66fec09298f30194f8b8ac36

LLM360/TxT360

LLM360

{"license": "odc-by"}

False

2024-10-15T15:35:56.000Z

82273e0bf5598f3178634478005a81c3bff8ce2e

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

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

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

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

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

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

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

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

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

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

6709054a623c91990fbef46d

NerdyRodent/NR-Flux-ComfyUI-Workflows

NerdyRodent

{"license": "mit"}

False

2024-10-12T19:05:59.000Z

d2f382ddd5b8a15adf925db1a51a407851c524fa

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

621ffdd236468d709f182a80

allenai/c4

allenai

{"pretty_name": "C4", "annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu"], "language_bcp47": ["bg-Latn", "el-Latn", "hi-Latn", "ja-Latn", "ru-Latn", "zh-Latn"], "license": ["odc-by"], "multilinguality": ["multilingual"], "size_categories": ["n<1K", "1K<n<10K", "10K<n<100K", "100K<n<1M", "1M<n<10M", "10M<n<100M", "100M<n<1B", "1B<n<10B"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "c4", "dataset_info": [{"config_name": "en", "features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 828589180707, "num_examples": 364868892}, {"name": "validation", "num_bytes": 825767266, "num_examples": 364608}], "download_size": 326778635540, "dataset_size": 1657178361414}, {"config_name": "en.noblocklist", "features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1029628201361, "num_examples": 393391519}, {"name": "validation", "num_bytes": 1025606012, "num_examples": 393226}], "download_size": 406611392434, "dataset_size": 2059256402722}, {"config_name": "realnewslike", "features": 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"multilingual/c4-ml.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ml-validation.*.json.gz"}]}, {"config_name": "mn", "data_files": [{"split": "train", "path": "multilingual/c4-mn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-mn-validation.*.json.gz"}]}, {"config_name": "mr", "data_files": [{"split": "train", "path": "multilingual/c4-mr.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-mr-validation.*.json.gz"}]}, {"config_name": "ms", "data_files": [{"split": "train", "path": "multilingual/c4-ms.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ms-validation.*.json.gz"}]}, {"config_name": "mt", "data_files": [{"split": "train", "path": "multilingual/c4-mt.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-mt-validation.*.json.gz"}]}, {"config_name": "my", "data_files": [{"split": "train", "path": "multilingual/c4-my.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-my-validation.*.json.gz"}]}, {"config_name": "ne", "data_files": [{"split": "train", "path": "multilingual/c4-ne.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ne-validation.*.json.gz"}]}, {"config_name": "nl", "data_files": [{"split": "train", "path": "multilingual/c4-nl.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-nl-validation.*.json.gz"}]}, {"config_name": "no", "data_files": [{"split": "train", "path": "multilingual/c4-no.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-no-validation.*.json.gz"}]}, {"config_name": "ny", "data_files": [{"split": "train", "path": "multilingual/c4-ny.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ny-validation.*.json.gz"}]}, {"config_name": "pa", "data_files": [{"split": "train", "path": "multilingual/c4-pa.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-pa-validation.*.json.gz"}]}, {"config_name": "pl", "data_files": [{"split": "train", "path": "multilingual/c4-pl.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-pl-validation.*.json.gz"}]}, {"config_name": "ps", "data_files": [{"split": "train", "path": "multilingual/c4-ps.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ps-validation.*.json.gz"}]}, {"config_name": "pt", "data_files": [{"split": "train", "path": "multilingual/c4-pt.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-pt-validation.*.json.gz"}]}, {"config_name": "ro", "data_files": [{"split": "train", "path": "multilingual/c4-ro.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ro-validation.*.json.gz"}]}, {"config_name": "ru", "data_files": [{"split": "train", "path": "multilingual/c4-ru.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ru-validation.*.json.gz"}]}, {"config_name": "ru-Latn", "data_files": [{"split": "train", "path": "multilingual/c4-ru-Latn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ru-Latn-validation.*.json.gz"}]}, {"config_name": "sd", "data_files": [{"split": "train", "path": "multilingual/c4-sd.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sd-validation.*.json.gz"}]}, {"config_name": "si", "data_files": [{"split": "train", "path": "multilingual/c4-si.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-si-validation.*.json.gz"}]}, {"config_name": "sk", "data_files": [{"split": "train", "path": "multilingual/c4-sk.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sk-validation.*.json.gz"}]}, {"config_name": "sl", "data_files": [{"split": "train", "path": "multilingual/c4-sl.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sl-validation.*.json.gz"}]}, {"config_name": "sm", "data_files": [{"split": "train", "path": "multilingual/c4-sm.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sm-validation.*.json.gz"}]}, {"config_name": "sn", "data_files": [{"split": "train", "path": "multilingual/c4-sn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sn-validation.*.json.gz"}]}, {"config_name": "so", "data_files": [{"split": "train", "path": "multilingual/c4-so.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-so-validation.*.json.gz"}]}, {"config_name": "sq", "data_files": [{"split": "train", "path": "multilingual/c4-sq.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sq-validation.*.json.gz"}]}, {"config_name": "sr", "data_files": [{"split": "train", "path": "multilingual/c4-sr.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sr-validation.*.json.gz"}]}, {"config_name": "st", "data_files": [{"split": "train", "path": "multilingual/c4-st.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-st-validation.*.json.gz"}]}, {"config_name": "su", "data_files": [{"split": "train", "path": "multilingual/c4-su.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-su-validation.*.json.gz"}]}, {"config_name": "sv", "data_files": [{"split": "train", "path": "multilingual/c4-sv.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sv-validation.*.json.gz"}]}, {"config_name": "sw", "data_files": [{"split": "train", "path": "multilingual/c4-sw.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sw-validation.*.json.gz"}]}, {"config_name": "ta", "data_files": [{"split": "train", "path": "multilingual/c4-ta.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ta-validation.*.json.gz"}]}, {"config_name": "te", "data_files": [{"split": "train", "path": "multilingual/c4-te.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-te-validation.*.json.gz"}]}, {"config_name": "tg", "data_files": [{"split": "train", "path": "multilingual/c4-tg.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-tg-validation.*.json.gz"}]}, {"config_name": "th", "data_files": [{"split": "train", "path": "multilingual/c4-th.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-th-validation.*.json.gz"}]}, {"config_name": "tr", "data_files": [{"split": "train", "path": "multilingual/c4-tr.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-tr-validation.*.json.gz"}]}, {"config_name": "uk", "data_files": [{"split": "train", "path": "multilingual/c4-uk.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-uk-validation.*.json.gz"}]}, {"config_name": "und", "data_files": [{"split": "train", "path": "multilingual/c4-und.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-und-validation.*.json.gz"}]}, {"config_name": "ur", "data_files": [{"split": "train", "path": "multilingual/c4-ur.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ur-validation.*.json.gz"}]}, {"config_name": "uz", "data_files": [{"split": "train", "path": "multilingual/c4-uz.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-uz-validation.*.json.gz"}]}, {"config_name": "vi", "data_files": [{"split": "train", "path": "multilingual/c4-vi.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-vi-validation.*.json.gz"}]}, {"config_name": "xh", "data_files": [{"split": "train", "path": "multilingual/c4-xh.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-xh-validation.*.json.gz"}]}, {"config_name": "yi", "data_files": [{"split": "train", "path": "multilingual/c4-yi.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-yi-validation.*.json.gz"}]}, {"config_name": "yo", "data_files": [{"split": "train", "path": "multilingual/c4-yo.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-yo-validation.*.json.gz"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "multilingual/c4-zh.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-validation.*.json.gz"}]}, {"config_name": "zh-Latn", "data_files": [{"split": "train", "path": "multilingual/c4-zh-Latn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-Latn-validation.*.json.gz"}]}, {"config_name": "zu", "data_files": [{"split": "train", "path": "multilingual/c4-zu.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zu-validation.*.json.gz"}]}]}

