diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..c2df6d438aa83aafba3dd60e716636e9577d5f03 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +*.jsonl filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index 3bbae99a5a2c894074e3d8a73f95764c41070319..45dca2179e206d3abedeca93ded8bdcf3ff4211b 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,8 @@ __pycache__/ *.py[codz] *$py.class *.pdf +# ... but the shipped appendix figure is part of the release payload +!data/analysis/figures/isic_frequencies.pdf # C extensions *.so diff --git a/HUGGINGFACE_UPLOAD.md b/HUGGINGFACE_UPLOAD.md new file mode 100644 index 0000000000000000000000000000000000000000..5f252465d711f5b325ee25ee4f095d7d46339af6 --- /dev/null +++ b/HUGGINGFACE_UPLOAD.md @@ -0,0 +1,257 @@ +# How to publish LEGEX to Hugging Face anonymously + +This walkthrough takes the contents of `submission/` and publishes **three +public Hugging Face dataset repositories** under the anonymous account +`legexbenchmark`: + +1. **`legexbenchmark/goldensets`** — expert-coded gold annotations + (19 jurisdictions, 1,548 judgments / 1,974 rows incl. secondary + re-annotations). +2. **`legexbenchmark/inference-results`** — Harvey (2 runs) / Gemini / GPT / + Legora (2 runs) outputs (19 jurisdictions × 6 runs = 114 files, JSONL) + plus the per-column prompts of the commercial runs (`prompts/`, 4 files). +3. **`legexbenchmark/code`** — Python pipeline (scrape → process → infer → + clean → evaluate). + +> **All data files are JSONL** (one JSON object per line). Inference was CSV +> in the first release and is now JSONL — see *Updating an existing release* +> below. + +--- + +## 0. Updating an existing release (goldensets + inference-results) + +The three repos already exist, and local clones live in +`../hf-uploads/{goldensets,inference-results,code}`. The staged bundle +(`submission/goldensets/`, `submission/inference-results/`) mirrors the two +data repos 1:1: data + the per-repo dataset card (`README.md`). + +Rebuild the bundle only if the underlying data changed +(`convert_goldenset_to_jsonl.py` needs the local-only +`data/reannotation/annotators.json` + `ANNOTATOR_SALT` from `.env`): + +```bash +uv run python submission/convert_goldenset_to_jsonl.py --data-dir data --out-dir submission/goldensets/data +(cd submission && uv run python build_inference_jsonl.py --data-dir ../data --gold-dir goldensets/data --out-dir inference-results/data) +``` + +When the working trees in `../hf-uploads/` are already synced from +`submission/` (data + README copied in, stale CSVs removed), pushing the +update is: + +```bash +# Log in once with the anonymous account's WRITE token. +pip install --upgrade huggingface_hub && git lfs install --skip-repo +huggingface-cli login # paste the hf_… write token +huggingface-cli whoami # must print: legexbenchmark + +# --- goldensets --- +cd ../hf-uploads/goldensets +git status && git diff --stat # review: data//*.jsonl + README.md +git config user.name anonymous && git config user.email anonymous@anonymous.invalid +git add -A +git commit -m "Update: re-annotations, Nepal/Singapore extensions, traceability fields, refreshed card" +git push +cd - + +# --- inference-results (CSV -> JSONL) --- +cd ../hf-uploads/inference-results +git status # review: CSVs deleted, 57 JSONL added, README.md, .gitattributes +git config user.name anonymous && git config user.email anonymous@anonymous.invalid +git add -A +git commit -m "Switch inference to JSONL; 19 jurisdictions; cleaning provenance; refreshed card" +git push +cd - +``` + +Then **verify usability** (the old CSV raised `ArrowInvalid`): + +```python +from datasets import load_dataset +load_dataset("legexbenchmark/goldensets", split="brazil") +load_dataset("legexbenchmark/inference-results", "harvey", split="united_states") +load_dataset("legexbenchmark/inference-results", "gpt", split="switzerland") +``` + +Also confirm on the web UI that every commit author reads **`anonymous`**, the +dataset viewer renders both repos, and no `inference_*.csv` remain in +`inference-results`. + +**`code`** is updated separately once the cleaned analysis package is staged — +see item 3 of [CHANGES_SINCE_SUBMISSION.md](CHANGES_SINCE_SUBMISSION.md). + +--- + +## 1. Create the anonymous Hugging Face account (once) + +1. Open a private / incognito browser window so cookies from any existing + HF account are isolated. +2. Visit . +3. Sign up with a **fresh email address** that is not tied to any of the + authors (a `proton.me` / `tutanota.com` mailbox works fine). +4. Pick the username **`legexbenchmark`** so the public URLs match the + names used in the paper. +5. Verify the email and log in. + +> **Why a fresh account?** HF attaches your account's display name, avatar +> and email to commits in each repo's history. Reusing an existing account +> would leak your identity even if every file in the repo is clean. + +## 2. Create a write-scoped access token (once) + +1. Visit . +2. Click **New token**, name it `upload`, role **Write**, click **Generate + token**, copy the value (it starts with `hf_`). + +## 3. Install the tooling and log in (once) + +```bash +pip install --upgrade huggingface_hub +git lfs install --skip-repo # macOS: brew install git-lfs first + +# Paste the write token created in step 2 when prompted. +huggingface-cli login +``` + +## 4. Push the three repositories (initial release) + +Run steps 4a → 4c **in order**. Each step is independent — if something +breaks in 4b you can fix it without redoing 4a. + +### 4a. Push `legexbenchmark/goldensets` + +```bash +# 1. Create the empty repo via the web UI: +# https://huggingface.co/new-dataset +# Owner: legexbenchmark | Name: goldensets | License: MIT | Public + +# 2. Clone, anonymise local git, copy in the staged files. +git clone https://huggingface.co/datasets/legexbenchmark/goldensets hf-goldensets +cd hf-goldensets +git config user.name "anonymous" +git config user.email "anonymous@anonymous.invalid" +git lfs track "*.jsonl" +git add .gitattributes + +cp -R ../submission/goldensets/. . + +# 3. Commit and push. +git add . +git commit -m "Initial release" +git push -u origin main +cd .. +``` + +Open . Verify: + +- The dataset card renders with the jurisdiction tables. +- `data//goldenset_.jsonl` is browsable for all 19 jurisdictions. +- The author next to every commit is **`anonymous`**. + +### 4b. Push `legexbenchmark/inference-results` + +```bash +# 1. Create the empty repo: +# https://huggingface.co/new-dataset +# Owner: legexbenchmark | Name: inference-results | License: MIT | Public + +# 2. Clone, anonymise, copy in. +git clone https://huggingface.co/datasets/legexbenchmark/inference-results hf-inference +cd hf-inference +git config user.name "anonymous" +git config user.email "anonymous@anonymous.invalid" +git lfs track "*.jsonl" +git add .gitattributes + +cp -R ../submission/inference-results/. . + +# 3. Commit and push. +git add . +git commit -m "Initial release" +git push -u origin main +cd .. +``` + +Verify on : + +- 19 `data//` folders (all jurisdictions). +- Each folder has `inference_harvey.jsonl`, `inference_gemini.jsonl`, + `inference_gpt.jsonl` (no `.csv`). + +### 4c. Push `legexbenchmark/code` + +```bash +# 1. Create the empty repo: +# https://huggingface.co/new-dataset +# Owner: legexbenchmark | Name: code | License: MIT | Public +# (HF dataset repos accept arbitrary files — we use "dataset" type as +# the most permissive container for source code.) + +# 2. Clone, anonymise, copy in the cleaned package (see +# CHANGES_SINCE_SUBMISSION.md item 3 for what goes into the code repo). +git clone https://huggingface.co/datasets/legexbenchmark/code hf-code +cd hf-code +git config user.name "anonymous" +git config user.email "anonymous@anonymous.invalid" + +# 3. Commit and push. +git add . +git commit -m "Initial release" +git push -u origin main +cd .. +``` + +Verify on : + +- The repo card lists the CLI entrypoints and the workflow. +- `legex/scrapers/` contains 19 jurisdiction modules. + +## 5. Cross-verify the org page + +Open in a fresh tab — you should +see three pinned datasets. Click each card and confirm: + +- The README renders correctly. +- The "Files and versions" tab is browsable. +- No identifying information appears next to commits (everything should + read `anonymous`). + +Also run one final string search per repo via the file viewer for your own +surname, your affiliation, and your work email domain: + +``` + # must return nothing + # must return nothing + # must return nothing +``` + +## 6. Share with reviewers + +The single URL that ties the three repos together is +. Add it to the supplementary +material of your OpenReview submission; reviewers can browse all three +datasets from that org page without logging into HF. + +--- + +## Troubleshooting + +**A commit shows your real name despite the local config.** Run +`git log -1 --pretty='%an <%ae>'` inside the clone. If it still shows your +real identity, redo `git config user.name / user.email` and then +`git commit --amend --reset-author` before pushing. + +**HF complains about LFS quota.** Free accounts get 1 GB of LFS storage; +this release uses ~30 MB so quota is not a concern. + +**A file exceeds 5 GB.** None of the files in this release do. If a future +addition does, `git lfs track` the path and recommit. + +**You want to delete a repo.** Each repo's Settings page has "Delete this +dataset repository" — that removes files and history. + +**You need to re-push after a fix.** `cd` into the local clone, edit, commit +with `--reset-author` if you also re-ran `git config`, then `git push`. + +**Reviewers ask for a single zip.** HF's "Use this dataset" → "Repository +size" → "Download repository" gives a `.tar.gz` of the whole repo. diff --git a/LICENSE b/LICENSE index 443d12fddccc2e0af4e3366cafb1eed7a6fcd9ee..0b702870d974991edd37d0a6e2a83aa8d34617bc 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2026 Anonymous Authors +Copyright (c) 2026 Engineers for Science Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 0402eff98ba3b8ac573eae4c4ed6735ba0e8297b..0804247b040acb7529d2eb50386f9ad2fe4811fb 100644 --- a/README.md +++ b/README.md @@ -1,91 +1,183 @@ ---- -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/`](legex/scrapers/). -- Inference pipeline that calls Harvey, Gemini and OpenAI APIs against - a schema-constrained 14-field review table - ([`legex/inference.py`](legex/inference.py), - [`legex/harvey.py`](legex/harvey.py), - [`legex/models/classification.py`](legex/models/classification.py)). -- Evaluation that compares system outputs against expert-coded gold - cells ([`legex/evaluation.py`](legex/evaluation.py)), aggregates across - jurisdictions ([`legex/analysis.py`](legex/analysis.py)), and renders - paper tables ([`legex/quant_results.py`](legex/quant_results.py)). -- Conversion script [`convert_goldenset_to_jsonl.py`](convert_goldenset_to_jsonl.py) - — turns the source XLSX goldensets into the JSONL format used by - [`legexbenchmark/goldensets`](https://huggingface.co/datasets/legexbenchmark/goldensets). - - -## Setup +# LEGEX — Reproduction Bundle (release/icml2026) + +LEGEX is an expert-coded benchmark for civil-judgment review-table +extraction: judgments from the highest civil courts of 19 jurisdictions, +annotated by legal experts on a shared schema (monetary outcomes, cost +allocation, party structure, industry classification), against which we +evaluate two commercial review-table systems (Harvey, Legora) and two +schema-constrained LLM pipelines (Gemini, ChatGPT). This bundle is the +versioned code + data payload behind the paper LEGEX: An Eight-Jurisdiction +Benchmark for Legal Review-Table Extraction (ICML 2026 Workshop on AI for +Law). It contains an eight-jurisdiction core benchmark (≥100 judgments each, +double-annotated on a 28–30-case overlap) and eleven preview jurisdictions. + +The release consists of three Hugging Face repositories, pinned to the +`release/icml2026` branch: + +| Artifact | Contents | URL | +|---|---|---| +| `goldensets` | Expert gold labels, `data//goldenset_.jsonl` | | +| `inference-results` | System predictions, `data//inference_{harvey,harvey_2,gemini,gpt,legora_1,legora_2}.jsonl`, plus the as-run per-column prompts of the commercial runs in `prompts/` | | +| `code` | This bundle (package, scripts, shipped analysis outputs) | | + +## Layout of this bundle + +``` +submission/ +├── README.md this file +├── pyproject.toml · LICENSE · .env.template +├── legex/ the LEGEX package, pruned to what the release uses: +│ ├── scrapers/ one scraper per jurisdiction +│ ├── fulltext/ full-text acquisition for the goldenset workbooks +│ ├── prompts/ versioned system prompts (paper runs use v3) +│ ├── inference.py legex-classify (LLM pipelines) +│ ├── harvey.py legex-harvey-ingest (review-table export ingest) +│ ├── evaluation/ tolerant cell comparator + scoring engine +│ └── analysis/ aggregates, IAA, AAT rendering, report, plots +├── scripts/ paper-facing scripts (tables, figures, AAT adapter) +├── tests/ unit tests (uv run pytest -q) +├── data/analysis/ SHIPPED analysis outputs (see "Recompute" below) +│ ├── per_country_per_column.csv · per_country.csv · per_column.csv +│ ├── per_tradition.csv · per_language.csv +│ ├── quant_results.tex paper Table tab:overall +│ ├── paper_tables.tex tab:metrics-by-jurisdiction / tab:metrics-by-field +│ ├── tables/ diversity.tex, currency_frequencies.tex, ... +│ ├── figures/ isic_frequencies.pdf, legal_subject_wordcloud.png +│ ├── iaa/ ANALYSIS.md, pairwise_agreement.csv, kappa_audit.csv, +│ │ alt_test_pooled.csv, alt_test_reference_*.csv +│ ├── hallucinations/ hallucination_review_ch.csv (hand-classified error case study) +│ └── quality/ by_country.csv, by_variable.csv +├── goldensets/ 1:1 copy of legexbenchmark/goldensets (dataset card + data) +├── inference-results/ 1:1 copy of legexbenchmark/inference-results +├── convert_goldenset_to_jsonl.py XLSX gold workbooks -> goldenset_.jsonl +├── build_inference_jsonl.py cleaned working JSONL -> inference_.jsonl +├── docs/Jurisdictions.md per-jurisdiction research catalogue +└── HUGGINGFACE_UPLOAD.md how this bundle maps onto the three HF repos +``` + +## Reproduce the paper tables and figures + +All commands are run **from this directory** (the bundle root). Requires +[uv](https://docs.astral.sh/uv/) and Python ≥ 3.11. ```bash -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`](.env.template). +**Table `tab:overall` (headline metrics).** Re-render from the shipped +per-cell aggregate and check it is byte-identical to the shipped table: -## End-to-end workflow +```bash +uv run legex-quant-results --input data/analysis/per_country_per_column.csv --out /tmp/quant_results.tex +diff /tmp/quant_results.tex data/analysis/quant_results.tex +``` + +**Tables `tab:metrics-by-jurisdiction` and `tab:metrics-by-field`.** ```bash -# Acquire raw judgments per jurisdiction. -uv run legex-run +uv run python scripts/paper_tables.py > /tmp/paper_tables.tex +diff /tmp/paper_tables.tex data/analysis/paper_tables.tex +``` -# 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 +**IAA / kappa / Alternative Annotator Test report** (`ANALYSIS.md`, the +source of the paper's `tab:iaa` and AAT numbers). The report is rendered +purely from the shipped CSVs: -# Evaluate one system on one jurisdiction. -uv run legex-evaluate --country us --system gpt +```bash +uv run legex-analysis-report --iaa-dir data/analysis/iaa --analysis-dir data/analysis --out /tmp/ANALYSIS.md +diff /tmp/ANALYSIS.md data/analysis/iaa/ANALYSIS.md +``` -# Aggregate across all 12 evaluated jurisdictions and 3 systems. -uv run legex-analysis --out data/analysis +**One-shot reproduction.