Datasets:

cfahlgren1

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hub-stats

like 21

63990f21cc50af73d29ecfa3

fka/awesome-chatgpt-prompts

fka

{"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]}

68ba7694e23014788dcc8ab5afe613824f45a05c

🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0

6782cb3d244c0e06b1362fed

NovaSky-AI/Sky-T1_data_17k

NovaSky-AI

{"size_categories": ["10K<n<100K"], "license": "apache-2.0"}

3e260822dae5d833d9b040e34265d5f9a2b8a6a5

Sky-T1_data_17k.json: The 17k training data used to train Sky-T1-32B-Preview. The final data contains 5k coding data from APPs and TACO, and 10k math data from AIME, MATH, and Olympiads subsets of the NuminaMATH dataset. In addition, we maintain 1k science and puzzle data from STILL-2.

67750882633d421965733171

DAMO-NLP-SG/multimodal_textbook

DAMO-NLP-SG

{"license": "apache-2.0", "task_categories": ["text-generation", "summarization"], "language": ["en"], "tags": ["Pretraining", "Interleaved", "Reasoning"], "size_categories": ["1M<n<10M"]}

b83d307b2682d6b12420f5b93f4360880ea89df4

Multimodal-Textbook-6.5M Overview This dataset is for "2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining", containing 6.5M images interleaving with 0.8B text from instructional videos. It contains pre-training corpus using interleaved image-text format. Specifically, our multimodal-textbook includes 6.5M keyframes extracted from instructional videos, interleaving with 0.8B ASR texts. All the images and text are extracted from… See the full description on the dataset page: https://huggingface.co/datasets/DAMO-NLP-SG/multimodal_textbook.

6649d353babc0b33565e1a4a

HumanLLMs/Human-Like-DPO-Dataset

HumanLLMs

{"language": ["en"], "license": "llama3", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.json"}]}]}

dd82ab6a284a15765964149e6a6603ff8ed7d672

Enhancing Human-Like Responses in Large Language Models 🤗 Models | 📊 Dataset | 📄 Paper Human-Like-DPO-Dataset This dataset was created as part of research aimed at improving conversational fluency and engagement in large language models. It is suitable for formats like Direct Preference Optimization (DPO) to guide models toward generating more human-like responses. The dataset includes 10,884 samples across 256 topics, including: Technology Daily Life Science… See the full description on the dataset page: https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset.

66cbf7ef92e9f5b19fcd65aa

cfahlgren1/react-code-instructions

cfahlgren1

{"license": "mit", "pretty_name": "React Code Instructions"}

11c8725b7c1027666f96bce02fe601b18417a23d

React Code Instructions Popular Queries Number of instructions by Model Unnested Messages Instructions Added Per Day Dataset of Claude Artifact esque React Apps generated by Llama 3.1 70B, Llama 3.1 405B, and Deepseek Chat V3. Examples Virtual Fitness Trainer Website LinkedIn Clone iPhone Calculator Chipotle Waitlist Apple Store

676f70846bf205795346d2be

FreedomIntelligence/medical-o1-reasoning-SFT

FreedomIntelligence

{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}]}

4c9573e7de1e8660b88158db2efa7c7204bbd269

Introduction This dataset is used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. This dataset is constructed using GPT-4o, which searches for solutions to verifiable medical problems and validates them through a medical verifier. For details, see our paper and GitHub repository. Citation If you find our data useful, please consider citing our work! @misc{chen2024huatuogpto1medicalcomplexreasoning, title={HuatuoGPT-o1… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.

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": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["aimo", "math"], "pretty_name": "NuminaMath CoT"}

9d8d210c9f6a36c8f3cd84045668c9b7800ef517

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.

6758176e04e2f15d7bfacd54

PowerInfer/QWQ-LONGCOT-500K

PowerInfer

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

10a787d967281599e9be6761717147817c018424

This repository contains approximately 500,000 instances of responses generated using QwQ-32B-Preview language model. The dataset combines prompts from multiple high-quality sources to create diverse and comprehensive training data. The dataset is available under the Apache 2.0 license. Over 75% of the responses exceed 8,000 tokens in length. The majority of prompts were carefully created using persona-based methods to create challenging instructions. Bias, Risks, and Limitations… See the full description on the dataset page: https://huggingface.co/datasets/PowerInfer/QWQ-LONGCOT-500K.

66a6da71f0dc7c8df2e0f979

OpenLeecher/lmsys_chat_1m_clean

OpenLeecher

{"language": ["en"], "size_categories": ["100K<n<1M"], "pretty_name": "Cleaned LMSYS dataset", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "grounded", "dtype": "bool"}, {"name": "deepseek_response", "struct": [{"name": "moralization", "dtype": "int64"}, {"name": "reward", "dtype": "float64"}, {"name": "value", "dtype": "string"}]}, {"name": "phi-3-mini_response", "struct": [{"name": "moralization", "dtype": "int64"}, {"name": "reward", "dtype": "float64"}, {"name": "value", "dtype": "string"}]}, {"name": "flaw", "dtype": "string"}, {"name": "agreement", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1673196622, "num_examples": 273402}], "download_size": 906472159, "dataset_size": 1673196622}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

e9f2f6838a2dbba87c216bb6bc406e8d7ce0f389

Cleaning and Categorizing A few weeks ago, I had the itch to do some data crunching, so I began this project - to clean and classify lmsys-chat-1m. The process was somewhat long and tedious, but here is the quick overview: 1. Removing Pure Duplicate Instructions The first step was to eliminate pure duplicate instructions. This involved: Removing whitespace and punctuation. Ensuring that if two instructions matched after that, only one was retained. This step… See the full description on the dataset page: https://huggingface.co/datasets/OpenLeecher/lmsys_chat_1m_clean.

67449661149efb6edaa63b98

HuggingFaceTB/finemath

HuggingFaceTB

{"license": "odc-by", "dataset_info": [{"config_name": "finemath-3plus", "features": [{"name": "url", "dtype": "string"}, {"name": "fetch_time", "dtype": "int64"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int32"}, {"name": "warc_record_length", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "token_count", "dtype": "int32"}, {"name": "char_count", "dtype": "int32"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "crawl", "dtype": "string"}, {"name": "snapshot_type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 137764105388.93857, "num_examples": 21405610}], "download_size": 65039196945, "dataset_size": 137764105388.93857}, {"config_name": "finemath-4plus", "features": [{"name": "url", "dtype": "string"}, {"name": "fetch_time", "dtype": "int64"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int32"}, {"name": "warc_record_length", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "token_count", "dtype": "int32"}, {"name": "char_count", "dtype": "int32"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "crawl", "dtype": "string"}, {"name": "snapshot_type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 39101488149.09091, "num_examples": 6699493}], "download_size": 18365184633, "dataset_size": 39101488149.09091}, {"config_name": "infiwebmath-3plus", "features": [{"name": "url", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "token_count", "dtype": "int64"}, {"name": "char_count", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 96485696853.10182, "num_examples": 13882669}], "download_size": 46808660851, "dataset_size": 96485696853.10182}, {"config_name": "infiwebmath-4plus", "features": [{"name": "url", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "token_count", "dtype": "int64"}, {"name": "char_count", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40002719500.1551, "num_examples": 6296212}], "download_size": 19234328998, "dataset_size": 40002719500.1551}], "configs": [{"config_name": "finemath-3plus", "data_files": [{"split": "train", "path": "finemath-3plus/train-*"}]}, {"config_name": "finemath-4plus", "data_files": [{"split": "train", "path": "finemath-4plus/train-*"}]}, {"config_name": "infiwebmath-3plus", "data_files": [{"split": "train", "path": "infiwebmath-3plus/train-*"}]}, {"config_name": "infiwebmath-4plus", "data_files": [{"split": "train", "path": "infiwebmath-4plus/train-*"}]}]}

8f233cf84cff0b817b3ffb26d5be7370990dd557

📐 FineMath What is it? 📐 FineMath consists of 34B tokens (FineMath-3+) and 54B tokens (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCrawl. To curate this dataset, we trained a mathematical content classifier using annotations generated by LLama-3.1-70B-Instruct. We used the classifier to retain only the most educational mathematics content, focusing on clear explanations and step-by-step problem solving rather than… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/finemath.

673e9e53cdad8a9744b0bf1b

O1-OPEN/OpenO1-SFT

O1-OPEN

{"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en", "zh"], "size_categories": ["10K<n<100K"]}

63112de109aa755e9cdfad63a13f08a92dd7df36

SFT Data for CoT Activation 🎉🎉🎉This repository contains the dataset used for fine-tuning a language model using SFT for Chain-of-Thought Activation. 🌈🌈🌈The dataset is designed to enhance the model's ability to generate coherent and logical reasoning sequences. ☄☄☄By using this dataset, the model can learn to produce detailed and structured reasoning steps, enhancing its performance on complex reasoning tasks. Statistics 1️⃣Total Records: 77,685… See the full description on the dataset page: https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT.

677c1f196b1653e3955dbce7

Rapidata/text-2-image-Rich-Human-Feedback

Rapidata

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "word_scores", "dtype": "string"}, {"name": "alignment_score_norm", "dtype": "float32"}, {"name": "coherence_score_norm", "dtype": "float32"}, {"name": "style_score_norm", "dtype": "float32"}, {"name": "alignment_heatmap", "sequence": {"sequence": "float16"}}, {"name": "coherence_heatmap", "sequence": {"sequence": "float16"}}, {"name": "alignment_score", "dtype": "float32"}, {"name": "coherence_score", "dtype": "float32"}, {"name": "style_score", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 25257389633.104, "num_examples": 13024}], "download_size": 17856619960, "dataset_size": 25257389633.104}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["text-to-image", "text-classification", "image-classification", "image-to-text", "image-segmentation"], "language": ["en"], "tags": ["t2i", "preferences", "human", "flux", "midjourney", "imagen", "dalle", "heatmap", "coherence", "alignment", "style", "plausiblity"], "pretty_name": "Rich Human Feedback for Text to Image Models", "size_categories": ["1M<n<10M"]}

e77afd00e481d9d2ca41a5b5c4f89cb704de45c6

Building upon Google's research Rich Human Feedback for Text-to-Image Generation we have collected over 1.5 million responses from 152'684 individual humans using Rapidata via the Python API. Collection took roughly 5 days. If you get value from this dataset and would like to see more in the future, please consider liking it. Overview We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback.

677e59ab4bf7f0d4735ea7da

llamaindex/vdr-multilingual-train

llamaindex

{"language": ["de", "it", "fr", "es", "en"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "pretty_name": "Multilingual Visual Document Retrieval", "dataset_info": [{"config_name": "en", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19695589638, "num_examples": 94225}], "download_size": 19695589638, "dataset_size": 19695589638}, {"config_name": "es", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19881676198, "num_examples": 102685}], "download_size": 19881676198, "dataset_size": 19881676198}, {"config_name": "it", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20278641470, "num_examples": 98747}], "download_size": 20278641470, "dataset_size": 20278641470}, {"config_name": "de", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19629975126, "num_examples": 100713}], "download_size": 19629975126, "dataset_size": 19629975126}, {"config_name": "fr", "features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "negatives", "sequence": {"dtype": "string"}}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20825335207, "num_examples": 99797}], "download_size": 20825335207, "dataset_size": 20825335207}], "configs": [{"config_name": "en", "data_files": [{"split": "train", "path": "en/train-*"}]}, {"config_name": "it", "data_files": [{"split": "train", "path": "it/train-*"}]}, {"config_name": "fr", "data_files": [{"split": "train", "path": "fr/train-*"}]}, {"config_name": "es", "data_files": [{"split": "train", "path": "es/train-*"}]}, {"config_name": "de", "data_files": [{"split": "train", "path": "de/train-*"}]}], "license": "apache-2.0"}

6b92b5cae23d44509f1e05d7062befe5ec77f7c9

Multilingual Visual Document Retrieval Dataset This dataset consists of 500k multilingual query image samples, collected and generated from scratch using public internet pdfs. The queries are synthetic and generated using VLMs (gemini-1.5-pro and Qwen2-VL-72B). It was used to train the vdr-2b-multi-v1 retrieval multimodal, multilingual embedding model. How it was created This is the entire data pipeline used to create the Italian subset of this dataset. Each… See the full description on the dataset page: https://huggingface.co/datasets/llamaindex/vdr-multilingual-train.