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

66a53dc7d40a13036c5f2ebe

mlabonne/FineTome-100k

mlabonne

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

False

2024-07-29T09:52:30.000Z

c2343c1372ff31f51aa21248db18bffa3193efdb

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

2024-07-27T18:34:47.000Z

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

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

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

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

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

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

670dc5114968ab91d80e2258

Marqo/marqo-GS-10M

Marqo

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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

650a9248d26103b6eee3ea7b

lmsys/lmsys-chat-1m

lmsys

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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

66fc03bc2d7c7dffd1d95786

argilla/Synth-APIGen-v0.1

argilla

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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

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

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

66ec6fc3fca6148a45e37e74

glaiveai/reflection-v1

glaiveai

{"license": "apache-2.0"}

False

2024-09-19T19:08:14.000Z

6becaa822b43dfd79490bb8b41f3cdba194f7b64

2024-09-19T18:38:59.000Z

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

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

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

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

66882f60655f93ab77bab62c

SkunkworksAI/reasoning-0.01

SkunkworksAI

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False

2024-09-14T16:06:30.000Z

7097753c0cd76874d3b60dbab4853830068fb98d

reasoning-0.01 subset synthetic dataset of reasoning chains for a wide variety of tasks. we leverage data like this across multiple reasoning experiments/projects. stay tuned for reasoning models and more data. Thanks to Hive Digital Technologies (https://x.com/HIVEDigitalTech) for their compute support in this project and beyond.

