datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
LongBench-v2
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
🌐 Project Page: https://longbench2.github.io
💻 Github Repo: https://github.com/THUDM/LongBench
📚 Arxiv Paper: https://arxiv.org/abs/2412.15204
LongBench v2 is designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks. LongBench v2 has the following features: (1) Length: Context length ranging from 8k to… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/LongBench-v2.orca-math-word-problems-200k
Dataset Card
This dataset contains ~200K grade school math word problems. All the answers in this dataset is generated using Azure GPT4-Turbo. Please refer to Orca-Math: Unlocking the potential of
SLMs in Grade School Math for details about the dataset construction.
Dataset Sources
Repository: microsoft/orca-math-word-problems-200k
Paper: Orca-Math: Unlocking the potential of
SLMs in Grade School Math
Direct Use
This dataset has been designed to… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k.OpenOrca🐋 The OpenOrca Dataset! 🐋
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
Official Models
Mistral-7B-OpenOrca
Our latest model, the first 7B to score better overall than all… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/OpenOrca.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llm/or-bench.LongAlign-10k
LongAlign-10k
🤗 [LongAlign Dataset] • 💻 [Github Repo] • 📃 [LongAlign Paper]
LongAlign is the first full recipe for LLM alignment on long context. We propose the LongAlign-10k dataset, containing 10,000 long instruction data of 8k-64k in length. We investigate on trianing strategies, namely packing (with loss weighting) and sorted batching, which are all implemented in our code. For real-world long context evaluation, we introduce LongBench-Chat that evaluate the… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/LongAlign-10k.HippoCamp
HippoCamp: Benchmarking Contextual Agents on Personal Computers
📖 Paper |
🏠 Project Page |
🛠️ GitHub |
🤗 Dataset |
🎬 Demo
Overview
HippoCamp is a benchmark for evaluating contextual agents in realistic, device-resident personal computing environments. Unlike agent benchmarks centered on web interaction, tool use, or generic software automation, HippoCamp focuses on multimodal file management over large personal file systems: agents must… See the full description on the dataset page: https://huggingface.co/datasets/MMMem-org/HippoCamp.SlimOrca
Overview
This is a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of our data, while only including ~500k GPT-4 completions.
The key change in this dataset is that we've done an additional pass, using GPT-4 to remove answers which appear wrong based on the human annotations from the FLAN dataset.
This reduces the dataset size to only ~500k entries, allowing training to a similar quality level… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/SlimOrca.orca_dpo_pairs
Dataset Card for Orca DPO Pair
Dataset Description
This is a pre-processed version of the OpenOrca dataset.
The original OpenOrca dataset is a collection of augmented FLAN data that aligns, as best as possible, with the distributions outlined in the Orca paper.
It has been instrumental in generating high-performing preference-tuned model checkpoints and serves as a valuable resource for all NLP researchers and developers!
Dataset Summary
The OrcaDPO Pair… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/orca_dpo_pairs.orca-agentinstruct-1M-v1
Dataset Card
This dataset is a fully synthetic set of instruction pairs where both the prompts and the responses have been synthetically generated, using the AgentInstruct framework.
AgentInstruct is an extensible agentic framework for synthetic data generation.
This dataset contains ~1 million instruction pairs generated by the AgentInstruct, using only raw text content publicly avialble on the Web as seeds. The data covers different capabilities, such as text editing, creative… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1.c3
Dataset Card for C3
Dataset Summary
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.
We… See the full description on the dataset page: https://huggingface.co/datasets/dataset-org/c3.general-reasoning-ift-pairs
Reasoning-IFT Pairs (General Domain)
This dataset provides the largest set of IFT and Reasoning answers pairs for a set of general domain queries (cf: math-domain).It is based on the Infinity-Instruct dataset, an extensive and high-quality collection of instruction fine-tuning data.
We curated 900k queries from the 7M_core subset of Infinity-Instruct, which covers multiple domains including general knowledge, commonsense Q&A, coding, and math.For each query… See the full description on the dataset page: https://huggingface.co/datasets/Scale-or-Reason/general-reasoning-ift-pairs.WM-ABench
WM-ABench: An Atomic Evaluation Benchmark of World Modeling abilities of Vision-Language Models
Paper: Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation
WM-ABench is a comprehensive benchmark that evaluates whether Vision-Language Models (VLMs) can truly understand and simulate physical world dynamics, or if they rely on shortcuts and pattern-matching. The benchmark covers 23 dimensions of world modeling across 6 physics simulators with over 100,000… See the full description on the dataset page: https://huggingface.co/datasets/maitrix-org/WM-ABench.SlimOrca-Dedup
Overview
"SlimOrca Dedup" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.