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"}}

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.

625552d2b339bb03abe3432d

openai/gsm8k

openai

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]}

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.

gsm8k

66bffb77453a7ef6c587560c

edinburgh-dawg/mmlu-redux-2.0

edinburgh-dawg

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63f54ebd32c36485c679f53b8e2f576d689b9b34

Dataset Card for MMLU-Redux-2.0 MMLU-Redux is a subset of 5,700 manually re-annotated questions across 57 MMLU subjects. Dataset Details Dataset Description Each data point in MMLU-Redux contains seven columns: question (str): The original MMLU question. choices (List[str]): The original list of four choices associated with the question from the MMLU dataset. answer (int): The MMLU ground truth label in the form of an array index between 0 and… See the full description on the dataset page: https://huggingface.co/datasets/edinburgh-dawg/mmlu-redux-2.0.

674dc01bf413e32210acb235

Rapidata/human-style-preferences-images

Rapidata

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79acd5ebcc535309c08d996ab1f88c01077a7b12

Rapidata Image Generation Preference Dataset This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. Explore our latest model rankings on our website. If you get value from this dataset and would like to see more in the future, please consider liking it. Overview One of the largest human preference datasets for text-to-image models, this release contains over 1,200,000 human… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/human-style-preferences-images.

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

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e31fdfd3918d4b48e837d69d274e624a067d7091

🍷 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.

677fdc0944145aefa9e3ca88

atlasia/TerjamaBench

atlasia

{"dataset_info": {"features": [{"name": "topic", "dtype": "string"}, {"name": "subtopic", "dtype": "string"}, {"name": "Arabizi", "dtype": "string"}, {"name": "English", "dtype": "string"}, {"name": "Darija", "dtype": "string"}, {"name": "annotator_dialect", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 132360, "num_examples": 850}, {"name": "train", "num_bytes": 126518, "num_examples": 850}], "download_size": 140184, "dataset_size": 258878}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}]}], "license": "mit", "task_categories": ["translation"], "size_categories": ["n<1K"], "language": ["ary", "en"]}

8ef552799373b205f12304d63191f3b8bad8b525

TerjamaBench: A Culturally Specific Dataset for Evaluating Translation Models for Moroccan Darija Moroccan Darija, the widely spoken dialect of Arabic in Morocco, is rich in cultural expressions, regional variations, and multilingual influences. Despite its prevalence, there is a lack of robust, culturally relevant datasets for evaluating models on Moroccan Darija, particularly for translation tasks. To address this gap, we introduce TerjamaBench, a dataset specifically… See the full description on the dataset page: https://huggingface.co/datasets/atlasia/TerjamaBench.

65fc5a783bc54054aa2e6e62

gretelai/synthetic_text_to_sql

gretelai

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

273a86f5f290e8d61b6767a9ff690c82bc990dc4

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

673a1149a7a311f5bed5c624

HuggingFaceTB/smoltalk

HuggingFaceTB

{"language": ["en"], "tags": ["synthetic"], "pretty_name": "SmolTalk", "size_categories": ["1M<n<10M"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "data/all/train-*"}, {"split": "test", "path": "data/all/test-*"}]}, {"config_name": "smol-magpie-ultra", "data_files": [{"split": "train", "path": "data/smol-magpie-ultra/train-*"}, {"split": "test", "path": "data/smol-magpie-ultra/test-*"}]}, {"config_name": "smol-constraints", "data_files": [{"split": "train", "path": "data/smol-constraints/train-*"}, {"split": "test", "path": "data/smol-constraints/test-*"}]}, {"config_name": "smol-rewrite", "data_files": [{"split": "train", "path": "data/smol-rewrite/train-*"}, {"split": "test", "path": "data/smol-rewrite/test-*"}]}, {"config_name": "smol-summarize", "data_files": [{"split": "train", "path": "data/smol-summarize/train-*"}, {"split": "test", "path": "data/smol-summarize/test-*"}]}, {"config_name": "apigen-80k", "data_files": [{"split": "train", "path": "data/apigen-80k/train-*"}, {"split": "test", "path": "data/apigen-80k/test-*"}]}, {"config_name": "everyday-conversations", "data_files": [{"split": "train", "path": "data/everyday-conversations/train-*"}, {"split": "test", "path": "data/everyday-conversations/test-*"}]}, {"config_name": "explore-instruct-rewriting", "data_files": [{"split": "train", "path": "data/explore-instruct-rewriting/train-*"}, {"split": "test", "path": "data/explore-instruct-rewriting/test-*"}]}, {"config_name": "longalign", "data_files": [{"split": "train", "path": "data/longalign/train-*"}, {"split": "test", "path": "data/longalign/test-*"}]}, {"config_name": "metamathqa-50k", "data_files": [{"split": "train", "path": "data/metamathqa-50k/train-*"}, {"split": "test", "path": "data/metamathqa-50k/test-*"}]}, {"config_name": "numina-cot-100k", "data_files": [{"split": "train", "path": "data/numina-cot-100k/train-*"}, {"split": "test", "path": "data/numina-cot-100k/test-*"}]}, {"config_name": "openhermes-100k", "data_files": [{"split": "train", "path": "data/openhermes-100k/train-*"}, {"split": "test", "path": "data/openhermes-100k/test-*"}]}, {"config_name": "self-oss-instruct", "data_files": [{"split": "train", "path": "data/self-oss-instruct/train-*"}, {"split": "test", "path": "data/self-oss-instruct/test-*"}]}, {"config_name": "systemchats-30k", "data_files": [{"split": "train", "path": "data/systemchats-30k/train-*"}, {"split": "test", "path": "data/systemchats-30k/test-*"}]}]}

5a40ecb185e55dd30edf3c24b77e67f6ea0d659b

SmolTalk Dataset description This is a synthetic dataset designed for supervised finetuning (SFT) of LLMs. It was used to build SmolLM2-Instruct family of models and contains 1M samples. During the development of SmolLM2, we observed that models finetuned on public SFT datasets underperformed compared to other models with proprietary instruction datasets. To address this gap, we created new synthetic datasets that improve instruction following while covering… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smoltalk.

674dc95c4c48b2c004b3b48f

Rapidata/human-alignment-preferences-images

Rapidata

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image1", "dtype": "image"}, {"name": "image2", "dtype": "image"}, {"name": "votes_image1", "dtype": "int64"}, {"name": "votes_image2", "dtype": "int64"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "detailed_results", "dtype": "string"}, {"name": "image1_path", "dtype": "string"}, {"name": "image2_path", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26216657746.75, "num_examples": 63721}], "download_size": 17892218611, "dataset_size": 26216657746.75}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cdla-permissive-2.0", "task_categories": ["text-to-image", "image-to-text", "reinforcement-learning", "question-answering"], "language": ["en"], "tags": ["Human", "Preference", "country", "language", "flux", "midjourney", "dalle3", "stabeldiffusion", "alignment", "flux1.1", "flux1", "imagen3"], "size_categories": ["1M<n<10M"], "pretty_name": "imagen-3 vs. Flux-1.1-pro vs. Flux-1-pro vs. Dalle-3 vs. Midjourney-5.2 vs. Stabel-Diffusion-3 - Human Alignment Dataset"}

804b92da58d614265377f9983d6715ef3bbb4d36

Rapidata Image Generation Alignment Dataset This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. Explore our latest model rankings on our website. If you get value from this dataset and would like to see more in the future, please consider liking it. Overview One of the largest human annotated alignment datasets for text-to-image models, this release contains over 1,200,000 human… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/human-alignment-preferences-images.

6761599ce5d10c2b3122000b

Rapidata/open-image-preferences-v1-more-results-binarized

Rapidata

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "image"}, {"name": "rejected", "dtype": "image"}, {"name": "chosen_model", "dtype": "string"}, {"name": "rejected_model", "dtype": "string"}, {"name": "evolution", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "sub_category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3039283260, "num_examples": 10480}], "download_size": 3035581905, "dataset_size": 3039283260}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

09c2763961cc51c87b4d41dddce21a265c0e42e6

We wanted to contribute to the challenge posed by the data-is-better-together community (description below). We collected 170'000 preferences using our API from people all around the world in rougly 3 days (docs.rapidata.ai): If you get value from this dataset and would like to see more in the future, please consider liking it. Dataset Card for image-preferences-results Original Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/open-image-preferences-v1-more-results-binarized.

676593a303cc6dbb6e857610

Rapidata/text-2-video-human-preferences

Rapidata

{"license": "apache-2.0", "task_categories": ["text-to-video", "video-classification"], "tags": ["human", "preferences", "coherence", "plausibilty", "style", "alignment"], "language": ["en"], "pretty_name": "Human Preferences for Text to Video Models", "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "video1", "dtype": "string"}, {"name": "video2", "dtype": "string"}, {"name": "weighted_results1_Alignment", "dtype": "float64"}, {"name": "weighted_results2_Alignment", "dtype": "float64"}, {"name": "detailedResults_Alignment", "list": [{"name": "userDetails", "struct": [{"name": "country", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "userScore", "dtype": "float64"}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Coherence", "dtype": "float64"}, {"name": "weighted_results2_Coherence", "dtype": "float64"}, {"name": "detailedResults_Coherence", "list": [{"name": "userDetails", "struct": [{"name": "country", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "userScore", "dtype": "float64"}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "weighted_results1_Preference", "dtype": "float64"}, {"name": "weighted_results2_Preference", "dtype": "float64"}, {"name": "detailedResults_Preference", "list": [{"name": "userDetails", "struct": [{"name": "country", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "userScore", "dtype": "float64"}]}, {"name": "votedFor", "dtype": "string"}]}, {"name": "file_name1", "dtype": "string"}, {"name": "file_name2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 540595, "num_examples": 316}], "download_size": 122082, "dataset_size": 540595}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

8d2db6367f00d60b6c94797298c8c61c7532fc0d

Rapidata Video Generation Preference Dataset If you get value from this dataset and would like to see more in the future, please consider liking it. This dataset was collected in ~12 hours using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. The data collected in this dataset informs our text-2-video model benchmark. We just started so currently only two models are represented in this set: Sora Hunyouan Pika 2.0 is currently… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences.

