name": "test", "num_bytes": 290340.31593470514, "num_examples": 100}], "download_size": 1234351634, "dataset_size": 2495747935.355769}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["aimo", "math"], "pretty_name": "NuminaMath CoT"}

False

2024-07-19T13:58:59.000Z

d5fb806061f3392fb8fddde7d45e18dfac409855

Dataset Card for NuminaMath CoT Dataset Summary Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-CoT.

2024-07-15T20:14:23.000Z

66ec6fc3fca6148a45e37e74

glaiveai/reflection-v1

glaiveai

{"license": "apache-2.0"}

False

2024-09-19T19:08:14.000Z

6becaa822b43dfd79490bb8b41f3cdba194f7b64

2024-09-19T18:38:59.000Z

66f4eabc4717373fb9ba43f5

facebook/Self-taught-evaluator-DPO-data

facebook

{"license": "other", "extra_gated_prompt": "## License", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "I accept the terms and conditions": "checkbox", "geo": "ip_location"}, "extra_gated_description": "Self-taught Evaluator Research License and Acceptable Use Policy", "extra_gated_button_content": "I Accept Self-taught Evaluator Research License and AUP"}

auto

2024-09-30T21:12:47.000Z

8c85b1974d24637f03423cc43882df51525ad421

This dataset is released as part of Self-taught evaluators research project. Please refer to our project materials here for training and evaluation details. Loading the dataset with transformers This dataset is built upon WildChat prompts by using Llama-3.1-70B-Instruct to generate responses and evaluation plans. Details on how to build such a self-taught dataset can be found in Self-taught evaluators. Minimal example below showing how to prepare training data. from datasets… See the full description on the dataset page: https://huggingface.co/datasets/facebook/Self-taught-evaluator-DPO-data.

2024-09-26T05:01:48.000Z

66f7b13cee826b5daa6b89bc

PKU-Alignment/align-anything-400k

PKU-Alignment

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"path": "text-audio-to-text/val_1k.parquet"}]}, {"config_name": "text-image-to-text", "data_files": [{"split": "train", "path": "text-image-to-text/train/train-*"}, {"split": "val", "path": "text-image-to-text/val_1k.parquet"}]}, {"config_name": "text-image-to-text-image", "data_files": [{"split": "train", "path": "text-image-to-text-image/*"}]}, {"config_name": "text-to-audio", "data_files": [{"split": "train", "path": "text-to-audio/train_12k.parquet"}]}, {"config_name": "text-to-image", "data_files": [{"split": "train", "path": "text-to-image/train_30k.parquet"}, {"split": "val", "path": "text-to-image/val_2k.parquet"}]}, {"config_name": "text-to-text", "data_files": [{"split": "train", "path": "text-to-text/train_30k.parquet"}, {"split": "val", "path": "text-to-text/val_1k.parquet"}]}, {"config_name": "text-to-video", "data_files": [{"split": "train", "path": "text-to-video/*"}]}, {"config_name": "text-video-to-text", "data_files": [{"split": "train", "path": "text-video-to-text/*"}]}], "lanuguage": ["en"]}

False

2024-10-16T15:59:54.000Z

d5ef4f793e608797216839e5a73ddc3b6fc044c4

Overview: Align-Anything 400K A Comprehensive All-Modality Alignment Dataset with Fine-grained Preference Annotations and Language Feedback. 🏠 Homepage | 🤗 Align-Anything-400K Dataset Our world is inherently multimodal. Humans perceive the world through multiple senses, and Language Models should operate similarly. However, the development of Current Multi-Modality Foundation Models faces limitations due to the availability and diversity of data across different modalities.… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/align-anything-400k.

2024-09-28T07:33:16.000Z

66fd4dcb7a83f74b96c71bd7

bansalaman18/yesbut

bansalaman18

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "difficulty_in_understanding", "dtype": "string"}, {"name": "left_image", "dtype": "string"}, {"name": "overall_description", "dtype": "string"}, {"name": "right_image", "dtype": "string"}, {"name": "stage", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 142007936.672, "num_examples": 1084}], "download_size": 139447610, "dataset_size": 142007936.672}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "language": ["en"], "pretty_name": "Y", "size_categories": ["1K<n<10K"], "tags": ["arxiv:2409.13592"]}

False

2024-10-12T06:53:32.000Z

cf66077fbaa93d16d4008b5f1409c6712346cfcd

YesBut Dataset Understanding satire and humor is a challenging task for even current Vision-Language models. In this paper, we propose the challenging tasks of Satirical Image Detection (detecting whether an image is satirical), Understanding (generating the reason behind the image being satirical), and Completion (given one half of the image, selecting the other half from 2 given options, such that the complete image is satirical) and release a high-quality dataset YesBut… See the full description on the dataset page: https://huggingface.co/datasets/bansalaman18/yesbut.

2024-10-02T13:42:35.000Z

66fe2d4de48de0216c6141a8

lmms-lab/llava-critic-113k

lmms-lab

{"license": "apache-2.0", "dataset_info": [{"config_name": "pairwise", "features": [{"name": "id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2013631739.368, "num_examples": 40154}], "download_size": 3092943481, "dataset_size": 2013631739.368}, {"config_name": "pointwise", "features": [{"name": "id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2877769500.932, "num_examples": 72782}], "download_size": 2847456218, "dataset_size": 2877769500.932}], "configs": [{"config_name": "pairwise", "data_files": [{"split": "train", "path": "pairwise/train-*"}]}, {"config_name": "pointwise", "data_files": [{"split": "train", "path": "pointwise/train-*"}]}], "tags": ["multimodal"], "pretty_name": "LLaVA-Critic-113k", "size_categories": ["100K<n<1M"]}

False

2024-10-05T16:08:22.000Z

7aa893ac47197c8300d7399d921836e12ceb5499

Dataset Card for LLaVA-Critic-113k 🪐 Project Page: https://llava-vl.github.io/blog/2024-10-03-llava-critic/ 📰 Paper: https://arxiv.org/abs/2410.02712 🤗 Huggingface Collection: https://huggingface.co/collections/lmms-lab/llava-critic-66fe3ef8c6e586d8435b4af8 👋 Point of Contact: Tianyi Xiong Dataset Summary LLaVA-Critic-113k is a high quality critic instruction-following dataset tailored to follow instructions in complex evaluation setting, providing both… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/llava-critic-113k.

