metadata
license: cc-by-4.0
language:
- en
tags:
- document-extraction
- information-extraction
- schema-driven
- benchmark
- evaluation
size_categories:
- 1K<n<10K
pretty_name: FieldBench Corpus
FieldBench Corpus
A cross-domain, field-level benchmark for schema-driven document extraction (document → structured JSON). 1,441 documents across 10 categories with per-field ground truth, released so extraction-accuracy claims become falsifiable and comparable.
- Code / scorer: https://github.com/fieldbench/fieldbench (
pip install fieldbench) - Full corpus + datasheet: https://github.com/fieldbench/corpus
- Datasheet: see
DATASHEET.mdin the corpus repo — read it before drawing conclusions.
Load
from datasets import load_dataset
ds = load_dataset("fieldbench/corpus")["test"]
ex = ds[0]
# ex["document"] -> the markdown representation to extract from
# ex["expected"] -> ground-truth field map (JSON string; json.loads it)
# ex["category"], ex["source"] ("real"|"synthetic"), ex["schema"]
Score predictions with the canonical scorer:
pip install fieldbench
fieldbench score --corpus <corpus-checkout> --results <your-predictions>/
Fields
| field | description |
|---|---|
doc_id |
document identifier |
category |
one of 10 categories (sec_filings, invoices, medical_records, …) |
source |
real or synthetic — always report results stratified by this |
original_format |
how the document reached the extractor (see composition note) |
document |
the markdown representation to extract from |
expected |
ground-truth {field: value} map, serialized as a JSON string |
schema |
schema file name defining the fields for this category |
Important caveats
- ~90% of documents are extraction-from-clean-text, not rendered-page extraction (only 9.8% came from an image/PDF). Parse-stage difficulty is largely absent by construction — scope accuracy claims accordingly. See the composition table in the corpus repo.
- Synthetic documents (~48%) overestimate accuracy relative to real ones. Never report a synthetic-inclusive number without the real/synthetic split.
- Licensing is per-source (see
ATTRIBUTION.md): synthetic CC0; SEC EDGAR public disclosure; SROIE CC BY 4.0; MTSamples educational-use with attribution; Caselaw Access Project public domain; government forms public domain. ACORD/ISO copyrighted forms are not included (those categories use synthetic equivalents).
Citation
See CITATION.cff in the corpus repo.