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.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.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.MUG-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.FARM_training_test
FARM Aerial Radio Map (ARM) Dataset
Paper:
FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking (https://arxiv.org/abs/2604.17362)
Overview
This repository releases the constructed ARM datasets based on ARM-Omni for FARM training, in-domain evaluation (D1-D10), and zero-shot evaluation (P1, F1, and A1). The dataset coverage is summarized below:
Dataset
Frequencies (GHz)
Max Rx Height (m)
Beamwidths
Map Grid Size
Volume
D1
2.1… See the full description on the dataset page: https://huggingface.co/datasets/jliang097/FARM_training_test.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.Bitext-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.Bitext-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.training_datatripclick-training
TripClick Baselines with Improved Training Data
Establishing Strong Baselines for TripClick Health Retrieval Sebastian Hofstätter, Sophia Althammer, Mete Sertkan and Allan Hanbury
https://arxiv.org/abs/2201.00365
tl;dr We create strong re-ranking and dense retrieval baselines (BERTCAT, BERTDOT, ColBERT, and TK) for TripClick (health ad-hoc retrieval). We improve the – originally too noisy – training data with a simple negative sampling policy. We achieve large gains over BM25 in the… See the full description on the dataset page: https://huggingface.co/datasets/sebastian-hofstaetter/tripclick-training.Bitext-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.Bitext-wealth-management-llm-chatbot-training-dataset
Bitext - Wealth Management 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 [Wealth Management] 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-wealth-management-llm-chatbot-training-dataset.isaac-gr00t-ikea-training-report
Isaac GR00T IKEA training report
Portable export of the W&B run unitree_g1_ikea_batch32_20260730. The run stopped after a clean host
shutdown; the last W&B metric is step 31,750 and the
last complete checkpoint is 31,000.
Summary
First logged training loss: 1.4667
Last logged training loss: 0.1256
Lowest 1,000-step rolling loss: 0.1249 at step 31,750
Mean GPU compute utilization: 51.8%
Mean allocated GPU memory: 29.0%
Provisional checkpoint choice: 30,000… See the full description on the dataset page: https://huggingface.co/datasets/ICRA-Competitions/isaac-gr00t-ikea-training-report.ga4-dataset-from-BQ-trainingset-2monthsBitext-hospitality-llm-chatbot-training-dataset
Bitext - Hospitality 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 [hospitality] 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-hospitality-llm-chatbot-training-dataset.Nemotron-3-Nano-RL-Training-Blend-prompt-only
Nemotron-3-Nano-RL-Training-Blend-prompt-only
Prompt-only extraction from nvidia/Nemotron-3-Nano-RL-Training-Blend.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.
null_or_empty_rows.md: row indexes where… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-3-Nano-RL-Training-Blend-prompt-only.bjj-kimura-lesson001-trial
BJJ Kimura from Side Control — Research Trial (sampled across the action arc)
Tier: Research / Evaluation (free, CC BY-NC-SA 4.0)
Source: RTK Motion Intelligence Platform · api.rtkmotion.io
Full commercial dataset: rtk-training/bjj-kimura-lesson001 (gated)
A temporally-sampled trial subset of a full 4D motion-capture session
of a Brazilian Jiu-Jitsu Kimura submission from side control, demonstrated
by a Former IBJJF World Champion (anonymized) with a training partner.… See the full description on the dataset page: https://huggingface.co/datasets/rtk-training/bjj-kimura-lesson001-trial.Bitext-media-llm-chatbot-training-dataset
Bitext - Media 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 [media] 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-media-llm-chatbot-training-dataset.cncf-question-and-answer-dataset-for-llm-training
CNCF QA Dataset for LLM Tuning
Description
This dataset, named cncf-qa-dataset-for-llm-tuning, is designed for fine-tuning large language models (LLMs) and is formatted in a question-answer (QA) style. The data is sourced from PDF and markdown (MD) files extracted from various project repositories within the CNCF (Cloud Native Computing Foundation) landscape. These files were processed and converted into a QA format to be fed into the LLM model.
