Datasets:

cfahlgren1

/

hub-stats

like 9

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"], 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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

67105fb3e506c35ff594baab

TIGER-Lab/MEGA-Bench

TIGER-Lab

{"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": 1317694, "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": 702288, "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": 851345, "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": 638460, "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-*"}]}], "license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"], "size_categories": ["1K<n<10K"]}

False

2024-10-17T18:30:52.000Z

b56c5cd480e1d607a1ce1e7c8d3267e30a97158b

MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks 🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub ❗❗ Data Information We put all image and video files into the parquet files to enable easier visualization. Videos are sub-sampled into a sequence of frames. The full MEGA-Bench contains two subsets, as described in our paper: Core: the Core task set (with 440 tasks), evaluated with a bunch of highly-customized… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MEGA-Bench.

2024-10-17T00:52:03.000Z

621ffdd236468d709f181ee4

google-research-datasets/natural_questions

google-research-datasets

{"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "natural-questions", "pretty_name": "Natural Questions", "dataset_info": [{"config_name": "default", "features": [{"name": "id", "dtype": "string"}, {"name": "document", "struct": [{"name": "html", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "tokens", "sequence": [{"name": "end_byte", "dtype": "int64"}, {"name": "is_html", "dtype": "bool"}, {"name": "start_byte", "dtype": "int64"}, {"name": "token", "dtype": "string"}]}, {"name": "url", "dtype": "string"}]}, {"name": "question", "struct": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}]}, {"name": "long_answer_candidates", "sequence": [{"name": "end_byte", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "start_token", "dtype": "int64"}, {"name": "top_level", "dtype": "bool"}]}, {"name": "annotations", "sequence": [{"name": "id", "dtype": "string"}, {"name": "long_answer", "struct": [{"name": "candidate_index", "dtype": "int64"}, {"name": "end_byte", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "start_token", "dtype": "int64"}]}, {"name": "short_answers", "sequence": [{"name": "end_byte", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "start_token", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}, {"name": "yes_no_answer", "dtype": {"class_label": {"names": {"0": "NO", "1": "YES"}}}}]}], "splits": [{"name": "train", "num_bytes": 143039948860, "num_examples": 307373}, {"name": "validation", "num_bytes": 3451288641, "num_examples": 7830}], "download_size": 56843626971, "dataset_size": 146491237501}, {"config_name": "dev", "features": [{"name": "id", "dtype": "string"}, {"name": "document", "struct": [{"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html", "dtype": "string"}, {"name": "tokens", "sequence": [{"name": "token", "dtype": "string"}, {"name": "is_html", "dtype": "bool"}, {"name": "start_byte", "dtype": "int64"}, {"name": "end_byte", "dtype": "int64"}]}]}, {"name": "question", "struct": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}]}, {"name": "long_answer_candidates", "sequence": [{"name": "start_token", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "end_byte", "dtype": "int64"}, {"name": "top_level", "dtype": "bool"}]}, {"name": "annotations", "sequence": [{"name": "id", "dtype": "string"}, {"name": "long_answer", "struct": [{"name": "start_token", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "end_byte", "dtype": "int64"}, {"name": "candidate_index", "dtype": "int64"}]}, {"name": "short_answers", "sequence": [{"name": "start_token", "dtype": "int64"}, {"name": "end_token", "dtype": "int64"}, {"name": "start_byte", "dtype": "int64"}, {"name": "end_byte", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}, {"name": "yes_no_answer", "dtype": {"class_label": {"names": {"0": "NO", "1": "YES"}}}}]}], "splits": [{"name": "validation", "num_bytes": 3451288639, "num_examples": 7830}], "download_size": 1337126358, "dataset_size": 3451288639}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "default/train-*"}, {"split": "validation", "path": "default/validation-*"}]}, {"config_name": "dev", "data_files": [{"split": "validation", "path": "dev/validation-*"}]}]}

False

2024-03-11T16:19:34.000Z

e8103d566bef4154c2c12b17c6095ec5275840cc

Dataset Card for Natural Questions Dataset Summary The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/natural_questions.

