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metadata

license: mit
pretty_name: 'LEGEX Code: Scrapers, Inference and Evaluation Pipeline'
tags:
  - legal
  - benchmark
  - code
  - llm-evaluation
  - information-extraction

LEGEX Code, Scrapers, Inference and Evaluation Pipeline

Python source for the LEGEX benchmark of civil-judgment review-table extraction. This repository contains:

Scrapers for 19 jurisdictions (per-court HTML / API / HuggingFace pull) in legex/scrapers/.
Inference pipeline that calls Harvey, Gemini and OpenAI APIs against a schema-constrained 14-field review table (legex/inference.py, legex/harvey.py, legex/models/classification.py).
Evaluation that compares system outputs against expert-coded gold cells (legex/evaluation.py), aggregates across jurisdictions (legex/analysis.py), and renders paper tables (legex/quant_results.py).
Conversion script convert_goldenset_to_jsonl.py — turns the source XLSX goldensets into the JSONL format used by legexbenchmark/goldensets.

Setup

git clone https://huggingface.co/datasets/legexbenchmark/code legex-code
cd legex-code
uv sync
cp .env.template .env

Required tokens depend on which scrapers / models you run, see .env.template.

End-to-end workflow

# Acquire raw judgments per jurisdiction.
uv run legex-run

# Run inference for one system on one jurisdiction, Harvey has do be done separately as this is a commercial tool
uv run legex-classify --country us --model gpt-5.4-mini --full_text

# Evaluate one system on one jurisdiction.
uv run legex-evaluate --country us --system gpt

# Aggregate across all 12 evaluated jurisdictions and 3 systems.
uv run legex-analysis --out data/analysis

# Render the paper-headline LaTeX table.
uv run legex-quant-results \
    --input data/analysis/per_country_per_column.csv \
    --out   data/analysis/quant_results.tex

To evaluate against the published goldensets and inference outputs, pull the two data repos into the expected layout:

huggingface-cli download legexbenchmark/goldensets         --repo-type dataset --local-dir data --include "data/*"
huggingface-cli download legexbenchmark/inference-results  --repo-type dataset --local-dir data --include "data/*"
# After these, data/<cc>/ contains goldenset_<cc>.jsonl + inference_*.csv
uv run legex-analysis --out data/analysis

CLI entrypoints

Command	Module	Purpose
`legex-run`	`legex.main:main`	Top-level scrape + filter + sample pipeline.
`legex-classify`	`legex.inference:main`	Run an LLM over the goldenset and write predictions to CSV.
`legex-harvey-ingest`	`legex.harvey:main`	Ingest a Harvey Vault Review export into the per-jurisdiction CSV format.
`legex-evaluate`	`legex.evaluation:main`	Per-country, per-field bucket counts and recall / hallucination.
`legex-analysis`	`legex.analysis:main`	Cross-jurisdiction analysis → CSV + LaTeX tables.
`legex-quant-results`	`legex.quant_results:main`	Paper-headline summary from the analysis CSV.
`legex-pdf`	`legex.pdf_export.cli:main`	Render per-row PDFs from a goldenset workbook.
`legex-plots`	`legex.plots:main`	Plot helpers used in the paper.

License

MIT.