datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MMMU_with_difficulty_level
MMMU with difficulty level tags
This dataset extends the 🤗 MMMU val benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs.
🚀 Data Usage
from datasets import load_dataset
dataset = load_dataset("JierunChen/MMMU_with_difficulty_level")
print(dataset)
📑… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MMMU_with_difficulty_level.MathVision_with_difficulty_level
MathVision with difficulty level tags
This dataset extends the 🤗 MathVision benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs.
🚀 Data Usage
from datasets import load_dataset
dataset = load_dataset("JierunChen/MathVision_with_difficulty_level")
print(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MathVision_with_difficulty_level.MathVista_with_difficulty_level
MathVista with difficulty level tags
This dataset extends the 🤗 MathVista testmini benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs.
🚀 Data Usage
from datasets import load_dataset
dataset = load_dataset("JierunChen/MathVista_with_difficulty_level")… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MathVista_with_difficulty_level.human_anatomy_qa_with_difficulty
Truth, Trust, and Trouble (TTT) – Medical Anatomy QA Benchmark
This repository hosts the dataset introduced in the EMNLP Industry Track 2025 paper “Truth, Trust, and Trouble: Medical AI on the Edge.”
The dataset contains 1,077 high-quality, clinically validated True/False anatomy questions, designed to evaluate medical LLMs along three critical axes:
Honesty (factual alignment)
Helpfulness (semantic relevance & completeness)
Harmlessness (safety under clinical constraints)
This… See the full description on the dataset page: https://huggingface.co/datasets/ekplatebiryani/human_anatomy_qa_with_difficulty.OpenMath-Difficulty-Annotated
📐 OpenMath-Difficulty-Annotated
🚀 Overview
OpenMath-Difficulty-Annotated is a curated subset of OpenMathInstruct-2 containing 10,176 math problems, enhanced with precise difficulty metadata.
While the original solutions are preserved from NVIDIA's dataset, we employed a 120B Parameter Model (LLM-as-a-Judge) to analyze and grade every single problem on a scale of 1 to 5.
This allows developers of Small Language Models (1B-3B) to filter out "Olympiad-level" noise… See the full description on the dataset page: https://huggingface.co/datasets/HAD653/OpenMath-Difficulty-Annotated.japanese-math-empirical-difficulty-pilot-50k
Japanese Math Empirical Difficulty Pilot 50k
This dataset is a 50,000-problem empirical difficulty pilot, not a full empirical labeling of the original 5.66M-row source dataset.
It was created for LLM-jp experiment 0399, Team Victory SFT, to validate empirical difficulty label distribution, downstream split behavior, and the rollout/scoring pipeline before attempting labeling at the full 5.6M scale.
Current Status
This upload uses the v3 scorer with assistant-only… See the full description on the dataset page: https://huggingface.co/datasets/argo11/japanese-math-empirical-difficulty-pilot-50k.MathVerse_with_difficulty_level
MathVerse with difficulty level tags
This dataset extends the 🤗 MathVerse testmini benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs.
🚀 Data Usage
from datasets import load_dataset
dataset = load_dataset("JierunChen/MathVerse_with_difficulty_level")… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MathVerse_with_difficulty_level.MMStar_with_difficulty_level
MMStar with difficulty level tags
This dataset extends the 🤗 MMStar benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs.
🚀 Data Usage
from datasets import load_dataset
dataset = load_dataset("JierunChen/MMStar_with_difficulty_level")
print(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MMStar_with_difficulty_level.clean_openthought312_difficulty_9_filterd元データ: https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M
diffculty 9でさらにフィルタリングしたもの
データ件数: 14,339
平均トークン数: 13370
最大トークン数: 16,808
合計トークン数: 191,708,678
ファイル形式: JSONL
ファイルサイズ: 723.9 MB
clean_openthought312_difficulty_9_qwentoken元データ: https://huggingface.co/datasets/LLMTeamAkiyama/clean_openthought312_difficulty_9_filterd
データ件数: 14,339
平均トークン数: 13,367
最大トークン数: 16,805
合計トークン数: 191,665,652
ファイル形式: JSONL
ファイル分割数: 3
合計ファイルサイズ: 724.7 MB
加工内容：
**tokenizeをQwen235B-A22Bで再度トークン化したものを出力
使用したコード
https://github.com/LLMTeamAkiyama/0-data_prepare/blob/master/src/openthoughts3/clean_openthoughts3_9_qwentoken.ipynb