2022-03-02T23:29:22.000Z

natural-questions

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}, }

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 }

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

6697abec43d9faa413ca745c

HuggingFaceM4/Docmatix

HuggingFaceM4

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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

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

66df0c09e17fb5ff05d87826

argilla/FinePersonas-v0.1

argilla

{"language": ["en"], "license": "llama3", "size_categories": ["10M<n<100M"], "task_categories": ["text-generation"], "pretty_name": "FinePersonas", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "model_name_embeddings", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 179098544944, "num_examples": 21071228}], "download_size": 141656480592, "dataset_size": 179098544944}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["synthetic", "distilabel"]}

False

2024-09-18T14:59:42.000Z

53f30d226c993ee059212a896cefdc14a1f9047f

FinePersonas Open dataset of 21 Million detailed personas for diverse and controllable synthetic text generation. FinePersonas contains detailed personas for creating customized, realistic synthetic data. With this dataset, AI researchers and engineers can easily integrate unique persona traits into text generation systems, enhancing the richness, diversity, and specificity of synthetic outputs without the complexity of crafting detailed attributes from… See the full description on the dataset page: https://huggingface.co/datasets/argilla/FinePersonas-v0.1.

2024-09-09T14:54:01.000Z

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

66f7b13cee826b5daa6b89bc

PKU-Alignment/align-anything-400k

PKU-Alignment

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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

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

6708f04ef8a1d7b2673dbc8b

dmis-lab/ChroKnowBench

dmis-lab

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

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

670befa7623c91990f914eb6

mlabonne/open-perfectblend

mlabonne

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False

2024-10-13T19:14:50.000Z

5c1a4a7e8cb9c8e31891fb6b6f79baaf464d9338

🎨 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. Here is the list of the datasets used: HuggingFaceH4/ultrachat_200k meta-math/MetaMathQA openbmb/UltraInteract_sft microsoft/orca-math-word-problems-200k HuggingFaceH4/ultrafeedback_binarized… See the full description on the dataset page: https://huggingface.co/datasets/mlabonne/open-perfectblend.

2024-10-13T16:04:55.000Z

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

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

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

621ffdd236468d709f181ecb

microsoft/ms_marco

microsoft

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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

621ffdd236468d709f181f09

Skylion007/openwebtext

Skylion007

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False

2024-05-17T17:56:27.000Z

f3808c30e817981b845ec549c43e82bb467d8144

An open-source replication of the WebText dataset from OpenAI.

2022-03-02T23:29:22.000Z

openwebtext

@misc{Gokaslan2019OpenWeb, title={OpenWebText Corpus}, author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex}, howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}}, year={2019} }

