| --- |
| license: apache-2.0 |
| viewer: false |
| pretty_name: ExtractBench Pro results |
| tags: |
| - document-extraction |
| - benchmark |
| - evaluation |
| - json-schema |
| --- |
| |
| # ExtractBench Pro results |
|
|
| Per-document inference outputs of every pipeline on the [ExtractBench Pro](https://huggingface.co/datasets/llamaindex/ExtractBenchPro) 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>/<challenge>.jsonl one line per document per challenge, see schema/record.v1.schema.json |
| (document_conventions, house_vocabulary, house_counting_rules, |
| drawn_relations, charts_inside_documents, long_chart_sheets) |
| 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 `<challenge>/<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. Records carry the real wall-clock latency of each document; the leaderboard's 30-minute view is a cutoff over `latency_in_ms`, the no-time-limit columns use every record. Documents a pipeline failed on have no record and are scored as missing. |
|
|
| ## 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/ExtractBenchPro-results --repo-type dataset --local-dir pro-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 --dataset pro # ground truth into ./data-pro |
| python ../pro-results/tools/explode.py --benchmark extractbenchpro ../pro-results/results/llamaextract_cost_effective output-pro/llamaextract_cost_effective |
| uv run extract-bench run llamaextract_cost_effective --dataset pro --skip_inference |
| ``` |
|
|
| The run reads `output-pro/<pipeline>/<challenge>/<stem>.result.json` and writes one report per challenge. Pass `--group document_conventions` to score one challenge. |
|
|
| ## Submit your pipeline |
|
|
| 1. Register the pipeline in the ExtractBench package and run it: `uv run extract-bench run <pipeline> --dataset pro`. |
| 2. Pack and check: |
| ```bash |
| python tools/pack.py --benchmark extractbenchpro --pipeline <pipeline> output-pro/<pipeline> results/<pipeline> |
| python tools/validate.py --benchmark extractbenchpro 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/ExtractBenchPro-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 Pro dataset card](https://huggingface.co/datasets/llamaindex/ExtractBenchPro). |
|
|