2024-10-03T05:36:13.000Z

66ff9766e70a593f92b28368

MMIE/MMIE

MMIE

{"license": "mit", "task_categories": ["question-answering", "visual-question-answering", "multiple-choice"], "language": ["en"], "size_categories": ["10K<n<100K"]}

auto

2024-10-15T01:59:30.000Z

d9f0566b2f8b7aa08aa4794894eb597474ba8ef8

MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models [📖 Project] [📄 Paper] [💻 Code] [📝 Dataset] [🤖 Evaluation Model] [🏆 Leaderboard] [🌟 Overview] [🔧 Dataset Details] [🚩 Citation] 🌟 Overview We present MMIE, a Massive Multimodal Interleaved understanding Evaluation benchmark, designed for Large Vision-Language Models (LVLMs). MMIE offers a robust framework for evaluating the interleaved comprehension and… See the full description on the dataset page: https://huggingface.co/datasets/MMIE/MMIE.

2024-10-04T07:21:10.000Z

670dc275108e588447fea628

neulab/MultiUI

neulab

{"license": "odc-by", "task_categories": ["question-answering", "visual-question-answering"], "language": ["en"], "tags": ["image", "web"], "size_categories": ["1M<n<10M"]}

auto

2024-10-18T02:13:16.000Z

a9f644ac663b21f148fe5df87a94ac99f308839e

MulitUI Dataset for the paper: Harnessing Webpage Uis For Text Rich Visual Understanding 🌐 Homepage | 🐍 GitHub | 📖 arXiv Introduction We introduce MultiUI, a dataset containing 7.3 million samples from 1 million websites, covering diverse multi- modal tasks and UI layouts. Models trained on MultiUI not only excel in web UI tasks—achieving up to a 48% improvement on VisualWebBench and a 19.1% boost in action accuracy on a web agent dataset… See the full description on the dataset page: https://huggingface.co/datasets/neulab/MultiUI.

2024-10-15T01:16:37.000Z

621ffdd236468d709f181ecb

microsoft/ms_marco

microsoft

{"language": ["en"], "paperswithcode_id": "ms-marco", "pretty_name": "Microsoft Machine Reading Comprehension Dataset", "dataset_info": [{"config_name": "v1.1", "features": [{"name": "answers", "sequence": "string"}, {"name": "passages", "sequence": [{"name": "is_selected", "dtype": "int32"}, {"name": "passage_text", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "query", "dtype": "string"}, {"name": "query_id", "dtype": "int32"}, {"name": "query_type", "dtype": "string"}, {"name": "wellFormedAnswers", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 42665198, "num_examples": 10047}, {"name": "train", "num_bytes": 350516260, "num_examples": 82326}, {"name": "test", "num_bytes": 40977580, "num_examples": 9650}], "download_size": 217328153, "dataset_size": 434159038}, {"config_name": "v2.1", "features": [{"name": "answers", "sequence": "string"}, {"name": "passages", "sequence": [{"name": "is_selected", "dtype": "int32"}, {"name": "passage_text", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "query", "dtype": "string"}, {"name": "query_id", "dtype": "int32"}, {"name": "query_type", "dtype": "string"}, {"name": "wellFormedAnswers", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 413765365, "num_examples": 101093}, {"name": "train", "num_bytes": 3462807709, "num_examples": 808731}, {"name": "test", "num_bytes": 405691932, "num_examples": 101092}], "download_size": 2105722550, "dataset_size": 4282265006}], "configs": [{"config_name": "v1.1", "data_files": [{"split": "validation", "path": "v1.1/validation-*"}, {"split": "train", "path": "v1.1/train-*"}, {"split": "test", "path": "v1.1/test-*"}]}, {"config_name": "v2.1", "data_files": [{"split": "validation", "path": "v2.1/validation-*"}, {"split": "train", "path": "v2.1/train-*"}, {"split": "test", "path": "v2.1/test-*"}]}]}

False

2024-01-04T16:01:29.000Z

a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a

Dataset Card for "ms_marco" Dataset Summary Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search. The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, keyphrase extraction dataset, crawling dataset, and a conversational… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/ms_marco.

2022-03-02T23:29:22.000Z

ms-marco

647d8a9532c471a7fa7e122b

kaist-ai/CoT-Collection

kaist-ai

{"license": "cc-by-4.0", "task_categories": ["text-generation", "text-classification"], "language": ["en"], "size_categories": ["1M<n<10M"]}

False

2023-10-14T12:10:16.000Z

c9d352cdc119df4a4f7526d100e4acb4a72a7a5c

""" _LICENSE = "CC BY 4.0" _HOMEPAGE = "https://github.com/kaistAI/CoT-Collection" _LANGUAGES = { "en": "English", } # _ALL_LANGUAGES = "all_languages" class CoTCollectionMultiConfig(datasets.BuilderConfig):

2023-06-05T07:11:17.000Z

@article{kim2023cot, title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning}, author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon}, journal={arXiv preprint arXiv:2305.14045}, year={2023} }

65a6ab59cc67787a8dd069c6

FreedomIntelligence/ALLaVA-4V

FreedomIntelligence

{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["GPT-4V", "LVLM", "Vision", "Language"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "allava_laion", "data_files": [{"split": "caption", "path": "allava_laion/ALLaVA-Caption-LAION-4V.json"}, {"split": "instruct", "path": "allava_laion/ALLaVA-Instruct-LAION-4V.json"}]}, {"config_name": "allava_vflan", "data_files": [{"split": "caption", "path": "allava_vflan/ALLaVA-Caption-VFLAN-4V.json"}, {"split": "instruct", "path": "allava_vflan/ALLaVA-Instruct-VFLAN-4V.json"}]}, {"config_name": "allava_text", "data_files": [{"split": "train", "path": "allava_text/Evol-Instruct-GPT4-Turbo-143K.json"}]}]}

False

2024-09-02T14:15:08.000Z

2087ab8efe1a76b11259bf14f257b50d9fbb9e47

📚 ALLaVA-4V Data Generation Pipeline LAION We leverage the superb GPT-4V to generate captions and complex reasoning QA pairs. Prompt is here. Vison-FLAN We leverage the superb GPT-4V to generate captions and detailed answer for the original instructions. Prompt is here. Wizard We regenerate the answer of Wizard_evol_instruct with GPT-4-Turbo. Dataset Cards All datasets can be found here. The structure of naming is shown below: ALLaVA-4V… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V.