6760cf1c46ba6c841069988a

O1-OPEN/OpenO1-SFT-Ultra

O1-OPEN

2762ca378dbb954419b053fa347835d14a0379a8

openo1-sft-ultra-35m-data Instruction We have released the openo1-sft-ultra-35m-data, which contains 35 million data points. It is based on existing open-source datasets and synthesized using the openo1-qwen-sft model. We first collected open-source datasets and then annotated the data based on difficulty, quality, and question types using the qwen-2.5-72b-instruct model. To ensure the difficulty and quality of the data, we only retained data where both the… See the full description on the dataset page: https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT-Ultra.

677c6dded25ebab44ca8267b

BIOMEDICA/biomedica_webdataset

BIOMEDICA

{"tags": ["medical", "biology", "chemistry"], "size_categories": ["n>1T"], "extra_gated_prompt": "I understand that this dataset contains articles grouped under three licensing categories: Commercial Use Allowed (CC0, CC BY, CC BY-SA, CC BY-ND licenses), Non-Commercial Use Only (CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses), and Other (no machine-readable Creative Commons license, no license, or a custom license). I acknowledge that each individual data point in the dataset specifies its corresponding license type, and I agree that it is my responsibility to verify compliance with the licensing terms before using any specific data point. I further agree to comply with the specific licensing terms of each group when using the dataset in accordance to what is established by the PubMed Central: PMC Open Acces Subset", "extra_gated_fields": {"I confirm that I have read and agree to the data usage agreement outlined above by checking this box": "checkbox", "I want to use this dataset for": "text"}}

f5c128c71123deb732786e895e3b464911b1707e

Dataset Card for Dataset Name Arxiv: Arxiv     |     Website: Biomedica     |     Training instructions: OpenCLIP     |     Tutorial: Google Colab BIOMEDICA Dataset is a large-scale, deep-learning-ready biomedical dataset containing over 24M imagecaption pairs and 30M image-references from 6M unique open-source articles. Each data point is highly annotated with over 27 unique metadata fields, including article level information (e.g., license… See the full description on the dataset page: https://huggingface.co/datasets/BIOMEDICA/biomedica_webdataset.

677e5956e84a20259e43d869

Rapidata/Translation-gpt4o_mini-v-gpt4o-v-deepl

Rapidata

{"dataset_info": {"features": [{"name": "original_text", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "total_responses", "dtype": "int64"}, {"name": "weighted_votes_1", "dtype": "float64"}, {"name": "weighted_votes_2", "dtype": "float64"}, {"name": "translation_model_1", "dtype": "string"}, {"name": "translation_model_2", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "detailed_results", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10792019, "num_examples": 746}], "download_size": 1059070, "dataset_size": 10792019}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["translation"], "tags": ["translation", "humanfeedback", "gpt", "deepl", "gpt4o", "gpt4o-mini", "DE", "PT", "ES", "FR"]}

6770337d65e354f89e8377a001b7004b020a89e6

If you get value from this dataset and would like to see more in the future, please consider liking it. Overview This dataset compares the translation capabilities of GPT-4o and GPT-4o-mini against DeepL across different languages. The comparison involved 100 distinct questions (found under raw_files) in 4 languages, with each translation being rated by 100 native speakers. Texts that were translated identically across platforms were excluded from the analysis. Results… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Translation-gpt4o_mini-v-gpt4o-v-deepl.

621ffdd236468d709f181e5e

cais/mmlu

cais

{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mmlu", "pretty_name": "Measuring Massive Multitask Language Understanding", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "abstract_algebra", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 17143, "dataset_size": 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"num_bytes": 161000625, "num_examples": 99842}], "download_size": 47518592, "dataset_size": 161000625}, {"config_name": "business_ethics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 31619, "dataset_size": 57303.3562203159}, {"config_name": "clinical_knowledge", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 131489.4633955277, 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c30699e8356da336a370243923dbaf21066bb9fe

Dataset Card for MMLU Dataset Summary Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu.

mmlu

649444227853dd12c3bbadd8

Amod/mental_health_counseling_conversations

Amod

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

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.

660e7b9b4636ce2b0e77b699

mozilla-foundation/common_voice_17_0

mozilla-foundation

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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.

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 }

66b5d7e4fadf33f0d54db784

microsoft/PEACE

microsoft

{"license": "mit", "task_categories": ["question-answering"], "language": ["en"], "tags": ["geology", "geologic_map", "benchmark"], "configs": [{"config_name": "default", "data_files": [{"split": "full", "path": ["usgs_qas.csv", "cgs_qas.csv"]}, {"split": "usgs", "path": "usgs_qas.csv"}, {"split": "cgs", "path": "cgs_qas.csv"}]}], "pretty_name": "GeoMap-Bench", "size_categories": ["1K<n<10K"], "viewer": true}

186bf40f140bdc7cd6f21dea8f61832e708bc6ac

PEACE: Empowering Geologic Map Holistic Understanding with MLLMs [Code] [Paper] [Data] Introduction We construct a geologic map benchmark, GeoMap-Bench, to evaluate the performance of MLLMs on geologic map understanding across different abilities, the overview of it is as shown in below Table. Property Description Source USGS(English) CGS(Chinese) Content Image-question… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/PEACE.

6763e94724dee5a47c7c77f7

agibot-world/AgiBotWorld-Alpha

agibot-world

{"pretty_name": "AgiBot World", "size_categories": ["n>1T"], "task_categories": ["other"], "language": ["en"], "tags": ["real-world", "dual-arm", "Robotics manipulation"], "extra_gated_prompt": "### AgiBot World COMMUNITY LICENSE AGREEMENT\nAgiBot World Alpha Release Date: December 30, 2024 All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Email": "text", "Country": "country", "Affiliation": "text", "Phone": "text", "Job title": {"type": "select", "options": ["Student", "Research Graduate", "AI researcher", "AI developer/engineer", "Reporter", "Other"]}, "Research interest": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the AgiBot Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the AgiBot Privacy Policy.", "extra_gated_button_content": "Submit"}

c666ffd4c310f8d3fd0cfecac931763774d8c9ef

Key Features 🔑 1 million+ trajectories from 100 robots. 100+ real-world scenarios across 5 target domains. Cutting-edge hardware: visual tactile sensors / 6-DoF dexterous hand / mobile dual-arm robots Tasks involving: Contact-rich manipulation Long-horizon planning Multi-robot collaboration Your browser does not support the video tag. Your browser does not support the video tag. Your… See the full description on the dataset page: https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha.

676f70968756741d47c691df

FreedomIntelligence/medical-o1-verifiable-problem

FreedomIntelligence

{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["medical", "biology"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "medical_o1_verifiable_problem.json"}]}]}

46d5175eb74fdef3516d51d52e8c40db04bbdf35

Introduction This dataset features open-ended medical problems designed to improve LLMs' medical reasoning. Each entry includes a open-ended question and a ground-truth answer based on challenging medical exams. The verifiable answers enable checking LLM outputs, refining their reasoning processes. For details, see our paper and GitHub repository. Citation If you find our data useful, please consider citing our work!… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-verifiable-problem.

677bb2afe4cf361eed72da2c

ngxson/MiniThinky-dataset

ngxson

{"language": ["en"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 444645709, "num_examples": 88218}], "download_size": 214646754, "dataset_size": 444645709}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

df7ed56101c76cb9dae350ff2ccbc8fa0d493f33

MiniThinky dataset Merged from: https://huggingface.co/datasets/TuneIt/o1-python https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT https://huggingface.co/datasets/KingNish/reasoning-base-20k Post processing: Replaced with the format below Remove any rows that does not have reasoning process (i.e remove straight responses) Deduplicated Response format <|thinking|>{thinking_process} <|answer|> {real_answer}

678127dbc5273cefdd648499

prithivMLmods/Math-symbols

prithivMLmods

{"license": "creativeml-openrail-m", "task_categories": ["image-classification"], "language": ["en"], "size_categories": ["10K<n<100K"]}

752b6428e1b5cf4e10695265ff720ad8b417da25

Math-Symbols Dataset Overview The Math-Symbols dataset is a collection of images representing various mathematical symbols. This dataset is designed for machine learning applications, particularly in the fields of image recognition, optical character recognition (OCR), and symbol classification. Dataset Details Name: Math-Symbols Type: Image dataset Format: Images with corresponding labels Size: 131MB (downloaded dataset files), 118MB… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Math-symbols.

621ffdd236468d709f184284

wikimedia/wikipedia

wikimedia

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b04c8d1ceb2f5cd4588862100d08de323dccfbaa

Dataset Card for Wikimedia Wikipedia Dataset Summary Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/) with one subset per language, each containing a single train split. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). All language subsets have already been processed for recent dump… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia.

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-*"}]}]}

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".

674dc4e248045e1aed1baa45

Rapidata/human-coherence-preferences-images

Rapidata

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72c0ebefc7ef3bebe22643fc709a6e94c22b5b02

Rapidata Image Generation Coherence Dataset This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. Explore our latest model rankings on our website. If you get value from this dataset and would like to see more in the future, please consider liking it. Overview One of the largest human annotated coherence datasets for text-to-image models, this release contains over 1,200,000 human… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/human-coherence-preferences-images.

677c3556e185a5dab36e2c98

omkarthawakar/VRC-Bench

omkarthawakar

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "idx", "dtype": "string"}, {"name": "final_answer", "dtype": "string"}, {"name": "steps", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 496944903, "num_examples": 1002}], "download_size": 490323379, "dataset_size": 496944903}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

d6d248133b873fc6f564fe21159077272e33b3e1

Dataset Card for VRC-Bench Dataset Sources Repository: [https://github.com/mbzuai-oryx/LlamaV-o1] Paper* Dataset Structure Each data sample contains following field: { "image": PIL.Image "question": "What is the difference of largest and smallest bar?", "idx": "MathVista_74", "final_answer": "47.6", "steps": [ "Step 1: Identify the largest bar in the chart. \nAction 1: The largest bar is for Iceland at 100%.", "\nStep 2:… See the full description on the dataset page: https://huggingface.co/datasets/omkarthawakar/VRC-Bench.

67812623537c35d61623efcb

prithivMLmods/Math-Equa

prithivMLmods

{"license": "apache-2.0", "task_categories": ["image-classification"], "language": ["en"], "size_categories": ["n<1K"]}

4ccd10f000247677bbe9a85f072c9ebd1c024d5d

Math-Equa Dataset Overview The Math-Equa dataset is a collection of mathematical equations designed for machine learning applications. This dataset can be used for tasks such as equation solving, symbolic mathematics, and other related research areas. Dataset Details Name: Math-Equa Type: Mathematical Equations Format: Text-based equations Size: [Insert size of the dataset] Source: [Insert source of the dataset, if applicable] Usage… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Math-Equa.

6788e67d22de2f9e878aa0be

prithivMLmods/Opendoc1-Analysis-Recognition

prithivMLmods

{"license": "apache-2.0", "task_categories": ["image-to-text", "text-classification", "image-feature-extraction"], "language": ["en"], "tags": ["image", "analysis", "vision-language"], "size_categories": ["n<1K"]}

2b768174e910c054e7bbe231ca4392a7644df2e5

Opendoc1-Analysis-Recognition Dataset Overview The Opendoc1-Analysis-Recognition dataset is designed for tasks involving image-to-text, text classification, and image feature extraction. It contains images paired with class labels, making it suitable for vision-language tasks. Dataset Details Modalities: Image Languages: English Size: Approximately 1,000 samples (n=1K) Tags: image, analysis, vision-language License: Apache 2.0 Tasks This dataset… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Opendoc1-Analysis-Recognition.

