datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
benchmark-bcplusHelpSteer2
HelpSteer2: Open-source dataset for training top-performing reward models
HelpSteer2 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.
This dataset has been created in partnership with Scale AI.
When used to tune a Llama 3.1 70B Instruct Model, we achieve 94.1% on RewardBench, which makes it the best Reward Model as… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer2.hermes-function-calling-v1
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 scenarios… See the full description on the dataset page: https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1.nfcorpus
NFCorpus
An MTEB dataset
Massive Text Embedding Benchmark
NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval
Task category
t2t
Domains
Medical, Academic, Written
Reference
https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["NFCorpus"])
evaluator = mteb.MTEB(task)
model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/nfcorpus.forecast-news
Forecast News
Deduplicated daily news corpus used by forecast-sim and future-sim.
Snapshot
31,859,020 articles
3,463 daily partitions
Coverage: 2016-08-26 through 2026-08-31
Snapshot published: 2026-09-18
Stored data size: approximately 158.5 GiB
The Parquet files are the canonical complete representation. The repository also
contains daily JSONL files where available and compact headline JSON files used
by article-browsing workflows.
Layout
Files… See the full description on the dataset page: https://huggingface.co/datasets/shash42/forecast-news.VideoChat3-LV116k
VideoChat3-LV116K
VideoChat3-LV116K is the long-video instruction data used by VideoChat3. It is designed to complement short academic video data with supervision over longer temporal contexts, where evidence can be sparse, delayed, and distributed across multiple video segments.
The dataset is constructed through a long-video synthesis pipeline. Candidate long videos are filtered for visual quality, semantic content, and temporal coherence. Videos are then split into manageable… See the full description on the dataset page: https://huggingface.co/datasets/MCG-NJU/VideoChat3-LV116k.cornstack-python-v1
CoRNStack Python Dataset
The CoRNStack Dataset, accepted to ICLR 2025, is a large-scale high quality training dataset specifically for code retrieval across multiple
programming languages. This dataset comprises of <query, positive, negative> triplets used to train nomic-embed-code,
CodeRankEmbed, and CodeRankLLM.
CoRNStack Dataset Curation
Starting with the deduplicated Stackv2, we create text-code pairs from function docstrings and respective code. We filtered out… See the full description on the dataset page: https://huggingface.co/datasets/nomic-ai/cornstack-python-v1.telegram-news-ua-dataset
Aisberg Telegram News UA
A continuously updated, de-identified corpus of Ukrainian Telegram news and the discussion around it, published by the Ukrainian non-profit Aisberg (ГО «АЙЗБЕРГ»). It comes in two layers. The first is the raw monthly stream: every post from a fixed set of public news channels, with its reactions and its comment thread. The second is the analysis behind every report Aisberg publishes: posts from different channels grouped into one event, the manipulation… See the full description on the dataset page: https://huggingface.co/datasets/aisbergpublicorganization/telegram-news-ua-dataset.FlashRAG_datasets
⚡FlashRAG: A Python Toolkit for Efficient RAG Research
FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms.
With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components.
For more information, please view our GitHub repo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets.Aiice
Dataset
Aiice benchmark dataset for Arctic sea ice concentration (SIC) forecasting,
based on OSI-SAF satellite products (CC BY 4.0).
Coverage
Period: October 1978 – April 2026
Resolution: 25 km spatial, daily temporal
Grid: 432×432 (Lambert Azimuthal Equal Area, EPSG:6931)
Source products
Product
Source
Period
OSI-450-a
SMMR, SSM/I, SSMIS
1978–2020
OSI-430-a
SSMIS
2021–Jul 2025
OSI-438
AMSR2
Jul 2025–present… See the full description on the dataset page: https://huggingface.co/datasets/ITMO-NSS/Aiice.xlam-function-calling-60kaime24_nofiguresThe 30 problems from AIME 2024 only with the ASY code for figures when it is necessary to solve the problem. Figure code that is not core to the problem was excluded.
Citation Information
@misc{muennighoff2025s1simpletesttimescaling,
title={s1: Simple test-time scaling},
author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto}… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/aime24_nofigures.Evol-Instruct-Code-80k-v1Open Source Implementation of Evol-Instruct-Code as described in the WizardCoder Paper.
Code for the intruction generation can be found on Github as Evol-Teacher.
20_newsgroupsThis is a version of the 20 newsgroups dataset that is provided in Scikit-learn. From the Scikit-learn docs:
The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). The split between the train and test set is based upon a messages posted before and after a specific date.
We followed the recommended practice to remove headers, signature blocks, and… See the full description on the dataset page: https://huggingface.co/datasets/SetFit/20_newsgroups.ner-jsonlOpenMathInstruct-1
OpenMathInstruct-1
OpenMathInstruct-1 is a math instruction tuning dataset with 1.8M problem-solution pairs
generated using permissively licensed Mixtral-8x7B model.
The problems are from GSM8K
and MATH training subsets and the solutions
are synthetically generated by allowing Mixtral model to use a mix of text reasoning and
code blocks executed by Python interpreter.
The dataset is split into train and validation subsets that we used in the ablations experiments.
These two subsets… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-1.Aegis-AI-Content-Safety-Dataset-2.0
🛡️ Nemotron Content Safety Dataset V2
The Nemotron Content Safety Dataset V2, formerly known as Aegis AI Content Safety Dataset 2.0, is comprised of 33,416 annotated interactions between humans and LLMs, split into 30,007 training samples, 1,445 validation samples, and 1,964 test samples. This release is an extension of the previously published Nemotron Content Safety Dataset V1.