2024-01-16T16:14:17.000Z

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

65fc5a783bc54054aa2e6e62

gretelai/synthetic_text_to_sql

gretelai

{"license": "apache-2.0", "task_categories": ["question-answering", "table-question-answering", "text-generation"], "language": ["en"], "tags": ["synthetic", "SQL", "text-to-SQL", "code"], "size_categories": ["100K<n<1M"]}

False

2024-05-10T22:30:56.000Z

273a86f5f290e8d61b6767a9ff690c82bc990dc4

Image generated by DALL-E. See prompt for more details synthetic_text_to_sql gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples, designed and generated using Gretel Navigator, and released under Apache 2.0. Please see our release blogpost for more details. The dataset includes: 105,851 records partitioned into 100,000 train and 5,851 test records ~23M total tokens, including ~12M SQL tokens Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql.

2024-03-21T16:04:08.000Z

660fa4fe1daa574c6dff4d5c

amaye15/NSFW

amaye15

{"dataset_info": {"features": [{"name": "pixel_values", "dtype": {"image": {"mode": "RGB"}}}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Barcode", "1": "Invoice", "2": "Object", "3": "Receipt", "4": "Non-Object"}}}}], "splits": [{"name": "train", "num_bytes": 112698691421.6, "num_examples": 54016}, {"name": "test", "num_bytes": 28174672855.4, "num_examples": 13504}], "download_size": 140879230907, "dataset_size": 140873364277}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-08-04T10:44:20.000Z

69c338e5ed5615e6cc04c213856e66192c84922d

2024-04-05T07:15:10.000Z

6697abec43d9faa413ca745c

HuggingFaceM4/Docmatix

HuggingFaceM4

{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["visual-question-answering"], "pretty_name": "Docmatix", "tags": ["docvqa"], "configs": [{"config_name": "images", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "pdf", "data_files": [{"split": "train", "path": "pdf/train-*"}]}, {"config_name": "zero-shot-exp", "data_files": [{"split": "train", "path": "zero-shot-exp/train-*"}, {"split": "test", "path": "zero-shot-exp/test-*"}]}], "dataset_info": [{"config_name": "images", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 552957537722.77, "num_examples": 1273215}], "download_size": 159404414330, "dataset_size": 552957537722.77}, {"config_name": "pdf", "features": [{"name": "pdf", "dtype": "binary"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 458612867150, "num_examples": 1273245}], "download_size": 431829972210, "dataset_size": 458612867150}, {"config_name": "zero-shot-exp", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 68900253, "num_examples": 200}, {"name": "train", "num_bytes": 578335690.5, "num_examples": 1700}], "download_size": 642963847, "dataset_size": 647235943.5}]}

False

2024-08-26T08:15:21.000Z

0725b65616e0e5f6024be10e38ddf8d8c48664fd

Dataset Card for Docmatix Dataset description Docmatix is part of the Idefics3 release (stay tuned). It is a massive dataset for Document Visual Question Answering that was used for the fine-tuning of the vision-language model Idefics3. Load the dataset To load the dataset, install the library datasets with pip install datasets. Then, from datasets import load_dataset ds = load_dataset("HuggingFaceM4/Docmatix") If you want the dataset to link to… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/Docmatix.

2024-07-17T11:33:00.000Z

66ec0c7c1cd3794eb4204469

YaoMarkMu/robotwin_dataset

YaoMarkMu

{"license": "mit"}

False

2024-09-27T02:49:16.000Z

347e296344796404093920f27081f8ea9c41b4e1

RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins Yao Mu* †, Tianxing Chen* , Zanxin Chen* , Shijia Peng*,Zeyu Gao, Zhiqian Lan, Yude Zou, Lunkai Lin, Zhiqiang Xie, Ping Luo†. RoboTwin (early version), accepted to ECCV Workshop 2024 (Oral): Webpage | PDF | arXiv 🛠️ Installation See INSTALLATION.md for installation instructions. It takes about 20 minutes for installation. ℹ️ Task Informaction Coming Soon 🧑🏻‍💻 Usage… See the full description on the dataset page: https://huggingface.co/datasets/YaoMarkMu/robotwin_dataset.

2024-09-19T11:35:24.000Z

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

66fed6e29b33512a612d59ef

WorldMedQA/V

WorldMedQA

{"task_categories": ["question-answering"], "language": ["en", "he", "ja", "es", "pt"], "tags": ["medical"], "size_categories": ["n<1K"]}

False

2024-10-17T09:24:52.000Z

7d4b008bdba5c961a249cf44c9a552675d351ca5

WorldMedQA-V: A Multilingual, Multimodal Medical Examination Dataset Overview WorldMedQA-V is a multilingual and multimodal benchmarking dataset designed to evaluate vision-language models (VLMs) in healthcare contexts. The dataset includes medical examination questions from four countries—Brazil, Israel, Japan, and Spain—in both their original languages and English translations. Each multiple-choice question is paired with a corresponding medical image… See the full description on the dataset page: https://huggingface.co/datasets/WorldMedQA/V.

2024-10-03T17:39:46.000Z

6705a06eddc1f8123862bd6f

rombodawg/Everything_Instruct_Multilingual

rombodawg

{"license": "apache-2.0", "language": ["en", "ru", "zh", "ko", "ur", "la", "ar", "de", "es", "fr", "hi", "it", "ja", "nl", "pt"], "tags": ["Num_Rows = 7,799,967", "Max_length = 8180"]}

False

2024-10-08T22:35:10.000Z

cb2f376b958efca637e4fa655c963cefec53220b

Everything Instruct (Multilingual Edition) Everything you need... all in one place 💘 Everything instruct (Multilingual Edition) 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) Note2: This version of the dataset supports the following languages: English Russian… See the full description on the dataset page: https://huggingface.co/datasets/rombodawg/Everything_Instruct_Multilingual.

2024-10-08T21:13:18.000Z

6706955a5691455e0ab1b35c

FunAILab/TVBench

FunAILab

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False

2024-10-15T13:46:04.000Z

ecaa679bce8fb253643d5077f2d2fa5b7854146d

TVBench: Redesigning Video-Language Evaluation Daniel Cores*, Michael Dorkenwald*, Manuel Mucientes, Cees G. M. Snoek, Yuki M. Asano *Equal contribution. TVBench TVBench is a new benchmark specifically created to evaluate temporal understanding in video QA. We identified three main issues in existing datasets: (i) static information from single frames is often sufficient to solve the tasks (ii) the text of the questions and candidate answers is overly informative… See the full description on the dataset page: https://huggingface.co/datasets/FunAILab/TVBench.