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

621ffdd236468d709f183300

facebook/multilingual_librispeech

facebook

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2194}, {"name": "1_hours", "num_bytes": 15714704, "num_examples": 241}], "download_size": 31526161821, "dataset_size": 31659423725.516}, {"config_name": "italian", "features": [{"name": "audio", "dtype": "audio"}, {"name": "original_path", "dtype": "string"}, {"name": "begin_time", "dtype": "float64"}, {"name": "end_time", "dtype": "float64"}, {"name": "transcript", "dtype": "string"}, {"name": "audio_duration", "dtype": "float64"}, {"name": "speaker_id", "dtype": "string"}, {"name": "chapter_id", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "dev", "num_bytes": 81607596.048, "num_examples": 1248}, {"name": "test", "num_bytes": 83216752.046, "num_examples": 1262}, {"name": "train", "num_bytes": 3896742625, "num_examples": 59623}, {"name": "9_hours", "num_bytes": 141671904.428, "num_examples": 2173}, {"name": "1_hours", "num_bytes": 15560398, "num_examples": 240}], "download_size": 4200633596, "dataset_size": 4218799275.522}, {"config_name": "polish", "features": [{"name": "audio", "dtype": "audio"}, {"name": "original_path", "dtype": "string"}, {"name": "begin_time", "dtype": "float64"}, {"name": "end_time", "dtype": "float64"}, {"name": "transcript", "dtype": "string"}, {"name": "audio_duration", "dtype": "float64"}, {"name": "speaker_id", "dtype": "string"}, {"name": "chapter_id", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "dev", "num_bytes": 32746725, "num_examples": 512}, {"name": "test", "num_bytes": 33735044, "num_examples": 520}, {"name": "train", "num_bytes": 1638889846, "num_examples": 25043}, {"name": "9_hours", "num_bytes": 142005461, "num_examples": 2173}, {"name": "1_hours", "num_bytes": 15681216, "num_examples": 238}], "download_size": 1855342312, "dataset_size": 1863058292}, {"config_name": "portuguese", "features": [{"name": "audio", "dtype": "audio"}, {"name": "original_path", "dtype": "string"}, {"name": "begin_time", "dtype": "float64"}, {"name": "end_time", "dtype": "float64"}, {"name": "transcript", "dtype": "string"}, {"name": "audio_duration", "dtype": "float64"}, {"name": "speaker_id", "dtype": "string"}, {"name": "chapter_id", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "dev", "num_bytes": 57533473, "num_examples": 826}, {"name": "test", "num_bytes": 59141979, "num_examples": 871}, {"name": "train", "num_bytes": 2518553713.946, "num_examples": 37533}, {"name": "9_hours", "num_bytes": 141641902.42, "num_examples": 2116}, {"name": "1_hours", "num_bytes": 15697139, "num_examples": 236}], "download_size": 2780836500, "dataset_size": 2792568207.366}, {"config_name": "spanish", "features": [{"name": "audio", "dtype": "audio"}, {"name": "original_path", "dtype": "string"}, {"name": "begin_time", "dtype": "float64"}, {"name": "end_time", "dtype": "float64"}, {"name": "transcript", "dtype": "string"}, {"name": "audio_duration", "dtype": "float64"}, {"name": "speaker_id", "dtype": "string"}, {"name": "chapter_id", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "dev", "num_bytes": 157804903.144, "num_examples": 2408}, {"name": "test", "num_bytes": 158526899.32, "num_examples": 2385}, {"name": "train", "num_bytes": 14562584188, "num_examples": 220701}, {"name": "9_hours", "num_bytes": 142473624.48, "num_examples": 2110}, {"name": "1_hours", "num_bytes": 15702048, "num_examples": 233}], "download_size": 14971394533, "dataset_size": 15037091662.944}], "configs": [{"config_name": "dutch", "data_files": [{"split": "dev", "path": "dutch/dev-*"}, {"split": "test", "path": "dutch/test-*"}, {"split": "train", "path": "dutch/train-*"}, {"split": "9_hours", "path": "dutch/9_hours-*"}, {"split": "1_hours", "path": "dutch/1_hours-*"}]}, {"config_name": "french", "data_files": [{"split": "dev", "path": "french/dev-*"}, {"split": "test", "path": "french/test-*"}, {"split": "train", "path": "french/train-*"}, {"split": "9_hours", "path": "french/9_hours-*"}, {"split": "1_hours", "path": "french/1_hours-*"}]}, {"config_name": "german", "data_files": [{"split": "dev", "path": "german/dev-*"}, {"split": "test", "path": "german/test-*"}, {"split": "train", "path": "german/train-*"}, {"split": "9_hours", "path": "german/9_hours-*"}, {"split": "1_hours", "path": "german/1_hours-*"}]}, {"config_name": "italian", "data_files": [{"split": "dev", "path": "italian/dev-*"}, {"split": "test", "path": "italian/test-*"}, {"split": "train", "path": "italian/train-*"}, {"split": "9_hours", "path": "italian/9_hours-*"}, {"split": "1_hours", "path": "italian/1_hours-*"}]}, {"config_name": "polish", "data_files": [{"split": "dev", "path": "polish/dev-*"}, {"split": "test", "path": "polish/test-*"}, {"split": "train", "path": "polish/train-*"}, {"split": "9_hours", "path": "polish/9_hours-*"}, {"split": "1_hours", "path": "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