6788eaf5640e4abf37f1428f

prithivMLmods/Opendoc2-Analysis-Recognition

prithivMLmods

{"license": "apache-2.0", "task_categories": ["image-to-text", "text-retrieval"], "language": ["en"], "tags": ["image", "vision"], "size_categories": ["10K<n<100K"]}

c40019e1246b4d6b662a52c8b1cc1260d3c97e16

Opendoc2-Analysis-Recognition Dataset Overview The Opendoc2-Analysis-Recognition dataset is a collection of data designed for tasks involving image analysis and recognition. It is suitable for various machine learning tasks, including image-to-text conversion, text classification, and image feature extraction. Dataset Details Modalities: Likely includes images and associated labels (specific modalities can be confirmed on the dataset's page). Languages:… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Opendoc2-Analysis-Recognition.

6655eb19d17e141dcb546ed5

HuggingFaceFW/fineweb-edu

HuggingFaceFW

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81fd597c805179172da5d94ac803cde08d95683d

📚 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.

666a59145c3bb7e4a6c8d180

Salesforce/xlam-function-calling-60k

Salesforce

{"extra_gated_heading": "Acknowledge to follow corresponding license and cite APIGen to access the repository", "extra_gated_button_content": "Agree and access repository", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Country": "country", "Affiliation": "text"}, "license": "cc-by-4.0", "task_categories": ["question-answering", "text-generation", "reinforcement-learning"], "language": ["en"], "tags": ["function-calling", "LLM Agent", "code", "synthetic"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "dataset", "data_files": [{"split": "train", "path": "xlam_function_calling_60k.json"}]}]}

1d5ae9b3285c9ab6ec147a2baba438a170ea7947

APIGen Function-Calling Datasets Paper | Website | Models This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness. We conducted human evaluation over 600 sampled data points… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k.

667c231902ffd4993eef43a5

joujiboi/japanese-anime-speech-v2

joujiboi

{"language": ["ja"], "license": "gpl", "size_categories": ["100K<n<1M"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "Japanese-Anime-Speech-V2", "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "sfw", "num_bytes": 19174765803.112, "num_examples": 271788}, {"name": "nsfw", "num_bytes": 2864808426.209, "num_examples": 20849}], "download_size": 24379492733, "dataset_size": 22039574229.321}, "tags": ["japanese", "anime", "speech", "\u65e5\u672c\u8a9e", "audio-text", "asr", "whisper", "voice"], "configs": [{"config_name": "default", "data_files": [{"split": "sfw", "path": "data/sfw-*"}, {"split": "nsfw", "path": "data/nsfw-*"}]}]}

1dea3fb40e0b1a224c011bab3efcde55893bf742

Japanese Anime Speech Dataset V2 日本語はこちら japanese-anime-speech-v2 is an audio-text dataset designed for training automatic speech recognition models. The dataset comprises 292,637 audio clips and their corresponding transcriptions from various visual novels. This dataset is not an expanded version of japanese-anime-speech-v1. For that reason, much of the audio from japanese-anime-speech-v1 is not included in this dataset. The goal of this dataset is to increase the accuracy of… See the full description on the dataset page: https://huggingface.co/datasets/joujiboi/japanese-anime-speech-v2.

66a1d16a27fd84b81d732482

TEAMREBOOTT-AI/SciCap-MLBCAP

TEAMREBOOTT-AI

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44f062ec4e5ec42898326cbea2f80f147a1ba861

MLBCAP: Multi-LLM Collaborative Caption Generation in Scientific Documents 📄 PaperMLBCAP has been accepted for presentation at AI4Research @ AAAI 2025. 🎉 📌 Introduction Scientific figure captioning is a challenging task that demands contextually accurate descriptions of visual content. Existing approaches often oversimplify the task by treating it as either an image-to-text conversion or text summarization problem, leading to suboptimal results. Furthermore… See the full description on the dataset page: https://huggingface.co/datasets/TEAMREBOOTT-AI/SciCap-MLBCAP.

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-*"}]}]}

469216e3f46f4dacf476b382e192485ea51a143e

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.

67289aeddfbd0f46c7b2326f

laion/laions_got_talent

laion

{"preview": {"files": ["voice_acting_outputs-alloy+de+_004.tar"], "rows": 10}}

3b2d1fa03639fec5b42233f7f239570edc2ab8f3

LAION's Got Talent: Generated Voice Acting Dataset Overview "LAION's Got Talent" is a generated dataset comprising voice acting samples that exhibit a wide range of emotions, vocal bursts, topics, and content. This dataset is a component of the BUD-E project, spearheaded by LAION with support from Intel. Dataset Composition The dataset includes: Emotional Diversity: Samples portraying various emotions to facilitate research in emotional recognition… See the full description on the dataset page: https://huggingface.co/datasets/laion/laions_got_talent.

6760406f6205e9e0d914a8ec

Rapidata/open-image-preferences-v1-more-results

Rapidata

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a5ced45e9d1f848d1d7dc1e87c0a4ece3a81799e

We wanted to contribute to the challenge posed by the data-is-better-together community (description below). We collected 170'000 preferences using our API from people all around the world in rougly 3 days (docs.rapidata.ai): If you get value from this dataset and would like to see more in the future, please consider liking it. Dataset Card for image-preferences-results Original Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/open-image-preferences-v1-more-results.

67744720363e2be467b7c2b5

qingy2024/FineQwQ-142k

qingy2024

{"language": ["en"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "10k", "num_bytes": 87273156.45129532, "num_examples": 10000}, {"name": "25k", "num_bytes": 218182891.12823832, "num_examples": 25000}, {"name": "50k", "num_bytes": 436365782.25647664, "num_examples": 50000}, {"name": "100k", "num_bytes": 872731564.5129533, "num_examples": 100000}, {"name": "142k", "num_bytes": 1239278821.6083937, "num_examples": 142000}], "download_size": 1265768860, "dataset_size": 2853832215.9573574}, "configs": [{"config_name": "default", "data_files": [{"split": "10k", "path": "data/10k-*"}, {"split": "25k", "path": "data/25k-*"}, {"split": "50k", "path": "data/50k-*"}, {"split": "100k", "path": "data/100k-*"}, {"split": "142k", "path": "data/142k-*"}]}]}

f7443bb54d207f590a5d13924c80c9eacfd66fe1

Shakker-Labs/FLUX.1-dev-LoRA-Logo-Design Original Sources: qingy2024/QwQ-LongCoT-Verified-130K (amphora/QwQ-LongCoT-130K), amphora/QwQ-LongCoT-130K-2, PowerInfer/QWQ-LONGCOT-500K. Source Information Rows % powerinfer/qwq-500k Only coding problems kept to avoid overlap 50,899 35.84% qwq-longcot-verified Verified math problems 64,096 45.14% amphora-magpie Diverse general purpose reasoning 27,015 19.02%

677f5729498775357d040e0d

RZ412/PokerBench

RZ412

{"language": ["en"], "pretty_name": "PokerBench", "task_categories": ["other"], "tags": ["poker", "decision-making"]}

b2430b78994121297c0797ce05f2feb9a11105db

PokerBench Overview This dataset contains natural language game scenarios and optimal decisions computed by solvers in No Limit Texas Hold’em. It is divided into pre-flop and post-flop datasets, each with training and test splits. The data is stored in both .json and .csv formats: JSON files: Contain the natural language prompts (instruction) and optimal decisions (output) derived from the game scenarios. CSV files: Contain structured game information from which the JSON files… See the full description on the dataset page: https://huggingface.co/datasets/RZ412/PokerBench.

627007d3becab9e2dcf15a40

ILSVRC/imagenet-1k

ILSVRC

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "license_details": "imagenet-agreement", "multilinguality": ["monolingual"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, 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 images that he or she may create from the Database.\n4. 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.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. 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.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "test", "num_bytes": 13613661561, "num_examples": 100000}, {"name": "train", "num_bytes": 146956944242, "num_examples": 1281167}, {"name": "validation", "num_bytes": 6709003386, "num_examples": 50000}], "download_size": 166009941208, "dataset_size": 167279609189}}

4603483700ee984ea9debe3ddbfdeae86f6489eb

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, ImageNet hopes to offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. ImageNet 2012 is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images

imagenet-1k-1

@article{imagenet15russakovsky, Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, Title = { {ImageNet Large Scale Visual Recognition Challenge} }, Year = {2015}, journal = {International Journal of Computer Vision (IJCV)}, doi = {10.1007/s11263-015-0816-y}, volume={115}, number={3}, pages={211-252} }

62a9dc9a471f7e0783124b0d

codeparrot/apps

codeparrot

{"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["mit"], "multilinguality": ["monolingual"], "pretty_name": "APPS", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"]}

21e74ddf8de1a21436da12e3e653065c5213e9d1

APPS is a benchmark for Python code generation, it includes 10,000 problems, which range from having simple oneline solutions to being substantial algorithmic challenges, for more details please refer to this paper: https://arxiv.org/pdf/2105.09938.pdf.

@article{hendrycksapps2021, title={Measuring Coding Challenge Competence With APPS}, author={Dan Hendrycks and Steven Basart and Saurav Kadavath and Mantas Mazeika and Akul Arora and Ethan Guo and Collin Burns and Samir Puranik and Horace He and Dawn Song and Jacob Steinhardt}, journal={NeurIPS}, year={2021} }

639244f571c51c43091df168

Anthropic/hh-rlhf

Anthropic

{"license": "mit", "tags": ["human-feedback"]}

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.

643dda8f317127fb1e30b27b

liuhaotian/LLaVA-Instruct-150K

liuhaotian

{"license": "cc-by-4.0", "task_categories": ["visual-question-answering", "question-answering"], "language": ["en"], "pretty_name": "LLaVA Visual Instruct 150K", "size_categories": ["100K<n<1M"]}

9d451dc7629cfe0469f6ae4432b765cd603d5fcb

LLaVA Visual Instruct 150K Dataset Card Dataset details Dataset type: LLaVA Visual Instruct 150K is a set of GPT-generated multimodal instruction-following data. It is constructed for visual instruction tuning and for building large multimodal towards GPT-4 vision/language capability. Dataset date: LLaVA Visual Instruct 150K was collected in April 2023, by prompting GPT-4-0314 API. Paper or resources for more information: https://llava-vl.github.io/ License:… See the full description on the dataset page: https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K.

6480d02ee1421e205fdd7f2a

cerebras/SlimPajama-627B

cerebras

{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "SlimPajama-627B"}

2d0accdd58c5d5511943ca1f5ff0e3eb5e293543

The dataset consists of 59166 jsonl files and is ~895GB compressed. It is a cleaned and deduplicated version of Together's RedPajama. Check out our blog post explaining our methods, our code on GitHub, and join the discussion on the Cerebras Discord. Getting Started You can download the dataset using Hugging Face datasets: from datasets import load_dataset ds = load_dataset("cerebras/SlimPajama-627B") Background Today we are releasing SlimPajama – the largest… See the full description on the dataset page: https://huggingface.co/datasets/cerebras/SlimPajama-627B.

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"]}

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.