Key Features
Removal of RLHF instances.
Deduplication using minhash and Jaccard similarity techniques.
Demo Models
Note: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.
* https://huggingface.co/openaccess-ai-collective/jackalope-7b
*… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llms/or-bench.math-reasoning-ift-pairs
Reasoning-IFT Pairs (Math Domain)
Paper | Project Page
This dataset provides the largest set of IFT and Reasoning answers pairs for a set of math queries (cf: general-domain).
It is based on the Llama-Nemotron-Post-Training dataset, an extensive and high-quality collection of math instruction fine-tuning data.
We curated 150k queries from the math subset of Llama-Nemotron-Post-Training, which covers multiple domains of math questions.For each query, we used… See the full description on the dataset page: https://huggingface.co/datasets/Scale-or-Reason/math-reasoning-ift-pairs.OR-Space
OR-Space
A full-lifecycle workspace benchmark for industrial optimization agents.
OR-Space evaluates whether language-model agents can work reliably with
operations research problems represented as executable, multi-file workspaces.
Rather than presenting a self-contained mathematical prompt, each task
distributes evidence across business requirements, structured data, source
code, execution logs, and solver records.
The benchmark contains 100 optimization topologies. Each… See the full description on the dataset page: https://huggingface.co/datasets/Chenyu-Zhou/OR-Space.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our leaderboard at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue… See the full description on the dataset page: https://huggingface.co/datasets/orbench-llm/or-bench.LongCite-45k
LongCite-45k
🤗 [LongCite Dataset] • 💻 [Github Repo] • 📃 [LongCite Paper]
LongCite-45k dataset contains 44,600 long-context QA instances paired with sentence-level citations (both English and Chinese, up to 128,000 words). The data can support training long-context LLMs to generate response and fine-grained citations within a single output.
Data Example
Each instance in LongCite-45k consists of an instruction, a long context (divided into sentences), a user… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/LongCite-45k.orca
Orca Dataset Collection
The Orca Dataset Collection is a unified compilation of multiple datasets from the Microsoft Orca and OpenOrca projects.
All duplicate entries have been removed, and any personally identifiable information (PII) has been carefully redacted.
All rows have been sorted by hash and split into JSONL files containing 100,000 entries each for easier handling and consistency.
Example Entry
id: MD5 hash of the system prompt, question, and answer JSON… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/orca.webglm-qa
WebGLM-QA
Dataset Description
WebGLM-QA is the dataset used to train the WebGLM generator module. It consists of 43,579 high-quality data samples for the train split, 1,000 for the validation split, and 400 for the test split. Refer to our paper for the data construction details.
Dataset Structure
To load the dataset, you can try the following code.
from datasets import load_dataset
load_dataset("THUDM/webglm-qa")
DatasetDict({
train: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/webglm-qa.or-bench-toxic-all
OR-Bench: An Over-Refusal Benchmark for Large Language Models
This dataset constains highly toxic prompts, use with caution!!!
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic… See the full description on the dataset page: https://huggingface.co/datasets/bench-llms/or-bench-toxic-all.spoken-multihop-rag
Spoken Multi-hop QA: ASR Transcripts Across Four English Accents
ASR transcriptions of 3,000 multi-hop QA questions, each spoken in four
English accents and transcribed with Whisper-large-v3. Released as the
data companion to Better Retrieval, Worse Robustness: How Multi-hop RAG
Amplifies Upstream ASR Errors
(EMNLP 2026, Main Conference).
The dataset exists to make one thing cheap to study: what happens to a
retrieval pipeline when its query arrives through ASR rather than as… See the full description on the dataset page: https://huggingface.co/datasets/orcarouter/spoken-multihop-rag.orgforge
OrgForge EpistemicBench
OrgForge generates synthetic but causally grounded enterprise corpora from a
deterministic simulation engine. Every artifact in this dataset — Jira tickets,
Slack threads, Confluence pages, customer emails, Zendesk tickets, invoices, Zoom
transcripts, Datadog alerts — traces back to a single event log. No LLM invented
any facts. The state machine controls what happened; LLMs only wrote the prose.
