Datasets:
metadata
pretty_name: J
dataset_info:
- config_name: Github_easy
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
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- name: val
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- name: test
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num_examples: 577
download_size: 540610
dataset_size: 1930980
- config_name: Github_hard
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
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- name: val
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- name: test
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download_size: 3562146
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- config_name: Github_medium
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- name: json_schema
dtype: string
- name: unique_id
dtype: string
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- name: val
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- name: test
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- config_name: Github_trivial
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- name: json_schema
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- name: unique_id
dtype: string
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- config_name: Github_ultra
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
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- name: val
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num_examples: 16
- name: test
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num_examples: 50
download_size: 2221455
dataset_size: 12235981
- config_name: Glaiveai2K
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
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- name: val
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num_examples: 168
- name: test
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num_examples: 513
download_size: 284264
dataset_size: 1440707
- config_name: JsonSchemaStore
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
splits:
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- name: val
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num_examples: 49
- name: test
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num_examples: 148
download_size: 4019966
dataset_size: 22195651
- config_name: Kubernetes
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
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num_examples: 639
- name: val
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num_examples: 105
- name: test
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num_examples: 320
download_size: 6819424
dataset_size: 25623424
- config_name: Snowplow
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
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num_examples: 242
- name: val
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num_examples: 40
- name: test
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num_examples: 121
download_size: 298277
dataset_size: 1613804
- config_name: WashingtonPost
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
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num_examples: 74
- name: val
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num_examples: 13
- name: test
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num_examples: 38
download_size: 565170
dataset_size: 2710348
- config_name: default
features:
- name: json_schema
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
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- name: val
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- name: test
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num_examples: 2867
download_size: 20765998
dataset_size: 96807978.39435147
configs:
- config_name: Github_easy
data_files:
- split: train
path: Github_easy/train-*
- split: val
path: Github_easy/val-*
- split: test
path: Github_easy/test-*
- config_name: Github_hard
data_files:
- split: train
path: Github_hard/train-*
- split: val
path: Github_hard/val-*
- split: test
path: Github_hard/test-*
- config_name: Github_medium
data_files:
- split: train
path: Github_medium/train-*
- split: val
path: Github_medium/val-*
- split: test
path: Github_medium/test-*
- config_name: Github_trivial
data_files:
- split: train
path: Github_trivial/train-*
- split: val
path: Github_trivial/val-*
- split: test
path: Github_trivial/test-*
- config_name: Github_ultra
data_files:
- split: train
path: Github_ultra/train-*
- split: val
path: Github_ultra/val-*
- split: test
path: Github_ultra/test-*
- config_name: Glaiveai2K
data_files:
- split: train
path: Glaiveai2K/train-*
- split: val
path: Glaiveai2K/val-*
- split: test
path: Glaiveai2K/test-*
- config_name: JsonSchemaStore
data_files:
- split: train
path: JsonSchemaStore/train-*
- split: val
path: JsonSchemaStore/val-*
- split: test
path: JsonSchemaStore/test-*
- config_name: Kubernetes
data_files:
- split: train
path: Kubernetes/train-*
- split: val
path: Kubernetes/val-*
- split: test
path: Kubernetes/test-*
- config_name: Snowplow
data_files:
- split: train
path: Snowplow/train-*
- split: val
path: Snowplow/val-*
- split: test
path: Snowplow/test-*
- config_name: WashingtonPost
data_files:
- split: train
path: WashingtonPost/train-*
- split: val
path: WashingtonPost/val-*
- split: test
path: WashingtonPost/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
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.
import datasets
from datasets import load_dataset
def main():
# Inspect the available subsets of the dataset
all_subsets = datasets.get_dataset_config_names("epfl-dlab/JSONSchemaBench")
print("Available subsets:", all_subsets)
# Example output: ['Github_easy', 'Github_hard', 'Github_medium', 'Github_trivial', 'Github_ultra', 'Glaiveai2K', 'JsonSchemaStore', 'Kubernetes', 'Snowplow', 'WashingtonPost', 'default']
# Access a specific subset of the dataset
subset_name = "Github_easy"
github_easy = load_dataset("epfl-dlab/JSONSchemaBench", subset_name)
print(f"Loaded subset '{subset_name}':", github_easy)
# Load the entire dataset as a whole
entire_dataset = load_dataset("epfl-dlab/JSONSchemaBench", "default")
print("Loaded entire dataset:", entire_dataset)
if __name__ == "__main__":
main()
Update (March 31st, 2025)
To improve inference efficiency and streamline data collation, we’ve decided to drop a small number of exceptionally long samples from the dataset.
We’re using the meta-llama/Llama-3.2-1B-instruct tokenizer, and the filtering criteria are as follows:
- Github_easy: Samples longer than 1024 tokens — 5 out of 582 removed
- Github_medium: Samples longer than 2048 tokens — 7 out of 593 removed
- Github_hard: Samples longer than 8192 tokens — 4 out of 372 removed
- Other subsets are not touched
Since the number of discarded samples is minimal, this change is expected to have at most a 1% impact on results.
⚠️ Important Update (March 10th, 2025)
We have restructured the dataset to include train/val/test splits. If you downloaded the dataset before this date, you might encounter errors like KeyError: 'Github_easy'.
To fix this issue, please follow one of the options below:
- Update How Subsets Are Accessed: If you previously used:
from datasets import load_dataset, concatenate_datasets, DatasetDict, Dataset
subset: DatasetDict = load_dataset("epfl-dlab/JSONSchemaBench")
subset["Github_easy"]
You can update it to:
from datasets import load_dataset, concatenate_datasets, DatasetDict, Dataset
subset: DatasetDict = load_dataset("epfl-dlab/JSONSchemaBench", name="Github_easy")
subset: Dataset = concatenate_datasets([subset["train"], subset["val"], subset["test"]])
- Load the Dataset in the Old Structure: If you need the previous structure, you can use a specific revision:
dataset = load_dataset("epfl-dlab/JSONSchemaBench", revision="e2ee5fdba65657c60d3a24b321172eb7141f8d73")
We apologize for the inconvenience and appreciate your understanding! 😊
📌 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.