2024-07-05T17:37:36.000Z

66a26e6812bd8c8ee66f5676

lmms-lab/LLaVA-OneVision-Data

lmms-lab

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"data_files": [{"split": "train", "path": "MapQA(MathV360K)/train-*"}]}, {"config_name": "PMC-VQA(MathV360K)", "data_files": [{"split": "train", "path": "PMC-VQA(MathV360K)/train-*"}]}, {"config_name": "Super-CLEVR(MathV360K)", "data_files": [{"split": "train", "path": "Super-CLEVR(MathV360K)/train-*"}]}, {"config_name": "TabMWP(MathV360K)", "data_files": [{"split": "train", "path": "TabMWP(MathV360K)/train-*"}]}, {"config_name": "UniGeo(MathV360K)", "data_files": [{"split": "train", "path": "UniGeo(MathV360K)/train-*"}]}, {"config_name": "VisualWebInstruct(filtered)", "data_files": [{"split": "train", "path": "VisualWebInstruct(filtered)/train-*"}]}, {"config_name": "VizWiz(MathV360K)", "data_files": [{"split": "train", "path": "VizWiz(MathV360K)/train-*"}]}, {"config_name": "ai2d(cauldron,llava_format)", "data_files": [{"split": "train", "path": "ai2d(cauldron,llava_format)/train-*"}]}, {"config_name": "ai2d(gpt4v)", "data_files": [{"split": "train", "path": "ai2d(gpt4v)/train-*"}]}, {"config_name": "ai2d(internvl)", "data_files": [{"split": "train", "path": "ai2d(internvl)/train-*"}]}, {"config_name": "allava_instruct_laion4v", "data_files": [{"split": "train", "path": "allava_instruct_laion4v/train-*"}]}, {"config_name": "allava_instruct_vflan4v", "data_files": [{"split": "train", "path": "allava_instruct_vflan4v/train-*"}]}, {"config_name": "aokvqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "aokvqa(cauldron,llava_format)/train-*"}]}, {"config_name": "chart2text(cauldron)", "data_files": [{"split": "train", "path": "chart2text(cauldron)/train-*"}]}, {"config_name": "chartqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "chartqa(cauldron,llava_format)/train-*"}]}, {"config_name": "chrome_writting", "data_files": [{"split": "train", "path": "chrome_writting/train-*"}]}, {"config_name": "clevr(cauldron,llava_format)", "data_files": [{"split": "train", "path": "clevr(cauldron,llava_format)/train-*"}]}, {"config_name": "diagram_image_to_text(cauldron)", "data_files": [{"split": "train", "path": "diagram_image_to_text(cauldron)/train-*"}]}, {"config_name": "dvqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "dvqa(cauldron,llava_format)/train-*"}]}, {"config_name": "figureqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "figureqa(cauldron,llava_format)/train-*"}]}, {"config_name": "geo170k(align)", "data_files": [{"split": "train", "path": "geo170k(align)/train-*"}]}, {"config_name": "geo170k(qa)", "data_files": [{"split": "train", "path": "geo170k(qa)/train-*"}]}, {"config_name": "geo3k", "data_files": [{"split": "train", "path": "geo3k/train-*"}]}, {"config_name": "geomverse(cauldron)", "data_files": [{"split": "train", "path": "geomverse(cauldron)/train-*"}]}, {"config_name": "hateful_memes(cauldron,llava_format)", "data_files": [{"split": "train", "path": "hateful_memes(cauldron,llava_format)/train-*"}]}, {"config_name": "hitab(cauldron,llava_format)", "data_files": [{"split": "train", "path": "hitab(cauldron,llava_format)/train-*"}]}, {"config_name": "hme100k", "data_files": [{"split": "train", "path": "hme100k/train-*"}]}, {"config_name": "iam(cauldron)", "data_files": [{"split": "train", "path": "iam(cauldron)/train-*"}]}, {"config_name": "iconqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "iconqa(cauldron,llava_format)/train-*"}]}, {"config_name": "iiit5k", "data_files": [{"split": "train", "path": "iiit5k/train-*"}]}, {"config_name": "image_textualization(filtered)", "data_files": [{"split": "train", "path": "image_textualization(filtered)/train-*"}]}, {"config_name": "infographic(gpt4v)", "data_files": [{"split": "train", "path": "infographic(gpt4v)/train-*"}]}, {"config_name": "infographic_vqa", "data_files": [{"split": "train", "path": "infographic_vqa/train-*"}]}, {"config_name": "infographic_vqa_llava_format", "data_files": [{"split": "train", "path": "infographic_vqa_llava_format/train-*"}]}, {"config_name": "intergps(cauldron,llava_format)", "data_files": [{"split": "train", "path": "intergps(cauldron,llava_format)/train-*"}]}, {"config_name": "k12_printing", "data_files": [{"split": "train", "path": "k12_printing/train-*"}]}, {"config_name": "llavar_gpt4_20k", "data_files": [{"split": "train", "path": "llavar_gpt4_20k/train-*"}]}, {"config_name": "lrv_chart", "data_files": [{"split": "train", "path": "lrv_chart/train