** `scripts/reproduce_paper.sh` regenerates every +artifact under `data/analysis/` — IAA CSVs, scoring aggregates, all tables, +the diversity/frequency figures, the hallucination shares, and `ANALYSIS.md` +— from the published bundles in `goldensets/data/` and +`inference-results/data/`. The run is byte-stable, so a clean +`git status data/analysis` afterwards is the verification that the shipped +numbers reproduce: -# 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 +```bash +bash scripts/reproduce_paper.sh +git status data/analysis ``` -To evaluate against the published goldensets and inference outputs, pull -the two data repos into the expected layout: +## Unpublished ata + +The expert-annotated XLSX workbooks (`data//Goldenset_*_final*.xlsx`) +behind the goldensets arenot published: Microsoft Office embeds author +metadata, and the re-annotation assignment structure could identify +individual annotators. The gold labels are published in full as +`goldensets/data//goldenset_.jsonl` with primary + anonymized secondary +annotator rows, and every analysis in this bundle runs from those published +files, the workbooks are not needed for reproduction. + +## Alternative Annotator Test (AAT). +The AAT (Calderon, Reichart & Dror, +ACL 2025, [arXiv:2501.10970](https://arxiv.org/abs/2501.10970)) is run with +the authors' original implementation via the adapter +`scripts/alt_test_reference.py`, which extracts `alt_test()` from the +upstream notebook at runtime and feeds it LEGEX data from the published +bundles: ```bash -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// contains goldenset_.jsonl + inference_*.csv -uv run legex-analysis --out data/analysis +git clone https://github.com/nitaytech/AltTest /tmp/AltTest +ALTTEST_DIR=/tmp/AltTest bash scripts/reproduce_paper.sh # or: +uv run python scripts/alt_test_reference.py --alttest /tmp/AltTest \ + --gold-dir goldensets/data --inference-dir inference-results/data \ + [--countries ge,sg,tw] [--epsilon 0.2] [--out data/analysis/iaa] [--per-field] ``` -## CLI entrypoints +Its outputs (`alt_test_pooled.csv`, `alt_test_reference_*.csv`) are shipped +under `data/analysis/iaa/` and rendered into `ANALYSIS.md` by +`legex-analysis-report`. -| 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. | +## Scraping + +The scrapers that built the corpus are included (`legex/scrapers/`, one file +per jurisdiction; see `docs/Jurisdictions.md` for the per-country catalogue). + +```bash +cp .env.template .env +uv run legex-run +``` + +`legex-run` executes the idempotent pipeline (scrape → filter/sample → +fill goldenset → dist) for every registered jurisdiction and skips countries +whose access is not set up or whose output already exists. Some sources need +manual prerequisites (all keys go into `.env`, see +[.env.template](.env.template)): + +- **Hugging Face** (`au`, `ch`, `de`, `us`): a read token as `HF_TOKEN`; + `de` and `us` additionally require accepting the gated-dataset terms of + `openlegaldata/court-decisions-germany` and + `free-law/Caselaw_Access_Project`. +- **France**: register at , subscribe to the + Judilibre API, set `JUDILIBRE_CLIENT_ID` / `JUDILIBRE_CLIENT_SECRET`. +- **New Zealand**: copy the WAF cookie from a justice.govt.nz session into + `NZ_WAF_COOKIE`. +- **India**: `legex-india-extract` pulls selected PDFs from the AWS Open + Data TAR archives (see `legex/scrapers/in_.py`). + +Model inference over the scraped full texts (the paper runs use prompt v3): + +```bash +uv run legex-classify --model --full_text --prompt_version v3 +``` + +Tests: `uv run pytest -q`. + +## Citation + +```bibtex +@inproceedings{legex2026, + title = {{LEGEX}: An Eight-Jurisdiction Benchmark for Legal Review-Table Extraction}, + author = {K{\"o}nig, Adrian and others}, + booktitle = {ICML 2026 Workshop on AI for Law (AI4Law)}, + year = {2026}, + url = {https://huggingface.co/legexbenchmark} +} +``` ## License -MIT. +MIT. See [LICENSE](LICENSE). diff --git a/build_harvey_prompts_jsonl.py b/build_harvey_prompts_jsonl.py new file mode 100644 index 0000000000000000000000000000000000000000..7e499f5fe150841a543a818077b75bbfb91403f7 --- /dev/null +++ b/build_harvey_prompts_jsonl.py @@ -0,0 +1,86 @@ +"""Build the published Harvey prompt-metadata JSONL from the VAULT exports. + +Each Harvey VAULT_REVIEW export carries the as-configured per-column prompt in +its question headers ("1. 1. Case ID ()"). This script extracts the +value-column header of each of the 12 question blocks (same block layout as +``legex.harvey``) and writes one record per (run, field): + + {"model": "harvey", "inference_date": "2026-05-18", "field": ..., "prompt": ...} + +``prompts_harvey.jsonl`` holds both dates of the paper run (2026-05-18 and +2026-06-30 — the 30 June re-created tables carry platform-rephrased prompts); +``prompts_harvey_2.jsonl`` holds the 2026-08-05 transparency run. + + python build_harvey_prompts_jsonl.py --raw-dir ../data/raw \\ + --out-dir inference-results/prompts +""" +import argparse +import json +import re +from pathlib import Path + +import openpyxl + +from legex.harvey import HARVEY_FIELDS_ORDER + +# run -> [(inference_date, export file)] +RUNS: dict[str, tuple[tuple[str, str], ...]] = { + "harvey": ( + ("2026-05-18", "harvey_2026-05-18.xlsx"), + ("2026-06-30", "harvey_2026-06-30.xlsx"), + ), + "harvey-2": (("2026-08-05", "harvey_2026-08-05.xlsx"),), +} +_FIRST_ANSWER_COL = 3 # after Name, Folder, Document Classification +_TITLE_PREFIX_RE = re.compile(r"^\s*(?:\d+\.\s*)+") + + +def _parse_header(header: str) -> str: + """The prompt is the parenthetical after the column title; parens may nest.""" + start = header.find("(") + if start == -1 or not header.rstrip().endswith(")"): + raise ValueError(f"header without a prompt parenthetical: {header[:80]!r}") + prompt = header[start + 1 : header.rindex(")")] + return " ".join(prompt.split()) + + +def read_prompts(xlsx: Path) -> dict[str, str]: + """Return ``{field: prompt}`` from the value-column headers of one export.""" + wb = openpyxl.load_workbook(xlsx, read_only=True) + ws = wb["Sheet1"]f + header = next(ws.iter_rows(min_row=1, max_row=1, values_only=True)) + wb.close() + width = (len(header) - _FIRST_ANSWER_COL) // len(HARVEY_FIELDS_ORDER) + if width < 1: + raise ValueError(f"unexpected Harvey sheet width in {xlsx.name}: {len(header)} columns") + out: dict[str, str] = {} + for j, field in enumerate(HARVEY_FIELDS_ORDER): + cell = header[_FIRST_ANSWER_COL + j * width] + out[field] = _parse_header(str(cell)) + return out + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--raw-dir", type=Path, default=Path("../data/raw"), + help="Directory holding the harvey_.xlsx exports.") + parser.add_argument("--out-dir", type=Path, default=Path("inference-results/prompts")) + args = parser.parse_args(argv) + + args.out_dir.mkdir(parents=True, exist_ok=True) + for run, exports in RUNS.items(): + dst = args.out_dir / f"prompts_{run.replace('-', '_')}.jsonl" + n = 0 + with dst.open("w", encoding="utf-8") as f: + for inference_date, filename in exports: + for field, prompt in read_prompts(args.raw_dir / filename).items(): + record = {"model": run, "inference_date": inference_date, + "field": field, "prompt": prompt} + f.write(json.dumps(record, ensure_ascii=False) + "\n") + n += 1 + print(f"wrote {n} prompt record(s) to {dst}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/build_inference_jsonl.py b/build_inference_jsonl.py new file mode 100644 index 0000000000000000000000000000000000000000..1a0ce6a07021132929f5f2fde77d4f0432ea2905 --- /dev/null +++ b/build_inference_jsonl.py @@ -0,0 +1,56 @@ +"""Build the published inference-results bundle from the cleaned working JSONL. + +For every annotated jurisdiction (one with a ``goldenset_.jsonl`` under +``--gold-dir//``) copy each model's cleaned inference file +``//Goldenset_*_v3_full_text_.jsonl`` to +``--out-dir//inference_.jsonl``. The records are already clean, +single-typed JSONL with ``comment`` / ``original_input`` provenance (see +``legex.evaluation.cleaning``), so this is a faithful copy — no transformation, +no CSV type-inference hazard. + + python build_inference_jsonl.py --data-dir ../data \\ + --gold-dir goldensets/data --out-dir inference-results/data +""" +import argparse +import re +from pathlib import Path + +# working model slug -> published short name +SLUG_TO_MODEL = { + "harvey": "harvey", + "harvey-2": "harvey_2", + "gpt-5.4-mini": "gpt", + "gemini_gemini-3.1-flash-lite": "gemini", + "legora-1": "legora_1", + "legora-2": "legora_2", +} +_SLUG_RE = re.compile(r"_v3_full_text_(.+)\.jsonl$") + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--data-dir", type=Path, default=Path("../data")) + parser.add_argument("--gold-dir", type=Path, default=Path("goldensets/data"), + help="Where goldenset_.jsonl live; used to enumerate annotated jurisdictions.") + parser.add_argument("--out-dir", type=Path, default=Path("inference-results/data")) + parser.add_argument("--prompt_version", default="v3") + args = parser.parse_args(argv) + + written = 0 + for gs in sorted(args.gold_dir.glob("*/goldenset_*.jsonl")): + cc = gs.parent.name + for src in sorted((args.data_dir / cc).glob(f"Goldenset_*_{args.prompt_version}_full_text_*.jsonl")): + m = _SLUG_RE.search(src.name) + model = SLUG_TO_MODEL.get(m.group(1)) if m else None + if model is None: + continue + dst = args.out_dir / cc / f"inference_{model}.jsonl" + dst.parent.mkdir(parents=True, exist_ok=True) + dst.write_text(src.read_text(encoding="utf-8"), encoding="utf-8") + written += 1 + print(f"wrote {written} inference JSONL file(s) to {args.out_dir}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/build_legora_prompts_jsonl.py b/build_legora_prompts_jsonl.py new file mode 100644 index 0000000000000000000000000000000000000000..15b83bbacb0278e918ec368f9a02a94348d780ff --- /dev/null +++ b/build_legora_prompts_jsonl.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +"""Build the published Legora prompt-metadata JSONL from the prompt-bearing export. + +The 2026-08-05 Legora re-export (``data/raw/legora_2026-08-05_prompts.xlsx``) +carries the per-column question text of both runs in row 2: the left column +group belongs to ``legora-1``, the right one to ``legora-2`` (see +``scripts/compare_legora_prompts.py`` / ``data/analysis/legora_prompt_comparison.md``). +This script writes one JSONL file per run with a record per field: + + {"model": "legora-1", "inference_date": "2026-08-01", "field": ..., "prompt": ...} + + python build_legora_prompts_jsonl.py --xlsx ../data/raw/legora_2026-08-05_prompts.xlsx \\ + --out-dir inference-results/prompts +""" +import argparse +import json +from pathlib import Path + +import openpyxl + +RUNS = ("legora-1", "legora-2") +INFERENCE_DATE = "2026-08-01" +PROMPT_SUFFIX = " (with prompt)" + + +def read_prompts(xlsx: Path) -> dict[str, dict[str, str]]: + """Return ``{model: {field: prompt}}`` from the export's prompt row.""" + wb = openpyxl.load_workbook(xlsx, data_only=True, read_only=True) + ws = wb.worksheets[0] + rows = ws.iter_rows(min_row=1, max_row=2, values_only=True) + header, prompt_row = next(rows), next(rows) + out: dict[str, dict[str, str]] = {run: {} for run in RUNS} + for head, prompt in zip(header, prompt_row): + if not head or not str(head).endswith(PROMPT_SUFFIX) or not prompt: + continue + field = str(head)[: -len(PROMPT_SUFFIX)] + run = RUNS[0] if field not in out[RUNS[0]] else RUNS[1] + out[run][field] = " ".join(str(prompt).split()) + return out + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--xlsx", type=Path, + default=Path("../data/raw/legora_2026-08-05_prompts.xlsx")) + parser.add_argument("--out-dir", type=Path, default=Path("inference-results/prompts")) + args = parser.parse_args(argv) + + prompts = read_prompts(args.xlsx) + args.out_dir.mkdir(parents=True, exist_ok=True) + for run, fields in prompts.items(): + dst = args.out_dir / f"prompts_{run.replace('-', '_')}.jsonl" + with dst.open("w", encoding="utf-8") as f: + for field, prompt in fields.items(): + record = {"model": run, "inference_date": INFERENCE_DATE, + "field": field, "prompt": prompt} + f.write(json.dumps(record, ensure_ascii=False) + "\n") + print(f"wrote {len(fields)} prompt record(s) to {dst}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/convert_goldenset_to_jsonl.py b/convert_goldenset_to_jsonl.py index e785347ac19e0c7893db362698c3acfdaf3fe2a1..cfc9f7f4f87714057f6e6a552636c97fd5a9f68b 100644 --- a/convert_goldenset_to_jsonl.py +++ b/convert_goldenset_to_jsonl.py @@ -2,11 +2,14 @@ """Convert handcrafted Goldenset XLSX files to anonymised JSONL. For each ``Goldenset_*_final*.xlsx`` workbook under ``//`` the -GOLDENSET sheet is read, rows that the expert annotator did not fully -classify are dropped (criterion: ``legal_subject_judgement`` must be -populated, which the annotators used as the marker that a row has been -substantively reviewed), and the remaining rows are written to -``//goldenset_.jsonl`` as one JSON object per line. +GOLDENSET sheet is read, rows that the expert annotator did not review are +dropped (criterion: at least one of the eleven evaluated label fields must +carry a value — the same row-inclusion rule the scoring pipeline applies, so +published-data scoring reproduces the paper), and the remaining rows are +written to ``//goldenset_.jsonl`` as one JSON object per +line. A handful of reviewed rows carry no ``legal_subject_judgement``; they +are excluded from the paper's judgment counts (which filter on that field) +but included here because their cells enter the scoring denominators. Each output record contains the case identifiers (``case_id``, ``link``, ``full_text``) followed by the 14 schema fields defined in @@ -24,10 +27,11 @@ Without arguments the script assumes ``./data`` for both inputs and outputs and processes the 19 jurisdictions of the paper. """ -from __future__ import annotations - import argparse +import hashlib import json +import os +import re import sys from datetime import date, datetime from pathlib import Path @@ -57,8 +61,36 @@ SCHEMA_FIELDS = ( "defendant_no1_ISIC1_industry_category", ) +# The evaluated label fields (currencies are scored via their amount field). +# Row inclusion must match `legex.evaluation.scoring._read_goldenset_rows`. +EVAL_FIELDS = tuple(f for f in SCHEMA_FIELDS if not f.startswith("Currency_")) + EMPTY_LITERALS = frozenset({"", "none", "null", "nan"}) +# Loose case_id key so full_text.jsonl matches the workbook even when separators +# differ (e.g. xlsx "4A_426/2024" vs jsonl "4A_426_2024"). +_CASE_ID_SEP_RE = re.compile(r"[\s/\\\-._;]+") + + +def _norm_case_id(s: str) -> str: + return _CASE_ID_SEP_RE.sub("_", str(s).strip()).strip("_").lower() + + +# Used to strip unnecessary prefixes +_SOURCE_PREFIX_RE = re.compile(r"^case_id:.*?Quelle Text:\s*\S+\s+", re.DOTALL) + + +def _strip_source_prefix(text): + if not text: + return text + return _SOURCE_PREFIX_RE.sub("", text, count=1) + + +# Find failed scrapes. +_SCRAPE_ERROR_RE = re.compile( + r"\(Fehlermeldung\)|Abruf fehlgeschlagen|Failed to resolve|Max retries exceeded" +) + def normalise(value: Any) -> Any: if value is None: @@ -81,6 +113,74 @@ def normalise(value: Any) -> Any: return s +# Field-type groups, so each JSONL column is single-typed +_DATE_FIELDS = {"trial_start_date", "trial_end_date"} +_INT_FIELDS = {"plaintiffs_all_count", "defendants_all_count"} +_NUMBER_FIELDS = { + "plaintiff_loosing_share", "court_cost_awarded_nominal", + "party_compensation_awarded_nominal", +} | _INT_FIELDS +_ISIC_FIELDS = { + "plaintiff_no1_ISIC1_industry_category", "defendant_no1_ISIC1_industry_category", +} +_STRING_FIELDS = { + "legal_subject_judgement", + "Currency_dispute_value_nominal", "Currency_court_cost_awarded_nominal", + "Currency_party_compensation_awarded_nominal", +} +_DISPUTE_FIELD = "dispute_value_nominal" +_ISO_DATE_RE = re.compile(r"^\d{4}-\d{2}-\d{2}$") + + +def _to_number(value: Any) -> int | float | None: + if isinstance(value, bool): + return None + if isinstance(value, (int, float)): + f = float(value) + return int(f) if f.is_integer() else f + s = str(value).strip().replace("'", "").replace(" ", "").replace(" ", "") + if "_" in s: # underscores here are range/typo separators, not digit grouping + return None + if re.fullmatch(r"-?