64dbd28f00b80a024c762bd8

glaiveai/glaive-function-calling-v2

glaiveai

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

e7f4b6456019f5d8bcb991ef0dd67d8ff23221ac

655b26e86a7098bc6e6f99e6

Lin-Chen/ShareGPT4V

Lin-Chen

{"license": "cc-by-nc-4.0", "task_categories": ["visual-question-answering", "question-answering"], "language": ["en"], "pretty_name": "ShareGPT4V Captions 1.2M Dataset Card", "size_categories": ["1M<n"], "configs": [{"config_name": "ShareGPT4V", "data_files": "sharegpt4v_instruct_gpt4-vision_cap100k.json"}, {"config_name": "ShareGPT4V-PT", "data_files": "share-captioner_coco_lcs_sam_1246k_1107.json"}]}

55d02b0bc53a2754095a14110dda6daedd95671d

News [2024/5/8] We released ShareGPT4Video, a large-scale video-caption dataset, with 40K captions annotated by GPT4V and 4.8M captions annotated by our ShareCaptioner-Video. The total videos last with 300 hours and 3000 hours separately! ShareGPT4V 1.2M Dataset Card Dataset details Dataset type: ShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data. It is constructed to enhance modality alignment and fine-grained visual… See the full description on the dataset page: https://huggingface.co/datasets/Lin-Chen/ShareGPT4V.

657d39813c65a5be2aeef7d2

LDJnr/Capybara

LDJnr

{"license": "apache-2.0", "task_categories": ["conversational", "question-answering", "text-generation"], "language": ["en"], "tags": ["Physics", "Biology", "Math", "Chemistry", "Culture", "Logic", "Roleplay"], "pretty_name": "LessWrong-Amplify-Instruct", "size_categories": ["10K<n<100K"]}

c2bc39ac72f24748f60f5fb55b77e08fb0660ba6

This is the Official Capybara dataset. Over 10,000 multi-turn examples. Capybara is the culmination of insights derived from synthesis techniques like Evol-instruct (used for WizardLM), Alpaca, Orca, Vicuna, Lamini, FLASK and others. The single-turn seeds used to initiate the Amplify-Instruct synthesis of conversations are mostly based on datasets that i've personally vetted extensively, and are often highly regarded for their diversity and demonstration of logical robustness… See the full description on the dataset page: https://huggingface.co/datasets/LDJnr/Capybara.

6582cfb305c177eea3bc2aba

BAAI/TACO

BAAI

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d593ed0a2becbbc952230bb89be09189bf1056dc

TACO is a benchmark for Python code generation, it includes 25443 problems and 1000 problems for train and test splits.

taco-topics-in-algorithmic-code-generation

65dc13085ca10be41fdd8b27

bigcode/the-stack-v2

bigcode

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If you are using the dataset to train models you must adhere to the SoftwareHeritage [principles for language model training](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/).\n3. The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 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.\n4. The Stack v2 is regularly updated to enact validated data removal requests. 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7408bfbcfd48e5833d62fd3dba48afd20d109473

The Stack v2 The dataset consists of 4 versions: bigcode/the-stack-v2: the full "The Stack v2" dataset <-- you are here bigcode/the-stack-v2-dedup: based on the bigcode/the-stack-v2 but further near-deduplicated bigcode/the-stack-v2-train-full-ids: based on the bigcode/the-stack-v2-dedup dataset but further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories. bigcode/the-stack-v2-train-smol-ids: based on the… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-v2.

661da64be6e166452da68324

PleIAs/YouTube-Commons

PleIAs

{"language": ["en", "fr", "es", "pt", "de", "ru"], "license": "cc-by-4.0", "task_categories": ["text-generation"], "pretty_name": "Youtube Commons Corpus", "tags": ["conversational"], "dataset_info": {"features": [{"name": "video_id", "dtype": "string"}, {"name": "video_link", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "channel", "dtype": "string"}, {"name": "channel_id", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "original_language", "dtype": "string"}, {"name": "source_language", "dtype": "string"}, {"name": "transcription_language", "dtype": "string"}, {"name": "word_count", "dtype": "int64"}, {"name": "character_count", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3284822536, "num_examples": 250000}], "download_size": 1830819739, "dataset_size": 3284822536}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

9addbabbfcd7409acbcd11a3b59ec2aef6da7eb0

📺 YouTube-Commons 📺 YouTube-Commons is a collection of audio transcripts of 2,063,066 videos shared on YouTube under a CC-By license. Content The collection comprises 22,709,724 original and automatically translated transcripts from 3,156,703 videos (721,136 individual channels). In total, this represents nearly 45 billion words (44,811,518,375). All the videos where shared on YouTube with a CC-BY license: the dataset provide all the necessary provenance… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/YouTube-Commons.

664a1c1f4fa4afb446afa8f7

openbmb/RLAIF-V-Dataset

openbmb

{"license": "cc-by-nc-4.0", "task_categories": ["visual-question-answering"], "language": ["en"], "pretty_name": "RLAIF-V-Dataset", "dataset_info": {"features": [{"name": "ds_name", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "origin_dataset", "dtype": "string"}, {"name": "origin_split", "dtype": "string"}, {"name": "idx", "dtype": "string"}, {"name": "image_path", "dtype": "string"}]}, "size_categories": ["10K<n<100K"]}

586aff0ea91b485a73fe99f65570f054c10c79d9

Dataset Card for RLAIF-V-Dataset GitHub | Paper News: [2024.05.28] 📃 Our paper is accesible at arxiv now! [2024.05.20] 🔥 Our data is used in MiniCPM-Llama3-V 2.5, which represents the first end-side MLLM achieving GPT-4V level performance! Dataset Summary RLAIF-V-Dataset is a large-scale multimodal feedback dataset. The dataset provides high-quality feedback with a total number of 83,132 preference pairs, where the instructions are collected… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset.

66e1a2fb91e57a0788b501cb

jackyhate/text-to-image-2M

jackyhate

{"license": "mit", "task_categories": ["text-to-image", "image-to-text", "image-classification"], "language": ["en"], "size_categories": ["1M<n<10M"]}

e4ece89e640210e9fc3fd0966f5a45291bdb665c

text-to-image-2M: A High-Quality, Diverse Text-to-Image Training Dataset Overview text-to-image-2M is a curated text-image pair dataset designed for fine-tuning text-to-image models. The dataset consists of approximately 2 million samples, carefully selected and enhanced to meet the high demands of text-to-image model training. The motivation behind creating this dataset stems from the observation that datasets with over 1 million samples tend to produce better… See the full description on the dataset page: https://huggingface.co/datasets/jackyhate/text-to-image-2M.

670bd71d721603bf001c0399

opencsg/chinese-fineweb-edu-v2

opencsg

{"language": ["zh"], "pipeline_tag": "text-generation", "license": "apache-2.0", "task_categories": ["text-generation"], "size_categories": ["10B<n<100B"]}

472afc63110922a8cb118fbdd2ba209739e0da82

Chinese Fineweb Edu Dataset V2 [中文] [English] [OpenCSG Community] [👾github] [wechat] [Twitter] 📖Technical Report Chinese Fineweb Edu Dataset V2 is a comprehensive upgrade of the original Chinese Fineweb Edu, designed and optimized for natural language processing (NLP) tasks in the education sector. This high-quality Chinese pretraining dataset has undergone significant improvements and expansions, aimed at providing researchers and developers with more… See the full description on the dataset page: https://huggingface.co/datasets/opencsg/chinese-fineweb-edu-v2.