-*"}]}, {"config_name": "lrv_normal(filtered)", "data_files": [{"split": "train", "path": "lrv_normal(filtered)/train-*"}]}, {"config_name": "magpie_pro(l3_80b_mt)", "data_files": [{"split": "train", "path": "magpie_pro(l3_80b_mt)/train-*"}]}, {"config_name": "magpie_pro(l3_80b_st)", "data_files": [{"split": "train", "path": "magpie_pro(l3_80b_st)/train-*"}]}, {"config_name": "magpie_pro(qwen2_72b_st)", "data_files": [{"split": "train", "path": "magpie_pro(qwen2_72b_st)/train-*"}]}, {"config_name": "mapqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "mapqa(cauldron,llava_format)/train-*"}]}, {"config_name": "mathqa", "data_files": [{"split": "train", "path": "mathqa/train-*"}]}, {"config_name": "mavis_math_metagen", "data_files": [{"split": "train", "path": "mavis_math_metagen/train-*"}]}, {"config_name": "mavis_math_rule_geo", "data_files": [{"split": "train", "path": "mavis_math_rule_geo/train-*"}]}, {"config_name": "multihiertt(cauldron)", "data_files": [{"split": "train", "path": "multihiertt(cauldron)/train-*"}]}, {"config_name": "orand_car_a", "data_files": [{"split": "train", "path": "orand_car_a/train-*"}]}, {"config_name": "raven(cauldron)", "data_files": [{"split": "train", "path": "raven(cauldron)/train-*"}]}, {"config_name": "rendered_text(cauldron)", "data_files": [{"split": "train", "path": "rendered_text(cauldron)/train-*"}]}, {"config_name": "robut_sqa(cauldron)", "data_files": [{"split": "train", "path": "robut_sqa(cauldron)/train-*"}]}, {"config_name": "robut_wikisql(cauldron)", "data_files": [{"split": "train", "path": "robut_wikisql(cauldron)/train-*"}]}, {"config_name": "robut_wtq(cauldron,llava_format)", "data_files": [{"split": "train", "path": "robut_wtq(cauldron,llava_format)/train-*"}]}, {"config_name": "scienceqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "scienceqa(cauldron,llava_format)/train-*"}]}, {"config_name": "scienceqa(nona_context)", "data_files": [{"split": "train", "path": "scienceqa(nona_context)/train-*"}]}, {"config_name": "screen2words(cauldron)", "data_files": [{"split": "train", "path": "screen2words(cauldron)/train-*"}]}, {"config_name": "sharegpt4o", "data_files": [{"split": "train", "path": "sharegpt4o/train-*"}]}, {"config_name": "sharegpt4v(coco)", "data_files": [{"split": "train", "path": "sharegpt4v(coco)/train-*"}]}, {"config_name": "sharegpt4v(knowledge)", "data_files": [{"split": "train", "path": "sharegpt4v(knowledge)/train-*"}]}, {"config_name": "sharegpt4v(llava)", "data_files": [{"split": "train", "path": "sharegpt4v(llava)/train-*"}]}, {"config_name": "sharegpt4v(sam)", "data_files": [{"split": "train", "path": "sharegpt4v(sam)/train-*"}]}, {"config_name": "sroie", "data_files": [{"split": "train", "path": "sroie/train-*"}]}, {"config_name": "st_vqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "st_vqa(cauldron,llava_format)/train-*"}]}, {"config_name": "tabmwp(cauldron)", "data_files": [{"split": "train", "path": "tabmwp(cauldron)/train-*"}]}, {"config_name": "tallyqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "tallyqa(cauldron,llava_format)/train-*"}]}, {"config_name": "textcaps", "data_files": [{"split": "train", "path": "textcaps/train-*"}]}, {"config_name": "textocr(gpt4v)", "data_files": [{"split": "train", "path": "textocr(gpt4v)/train-*"}]}, {"config_name": "tqa(cauldron,llava_format)", "data_files": [{"split": "train", "path": "tqa(cauldron,llava_format)/train-*"}]}, {"config_name": "ureader_cap", "data_files": [{"split": "train", "path": "ureader_cap/train-*"}]}, {"config_name": "ureader_ie", "data_files": [{"split": "train", "path": "ureader_ie/train-*"}]}, {"config_name": "vision_flan(filtered)", "data_files": [{"split": "train", "path": "vision_flan(filtered)/train-*"}]}, {"config_name": "vistext(cauldron)", "data_files": [{"split": "train", "path": "vistext(cauldron)/train-*"}]}, {"config_name": "visual7w(cauldron,llava_format)", "data_files": [{"split": "train", "path": "visual7w(cauldron,llava_format)/train-*"}]}, {"config_name": "visualmrc(cauldron)", "data_files": [{"split": "train", "path": "visualmrc(cauldron)/train-*"}]}, {"config_name": "vqarad(cauldron,llava_format)", "data_files": [{"split": "train", "path": "vqarad(cauldron,llava_format)/train-*"}]}, {"config_name": "vsr(cauldron,llava_format)", "data_files": [{"split": "train", "path": "vsr(cauldron,llava_format)/train-*"}]}, {"config_name": "websight(cauldron)", "data_files": [{"split": "train", "path": "websight(cauldron)/train-*"}]}]}