2024-10-09T14:38:18.000Z

6708f04ef8a1d7b2673dbc8b

dmis-lab/ChroKnowBench

dmis-lab

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False

2024-10-15T09:17:30.000Z

3cd6f7d2a310662c27ac0fc9a44c5a9f68d8e14e

ChroKnowBench ChroKnowBench is a benchmark dataset designed to evaluate the performance of language models on temporal knowledge across multiple domains. The dataset consists of both time-variant and time-invariant knowledge, providing a comprehensive assessment for understanding knowledge evolution and constancy over time. Dataset is introduced by Park et al. in ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains Dataset… See the full description on the dataset page: https://huggingface.co/datasets/dmis-lab/ChroKnowBench.

2024-10-11T09:30:54.000Z

670dd548aa00e89d82f818ba

NinaKarine/t2i-compbench

NinaKarine

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{"config_name": "texture_val_unseen", "data_files": [{"split": "val_unseen", "path": "texture_val_unseen/val_unseen-*"}]}], "license": "mit", "task_categories": ["text-to-image"], "language": ["en"], "tags": ["image"]}

False

2024-10-15T02:50:52.000Z

18f3f268acaf39c89f8a3afcc2b471243042c927

Hub version of the T2I-CompBench dataset. All credits and licensing belong to the creators of the dataset. This version was obtained as described below. First, the ".txt" files were obtained from this directory. Code import requests import os # Set the necessary parameters owner = "Karine-Huang" repo = "T2I-CompBench" branch = "main" directory = "examples/dataset" local_directory = "." # GitHub API URL to get contents of the directory url =… See the full description on the dataset page: https://huggingface.co/datasets/NinaKarine/t2i-compbench.

2024-10-15T02:36:56.000Z

67118b6445f13cb79173176c

fairchem/OMAT24

fairchem

{"license": "cc-by-4.0", "tags": ["chemistry", "materials"]}

False

2024-10-19T19:03:16.000Z

f7a183f7f24221acf316d65998d819193c3c6e68

Meta Open Materials 2024 (OMat24) Dataset Overview Several datasets were utilized in this work. We provide open access to all datasets used to help accelerate research in the community. This includes the OMat24 dataset as well as our modified sAlex dataset. Details on the different datasets are provided below. Datasets OMat24 Dataset The OMat24 dataset contains a mix of single point calculations of non-equilibrium structures and structural… See the full description on the dataset page: https://huggingface.co/datasets/fairchem/OMAT24.

2024-10-17T22:10:44.000Z

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

621ffdd236468d709f181f95

rajpurkar/squad

rajpurkar

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False

2024-03-04T13:54:37.000Z

7b6d24c440a36b6815f21b70d25016731768db1f

Dataset Card for SQuAD Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles. Supported Tasks and Leaderboards Question… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad.

2022-03-02T23:29:22.000Z

squad

621ffdd236468d709f184284

wikimedia/wikipedia

wikimedia

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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

630893c1f48eff2e8eb7600d

ShapeNet/ShapeNetCore

ShapeNet

{"language": ["en"], "pretty_name": "ShapeNetCore", "tags": ["3D shapes"], "license": "other", "extra_gated_heading": "Acknowledge license to accept the repository", "extra_gated_prompt": "To request access to this ShapeNet repo, you will need to provide your **full name** (please provide both your first and last name), the name of your **advisor or the principal investigator (PI)** of your lab (in the PI/Advisor) fields, and the **school or company** that you are affiliated with (the **Affiliation** field). After requesting access to this ShapeNet repo, you will be considered for access approval. \n\nAfter access approval, you (the \"Researcher\") receive permission to use the ShapeNet database (the \"Database\") at Princeton University and Stanford University. In exchange for being able to join the ShapeNet community and receive such permission, Researcher hereby agrees to the following terms and conditions: Researcher shall use the Database only for non-commercial research and educational purposes. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, 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 3D models that he or she may create from the Database. 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. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 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. The law of the State of New Jersey shall apply to all disputes under this agreement.\n\nFor access to the data, please fill in your **full name** (both first and last name), the name of your **advisor or principal investigator (PI)**, and the name of the **school or company** you are affliated with. Please actually fill out the fields (DO NOT put the word \"Advisor\" for PI/Advisor and the word \"School\" for \"Affiliation\", please specify the name of your advisor and the name of your school).", "extra_gated_fields": {"Name": "text", "PI/Advisor": "text", "Affiliation": "text", "Purpose": "text", "Country": "text", "I agree to use this dataset for non-commercial use ONLY": "checkbox"}}

manual

2023-09-20T15:05:48.000Z

0efb24cbe6828a85771a28335c5f7b5626514d9b

This repository contains ShapeNetCore (v2), a subset of ShapeNet.ShapeNetCore is a densely annotated subset of ShapeNet covering 55 common object categories with ~51,300 unique 3D models. Each model in ShapeNetCore are linked to an appropriate synset in WordNet 3.0. Please see DATA.md for details about the data. If you use ShapeNet data, you agree to abide by the ShapeNet terms of use. You are only allowed to redistribute the data to your research associates and colleagues provided that… See the full description on the dataset page: https://huggingface.co/datasets/ShapeNet/ShapeNetCore.

2022-08-26T09:34:57.000Z

641f0c169c1013836ce2e5eb

QingyiSi/Alpaca-CoT

QingyiSi

{"language": ["en", "zh", "ml"], "tags": ["Instruction", "Cot"], "license": "apache-2.0", "datasets": ["dataset1", "dataset2"]}

False

2023-09-14T08:52:10.000Z

18add89e3b884703ec869a5c6e2bcf1412ee7edc

Instruction-Finetuning Dataset Collection (Alpaca-CoT) This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the code of Alpaca model. We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in https://github.com/PhoebusSi/alpaca-CoT. If you think this dataset collection is helpful to you, please like… See the full description on the dataset page: https://huggingface.co/datasets/QingyiSi/Alpaca-CoT.

2023-03-25T14:58:30.000Z

64358e2179c45fcf1ada09f4

databricks/databricks-dolly-15k

databricks

{"license": "cc-by-sa-3.0", "task_categories": ["question-answering", "summarization"], "language": ["en"], "size_categories": ["10K<n<100K"]}

False

2023-06-30T18:34:13.000Z

bdd27f4d94b9c1f951818a7da7fd7aeea5dbff1a

Summary databricks-dolly-15k is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported… See the full description on the dataset page: https://huggingface.co/datasets/databricks/databricks-dolly-15k.