\d{1,3}(\.\d{3})+,\d+", s): # 1.000,50 (EU) + s = s.replace(".", "").replace(",", ".") + elif re.fullmatch(r"-?\d+,\d+", s): # 1234,56 + s = s.replace(",", ".") + elif re.fullmatch(r"-?\d{1,3}(,\d{3})+(\.\d+)?", s): # 1,000.50 (US) + s = s.replace(",", "") + try: + f = float(s) + if f != f or f in (float("inf"), float("-inf")): # reject nan / inf + return None + return int(f) if f.is_integer() else f + except ValueError: + return None + + +def normalise_field(field: str, value: Any) -> Any: + """Type-aware normalisation keeping each schema column single-typed.""" + v = normalise(value) + if v is None: + return None + if field in _DATE_FIELDS: + s = str(v).strip() + return s if _ISO_DATE_RE.match(s) else None + if field == _DISPUTE_FIELD: + s = str(v).strip() + return "nonpecuniary" if s.lower() == "nonpecuniary" else s + if field in _ISIC_FIELDS: # The categories are lower case + return str(v).strip().lower() + if field in _NUMBER_FIELDS: + n = _to_number(v) + if n is None: + return None + # counts -> int; money/ratio -> natural form (int when whole, else float). + # pyarrow promotes int/float within a split, so the viewer renders fine. + return int(n) if field in _INT_FIELDS else n + if field in _STRING_FIELDS: + return str(v).strip() + return v + + def find_goldenset_xlsx(data_dir: Path, cc: str) -> Path | None: """Return the *_final*.xlsx workbook for a jurisdiction, if any.""" jurisdiction_dir = data_dir / cc @@ -104,6 +204,7 @@ def load_full_text_fallback(data_dir: Path, cc: str) -> dict[str, str]: if not path.exists(): return {} fallback: dict[str, str] = {} + norm_extra: dict[str, str] = {} for line in path.read_text(encoding="utf-8").splitlines(): if not line.strip(): continue @@ -112,6 +213,9 @@ def load_full_text_fallback(data_dir: Path, cc: str) -> dict[str, str]: text = record.get("full_text") or record.get("text") if case_id and text: fallback[str(case_id)] = str(text) + norm_extra.setdefault(_norm_case_id(case_id), str(text)) + for k, v in norm_extra.items(): # normalized keys never overwrite exact ones + fallback.setdefault(k, v) return fallback @@ -132,18 +236,30 @@ def convert_workbook(xlsx_path: Path, fallback: dict[str, str]) -> list[dict[str case_id = normalise(cells.get("case_id")) if not case_id: continue - labels = {field: normalise(cells.get(field)) for field in SCHEMA_FIELDS} - if labels["legal_subject_judgement"] is None: + # Same row-inclusion rule as the scoring pipeline: reviewed = at least one + # evaluated field carries a value in the workbook (raw, pre-typing — a non-ISO + # date string still marks the row as reviewed even though it publishes as null). + if all(normalise(cells.get(field)) is None for field in EVAL_FIELDS): continue + labels = {field: normalise_field(field, cells.get(field)) for field in SCHEMA_FIELDS} full_text = normalise(cells.get("full_text")) + if full_text and _SCRAPE_ERROR_RE.search(full_text): + full_text = None # Broken scrape if not full_text: - full_text = fallback.get(str(case_id)) + full_text = fallback.get(str(case_id)) or fallback.get(_norm_case_id(case_id)) + full_text = _strip_source_prefix(full_text) record: dict[str, Any] = { "case_id": str(case_id), "link": normalise(cells.get("link")), "full_text": full_text, } record.update(labels) + # Provenance columns (workbook-only otherwise): included on request so the + # published JSONL carries the sanitization audit trail. original_input is a + # JSON object of the pre-sanitization values for corrected fields; rows that + # needed no correction get an empty object "{}" (never null). + record["comment"] = normalise(cells.get("comment")) + record["original_input"] = normalise(cells.get("original_input")) or "{}" records.append(record) return records @@ -155,6 +271,24 @@ def write_jsonl(records: list[dict[str, Any]], out_path: Path) -> None: f.write(json.dumps(record, ensure_ascii=False) + "\n") +def _load_annotators(data_dir: Path) -> dict | None: + """Local annotator lookup (gitignored): {primary: {cc: name}, reannotations: [...]}. + + Names never reach the published JSONL — only the salted-hash annotator_id does. + """ + path = data_dir / "reannotation" / "annotators.json" + if not path.exists(): + return None + return json.loads(path.read_text(encoding="utf-8")) + + +def _annotator_id(name: str | None, salt: str) -> str | None: + """Pseudonymous, stable per-name id (same name -> same id across countries).""" + if not name: + return None + return hashlib.sha256(f"{salt}|{name}".encode("utf-8")).hexdigest()[:10] + + def main(argv: list[str] | None = None) -> int: parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) parser.add_argument( @@ -185,6 +319,16 @@ def main(argv: list[str] | None = None) -> int: data_dir: Path = args.data_dir.resolve() out_dir: Path = args.out_dir.resolve() + try: # pick up ANNOTATOR_SALT from .env when available + from dotenv import load_dotenv + load_dotenv() + except ImportError: + pass + annotators = _load_annotators(data_dir) + salt = os.environ.get("ANNOTATOR_SALT", "") + if annotators and not salt: + print("warning: annotators.json found but ANNOTATOR_SALT is unset", file=sys.stderr) + total = 0 missing: list[str] = [] for cc in args.jurisdictions: @@ -195,10 +339,29 @@ def main(argv: list[str] | None = None) -> int: continue fallback = load_full_text_fallback(data_dir, cc) records = convert_workbook(xlsx, fallback) + n_reann = 0 + if annotators: # stamp annotator_id; append second-annotator rows + pid = _annotator_id(annotators.get("primary", {}).get(cc), salt) + for r in records: + r["annotator_id"] = pid + for entry in annotators.get("reannotations", []): + if entry.get("cc") != cc: + continue + rxlsx = data_dir / entry["file"] + if not rxlsx.exists(): + print(f"[{cc}] reannotation missing: {rxlsx}", file=sys.stderr) + continue + rid = _annotator_id(entry["name"], salt) + rrecords = convert_workbook(rxlsx, fallback) + for r in rrecords: + r["annotator_id"] = rid + records.extend(rrecords) + n_reann += len(rrecords) out_path = out_dir / cc / f"goldenset_{cc}.jsonl" if not args.dry_run: write_jsonl(records, out_path) - print(f"[{cc}] {xlsx.name} -> {out_path.relative_to(out_dir.parent)}: {len(records)} rows") + extra = f" (+{n_reann} reann)" if n_reann else "" + print(f"[{cc}] {xlsx.name} -> {out_path.relative_to(out_dir.parent)}: {len(records)} rows{extra}") total += len(records) print(f"\nTotal: {total} rows across {len(args.jurisdictions) - len(missing)} jurisdictions.") diff --git a/data/analysis/figures/hallu_vs_recall.png b/data/analysis/figures/hallu_vs_recall.png new file mode 100644 index 0000000000000000000000000000000000000000..ebf04b49c8c713582c11289cf41b838cb9699e90 --- /dev/null +++ b/data/analysis/figures/hallu_vs_recall.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97f4b07b7adc90f339c6aca459c15922540f411f3e8a24666fd377ea1f224395 +size 101053 diff --git a/data/analysis/figures/hallucination_by_country.png b/data/analysis/figures/hallucination_by_country.png new file mode 100644 index 0000000000000000000000000000000000000000..cc6c966b1fa1791ed911fac9ae457a6ca55bdc5b --- /dev/null +++ b/data/analysis/figures/hallucination_by_country.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:16fd147cb9128258094da80330bf931723ccdb406d532ea00ef93e5667db8e7e +size 50330 diff --git a/data/analysis/figures/isic_frequencies.pdf b/data/analysis/figures/isic_frequencies.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b4be9e228224b8f96d6e90e9dbee6296932337b7 Binary files /dev/null and b/data/analysis/figures/isic_frequencies.pdf differ diff --git a/data/analysis/figures/legal_subject_wordcloud.png b/data/analysis/figures/legal_subject_wordcloud.png new file mode 100644 index 0000000000000000000000000000000000000000..414b324a0a61f71c9cdccf7592ef952a8031b3c9 --- /dev/null +++ b/data/analysis/figures/legal_subject_wordcloud.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fdabb1f4ca54e8bb524952c4f93ece586f42d9ffe9481ad81db2d20ecc89141d +size 339987 diff --git a/data/analysis/figures/per_variable_heatmap.png b/data/analysis/figures/per_variable_heatmap.png new file mode 100644 index 0000000000000000000000000000000000000000..58a93a3976373fdef47bb55517f768b532596769 --- /dev/null +++ b/data/analysis/figures/per_variable_heatmap.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f9ea6f983964a70361ce4d9be45f1e43016bc8687214b70d5e08772a705706f +size 326863 diff --git a/data/analysis/figures/recall_by_country.png b/data/analysis/figures/recall_by_country.png new file mode 100644 index 0000000000000000000000000000000000000000..7b78f9d9a874f9b69aae1249aeccbaa706116e1c --- /dev/null +++ b/data/analysis/figures/recall_by_country.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b10e962a0dc5cdc474ef1c3d892cc847db1a2a0e0793ca6bdc3025cae5c1c280 +size 46068 diff --git a/data/analysis/hallucinations/hallucination_review_ch.csv b/data/analysis/hallucinations/hallucination_review_ch.csv new file mode 100644 index 0000000000000000000000000000000000000000..05d0f4987d83d49ec29c7e2fcf19f3e708c263cf --- /dev/null +++ b/data/analysis/hallucinations/hallucination_review_ch.csv @@ -0,0 +1,197 @@ +case_id,field,model,category +4A_211/2024,defendant_no1_ISIC1_industry_category,gemini/gemini-3.1-flash-lite,D +4A_211/2024,defendant_no1_ISIC1_industry_category,gpt-5.4-mini,D +4A_211/2024,plaintiff_no1_ISIC1_industry_category,gemini/gemini-3.1-flash-lite,B +4A_211/2024,plaintiff_no1_ISIC1_industry_category,gpt-5.4-mini,A +4A_211/2024,trial_start_date,gpt-5.4-mini,B +4A_312/2024,defendant_no1_ISIC1_industry_category,gemini/gemini-3.1-flash-lite,D +4A_312/2024,defendant_no1_ISIC1_industry_category,gpt-5.4-mini,D +4A_312/2024,defendant_no1_ISIC1_industry_category,harvey,D +4A_312/2024,plaintiff_no1_ISIC1_industry_category,gemini/gemini-3.1-flash-lite,D +4A_312/2024,plaintiff_no1_ISIC1_industry_category,gpt-5.4-mini,D +4A_312/2024,plaintiff_no1_ISIC1_industry_category,harvey,D +4A_312/2024,trial_start_date,gemini/gemini-3.1-flash-lite,B +4A_312/2024,trial_start_date,gpt-5.4-mini,B +4A_317/2024,defendant_no1_ISIC1_industry_category,gemini/gemini-3.1-flash-lite,A +4A_317/2024,defendant_no1_ISIC1_industry_category,gpt-5.4-mini,A 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+4A_606/2024,plaintiff_no1_ISIC1_industry_category,legora-1,C +4A_608/2023,dispute_value_nominal,legora-1,A +4D_115/2024,defendant_no1_ISIC1_industry_category,legora-1,D +4D_40/2025,dispute_value_nominal,legora-1,C +5A_107/2024,dispute_value_nominal,legora-1,C +5A_119/2024,dispute_value_nominal,legora-1,D +5A_169/2024,party_compensation_awarded_nominal,legora-1,C +5A_341/2023,dispute_value_nominal,legora-1,B +5A_358/2024,dispute_value_nominal,legora-1,C +5A_47/2024,dispute_value_nominal,legora-1,C +5A_503/2023,plaintiff_no1_ISIC1_industry_category,legora-1,D +5A_891/2024,plaintiff_loosing_share,legora-1,C +5D_63/2024,defendant_no1_ISIC1_industry_category,legora-1,B +5D_63/2024,dispute_value_nominal,legora-1,C diff --git a/data/analysis/iaa/ANALYSIS.md b/data/analysis/iaa/ANALYSIS.md new file mode 100644 index 0000000000000000000000000000000000000000..555657a6d5859da5613f3aba946c844da5d7ba24 --- /dev/null +++ b/data/analysis/iaa/ANALYSIS.md @@ -0,0 +1,362 @@ +# Inter-Annotator Agreement & Alternative-Annotator Test + +_Generated by `legex-analysis-report` from the CSVs in this directory — do not edit by hand; numbers always reflect the current CSVs._ + +Regenerate the whole chain with `scripts/reproduce_paper.sh`. + +--- + +## 1. Scope + +- Countries with ≥2 annotators: **8** — br, ch, de, ge, sg, tw, uk, us +- Annotators: **11** (primary + 10 secondary: 359f544380, 40dd94f4cc, 5c9b11ec0c, 93839740f5, 9bbad8ca9e, ab2d3cfed5, b36683f5be, c7cd8dc26a, cf5a561621, dd79b903af) +- Annotator pairs (country × pair): **24** + +Countries: **br** Brazil, **ch** Switzerland, **de** Germany, **ge** Georgia, **sg** Singapore, **tw** Taiwan, **uk** United Kingdom, **us** United States. + +## 2. Human–human agreement + +> **Per-variable agreement.** Percent agreement is reported for every variable, using the same value-matching as the evaluation (ISO-date / number-format aware; `0` and empty treated alike). For the two **nominal ISIC** fields — the only variables with a fixed controlled vocabulary — we additionally report **Cohen's κ**. For the other variables the category space is unbounded (free text) or continuous (dates, counts, money, ratios): there κ's chance correction collapses toward percent agreement (as the number of categories → ∞, expected agreement pₑ → 0 and κ → observed agreement) and awards no partial credit for near-misses, so percent agreement is the appropriate summary. Correlation coefficients are deliberately not used — for these labels a wrong value is wrong, not partially correct. Cell detail: `kappa_audit.csv`. + +_n = paired case comparisons (shared cases × annotator pairs). We report ±1 SE = √(p(1-p)/n) in percentage points, a descriptive precision marker. This is not a significance test._ + +### 2.1 By variable + +| Variable | Type | n | % agreement | Cohen's κ | +| --- | --- | --- | --- | --- | +| `defendant_no1_ISIC1_industry_category` | nominal | 614 | 53.9% ±2.0 | 0.426 | +| `plaintiff_no1_ISIC1_industry_category` | nominal | 614 | 47.2% ±2.0 | 0.316 | +| `legal_subject_judgement` | free text | 614 | 2.4% ±0.6 | — | +| `trial_end_date` | date | 614 | 74.8% ±1.8 | — | +| `trial_start_date` | date | 614 | 70.2% ±1.8 | — | +| `plaintiffs_all_count` | count | 614 | 84.7% ±1.5 | — | +| `defendants_all_count` | count | 614 | 76.9% ±1.7 | — | +| `party_compensation_awarded_nominal` | monetary | 614 | 89.4% ±1.2 | — | +| `court_cost_awarded_nominal` | monetary | 614 | 86.0% ±1.4 | — | +| `dispute_value_nominal` | monetary | 614 | 65.3% ±1.9 | — | +| `plaintiff_loosing_share` | ratio | 614 | 72.5% ±1.8 | — | + +### 2.2 By country + +| Country | Pairs | n | % agreement | +| --- | --- | --- | --- | +| br | 3 | 68 | 61.1% | +| ch | 3 | 90 | 76.4% | +| de | 3 | 70 | 71.4% | +| ge | 3 | 68 | 51.7% | +| sg | 3 | 68 | 55.9% | +| tw | 3 | 70 | 71.0% | +| uk | 3 | 90 | 57.1% | +| us | 3 | 90 | 76.9% | + +### 2.3 By pair + +| Country | Pair | n | % agreement | +| --- | --- | --- | --- | +| br | 93839740f5 – ab2d3cfed5 | 19 | 65.5% | +| br | 93839740f5 – primary | 19 | 56.9% | +| br | ab2d3cfed5 – primary | 30 | 60.9% | +| ch | 5c9b11ec0c – ab2d3cfed5 | 30 | 71.8% | +| ch | 5c9b11ec0c – primary | 30 | 73.6% | +| ch | ab2d3cfed5 – primary | 30 | 83.6% | +| de | 40dd94f4cc – ab2d3cfed5 | 20 | 73.2% | +| de | 40dd94f4cc – primary | 20 | 75.5% | +| de | ab2d3cfed5 – primary | 30 | 67.6% | +| ge | 359f544380 – dd79b903af | 19 | 55.0% | +| ge | 359f544380 – primary | 20 | 53.6% | +| ge | dd79b903af – primary | 29 | 48.3% | +| sg | c7cd8dc26a – dd79b903af | 20 | 47.7% | +| sg | c7cd8dc26a – primary | 27 | 59.6% | +| sg | dd79b903af – primary | 21 | 58.9% | +| tw | ab2d3cfed5 – cf5a561621 | 20 | 67.7% | +| tw | ab2d3cfed5 – primary | 30 | 67.9% | +| tw | cf5a561621 – primary | 20 | 79.1% | +| uk | ab2d3cfed5 – b36683f5be | 30 | 54.8% | +| uk | ab2d3cfed5 – primary | 30 | 62.7% | +| uk | b36683f5be – primary | 30 | 53.6% | +| us | 9bbad8ca9e – ab2d3cfed5 | 30 | 76.4% | +| us | 9bbad8ca9e – primary | 30 | 76.7% | +| us | ab2d3cfed5 – primary | 30 | 77.6% | + +## 3. Alternative-annotator test + +> **Alternative Annotator Test** (Calderon et al. 2025, [arXiv:2501.10970](https://arxiv.org/abs/2501.10970)): leave each human annotator out in turn and score, per instance, both the candidate and the excluded