| --- |
| 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.md` in the corpus repo — read it before drawing conclusions. |
|
|
| ## Load |
|
|
| ```python |
| 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: |
|
|
| ```bash |
| 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. |
|
|
