datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
JSONSchemaBench
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 datasetall_subsets = datasets.get_dataset_config_names("epfl-dlab/JSONSchemaBench")
print("Available subsets:"… See the full description on the dataset page: https://huggingface.co/datasets/epfl-dlab/JSONSchemaBench.json-schema-instances-training-pool
JSON schema and instance training pool
Real JSON Schemas from the public collections named below, read at the pinned revisions given there,
each paired where possible with documents that satisfy it, laid out twice. Train on either layer or
on both.
pool.jsonl
Every source rewritten into one shape, 20004 rows, one JSON object per line, with these fields.
Field
What it holds
id
a row identifier unique within this file
prompt
the request a model would… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/json-schema-instances-training-pool.cybersec-jsonschemabench
CybersecJSONSchemaBench Hard
This hard split is a JSONSchemaBench-style cybersecurity benchmark built from
normalized CloudTrail and Suricata EVE records. It replaces anchored lookup
questions with unanchored, deterministic multi-hop reasoning programs over
large nested JSONL slices.
Each row includes:
unique_id
json_schema
prompt
input_jsonl
ground_truth_json
reasoning_family
candidate_count
distractor_count
Current Version
benchmark version: 1.0.0-hard
total… See the full description on the dataset page: https://huggingface.co/datasets/achinta3/cybersec-jsonschemabench.