2023-04-11T16:43:13.000Z

644201ac55a16ae60fa855ad

b-mc2/sql-create-context

b-mc2

{"license": "cc-by-4.0", "task_categories": ["text-generation", "question-answering", "table-question-answering"], "language": ["en"], "tags": ["SQL", "code", "NLP", "text-to-sql", "context-sql", "spider", "wikisql", "sqlglot"], "pretty_name": "sql-create-context", "size_categories": ["10K<n<100K"]}

False

2024-01-25T22:01:25.000Z

9d80a6a118b838d9defc3798d659a54a2ac2ff37

Overview This dataset builds from WikiSQL and Spider. There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names… See the full description on the dataset page: https://huggingface.co/datasets/b-mc2/sql-create-context.

2023-04-21T03:23:24.000Z

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}, }

64e77c453e1237c874d34ffe

bitext/Bitext-customer-support-llm-chatbot-training-dataset

bitext

{"license": "cdla-sharing-1.0", "task_categories": ["question-answering", "table-question-answering"], "language": ["en"], "tags": ["question-answering", "llm", "chatbot", "customer-support", "conversional-ai", "generative-ai", "natural-language-understanding", "fine-tuning", "Retail"], "pretty_name": "Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants", "size_categories": ["10K<n<100K"]}

False

2024-07-18T18:19:33.000Z

430d1a89bd93bd1fa23c16f29dd53e73f0087443

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants Overview This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.

2023-08-24T15:50:29.000Z

650c7be62a602ba34904a362

meta-math/MetaMathQA

meta-math

{"tags": ["math", "math-qa"], "license": "mit"}

False

2023-12-21T01:35:53.000Z

aa4f34d3d2d3231299b5b03d9b3e5a20da45aa18

View the project page: https://meta-math.github.io/ see our paper at https://arxiv.org/abs/2309.12284 Note All MetaMathQA data are augmented from the training sets of GSM8K and MATH. None of the augmented data is from the testing set. You can check the original_question in meta-math/MetaMathQA, each item is from the GSM8K or MATH train set. Model Details MetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-7B model.… See the full description on the dataset page: https://huggingface.co/datasets/meta-math/MetaMathQA.

2023-09-21T17:22:46.000Z

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

6582baf221aa786b76be285e

google/Synthetic-Persona-Chat

google

{"license": "cc-by-4.0", "task_categories": ["text2text-generation"], "language": ["en"], "size_categories": ["10K<n<100K"]}

False

2024-03-01T01:01:01.000Z

a520ad7f999ca7e6dfdc25fed9f5070bf6f87b42

Dataset Card for SPC: Synthetic-Persona-Chat Dataset Abstract from the paper introducing this dataset: High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and… See the full description on the dataset page: https://huggingface.co/datasets/google/Synthetic-Persona-Chat.

2023-12-20T09:59:14.000Z

65bbbc6e8e75e88ea0aa4103

textdetox/multilingual_toxicity_dataset

textdetox

{"language": ["en", "ru", "uk", "de", "es", "am", "zh", "ar", "hi"], "license": "openrail++", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "toxic", "dtype": "int64"}], "splits": [{"name": "en", "num_bytes": 411178, "num_examples": 5000}, {"name": "ru", "num_bytes": 710001, "num_examples": 5000}, {"name": "uk", "num_bytes": 630595, "num_examples": 5000}, {"name": "de", "num_bytes": 941017, "num_examples": 5000}, {"name": "es", "num_bytes": 978750, "num_examples": 5000}, {"name": "am", "num_bytes": 1102628, "num_examples": 5000}, {"name": "zh", "num_bytes": 359235, "num_examples": 5000}, {"name": "ar", "num_bytes": 889661, "num_examples": 5000}, {"name": "hi", "num_bytes": 1842662, "num_examples": 5000}], "download_size": 4470012, "dataset_size": 7865727}, "configs": [{"config_name": "default", "data_files": [{"split": "en", "path": "data/en-*"}, {"split": "ru", "path": "data/ru-*"}, {"split": "uk", "path": "data/uk-*"}, {"split": "de", "path": "data/de-*"}, {"split": "es", "path": "data/es-*"}, {"split": "am", "path": "data/am-*"}, {"split": "zh", "path": "data/zh-*"}, {"split": "ar", "path": "data/ar-*"}, {"split": "hi", "path": "data/hi-*"}]}]}

False

2024-06-10T09:43:48.000Z

684bfd68651993aafc5ca653c35747e43f5b5c43

For the shared task CLEF TextDetox 2024, we provide a compilation of binary toxicity classification datasets for each language. Namely, for each language, we provide 5k subparts of the datasets -- 2.5k toxic and 2.5k non-toxic samples. The list of original sources: English: Jigsaw, Unitary AI Toxicity Dataset Russian: Russian Language Toxic Comments, Toxic Russian Comments Ukrainian: our labeling based on Ukrainian Twitter texts Spanish: CLANDESTINO, the Spanish toxic language dataset German:… See the full description on the dataset page: https://huggingface.co/datasets/textdetox/multilingual_toxicity_dataset.

2024-02-01T15:44:46.000Z

65d2675495e8d86e2fe4124d

HuggingFaceTB/cosmopedia

HuggingFaceTB

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False

2024-08-12T22:05:49.000Z

0ae6ec63f91742bd2d1eaef4f02232c55d719385

Cosmopedia v0.1 Image generated by DALL-E, the prompt was generated by Mixtral-8x7B-Instruct-v0.1 Note: Cosmopedia v0.2 is available at smollm-corpus User: What do you think "Cosmopedia" could mean? Hint: in our case it's not related to cosmology. Mixtral-8x7B-Instruct-v0.1: A possible meaning for "Cosmopedia" could be an encyclopedia or collection of information about different cultures, societies, and topics from around the world, emphasizing diversity and global… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/cosmopedia.

2024-02-18T20:23:48.000Z

661f3cac5f47948bfd541f77

nvidia/Aegis-AI-Content-Safety-Dataset-1.0

nvidia

{"license": "cc-by-4.0", "language": ["en"], "pretty_name": "Aegis AI Content Safety Dataset 1.0", "task_categories": ["text-classification"], "tags": ["safety", "content moderation", "LLM safety", "toxicity detection"], "size_categories": ["10K<n<100K"]}

False

2024-06-28T18:59:50.000Z

803da1d97cc57d79f18ade2ec406040011139a62

🛡️ Aegis AI Content Safety Dataset 1.0 Aegis AI Content Safety Dataset is an open-source content safety dataset (CC-BY-4.0), which adheres to Nvidia's content safety taxonomy, covering 13 critical risk categories (see Dataset Description). Dataset Details Dataset Description The Aegis AI Content Safety Dataset is comprised of approximately 11,000 manually annotated interactions between humans and LLMs, split into 10,798 training samples and 1,199… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-1.0.