670f99838b9d35b27a752641

OpenLLM-France/Lucie-Training-Dataset

OpenLLM-France

{"pretty_name": "Lucie Training Dataset", "license": "cc-by-nc-sa-4.0", "language": ["en", "fr", "de", "es", "it", "code"], "multilinguality": ["multilingual"], "task_categories": ["text-generation", "text2text-generation"], "task_ids": ["language-modeling"], "tags": ["text-generation", "conditional-text-generation"], "size_categories": ["n>1T"], "viewer": true, "configs": [{"config_name": "default", "data_files": [{"path": "data/v*/*/*/*/*parquet", "split": "train"}]}, {"config_name": "en", "data_files": [{"path": "data/v*/natural/en/*/*parquet", "split": "train"}]}, {"config_name": "fr", "data_files": [{"path": "data/v*/natural/fr/*/*parquet", "split": "train"}]}, {"config_name": "de", "data_files": [{"path": "data/v*/natural/de/*/*parquet", "split": "train"}]}, {"config_name": "es", "data_files": [{"path": "data/v*/natural/es/*/*parquet", "split": "train"}]}, {"config_name": "it", "data_files": [{"path": "data/v*/natural/it/*/*parquet", "split": "train"}]}, {"config_name": "de,fr", "data_files": [{"path": "data/v*/natural/de-fr/*/*.parquet", "split": "train"}]}, {"config_name": "es,en", "data_files": [{"path": "data/v*/natural/es-en/*/*.parquet", "split": "train"}]}, {"config_name": "fr,en", "data_files": [{"path": "data/v*/natural/fr-en/*/*.parquet", "split": "train"}]}, {"config_name": "it,en", "data_files": [{"path": "data/v*/natural/it-en/*/*.parquet", "split": "train"}]}, {"config_name": "natural", "data_files": [{"path": "data/v*/natural/*/*/*.parquet", "split": "train"}]}, {"config_name": "code", "data_files": [{"path": "data/v*/code/*/*/*parquet", "split": "train"}]}, {"config_name": "code-assembly", "data_files": [{"path": "data/v*/code/assembly/*/*.parquet", "split": "train"}]}, {"config_name": "code-c", "data_files": [{"path": "data/v*/code/c/*/*.parquet", "split": "train"}]}, {"config_name": "code-c#", "data_files": [{"path": "data/v*/code/c#/*/*.parquet", "split": "train"}]}, {"config_name": "code-c++", "data_files": [{"path": "data/v*/code/c++/*/*.parquet", "split": "train"}]}, {"config_name": "code-clojure", "data_files": [{"path": "data/v*/code/clojure/*/*.parquet", "split": "train"}]}, {"config_name": "code-dart", "data_files": [{"path": "data/v*/code/dart/*/*.parquet", "split": "train"}]}, {"config_name": "code-elixir", "data_files": [{"path": "data/v*/code/elixir/*/*.parquet", "split": "train"}]}, {"config_name": "code-erlang", "data_files": [{"path": "data/v*/code/erlang/*/*.parquet", "split": "train"}]}, {"config_name": "code-fortran", "data_files": [{"path": "data/v*/code/fortran/*/*.parquet", "split": "train"}]}, {"config_name": "code-go", "data_files": [{"path": "data/v*/code/go/*/*.parquet", "split": "train"}]}, {"config_name": "code-haskell", "data_files": [{"path": "data/v*/code/haskell/*/*.parquet", "split": "train"}]}, {"config_name": "code-java", "data_files": [{"path": "data/v*/code/java/*/*.parquet", "split": "train"}]}, {"config_name": "code-javascript", "data_files": [{"path": "data/v*/code/javascript/*/*.parquet", "split": "train"}]}, {"config_name": "code-julia", "data_files": [{"path": "data/v*/code/julia/*/*.parquet", "split": "train"}]}, {"config_name": "code-kotlin", "data_files": [{"path": "data/v*/code/kotlin/*/*.parquet", "split": "train"}]}, {"config_name": "code-lua", "data_files": [{"path": "data/v*/code/lua/*/*.parquet", "split": "train"}]}, {"config_name": "code-mathematica", "data_files": [{"path": "data/v*/code/mathematica/*/*.parquet", "split": "train"}]}, {"config_name": "code-matlab", "data_files": [{"path": "data/v*/code/matlab/*/*.parquet", "split": "train"}]}, {"config_name": "code-ocaml", "data_files": [{"path": "data/v*/code/ocaml/*/*.parquet", "split": "train"}]}, {"config_name": "code-perl", "data_files": [{"path": "data/v*/code/perl/*/*.parquet", "split": "train"}]}, {"config_name": "code-php", "data_files": [{"path": "data/v*/code/php/*/*.parquet", "split": "train"}]}, {"config_name": "code-python", "data_files": [{"path": "data/v*/code/python/*/*.parquet", "split": "train"}]}, {"config_name": "code-r", "data_files": [{"path": "data/v*/code/r/*/*.parquet", "split": "train"}]}, {"config_name": "code-racket", "data_files": [{"path": "data/v*/code/racket/*/*.parquet", "split": "train"}]}, {"config_name": "code-ruby", "data_files": [{"path": "data/v*/code/ruby/*/*.parquet", "split": "train"}]}, {"config_name": "code-rust", "data_files": [{"path": "data/v*/code/rust/*/*.parquet", "split": "train"}]}, {"config_name": "code-scala", "data_files": [{"path": "data/v*/code/scala/*/*.parquet", "split": "train"}]}, {"config_name": "code-swift", "data_files": [{"path": "data/v*/code/swift/*/*.parquet", "split": "train"}]}, {"config_name": "code-tex", "data_files": [{"path": "data/v*/code/tex/*/*.parquet", "split": "train"}]}, {"config_name": "code-typescript", "data_files": [{"path": "data/v*/code/typescript/*/*.parquet", "split": "train"}]}, {"config_name": "AmendementsParlement", "data_files": [{"path": "data/v*/natural/*/AmendementsParlement/*.parquet", "split": "train"}]}, {"config_name": "AmericanStories", "data_files": [{"path": "data/v*/natural/*/AmericanStories/*.parquet", "split": "train"}]}, {"config_name": "Claire", "data_files": [{"path": "data/v*/natural/*/Claire/*.parquet", "split": "train"}]}, {"config_name": "Claire-en", "data_files": [{"path": "data/v*/natural/en/Claire/*.parquet", "split": "train"}]}, {"config_name": "Claire-fr", "data_files": [{"path": "data/v*/natural/fr/Claire/*.parquet", "split": "train"}]}, {"config_name": "CroissantAligned", "data_files": [{"path": "data/v*/natural/*/CroissantAligned/*.parquet", "split": "train"}]}, {"config_name": "DiscoursPublics", "data_files": [{"path": "data/v*/natural/*/DiscoursPublics/*.parquet", "split": "train"}]}, {"config_name": "Europarl", "data_files": [{"path": "data/v*/natural/*/Europarl/*.parquet", "split": "train"}]}, {"config_name": "Europarl-de", "data_files": [{"path": "data/v*/natural/de/Europarl/*.parquet", "split": "train"}]}, {"config_name": "Europarl-en", "data_files": [{"path": "data/v*/natural/en/Europarl/*.parquet", "split": "train"}]}, {"config_name": "Europarl-es", "data_files": [{"path": "data/v*/natural/es/Europarl/*.parquet", "split": "train"}]}, {"config_name": "Europarl-fr", "data_files": [{"path": "data/v*/natural/fr/Europarl/*.parquet", "split": "train"}]}, {"config_name": "EuroparlAligned", "data_files": [{"path": "data/v*/natural/*/EuroparlAligned/*.parquet", "split": "train"}]}, {"config_name": "EuroparlAligned-de,fr", "data_files": [{"path": "data/v*/natural/de-fr/EuroparlAligned/*.parquet", "split": "train"}]}, {"config_name": "EuroparlAligned-es,en", "data_files": [{"path": "data/v*/natural/es-en/EuroparlAligned/*.parquet", "split": "train"}]}, {"config_name": "EuroparlAligned-fr,en", "data_files": [{"path": "data/v*/natural/fr-en/EuroparlAligned/*.parquet", "split": "train"}]}, {"config_name": "EuroparlAligned-it,en", "data_files": [{"path": "data/v*/natural/it-en/EuroparlAligned/*.parquet", "split": "train"}]}, {"config_name": "Eurovoc", "data_files": [{"path": "data/v*/natural/*/Eurovoc/*.parquet", "split": "train"}]}, {"config_name": "Eurovoc-de", "data_files": [{"path": "data/v*/natural/de/Eurovoc/*.parquet", "split": "train"}]}, {"config_name": "Eurovoc-en", "data_files": [{"path": "data/v*/natural/en/Eurovoc/*.parquet", "split": "train"}]}, {"config_name": "Eurovoc-es", "data_files": [{"path": "data/v*/natural/es/Eurovoc/*.parquet", "split": "train"}]}, {"config_name": "Eurovoc-it", "data_files": [{"path": "data/v*/natural/it/Eurovoc/*.parquet", "split": "train"}]}, {"config_name": "FineWebEdu", "data_files": [{"path": "data/v*/natural/*/FineWebEdu/*.parquet", "split": "train"}]}, {"config_name": "GallicaMonographies", "data_files": [{"path": "data/v*/natural/*/GallicaMonographies/*.parquet", "split": "train"}]}, {"config_name": "GallicaPress", "data_files": [{"path": "data/v*/natural/*/GallicaPress/*.parquet", "split": "train"}]}, {"config_name": "Gutenberg", "data_files": [{"path": "data/v*/natural/*/Gutenberg/*.parquet", "split": "train"}]}, {"config_name": "Gutenberg-de", "data_files": [{"path": "data/v*/natural/de/Gutenberg/*.parquet", "split": "train"}]}, {"config_name": "Gutenberg-en", "data_files": [{"path": "data/v*/natural/en/Gutenberg/*.parquet", "split": "train"}]}, {"config_name": "Gutenberg-es", "data_files": [{"path": "data/v*/natural/es/Gutenberg/*.parquet", "split": "train"}]}, {"config_name": "Gutenberg-fr", "data_files": [{"path": "data/v*/natural/fr/Gutenberg/*.parquet", "split": "train"}]}, {"config_name": "Gutenberg-it", "data_files": [{"path": "data/v*/natural/it/Gutenberg/*.parquet", "split": "train"}]}, {"config_name": "HAL", "data_files": [{"path": "data/v*/natural/*/HAL/*.parquet", "split": "train"}]}, {"config_name": "InterventionsParlement", "data_files": [{"path": "data/v*/natural/*/InterventionsParlement/*.parquet", "split": "train"}]}, {"config_name": "LEGI", "data_files": [{"path": "data/v*/natural/*/LEGI/*.parquet", "split": "train"}]}, {"config_name": "MathPile", "data_files": [{"path": "data/v*/natural/*/MathPile/*.parquet", "split": "train"}]}, {"config_name": "OpenData", "data_files": [{"path": "data/v*/natural/*/OpenData/*.parquet", "split": "train"}]}, {"config_name": "OpenEdition", "data_files": [{"path": "data/v*/natural/*/OpenEdition/*.parquet", "split": "train"}]}, {"config_name": "PeS2o", "data_files": [{"path": "data/v*/natural/*/PeS2o/*.parquet", "split": "train"}]}, {"config_name": "PeS2o-s2ag", "data_files": [{"path": "data/v*/natural/*/PeS2o/*s2ag.parquet", "split": "train"}]}, {"config_name": "PeS2o-s2orc", "data_files": [{"path": "data/v*/natural/*/PeS2o/*s2orc.parquet", "split": "train"}]}, {"config_name": "Pile", "data_files": [{"path": "data/v*/natural/*/Pile/*.parquet", "split": "train"}]}, {"config_name": "Pile-DM_Mathematics", "data_files": [{"path": "data/v*/natural/*/Pile/*DM_Mathematics.parquet", "split": "train"}]}, {"config_name": "Pile-FreeLaw", "data_files": [{"path": "data/v*/natural/*/Pile/*FreeLaw.parquet", "split": "train"}]}, {"config_name": "Pile-NIH_ExPorter", "data_files": [{"path": "data/v*/natural/*/Pile/*NIH_ExPorter.parquet", "split": "train"}]}, {"config_name": "Pile-PhilPapers", "data_files": [{"path": "data/v*/natural/*/Pile/*PhilPapers.parquet", "split": "train"}]}, {"config_name": "Pile-StackExchange", "data_files": [{"path": "data/v*/natural/*/Pile/*StackExchange.parquet", "split": "train"}]}, {"config_name": "Pile-USPTO_Backgrounds", "data_files": [{"path": "data/v*/natural/*/Pile/*USPTO_Backgrounds.parquet", "split": "train"}]}, {"config_name": "Pile-Ubuntu_IRC", "data_files": [{"path": "data/v*/natural/*/Pile/*Ubuntu_IRC.parquet", "split": "train"}]}, {"config_name": "QuestionsEcritesParlement", "data_files": [{"path": "data/v*/natural/*/QuestionsEcritesParlement/*.parquet", "split": "train"}]}, {"config_name": "RedPajama", "data_files": [{"path": "data/v*/natural/*/RedPajama/*.parquet", "split": "train"}]}, {"config_name": "RedPajama-de", "data_files": [{"path": "data/v*/natural/de/RedPajama/*.parquet", "split": "train"}]}, {"config_name": "RedPajama-es", "data_files": [{"path": "data/v*/natural/es/RedPajama/*.parquet", "split": "train"}]}, {"config_name": "RedPajama-fr", "data_files": [{"path": "data/v*/natural/fr/RedPajama/*.parquet", "split": "train"}]}, {"config_name": "RedPajama-it", "data_files": [{"path": "data/v*/natural/it/RedPajama/*.parquet", "split": "train"}]}, {"config_name": "Stac", "data_files": [{"path": "data/v*/natural/*/Stac/*.parquet", "split": "train"}]}, {"config_name": "TheStack", "data_files": [{"path": "data/v*/code/*/TheStack/*.parquet", "split": "train"}]}, {"config_name": "Theses", "data_files": [{"path": "data/v*/natural/*/Theses/*.parquet", "split": "train"}]}, {"config_name": "Wikipedia", "data_files": [{"path": "data/v*/natural/*/Wikipedia/*.parquet", "split": "train"}]}, {"config_name": "Wikipedia-de", "data_files": [{"path": "data/v*/natural/de/Wikipedia/*.parquet", "split": "train"}]}, {"config_name": "Wikipedia-en", "data_files": [{"path": "data/v*/natural/en/Wikipedia/*.parquet", "split": "train"}]}, {"config_name": "Wikipedia-es", "data_files": [{"path": "data/v*/natural/es/Wikipedia/*.parquet", "split": "train"}]}, {"config_name": "Wikipedia-fr", "data_files": [{"path": "data/v*/natural/fr/Wikipedia/*.parquet", "split": "train"}]}, {"config_name": "Wikipedia-it", "data_files": [{"path": "data/v*/natural/it/Wikipedia/*.parquet", "split": "train"}]}, {"config_name": "Wikisource", "data_files": [{"path": "data/v*/natural/*/Wikisource/*.parquet", "split": "train"}]}, {"config_name": "Wiktionary", "data_files": [{"path": "data/v*/natural/*/Wiktionary/*.parquet", "split": "train"}]}, {"config_name": "YouTube", "data_files": [{"path": "data/v*/natural/*/YouTube/*.parquet", "split": "train"}]}]}

a90fd57383929416de159c378ae624993667f03d

Lucie Training Dataset Card The Lucie Training Dataset is a curated collection of text data in English, French, German, Spanish and Italian culled from a variety of sources including: web data, video subtitles, academic papers, digital books, newspapers, and magazines, some of which were processed by Optical Character Recognition (OCR). It also contains samples of diverse programming languages. The Lucie Training Dataset was used to pretrain Lucie-7B, a foundation LLM with strong… See the full description on the dataset page: https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset.

