---
license: apache-2.0
viewer: false
pretty_name: ExtractBench results
tags:
- document-extraction
- benchmark
- evaluation
- json-schema
---

# ExtractBench results

Per-document inference outputs of every pipeline on the [ExtractBench](https://huggingface.co/datasets/llamaindex/ExtractBench) leaderboard, so anyone can re-run the evaluation locally without calling any provider, inspect what a pipeline returned for a given document, and re-score when the ground truth changes.

Scores come from the [ExtractBench](https://github.com/run-llama/ExtractBench) package. Pipeline results were produced by the same package or by the equivalent provider API calls; only the normalized output and the cost, usage and timing counters are published.

## Layout

```
pipelines.jsonl                  one line per pipeline: name, display name, category, documents, data revision
results/<pipeline>/short.jsonl   one line per document, see schema/record.v1.schema.json
results/<pipeline>/medium.jsonl
results/<pipeline>/long.jsonl
data_manifest.json               document ids of the data revision these results were scored on
tools/                           explode.py, pack.py, validate.py (Python 3.9+, standard library)
```

Each line is a minimal `InferenceResult` as the evaluator loads it: `request.example_id` is `<split>/<document stem>`, `output` holds `extracted_data` and `field_citations` (the citations drive the word- and page-grounding scores), and `raw_output` holds cost, page and token counters.

## Two tiers: eval records and full results

- `results/<pipeline>/<category>.jsonl` is the small tier: only the fields the evaluator needs. Download this to re-score a pipeline quickly.
- `raw/<pipeline>/<category>.jsonl` is the full tier: one line per document with everything a user of that pipeline receives, meaning the complete provider response (`raw_output`), the normalized output with all citation metadata, the parse job spawned by LlamaExtract (`stages`), timing and cost. Fields that exist only because of our harness (internal paths, run ids, job ids, signed download links) are removed.
- `traces/<pipeline>/<category>/<stem>.*` holds the complete transcripts of coding-agent pipelines: the prompt, every tool call and tool output, thinking, token usage and cost (`*.trace.jsonl`, `*.attempt-N.jsonl`), the agent's final message (`*.last_message.txt`), its structured output (`*.output.json`) and stderr (`*.stderr.log`). Only our runner paths and identity fields are rewritten or removed.

Download one tier at a time with `--include "results/<pipeline>/*"`, `--include "raw/<pipeline>/*"` or `--include "traces/<pipeline>/*"`.

## Replay a pipeline locally

```bash
pip install -U huggingface_hub
hf download llamaindex/ExtractBench-results --repo-type dataset --local-dir eb-results \
  --include "results/llamaextract_cost_effective/*" "tools/*" "data_manifest.json" "pipelines.jsonl"

git clone https://github.com/run-llama/ExtractBench && cd ExtractBench && uv sync
uv run extract-bench download                           # ground truth into ./data
python ../eb-results/tools/explode.py --benchmark extractbench ../eb-results/results/llamaextract_cost_effective output/llamaextract_cost_effective
uv run extract-bench run llamaextract_cost_effective --skip_inference
```

The run reads `output/<pipeline>/<split>/<stem>.result.json`, scores every document, and writes one report per split. Pass `--group short` to score one split.

## Submit your pipeline

1. Register the pipeline in the ExtractBench package and run it: `uv run extract-bench run <pipeline>`.
2. Pack and check:
   ```bash
   python tools/pack.py --benchmark extractbench --pipeline <pipeline> output/<pipeline> results/<pipeline>
   python tools/validate.py --benchmark extractbench results/<pipeline>
   ```
   `validate.py` fails on anything that is not a public document id, on local paths or hostnames, on credentials, and on non-canonical JSON. Identical model output always packs to identical files; only timestamps, latency and cost differ between runs.
3. Open a pull request on this dataset:
   ```bash
   hf upload llamaindex/ExtractBench-results results/<pipeline> results/<pipeline> --repo-type dataset --create-pr
   ```
   and link it from your leaderboard pull request on GitHub. The leaderboard row is re-scored from the uploaded results before merge.

## Citation

See the [ExtractBench dataset card](https://huggingface.co/datasets/llamaindex/ExtractBench).