metadata

pretty_name: J
dataset_info:
  features:
    - name: json_schema
      dtype: string
    - name: unique_id
      dtype: string
  splits:
    - name: WashingtonPost
      num_bytes: 2710348
      num_examples: 125
    - name: Snowplow
      num_bytes: 1613804
      num_examples: 403
    - name: Kubernetes
      num_bytes: 25623424
      num_examples: 1064
    - name: Github_trivial
      num_bytes: 780060
      num_examples: 444
    - name: Github_easy
      num_bytes: 1980784
      num_examples: 1943
    - name: Github_medium
      num_bytes: 7994298
      num_examples: 1976
    - name: Github_hard
      num_bytes: 20240875
      num_examples: 1240
    - name: Github_ultra
      num_bytes: 12235981
      num_examples: 164
    - name: JsonSchemaStore
      num_bytes: 22195651
      num_examples: 492
    - name: Glaiveai2K
      num_bytes: 1440707
      num_examples: 1707
  download_size: 19019152
  dataset_size: 96815932
configs:
  - config_name: default
    data_files:
      - split: WashingtonPost
        path: data/WashingtonPost-*
      - split: Snowplow
        path: data/Snowplow-*
      - split: Kubernetes
        path: data/Kubernetes-*
      - split: Github_trivial
        path: data/Github_trivial-*
      - split: Github_easy
        path: data/Github_easy-*
      - split: Github_medium
        path: data/Github_medium-*
      - split: Github_hard
        path: data/Github_hard-*
      - split: Github_ultra
        path: data/Github_ultra-*
      - split: JsonSchemaStore
        path: data/JsonSchemaStore-*
      - split: Glaiveai2K
        path: data/Glaiveai2K-*
license: mit
task_categories:
  - text-generation

JSONSchemaBench

JSONSchemaBench is a benchmark of real-world JSON schemas designed to evaluate structured output generation for Large Language Models (LLMs). It contains approximately 10,000 JSON schemas, capturing diverse constraints and complexities.

📌 Dataset Overview

Purpose: Evaluate the efficiency and coverage of structured output generation.
Sources: GitHub, Kubernetes, API specifications, curated collections.
Schemas: Categorized based on complexity and domain.

📊 Dataset Breakdown

Dataset	Category	Count
GlaiveAI-2K	Function Call	1707
Github-Trivial	Misc	444
Github-Easy	Misc	1943
Snowplow	Operational API	403
Github-Medium	Misc	1976
Kubernetes	Kubernetes API	1064
Washington Post	Resource Access API	125
Github-Hard	Misc	1240
JSONSchemaStore	Misc	492
Github-Ultra	Misc	164
Total		9558

📥 Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("epfl-dlab/JSONSchemaBench")
print(dataset)

🔍 Data Structure

Each dataset split contains:

"json_schema": The schema definition.
"unique_id": A unique identifier for the schema.

🚀 For more details, check out the paper.

📚 Citation

@misc{geng2025jsonschemabench,
      title={Generating Structured Outputs from Language Models: Benchmark and Studies},
      author={Saibo Geng et al.},
      year={2025},
      eprint={2501.10868},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.10868}
}

License

This dataset is provided under the MIT License. Please ensure that you comply with the license terms when using or distributing this dataset.

Acknowledgements

We would like to thank the contributors and maintainers of the JSON schema projects and the open-source community for their invaluable work and support.