67162ffb3155cb90a534be53

Rapidata/image-preference-demo

Rapidata

{"language": ["en"], "size_categories": ["n<1K"], "pretty_name": "Image dataset for preference aquisition demo", "tags": ["preference", "text-to-image", "flux"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "matchups.csv"}]}]}

3c4d7fd9da793cfb3cc3651602287e29e4788148

Image dataset for preference aquisition demo This dataset provides the files used to run the example that we use in this blog post to illustrate how easily you can set up and run the annotation process to collect a huge preference dataset using Rapidata's API. The goal is to collect human preferences based on pairwise image matchups. The dataset contains: Generated images: A selection of example images generated using Flux.1 and Stable Diffusion. The images are provided in a… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/image-preference-demo.

6734a325be618c1a37a20040

Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry

Rapidata

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image1", "dtype": "image"}, {"name": "image2", "dtype": "image"}, {"name": "votes_image1", "dtype": "int64"}, {"name": "votes_image2", "dtype": "int64"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "detailed_results", "dtype": "string"}, {"name": "image1_path", "dtype": "string"}, {"name": "image2_path", "dtype": "string"}], "splits": [{"name": "train_0001", "num_bytes": 605226469, "num_examples": 1000}, {"name": "train_0002", "num_bytes": 642274651, "num_examples": 1000}, {"name": "train_0003", "num_bytes": 691292204, "num_examples": 1000}, {"name": "train_0004", "num_bytes": 738469071, "num_examples": 1000}, {"name": "train_0005", "num_bytes": 342763220, "num_examples": 496}], "download_size": 820299961, "dataset_size": 3020025615}, "configs": [{"config_name": "default", "data_files": [{"split": "train_0001", "path": "data/train_0001-*"}, {"split": "train_0002", "path": "data/train_0002-*"}, {"split": "train_0003", "path": "data/train_0003-*"}, {"split": "train_0004", "path": "data/train_0004-*"}, {"split": "train_0005", "path": "data/train_0005-*"}]}], "language": ["en"]}

0e768695d5e647708b7931fafa89de91880dddbf

Rapidata Image Generation Alignment Dataset This Dataset is a 1/3 of a 340k human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment. Link to the Preference dataset: https://huggingface.co/datasets/Rapidata/117k_human_preferences_flux1.0_V_flux1.1Blueberry Link to the Text-2-Image Alignment dataset: https://huggingface.co/datasets/Rapidata/117k_human_alignment_flux1.0_V_flux1.1Blueberry It was collected in ~2 Days using… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry.

6735ab5ada0aa544a31cb334

Laxhar/noob-wiki

Laxhar

{"license": "apache-2.0", "task_categories": ["text-to-image"], "language": ["en"], "tags": ["wiki"]}

929c972dcc8aeecde42b7cd8931afe82cd864424

Noob SDXL Wiki This is the WIKI database for Noob SDXL Models.

6749bb67ed72b6f7d98bfb71

hpprc/kaken-trans-ja-en

hpprc

{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "text_ja", "dtype": "string"}, {"name": "text_en", "dtype": "string"}, {"name": "model", "dtype": {"class_label": {"names": {"0": "qwen2.5-32b"}}}}], "splits": [{"name": "train", "num_bytes": 14898659332, "num_examples": 3976575}], "download_size": 4595849673, "dataset_size": 14898659332}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cc-by-4.0", "task_categories": ["translation"], "language": ["ja", "en"], "tags": ["machine-translation", "synthetic"]}

5f33a0d81885ef7aca7766623f6a5240587c8b80

llm-jp-corpus-v3のkakenサブセット中の日本語テキストを、Qwen/Qwen2.5-32B-Instructを用いて日本語から英語に翻訳したデータセットです。 オープンな日英パラレルコーパスを意図して作成・公開しました。 id列は翻訳の際に使用したカラムであり、元データセットに存在するidカラムとは異なっています。 kakenサブセット自体のHF版データセットも合わせてご覧ください: hpprc/llmjp-kaken。 本データセットのライセンスは元データセットのライセンスを継承し、CC-BY 4.0とします。

674dae8c3416d4f1bbfc4fbd

kenhktsui/longtalk-cot-v0.1

kenhktsui

{"dataset_info": {"features": [{"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source", "dtype": "string"}, {"name": "chosen_source", "dtype": "string"}, {"name": "rejected_source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 580076398, "num_examples": 61246}], "download_size": 284759903, "dataset_size": 580076398}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "language": ["en"], "tags": ["reasoning"], "size_categories": ["10K<n<100K"]}

437e69bf3d3744be737af7cf4d2ef88bbf7c2840

LongTalk-CoT v0.1: A Very Long Chain-of-Thought Dataset for Reasoning Model Post-Training Generated by Datou1111/shou_xin "Reasoning is About Process, not Outcome" I’m excited to release LongTalk-CoT v0.1, a dataset designed for post training o1-like reasoning model. Each response is prompted using QwQ-32B-Preview, and specifically handcrafted system message that encourages more vocalised thinking, and self reflection. Features post-training dataset contains… See the full description on the dataset page: https://huggingface.co/datasets/kenhktsui/longtalk-cot-v0.1.

67711b7130664bf3f0e9da12

laion/LAION-Audio-300M

laion

{"license": "apache-2.0"}

29eaacba2d0815aaf608ab34303555b9c895792e

67734d5c7ec2413faa8d3c85

PowerInfer/LONGCOT-Refine-500K

PowerInfer

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

88bf8410db01197006e572a46c88311720a23577

This repository contains approximately 500,000 instances of responses generated using Qwen2.5-72B-Instruct. The dataset combines prompts from multiple high-quality sources to create diverse and comprehensive training data. The dataset is available under the Apache 2.0 license. Bias, Risks, and Limitations This dataset is mainly in English. The dataset inherits the biases, errors, and omissions known to exist in data used for seed sources and models used for data generation.… See the full description on the dataset page: https://huggingface.co/datasets/PowerInfer/LONGCOT-Refine-500K.

677562e931db4dedcf1c7805

AkitoP/MiSide-Japanese

AkitoP

{"license": "apache-2.0", "task_categories": ["text-to-speech"], "language": ["ja"], "size_categories": ["1K<n<10K"]}

365a44f821b6efd55b7e6573fe321460b8aa075d

6781cbdbbf78248d0eb507a4

Avelina/smollm-corpus

Avelina

{"license": "odc-by", "dataset_info": [{"config_name": "default", "features": [{"name": "text", "dtype": "string"}]}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data*/train-*"}]}], "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["100M<n<1B"]}

f2f6895e53c967882a4c18009c536c41e6d7ffb4

SmolLM-Corpus: Now shuffled and sharded! This is a version of the SmolLM-Corpus where the 3 subsets have been interleved, shuffled and sharded as 23698 jsonl.zst files for easy streaming! The dataset is comprised of the cosmopedia-v2 and fineweb-edu-dedup subsets from the original SmolLM-Corpus repo, with the python-edu subset being pulled from my python-edu repo. Dataset Structure The dataset is split into 24 subdirectories, with the first 23 containing 1000… See the full description on the dataset page: https://huggingface.co/datasets/Avelina/smollm-corpus.

6784a48357147f61bc3fb7d3

HAERAE-HUB/HRM8K

HAERAE-HUB

{"license": "mit", "configs": [{"config_name": "MATH", "data_files": [{"split": "test", "path": "HRM8K/math_do_test.csv"}]}, {"config_name": "GSM8K", "data_files": [{"split": "test", "path": "HRM8K/gsm8k_test.csv"}]}, {"config_name": "OMNI_MATH", "data_files": [{"split": "test", "path": "HRM8K/omni-math_do_test.csv"}]}, {"config_name": "MMMLU", "data_files": [{"split": "test", "path": "HRM8K/mmmlu_test.csv"}]}, {"config_name": "KSM", "data_files": [{"split": "test", "path": "HRM8K/ksm_test.csv"}]}], "language": ["ko", "en"], "tags": ["haerae"]}

119f413a404c8b123618f6e7482d744c8afd6916

| 📖 Paper | 📝 Blog | 🖥️ Code(Coming soon!) | HRM8K We introduce HAE-RAE Math 8K (HRM8K), a bilingual math reasoning benchmark for Korean and English. HRM8K comprises 8,011 instances for evaluation, sourced through a combination of translations from established English benchmarks (e.g., GSM8K, MATH, OmniMath, MMMLU) and original problems curated from existing Korean math exams. Benchmark Overview The HRM8K benchmark consists of two subsets: Korean School… See the full description on the dataset page: https://huggingface.co/datasets/HAERAE-HUB/HRM8K.

678591b07ecdfd2fdb1a3f9f

mlabonne/smoltalk-semhashed

mlabonne

{"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3319564806.469319, "num_examples": 861102}], "download_size": 1809087925, "dataset_size": 3319564806.469319}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

c4bd9c0dfc79986b9a5a29b976eeeb3b50804e23

SmolTalk SemHashed This is a near-deduplicated version of smoltalk created with the semhash library. Instead of MinHash deduplication, it uses embeddings generated with minishlab/potion-base-8M, a distilled version of BAAI/bge-base-en-v1.5, and a threshold of 0.95 (see the vicinity library). ❤️ Kudos to minishlab for this super cool stuff!

621ffdd236468d709f181dd1

hendrycks/competition_math

hendrycks

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71b758ecc688b2822d07ffa7f8393299f1dc7cac

The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations.

@article{hendrycksmath2021, title={Measuring Mathematical Problem Solving With the MATH Dataset}, author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, journal={arXiv preprint arXiv:2103.03874}, year={2021} }

625e8e36d28969004c120d8b

google/fleurs

google

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d7c758a6dceecd54a98cac43404d3d576e721f07

FLEURS Fleurs is the speech version of the FLoRes machine translation benchmark. We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven… See the full description on the dataset page: https://huggingface.co/datasets/google/fleurs.

64358e2179c45fcf1ada09f4

databricks/databricks-dolly-15k

databricks

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

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.

64ff224ee7ef4e223d949733

TIGER-Lab/MathInstruct

TIGER-Lab

{"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MathInstruct", "size_categories": ["100K<n<1M"], "tags": ["math"]}

b4fdc323a7be1379c9c7c0b67b1de72dfee2111a

🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields. Project Page:… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MathInstruct.

6564d741cfdc8b6433bfba49

MMMU/MMMU

MMMU

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171b0ef74cd1704464e6940860968009d8cdd59a

MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI) 🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub 🔔News 🛠️[2024-05-30]: Fixed duplicate option issues in Materials dataset items (validation_Materials_25; test_Materials_17, 242) and content error in validation_Materials_25. 🛠️[2024-04-30]: Fixed missing "-" or "^" signs in Math dataset items (dev_Math_2, validation_Math_11, 12, 16;… See the full description on the dataset page: https://huggingface.co/datasets/MMMU/MMMU.

