datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
NuminaMath-CoT
Dataset Card for NuminaMath CoT
Dataset Summary
Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-CoT.NuminaMath-1.5
Dataset Card for NuminaMath 1.5
Dataset Summary
This is the second iteration of the popular NuminaMath dataset, bringing high quality post-training data for approximately 900k competition-level math problems. Each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-1.5.NuminaMath-TIR
Dataset Card for NuminaMath CoT
Dataset Summary
Tool-integrated reasoning (TIR) plays a crucial role in this competition. However, collecting and annotating such data is both costly and time-consuming. To address this, we selected approximately 70k problems from the NuminaMath-CoT dataset, focusing on those with numerical outputs, most of which are integers. We then utilized a pipeline leveraging GPT-4 to generate TORA-like reasoning paths, executing the code and… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-TIR.PolyMath
Dataset Card for PolyMath
Dataset Summary
PolyMath is a curated dataset of 11,090 high-difficulty mathematical problems designed for training reasoning models. Built for the AIMO Math Corpus Prize. Existing math datasets (NuminaMath-1.5, OpenMathReasoning) suffer from high noise rates in their hardest samples and largely unusable proof-based problems.
PolyMath addresses both issues through:
Data scraping: problems sourced from official competition PDFs absent from… See the full description on the dataset page: https://huggingface.co/datasets/AIMO-Corpus/PolyMath.AI-MO-NuminaMath-CoT-Ko
Dataset Card for NuminaMath CoT Korean
🎉 Translation finished! If there are any errors, please open the PR. 🎉
If you use this data, please make sure to credit my source!
⚠️ There may be errors in the translation of mathematical terms. (ex: trivial-사소한X/자명한O, negative-부정?음수?)
Translation
The original data AI-MO/NuminaMath-CoT is in English.
I used solar-1-mini-translate-enko-240507 to translate into Korean. To see the detailed script on how I did it, please refer to the… See the full description on the dataset page: https://huggingface.co/datasets/ChuGyouk/AI-MO-NuminaMath-CoT-Ko.AIMO3_CoT
AIMO3 CoT Dataset
数据集来源与目的 (Dataset Origin and Purpose)
本数据集源自 Kaggle 竞赛 AI Mathematical Olympiad - Progress Prize 3。
动机 (Motivation)
原始数据集仅包含问题和答案，缺乏思维链（Chain of Thought, CoT）。直接使用原始数据训练如 DeepSeek Math 或 Qwen Math 等模型效果不佳。因此，本项目的目的是利用 Gemini 3 Pro 为这些问题补充详细的 CoT，以提升模型在数学推理任务上的表现。
CoT 格式 (CoT Format)
生成的 CoT 遵循 ReAct 风格的推理过程，并使用中文叙述：
Thought: 分析问题并规划下一步。
Code: 编写 Python 代码进行计算或验证。
Observation: 代码的执行输出。
... (重复上述步骤)
Final Answer: 得出的最终答案。… See the full description on the dataset page: https://huggingface.co/datasets/UR-xiaoyang/AIMO3_CoT.aimo3-math-dataset
AIMO3 Math Dataset
Training data for AI Mathematical Olympiad Progress Prize 3.
Files
train_cot.jsonl - Chain-of-Thought examples
train_tir.jsonl - Tool-Integrated Reasoning examples
Author
Ryan J Cardwell (Archer Phoenix) - AIMO3 Competitor
AI-MO-NuminaMath-TIR-korean-240918
IMPORTANT NOTE
This data is part of the progress. Current translation progress: 24.85% (2024-09-18 01:32 KST)
I'm taking a short break due to personal reasons. I'll be back in a month.
TODO-LIST
Finish translation
Translation
I used gemini-1.5-pro-exp-0827. The prompt used for translation will be disclosed at the end.
Dataset Card for NuminaMath CoT
Dataset Summary
Tool-integrated reasoning (TIR) plays a crucial role in this… See the full description on the dataset page: https://huggingface.co/datasets/ChuGyouk/AI-MO-NuminaMath-TIR-korean-240918.QWQ-LongCOT-AIMOQWQ-LongCOT-AIMO is a derived dataset created by processing the amphora/QwQ-LongCoT-130K dataset. It filters the original dataset to focus specifically on question-answering pairs where the final answer is a numerical value between 0 and 999, explicitly marked using the \boxed{...} format within the original chain-of-thought answer.
Dataset Structure
Data Splits
The dataset is split into training, validation, and test sets with an 80/10/10 ratio based on the filtered… See the full description on the dataset page: https://huggingface.co/datasets/Floppanacci/QWQ-LongCOT-AIMO.ai_model_weight_poisoning_deserialization_guard_teaser
🚀 Model Security - AI Model Weight Poisoning & Deserialization Guard (Evaluation Teaser)
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🌟 Domain Focus & Capabilities
Detects hidden pickle exploits, malicious backdoor tensors, and weight corruption across… See the full description on the dataset page: https://huggingface.co/datasets/emgena/ai_model_weight_poisoning_deserialization_guard_teaser.moh_8_fake_rollouts
MOH-8 Fake Rollouts
480 math competition problems, each with 8 candidate solution rollouts from OSS 120B.
A controlled number of rollouts per problem are correct — use this to train/test a
verifier model that must identify which solutions are right.
Source
Problems and rollouts sampled from aimosprite/training-data-oss120b (the oss128-fixed-FINAL.jsonl file).
Only polymath-source problems in the 2/8–4/8 pass rate range (32–64 correct out of 128 attempts).
4 problems… See the full description on the dataset page: https://huggingface.co/datasets/aimosprite/moh_8_fake_rollouts.aimodels.fyi-papers
BEE-spoke-data/aimodels.fyi-papers
paper overviews from https://www.aimodels.fyi/papers
dates:
pulled may 28, 2024
updated/supplemental articles added sept 21, 2024