2024-04-17T03:06:20.000Z

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

6669881c7df8f73f1a8c9ae3

GTSinger/GTSinger

GTSinger

False

2024-10-13T13:49:46.000Z

dfadc1cb9cf8fe47efccfdfa00be0d043a940fb1

GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks Yu Zhang*, Changhao Pan*, Wenxiang Guo*, Ruiqi Li, Zhiyuan Zhu, Jialei Wang, Wenhao Xu, Jingyu Lu, Zhiqing Hong, Chuxin Wang, LiChao Zhang, Jinzheng He, Ziyue Jiang, Yuxin Chen, Chen Yang, Jiecheng Zhou, Xinyu Cheng, Zhou Zhao | Zhejiang University Dataset of GTSinger (NeurIPS 2024 Spotlight): A Global Multi-Technique Singing Corpus with Realistic Music Scores for… See the full description on the dataset page: https://huggingface.co/datasets/GTSinger/GTSinger.

2024-06-12T11:35:56.000Z

6669ed3bcf27eed0f2ecc297

namkoong-lab/PersonalLLM

namkoong-lab

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "subset", "dtype": "string"}, {"name": "prompt_id", "dtype": "int64"}, {"name": "response_1", "dtype": "string"}, {"name": "response_1_model", "dtype": "string"}, {"name": "response_2", "dtype": "string"}, {"name": "response_2_model", "dtype": "string"}, {"name": "response_3", "dtype": "string"}, {"name": "response_3_model", "dtype": "string"}, {"name": "response_4", "dtype": "string"}, {"name": "response_4_model", "dtype": "string"}, {"name": "response_5", "dtype": "string"}, {"name": "response_5_model", "dtype": "string"}, {"name": "response_6", "dtype": "string"}, {"name": "response_6_model", "dtype": "string"}, {"name": "response_7", "dtype": "string"}, {"name": "response_7_model", "dtype": "string"}, {"name": "response_8", "dtype": "string"}, {"name": "response_8_model", "dtype": "string"}, {"name": "response_1_gemma_2b", "dtype": "float64"}, {"name": "response_2_gemma_2b", "dtype": "float64"}, {"name": 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"dtype": "float64"}, {"name": "response_6_mistral_raft", "dtype": "float64"}, {"name": "response_7_mistral_raft", "dtype": "float64"}, {"name": "response_8_mistral_raft", "dtype": "float64"}, {"name": "response_1_mistral_ray", "dtype": "float64"}, {"name": "response_2_mistral_ray", "dtype": "float64"}, {"name": "response_3_mistral_ray", "dtype": "float64"}, {"name": "response_4_mistral_ray", "dtype": "float64"}, {"name": "response_5_mistral_ray", "dtype": "float64"}, {"name": "response_6_mistral_ray", "dtype": "float64"}, {"name": "response_7_mistral_ray", "dtype": "float64"}, {"name": "response_8_mistral_ray", "dtype": "float64"}, {"name": "response_1_mistral_weqweasdas", "dtype": "float64"}, {"name": "response_2_mistral_weqweasdas", "dtype": "float64"}, {"name": "response_3_mistral_weqweasdas", "dtype": "float64"}, {"name": "response_4_mistral_weqweasdas", "dtype": "float64"}, {"name": "response_5_mistral_weqweasdas", "dtype": "float64"}, {"name": "response_6_mistral_weqweasdas", "dtype": "float64"}, {"name": "response_7_mistral_weqweasdas", "dtype": "float64"}, {"name": "response_8_mistral_weqweasdas", "dtype": "float64"}, {"name": "response_1_llama3_sfairx", "dtype": "float64"}, {"name": "response_2_llama3_sfairx", "dtype": "float64"}, {"name": "response_3_llama3_sfairx", "dtype": "float64"}, {"name": "response_4_llama3_sfairx", "dtype": "float64"}, {"name": "response_5_llama3_sfairx", "dtype": "float64"}, {"name": "response_6_llama3_sfairx", "dtype": "float64"}, {"name": "response_7_llama3_sfairx", "dtype": "float64"}, {"name": "response_8_llama3_sfairx", "dtype": "float64"}, {"name": "response_1_oasst_deberta_v3", "dtype": "float64"}, {"name": "response_2_oasst_deberta_v3", "dtype": "float64"}, {"name": "response_3_oasst_deberta_v3", "dtype": "float64"}, {"name": "response_4_oasst_deberta_v3", "dtype": "float64"}, {"name": "response_5_oasst_deberta_v3", "dtype": "float64"}, {"name": "response_6_oasst_deberta_v3", "dtype": "float64"}, {"name": "response_7_oasst_deberta_v3", "dtype": "float64"}, {"name": "response_8_oasst_deberta_v3", "dtype": "float64"}, {"name": "response_1_beaver_7b", "dtype": "float64"}, {"name": "response_2_beaver_7b", "dtype": "float64"}, {"name": "response_3_beaver_7b", "dtype": "float64"}, {"name": "response_4_beaver_7b", "dtype": "float64"}, {"name": "response_5_beaver_7b", "dtype": "float64"}, {"name": "response_6_beaver_7b", "dtype": "float64"}, {"name": "response_7_beaver_7b", "dtype": "float64"}, {"name": "response_8_beaver_7b", "dtype": "float64"}, {"name": "response_1_oasst_pythia_7b", "dtype": "float64"}, {"name": "response_2_oasst_pythia_7b", "dtype": "float64"}, {"name": "response_3_oasst_pythia_7b", "dtype": "float64"}, {"name": "response_4_oasst_pythia_7b", "dtype": "float64"}, {"name": "response_5_oasst_pythia_7b", "dtype": "float64"}, {"name": "response_6_oasst_pythia_7b", "dtype": "float64"}, {"name": "response_7_oasst_pythia_7b", "dtype": "float64"}, {"name": "response_8_oasst_pythia_7b", "dtype": "float64"}, {"name": "response_1_oasst_pythia_1b", "dtype": "float64"}, {"name": "response_2_oasst_pythia_1b", "dtype": "float64"}, {"name": "response_3_oasst_pythia_1b", "dtype": "float64"}, {"name": "response_4_oasst_pythia_1b", "dtype": "float64"}, {"name": "response_5_oasst_pythia_1b", "dtype": "float64"}, {"name": "response_6_oasst_pythia_1b", "dtype": "float64"}, {"name": "response_7_oasst_pythia_1b", "dtype": "float64"}, {"name": "response_8_oasst_pythia_1b", "dtype": "float64"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 141372032, "num_examples": 9402}, {"name": "test", "num_bytes": 15120618, "num_examples": 1000}], "download_size": 92172816, "dataset_size": 156492650}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "cc-by-4.0", "language": ["en"], "size_categories": ["10K<n<100K"]}

