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.jsonschemabench-maskbenchJSONSchemaBench__subsampledjson-ld-schema-meta-tag-extractor-sample-data
JSON-LD Schema & Meta Tag Extractor
Extract JSON-LD/Schema.org structured data, Meta tags, OpenGraph and Twitter Cards from any URL. Get page title + meta description with a clean JSON output for SEO audits, validation, competitor research and AI datasets. Proxy-ready for large crawls.
What the actor scrapes
🧩 JSON-LD Schema & Meta Tag Extractor — Scrape Schema.org, OpenGraph & Meta Tags Extract structured data and SEO metadata from any webpage in seconds. This… See the full description on the dataset page: https://huggingface.co/datasets/logiover/json-ld-schema-meta-tag-extractor-sample-data.fineweb-edu-json-schema-deduction
FineWeb-Edu json-schema-deduction
Source: fineweb-edu dataset.
Task: JSON schema deduction.
5,000 entries from fineweb-edu dataset
btw, every single key in the schema is unique. The model reasoning was high. The ontology went too deep haha.
It generated over 51,000 unique keys across 5,000 documents. it basically baked raw text directly into the structural keys.
however! json is 100% valid and correct so theres that
Columns are raw_text and schema
json-schema-store-reasoningjsonschema-sftjsonschema-goldagentic_dpo_json_schema_v1