Why it exists
Evaluating agents that reason… See the full description on the dataset page: https://huggingface.co/datasets/aeriesec/orgforge.med-qa-orpo-dpo
MED QA ORPO-DPO Dataset
This dataset is restructured from several existing datasource on medical literature and research, hosted here on hugging face. The dataset is shaped in question, choosen
and rejected pairs to match the ORPO-DPO trainset requirements.
Features
The dataset consists of the following features:
question: MCQ or yes/no/maybe based questions on medical questions
direct-answer: correct answer to the above question
chosen: the correct answer along with… See the full description on the dataset page: https://huggingface.co/datasets/empirischtech/med-qa-orpo-dpo.OracleProto
OracleProto: Forecasting Evaluation Set
Chinese doc: [中文文档]
GitHub repo: [MaYiding/OracleProto]
Visit Our Leaderboards: [Website]
View Our Paper: [arXiv]
A SQLite-packaged evaluation set of 80 hand-curated forecasting questions on real-world events, with resolution dates between 2026-03-12 and 2026-04-14, released alongside the GitHub Repo. Both the rows and the byte-stable prompt-reconstruction recipe are packaged in a single file, forecast_eval_set_example.db, which exposes two… See the full description on the dataset page: https://huggingface.co/datasets/MaYiding/OracleProto.orz_math_72k_collection_extended
Open Reasoner Zero
An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
Paper Arxiv Link 👁️
Overview 🌊
We introduce Open-Reasoner-Zero, the first open source implementation of large-scale reasoning-oriented RL training focusing on scalability, simplicity and accessibility.
Using the same base model as DeepSeek-R1-Zero-Qwen-32B, our implementation achieves superior performance on AIME2024, MATH500, and the… See the full description on the dataset page: https://huggingface.co/datasets/Open-Reasoner-Zero/orz_math_72k_collection_extended.simplequestions-sparqltotext
Dataset Card for SimpleQuestions-SPARQLtoText
Dataset Summary
Special version of SimpleQuestions with SPARQL queries formatted for the SPARQL-to-Text task.
JSON fields
The original version of SimpleQuestions is a raw text file listing triples and the natural language question. A JSON version has been generated and augmented with the following fields:
rdf_subject, rdf_property, rdf_object: triple in the Wikidata format (IDs)
nl_subject, nl_property, nl_object:… See the full description on the dataset page: https://huggingface.co/datasets/Orange/simplequestions-sparqltotext.orca-agentinstruct-1M-v1-cleaned
🐋 Orca-AgentInstruct-1M-v1-cleaned
This is a cleaned version of the microsoft/orca-agentinstruct-1M-v1 dataset released by Microsoft.
orca-agentinstruct-1M-v1 is a fully synthetic dataset using only raw text publicly available on the web as seed data. It is a subset of the full AgentInstruct dataset (~25M samples) that created Orca-3-Mistral. Compared to Mistral 7B Instruct, the authors claim 40% improvement on AGIEval, 19% improvement on MMLU, 54% improvement on GSM8K, 38%… See the full description on the dataset page: https://huggingface.co/datasets/mlabonne/orca-agentinstruct-1M-v1-cleaned.OR-Clarify
OR-Clarify
📄 Paper: Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
OR-Clarify is a benchmark for testing whether an agent asks the right questions before formulating an optimization model.
Most optimization benchmarks give an agent a complete problem statement. OR-Clarify instead starts with an incomplete business brief. The agent must identify missing requirements that could change the optimization formulation, ask for the relevant… See the full description on the dataset page: https://huggingface.co/datasets/AIOR-Research/OR-Clarify.csqa-sparqltotext
Dataset Card for CSQA-SPARQLtoText
Dataset Summary
CSQA corpus (Complex Sequential Question-Answering, see https://amritasaha1812.github.io/CSQA/) is a large corpus for conversational knowledge-based question answering. The version here is augmented with various fields to make it easier to run specific tasks, especially SPARQL-to-text conversion.
The original data has been post-processing as follows:
Verbalization templates were applied on the answers and their entities… See the full description on the dataset page: https://huggingface.co/datasets/Orange/csqa-sparqltotext.