| --- |
| 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 |
|
|
| [](https://arxiv.org/abs/2501.10868) |
| [](https://github.com/guidance-ai/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 |
|
|
| ```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 |
| ```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. |
