Abimael Martell
a910b7df1d
* docs: refresh benchmark comparison * docs(site): refresh benchmark section * docs: reframe benchmark positioning * docs: focus benchmark positioning on best fit
60 lines
2.6 KiB
Markdown
60 lines
2.6 KiB
Markdown
# Benchmarking against OpenDataLoader
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The paired harness runs two `pdf2md` binaries through the same local
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OpenDataLoader corpus, evaluates both outputs, and reports aggregate and
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per-document deltas. This avoids comparing results produced from different
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corpus revisions or evaluator versions.
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Build a candidate and provide a released or worktree build as the baseline:
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```bash
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cargo build --release
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python3 scripts/bench_opendataloader.py \
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--bench-dir ../opendataloader-bench \
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--baseline ../pdf-inspector-main/target/release/pdf2md \
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--candidate target/release/pdf2md \
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--max-document-regression 0.02 \
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--json-output /tmp/pdf-inspector-benchmark.json
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```
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Pass `--reference-evaluation path/to/evaluation.json` to report the candidate
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delta against another evaluation, and add `--require-reference-lead` to make a
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negative reference delta fail the run. By default, the candidate must not
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regress the baseline overall score or introduce missing predictions. Use
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`--min-overall-delta` to require a specific aggregate gain.
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The OpenDataLoader repository is external and keeps its normal
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`prediction/pdf-inspector` output. Paired evaluation copies each run into a
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temporary directory before evaluating it, so the baseline and candidate cannot
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overwrite one another.
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## Published comparison protocol
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The public benchmark table was refreshed on July 16, 2026, on an Apple M4 Pro
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using pdf-inspector 0.1.6, LiteParse 2.6.0, OpenDataLoader 2.1.1,
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PyMuPDF4LLM 0.2.0, and MarkItDown 0.1.4. Every engine processed the same 200
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PDFs with OCR disabled. Reported speed is the median of three complete corpus
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runs; quality scores come from the benchmark evaluator over all 200 outputs.
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## Optional backend evidence probe
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The evidence probe compares positioned `pdf2md` items with MuPDF structured
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text on the same pages. It is intended to find deterministic extraction or
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layout evidence that could justify a future native implementation; it does not
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merge MuPDF output into Markdown, invoke OCR, or add a runtime dependency.
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Install MuPDF's `mutool`, build `pdf2md`, then run:
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```bash
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python3 scripts/probe_backend_evidence.py document.pdf \
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--pdf2md target/release/pdf2md \
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--json-output /tmp/backend-evidence.json
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```
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The report flags pages when MuPDF exposes a material net token gain, repeated
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alignment anchors absent from local evidence, or additional image blocks. The
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JSON includes bounded token samples and page-level counts so promising cases
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can be inspected without treating backend disagreement as automatically
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correct. Thresholds are configurable with `--min-token-gain`,
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`--min-alternate-only-ratio`, and `--min-anchor-gain`.
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