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.

66cbf7ef92e9f5b19fcd65aa

cfahlgren1/react-code-instructions

cfahlgren1

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

787d9ed92d5b68adda1056c05d256a8e54106c42

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Translation-gpt4o_mini-v-gpt4o-v-deepl.

66bffb77453a7ef6c587560c

edinburgh-dawg/mmlu-redux-2.0

edinburgh-dawg

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{"config_name": "high_school_physics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "high_school_psychology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "high_school_statistics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": 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"string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "virology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}, {"config_name": "world_religions", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "error_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "potential_reason", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 100}]}], "configs": [{"config_name": "abstract_algebra", "data_files": [{"split": "test", "path": "abstract_algebra/data-*"}]}, {"config_name": "anatomy", "data_files": [{"split": "test", "path": "anatomy/data-*"}]}, {"config_name": "astronomy", "data_files": [{"split": "test", "path": "astronomy/data-*"}]}, {"config_name": "business_ethics", "data_files": [{"split": "test", "path": "business_ethics/data-*"}]}, {"config_name": "clinical_knowledge", "data_files": [{"split": "test", "path": "clinical_knowledge/data-*"}]}, {"config_name": "college_biology", "data_files": [{"split": "test", "path": "college_biology/data-*"}]}, {"config_name": "college_chemistry", "data_files": [{"split": "test", "path": "college_chemistry/data-*"}]}, {"config_name": "college_computer_science", "data_files": [{"split": "test", "path": "college_computer_science/data-*"}]}, {"config_name": "college_mathematics", "data_files": [{"split": "test", "path": "college_mathematics/data-*"}]}, {"config_name": "college_medicine", "data_files": [{"split": "test", "path": "college_medicine/data-*"}]}, {"config_name": "college_physics", "data_files": [{"split": "test", "path": "college_physics/data-*"}]}, {"config_name": "computer_security", "data_files": [{"split": "test", "path": "computer_security/data-*"}]}, {"config_name": "conceptual_physics", "data_files": [{"split": "test", "path": "conceptual_physics/data-*"}]}, {"config_name": "econometrics", "data_files": [{"split": "test", "path": "econometrics/data-*"}]}, {"config_name": "electrical_engineering", "data_files": [{"split": "test", "path": "electrical_engineering/data-*"}]}, {"config_name": "elementary_mathematics", "data_files": [{"split": "test", "path": "elementary_mathematics/data-*"}]}, {"config_name": "formal_logic", "data_files": [{"split": "test", "path": "formal_logic/data-*"}]}, {"config_name": "global_facts", "data_files": [{"split": "test", "path": "global_facts/data-*"}]}, {"config_name": "high_school_biology", "data_files": [{"split": "test", "path": "high_school_biology/data-*"}]}, {"config_name": "high_school_chemistry", "data_files": [{"split": "test", "path": "high_school_chemistry/data-*"}]}, {"config_name": "high_school_computer_science", "data_files": [{"split": "test", "path": "high_school_computer_science/data-*"}]}, {"config_name": "high_school_european_history", "data_files": [{"split": "test", "path": "high_school_european_history/data-*"}]}, {"config_name": "high_school_geography", "data_files": [{"split": "test", "path": "high_school_geography/data-*"}]}, {"config_name": "high_school_government_and_politics", "data_files": [{"split": "test", "path": "high_school_government_and_politics/data-*"}]}, {"config_name": "high_school_macroeconomics", "data_files": [{"split": "test", "path": "high_school_macroeconomics/data-*"}]}, {"config_name": "high_school_mathematics", "data_files": [{"split": "test", "path": "high_school_mathematics/data-*"}]}, {"config_name": "high_school_microeconomics", "data_files": [{"split": "test", "path": "high_school_microeconomics/data-*"}]}, {"config_name": "high_school_physics", "data_files": [{"split": "test", "path": "high_school_physics/data-*"}]}, {"config_name": "high_school_psychology", "data_files": [{"split": "test", "path": "high_school_psychology/data-*"}]}, {"config_name": "high_school_statistics", "data_files": [{"split": "test", "path": "high_school_statistics/data-*"}]}, {"config_name": "high_school_us_history", "data_files": [{"split": "test", "path": "high_school_us_history/data-*"}]}, {"config_name": "high_school_world_history", "data_files": [{"split": "test", "path": "high_school_world_history/data-*"}]}, {"config_name": "human_aging", "data_files": [{"split": "test", "path": "human_aging/data-*"}]}, {"config_name": "human_sexuality", "data_files": [{"split": "test", "path": "human_sexuality/data-*"}]}, {"config_name": "international_law", "data_files": [{"split": "test", "path": "international_law/data-*"}]}, {"config_name": "jurisprudence", "data_files": [{"split": "test", "path": "jurisprudence/data-*"}]}, {"config_name": "logical_fallacies", "data_files": [{"split": "test", "path": "logical_fallacies/data-*"}]}, {"config_name": "machine_learning", "data_files": [{"split": "test", "path": "machine_learning/data-*"}]}, {"config_name": "management", "data_files": [{"split": "test", "path": "management/data-*"}]}, {"config_name": "marketing", "data_files": [{"split": "test", "path": "marketing/data-*"}]}, {"config_name": "medical_genetics", "data_files": [{"split": "test", "path": "medical_genetics/data-*"}]}, {"config_name": "miscellaneous", "data_files": [{"split": "test", "path": "miscellaneous/data-*"}]}, {"config_name": "moral_disputes", "data_files": [{"split": "test", "path": "moral_disputes/data-*"}]}, {"config_name": "moral_scenarios", "data_files": [{"split": "test", "path": "moral_scenarios/data-*"}]}, {"config_name": "nutrition", "data_files": [{"split": "test", "path": "nutrition/data-*"}]}, {"config_name": "philosophy", "data_files": [{"split": "test", "path": "philosophy/data-*"}]}, {"config_name": "prehistory", "data_files": [{"split": "test", "path": "prehistory/data-*"}]}, {"config_name": "professional_accounting", "data_files": [{"split": "test", "path": "professional_accounting/data-*"}]}, {"config_name": "professional_law", "data_files": [{"split": "test", "path": "professional_law/data-*"}]}, {"config_name": "professional_medicine", "data_files": [{"split": "test", "path": "professional_medicine/data-*"}]}, {"config_name": "professional_psychology", "data_files": [{"split": "test", "path": "professional_psychology/data-*"}]}, {"config_name": "public_relations", "data_files": [{"split": "test", "path": "public_relations/data-*"}]}, {"config_name": "security_studies", "data_files": [{"split": "test", "path": "security_studies/data-*"}]}, {"config_name": "sociology", "data_files": [{"split": "test", "path": "sociology/data-*"}]}, {"config_name": "us_foreign_policy", "data_files": [{"split": "test", "path": "us_foreign_policy/data-*"}]}, {"config_name": "virology", "data_files": [{"split": "test", "path": "virology/data-*"}]}, {"config_name": "world_religions", "data_files": [{"split": "test", "path": "world_religions/data-*"}]}], "license": "cc-by-4.0", "task_categories": ["question-answering"], "language": ["en"], "pretty_name": "MMLU-Redux-2.0", "size_categories": ["1K<n<10K"]}