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

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

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

66f14a7926413db5f6c63b5b

FBK-MT/mosel

FBK-MT

{"task_categories": ["automatic-speech-recognition"], "language": ["en", "bg", "hr", "cs", "da", "nl", "et", "fi", "fr", "de", "el", "hu", "ga", "it", "lv", "lt", "mt", "pl", "pt", "ro", "sk", "sl", "es", "sv"], "pretty_name": "MOSEL", "license": "cc-by-4.0"}

False

2024-10-02T08:44:59.000Z

0f884d2d8ee47a646666e6407d340e38da62d82b

Dataset Description, Collection, and Source The MOSEL corpus is a multilingual dataset collection including up to 950K hours of open-source speech recordings covering the 24 official languages of the European Union. We collect data by surveying labeled and unlabeled speech corpora under open-source compliant licenses. In particular, MOSEL includes the automatic transcripts of 441k hours of unlabeled speech from VoxPopuli and LibriLight. The data is transcribed using Whisper… See the full description on the dataset page: https://huggingface.co/datasets/FBK-MT/mosel.

2024-09-23T11:01:13.000Z

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

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

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

64be50954b4ff0d509698f72

iamtarun/python_code_instructions_18k_alpaca

iamtarun

{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25180782, "num_examples": 18612}], "download_size": 11357076, "dataset_size": 25180782}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["question-answering", "text2text-generation", "text-generation"], "tags": ["code"], "size_categories": ["10K<n<100K"]}

False

2023-07-27T15:51:36.000Z

7cae181e29701a8663a07a3ea43c8e105b663ba1

Dataset Card for python_code_instructions_18k_alpaca The dataset contains problem descriptions and code in python language. This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. Refer to the source here.

2023-07-24T10:21:09.000Z

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

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

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

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

67094a36d0d7527b66c56266

ai-safety-institute/AgentHarm

ai-safety-institute

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

False

2024-10-14T07:58:31.000Z

a82cbefaf77e194849d7bc829a92bf502894c31a

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

2024-10-11T15:54:30.000Z