62ceef6d2822f319618afc3d

nreimers/reddit_question_best_answers

nreimers

False

2022-07-13T17:25:49.000Z

c4821b678115e52620027e77f76919953581236c

Question & question body together with the best answers to that question from Reddit. The score for the question / answer is the upvote count (i.e. positive-negative upvotes). Only questions / answers that have these properties were extracted: min_score = 3 min_title_len = 20 min_body_len = 100

2022-07-13T16:14:37.000Z

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

641debae1d05404efd046a4f

yahma/alpaca-cleaned

yahma

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

False

2023-04-10T20:29:06.000Z

12567cabf869d7c92e573c7c783905fc160e9639

Dataset Card for Alpaca-Cleaned Repository: https://github.com/gururise/AlpacaDataCleaned Dataset Description This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset: Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer. "instruction":"Summarize… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned.

2023-03-24T18:27:58.000Z

646fcd74d1f1b73079ed6732

rubend18/ChatGPT-Jailbreak-Prompts

rubend18

{"task_categories": ["question-answering", "text-generation", "fill-mask", "zero-shot-classification", "table-question-answering"], "language": ["en", "aa"], "tags": ["ChatGPT", "JailbreakPrompts", "LanguageModeling", "ArtificialIntelligence", "TextGeneration", "Dataset", "OpenAI", "Jailbreak", "Prompts"], "size_categories": ["n<1K"], "pretty_name": "ChatGPT Jailbreak Prompts"}

False

2023-08-24T18:24:29.000Z

b93e4982f8f8ad2d82c6d35e3c00d161844ad70a

Dataset Card for Dataset Name Name ChatGPT Jailbreak Prompts Dataset Summary ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT. Languages [English]

2023-05-25T21:04:52.000Z

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} }

648b556b363cf923caddc497

Open-Orca/OpenOrca

Open-Orca

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

False

2023-10-21T10:09:31.000Z

3e85783ecb0db83df8b30dbbd94107857b5ac830

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

2023-06-15T18:16:11.000Z

649444227853dd12c3bbadd8

Amod/mental_health_counseling_conversations

Amod

{"license": "openrail", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical"], "size_categories": ["1K<n<10K"]}

False

2024-04-05T08:30:03.000Z

4672e03c7f1a7b2215eb4302b83ca50449ce2553

Amod/mental_health_counseling_conversations Dataset Summary This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice. Supported Tasks and Leaderboards The… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations.

2023-06-22T12:52:50.000Z

64dbd28f00b80a024c762bd8

glaiveai/glaive-function-calling-v2

glaiveai

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["100K<n<1M"]}

False

2023-09-27T18:04:08.000Z

e7f4b6456019f5d8bcb991ef0dd67d8ff23221ac

2023-08-15T19:31:27.000Z

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

65a3043fbfaec7e7ca07c57d

jtatman/python-code-dataset-500k

jtatman

{"dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "system", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 922266591, "num_examples": 559515}], "download_size": 346944286, "dataset_size": 922266591}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "mit", "task_categories": ["text-generation"], "tags": ["instructional", "python", "code"], "pretty_name": "github_python", "size_categories": ["100K<n<1M"]}

False

2024-01-23T21:39:13.000Z

060ad3df88a6ba5f5546c622652290f38e73ceba

Attention: This dataset is a summary and reformat pulled from github code. You should make your own assumptions based on this. In fact, there is another dataset I formed through parsing that addresses several points: out of 500k python related items, most of them are python-ish, not pythonic the majority of the items here contain excessive licensing inclusion of original code the items here are sometimes not even python but have references There's a whole lot of gpl summaries… See the full description on the dataset page: https://huggingface.co/datasets/jtatman/python-code-dataset-500k.