To curate the dataset, we use the HuggingFace version of human preference data about harmlessness… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0.OpenScience
Dataset Description:
OpenScience is a multi-domain synthetic dataset designed to improve general-purpose reasoning in large language models (LLMs). The dataset contains multiple-choice question-answer pairs with detailed reasoning traces and spans across diverse scientific domains, including STEM, law, economics, and humanities. OpenScience aims to boost accuracy on advanced benchmarks such as GPQA-Diamond and MMLU-Pro via supervised finetuning or reinforcement learning.
This… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenScience.svgrepo
Dataset Card for SVGRepo Icons
Dataset Summary
This dataset contains a large collection of Scalable Vector Graphics (SVG) icons sourced from SVGRepo.com. The icons cover a wide range of categories and styles, suitable for user interfaces, web development, presentations, and potentially for training vector graphics or icon classification models. Each icon is provided under a specific open-source or permissive license, clearly indicated in its metadata. The SVG… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/svgrepo.LeetCodeDataset
LeetCodeDataset
LeetCodeDataset is a dataset consists of Python leetcode problems that can be used for LLM training and evaluation.
💻 GitHub
📄 LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs
📄 Policy Filtration for RLHF to Mitigate Noise in Reward Models
Nemotron-VLM-Dataset-v2
Nemotron-VLM-Dataset v2
Versions
Date
Commit
Changes
2025-11-05
head
Fix nights_cot dataset. Fix/filter broken <think> entries. Update fintabnet instructions. Update indexes.
2025-10-28
214051e
Initial Release
Dataset Description
Following up on Llama Nemotron VLM Dataset V1 with 3 million samples, we are releasing the Nemotron VLM Dataset V2 with almost three times as many high-quality samples.
This time, our focus was on three… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-VLM-Dataset-v2.HelpSteer3
HelpSteer3
HelpSteer3 is an open-source dataset (CC-BY-4.0) that supports aligning models to become more helpful in responding to user prompts.
HelpSteer3-Preference can be used to train Llama 3.3 Nemotron Super 49B v1 (for Generative RMs) and Llama 3.3 70B Instruct Models (for Bradley-Terry RMs) to produce Reward Models that score as high as 85.5% on RM-Bench and 78.6% on JudgeBench, which substantially surpass existing Reward Models on these benchmarks.
HelpSteer3-Feedback and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer3.Nemotron-ClimbMix
ClimbMix Dataset
🚀 Creating the highest-quality pre-training datasets for LLMs 🌟
📄 PAPER
🤗 CLIMBLAB
🤗 CLIMBMIX
🏠 HOMEPAGE
Figure 1: Continuously training a 1B model yields a 2.0% improvement over Llama-3.2-1B, demonstrating a more efficient scaling trend compared to prior models.
Figure 2: Pre-training a 1B model from scratch on ClimbMix shows better scaling effects than training on other datasets.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-ClimbMix.rlvr-reward-hacking-scale-no-conftest-20260909-completion
Matched no-conftest RLVR study 20260909-completion
Lossless research records, grouped by model and trajectory type. Only the listed
configurations have published records. Canary diagnostics are excluded from study
estimates; run status in provenance distinguishes retired diagnostics from active
or completed training. Valid failures, refusals and truncations are retained.
The train split name is a dataset-loader convention; record_type identifies
whether a record is training… See the full description on the dataset page: https://huggingface.co/datasets/lucabaroni/rlvr-reward-hacking-scale-no-conftest-20260909-completion.tokenized_datasetNeko_Audio-80K_Short
Nemotron-Cascade-2-SFT-Data
Nemotron-Cascade-2-SFT-Data
We release the SFT data used for training Nemotron-Cascade-2.
Data sources
Math
Our non-proof math prompts are sourced from Nemotron-Cascade-1-SFT and Nemotron-Math-v2, with responses generated by DeepSeek-V3.2, DeepSeek-V3.2-Speciale, and GPT-OSS-120B. For mathematical proofs, prompts are taken from Nemotron-Math-Proofs-v1 and generated using DeepSeek-V3.2-Speciale.
Science
We collect science prompts from… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Cascade-2-SFT-Data.Nemotron-Image-Training-v3
Nemotron Image Training v3
Versions
Date
Commit
Changes
2026-04-28
HEAD
Initial commit.
Dataset Description
Nemotron Image Training v3 is a collection of image-centric multimodal training data for vision–language models. Similar to Nemotron-VLM-Dataset v2, it was curated as a large-scale, multi-subdataset release where each subset ships a standardized conversation JSONL alongside a dataset card describing sources, licensing, and media layout.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Image-Training-v3.Llama-Nemotron-Post-Training-Dataset
Llama-Nemotron-Post-Training-Dataset-v1.1 Release
Update [4/8/2025]:
v1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset.Nemotron-SFT-Science-v2
Dataset Description:
Nemotron-Science-v2 is a science reasoning dataset with synthetic (synthetic MCQ, RQA) and non-synthetic vendor problems and LLM-generated solutions. It comprises three domains (Physics, Biology, and Chemistry), two question formats (multiple-choice questions [MCQ] and open questions [OpenQ]), and three generation setups: chain-of-thought (CoT) reasoning without tools, Python tool usage, and search tools usage with the Tavily API.
The solutions were… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Science-v2.