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README.md

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+---
+pretty_name: J
+dataset_info:
+- config_name: Github_medium
+  features:
+  - name: json_schema
+    dtype: string
+  - name: unique_id
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 1
+  - name: val
+    num_examples: 1
+  - name: test
+    num_examples: 100
+configs:
+- config_name: Github_medium
+  data_files:
+  - split: train
+    path: Github_medium/train-*
+  - split: val
+    path: Github_medium/val-*
+  - split: test
+    path: Github_medium/test-*
+license: mit
+task_categories:
+- text-generation
+---
+
+This is a pruned eval dataset from [epfl-dlab/JSONSchemaBench](https://huggingface.co/datasets/epfl-dlab/JSONSchemaBench) for personal debugging purposes.
+
+Below is the original model card.
+
+***
+# JSONSchemaBench
+
+[![Paper](https://img.shields.io/badge/Paper-arXiv-blue)](https://arxiv.org/abs/2501.10868)
+[![GitHub](https://img.shields.io/badge/Code-GitHub-blue)](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.
+
+
+```python
+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:
+
+1. Update How Subsets Are Accessed:
+If you previously used:
+
+```python
+from datasets import load_dataset, concatenate_datasets, DatasetDict, Dataset
+
+subset: DatasetDict = load_dataset("epfl-dlab/JSONSchemaBench")
+subset["Github_easy"]
+```
+You can update it to:
+
+```python
+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"]])
+```
+
+2. Load the Dataset in the Old Structure:
+If you need the previous structure, you can use a specific revision:
+
+```python
+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
+
+```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.