2024-01-13T21:44:31.000Z

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

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

65dea52c2915c5bcbbc3b733

CyberNative/Code_Vulnerability_Security_DPO

CyberNative

{"license": "apache-2.0", "tags": ["dpo", "cybersecurity", "programming", "code", "Python"], "pretty_name": "Code Vulnerability and Security DPO Dataset"}

False

2024-02-29T15:24:07.000Z

81aeacf06cf43b16d7278a3a01f019a496a53c51

Cybernative.ai Code Vulnerability and Security Dataset Dataset Description The Cybernative.ai Code Vulnerability and Security Dataset is a dataset of synthetic Data Programming by Demonstration (DPO) pairs, focusing on the intricate relationship between secure and insecure code across a variety of programming languages. This dataset is meticulously crafted to serve as a pivotal resource for researchers, cybersecurity professionals, and AI developers who are keen… See the full description on the dataset page: https://huggingface.co/datasets/CyberNative/Code_Vulnerability_Security_DPO.

2024-02-28T03:14:52.000Z

65f40c0253fb04fa76f8ac37

mokyu2106/iroiro_data

mokyu2106

{"license": "unknown"}

False

2024-10-16T07:37:14.000Z

8e7350aff827e010fbd5dbf25175c054a0bf514d

■■LECO&DIFF置き場■■ 主にXLで使用するLECOが格納されています。 作成者の都合上、数としてはponyV6関連が一番充実しています 特に『7th anime XL-Pony A (V1.0)』用が一番多いです。 ※2020/10/11 追記 IllustriousXLv01　向けのLECOを大量に追加中です。 興味のある方は随時確認して頂くと良いかと思います。 ※簡易な使い方説明は下位フォルダ内txt参照の事

2024-03-15T08:51:14.000Z

666513f121aa69e38699e6d3

UCSC-VLAA/MedTrinity-25M

UCSC-VLAA

{"language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["question-answering"], "dataset_info": [{"config_name": "25M_full", "features": [{"name": "id", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25234102586, "num_examples": 24760560}], "download_size": 7353330306, "dataset_size": 25234102586}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4781050841.25, "num_examples": 161630}], "download_size": 8300138103, "dataset_size": 4781050841.25}], "configs": [{"config_name": "25M_full", "data_files": [{"split": "train", "path": "25M_full/train-*"}]}, {"config_name": "25M_demo", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["medical"]}

auto

2024-10-11T00:47:43.000Z

89e5c684794e5c4cc1af9e8f1a7798af7c937dbf

Tutorial of using Medtrinity-25M MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass both global textual information, such as disease/lesion type, modality, region-specific descriptions, and inter-regional relationships, as well as detailed local annotations for regions of interest (ROIs), including… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/MedTrinity-25M.

2024-06-09T02:31:13.000Z

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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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

667ab99edb56acf219d8d646

FreedomIntelligence/PubMedVision

FreedomIntelligence

{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["GPT-4V", "Vision", "medical", "biology"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "PubMedVision_Alignment_VQA", "data_files": "PubMedVision_Alignment_VQA.json"}, {"config_name": "PubMedVision_InstructionTuning_VQA", "data_files": "PubMedVision_InstructionTuning_VQA.json"}]}

False

2024-07-01T04:55:12.000Z

8c7db81c018b4b6d0f481ec9b92e957c5fa720db

News [2024/07/01]: We add annotations for 'body_part' and 'modality' of images, utilizing the HuatuoGPT-Vision-7B model. PubMedVision PubMedVision is a large-scale medical VQA dataset. We extracted high-quality image-text pairs from PubMed and used GPT-4V to reformat them to enhance their quality. PubMedVision significantly improves the multimodal capabilities of MLLMs in the medical field. For more details, refer to our paper and github. Data Volume… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/PubMedVision.