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.

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.

66a1d16a27fd84b81d732482

TEAMREBOOTT-AI/SciCap-MLBCAP

TEAMREBOOTT-AI

{"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "image-to-text"], "language": ["en"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "figure_type", "dtype": "string"}, {"name": "ocr", "dtype": "string"}, {"name": "paragraph", "dtype": "string"}, {"name": "mention", "dtype": "string"}, {"name": "figure_description", "dtype": "string"}, {"name": "mlbcap_long", "dtype": "string"}, {"name": "mlbcap_short", "dtype": "string"}, {"name": "categories", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2444177418.129, "num_examples": 47639}], "download_size": 2487129056, "dataset_size": 2444177418.129}, "size_categories": ["10K<n<100K"]}

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.

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.

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"}

53f3739cc041164023f988d7c7b98f6af3f0d2c0

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.… See the full description on the dataset page: https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha.

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.

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.

674dc01bf413e32210acb235

Rapidata/human-style-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": 26229461236, "num_examples": 63752}], "download_size": 17935847407, "dataset_size": 26229461236}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cdla-permissive-2.0", "task_categories": ["text-to-image", "image-to-text", "image-classification", "reinforcement-learning"], "language": ["en"], "tags": ["Human", "Preference", "country", "language", "flux", "midjourney", "dalle3", "stabeldiffusion", "alignment", "flux1.1", "flux1", "imagen3"], "size_categories": ["100K<n<1M"], "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 Preference Dataset"}

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.

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.

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.

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.

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.

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.

67744720363e2be467b7c2b5

qingy2024/FineQwQ-142k

qingy2024

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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%

677e02dad71b5f108df18381

permutans/fineweb-bbc-news

permutans

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5b14c5e10efd1c2103d1b425ac06876021258bc2

Dataset Card for BBC News from FineWeb This dataset provides a filtered subset of BBC News articles from each subset of the FineWeb dataset, expected to contain approximately 300M articles from BBC News domains. Dataset Details Dataset Sources Repository: https://huggingface.co/datasets/permutans/fineweb-bbc-news Source Dataset: HuggingFaceFW/fineweb Paper: https://arxiv.org/abs/2406.17557 (FineWeb paper) Uses Direct Use… See the full description on the dataset page: https://huggingface.co/datasets/permutans/fineweb-bbc-news.

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

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.

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.

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".

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.

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

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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.