language:en license:mit size_categories:10K<n<100K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
language:
- en
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: raw_labels
    sequence: int64
  - name: labels
    sequence: string
  - name: result
    dtype: string
  - name: phase
    dtype: string
  splits:
  - name: train
    num_bytes: 97353477
    num_examples: 94751
  - name: test
    num_bytes: 2825567
    num_examples: 2754
  download_size: 37655205
  dataset_size: 100179044
---

This is a refined format for OpenAI PRM800K dataset.
The original dataset can be found here: https://github.com/openai/prm800k/tree/main

To fit into the main stream open source PRM training manner, such as [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF), we change the format of original PRM800K into Math-Shepherd.

For each data item, it contains below keys:
- question: the original quesiton text.
- answer: the step-wise solutions. Steps are separated by 'ки' token following the manner of math-shepherd
- raw_labels: the raw label from PRM800K, the values are 1, -1 or 0.
- labels: '+' if the raw_label is 1 otherwise '-'
- result: the ground truth answer to the question. Usually a number or a short math expression.
- phase: the phase from original PRM800K, the values are "phase1" or "phase2".

The preprocess script can be found at preprocess.py