2024-06-25T12:35:42.000Z

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

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

2024-09-05T05:11:29.000Z

4a8dbc75e4eaff1661fdd71f5d00a2e4017fe38d

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

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

66fb280ee66d0cc6ac1fb6de

argilla/ifeval-like-data

argilla

{"language": ["en"], "license": "other", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "pretty_name": "IFEval Like Data", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "dataset_info": [{"config_name": "default", "features": [{"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "model_name", "dtype": "string"}, {"name": "instruction_id_list", "sequence": "string"}, {"name": "distilabel_metadata", "struct": [{"name": "raw_input_i_f_eval_kwargs_assignator_0", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_output_i_f_eval_kwargs_assignator_0", "dtype": "string"}]}, {"name": "kwargs", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4946738037, "num_examples": 550000}], "download_size": 590155799, "dataset_size": 4946738037}, {"config_name": "filtered", "features": [{"name": "key", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "instruction_id_list", "sequence": "string"}, {"name": "kwargs", "dtype": "string"}, {"name": "prompt_level_strict_acc", "dtype": "bool"}, {"name": "inst_level_strict_acc", "sequence": "bool"}, {"name": "prompt_level_loose_acc", "dtype": "bool"}, {"name": "inst_level_loose_acc", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 83762994.47804716, "num_examples": 56339}], "download_size": 31864315, "dataset_size": 83762994.47804716}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "filtered", "data_files": [{"split": "train", "path": "filtered/train-*"}]}], "tags": ["synthetic", "distilabel", "rlaif"]}

False

2024-10-17T09:43:39.000Z

56505d1771e36fa5aa07aa9a1b39008fe60a8487

IFEval Like Data This dataset contains instruction-response pairs synthetically generated using Qwen/Qwen2.5-72B-Instruct following the style of google/IFEval dataset and verified for correctness with lm-evaluation-harness. The dataset contains two subsets: default: which contains 550k unfiltered rows synthetically generated with Qwen2.5-72B-Instruct, a few system prompts and MagPie prompting technique. The prompts can contain conflicting instructions as defined… See the full description on the dataset page: https://huggingface.co/datasets/argilla/ifeval-like-data.

2024-09-30T22:37:02.000Z

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

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

670d7aa55c92b2893293425c

DEVAI-benchmark/DEVAI

DEVAI-benchmark

{"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "main", "path": "instances/*.json"}]}]}

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

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

621ffdd236468d709f181eaa

keithito/lj_speech

keithito

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False

2024-08-14T11:13:15.000Z

1532a199abd253d1d9511ea04d6d7f45a12a39f9

This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .wav format and is not converted to a float32 array. To convert the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```

2022-03-02T23:29:22.000Z

ljspeech

@misc{ljspeech17, author = {Keith Ito and Linda Johnson}, title = {The LJ Speech Dataset}, howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}}, year = 2017 }

621ffdd236468d709f181ec6

ylecun/mnist

ylecun

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False

2024-08-08T06:07:00.000Z

77f3279092a1c1579b2250db8eafed0ad422088c

Dataset Card for MNIST Dataset Summary The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class. Half of the image were drawn by Census Bureau employees and the other half by high school… See the full description on the dataset page: https://huggingface.co/datasets/ylecun/mnist.

2022-03-02T23:29:22.000Z

mnist

621ffdd236468d709f181f0c

Helsinki-NLP/opus_books

Helsinki-NLP

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False

2024-03-29T16:50:29.000Z

1f9f6191d0e91a3c539c2595e2fe48fc1420de9b

Dataset Card for OPUS Books Dataset Summary This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php Note that the texts are rather dated due to copyright issues and that some of them are manually reviewed (check the meta-data at the top of the corpus files in XML). The source is multilingually aligned, which is available from… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/opus_books.

2022-03-02T23:29:22.000Z

621ffdd236468d709f181ff1

CSTR-Edinburgh/vctk

CSTR-Edinburgh

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False

2024-08-14T11:27:34.000Z

31539806e8a6ee3b0c0fef88659ed518542bc564

The CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents.

2022-03-02T23:29:22.000Z

vctk

@inproceedings{Veaux2017CSTRVC, title = {CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit}, author = {Christophe Veaux and Junichi Yamagishi and Kirsten MacDonald}, year = 2017 }