False

2024-08-19T14:35:13.000Z

0382e87d51cd919e8c5e940ef0fe6e50f7c9f75f

Dataset Card for Dataset Name The PersonalLLM dataset is a collection of prompts, responses, and rewards designed for personalized language model methodology development and evaluation. Dataset Details Dataset Description Curated by: Andrew Siah*, Tom Zollo*, Naimeng Ye, Ang Li, Namkoong Hongseok Funded by: Digital Future Initiative at Columbia Business School Language(s) (NLP): English License: CC BY 4.0 License Dataset Sources… See the full description on the dataset page: https://huggingface.co/datasets/namkoong-lab/PersonalLLM.

2024-06-12T18:47:23.000Z

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

6696b66e57a5a55a1f1638dd

princeton-nlp/gemma2-ultrafeedback-armorm

princeton-nlp

{"dataset_info": {"features": [{"name": "prompt_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "all_generated_responses", "sequence": "string"}, {"name": "all_rm_scores", "sequence": "float64"}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 790191703, "num_examples": 59569}, {"name": "test", "num_bytes": 26074764, "num_examples": 1941}], "download_size": 412580611, "dataset_size": 816266467}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "mit"}

False

2024-07-18T19:37:24.000Z

59b3436bf2760c735828a0fb8cf10c5e836e6322

Dataset Card for gemma2-ultrafeedback-armorm This dataset was used to train princeton-nlp/gemma-2-9b-it-SimPO. If you are interested in training other model types (e.g., Mistral, Llama-3), please refer to their corresponding datasets: princeton-nlp/mistral-instruct-ultrafeedback, princeton-nlp/llama3-ultrafeedback, and princeton-nlp/llama3-ultrafeedback-armorm. Dataset Structure This dataset contains around 60k training samples and 2k testing samples, following… See the full description on the dataset page: https://huggingface.co/datasets/princeton-nlp/gemma2-ultrafeedback-armorm.

2024-07-16T18:05:34.000Z

66d55e3455fa2d5aa561ba19

THUDM/LongCite-45k

THUDM

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "long.jsonl"}]}], "license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en", "zh"], "tags": ["long context", "citation generation", "SFT"], "size_categories": ["10K<n<100K"]}

False

2024-10-18T09:59:40.000Z

b776b01faf6e132a3d9cc373de1b46cd6630d772

LongCite-45k 🤗 [LongCite Dataset] • 💻 [Github Repo] • 📃 [LongCite Paper] LongCite-45k dataset contains 44,600 long-context QA instances paired with sentence-level citations (both English and Chinese, up to 128,000 words). The data can support training long-context LLMs to generate response and fine-grained citations within a single output. Data Example Each instance in LongCite-45k consists of an instruction, a long context (divided into sentences), a… See the full description on the dataset page: https://huggingface.co/datasets/THUDM/LongCite-45k.

2024-09-02T06:41:56.000Z

66d95bfaad2d8eb9febf4a19

nlpai-lab/ko-triplet-v1.0

nlpai-lab

{"language": ["ko"], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "document", "dtype": "string"}, {"name": "hard_negative", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 628315763, "num_examples": 744862}], "download_size": 270060556, "dataset_size": 628315763}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-09-07T10:56:13.000Z

9cc1d6aeecd44fef05cb5955d9290f6213feb863

2024-09-05T07:21:30.000Z

66dab5c4204cd0a4f82c05be

TommyChien/UltraDomain

TommyChien

{"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"]}

False

2024-09-09T02:48:23.000Z

aa8a51d523f8fc3c5a0ab90dd16b7f6b9dbb5d0d

For the usage of this benchmark dataset, please refer to this repo.

2024-09-06T07:56:52.000Z

66e0648d5dbcd069c2fd466d

arcee-ai/EvolKit-20k

arcee-ai

{"license": "mit", "tags": ["synthetic"]}

False

2024-09-10T22:00:56.000Z

aaaf9475dc8f12e5c83a35df142891ddd6fd8801

EvolKit-20k This is a 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.

2024-09-10T15:23:57.000Z

66e2b0374552c7fe034da12e

gpt-omni/VoiceAssistant-400K

gpt-omni

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "split_name", "dtype": "string"}, {"name": "index", "dtype": "string"}, {"name": "round", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_audio", "dtype": "audio"}, {"name": "answer", "dtype": "string"}, {"name": "answer_snac", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 162359768796.944, "num_examples": 470054}], "download_size": 219464903276, "dataset_size": 162359768796.944}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-09-13T12:01:42.000Z

65dab707fcdd3d43dcfb834398aa9fed4116be3a

2024-09-12T09:11:19.000Z

66f0fc29bc14af0cbb4d4e77

recursal/SuperWikiImage-7M

recursal

{"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "pretty_name": "SuperWikiImages-7M", "task_categories": ["image-classification", "image-to-text", "text-to-image", "image-to-image"], "task_ids": ["language-modeling", "masked-language-modeling"], "source_datasets": ["original"], "multilinguality": ["multilingual"], "language": ["af", "ar", "ast", "az", "be", "bg", "bn", "ca", "ce", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "gl", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "kk", "ko", "la", "lt", "lv", "mk", "ms", "my", "nl", "nn", "no", "pl", "pt", "ro", "ru", "sh", "sk", "sl", "sr", "sv", "ta", "tg", "th", "tr", "uk", "ur", "uz", "vi", "zh"], "size_categories": ["10B<n<100B"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": ["chunk_00/*.tar", "chunk_01/*.tar", "chunk_02/*.tar", "chunk_03/*.tar"]}]}]}

False

2024-10-07T06:49:22.000Z

e2d8a5d8dd029b4b676039cf88cecf47ac4cb888

Dataset Card for SuperWikiImage (SWI) Waifu to catch your attention. Dataset Details Dataset Description Off from the presses of SuperWikipedia-NEXT comes SuperWikiImage: A ~15TiB (~7 Million) collection of images from wikipedia. Curated by: KaraKaraWitch Funded by: Recursal.ai Shared by: KaraKaraWitch Language(s) (NLP): Many. Refer to the data below for a list of languages. License: Mixed. Refer to lower section on licensing Dataset… See the full description on the dataset page: https://huggingface.co/datasets/recursal/SuperWikiImage-7M.