annotator against the remaining annotators; a one-sided test per annotator asks whether the candidate's advantage probability trails the human's by less than ε = 0.2 (the expert-annotator tolerance), under Benjamini–Yekutieli FDR control at q = 0.05. `passes` = winning rate ≥ 0.5. Requires ≥ 3 independent annotators per country; free-text fields are excluded. ρ is the advantage probability — how likely the candidate annotates as well as or better than a randomly chosen human. The *non-trivial* variant drops instances every expert left empty: an empty prediction ties those for free, so the gap between the two columns shows how much of a pass rests on empty cells. Untestable cells (too few non-empty judgements) are excluded from the denominators. +> +> These numbers come from the **authors' reference implementation** ([github.com/nitaytech/AltTest](https://github.com/nitaytech/AltTest)) executed on the LEGEX data via `scripts/alt_test_reference.py`. See the README section "Alternative Annotator Test (AAT)" for how to run it. `legex-iaa` does not produce these CSVs. + +### 3.1 How ω and ρ are computed + +_Definitions from Calderon et al. (2025), as implemented in the authors' `alt_test`. +`values_agree` is the tolerant comparator LEGEX passes in as the scoring function._ + +```text +One jurisdiction, one candidate f, human annotators H = {1..M}, and instances i +(here: one (judgment, variable) cell over the 10 structured fields). + +LEAVE-ONE-OUT. For human j and every instance i the other humans also labelled, +let A(i,-j) be the remaining humans' labels and + + s_f(i,j) = score( f(i), A(i,-j) ) candidate vs. the other humans + s_h(i,j) = score( h_j(i), A(i,-j) ) held-out human vs. the same humans + + score(p, A) = (1/|A|) * SUM_{a in A} values_agree(p, a) in {0, 1/2, 1} + +ADVANTAGE PROBABILITY of the candidate against human j. Note the ">=", which +credits every tie to the candidate: + + rho_j = (1/n_j) * SUM_i 1[ s_f(i,j) >= s_h(i,j) ] + +PER-ANNOTATOR TEST. With d_i = 1[s_f < s_h] - 1[s_f >= s_h], so E[d] = 1 - 2*rho_j: + + H0: E[d] >= epsilon vs. H1: E[d] < epsilon + +one-sided paired t-test (Wilcoxon signed-rank when n_j < 30), the M p-values +corrected by Benjamini-Yekutieli at q = 0.05. Rejecting H0 is therefore +equivalent to rho_j being significantly greater than + + (1 - epsilon) / 2 = 0.4 for the expert tolerance epsilon = 0.2 + +VERDICT. + + omega = |{ j : H0_j rejected }| / M winning rate + rho = (1/M) * SUM_j rho_j advantage probability + "f may substitute a human annotator" <=> omega >= 0.5 +``` + +Two consequences worth keeping in view when reading §3.2. First, ρ is a +**≥**-comparison, so a candidate that merely matches the held-out human on an +instance is scored as winning it. Second, with ε = 0.2 the hypothesis test +clears at ρ_j > 0.4, not at 0.5. Both are deliberate: the alt-test asks whether +a candidate can *substitute* a human annotator, not whether it is *better* than +one. §3.4 separates the two. + + +### 3.2 Headline: pooled per jurisdiction (paper numbers) + +_One alt-test per jurisdiction; instance = (judgment, variable) cell over the 10 structured fields, so each annotator contributes ~190+ effective instances and the paired t-test applies without the paper's n<30 caveat._ + +| Candidate | Country | ω | ρ | ω (non-triv) | ρ (non-triv) | +| --- | --- | --- | --- | --- | --- | +| gpt-5.4-mini | br | 1.00 | 0.90 | 1.00 | 0.94 | +| gpt-5.4-mini | ch | 1.00 | 0.89 | 1.00 | 0.97 | +| gpt-5.4-mini | de | 0.33 | 0.80 | 0.33 | 0.78 | +| gpt-5.4-mini | ge | 1.00 | 0.82 | 1.00 | 0.84 | +| gpt-5.4-mini | sg | 1.00 | 0.86 | 1.00 | 0.87 | +| gpt-5.4-mini | tw | 1.00 | 0.86 | 1.00 | 0.87 | +| gpt-5.4-mini | uk | 1.00 | 0.81 | 1.00 | 0.80 | +| gpt-5.4-mini | us | 0.00 | 0.78 | 0.00 | 0.74 | +| gemini/gemini-3.1-flash-lite | br | 1.00 | 0.93 | 1.00 | 0.92 | +| gemini/gemini-3.1-flash-lite | ch | 1.00 | 0.90 | 1.00 | 0.93 | +| gemini/gemini-3.1-flash-lite | de | 1.00 | 0.84 | 0.33 | 0.80 | +| gemini/gemini-3.1-flash-lite | ge | 1.00 | 0.86 | 1.00 | 0.87 | +| gemini/gemini-3.1-flash-lite | sg | 1.00 | 0.88 | 1.00 | 0.90 | +| gemini/gemini-3.1-flash-lite | tw | 1.00 | 0.86 | 1.00 | 0.86 | +| gemini/gemini-3.1-flash-lite | uk | 1.00 | 0.82 | 1.00 | 0.79 | +| gemini/gemini-3.1-flash-lite | us | 0.00 | 0.75 | 0.00 | 0.69 | +| harvey | br | 1.00 | 0.91 | 1.00 | 0.87 | +| harvey | ch | 1.00 | 0.93 | 1.00 | 0.93 | +| harvey | de | 0.67 | 0.83 | 0.67 | 0.77 | +| harvey | ge | 1.00 | 0.84 | 1.00 | 0.84 | +| harvey | sg | 1.00 | 0.92 | 1.00 | 0.91 | +| harvey | tw | 1.00 | 0.85 | 1.00 | 0.82 | +| harvey | uk | 1.00 | 0.92 | 1.00 | 0.92 | +| harvey | us | 0.33 | 0.78 | 0.00 | 0.74 | +| legora-1 | br | 1.00 | 0.91 | 1.00 | 0.87 | +| legora-1 | ch | 1.00 | 0.97 | 1.00 | 0.97 | +| legora-1 | de | 0.00 | 0.70 | 0.00 | 0.57 | +| legora-1 | sg | 1.00 | 0.85 | 1.00 | 0.82 | +| legora-1 | tw | 1.00 | 0.88 | 1.00 | 0.85 | +| legora-1 | uk | 1.00 | 0.90 | 1.00 | 0.87 | +| legora-1 | us | 0.00 | 0.70 | 0.00 | 0.62 | +| legora-2 | br | 1.00 | 0.92 | 1.00 | 0.87 | +| legora-2 | ch | 1.00 | 0.96 | 1.00 | 0.95 | +| legora-2 | de | 0.00 | 0.69 | 0.00 | 0.55 | +| legora-2 | sg | 1.00 | 0.86 | 1.00 | 0.84 | +| legora-2 | tw | 1.00 | 0.88 | 1.00 | 0.85 | +| legora-2 | uk | 1.00 | 0.91 | 1.00 | 0.88 | +| legora-2 | us | 0.00 | 0.71 | 0.00 | 0.64 | + +### 3.3 Per-field diagnostic (cells passed / testable) + +| Candidate | ρ̄ (all) | Pass (all) | ρ̄ (non-triv) | Pass (non-triv) | br (all · non-triv) | ch (all · non-triv) | de (all · non-triv) | ge (all · non-triv) | sg (all · non-triv) | tw (all · non-triv) | uk (all · non-triv) | us (all · non-triv) | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| gemini-3.1-flash-lite | 0.86 | 47/80 | 0.85 | 40/69 | 9/10 · 6/7 | 7/10 · 7/10 | 7/10 · 4/7 | 5/10 · 6/10 | 5/10 · 6/10 | 6/10 · 6/9 | 3/10 · 2/8 | 5/10 · 3/8 | +| gpt-5.4-mini | 0.84 | 40/80 | 0.85 | 35/69 | 6/10 · 5/7 | 6/10 · 7/10 | 4/10 · 3/7 | 5/10 · 6/10 | 6/10 · 5/10 | 5/10 · 4/9 | 3/10 · 2/8 | 5/10 · 3/8 | +| harvey | 0.87 | 54/80 | 0.85 | 38/69 | 7/10 · 4/7 | 8/10 · 7/10 | 6/10 · 4/7 | 6/10 · 4/10 | 8/10 · 6/10 | 6/10 · 4/9 | 7/10 · 5/8 | 6/10 · 4/8 | +| legora-1 | 0.84 | 45/70 | 0.80 | 29/59 | 7/10 · 4/7 | 9/10 · 7/10 | 5/10 · 2/7 | – · – | 5/10 · 4/10 | 6/10 · 4/9 | 8/10 · 5/8 | 5/10 · 3/8 | +| legora-2 | 0.85 | 46/70 | 0.81 | 34/59 | 7/10 · 4/7 | 8/10 · 8/10 | 6/10 · 2/7 | – · – | 6/10 · 6/10 | 6/10 · 5/9 | 9/10 · 7/8 | 4/10 · 2/8 | + +> Per-cell winning rates and advantage probabilities are in `alt_test_reference_.csv`; an empty cell there means the (country, field, variant) combination was untestable. + +### 3.4 Substitutable vs. better: win / tie / loss decomposition + +> ρ collapses the leave-one-out comparison of §3.1 into one number, and its `≥` hands every tie to the candidate. This section keeps the same comparisons and reports the counts instead: per held-out human and instance, whether the candidate scored **better than**, **the same as**, or **worse than** that human against the two remaining humans. Restricted to judgments all three experts labelled, so every comparison has exactly two references and a score is 0, ½ or 1. `ρ (alt-test)` is the reproduced advantage probability (ties → candidate); `ρ (ties split)` counts a tie as half a win for each side. Source: `alt_test_decomposition.csv` from `scripts/alt_test_decomposition.py`, which reproduces the reference ρ of §3.2 to within 0.02. + +| Candidate | Country | n | Candidate better | Tie | Human better | ρ (alt-test) | ρ (ties split) | +| --- | --- | --- | --- | --- | --- | --- | --- | +| gemini-3.1-flash-lite | br | 570 | 74 (13%) | 463 (81%) | 33 (6%) | 0.94 | 0.54 | +| gemini-3.1-flash-lite | ch | 900 | 53 (6%) | 758 (84%) | 89 (10%) | 0.90 | 0.48 | +| gemini-3.1-flash-lite | de | 600 | 37 (6%) | 470 (78%) | 93 (16%) | 0.84 | 0.45 | +| gemini-3.1-flash-lite | ge | 570 | 65 (11%) | 427 (75%) | 78 (14%) | 0.86 | 0.49 | +| gemini-3.1-flash-lite | sg | 600 | 68 (11%) | 465 (78%) | 67 (11%) | 0.89 | 0.50 | +| gemini-3.1-flash-lite | tw | 600 | 38 (6%) | 488 (81%) | 74 (12%) | 0.88 | 0.47 | +| gemini-3.1-flash-lite | uk | 900 | 87 (10%) | 650 (72%) | 163 (18%) | 0.82 | 0.46 | +| gemini-3.1-flash-lite | us | 900 | 59 (7%) | 617 (69%) | 224 (25%) | 0.75 | 0.41 | +| gemini-3.1-flash-lite | **all three** | 5640 | 481 (9%) | 4338 (77%) | 821 (15%) | 0.85 | 0.47 | +| gpt-5.4-mini | br | 570 | 72 (13%) | 445 (78%) | 53 (9%) | 0.91 | 0.52 | +| gpt-5.4-mini | ch | 900 | 46 (5%) | 759 (84%) | 95 (11%) | 0.89 | 0.47 | +| gpt-5.4-mini | de | 600 | 34 (6%) | 452 (75%) | 114 (19%) | 0.81 | 0.43 | +| gpt-5.4-mini | ge | 570 | 75 (13%) | 391 (69%) | 104 (18%) | 0.82 | 0.47 | +| gpt-5.4-mini | sg | 600 | 50 (8%) | 467 (78%) | 83 (14%) | 0.86 | 0.47 | +| gpt-5.4-mini | tw | 600 | 34 (6%) | 483 (80%) | 83 (14%) | 0.86 | 0.46 | +| gpt-5.4-mini | uk | 900 | 83 (9%) | 648 (72%) | 169 (19%) | 0.81 | 0.45 | +| gpt-5.4-mini | us | 900 | 60 (7%) | 642 (71%) | 198 (22%) | 0.78 | 0.42 | +| gpt-5.4-mini | **all three** | 5640 | 454 (8%) | 4287 (76%) | 899 (16%) | 0.84 | 0.46 | +| harvey | br | 570 | 72 (13%) | 451 (79%) | 47 (8%) | 0.92 | 0.52 | +| harvey | ch | 900 | 59 (7%) | 780 (87%) | 61 (7%) | 0.93 | 0.50 | +| harvey | de | 600 | 46 (8%) | 455 (76%) | 99 (16%) | 0.83 | 0.46 | +| harvey | ge | 570 | 79 (14%) | 399 (70%) | 92 (16%) | 0.84 | 0.49 | +| harvey | sg | 600 | 78 (13%) | 473 (79%) | 49 (8%) | 0.92 | 0.52 | +| harvey | tw | 600 | 55 (9%) | 466 (78%) | 79 (13%) | 0.87 | 0.48 | +| harvey | uk | 900 | 139 (15%) | 693 (77%) | 68 (8%) | 0.92 | 0.54 | +| harvey | us | 900 | 66 (7%) | 639 (71%) | 195 (22%) | 0.78 | 0.43 | +| harvey | **all three** | 5640 | 594 (11%) | 4356 (77%) | 690 (12%) | 0.88 | 0.49 | +| legora-1 | br | 540 | 68 (13%) | 434 (80%) | 38 (7%) | 0.93 | 0.53 | +| legora-1 | ch | 900 | 59 (7%) | 818 (91%) | 23 (3%) | 0.97 | 0.52 | +| legora-1 | de | 570 | 49 (9%) | 352 (62%) | 169 (30%) | 0.70 | 0.39 | +| legora-1 | sg | 600 | 74 (12%) | 432 (72%) | 94 (16%) | 0.84 | 0.48 | +| legora-1 | tw | 600 | 57 (10%) | 480 (80%) | 63 (10%) | 0.90 | 0.49 | +| legora-1 | uk | 900 | 128 (14%) | 679 (75%) | 93 (10%) | 0.90 | 0.52 | +| legora-1 | us | 900 | 71 (8%) | 560 (62%) | 269 (30%) | 0.70 | 0.39 | +| legora-1 | **all three** | 5010 | 506 (10%) | 3755 (75%) | 749 (15%) | 0.85 | 0.48 | +| legora-2 | br | 510 | 65 (13%) | 408 (80%) | 37 (7%) | 0.93 | 0.53 | +| legora-2 | ch | 900 | 62 (7%) | 804 (89%) | 34 (4%) | 0.96 | 0.52 | +| legora-2 | de | 600 | 50 (8%) | 362 (60%) | 188 (31%) | 0.69 | 0.39 | +| legora-2 | sg | 600 | 72 (12%) | 440 (73%) | 88 (15%) | 0.85 | 0.49 | +| legora-2 | tw | 600 | 56 (9%) | 484 (81%) | 60 (10%) | 0.90 | 0.50 | +| legora-2 | uk | 900 | 131 (15%) | 686 (76%) | 83 (9%) | 0.91 | 0.53 | +| legora-2 | us | 900 | 72 (8%) | 570 (63%) | 258 (29%) | 0.71 | 0.40 | +| legora-2 | **all three** | 5010 | 508 (10%) | 3754 (75%) | 748 (15%) | 0.85 | 0.48 | + +**What is in the “Tie” bucket.** A tie only means *same score against the same two references*, so it merges several different situations. Split by score level, and independently by whether the candidate actually produced the held-out expert's answer: + +| Candidate | Ties | Same answer as expert | Different answer, equal score | at 1 (all agree) | at ½ (experts conflict) | at 0 (both differ) | +| --- | --- | --- | --- | --- | --- | --- | +| gemini-3.1-flash-lite | 4338 | 3757 (87%) | 581 (13%) | 2930 (68%) | 901 (21%) | 507 (12%) | +| gpt-5.4-mini | 4287 | 3654 (85%) | 633 (15%) | 2888 (67%) | 855 (20%) | 544 (13%) | +| harvey | 4356 | 3926 (90%) | 430 (10%) | 2986 (69%) | 972 (22%) | 398 (9%) | +| legora-1 | 3755 | 3437 (92%) | 318 (8%) | 2639 (70%) | 796 (21%) | 320 (9%) | +| legora-2 | 3754 | 3446 (92%) | 308 (8%) | 2665 (71%) | 770 (21%) | 319 (8%) | + +A tie at ½ is only possible when the two reference experts contradict each other — that caps every achievable score at ½, for the candidate and the held-out expert alike. The two references disagree in 1543 of 5640 comparisons (27%; a property of the human labels, identical for every candidate). Note that reference disagreement is *not* the same thing as a tie: ties also arise, and in fact more often, where the two references agree and the candidate simply matches them. + +**Reading.** + +- **gemini-3.1-flash-lite** — 77% of the 5640 comparisons are ties. 87% of those ties are real agreement (candidate gave the held-out expert's answer); the other 13% are comparisons where candidate and expert gave *different* answers that happened to score the same, and ρ credits every one of them to the candidate. On the 1302 comparisons that actually discriminate, the human wins 63% (821 vs 481). Dropping instances whose reference is empty throughout leaves ρ (ties split) at 0.46. +- **gpt-5.4-mini** — 76% of the 5640 comparisons are ties. 85% of those ties are real agreement (candidate gave the held-out expert's answer); the other 15% are comparisons where candidate and expert gave *different* answers that happened to score the same, and ρ credits every one of them to the candidate. On the 1353 comparisons that actually discriminate, the human wins 66% (899 vs 454). Dropping instances whose reference is empty throughout leaves ρ (ties split) at 0.47. +- **harvey** — 77% of the 5640 comparisons are ties. 90% of those ties are real agreement (candidate gave the held-out expert's answer); the other 10% are comparisons where candidate and expert gave *different* answers that happened to score the same, and ρ credits every one of them to the candidate. On the 1284 comparisons that actually discriminate, the human wins 54% (690 vs 594). Dropping instances whose reference is empty throughout leaves ρ (ties split) at 0.48. +- **legora-1** — 75% of the 5010 comparisons are ties. 92% of those ties are real agreement (candidate gave the held-out expert's answer); the other 8% are comparisons where candidate and expert gave *different* answers that happened to score the same, and ρ credits every one of them to the candidate. On the 1255 comparisons that actually discriminate, the human wins 60% (749 vs 506). Dropping instances whose reference is empty throughout leaves ρ (ties split) at 0.45. +- **legora-2** — 75% of the 5010 comparisons are ties. 92% of those ties are real agreement (candidate gave the held-out expert's answer); the other 8% are comparisons where candidate and expert gave *different* answers that happened to score the same, and ρ credits every one of them to the candidate. On the 1256 comparisons that actually discriminate, the human wins 60% (748 vs 508). Dropping instances whose reference is empty throughout leaves ρ (ties split) at 0.45. + +This is what the alt-test is and is not evidence for. The tie rate is high and mostly genuine, so on this data the models are largely *indistinguishable* from an additional expert — which is exactly the substitutability claim ω and ρ are designed to support, and §3.2 supports it. It is not evidence of superiority: once ties stop counting as wins, ρ sits at chance, and on the comparisons that separate the two the human expert is still ahead. "Can this model replace a human annotator?" and "is this model better than a human annotator?" are different questions, and only the first one is being tested. + +## 4. Headline extraction metrics + +> Recall = TP / (TP + Mismatch + Missed) over cells the expert filled; precision = TP / (TP + Mismatch + Hallucinated); hallucination = invented values on empty-gold cells. Buckets from `legex/evaluation.py`; per-cell source `../per_column.csv`. n = evaluated label cells; recall and precision carry ±1 SE over their gold-filled / emitted denominators. The per-field grids (§4.2–4.5) break each metric out by variable across models; their `n` is the metric's model-independent denominator (gold-filled for recall, gold-empty for hallucination) and is omitted for precision (emitted; model-dependent) and F1 (composite). + +### 4.1 Overall (all countries, summed across fields) + +| Model | n | Accuracy | Recall (filled) | Precision | Hallu. rate | F1 | +| --- | --- | --- | --- | --- | --- | --- | +| gemini-3.1-flash-lite | 17017 | 61.5% | 57.8% ±0.5 | 57.4% ±0.5 | 30.3% | 0.576 | +| gpt-5.4-mini | 17006 | 58.5% | 58.7% ±0.5 | 54.2% ±0.4 | 41.9% | 0.563 | +| harvey | 15158 | 61.7% | 51.5% ±0.5 | 64.9% ±0.5 | 16.3% | 0.574 | +| harvey-2 | 15323 | 61.1% | 48.3% ±0.5 | 68.6% ±0.5 | 11.4% | 0.567 | +| legora-1 | 13827 | 65.0% | 52.5% ±0.5 | 73.6% ±0.5 | 8.7% | 0.613 | +| legora-2 | 13981 | 65.0% | 52.7% ±0.5 | 73.3% ±0.5 | 9.1% | 0.613 | + +### 4.2 Recall (filled) by field (all countries) + +| Field | n | gemini-3.1-flash-lite | gpt-5.4-mini | harvey | harvey-2 | legora-1 | legora-2 | +| --- | --- | --- | --- | --- | --- | --- | --- | +| `court_cost_awarded_nominal` | 501 | 63.9% | 65.5% | 57.9% | 57.0% | 70.1% | 70.4% | +| `defendant_no1_ISIC1_industry_category` | 1075 | 58.0% | 64.7% | 49.2% | 33.7% | 55.5% | 50.7% | +| `defendants_all_count` | 1505 | 76.9% | 73.8% | 71.6% | 71.8% | 66.6% | 65.8% | +| `dispute_value_nominal` | 708 | 56.4% | 57.1% | 47.0% | 33.1% | 34.7% | 39.7% | +| `legal_subject_judgement` | 1541 | 3.6% | 4.9% | 3.9% | 0.7% | 1.8% | 2.2% | +| `party_compensation_awarded_nominal` | 364 | 71.4% | 67.3% | 66.9% | 63.1% | 79.1% | 79.7% | +| `plaintiff_loosing_share` | 1323 | 71.3% | 69.9% | 71.4% | 69.7% | 69.3% | 70.8% | +| `plaintiff_no1_ISIC1_industry_category` | 865 | 54.1% | 53.9% | 41.8% | 31.3% | 36.8% | 36.9% | +| `plaintiffs_all_count` | 1532 | 88.8% | 85.4% | 74.9% | 77.6% | 70.5% | 72.4% | +| `trial_end_date` | 1487 | 67.6% | 66.9% | 65.0% | 65.0% | 77.8% | 77.0% | +| `trial_start_date` | 916 | 26.2% | 41.6% | 13.3% | 15.1% | 27.3% | 27.2% | + +### 4.3 Precision by field (all countries) + +| Field | gemini-3.1-flash-lite | gpt-5.4-mini | harvey | harvey-2 | legora-1 | legora-2 | +| --- | --- | --- | --- | --- | --- | --- | +| `court_cost_awarded_nominal` | 82.9% | 72.9% | 85.2% | 83.7% | 94.7% | 94.8% | +| `defendant_no1_ISIC1_industry_category` | 50.6% | 52.1% | 55.8% | 51.1% | 70.6% | 65.1% | +| `defendants_all_count` | 76.6% | 80.3% | 85.0% | 84.6% | 85.7% | 88.5% | +| `dispute_value_nominal` | 45.6% | 42.3% | 45.5% | 46.7% | 63.0% | 63.2% | +| `legal_subject_judgement` | 3.6% | 4.9% | 4.8% | 1.3% | 3.0% | 3.7% | +| `party_compensation_awarded_nominal` | 74.1% | 55.9% | 77.2% | 75.9% | 80.2% | 79.7% | +| `plaintiff_loosing_share` | 74.1% | 68.6% | 76.7% | 85.2% | 90.4% | 85.6% | +| `plaintiff_no1_ISIC1_industry_category` | 35.5% | 35.2% | 49.2% | 48.2% | 54.0% | 55.8% | +| `plaintiffs_all_count` | 89.1% | 89.8% | 88.6% | 89.6% | 94.5% | 93.4% | +| `trial_end_date` | 68.3% | 71.1% | 91.8% | 85.9% | 87.8% | 88.2% | +| `trial_start_date` | 58.8% | 32.4% | 57.1% | 67.4% | 78.7% | 80.7% | + +### 4.4 Hallucination rate by field (all countries) + +| Field | n | gemini-3.1-flash-lite | gpt-5.4-mini | harvey | harvey-2 | legora-1 | legora-2 | +| --- | --- | --- | --- | --- | --- | --- | --- | +| `court_cost_awarded_nominal` | 1046 | 4.1% | 8.3% | 1.9% | 0.8% | 0.9% | 0.7% | +| `defendant_no1_ISIC1_industry_category` | 472 | 65.9% | 74.7% | 37.3% | 27.4% | 22.4% | 28.5% | +| `defendants_all_count` | 42 | 90.5% | 56.1% | 48.6% | 62.2% | 37.1% | 18.4% | +| `dispute_value_nominal` | 839 | 44.0% | 53.8% | 32.9% | 20.4% | 12.3% | 12.9% | +| `legal_subject_judgement` | 6 | 83.3% | 100.0% | 83.3% | 0.0% | 16.7% | 16.7% | +| `party_compensation_awarded_nominal` | 1183 | 6.6% | 13.6% | 4.7% | 4.9% | 4.2% | 4.5% | +| `plaintiff_loosing_share` | 224 | 36.6% | 55.2% | 33.3% | 15.9% | 11.4% | 16.3% | +| `plaintiff_no1_ISIC1_industry_category` | 682 | 79.5% | 80.5% | 25.4% | 18.8% | 16.9% | 14.8% | +| `plaintiffs_all_count` | 15 | 100.0% | 71.4% | 83.3% | 66.7% | 28.6% | 35.7% | +| `trial_end_date` | 60 | 50.0% | 44.1% | 12.3% | 14.0% | 13.5% | 13.2% | +| `trial_start_date` | 631 | 9.7% | 61.3% | 4.4% | 3.3% | 3.6% | 3.6% | + +### 4.5 F1 by field (all countries) + +| Field | gemini-3.1-flash-lite | gpt-5.4-mini | harvey | harvey-2 | legora-1 | legora-2 | +| --- | --- | --- | --- | --- | --- | --- | +| `court_cost_awarded_nominal` | 0.722 | 0.690 | 0.689 | 0.679 | 0.806 | 0.808 | +| `defendant_no1_ISIC1_industry_category` | 0.540 | 0.578 | 0.523 | 0.406 | 0.621 | 0.570 | +| `defendants_all_count` | 0.768 | 0.769 | 0.777 | 0.777 | 0.750 | 0.755 | +| `dispute_value_nominal` | 0.504 | 0.486 | 0.463 | 0.387 | 0.447 | 0.488 | +| `legal_subject_judgement` | 0.036 | 0.049 | 0.043 | 0.009 | 0.023 | 0.028 | +| `party_compensation_awarded_nominal` | 0.727 | 0.611 | 0.717 | 0.689 | 0.797 | 0.797 | +| `plaintiff_loosing_share` | 0.727 | 0.692 | 0.740 | 0.767 | 0.784 | 0.775 | +| `plaintiff_no1_ISIC1_industry_category` | 0.429 | 0.426 | 0.452 | 0.379 | 0.438 | 0.444 | +| `plaintiffs_all_count` | 0.889 | 0.875 | 0.812 | 0.832 | 0.807 | 0.816 | +| `trial_end_date` | 0.679 | 0.690 | 0.761 | 0.740 | 0.825 | 0.822 | +| `trial_start_date` | 0.362 | 0.364 | 0.216 | 0.247 | 0.405 | 0.406 | + +## Appendix + +### Landis–Koch (1977) κ scale (for interpreting the ISIC κ) + +| κ | Strength | +| --- | --- | +| < 0.00 | Poor (worse than chance) | +| 0.00 – 0.20 | Slight | +| 0.21 – 0.40 | Fair | +| 0.41 – 0.60 | Moderate | +| 0.61 – 0.80 | Substantial | +| 0.81 – 1.00 | Almost perfect | + +### Provenance + +- Pairwise agreement & kappa: `legex/analysis/iaa.py` (`pairwise_agreement`, `write_kappa_audit_csv`). +- Alt-test: authors' reference implementation ([github.com/nitaytech/AltTest](https://github.com/nitaytech/AltTest)) run via `scripts/alt_test_reference.py` → `alt_test_reference_*.csv` (see README). +- Alt-test win/tie/loss decomposition: `scripts/alt_test_decomposition.py` → `alt_test_decomposition.csv`. +- Tolerant comparator: `legex/evaluation/comparison.py` (`values_agree`, `normalise`). +- Headline buckets: `legex/analysis/aggregate.py` over `legex/evaluation.score_country`. +- This report: `legex/analysis/report.py`. diff --git a/data/analysis/iaa/alt_test_decomposition.csv b/data/analysis/iaa/alt_test_decomposition.csv new file mode 100644 index 0000000000000000000000000000000000000000..f1d1f67f6ccc6e9d8c76825a1f1ed655b0a6c802 --- /dev/null +++ b/data/analysis/iaa/alt_test_decomposition.csv @@ -0,0 +1,77 @@ +candidate,country,variant,n_comparisons,refs_disagree,llm_better,human_better,tie,tie_same,tie_diff,tie_at_1,tie_at_half,tie_at_0,rho_alttest,rho_tiebroken +gpt-5.4-mini,br,all,570,185,72,53,445,401,44,283,123,39,0.907,0.5167 +gpt-5.4-mini,br,nontrivial,320,185,52,22,246,208,38,97,123,26,0.9313,0.5469 +gpt-5.4-mini,ch,all,900,153,46,95,759,704,55,628,90,41,0.8944,0.4728 +gpt-5.4-mini,ch,nontrivial,736,153,46,26,664,615,49,550,90,24,0.9647,0.5136 +gpt-5.4-mini,de,all,600,127,34,114,452,394,58,326,74,52,0.81,0.4333 +gpt-5.4-mini,de,nontrivial,397,127,25,87,285,230,55,173,74,38,0.7809,0.4219 +gpt-5.4-mini,ge,all,570,238,75,104,391,281,110,153,153,85,0.8175,0.4746 +gpt-5.4-mini,ge,nontrivial,490,238,68,85,337,251,86,120,153,64,0.8265,0.4827 +gpt-5.4-mini,sg,all,600,233,50,83,467,350,117,250,119,98,0.8617,0.4725 +gpt-5.4-mini,sg,nontrivial,498,233,39,74,385,288,97,183,119,83,0.8514,0.4649 +gpt-5.4-mini,tw,all,600,129,34,83,483,419,64,347,92,44,0.8617,0.4592 +gpt-5.4-mini,tw,nontrivial,416,129,32,53,331,270,61,216,92,23,0.8726,0.4748 +gpt-5.4-mini,uk,all,900,337,83,169,648,502,146,358,156,134,0.8122,0.4522 +gpt-5.4-mini,uk,nontrivial,671,337,74,142,455,319,136,177,156,122,0.7884,0.4493 +gpt-5.4-mini,us,all,900,141,60,198,642,603,39,543,48,51,0.78,0.4233 +gpt-5.4-mini,us,nontrivial,688,141,52,186,450,412,38,360,48,42,0.7297,0.4026 +gemini/gemini-3.1-flash-lite,br,all,570,185,74,33,463,422,41,303,123,37,0.9421,0.536 +gemini/gemini-3.1-flash-lite,br,nontrivial,320,185,54,26,240,206,34,93,123,24,0.9187,0.5437 +gemini/gemini-3.1-flash-lite,ch,all,900,153,53,89,758,721,37,638,86,34,0.9011,0.48 +gemini/gemini-3.1-flash-lite,ch,nontrivial,736,153,46,53,637,605,32,527,86,24,0.928,0.4952 +gemini/gemini-3.1-flash-lite,de,all,600,127,37,93,470,416,54,347,74,49,0.845,0.4533 +gemini/gemini-3.1-flash-lite,de,nontrivial,397,127,30,81,286,235,51,179,74,33,0.796,0.4358 +gemini/gemini-3.1-flash-lite,ge,all,570,238,65,78,427,310,117,165,177,85,0.8632,0.4886 +gemini/gemini-3.1-flash-lite,ge,nontrivial,490,238,53,69,368,273,95,122,177,69,0.8592,0.4837 +gemini/gemini-3.1-flash-lite,sg,all,600,233,68,67,465,371,94,250,135,80,0.8883,0.5008 +gemini/gemini-3.1-flash-lite,sg,nontrivial,498,233,50,64,384,304,80,177,135,72,0.8715,0.4859 +gemini/gemini-3.1-flash-lite,tw,all,600,129,38,74,488,432,56,356,92,40,0.8767,0.47 +gemini/gemini-3.1-flash-lite,tw,nontrivial,416,129,29,56,331,278,53,213,92,26,0.8654,0.4675 +gemini/gemini-3.1-flash-lite,uk,all,900,337,87,163,650,508,142,352,168,130,0.8189,0.4578 +gemini/gemini-3.1-flash-lite,uk,nontrivial,671,337,74,145,452,316,136,162,168,122,0.7839,0.4471 +gemini/gemini-3.1-flash-lite,us,all,900,141,59,224,617,577,40,519,46,52,0.7511,0.4083 +gemini/gemini-3.1-flash-lite,us,nontrivial,688,141,52,218,418,381,37,330,46,42,0.6831,0.3794 +harvey,br,all,570,185,72,47,451,405,46,289,121,41,0.9175,0.5219 +harvey,br,nontrivial,320,185,51,43,226,187,39,76,121,29,0.8656,0.5125 +harvey,ch,all,900,153,59,61,780,747,33,656,96,28,0.9322,0.4989 +harvey,ch,nontrivial,736,153,49,55,632,601,31,515,96,21,0.9253,0.4959 +harvey,de,all,600,127,46,99,455,421,34,341,74,40,0.835,0.4558 +harvey,de,nontrivial,397,127,28,93,276,246,30,167,74,35,0.7657,0.4181 +harvey,ge,all,570,238,79,92,399,296,103,151,175,73,0.8386,0.4886 +harvey,ge,nontrivial,490,238,56,89,345,263,82,102,175,68,0.8184,0.4663 +harvey,sg,all,600,233,78,49,473,391,82,264,139,70,0.9183,0.5242 +harvey,sg,nontrivial,498,233,57,49,392,324,68,188,139,65,0.9016,0.508 +harvey,tw,all,600,129,55,79,466,440,26,347,96,23,0.8683,0.48 +harvey,tw,nontrivial,416,129,35,70,311,288,23,195,96,20,0.8317,0.4579 +harvey,uk,all,900,337,139,68,693,615,78,398,217,78,0.9244,0.5394 +harvey,uk,nontrivial,671,337,124,56,491,417,74,202,217,72,0.9165,0.5507 +harvey,us,all,900,141,66,195,639,611,28,540,54,45,0.7833,0.4283 +harvey,us,nontrivial,688,141,51,186,451,424,27,354,54,43,0.7297,0.4019 +legora-1,br,all,540,174,68,38,434,395,39,275,125,34,0.9296,0.5278 +legora-1,br,nontrivial,306,174,40,35,231,196,35,73,125,33,0.8856,0.5082 +legora-1,ch,all,900,153,59,23,818,783,35,684,106,28,0.9744,0.52 +legora-1,ch,nontrivial,736,153,45,23,668,633,35,537,106,25,0.9688,0.5149 +legora-1,de,all,570,123,49,169,352,323,29,242,76,34,0.7035,0.3947 +legora-1,de,nontrivial,378,123,29,169,180,151,29,71,76,33,0.5529,0.3148 +legora-1,sg,all,600,233,74,94,432,350,82,229,129,74,0.8433,0.4833 +legora-1,sg,nontrivial,498,233,49,94,355,287,68,153,129,73,0.8112,0.4548 +legora-1,tw,all,600,129,57,63,480,457,23,359,100,21,0.895,0.495 +legora-1,tw,nontrivial,416,129,35,57,324,303,21,204,100,20,0.863,0.4736 +legora-1,uk,all,900,337,128,93,679,590,89,382,208,89,0.8967,0.5194 +legora-1,uk,nontrivial,671,337,109,93,469,382,87,174,208,87,0.8614,0.5119 +legora-1,us,all,900,141,71,269,560,539,21,468,52,40,0.7011,0.39 +legora-1,us,nontrivial,688,141,58,266,364,344,20,276,52,36,0.6134,0.3488 +legora-2,br,all,510,166,65,37,408,370,38,262,111,35,0.9275,0.5275 +legora-2,br,nontrivial,297,166,41,33,223,188,35,79,111,33,0.8889,0.5135 +legora-2,ch,all,900,153,62,34,804,774,30,679,100,25,0.9622,0.5156 +legora-2,ch,nontrivial,736,153,46,34,656,627,29,532,100,24,0.9538,0.5082 +legora-2,de,all,600,127,50,188,362,332,30,248,78,36,0.6867,0.385 +legora-2,de,nontrivial,397,127,29,188,180,150,30,68,78,34,0.5264,0.2997 +legora-2,sg,all,600,233,72,88,440,357,83,241,123,76,0.8533,0.4867 +legora-2,sg,nontrivial,498,233,47,88,363,294,69,165,123,75,0.8233,0.4588 +legora-2,tw,all,600,129,56,60,484,460,24,362,100,22,0.9,0.4967 +legora-2,tw,nontrivial,416,129,33,54,329,307,22,207,100,22,0.8702,0.4748 +legora-2,uk,all,900,337,131,83,686,600,86,390,210,86,0.9078,0.5267 +legora-2,uk,nontrivial,671,337,111,82,478,393,85,183,210,85,0.8778,0.5216 +legora-2,us,all,900,141,72,258,570,553,17,483,48,39,0.7133,0.3967 +legora-2,us,nontrivial,688,141,57,255,376,361,15,291,48,37,0.6294,0.3561 diff --git a/data/analysis/iaa/alt_test_pooled.csv b/data/analysis/iaa/alt_test_pooled.csv new file mode 100644 index 0000000000000000000000000000000000000000..583a614b467ab55a366cc2db4945fed572861711 --- /dev/null +++ b/data/analysis/iaa/alt_test_pooled.csv @@ -0,0 +1,39 @@ +candidate,country,omega,rho,passes,omega_nontrivial,rho_nontrivial,passes_nontrivial +gpt-5.4-mini,br,1.0,0.9,1,1.0,0.9352,1 +gpt-5.4-mini,ch,1.0,0.8944,1,1.0,0.9655,1 +gpt-5.4-mini,de,0.3333,0.7961,0,0.3333,0.7783,0 +gpt-5.4-mini,ge,1.0,0.8189,1,1.0,0.8417,1 +gpt-5.4-mini,sg,1.0,0.8583,1,1.0,0.8698,1 +gpt-5.4-mini,tw,1.0,0.8561,1,1.0,0.8703,1 +gpt-5.4-mini,uk,1.0,0.8122,1,1.0,0.7951,1 +gpt-5.4-mini,us,0.0,0.78,0,0.0,0.7362,0 +gemini/gemini-3.1-flash-lite,br,1.0,0.934,1,1.0,0.9207,1 +gemini/gemini-3.1-flash-lite,ch,1.0,0.9011,1,1.0,0.9296,1 +gemini/gemini-3.1-flash-lite,de,1.0,0.8433,1,0.3333,0.7992,0 +gemini/gemini-3.1-flash-lite,ge,1.0,0.8611,1,1.0,0.8695,1 +gemini/gemini-3.1-flash-lite,sg,1.0,0.8815,1,1.0,0.8977,1 +gemini/gemini-3.1-flash-lite,tw,1.0,0.8633,1,1.0,0.8565,1 +gemini/gemini-3.1-flash-lite,uk,1.0,0.8189,1,1.0,0.7908,1 +gemini/gemini-3.1-flash-lite,us,0.0,0.7511,0,0.0,0.6908,0 +harvey,br,1.0,0.9108,1,1.0,0.8697,1 +harvey,ch,1.0,0.9322,1,1.0,0.927,1 +harvey,de,0.6667,0.8272,1,0.6667,0.7678,1 +harvey,ge,1.0,0.8355,1,1.0,0.8358,1 +harvey,sg,1.0,0.9227,1,1.0,0.9149,1 +harvey,tw,1.0,0.8461,1,1.0,0.8157,1 +harvey,uk,1.0,0.9244,1,1.0,0.9192,1 +harvey,us,0.3333,0.7833,0,0.0,0.7362,0 +legora-1,br,1.0,0.9143,1,1.0,0.8685,1 +legora-1,ch,1.0,0.9744,1,1.0,0.9695,1 +legora-1,de,0.0,0.6993,0,0.0,0.5682,0 +legora-1,sg,1.0,0.8454,1,1.0,0.8241,1 +legora-1,tw,1.0,0.8783,1,1.0,0.8501,1 +legora-1,uk,1.0,0.8967,1,1.0,0.8658,1 +legora-1,us,0.0,0.7011,0,0.0,0.6227,0 +legora-2,br,1.0,0.915,1,1.0,0.8719,1 +legora-2,ch,1.0,0.9622,1,1.0,0.9548,1 +legora-2,de,0.0,0.6861,0,0.0,0.5492,0 +legora-2,sg,1.0,0.8582,1,1.0,0.8387,1 +legora-2,tw,1.0,0.8778,1,1.0,0.8524,1 +legora-2,uk,1.0,0.9078,1,1.0,0.8802,1 +legora-2,us,0.0,0.7133,0,0.0,0.6383,0 diff --git a/data/analysis/iaa/alt_test_reference_gemini_gemini-3.1-flash-lite.csv b/data/analysis/iaa/alt_test_reference_gemini_gemini-3.1-flash-lite.csv new file mode 100644 index 0000000000000000000000000000000000000000..92aebf7dcdd4bfaec057466535c043c800a754b3 --- /dev/null +++ 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+ge,ას_1096_2025,359f544380,primary,legal_subject_judgement,"Recognition of co-ownership of real estate, enforcement of property jointly owned by spouses",co-ownership rights over immovable property,no,no +ge,ას_1096_2025,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_1096_2025,359f544380,primary,plaintiff_loosing_share,,,yes,yes +ge,ას_1096_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_1096_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_1096_2025,359f544380,primary,trial_end_date,2025-04-14,2026-04-14,no,no +ge,ას_1096_2025,359f544380,primary,trial_start_date,,2005-08-04,no,no +ge,ას_1144_2025,359f544380,primary,court_cost_awarded_nominal,0,0,yes,yes +ge,ას_1144_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,,r_human_health_social_work,no,no +ge,ას_1144_2025,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_1144_2025,359f544380,primary,dispute_value_nominal,,67588.5,no,no +ge,ას_1144_2025,359f544380,primary,legal_subject_judgement,"Reimbursement_of_salary_arrears_and_the_imposition_of_statutory_interest_for_delayed_payment +delay in payment.",Wage arrears (/ delayed payment interest,no,no +ge,ას_1144_2025,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_1144_2025,359f544380,primary,plaintiff_loosing_share,,1,no,no +ge,ას_1144_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,n_professional_scientific_technical,no_allocation_possible,no,no +ge,ას_1144_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_1144_2025,359f544380,primary,trial_end_date,2025-11-06,2025-11-06,yes,yes +ge,ას_1144_2025,359f544380,primary,trial_start_date,,2025-07-07,no,no +ge,ას_1174_2024,359f544380,primary,court_cost_awarded_nominal,0,0,yes,yes +ge,ას_1174_2024,359f544380,primary,defendant_no1_ISIC1_industry_category,p_public_admin_defence,m_real_estate,no,no +ge,ას_1174_2024,359f544380,primary,defendants_all_count,1,3,no,no +ge,ას_1174_2024,359f544380,primary,dispute_value_nominal,0,39500,no,no +ge,ას_1174_2024,359f544380,primary,legal_subject_judgement,"Lifting of the Property Lien and Release from Public Legal Restrictions, Recognition of +Property Ownership",Release of property from seizure,no,no +ge,ას_1174_2024,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_1174_2024,359f544380,primary,plaintiff_loosing_share,0,0,yes,yes +ge,ას_1174_2024,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,m_real_estate,no,no +ge,ას_1174_2024,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_1174_2024,359f544380,primary,trial_end_date,2025-02-25,2025-02-25,yes,yes +ge,ას_1174_2024,359f544380,primary,trial_start_date,,,yes,yes +ge,ას_120_2025,359f544380,primary,court_cost_awarded_nominal,0,1330,no,no +ge,ას_120_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +ge,ას_120_2025,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_120_2025,359f544380,primary,dispute_value_nominal,,2000,no,no +ge,ას_120_2025,359f544380,primary,legal_subject_judgement,Annulment_of_Employment_Contract_Termination_and_Claim for Compensation,employment dismissal / compensation,no,no +ge,ას_120_2025,359f544380,primary,party_compensation_awarded_nominal,2000,2000,yes,yes +ge,ას_120_2025,359f544380,primary,plaintiff_loosing_share,0,0,yes,yes +ge,ას_120_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,n_professional_scientific_technical,no_allocation_possible,no,no +ge,ას_120_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_120_2025,359f544380,primary,trial_end_date,2026-04-07,2026-04-07,yes,yes +ge,ას_120_2025,359f544380,primary,trial_start_date,,2025-03-21,no,no +ge,ას_1305_2025,359f544380,primary,court_cost_awarded_nominal,0,0,yes,yes +ge,ას_1305_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,,,yes,yes +ge,ას_1305_2025,359f544380,primary,defendants_all_count,1,3,no,no +ge,ას_1305_2025,359f544380,primary,dispute_value_nominal,,60000,no,no +ge,ას_1305_2025,359f544380,primary,legal_subject_judgement,Imposition_of_Monetary_Amount,Monetary claim,no,no +ge,ას_1305_2025,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_1305_2025,359f544380,primary,plaintiff_loosing_share,0,1,no,no +ge,ას_1305_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,,yes,yes +ge,ას_1305_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_1305_2025,359f544380,primary,trial_end_date,2025-11-04,2025-11-04,yes,yes +ge,ას_1305_2025,359f544380,primary,trial_start_date,,,yes,yes +ge,ას_1450_2025,359f544380,primary,court_cost_awarded_nominal,210,210,yes,yes +ge,ას_1450_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_1450_2025,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_1450_2025,359f544380,primary,dispute_value_nominal,500,,no,no +ge,ას_1450_2025,359f544380,primary,legal_subject_judgement,Imposition_of_Child_Alimony,Child support / alimony,no,no +ge,ას_1450_2025,359f544380,primary,party_compensation_awarded_nominal,500,,no,no +ge,ას_1450_2025,359f544380,primary,plaintiff_loosing_share,0,0,yes,yes +ge,ას_1450_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_1450_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_1450_2025,359f544380,primary,trial_end_date,2026-04-07,2026-04-07,yes,yes +ge,ას_1450_2025,359f544380,primary,trial_start_date,2024-04-10,2026-02-02,no,no +ge,ას_1472_2025,359f544380,primary,court_cost_awarded_nominal,0,,no,yes +ge,ას_1472_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_1472_2025,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_1472_2025,359f544380,primary,dispute_value_nominal,7206.3,3600,no,no +ge,ას_1472_2025,359f544380,primary,legal_subject_judgement,"Invalidation_of_the_agreement_on_recognition_of_debt,_invalidation_of_enforcement_document_and_imposition_of_sum_of_money",Debt acknowledgment / enforcement writ,no,no +ge,ას_1472_2025,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_1472_2025,359f544380,primary,plaintiff_loosing_share,0,1,no,no +ge,ას_1472_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_1472_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_1472_2025,359f544380,primary,trial_end_date,2026-03-26,2026-03-26,yes,yes +ge,ას_1472_2025,359f544380,primary,trial_start_date,,2025-12-23,no,no +ge,ას_1551_2025,359f544380,primary,court_cost_awarded_nominal,105,105,yes,yes +ge,ას_1551_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_1551_2025,359f544380,primary,defendants_all_count,2,2,yes,yes +ge,ას_1551_2025,359f544380,primary,dispute_value_nominal,0,,no,yes +ge,ას_1551_2025,359f544380,primary,legal_subject_judgement,Recovery_of_Real_Estate_from_Illegal_Possession,Recovery of immovable propoerty,no,no +ge,ას_1551_2025,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_1551_2025,359f544380,primary,plaintiff_loosing_share,0,0,yes,yes +ge,ას_1551_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,l_financial_insurance,no,no +ge,ას_1551_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_1551_2025,359f544380,primary,trial_end_date,2026-04-07,2026-04-07,yes,yes +ge,ას_1551_2025,359f544380,primary,trial_start_date,,2026-02-09,no,no +ge,ას_322_2025,359f544380,primary,court_cost_awarded_nominal,0,0,yes,yes +ge,ას_322_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,,,yes,yes +ge,ას_322_2025,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_322_2025,359f544380,primary,dispute_value_nominal,35218.23,,no,no +ge,ას_322_2025,359f544380,primary,legal_subject_judgement,Imposition_of_Fine,Lease penalty,no,no +ge,ას_322_2025,359f544380,primary,party_compensation_awarded_nominal,10000,10000,yes,yes +ge,ას_322_2025,359f544380,primary,plaintiff_loosing_share,,,yes,yes +ge,ას_322_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +ge,ას_322_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_322_2025,359f544380,primary,trial_end_date,2025-10-15,2025-10-15,yes,yes +ge,ას_322_2025,359f544380,primary,trial_start_date,,2025-11-14,no,no +ge,ას_639_2025,359f544380,primary,court_cost_awarded_nominal,0,630,no,no +ge,ას_639_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_639_2025,359f544380,primary,defendants_all_count,,1,no,no +ge,ას_639_2025,359f544380,primary,dispute_value_nominal,1000,25000,no,no +ge,ას_639_2025,359f544380,primary,legal_subject_judgement,Recovery_of_Loan_Amount,Loan repayment,no,no +ge,ას_639_2025,359f544380,primary,party_compensation_awarded_nominal,0,25000,no,no +ge,ას_639_2025,359f544380,primary,plaintiff_loosing_share,0,0,yes,yes +ge,ას_639_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_639_2025,359f544380,primary,plaintiffs_all_count,,1,no,no +ge,ას_639_2025,359f544380,primary,trial_end_date,2025-11-14,2025-11-14,yes,yes +ge,ას_639_2025,359f544380,primary,trial_start_date,,2025-07-21,no,no +ge,ას_687_2025,359f544380,primary,court_cost_awarded_nominal,0,1245.35,no,no +ge,ას_687_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,,f_construction,no,no +ge,ას_687_2025,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_687_2025,359f544380,primary,dispute_value_nominal,15905,,no,no +ge,ას_687_2025,359f544380,primary,legal_subject_judgement,Imposition_of_Penalty for Damages,Penalty / damage compensation,no,no +ge,ას_687_2025,359f544380,primary,party_compensation_awarded_nominal,13450,13450,yes,yes +ge,ას_687_2025,359f544380,primary,plaintiff_loosing_share,0,0,yes,yes +ge,ას_687_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,m_real_estate,m_real_estate,yes,yes +ge,ას_687_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_687_2025,359f544380,primary,trial_end_date,2025-11-04,2025-09-26,no,no +ge,ას_687_2025,359f544380,primary,trial_start_date,,,yes,yes +ge,ას_71_2024,359f544380,primary,court_cost_awarded_nominal,5600,5600,yes,yes +ge,ას_71_2024,359f544380,primary,defendant_no1_ISIC1_industry_category,p_public_admin_defence,m_real_estate,no,no +ge,ას_71_2024,359f544380,primary,defendants_all_count,2,2,yes,yes +ge,ას_71_2024,359f544380,primary,dispute_value_nominal,760656,760656,yes,yes +ge,ას_71_2024,359f544380,primary,legal_subject_judgement,Compensation for damages,Damage compensation,no,no +ge,ას_71_2024,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_71_2024,359f544380,primary,plaintiff_loosing_share,,1,no,no +ge,ას_71_2024,359f544380,primary,plaintiff_no1_ISIC1_industry_category,m_real_estate,m_real_estate,yes,yes +ge,ას_71_2024,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_71_2024,359f544380,primary,trial_end_date,2024-12-25,2024-12-25,yes,yes +ge,ას_71_2024,359f544380,primary,trial_start_date,,,yes,yes +ge,ას_730_2025,359f544380,primary,court_cost_awarded_nominal,0,,no,yes +ge,ას_730_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +ge,ას_730_2025,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_730_2025,359f544380,primary,dispute_value_nominal,870057.46,17843.81,no,no +ge,ას_730_2025,359f544380,primary,legal_subject_judgement,Fine_Reduction,Penalty reduction,no,no +ge,ას_730_2025,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_730_2025,359f544380,primary,plaintiff_loosing_share,,1,no,no +ge,ას_730_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,f_construction,f_construction,yes,yes +ge,ას_730_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_730_2025,359f544380,primary,trial_end_date,2024-04-02,2026-04-02,no,no +ge,ას_730_2025,359f544380,primary,trial_start_date,,2025-06-06,no,no +ge,ას_738_2024,359f544380,primary,court_cost_awarded_nominal,0,0,yes,yes +ge,ას_738_2024,359f544380,primary,defendant_no1_ISIC1_industry_category,,m_real_estate,no,no +ge,ას_738_2024,359f544380,primary,defendants_all_count,4,5,no,no +ge,ას_738_2024,359f544380,primary,dispute_value_nominal,32300,32300,yes,yes +ge,ას_738_2024,359f544380,primary,legal_subject_judgement,Real_estate_ownership_recognition_ and_release_from_encumbrance_of_real_estate,Ownership recognition / release from seizure,no,no +ge,ას_738_2024,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_738_2024,359f544380,primary,plaintiff_loosing_share,0,0,yes,yes +ge,ას_738_2024,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,m_real_estate,no,no +ge,ას_738_2024,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_738_2024,359f544380,primary,trial_end_date,2025-11-14,2025-11-14,yes,yes +ge,ას_738_2024,359f544380,primary,trial_start_date,,2024-06-17,no,no +ge,ას_75_2026,359f544380,primary,court_cost_awarded_nominal,0,4200,no,no +ge,ას_75_2026,359f544380,primary,defendant_no1_ISIC1_industry_category,c_manufacturing,c_manufacturing,yes,yes +ge,ას_75_2026,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_75_2026,359f544380,primary,dispute_value_nominal,0,,no,yes +ge,ას_75_2026,359f544380,primary,legal_subject_judgement,Invalidation_of_trademark_registration_and_compensation_for_damages,Trademark invalidation / damage compensation,no,no +ge,ას_75_2026,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_75_2026,359f544380,primary,plaintiff_loosing_share,0,1,no,no +ge,ას_75_2026,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,c_manufacturing,no,no +ge,ას_75_2026,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_75_2026,359f544380,primary,trial_end_date,2026-04-02,2026-04-02,yes,yes +ge,ას_75_2026,359f544380,primary,trial_start_date,,2026-02-17,no,no +ge,ას_80_2026,359f544380,primary,court_cost_awarded_nominal,0,,no,yes +ge,ას_80_2026,359f544380,primary,defendant_no1_ISIC1_industry_category,,,yes,yes +ge,ას_80_2026,359f544380,primary,defendants_all_count,1,1,yes,yes +ge,ას_80_2026,359f544380,primary,dispute_value_nominal,0,,no,yes +ge,ას_80_2026,359f544380,primary,legal_subject_judgement,Compensation_for_salary_arrears_severance_pay_and_unused_vacation,Wage arrears and unused leave compensations,no,no +ge,ას_80_2026,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_80_2026,359f544380,primary,plaintiff_loosing_share,0,0,yes,yes +ge,ას_80_2026,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,ას_80_2026,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_80_2026,359f544380,primary,trial_end_date,2026-04-15,2026-04-15,yes,yes +ge,ას_80_2026,359f544380,primary,trial_start_date,,,yes,yes +ge,ას_906_2025,359f544380,primary,court_cost_awarded_nominal,0,0,yes,yes +ge,ას_906_2025,359f544380,primary,defendant_no1_ISIC1_industry_category,r_human_health_social_work,r_human_health_social_work,yes,yes +ge,ას_906_2025,359f544380,primary,defendants_all_count,1,2,no,no +ge,ას_906_2025,359f544380,primary,dispute_value_nominal,42500,42500,yes,yes +ge,ას_906_2025,359f544380,primary,legal_subject_judgement,Compensation_for_moral_and_material_damage,Medical malpractice compensation,no,no +ge,ას_906_2025,359f544380,primary,party_compensation_awarded_nominal,0,,no,yes +ge,ას_906_2025,359f544380,primary,plaintiff_loosing_share,,1,no,no +ge,ას_906_2025,359f544380,primary,plaintiff_no1_ISIC1_industry_category,,r_human_health_social_work,no,no +ge,ას_906_2025,359f544380,primary,plaintiffs_all_count,1,1,yes,yes +ge,ას_906_2025,359f544380,primary,trial_end_date,2025-10-30,2025-10-30,yes,yes +ge,ას_906_2025,359f544380,primary,trial_start_date,,2025-07-07,no,no +ge,case_75522,dd79b903af,primary,court_cost_awarded_nominal,,,yes,yes +ge,case_75522,dd79b903af,primary,defendant_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,case_75522,dd79b903af,primary,defendants_all_count,1,1,yes,yes +ge,case_75522,dd79b903af,primary,dispute_value_nominal,,,yes,yes +ge,case_75522,dd79b903af,primary,legal_subject_judgement,Recognition_Of_Foreign_Court_Decision/Divorce,Reognition of foreign divorce judgment,no,no +ge,case_75522,dd79b903af,primary,party_compensation_awarded_nominal,,,yes,yes +ge,case_75522,dd79b903af,primary,plaintiff_loosing_share,0,0,yes,yes +ge,case_75522,dd79b903af,primary,plaintiff_no1_ISIC1_industry_category,,no_allocation_possible,no,no +ge,case_75522,dd79b903af,primary,plaintiffs_all_count,1,1,yes,yes +ge,case_75522,dd79b903af,primary,trial_end_date,,2025-11-06,no,no +ge,case_75522,dd79b903af,primary,trial_start_date,,2025-10-27,no,no +ge,case_75580,dd79b903af,primary,court_cost_awarded_nominal,,,yes,yes +ge,case_75580,dd79b903af,primary,defendant_no1_ISIC1_industry_category,,,yes,yes +ge,case_75580,dd79b903af,primary,defendants_all_count,0,0,yes,yes +ge,case_75580,dd79b903af,primary,dispute_value_nominal,,,yes,yes 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+sg,2026_sghc_50,dd79b903af,primary,court_cost_awarded_nominal,,,yes,yes +sg,2026_sghc_50,dd79b903af,primary,defendant_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +sg,2026_sghc_50,dd79b903af,primary,defendants_all_count,1,1,yes,yes +sg,2026_sghc_50,dd79b903af,primary,dispute_value_nominal,,,yes,yes +sg,2026_sghc_50,dd79b903af,primary,legal_subject_judgement,Criminal_Procedure_and_Sentencing,criminal_procedure_and _sentencing_mutual_legal_assistance,no,no +sg,2026_sghc_50,dd79b903af,primary,party_compensation_awarded_nominal,,,yes,yes +sg,2026_sghc_50,dd79b903af,primary,plaintiff_loosing_share,1,0,no,no +sg,2026_sghc_50,dd79b903af,primary,plaintiff_no1_ISIC1_industry_category,,g_wholesale_retail_trade,no,no +sg,2026_sghc_50,dd79b903af,primary,plaintiffs_all_count,1,1,yes,yes +sg,2026_sghc_50,dd79b903af,primary,trial_end_date,2026-03-06,2026-03-06,yes,yes +sg,2026_sghc_50,dd79b903af,primary,trial_start_date,2025-08-26,2026-02-09,no,no 