Datasets:

llamaindex
/

ExtractBenchPro

license: apache-2.0
configs:
  - config_name: extract-bench-pro
    features:
      - name: id
        dtype: string
      - name: category
        dtype: string
      - name: pdf
        dtype: string
      - name: data_schema
        dtype: string
      - name: expected_output
        dtype: string
      - name: field_rules
        dtype: string
      - name: repeated_structure
        dtype: string
      - name: tags
        sequence: string
    data_files:
      - split: document_conventions
        path: document_conventions.jsonl
      - split: house_vocabulary
        path: house_vocabulary.jsonl
      - split: house_counting_rules
        path: house_counting_rules.jsonl
      - split: drawn_relations
        path: drawn_relations.jsonl
      - split: charts_inside_documents
        path: charts_inside_documents.jsonl
      - split: long_chart_sheets
        path: long_chart_sheets.jsonl
language:
  - en
pretty_name: ExtractBench Pro
size_categories:
  - n<1K
tags:
  - document-extraction
  - structured-extraction
  - information-extraction
  - pdf
  - benchmark
  - evaluation
  - json-schema
  - visual-grounding
  - synthetic

ExtractBench Pro

Quick links: [🌐 Website] [💻 Code] [📚 ExtractBench]

ExtractBench Pro is the hard companion to ExtractBench. The task is the same: given a full document and a user-written schema, return a schema-valid JSON object with the correct, exhaustive values and evidence for each one. The difference is what makes a value hard. In ExtractBench Pro, many values cannot be copied from the page. They have to be read off a chart or a drawing, counted under the document’s own rules, or decoded with its own vocabulary. The documents are derived from real long-tail documents, and grouped into six challenges.

ExtractBench Pro contains 100 documents (2,622 pages, 1–83 pages each) across 86 document types, with 144,141 expected values. Every document is synthetic: its values are fictional and fixed before the page is rendered, so the expected output is exact by construction.

Challenge subsets

Each document belongs to exactly one subset: the challenge a naive reader most often gets wrong in it. A document can also contain values of other challenges, and values that are read directly from the page.

Subset	Challenge	Documents	Pages	Expected values
`document_conventions`	Document conventions. One part of the document, such as a legend, rule or amendment, changes how the rest must be read	22	875	121,287
`house_vocabulary`	House vocabulary. The organization’s own definitions of its jargon decide the value	14	606	15,242
`house_counting_rules`	House counting rules. Counts and sums computed under definitions from the schema and the document	12	225	409
`drawn_relations`	Drawn relations. All or part of the value depends on a line in a diagram rather than a printed value	20	352	2,349
`charts_inside_documents`	Charts inside documents. Values read off charts, as the range between the tick marks the value falls in	25	557	1,347
`long_chart_sheets`	Long chart sheets. Hundreds of values on one extremely long, narrow chart sheet, such as a well log or a time-location chart	7	7	3,507
Total		100	2,622	144,141

Document types per subset

Document conventions: GL trial balances (2), loss runs (2), regulations (2), utility invoices (2), benefits census, clinical listing, customs entry (CBP 7501), grant award form, insurance schedule, investor report, mortgage application (URLA 1003), patient record, payment remittance, payroll audit packet, property bulletin, SEC 13F report, UB-04 claim form, UCC search form
House vocabulary: care home census review, circulation audit, demand response settlement, export credit policy results, hotel room block review, import duty reconciliation, joint interest billing review, lab safety review, purchase contract review, school enrollment review, show entry check, spread betting account, subscription seat review, transit ridership review
House counting rules: college cohort file, container demurrage claim, election canvass binder, engine utilisation return, franchise royalty report, landfill diversion return, library aid claim, mortgage servicing report, premium audit worksheet, travel expense binder, TV post-buy affidavit, vendor statement reconciliation
Drawn relations: credit facility binders (2), fund structure files (2), HVAC balancing reports (2), insurance programme files (2), network change requests (2), one-line commissioning binders (2), water loss audits (2), audit findings report, capital call notice, fibre splice record, probate court file, procurement audit pack, tournament results pack
Charts inside documents: agency staffing invoice, aggregate QC report, assessment billing run, capacity change pack, chemotherapy verification, claims triage bundle, contractor dispute binder, dam operations report, drilling report bundle, earthwork quantity packet, eligibility appeals file, Gantt schedule board, integrity filing annex, medical gas shutdown notice, parking variance hearing, pipeline inspection report, pump station calculation, QT audit bulletin, rezoning notice packet, right-of-way acquisition packet, site plan review, stock declaration file, titration packet, tower coverage credit, well completion filing
Long chart sheets: wireline logs (3), time-location charts (2), borehole core log, track chart

Capture conditions and length

Each document is also tagged by how most of its pages were captured, by length and by business domain. The axes are independent of the challenge subset.

Perception	Documents	What most pages look like
`digital`	25	Born-digital pages with a text layer
`image`	8	Clean page images without scanner noise
`scan`	44	Light or medium scan
`heavy_scan`	16	Heavy or extreme scan
`handwritten`	7	Hand-filled forms or handwritten values

Length: short (10 pages or fewer) 8 documents, medium (11–50 pages) 81 documents, long (more than 50 pages) 11 documents.
Domain: D1 finance (27), D2 energy (19), D3 government (16), D5 supply chain (9), D6 healthcare (13), D7 legal (3), D8 real estate (13), using the ExtractBench domain codes.

