README.md

metadata

language:
  - en
tags:
  - math
  - reasoning
size_categories:
  - 1K<n<10K
license: apache-2.0
configs:
  - config_name: default
    data_files:
      - split: gsm8k
        path: gsm8k.json
      - split: math
        path: math.json
      - split: olympiadbench
        path: olympiadbench.json
      - split: omnimath
        path: omnimath.json

Neural Metrics · Step-level evaluation, because extraction is a chain.

Document extraction is a multi-step process, and an end-to-end accuracy number hides where it broke. ProcessBench evaluates reasoning step by step, which is the shape of evaluation we want for our own pipelines.

We use it for: benchmarking step-level error localization - studying how failures propagate through a chain rather than just counting final-answer mistakes.

Attribution

This is an unmodified fork of Qwen/ProcessBench, created by the Qwen team. All weights, files and behaviour are identical to upstream — we rehost it so our experiments stay reproducible and version-pinned. The original license and all credit remain with the Qwen team. If you want the canonical dataset, please use the original.

---

Original dataset card from Qwen/ProcessBench (click to expand)

Neural Metrics · Step-level evaluation, because extraction is a chain.

Document extraction is a multi-step process, and an end-to-end accuracy number hides where it broke. ProcessBench evaluates reasoning step by step, which is the shape of evaluation we want for our own pipelines.

We use it for: benchmarking step-level error localization - studying how failures propagate through a chain rather than just counting final-answer mistakes.

Attribution

This is an unmodified fork of Qwen/ProcessBench, created by the Qwen team. All weights, files and behaviour are identical to upstream — we rehost it so our experiments stay reproducible and version-pinned. The original license and all credit remain with the Qwen team. If you want the canonical dataset, please use the original.

---

Original dataset card from Qwen/ProcessBench (click to expand)

ProcessBench

This repository contains the dataset of the ProcessBench benchmark proposed by Qwen Team.

You can refer to our GitHub repository for the evaluation code and the prompt templates we use in this work.

If you find this work relevant or helpful to your work, please kindly cite us:

@article{processbench,
  title={ProcessBench: Identifying Process Errors in Mathematical Reasoning}, 
  author={
    Chujie Zheng and Zhenru Zhang and Beichen Zhang and Runji Lin and Keming Lu and
    Bowen Yu and Dayiheng Liu and Jingren Zhou and Junyang Lin
  },
  journal={arXiv preprint arXiv:2412.06559},
  year={2024}
}

Data Usage

You can use the following code to preview the dataset:

import json
from datasets import load_dataset

dataset = load_dataset('Qwen/ProcessBench', split='gsm8k')
print(json.dumps(dataset[0], indent=2))

# Expected output:
"""
{
  "id": "gsm8k-0",
  "generator": "Qwen2-7B-Instruct",
  "problem": "Sue lives in a fun neighborhood...",
  "steps": [
    "To find out how many more pink plastic flamingos were out than...",
    ...
  ],
  "final_answer_correct": false,
  "label": 1
}
"""