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+tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,"Assumption_of_Litigation_after_Change_of_Legal_Representative_in_Damages_Case +trial_start_date: None",Damages,no,no +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,,,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-02-12,2026-02-12,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,defendants_all_count,,6,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,legal_subject_judgement,No record,Demolition_of_Building_and_Return_of_Land,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,,0,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,plaintiffs_all_count,,1,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,trial_end_date,,2026-04-08,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,cf5a561621,trial_start_date,,2025-06-03,no,no +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,no_allocation_possible,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,defendants_all_count,2,1,no,no +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,dispute_value_nominal,,nonpecuniary,no,no +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,legal_subject_judgement,Confirmation_of_Invalidity_of_Owners_Meeting_Resolution,Confirmation_of_Invalidity_of_Owners_Meeting_Resolution,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,0,0,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,l_real_estate_activities,,no,no +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,2,no,no +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-05,2026-03-05,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,cf5a561621,trial_start_date,2025-08-27,2025-08-27,yes,yes +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,m_professional_scientific_technical,n_professional_scientific_technical,no,no +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,4,5,no,no +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Reexamination_of_Damages_Compensation,Damages,no,no +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-02-24,2026-02-24,yes,yes +tw,最高法院_115_年度_台上_字第_280_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,2025-10-01,2025-10-01,yes,yes +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,l_real_estate_activities,m_real_estate,no,no +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,defendants_all_count,3,4,no,no +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,dispute_value_nominal,3924921,3924921,yes,yes +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,legal_subject_judgement,Damages_for_Tortious_Acts_in_Investment_Solicitation_Dispute,Tort_Damages,no,no +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,0,0,yes,yes +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,trial_end_date,2026-04-15,2026-04-15,yes,yes +tw,最高法院_115_年度_台上_字第_311_號民事判決,ab2d3cfed5,cf5a561621,trial_start_date,2025-10-22,2025-10-22,yes,yes +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,7,4,no,no +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Cancellation_and_Determination_of_Surface_Rights,Cancellation_of_Superficies,no,no +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,4,7,no,no +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-19,2026-03-19,yes,yes +tw,最高法院_115_年度_台上_字第_377_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,2025-10-21,2025-10-21,yes,yes +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,f_construction,f_construction,yes,yes +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,11600000,,no,no +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Transfer_of_Real_Estate_Ownership_Registration,Transfer_Registration_of_Real_Estate_Ownership,no,no +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,m_real_estate,,no,no +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-26,2026-03-26,yes,yes +tw,最高法院_115_年度_台上_字第_501_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,2025-12-24,2025-12-24,yes,yes +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,3,no,no +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Division_of_Common_Property,Partition_of_Joint_Property,no,no +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-26,2026-03-26,yes,yes +tw,最高法院_115_年度_台上_字第_505_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,2026-01-07,2026-01-07,yes,yes +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,21885,no,no +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,c_manufacturing,,no,no +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,1510000,1510000,yes,yes +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Payment_of_Share_Redemption_and_Others,Payment_of_Share_Redemption,no,no +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-26,2026-03-26,yes,yes +tw,最高法院_115_年度_台上_字第_506_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,2025-11-12,2025-11-12,yes,yes +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,203580,no,no +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,15586274,15586274,yes,yes +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Damages_for_Erroneous_Land_Registration,Damages,no,no +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,q_human_health_social_work,t_other_service_activities,no,no +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2025-12-23,2026-04-08,no,no +tw,最高法院_115_年度_台上_字第_576_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,2025-12-23,2025-12-23,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,30255,no,no +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,f_construction,f_construction,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,2120590,2120590,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Payment_of_Construction_Fees,Payment_of_Construction_Fees,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,f_construction,f_construction,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-04-08,2026-04-08,yes,yes +tw,最高法院_115_年度_台上_字第_579_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,2026-01-13,2026-01-13,yes,yes +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,defendants_all_count,1,6,no,no +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,legal_subject_judgement,Division_of_Joint_Property,Partition_of_Joint_Property,no,no +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,0,0,yes,yes +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,plaintiffs_all_count,6,1,no,no +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-10,2026-03-10,yes,yes +tw,最高法院_115_年度_台上_字第_81_號民事判決,ab2d3cfed5,cf5a561621,trial_start_date,2025-09-16,2025-09-16,yes,yes +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,1000,no,no +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Debtors_Objection_Lawsuit,Debtor_Objection_Action,no,no +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,0,0,yes,yes +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,3,3,yes,yes +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-02-11,2026-02-11,yes,yes +tw,最高法院_115_年度_台簡抗_字第_21_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,2025-10-28,2025-10-28,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Approval_of_Third_Instance_Attorney_Fees_in_Land_Ownership_Confirmation_Dispute,Confirmation_of_Land_Ownership_Existence,no,no +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,40000,40000,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,,,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-02-11,2026-02-11,yes,yes +tw,最高法院_115_年度_台聲_字第_156_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,,,yes,yes +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,1500,1500,yes,yes +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,6,6,yes,yes +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Reexamination_of_Ownership_Transfer_Registration_and_Other_Matters,Transfer_Registration_of_Ownership,no,no +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-26,2026-03-26,yes,yes +tw,最高法院_115_年度_台聲_字第_204_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,,2025-05-29,no,no +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,1500,1500,yes,yes +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,k_financial_insurance,l_financial_insurance,no,no +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Return_of_Money_and_Appointment_of_Litigation_Agent_for_Reexamination,Return_of_Money,no,no +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-12,2026-03-12,yes,yes +tw,最高法院_115_年度_台聲_字第_208_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,,2025-07-16,no,no +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Determination_of_Third_Instance_Attorney_Fees_in_Child_Recognition_Case,Recognition_of_Child,no,no +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,40000,40000,yes,yes +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,,,yes,yes +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-03-12,2026-03-12,yes,yes +tw,最高法院_115_年度_台聲_字第_225_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,,,yes,yes +tw,最高法院_115_年度_台聲_字第_231_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_231_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no 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+tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Approval_of_Third_Instance_Attorney_Fees_in_Price_Reduction_Dispute,Reduction_of_Price,no,no +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,40000,40000,yes,yes +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,,,yes,yes +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,trial_end_date,2026-04-08,2026-04-08,yes,yes +tw,最高法院_115_年度_台聲_字第_303_號民事裁定,ab2d3cfed5,cf5a561621,trial_start_date,,,yes,yes +tw,最高法院_115_年度_台聲_字第_318_號民事裁定,ab2d3cfed5,cf5a561621,court_cost_awarded_nominal,,1000,no,no +tw,最高法院_115_年度_台聲_字第_318_號民事裁定,ab2d3cfed5,cf5a561621,defendant_no1_ISIC1_industry_category,k_financial_insurance,l_financial_insurance,no,no +tw,最高法院_115_年度_台聲_字第_318_號民事裁定,ab2d3cfed5,cf5a561621,defendants_all_count,1,1,yes,yes +tw,最高法院_115_年度_台聲_字第_318_號民事裁定,ab2d3cfed5,cf5a561621,dispute_value_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_318_號民事裁定,ab2d3cfed5,cf5a561621,legal_subject_judgement,Suspension_of_Compulsory_Execution_in_Debt_Repayment_Case,Objection_to_Compulsory_Execution,no,no +tw,最高法院_115_年度_台聲_字第_318_號民事裁定,ab2d3cfed5,cf5a561621,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_115_年度_台聲_字第_318_號民事裁定,ab2d3cfed5,cf5a561621,plaintiff_loosing_share,1,1,yes,yes 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None",Claim_For_Damages/Declaration _Of_Succession_To_Litigation,no,no +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,primary,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,primary,plaintiff_loosing_share,,0,no,no +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,primary,plaintiff_no1_ISIC1_industry_category,p_public_admin_defence,p_public_admin_defence,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,primary,plaintiffs_all_count,1,1,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,primary,trial_end_date,2026-02-12,2026-02-12,yes,yes +tw,最高法院_113_年度_台上_字第_2210_號民事裁定,ab2d3cfed5,primary,trial_start_date,,,yes,yes +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,court_cost_awarded_nominal,,29979,no,no +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,defendant_no1_ISIC1_industry_category,no_allocation_possible,f_construction,no,no +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,defendants_all_count,1,1,yes,yes +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,dispute_value_nominal,1575859,1575859,yes,yes +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,legal_subject_judgement,Damages_for_Tortious_Acts_in_Occupational_Injury_Case,Claim_For_Damages_Tort,no,no +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,plaintiff_loosing_share,0,,no,yes +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,plaintiff_no1_ISIC1_industry_category,e_water_supply_sewerage_waste_management_remediation,g_wholesale_retail_trade,no,no +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,plaintiffs_all_count,2,2,yes,yes +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,trial_end_date,2026-03-31,2026-03-31,yes,yes +tw,最高法院_114_年度_台上_字第_1281_號民事判決,ab2d3cfed5,primary,trial_start_date,2025-05-07,2025-05-07,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,defendant_no1_ISIC1_industry_category,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,defendants_all_count,,6,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,dispute_value_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,legal_subject_judgement,No record,Action_Seeking_Removal_Of_Building_And_Return_Of_The_Land_Property_Rights,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,plaintiff_loosing_share,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,plaintiff_no1_ISIC1_industry_category,,,yes,yes +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,plaintiffs_all_count,,1,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,trial_end_date,,2026-04-08,no,no +tw,最高法院_114_年度_台上_字第_1964_號民事判決,ab2d3cfed5,primary,trial_start_date,,2025-06-03,no,no +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,primary,court_cost_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,primary,defendant_no1_ISIC1_industry_category,no_allocation_possible,,no,no +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,primary,defendants_all_count,2,2,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,primary,dispute_value_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,primary,legal_subject_judgement,Confirmation_of_Invalidity_of_Owners_Meeting_Resolution,Declaratory_Action_Concerning_Invalidity_Of_A_Condominium_Owners’_Association_Resolution,no,no +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,primary,party_compensation_awarded_nominal,,,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,primary,plaintiff_loosing_share,0,0,yes,yes +tw,最高法院_114_年度_台上_字第_2124_號民事判決,ab2d3cfed5,primary,plaintiff_no1_ISIC1_industry_category,l_real_estate_activities,no_allocation_possible,no,no 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structured fields (recall on expert-filled cells and false-fill rate on expert-empty cells, each $\pm$1\,SE). $n$ lists the goldenset-filled/goldenset-empty denominators; where a system's coverage differs its denominators follow in parentheses (G = Gemini, H = Harvey, L = Legora); \mbox{---} marks jurisdictions a system did not cover.} +\label{tab:metrics-by-jurisdiction} +\centering\small +\resizebox{\textwidth}{!