datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
math-500math500math500math-500_Qwen3-30B-A3B_moe_patternsmath500MATH-500MATH-500 test set with the remaining 12000 examples in train.
import datasets
# https://github.com/volcengine/verl/blob/30911f133aa300ae9d8e341dba8e63192335705e/verl/utils/reward_score/math.py
from math_utils import last_boxed_only_string, remove_boxed
math = datasets.load_dataset('DigitalLearningGmbH/MATH-lighteval', 'default')
math500 = datasets.load_dataset('HuggingFaceH4/MATH-500')
# convert math to math500 format
def map_to_500(example):
return {
'problem':… See the full description on the dataset page: https://huggingface.co/datasets/ricdomolm/MATH-500.Eval-MATH500math500math_full_minus_math500
MATH (minus MATH-500)
This dataset is derived from the original MATH dataset by Hendrycks et al.
(qwedsacf/competition_math) with all problems from the MATH-500 benchmark set removed.
Construction
Source: 12,500 problems from the MATH dataset by Hendrycks et al. (qwedsacf/competition_math)
Benchmark held out: 500 problems from the MATH-500 dataset (HuggingFaceH4/MATH-500)
Matching criterion: exact match on the problem field (see… See the full description on the dataset page: https://huggingface.co/datasets/rasbt/math_full_minus_math500.MATH500_with_Llama_3.1_8B_Instruct_v1
MATH-500 with Llama-3.1-8B-Instruct
This dataset contains 500 mathematical reasoning problems from the MATH benchmark with 100 candidate responses generated by Llama-3.1-8B-Instruct for each problem. Each response has been evaluated for correctness using a mixture of GPT-4o-mini and procedural Python code to robustly parse different answer formats, and scored by multiple reward models (scalar values) and LM judges (boolean verdicts).
Dataset Structure
Split: Single… See the full description on the dataset page: https://huggingface.co/datasets/hazyresearch/MATH500_with_Llama_3.1_8B_Instruct_v1.MATH500_with_Llama_3.1_70B_Instruct_v1
MATH-500 with Llama-3.1-70B-Instruct
This dataset contains 500 mathematical reasoning problems from the MATH benchmark with 100 candidate responses generated by Llama-3.1-70B-Instruct for each problem. Each response has been evaluated for correctness using a mixture of GPT-4o-mini and procedural Python code to robustly parse different answer formats, and scored by multiple reward models (scalar values) and LM judges (boolean verdicts).
Dataset Structure
Split: Single… See the full description on the dataset page: https://huggingface.co/datasets/hazyresearch/MATH500_with_Llama_3.1_70B_Instruct_v1.math-500_deepseek-moe-16b-chat_moe_patternsmath500-cot-deepseek-r1-1.5b
MATH-500 CoT completions (DeepSeek-R1-Distill-Qwen-1.5B)
Successful chain-of-thought completions for HuggingFaceH4/MATH-500 test problems, generated with deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B via vLLM.
Files
File
Description
records.parquet
Main dataset: correct completions as token IDs
manifest.json
Schema, tokenizer, run ids, decoding config
problem_index.json
unique_id → problem_idx in MATH-500 test
subject_max_tokens.json
Per-subject completion… See the full description on the dataset page: https://huggingface.co/datasets/ChrisMcCormick/math500-cot-deepseek-r1-1.5b.math-500-th
Math-500-th
A Thai translation of MATH-500: the 500-problem subset of the MATH benchmark used
in OpenAI's Let's Verify Step by Step. Every row corresponds 1:1, in order, to a
row of the English original, so the Thai and English scores of a model are directly
comparable.
Source and licence
Original benchmark
hendrycks/math — MIT
500-problem subset
openai/prm800k — MIT
File we translated from
HuggingFaceH4/MATH-500
This dataset
MIT, see LICENSE… See the full description on the dataset page: https://huggingface.co/datasets/iapp/math-500-th.MATH-500_with_Llama_3.1_8B_Instruct_v1Qwen2.5-1.5B-Instruct-uPRM-70B-T80-math500-best_of_n-completionsDeepseek-MATH500-Testmath500-cot-experiment
MATH-500 Chain of Thought Experiment Dataset
This dataset contains the results of an experiment testing different prompting strategies (standard, chain of thought, and gibberish chain of thought) on the MATH-500 benchmark using the Llama-3.2-1B-Instruct model.
Dataset Structure
The dataset is split into three parts:
standard: Direct prompting with no reasoning steps (500 examples)
cot: Chain of thought prompting with structured reasoning (500 examples)
gibberish:… See the full description on the dataset page: https://huggingface.co/datasets/codelion/math500-cot-experiment.math-500_deepseek_moe_16b_token_patternsLlama-3.2-1B-Instruct-uPRM-32B-T80-math500-best_of_n-completionsMistral-MATH500-TestLlama-3.2-1B-Instruct-uPRM-70B-T80-math500-best_of_n-completionsmath_500math500-olmo-3-7b-instruct-temp0.9-samples99-logprobs
OLMo-3-7B-Instruct self-consistency generations with logprobs on MATH500
This dataset contains 99 self-consistency generations per question for the
MATH500 benchmark, produced with allenai/OLMo-3-7B-Instruct at temperature
0.9, together with token-level log probabilities for each completion.
The file is intended for post-hoc analysis, self-consistency curves, adaptive
stopping, and related aggregation methods.
Source
Base benchmark: HuggingFaceH4/MATH-500
Model:… See the full description on the dataset page: https://huggingface.co/datasets/s-nlp/math500-olmo-3-7b-instruct-temp0.9-samples99-logprobs.Qwen2.5-1.5B-Instruct-uPRM-32B-T80-math500-best_of_n-completionsMATH-500-french-thoughts
➕🇫🇷💭 Dataset Card for MATH-500-french-thoughts
A french subset from OpenAI's MATH benchmark adapted from bezir/MATH-500-multilingual with additionnal thoughts from stelterlab/DeepSeek-R1-Distill-Qwen-14B-AWQ.
Perfect for testing math skills in French.
📂 Source & Attribution
Original Dataset: Sourced from HuggingFaceH4/MATH-500.
Adapted Dataset: Taken from bezir/MATH-500-multilingual.
Thoughts, answers and predicted answers: Generated with… See the full description on the dataset page: https://huggingface.co/datasets/Cotum/MATH-500-french-thoughts.MATH-500-translatedm-math500MATH-500-zhtwmixed_sft_math500_128_s1_tulu2_sft_s1_1.0pct