The dataset includes the… See the full description on the dataset page: https://huggingface.co/datasets/Kubermatic/cncf-question-and-answer-dataset-for-llm-training.remote-ai-evaluation-training-market-snapshot
Dataset Description
This is an aggregate August 22, 2026 research snapshot from Specialist AI Work, an independent PatchMedia tracker of reviewed remote AI evaluation, AI training, data annotation-adjacent, and expert-review opportunities.
The live Specialist AI Work inventory has advanced since this snapshot. The counts in this repository describe the immutable August 22 research object; they are not a claim about today's inventory.
Reporting date: 2026-08-22
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/patchmedia-org/remote-ai-evaluation-training-market-snapshot.water-conflict-training-data
Water Conflict Training Dataset (Training-Ready)
Version: d2.0
🔬 Experimental Research
This experimental research draws on Pacific Institute's Water Conflict Chronology, which tracks water-related conflicts spanning over 4,500 years of human history. The work is conducted independently and is not affiliated with Pacific Institute.
This dataset is designed to assist researchers in training models to classify water-related conflict events at scale. The Pacific Institute… See the full description on the dataset page: https://huggingface.co/datasets/baobabtech/water-conflict-training-data.Bitext-restaurants-llm-chatbot-training-dataset
Bitext - Restaurants 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 [restaurants] 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-restaurants-llm-chatbot-training-dataset.seq_level_training_datatr_movie_reviews_trainingannotations_creators:
found
language_creators:
found
languages:
tr
licenses:
unknown
multilinguality:
monolingual
paperswithcode_id: null
pretty_name: turkish_movie_reviews
size_categories:
10K<n<100K
source_datasets:
original
task_categories:
text-classification
task_ids:
sentiment-classification
sentiment-scoring
GPU-Resources-Estimation-for-Deep-Learning-Training-Tasks
GPUMemNet and GPUUtilNet Dataset
This dataset accompanies the paper
“GPU Memory and Utilization Estimation for Training-Aware Resource
Management: Opportunities and Limitations.”
It contains synthetic deep learning training configurations and their measured
GPU memory consumption and utilization characteristics.
Dataset configurations
The dataset is divided into separate configurations because MLP, CNN, and
Transformer workloads use different feature schemas.… See the full description on the dataset page: https://huggingface.co/datasets/ehyo/GPU-Resources-Estimation-for-Deep-Learning-Training-Tasks.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/abhi23457/Bitext-customer-support-llm-chatbot-training-dataset.ozone_training_data
Ozone Training Data
Dataset Summary
The Ozone training dataset contains information about ozone levels, temperature, wind speed, pressure, and other related atmospheric variables across various geographic locations and time periods. It includes detailed daily observations from multiple data sources for comprehensive environmental and air quality analysis. Geographic coordinates (latitude and longitude) and timestamps (month, day, and hour) provide spatial and temporal… See the full description on the dataset page: https://huggingface.co/datasets/Geoweaver/ozone_training_data.STRING_V12_TrainingSet**Repository: https://stringdb-downloads.org/download/protein.physical.links.v12.0.txt.gz
**Reference: Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51, D638–D646 (2023).
llm-training-dataset
LLM Fine-Tuning Dataset - 4,000,000+ logs, 32 languages
The dataset contains over 4 million+ logs written in 32 languages and is tailored for LLM training. It includes log and response pairs from 3 models, and is designed for language models and instruction fine-tuning to achieve improved performance in various NLP tasks - Get the data
Models used for text generation:
GPT-3.5
GPT-4
Uncensored GPT Version (is not included inthe sample)
Languages in… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/llm-training-dataset.Nemotron-RL-Ultra-Training-Blends-prompt-only
Nemotron-RL-Ultra-Training-Blends-prompt-only
Prompt-only extraction from nvidia/Nemotron-RL-Ultra-Training-Blends.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.
null_or_empty_rows.md: row indexes where… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-RL-Ultra-Training-Blends-prompt-only.