task_categories:question-answering annotations_creators:expert-generated size_categories:100K<n<1M language:en license:mit reasoning region:us
---
# Full dataset card template at https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
language:
- en
license: mit
tags:
- reasoning
annotations_creators:
- expert-generated
pretty_name: PRM800K
size_categories:
- 100K<n<1M
task_categories:
- question-answering

# Optional. This part can be used to store the feature types and size of the dataset to be used in python. This can be automatically generated using the datasets-cli.
dataset_info:
  - config_name: all_positive_responses
    features:
      - name: instruction
        dtype: string
      - name: responses
        sequence:
          dtype: string
      - name: next_response
        dtype: string
      - name: answer
        dtype: string
  - config_name: solutions_only
    features:
      - name: instruction
        dtype: string
      - name: responses
        sequence:
          dtype: string
      - name: next_response
        dtype: string
      - name: answer
        dtype: string
    # splits:
    #   - name: {split_name_0}                  # Example: train
    #     num_bytes: {split_num_bytes_0}        # Example for SQuAD: 79317110
    #     num_examples: {split_num_examples_0}  # Example for SQuAD: 87599
    # download_size: {dataset_download_size}   # Example for SQuAD: 35142551
    # dataset_size: {dataset_size}             # Example for SQuAD: 89789763
---