2024-09-23T05:27:05.000Z

66f3907ead897b1a4512d93b

mllmTeam/MobileViews

mllmTeam

{"language": ["en"], "license": "mit", "datasets": ["MobileViews"], "pretty_name": "MobileViews: A Large-Scale Mobile GUI Dataset", "tags": ["mobile-ui", "user-interfaces", "view-hierarchy", "android-apps", "screenshots"], "task_categories": ["question-answering", "image-to-text"], "task_ids": ["task-planning", "visual-question-answering"]}

False

2024-10-14T06:30:07.000Z

ba7f2f36d2d0e6778df286c42961fab10a0a0ac0

MobileViews: A Large-Scale Mobile GUI Dataset Read the paper MobileViews is a large-scale dataset designed to support research in mobile user interface (UI) analysis and mobile agents. Our first version — MobileViews-600K — contains over 600,000 mobile UI screenshot-view hierarchy (VH) pairs, collected from approximately 20,000 apps on the Google Play Store. Dataset Overview The dataset consists of multiple .tar.gz files containing screenshot-VH pairs and… See the full description on the dataset page: https://huggingface.co/datasets/mllmTeam/MobileViews.

2024-09-25T04:24:30.000Z

670760996d79c9796255a647

litagin/Galgame_Speech_ASR_16kHz

litagin

{"language": ["ja"], "license": "gpl-3.0", "license_link": "LICENSE.md", "multilinguality": ["monolingual"], "pretty_name": "Galgame_Speech_ASR_16kHz", "size_categories": ["1M<n<10M"], "task_categories": ["automatic-speech-recognition"], "tags": ["speech", "audio", "text", "japanese", "anime", "voice", "visual novel", "galgame"]}

False

2024-10-14T06:37:25.000Z

3fb86654222b3f0af0f7c332ae6a0ef9752a9451

Dataset Card for Galgame_Speech_ASR_16kHz The following rules (in the original repository) must be followed: 必须遵守GNU General Public License v3.0内的所有协议！附加：禁止商用，本数据集以及使用本数据集训练出来的任何模型都不得用于任何商业行为，如要用于商业用途，请找数据列表内的所有厂商授权（笑），因违反开源协议而出现的任何问题都与本人无关！ 训练出来的模型必须开源，是否在README内引用本数据集由训练者自主决定，不做强制要求。 English: You must comply with all the terms of the GNU General Public License v3.0!Additional note: Commercial use is prohibited. This dataset and any model trained using this dataset cannot be… See the full description on the dataset page: https://huggingface.co/datasets/litagin/Galgame_Speech_ASR_16kHz.

2024-10-10T05:05:29.000Z

670a2c60705db29c00ed4b6d

SafeMTData/SafeMTData

SafeMTData

{"license": "mit", "task_categories": ["text-generation", "question-answering"], "pretty_name": "SafeMTData", "size_categories": ["1K<n<10K"], "configs": [{"config_name": "SafeMTData_1K", "data_files": [{"split": "SafeMTData_1K", "path": "SafeMTData/SafeMTData_1K.json"}]}, {"config_name": "Attack_600", "data_files": [{"split": "Attack_600", "path": "SafeMTData/Attack_600.json"}]}]}

False

2024-10-15T03:29:47.000Z

1fcc09a3455974388785b8af56c7d5a32c4f32a8

💥Derail Yourself: Multi-turn LLM Jailbreak Attack through Self-discovered Clues 🌐 GitHub | 🛎 Paper If you like our project, please give us a star ⭐ on Hugging Face for the latest update. 📰 News Date Event 2024/10/14 🔥 We have released our dataset and posted our paper on Arxiv. 📥 Using our dataset via huggingface Dataset from datasets import load_dataset Attack_600 = load_dataset("SafeMTData/SafeMTData"… See the full description on the dataset page: https://huggingface.co/datasets/SafeMTData/SafeMTData.

2024-10-12T07:59:28.000Z

670cf5229152200f723ae743

Fsoft-AIC/CodeMMLU

Fsoft-AIC

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False

2024-10-15T06:20:51.000Z

4cbafc55c17df4783b069772a3e33c187d9e20ec

CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities 📌 CodeMMLU CodeMMLU is a comprehensive benchmark designed to evaluate the capabilities of large language models (LLMs) in coding and software knowledge. It builds upon the structure of multiple-choice question answering (MCQA) to cover a wide range of programming tasks and domains, including code generation, defect detection, software engineering principles, and much more.… See the full description on the dataset page: https://huggingface.co/datasets/Fsoft-AIC/CodeMMLU.

2024-10-14T10:40:34.000Z

670cf8d40b54b420bb668415

Pendrokar/open_tts_tracker

Pendrokar

{"configs": [{"config_name": "default", "data_files": "*.tsv", "sep": "\t"}], "size_categories": ["n<1K"]}

False

2024-10-19T13:48:28.000Z

4670823531f557719170525cb6f8ccf21b8d1535

above models sorted by amount of capabilities (lazy method - character count); #legend Cloned the GitHub repo for easier viewing and embedding the above table as once requested by @reach-vb: https://github.com/Vaibhavs10/open-tts-tracker/issues/30#issuecomment-1946367525 🗣️ Open TTS Tracker A one stop shop to track all open-access/ source TTS models as they come out. Feel free to make a PR for all those that aren't linked here. This is aimed as a resource to increase awareness… See the full description on the dataset page: https://huggingface.co/datasets/Pendrokar/open_tts_tracker.

2024-10-14T10:56:20.000Z

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

621ffdd236468d709f181e3f

nyu-mll/glue

nyu-mll

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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

621ffdd236468d709f181eb4

google-research-datasets/mbpp

google-research-datasets

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False

2024-01-04T14:26:37.000Z

4bb6404fdc6cacfda99d4ac4205087b89d32030c

Dataset Card for Mostly Basic Python Problems (mbpp) Dataset Summary The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. Released here as part… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/mbpp.

2022-03-02T23:29:22.000Z