65a6c16a8d4eb84e00341759

satellogic/EarthView

satellogic

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7cc63f0d0ed182409f7ac101287f84ed96307afd

EarthView dataset Overview The EarthView Dataset is a comprehensive collection of multispectral earth imagery. The dataset is divided into four distinct subsets sourced from Satellogic, Sentinel-1, Sentinel-2, and NEON imagers, each providing unique data. Dataset Viewer Check the EarthView Dataset Viewer and it's code for examples on how to access the images and navigate the dataset. EarthView dataset Overview Dataset Viewer Data Sources Available Subsets… See the full description on the dataset page: https://huggingface.co/datasets/satellogic/EarthView.

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}]}}

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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

65d79d224f7ca8579b9e5e84

MathLLMs/MathVision

MathLLMs

{"license": "mit", "annotations_creators": ["expert-generated", "found"], "language_creators": ["expert-generated", "found"], "task_categories": ["question-answering", "multiple-choice", "visual-question-answering", "text-generation"], "language": ["en"], "tags": ["mathematics", "reasoning", "multi-modal-qa", "math-qa", "figure-qa", "geometry-qa", "math-word-problem", "textbook-qa", "vqa", "geometry-diagram", "synthetic-scene", "chart", "plot", "scientific-figure", "table", "function-plot", "abstract-scene", "puzzle-test", "document-image", "science"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "testmini", "path": "data/testmini-*"}]}], "pretty_name": "MATH-V", "size_categories": ["1K<n<10K"]}

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Measuring Multimodal Mathematical Reasoning with the MATH-Vision Dataset [💻 Github] [🌐 Homepage] [📊 Leaderboard ] [🔍 Visualization] [📖 ArXiv Paper] 🚀 Data Usage from datasets import load_dataset dataset = load_dataset("MathLLMs/MathVision") print(dataset) 💥 News [2024-09-27] MATH-V is accepted by NeurIPS DB Track, 2024! 🎉🎉🎉 [2024-08-29] 🔥🔥🔥 Qwen2-VL-72B achieves new open-sourced SOTA on MATH-Vision with 25.9! 🎉 Congratulations!… See the full description on the dataset page: https://huggingface.co/datasets/MathLLMs/MathVision.

65e2df2f75019230a03c0ca6

Shitao/bge-m3-data

Shitao

{}

a69db8b86e9c1767d193ee0de95e5c4001a71eae

Dataset Summary This depository contains all the fine-tuning data for the bge-m3 model, including: Dataset Language MS MARCO English NQ English HotpotQA English TriviaQA English SQuAD English COLIEE English PubMedQA English NLI from SimCSE English DuReader Chinese mMARCO-zh Chinese T2Ranking Chinese Law-GPT Chinese cMedQAv2 Chinese NLI-zh Chinese LeCaRDv2 Chinese Mr.TyDi 11 languages MIRACL 16 languages MLDR 13 languages Note:… See the full description on the dataset page: https://huggingface.co/datasets/Shitao/bge-m3-data.

65fd74a5f31aac18cc0baaae

deepcs233/Visual-CoT

deepcs233

{"license": "apache-2.0"}

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VisCoT Dataset Card There is a shortage of multimodal datasets for training multi-modal large language models (MLLMs) that require to identify specific regions in an image for additional attention to improve response performance. This type of dataset with grounding bbox annotations could possibly help the MLLM output intermediate interpretable attention area and enhance performance. To fill the gap, we curate a visual CoT dataset. This dataset specifically focuses on… See the full description on the dataset page: https://huggingface.co/datasets/deepcs233/Visual-CoT.

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"], "license": "cc-by-sa-4.0", "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}}

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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.

6690566cd7741cade02b8fe2

Magpie-Align/Magpie-Reasoning-V1-150K

Magpie-Align

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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-V1-150K.

66bc06dc6da7aec8413d35ba

NousResearch/hermes-function-calling-v1

NousResearch

{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering", "feature-extraction"], "language": ["en"], "configs": [{"config_name": "func_calling_singleturn", "data_files": "func-calling-singleturn.json", "default": true}, {"config_name": "func_calling", "data_files": "func-calling.json"}, {"config_name": "glaive_func_calling", "data_files": "glaive-function-calling-5k.json"}, {"config_name": "json_mode_agentic", "data_files": "json-mode-agentic.json"}, {"config_name": "json_mode_singleturn", "data_files": "json-mode-singleturn.json"}]}

8f025148382537ba84cd325e1834b706e1461692

Hermes Function-Calling V1 This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models. This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational… See the full description on the dataset page: https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1.

67095bd63d70a28512ce9e76

Skywork/Skywork-Reward-Preference-80K-v0.2

Skywork

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9757870d81894c90216e92536bad48f41475d5d0

Skywork Reward Preference 80K IMPORTANT: This dataset is the decontaminated version of Skywork-Reward-Preference-80K-v0.1. We removed 4,957 pairs from the magpie-ultra-v0.1 subset that have a significant n-gram overlap with the evaluation prompts in RewardBench. You can find the set of removed pairs here. For more information, see this GitHub gist. If your task involves evaluation on RewardBench, we strongly encourage you to use v0.2 instead of v0.1 of the dataset. We will soon… See the full description on the dataset page: https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.2.

6709f419db7d982eb6235de3

Koala-36M/Koala-36M-v1

Koala-36M

d22de7a8906c9555a51ea59fb503d88726d5bda3

671b996b412818afaa0bc60d

Rapidata/flux1.1-likert-scale-preference

Rapidata

{"task_categories": ["text-to-image"], "language": ["en"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "uid", "dtype": "int64"}, {"name": "1: Not at all", "dtype": "int64"}, {"name": "2: A little", "dtype": "int64"}, {"name": "3: Moderately", "dtype": "int64"}, {"name": "4: Very well", "dtype": "int64"}, {"name": "5: Perfectly", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "prompt source", "dtype": "string"}, {"name": "tag", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 467753187.988, "num_examples": 1124}], "download_size": 470650143, "dataset_size": 467753187.988}, "tags": ["preference", "likert", "flux"]}

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Flux1.1 Likert Scale Text-to-Image Alignment Evaluation This dataset contains images generated using Flux1.1 [pro] based on the prompts from our text-to-image generation benchmark. Where the benchmark generally focuses on pairwise comparisons to rank different image generation models against each other, this Likert-scale dataset focuses on one particular model and aims to reveal the particular nuances and highlight strong and weaks points of the model. If you get value from… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/flux1.1-likert-scale-preference.

671fc14579275e038e3e299b

Rapidata/Animals-10

Rapidata

{"license": "gpl-2.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Butterfly", "1": "Cat", "2": "Chicken", "3": "Cow", "4": "Dog", "5": "Elephant", "6": "Horse", "7": "Sheep", "8": "Spider", "9": "Squirrel"}}}}], "splits": [{"name": "train", "num_bytes": 300304734.49, "num_examples": 23554}], "download_size": 318523927, "dataset_size": 300304734.49}}

f4aa57bb6d694a5e9cd478f0df823addb770a118

Rapidata Animals-10 We took this existing Animals-10 dataset from kaggle and cleaned it using Rapidata's crowd, as detailed in this blog post. If you get value from this dataset and would like to see more in the future, please consider liking it. Dataset Details 10 classes: Butterfly, Cat, Chicken, Cow, Dog, Elephant, Horse, Sheep Spider, Squirrel 23554 Images In total, 124k labels were collected by human annotators, so each image is cross-validated on average by… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Animals-10.

672ce04d6451fbe8185909be

Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3

Rapidata

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96a4db1d70fbf08f1054dff771f465dccab94535

NOTE: A newer version of this dataset is available Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Preference_Dataset Rapidata Image Generation Preference Dataset This Dataset is a 1/3 of a 2M+ human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment. Link to the Coherence dataset: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset Link to the Text-2-Image Alignment dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3.

67324e20809e988d76c9e982

eltorio/ROCOv2-radiology

eltorio

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "cui", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 13464639396.75, "num_examples": 59962}, {"name": "validation", "num_bytes": 2577450447, "num_examples": 9904}, {"name": "test", "num_bytes": 2584850128.125, "num_examples": 9927}], "download_size": 18621371902, "dataset_size": 18626939971.875}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "language": ["en"], "license": "cc-by-nc-sa-4.0", "pretty_name": "ROCOv2", "tags": ["medical"]}

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ROCOv2: Radiology Object in COntext version 2 Introduction ROCOv2 is a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access Subset. It is an updated version of the ROCO dataset, adding 35,705 new images and improving concept extraction and filtering. Dataset Overview The ROCOv2 dataset contains 79,789 radiological images, each with a corresponding caption and medical… See the full description on the dataset page: https://huggingface.co/datasets/eltorio/ROCOv2-radiology.

673c32238052ec9866ba023c

maum-ai/General-Evol-VQA

maum-ai

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "korean", "num_bytes": 343402628, "num_examples": 587441}, {"name": "english", "num_bytes": 626167802, "num_examples": 598255}], "download_size": 464324435, "dataset_size": 969570430}, "configs": [{"config_name": "default", "data_files": [{"split": "korean", "path": "data/korean-*"}, {"split": "english", "path": "data/english-*"}]}], "task_categories": ["visual-question-answering", "question-answering"], "language": ["ko", "en"], "size_categories": ["100K<n<1M"]}

c0a0e1d22bfb95c81543c76bae8b16a10a8ce6b9

Dataset Card for General-Evol-VQA-1.2M This dataset has been carefully curated to enhance the general instruction capabilities of Vision-Language Models (VLMs). It comprises two subsets: 600k English samples 600k Korean samples We recommend using this dataset alongside other task-specific datasets (e.g., OCR, Language, code, math, ...) to improve performance and achieve more robust model capabilities. Made by: maum.ai Brain NLP. Jaeyoon Jung, Yoonshik Kim Dataset Target… See the full description on the dataset page: https://huggingface.co/datasets/maum-ai/General-Evol-VQA.

6749eaea4ccd2d5607847b71

MAmmoTH-VL/MAmmoTH-VL-Instruct-12M

MAmmoTH-VL

{"license": "apache-2.0", "language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["visual-question-answering", "question-answering"], "tags": ["reasoning", "CoT", "math"]}

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MAmmoTH-VL-Instruct-12M 🏠 Homepage | 🤖 MAmmoTH-VL-8B | 💻 Code | 📄 Arxiv | 📕 PDF | 🖥️ Demo Introduction Our simple yet scalable visual instruction data rewriting pipeline consists of three steps: manual data source collection, rewriting using MLLMs/LLMs, and filtering via the same MLLM as a judge. Examples below illustrate transformations in math and science categories, showcasing detailed, step-by-step responses. The data distribution of… See the full description on the dataset page: https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M.

67514cb8ff3dfacd1b313a33

amphora/QwQ-LongCoT-130K

amphora

{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "qwq", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 969051509, "num_examples": 133102}], "download_size": 420996585, "dataset_size": 969051509}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"]}

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Also have a look on the second version here => QwQ-LongCoT-2 Figure 1: Just a cute picture generate with [Flux](https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-Logo-Design) Today, I’m excited to release QwQ-LongCoT-130K, a SFT dataset designed for training O1-like large language models (LLMs). This dataset includes about 130k instances, each with responses generated using QwQ-32B-Preview. The dataset is available under the Apache 2.0 license, so feel free to use it as you like.… See the full description on the dataset page: https://huggingface.co/datasets/amphora/QwQ-LongCoT-130K.