Seven of the eight short documents are single plotter sheets 13 to 17 feet long (well logs, a borehole core log, a track chart and time-location charts), scanned or photocopied, with no text layer; each asks for 390 to 715 values. The eighth is a two-page Gantt schedule board. Charts and drawings in other documents are often page images too, so their values cannot be read from a text layer. Some scanned documents carry a text layer that does not match the printed page. The printed page is authoritative. One document prints two pages on each sheet, turned sideways.

Usage

Load a subset with datasets:

from datasets import load_dataset
import json

ds = load_dataset("llamaindex/ExtractBenchPro", split="charts_inside_documents")
case = ds[0]
schema = json.loads(case["data_schema"])
expected = json.loads(case["expected_output"])

The rows use the same format as ExtractBench, so the evaluation framework scores them unchanged. To rebuild the <stem>.pdf + <stem>.test.json layout that the framework reads:

import json, shutil
from pathlib import Path
from huggingface_hub import snapshot_download

root = Path(snapshot_download("llamaindex/ExtractBenchPro", repo_type="dataset"))
out = Path("data/extract_bench_pro")
for split in ["document_conventions", "house_vocabulary", "house_counting_rules", "drawn_relations", "charts_inside_documents", "long_chart_sheets"]:
    for line in (root / f"{split}.jsonl").read_text().splitlines():
        row = json.loads(line)
        stem = Path(row["pdf"]).stem
        (out / split).mkdir(parents=True, exist_ok=True)
        shutil.copyfile(root / row["pdf"], out / split / f"{stem}.pdf")
        sidecar = {
            "tags": row["tags"],
            "data_schema": json.loads(row["data_schema"]),
            "expected_output": json.loads(row["expected_output"]),
            "_field_rules": json.loads(row["field_rules"]),
        }
        (out / split / f"{stem}.test.json").write_text(json.dumps(sidecar))

Scoring follows ExtractBench and is fully deterministic:

Unified value F1: whether the extracted values match the expected output, under one definition for scalar fields and arrays of records.
Near-perfect value F1: the share of documents with a unified value F1 of at least 99%.
Word-level grounding F1: a field counts as grounded only when its value is correct and its predicted box overlaps an accepted box for that field at IoU 0.5.
Page-level grounding F1: requires the correct source page rather than a box.

The evaluation dataset files are:

document_conventions.jsonl: 22 test cases across 875 pages
house_vocabulary.jsonl: 14 test cases across 606 pages
house_counting_rules.jsonl: 12 test cases across 225 pages
drawn_relations.jsonl: 20 test cases across 352 pages
charts_inside_documents.jsonl: 25 test cases across 557 pages
long_chart_sheets.jsonl: 7 test cases across 7 pages
docs/: source PDFs organized by subset

Dataset Format

Each line in a JSONL file is one document-schema test case:

{
    "id": "long_chart_sheets/wireline_log_01",      // Unique test-case id: <subset>/<stem>
    "category": "long_chart_sheets",                // Subset name
    "pdf": "docs/long_chart_sheets/wireline_log_01.pdf", // Relative path to the source document
    "data_schema": "{...}",         // JSON-encoded JSON Schema the output must validate against
    "expected_output": "{...}",     // JSON-encoded ground-truth extraction
    "field_rules": "{...}",         // JSON-encoded per-field scoring rules
    "repeated_structure": "{}",     // JSON-encoded record-identity config (empty: records are matched on their values)
    "tags": ["source:synthetic", "challenge:long_chart_sheets", "perception:scan", "length:short", "domain:D2"]
}

data_schema, expected_output, field_rules and repeated_structure are stored as JSON-encoded strings because schemas differ by document. Decode them with json.loads. The largest field_rules cell is about 6 MB, so the dataset viewer shows only a preview of each cell. The downloaded file contains the complete data.

field_rules maps each value path to a scoring rule:

evidence: accepted readings and their source locations, stored as {page, bbox, quote, value}; any accepted reading counts as correct
source_policy: whether the value must appear verbatim in the document
evidence_required / verified: whether located evidence exists for the value
normalizers: value normalization applied before comparison

Data Display

_{ECG rhythm strips}	_{Decision flowchart}	_{Zoning atlas map}	_{Single-line power diagram}
_{Colour-coded shift roster}	_{Probate family tree}	_{Fund structure chart}	_{Wireline well-log tracks}
_{Nomogram with straightedge}	_{Pipeline inspection strip log}	_{Bars-and-line combo chart}	_{Warehouse slot hatch map}
_{Site plan with review marks}	_{Hand-drawn ownership network}	_{Time-location chart}	_{Dark-theme bar panel}
_{Subdivision plat}	_{Sieve grading curves}	_{Fibre splice diagram}	_{Reservoir hydrograph strips}

Copyright Statement

All documents are synthetic and contain fictional values. The dataset is released under the Apache 2.0 License. For questions, contact us through the GitHub repository.