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license: apache-2.0
configs:
- config_name: extract-bench
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: short
path: short.jsonl
- split: medium
path: medium.jsonl
- split: long
path: long.jsonl
language:
- en
pretty_name: ExtractBench
size_categories:
- n<1K
tags:
- document-extraction
- structured-extraction
- information-extraction
- pdf
- benchmark
- evaluation
- json-schema
- visual-grounding
- forms
- tables
citation: |
@misc{extractbench2026,
title={ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction},
author={LlamaIndex},
year={2026},
url={https://github.com/run-llama/ExtractBench},
}
---
# ExtractBench — smoke subset
This is the `test-data` branch: a 6-document sample (3 short, 2 medium, 1 long) for quick smoke runs. **The benchmark itself lives on the [`main`](https://huggingface.co/datasets/llamaindex/ExtractBench) branch** — 370 documents across 4,869 pages — and that is what any reported score must be run against.
The rows here are byte-identical to their counterparts on `main`, in the same format and with the same tags; only the number of documents differs. Use it to check that a pipeline is wired up correctly before spending a full run.
```bash
uv run extract-bench download --test # -> data/test/<split>/
uv run extract-bench run <pipeline> --test # inference -> evaluation -> reports
```
| Split | File | Documents | Example |
|-------|------|----------:|---------|
| Short | [short.jsonl](short.jsonl) | 3 | scanned tax return, handwritten-annotated disposal permit, pivoted election results |
| Medium | [medium.jsonl](medium.jsonl) | 2 | district check register, quarterly earnings deck |
| Long | [long.jsonl](long.jsonl) | 1 | 66-page clinical measurement listing |
The sample spans eight challenge tags (long lists, cross-page continuation, pivoted tables, packed cells, needle-in-haystack, dense forms, filer-versus-reviewer edits), scanned and handwritten capture, and four business domains.
Five of the six documents carry word-level bounding boxes in their ground truth, so every split reports grounding as well as value accuracy. The earnings deck has value-level ground truth only; grounding metrics are omitted for it, as they are for any document whose ground truth carries no boxes.
- **Code**: [run-llama/ExtractBench](https://github.com/run-llama/ExtractBench)
- **Full dataset**: [`main` branch](https://huggingface.co/datasets/llamaindex/ExtractBench)
|