| --- |
| 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" |
| --- |
| <div align="center"> |
|
|
| # Neural Metrics · Step-level evaluation, because extraction is a chain. |
|
|
| <img src="https://img.shields.io/badge/Neural%20Metrics-document%20extraction-4F46E5?style=for-the-badge" alt="Neural Metrics" /> |
| <img src="https://img.shields.io/badge/fork%20of-Qwen%2FProcessBench-2563EB?style=flat-square" alt="fork" /> |
|
|
| </div> |
|
|
| 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`](https://huggingface.co/datasets/Qwen/ProcessBench), created by the [Qwen team](https://huggingface.co/Qwen). |
| > 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](https://huggingface.co/datasets/Qwen/ProcessBench). |
|
|
| --- |
|
|
| <details> |
| <summary><b>Original dataset card from Qwen/ProcessBench</b> (click to expand)</summary> |
|
|
| <div align="center"> |
|
|
| # Neural Metrics · Step-level evaluation, because extraction is a chain. |
|
|
| <img src="https://img.shields.io/badge/Neural%20Metrics-document%20extraction-4F46E5?style=for-the-badge" alt="Neural Metrics" /> |
| <img src="https://img.shields.io/badge/fork%20of-Qwen%2FProcessBench-2563EB?style=flat-square" alt="fork" /> |
|
|
| </div> |
|
|
| 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`](https://huggingface.co/datasets/Qwen/ProcessBench), created by the [Qwen team](https://huggingface.co/Qwen). |
| > 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](https://huggingface.co/datasets/Qwen/ProcessBench). |
|
|
| --- |
|
|
| <details> |
| <summary><b>Original dataset card from Qwen/ProcessBench</b> (click to expand)</summary> |
|
|
| # ProcessBench |
|
|
| This repository contains the dataset of the [ProcessBench](https://huggingface.co/papers/2412.06559) benchmark proposed by Qwen Team. |
|
|
| You can refer to our [GitHub repository](https://github.com/QwenLM/ProcessBench) 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: |
|
|
| ```python |
| 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 |
| } |
| """ |
| ``` |
|
|
| </details> |
|
|
| </details> |
|
|
