|
|
--- |
|
|
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-* |
|
|
pretty_name: J |
|
|
--- |
|
|
|
|
|
# JSONSchemaBench |
|
|
|
|
|
[](https://arxiv.org/abs/2501.10868) |
|
|
|
|
|
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 |
|
|
|
|
|
```python |
|
|
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](https://arxiv.org/abs/2501.10868).** |
|
|
|
|
|
## 📚 Citation |
|
|
If you use this dataset, please cite: |
|
|
```bibtex |
|
|
@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](https://opensource.org/licenses/MIT). 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. |
