datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.cyp-challenge-train-test
CYP Challenge Train/Test Dataset
A high-quality experimental dataset for predicting inhibition of the major drug-metabolizing Cytochrome P450 enzymes (CYP1A2, CYP2C9, CYP2D6, CYP3A4), released as part of the OpenADMET CYP Inhibition Blind Challenge.
Blog post: Announcing OpenADMET’s CYP inhibition blind challenge
Challenge Space: OpenADMET CYP Inhibition Blind Challenge
Challenge period: August 17, 2026 - November 3, 2026
Produced by: OpenADMET
CHANGELOG
Updated… See the full description on the dataset page: https://huggingface.co/datasets/openadmet/cyp-challenge-train-test.pxr-challenge-train-test
PXR Challenge Train/Test Dataset
A high-quality experimental dataset for predicting human Pregnane-X Receptor (PXR) induction, comprising over 11,000 compounds screened using a high-fidelity in-house assay. This is the largest publicly available PXR activity dataset, released as part of the OpenADMET PXR Induction Blind Challenge.
Blog post: Announcing the Next OpenADMET Blind Challenge: Predicting PXR Induction
Challenge Space: openadmet/pxr-challenge
Challenge period: April 1… See the full description on the dataset page: https://huggingface.co/datasets/openadmet/pxr-challenge-train-test.Bitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.MMLU-Pro-CoT-Train-Labeled
Dataset Details
Modality: Text
Format: CSV
Size: 10K - 100K rows
Total Rows: 84,098
License: MIT
Libraries Supported: datasets, pandas, croissant
Structure
Each row in the dataset includes:
question: The query posed in the dataset.
answer: The correct response.
category: The domain of the question (e.g., math, science).
src: The source of the question.
id: A unique identifier for each entry.
chain_of_thoughts: Step-by-step reasoning steps leading to the answer.
labels:… See the full description on the dataset page: https://huggingface.co/datasets/UW-Madison-Lee-Lab/MMLU-Pro-CoT-Train-Labeled.plant-disease-trainopenadmet-expansionrx-challenge-train-data
OpenADMET-ExpansionRx Challenge training dataset
This dataset contains real-work ADMET data from a recently prosecuted series of drug discovery campaigns by Expansion Therapeutics on RNA mediated diseases. While optimising candidate molecules for their preclinical programs Expansion collected a variety of ADMET data for off-targets and properties of interest in the traditional game of “whack-a-mole” familiar to all drug hunters. Now, they’ve made the bold and generous decision to… See the full description on the dataset page: https://huggingface.co/datasets/openadmet/openadmet-expansionrx-challenge-train-data.Bitext-events-ticketing-llm-chatbot-training-dataset
Bitext - Events and Ticketing Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [events and ticketing] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset.pizza_st_human_mouse_v1_train_with_labelsMUG-V-Training-Samples
MUG-V Training Samples
Sample training dataset for the MUG-V 10B video generation model training framework.
Dataset Description
This dataset contains pre-processed training samples for quick-start validation and testing of the MUG-V Megatron-LM training pipeline. It includes:
VideoVAE-encoded latents (8×8×8 compressed video representations)
T5-XXL text features (4096-dim embeddings)
Training metadata CSV (sample mapping and configuration)
⚠️ Note: This is a sample… See the full description on the dataset page: https://huggingface.co/datasets/MUG-V/MUG-V-Training-Samples.STARCOP_allbands_Train1
STARCOP dataset
STARCOP dataset: Semantic Segmentation of Methane Plumes with Hyperspectral Machine Learning Models 🌈🛰️Authors: Vít Růžička, Gonzalo Mateo-Garcia, Luis Gómez-Chova, Anna Vaughan, Luis Guanter and Andrew Markham
Fast data preview in: dataset_exploration.ipynb Main repository: github/spaceml-org/STARCOP
Task:
Methane is the second most important greenhouse gas contributor to climate change; at the same time its reduction has been denoted as one of the… See the full description on the dataset page: https://huggingface.co/datasets/previtus/STARCOP_allbands_Train1.Bitext-telco-llm-chatbot-training-dataset
Bitext - Telco Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [telco] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset.Dream_TrainBitext-insurance-llm-chatbot-training-dataset
Bitext - Insurance Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [insurance] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset.FrontierCO-TrainReverse-hybrid-train-no-persona-meanBitext-travel-llm-chatbot-training-dataset
Bitext - Travel Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Travel] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-travel-llm-chatbot-training-dataset.Reverse-hybrid-correct-train-no-persona-meanDeepSeek-R1-Distill-Qwen-32B_NUMINA_train_amc_aime-llama3.1OmniVCus-Train
[NeurIPS 2025] OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
Dataset Description
This dataset supports multi-modal constrol video generation. It contains ~80K data samples processed from 140K videos by our VideoCus-Factory pipeline. Each data sample includes the original
video, text prompts, subject reference image, depth video, mask video, and motion video conditions.
Here is a data example:
Generated… See the full description on the dataset page: https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train.training_dataOriginal-hybrid-correct-train-no-persona-meanOriginal-hybrid-train-no-persona-meanpii_train
Russian PII NER Training Dataset
Dataset Description
This is the training corpus for PII (Personally Identifiable Information)
detection and Named Entity Recognition (NER) on Russian-language text. It
targets guardrail and anonymization pipelines that must reliably find personal
data (names, addresses, contacts) and Russian identity-document numbers
(passport, SNILS, INN, OMS, etc.) in text.
The corpus combines real, manually annotated examples from production… See the full description on the dataset page: https://huggingface.co/datasets/redmadrobot-rnd/pii_train.xnli2.0_train_arabicROCOv2-trainBitext-mortgage-loans-llm-chatbot-training-dataset
Bitext - Mortgage and Loans Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Mortgage and Loans] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-mortgage-loans-llm-chatbot-training-dataset.aihub-koen-translation-integrated-large-10m
AI Hub Ko-En Translation Dataset (Integrated)
AI Hub의 한-영 번역 관련 데이터셋 8개를 병합한 자료입니다.
병합 시 총 데이터 개수는 10,416,509개 이며, train / validation / test는 8:1:1 비율로 분할되었습니다.
base-10m: 병합 데이터 100% 사용, 총 10,416,509개
mini-1m: 병합 데이터 10% 사용 (base-10m의 각 세트 내에서 10% 임의 선택), 총 1,041,651개
tiny-100k: 병합 데이터 1% 사용 (base-10m의 각 세트 내에서 1% 임의 선택), 총 104,165개
Subsets
활용한 데이터셋 목록은 다음과 같으며, 데이터셋 이름 옆 번호는 aihubshell에서의 datasetkey입니다.
전문분야 한영 말뭉치 (111)
총 개수: 1,350,000
중복 제거 후 개수: 1,350,000
사용 칼럼:… See the full description on the dataset page: https://huggingface.co/datasets/traintogpb/aihub-koen-translation-integrated-large-10m.SC-train-valid-test_SDG-Descriptionsnli-label:
(0) entailment
(2) contradiction
train-deid