}{% +\begin{tabular}{@{}lrr@{\hskip 8pt}rr@{\hskip 8pt}rr@{\hskip 8pt}rr@{\hskip 8pt}r@{}} +\toprule +& \multicolumn{2}{c}{\textbf{Gemini}} & \multicolumn{2}{c}{\textbf{GPT-5.4-mini}} & \multicolumn{2}{c}{\textbf{Harvey}} & \multicolumn{2}{c}{\textbf{Legora}} & \\ +\cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-7}\cmidrule(lr){8-9} +\textbf{Jurisdiction} & \textbf{Recall} & \textbf{False-fill} & \textbf{Recall} & \textbf{False-fill} & \textbf{Recall} & \textbf{False-fill} & \textbf{Recall} & \textbf{False-fill} & \textbf{$n$ (filled/empty)} \\ +\midrule +Armenia & 55.1\% $\pm$ 2.3\% & 10.6\% $\pm$ 2.8\% & 58.9\% $\pm$ 2.3\% & 15.4\% $\pm$ 3.3\% & 9.4\% $\pm$ 1.4\% & 0.0\% & --- & --- & 457/123 (H: 448/122) \\ +Australia & 77.3\% $\pm$ 3.3\% & 34.3\% $\pm$ 4.1\% & 74.8\% $\pm$ 3.4\% & 50.4\% $\pm$ 4.3\% & 79.8\% $\pm$ 3.1\% & 21.2\% $\pm$ 3.5\% & 69.9\% $\pm$ 3.6\% & 5.1\% $\pm$ 1.9\% & 163/137 \\ +Belgium & 78.0\% $\pm$ 2.2\% & 9.7\% $\pm$ 2.1\% & 73.2\% $\pm$ 2.3\% & 27.2\% $\pm$ 3.2\% & 71.3\% $\pm$ 2.4\% & 8.2\% $\pm$ 2.0\% & 65.9\% $\pm$ 2.5\% & 2.6\% $\pm$ 1.1\% & 355/195 \\ +Brazil & 64.8\% $\pm$ 2.0\% & 28.0\% $\pm$ 1.7\% & 57.0\% $\pm$ 2.1\% & 36.0\% $\pm$ 1.8\% & 49.7\% $\pm$ 2.1\% & 22.0\% $\pm$ 1.5\% & 48.7\% $\pm$ 2.2\% & 11.7\% $\pm$ 1.2\% & 563/737 (L: 538/702) \\ +France & 69.1\% $\pm$ 3.3\% & 36.8\% $\pm$ 4.7\% & 78.4\% $\pm$ 3.0\% & 61.3\% $\pm$ 4.7\% & 67.5\% $\pm$ 3.4\% & 29.2\% $\pm$ 4.4\% & 64.9\% $\pm$ 3.4\% & 25.5\% $\pm$ 4.2\% & 194/106 \\ +Georgia & 55.1\% $\pm$ 1.7\% & 25.9\% $\pm$ 2.9\% & 55.2\% $\pm$ 1.7\% & 49.6\% $\pm$ 3.3\% & 40.1\% $\pm$ 1.6\% & 16.1\% $\pm$ 2.5\% & --- & --- & 896/224 \\ +Germany & 56.7\% $\pm$ 2.0\% & 29.0\% $\pm$ 1.8\% & 53.9\% $\pm$ 2.0\% & 33.0\% $\pm$ 1.8\% & 53.7\% $\pm$ 2.0\% & 16.1\% $\pm$ 1.5\% & 22.7\% $\pm$ 1.7\% & 8.4\% $\pm$ 1.2\% & 642/658 (H: 631/639) (L: 599/581) \\ +Hong Kong & 74.2\% $\pm$ 5.6\% & 31.6\% $\pm$ 7.5\% & 71.0\% $\pm$ 5.8\% & 28.9\% $\pm$ 7.4\% & 61.3\% $\pm$ 6.2\% & 10.5\% $\pm$ 5.0\% & 62.9\% $\pm$ 6.1\% & 2.6\% $\pm$ 2.6\% & 62/38 \\ +India & 73.9\% $\pm$ 3.8\% & 37.5\% $\pm$ 4.9\% & 81.3\% $\pm$ 3.4\% & 59.4\% $\pm$ 5.0\% & 78.7\% $\pm$ 3.4\% & 20.2\% $\pm$ 4.0\% & 78.0\% $\pm$ 3.5\% & 10.1\% $\pm$ 3.0\% & 134/96 (H: 141/99) (L: 141/99) \\ +Nepal & 56.8\% $\pm$ 1.6\% & 64.1\% $\pm$ 2.5\% & 59.0\% $\pm$ 1.6\% & 78.0\% $\pm$ 2.2\% & 38.8\% $\pm$ 2.0\% & 26.5\% $\pm$ 3.1\% & 34.2\% $\pm$ 3.3\% & 17.9\% $\pm$ 4.3\% & 932/368 (H: 616/204) (L: 202/78) \\ +New Zealand & 91.1\% $\pm$ 2.3\% & 32.6\% $\pm$ 4.1\% & 86.1\% $\pm$ 2.8\% & 51.5\% $\pm$ 4.3\% & 89.2\% $\pm$ 2.5\% & 14.4\% $\pm$ 3.1\% & 86.1\% $\pm$ 2.8\% & 6.1\% $\pm$ 2.1\% & 158/132 \\ +Philippines & 41.8\% $\pm$ 6.7\% & 42.2\% $\pm$ 7.4\% & 56.4\% $\pm$ 6.7\% & 48.9\% $\pm$ 7.5\% & 72.2\% $\pm$ 6.1\% & 27.8\% $\pm$ 7.5\% & 78.2\% $\pm$ 5.6\% & 24.4\% $\pm$ 6.4\% & 55/45 (H: 54/36) \\ +Serbia & 85.6\% $\pm$ 2.8\% & 40.0\% $\pm$ 4.9\% & 85.0\% $\pm$ 2.8\% & 36.0\% $\pm$ 4.8\% & 85.1\% $\pm$ 2.9\% & 10.4\% $\pm$ 3.1\% & 78.1\% $\pm$ 3.3\% & 12.0\% $\pm$ 3.2\% & 160/100 (H: 154/96) \\ +Singapore & 58.8\% $\pm$ 1.7\% & 24.2\% $\pm$ 2.2\% & 58.7\% $\pm$ 1.7\% & 30.1\% $\pm$ 2.4\% & 57.9\% $\pm$ 1.7\% & 12.0\% $\pm$ 1.7\% & 47.5\% $\pm$ 1.7\% & 5.1\% $\pm$ 1.1\% & 855/375 (G: 855/385) \\ +Spain & 75.7\% $\pm$ 1.3\% & 29.0\% $\pm$ 3.0\% & 77.7\% $\pm$ 1.3\% & 44.2\% $\pm$ 3.3\% & 53.1\% $\pm$ 4.4\% & 22.7\% $\pm$ 8.9\% & 58.6\% $\pm$ 1.5\% & 10.8\% $\pm$ 2.0\% & 1069/231 (H: 128/22) \\ +Switzerland & 88.5\% $\pm$ 1.0\% & 42.8\% $\pm$ 2.8\% & 92.0\% $\pm$ 0.9\% & 79.4\% $\pm$ 2.3\% & 87.3\% $\pm$ 1.1\% & 16.1\% $\pm$ 2.1\% & 89.5\% $\pm$ 1.0\% & 7.7\% $\pm$ 1.5\% & 989/311 \\ +Taiwan & 69.3\% $\pm$ 1.6\% & 29.5\% $\pm$ 2.1\% & 74.0\% $\pm$ 1.5\% & 43.6\% $\pm$ 2.2\% & 63.5\% $\pm$ 1.7\% & 9.8\% $\pm$ 1.3\% & 67.2\% $\pm$ 1.6\% & 4.7\% $\pm$ 1.0\% & 812/488 \\ +United Kingdom & 48.9\% $\pm$ 1.7\% & 25.2\% $\pm$ 2.0\% & 52.8\% $\pm$ 1.7\% & 28.7\% $\pm$ 2.1\% & 64.6\% $\pm$ 1.6\% & 22.0\% $\pm$ 2.0\% & 62.6\% $\pm$ 1.7\% & 9.4\% $\pm$ 1.4\% & 851/449 \\ +United States & 66.7\% $\pm$ 1.5\% & 16.2\% $\pm$ 1.9\% & 64.0\% $\pm$ 1.6\% & 22.4\% $\pm$ 2.2\% & 60.6\% $\pm$ 1.6\% & 8.9\% $\pm$ 1.5\% & 55.7\% $\pm$ 1.6\% & 7.0\% $\pm$ 1.3\% & 929/371 \\ +\bottomrule +\end{tabular}% +} +\end{table*} + +\begin{table*}[htbp] +\caption{Extraction metrics by field over the 19 release jurisdictions (recall and false-fill rate, each $\pm$1\,SE). $n$ as in \cref{tab:metrics-by-jurisdiction}.} +\label{tab:metrics-by-field} +\centering\small +\resizebox{\textwidth}{!}{% +\begin{tabular}{@{}lrr@{\hskip 8pt}rr@{\hskip 8pt}rr@{\hskip 8pt}rr@{\hskip 8pt}r@{}} +\toprule +& \multicolumn{2}{c}{\textbf{Gemini}} & \multicolumn{2}{c}{\textbf{GPT-5.4-mini}} & \multicolumn{2}{c}{\textbf{Harvey}} & \multicolumn{2}{c}{\textbf{Legora}} & \\ +\cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-7}\cmidrule(lr){8-9} +\textbf{Variable} & \textbf{Recall} & \textbf{False-fill} & \textbf{Recall} & \textbf{False-fill} & \textbf{Recall} & \textbf{False-fill} & \textbf{Recall} & \textbf{False-fill} & \textbf{$n$ (filled/empty)} \\ +\midrule +\texttt{court\_cost\_awarded\_nominal} & 63.9\% $\pm$ 2.1\% & 4.1\% $\pm$ 0.6\% & 65.5\% $\pm$ 2.1\% & 8.3\% $\pm$ 0.9\% & 57.8\% $\pm$ 2.4\% & 1.9\% $\pm$ 0.4\% & 70.1\% $\pm$ 2.6\% & 0.9\% $\pm$ 0.3\% & 501/1045 (G: 501/1046) (H: 427/951) (L: 308/949) \\ +\texttt{defendant\_no1\_ISIC1\_industry\_category} & 58.0\% $\pm$ 1.5\% & 65.9\% $\pm$ 2.2\% & 64.7\% $\pm$ 1.5\% & 74.7\% $\pm$ 2.0\% & 49.3\% $\pm$ 1.6\% & 37.3\% $\pm$ 2.3\% & 55.5\% $\pm$ 1.7\% & 22.5\% $\pm$ 2.1\% & 1075/471 (G: 1075/472) (H: 938/440) (L: 874/383) \\ +\texttt{defendants\_all\_count} & 76.9\% $\pm$ 1.1\% & 90.5\% $\pm$ 4.5\% & 73.8\% $\pm$ 1.1\% & 56.1\% $\pm$ 7.8\% & 71.6\% $\pm$ 1.2\% & 48.6\% $\pm$ 8.4\% & 66.6\% $\pm$ 1.3\% & 37.1\% $\pm$ 8.2\% & 1505/41 (G: 1505/42) (H: 1343/35) (L: 1222/35) \\ +\texttt{dispute\_value\_nominal} & 56.4\% $\pm$ 1.9\% & 44.0\% $\pm$ 1.7\% & 57.1\% $\pm$ 1.9\% & 53.8\% $\pm$ 1.7\% & 47.0\% $\pm$ 2.1\% & 32.9\% $\pm$ 1.7\% & 34.7\% $\pm$ 2.1\% & 12.3\% $\pm$ 1.2\% & 708/838 (G: 708/839) (H: 591/787) (L: 539/718) \\ +\texttt{party\_compensation\_awarded\_nominal} & 71.4\% $\pm$ 2.4\% & 6.6\% $\pm$ 0.7\% & 67.3\% $\pm$ 2.5\% & 13.6\% $\pm$ 1.0\% & 66.9\% $\pm$ 2.8\% & 4.7\% $\pm$ 0.6\% & 79.1\% $\pm$ 2.7\% & 4.2\% $\pm$ 0.6\% & 364/1182 (G: 364/1183) (H: 284/1094) (L: 235/1022) \\ +\texttt{plaintiff\_loosing\_share} & 71.3\% $\pm$ 1.2\% & 36.6\% $\pm$ 3.2\% & 69.9\% $\pm$ 1.3\% & 55.2\% $\pm$ 3.3\% & 71.4\% $\pm$ 1.3\% & 33.3\% $\pm$ 3.4\% & 69.3\% $\pm$ 1.4\% & 11.4\% $\pm$ 2.5\% & 1323/223 (G: 1323/224) (H: 1189/189) (L: 1090/167) \\ +\texttt{plaintiff\_no1\_ISIC1\_industry\_category} & 54.1\% $\pm$ 1.7\% & 79.5\% $\pm$ 1.5\% & 53.9\% $\pm$ 1.7\% & 80.5\% $\pm$ 1.5\% & 41.8\% $\pm$ 1.8\% & 25.4\% $\pm$ 1.7\% & 36.8\% $\pm$ 1.8\% & 16.9\% $\pm$ 1.6\% & 865/681 (G: 865/682) (H: 752/626) (L: 682/575) \\ +\texttt{plaintiffs\_all\_count} & 88.8\% $\pm$ 0.8\% & 100.0\% & 85.4\% $\pm$ 0.9\% & 71.4\% $\pm$ 12.1\% & 74.9\% $\pm$ 1.2\% & 83.3\% $\pm$ 10.8\% & 70.5\% $\pm$ 1.3\% & 28.6\% $\pm$ 12.1\% & 1532/14 (G: 1532/15) (H: 1366/12) (L: 1243/14) \\ +\texttt{trial\_end\_date} & 67.6\% $\pm$ 1.2\% & 50.0\% $\pm$ 6.5\% & 66.9\% $\pm$ 1.2\% & 44.1\% $\pm$ 6.5\% & 65.0\% $\pm$ 1.3\% & 12.3\% $\pm$ 4.3\% & 77.8\% $\pm$ 1.2\% & 13.5\% $\pm$ 4.7\% & 1487/59 (G: 1487/60) (H: 1321/57) (L: 1205/52) \\ +\texttt{trial\_start\_date} & 26.2\% $\pm$ 1.5\% & 9.7\% $\pm$ 1.2\% & 41.6\% $\pm$ 1.6\% & 61.3\% $\pm$ 1.9\% & 13.3\% $\pm$ 1.2\% & 4.4\% $\pm$ 0.8\% & 27.2\% $\pm$ 1.6\% & 3.6\% $\pm$ 0.8\% & 916/630 (G: 916/631) (H: 788/590) (L: 734/523) \\ +\bottomrule +\end{tabular}% +} +\end{table*} diff --git a/data/analysis/per_column.csv b/data/analysis/per_column.csv new file mode 100644 index 0000000000000000000000000000000000000000..f3431196ec5453ed6402b4e3c6a0548574e98a60 --- /dev/null +++ b/data/analysis/per_column.csv @@ -0,0 +1,67 @@ +model,column,tp,mismatch,missed,hallucinated,tn,accuracy,recall_when_filled,precision_when_emitted,hallucination_rate,miss_rate,wrong_when_both_filled,f1 +gemini/gemini-3.1-flash-lite,court_cost_awarded_nominal,320,23,158,43,1003,0.8552,0.6387,0.8290,0.0411,0.3154,0.0671,0.7215 +gemini/gemini-3.1-flash-lite,defendant_no1_ISIC1_industry_category,623,298,154,311,161,0.5068,0.5795,0.5057,0.6589,0.1433,0.3236,0.5401 +gemini/gemini-3.1-flash-lite,defendants_all_count,1158,315,32,38,4,0.7511,0.7694,0.7664,0.9048,0.0213,0.2138,0.7679 +gemini/gemini-3.1-flash-lite,dispute_value_nominal,399,107,202,369,470,0.5617,0.5636,0.4560,0.4398,0.2853,0.2115,0.5041 +gemini/gemini-3.1-flash-lite,legal_subject_judgement,55,1482,4,5,1,0.0362,0.0357,0.0357,0.8333,0.0026,0.9642,0.0357 +gemini/gemini-3.1-flash-lite,party_compensation_awarded_nominal,260,13,91,78,1105,0.8824,0.7143,0.7407,0.0659,0.2500,0.0476,0.7273 +gemini/gemini-3.1-flash-lite,plaintiff_loosing_share,943,247,133,82,142,0.7014,0.7128,0.7414,0.3661,0.1005,0.2076,0.7268 +gemini/gemini-3.1-flash-lite,plaintiff_no1_ISIC1_industry_category,468,309,88,542,140,0.3930,0.5410,0.3548,0.7947,0.1017,0.3977,0.4286 +gemini/gemini-3.1-flash-lite,plaintiffs_all_count,1360,151,21,15,0,0.8791,0.8877,0.8912,1.0000,0.0137,0.0999,0.8895 +gemini/gemini-3.1-flash-lite,trial_end_date,1005,436,46,30,30,0.6690,0.6759,0.6832,0.5000,0.0309,0.3026,0.6795 +gemini/gemini-3.1-flash-lite,trial_start_date,240,107,569,61,570,0.5236,0.2620,0.5882,0.0967,0.6212,0.3084,0.3625 +gpt-5.4-mini,court_cost_awarded_nominal,328,35,138,87,958,0.8318,0.6547,0.7289,0.0833,0.2754,0.0964,0.6898 +gpt-5.4-mini,defendant_no1_ISIC1_industry_category,696,287,92,352,119,0.5272,0.6474,0.5213,0.7473,0.0856,0.2920,0.5776 +gpt-5.4-mini,defendants_all_count,1111,249,145,23,18,0.7303,0.7382,0.8033,0.5610,0.0963,0.1831,0.7694 +gpt-5.4-mini,dispute_value_nominal,404,100,204,451,387,0.5116,0.5706,0.4230,0.5382,0.2881,0.1984,0.4859 +gpt-5.4-mini,legal_subject_judgement,75,1458,7,6,0,0.0485,0.0487,0.0487,1.0000,0.0045,0.9511,0.0487 +gpt-5.4-mini,party_compensation_awarded_nominal,245,32,87,161,1021,0.8189,0.6731,0.5594,0.1362,0.2390,0.1155,0.6110 +gpt-5.4-mini,plaintiff_loosing_share,925,301,97,123,100,0.6630,0.6992,0.6857,0.5516,0.0733,0.2455,0.6924 +gpt-5.4-mini,plaintiff_no1_ISIC1_industry_category,466,308,91,548,133,0.3875,0.5387,0.3525,0.8047,0.1052,0.3979,0.4262 +gpt-5.4-mini,plaintiffs_all_count,1308,139,85,10,4,0.8486,0.8538,0.8977,0.7143,0.0555,0.0961,0.8752 +gpt-5.4-mini,trial_end_date,995,378,114,26,33,0.6649,0.6691,0.7112,0.4407,0.0767,0.2753,0.6895 +gpt-5.4-mini,trial_start_date,381,410,125,386,244,0.4043,0.4159,0.3237,0.6127,0.1365,0.5183,0.3641 +harvey,court_cost_awarded_nominal,247,25,155,18,933,0.8563,0.5785,0.8517,0.0189,0.3630,0.0919,0.6890 +harvey,defendant_no1_ISIC1_industry_category,462,202,274,164,276,0.5356,0.4925,0.5580,0.3727,0.2921,0.3042,0.5232 +harvey,defendants_all_count,961,153,229,17,18,0.7104,0.7156,0.8497,0.4857,0.1705,0.1373,0.7769 +harvey,dispute_value_nominal,278,74,239,259,528,0.5849,0.4704,0.4550,0.3291,0.4044,0.2102,0.4626 +harvey,legal_subject_judgement,53,1052,267,5,1,0.0392,0.0386,0.0477,0.8333,0.1946,0.9520,0.0427 +harvey,party_compensation_awarded_nominal,190,5,89,51,1043,0.8948,0.6690,0.7724,0.0466,0.3134,0.0256,0.7170 +harvey,plaintiff_loosing_share,849,195,145,63,126,0.7075,0.7140,0.7669,0.3333,0.1220,0.1868,0.7395 +harvey,plaintiff_no1_ISIC1_industry_category,314,165,273,159,467,0.5668,0.4176,0.4922,0.2540,0.3630,0.3445,0.4518 +harvey,plaintiffs_all_count,1023,121,222,10,2,0.7438,0.7489,0.8865,0.8333,0.1625,0.1058,0.8119 +harvey,trial_end_date,859,70,392,7,50,0.6597,0.6503,0.9177,0.1228,0.2967,0.0753,0.7612 +harvey,trial_start_date,105,53,630,26,564,0.4855,0.1332,0.5707,0.0441,0.7995,0.3354,0.2160 +harvey-2,court_cost_awarded_nominal,247,40,146,8,952,0.8607,0.5704,0.8373,0.0083,0.3372,0.1394,0.6786 +harvey-2,defendant_no1_ISIC1_industry_category,320,185,446,121,321,0.4602,0.3365,0.5112,0.2738,0.4690,0.3663,0.4058 +harvey-2,defendants_all_count,974,154,228,23,14,0.7093,0.7183,0.8462,0.6216,0.1681,0.1365,0.7770 +harvey-2,dispute_value_nominal,201,69,338,160,625,0.5930,0.3306,0.4674,0.2038,0.5559,0.2556,0.3873 +harvey-2,legal_subject_judgement,10,778,599,0,6,0.0115,0.0072,0.0127,0.0000,0.4319,0.9873,0.0092 +harvey-2,party_compensation_awarded_nominal,183,4,103,54,1049,0.8844,0.6310,0.7593,0.0490,0.3552,0.0214,0.6893 +harvey-2,plaintiff_loosing_share,831,112,249,32,169,0.7179,0.6971,0.8523,0.1592,0.2089,0.1188,0.7670 +harvey-2,plaintiff_no1_ISIC1_industry_category,236,134,384,120,519,0.5420,0.3130,0.4816,0.1878,0.5093,0.3622,0.3794 +harvey-2,plaintiffs_all_count,1072,116,193,8,4,0.7724,0.7762,0.8963,0.6667,0.1398,0.0976,0.8320 +harvey-2,trial_end_date,868,134,334,8,49,0.6583,0.6497,0.8594,0.1404,0.2500,0.1337,0.7400 +harvey-2,trial_start_date,118,37,627,20,591,0.5090,0.1509,0.6743,0.0327,0.8018,0.2387,0.2466 +legora-1,court_cost_awarded_nominal,216,3,89,9,940,0.9196,0.7013,0.9474,0.0095,0.2890,0.0137,0.8060 +legora-1,defendant_no1_ISIC1_industry_category,485,116,273,86,297,0.6221,0.5549,0.7060,0.2245,0.3124,0.1930,0.6214 +legora-1,defendants_all_count,814,123,285,13,22,0.6651,0.6661,0.8568,0.3714,0.2332,0.1313,0.7495 +legora-1,dispute_value_nominal,187,22,330,88,630,0.6500,0.3469,0.6296,0.1226,0.6122,0.1053,0.4474 +legora-1,legal_subject_judgement,23,735,493,1,5,0.0223,0.0184,0.0303,0.1667,0.3941,0.9697,0.0229 +legora-1,party_compensation_awarded_nominal,186,3,46,43,979,0.9268,0.7915,0.8017,0.0421,0.1957,0.0159,0.7966 +legora-1,plaintiff_loosing_share,755,61,274,19,148,0.7184,0.6927,0.9042,0.1138,0.2514,0.0748,0.7844 +legora-1,plaintiff_no1_ISIC1_industry_category,251,117,314,97,478,0.5800,0.3680,0.5398,0.1687,0.4604,0.3179,0.4377 +legora-1,plaintiffs_all_count,876,47,320,4,10,0.7049,0.7047,0.9450,0.2857,0.2574,0.0509,0.8074 +legora-1,trial_end_date,937,123,145,7,45,0.7812,0.7776,0.8782,0.1346,0.1203,0.1160,0.8248 +legora-1,trial_start_date,200,35,499,19,504,0.5601,0.2725,0.7874,0.0363,0.6798,0.1489,0.4049 +legora-2,court_cost_awarded_nominal,221,5,88,7,950,0.9213,0.7038,0.9485,0.0073,0.2803,0.0221,0.8080 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+legora-2,trial_start_date,201,29,510,19,512,0.5610,0.2716,0.8072,0.0358,0.6892,0.1261,0.4065 diff --git a/data/analysis/per_country.csv b/data/analysis/per_country.csv new file mode 100644 index 0000000000000000000000000000000000000000..9665e07ae95574c60d490f6e92c597693ad69fb2 --- /dev/null +++ b/data/analysis/per_country.csv @@ -0,0 +1,115 @@ +country,model,legal_tradition,language_family,tp,mismatch,missed,hallucinated,tn,accuracy,recall_when_filled,precision_when_emitted,hallucination_rate,miss_rate,wrong_when_both_filled,f1,cost_tp,cost_mismatch,cost_missed,cost_hallucinated,cost_tn,cost_accuracy,cost_recall_when_filled,cost_precision_when_emitted,cost_hallucination_rate,cost_miss_rate,cost_wrong_when_both_filled,cost_f1 +am,gemini/gemini-3.1-flash-lite,civil,non-latin,259,77,179,13,110,0.5784,0.5029,0.7421,0.1057,0.3476,0.2292,0.5995,41,2,104,0,85,0.5431,0.2789,0.9535,0.0000,0.7075,0.0465,0.4316 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0000000000000000000000000000000000000000..9075f7b511e390c1f5b5ad7ce11ab31e06ae6ce0 --- /dev/null +++ b/data/analysis/per_country_per_column.csv @@ -0,0 +1,1255 @@ +country,model,column,tp,mismatch,missed,hallucinated,tn,accuracy,recall_when_filled,precision_when_emitted,hallucination_rate,miss_rate,wrong_when_both_filled,f1 +am,gemini/gemini-3.1-flash-lite,legal_subject_judgement,7,50,1,0,0,0.1207,0.1207,0.1228,0.0000,0.0172,0.8772,0.1217 +am,gemini/gemini-3.1-flash-lite,trial_start_date,0,0,41,0,17,0.2931,0.0000,0.0000,0.0000,1.0000,0.0000,0.0000 +am,gemini/gemini-3.1-flash-lite,trial_end_date,56,1,0,1,0,0.9655,0.9825,0.9655,1.0000,0.0000,0.0175,0.9739 +am,gemini/gemini-3.1-flash-lite,dispute_value_nominal,1,2,55,0,0,0.0172,0.0172,0.3333,0.0000,0.9483,0.6667,0.0328 +am,gemini/gemini-3.1-flash-lite,plaintiff_loosing_share,35,0,23,0,0,0.6034,0.6034,1.0000,0.0000,0.3966,0.0000,0.7527 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--- /dev/null +++ b/data/analysis/quality/by_country.csv @@ -0,0 +1,287 @@ +country,variable,n,empty,valid,invalid,pct_empty,pct_valid,pct_invalid +al,court_cost_awarded_nominal,0,0,0,0,0.0000,0.0000,0.0000 +al,defendant_no1_ISIC1_industry_category,0,0,0,0,0.0000,0.0000,0.0000 +al,defendants_all_count,0,0,0,0,0.0000,0.0000,0.0000 +al,dispute_value_nominal,0,0,0,0,0.0000,0.0000,0.0000 +al,legal_subject_judgement,0,0,0,0,0.0000,0.0000,0.0000 +al,party_compensation_awarded_nominal,0,0,0,0,0.0000,0.0000,0.0000 +al,plaintiff_loosing_share,0,0,0,0,0.0000,0.0000,0.0000 +al,plaintiff_no1_ISIC1_industry_category,0,0,0,0,0.0000,0.0000,0.0000 +al,plaintiffs_all_count,0,0,0,0,0.0000,0.0000,0.0000 +al,trial_end_date,0,0,0,0,0.0000,0.0000,0.0000 +al,trial_start_date,0,0,0,0,0.0000,0.0000,0.0000 +am,court_cost_awarded_nominal,58,32,26,0,0.5517,0.4483,0.0000 +am,defendant_no1_ISIC1_industry_category,58,13,45,0,0.2241,0.7759,0.0000 +am,defendants_all_count,58,0,58,0,0.0000,1.0000,0.0000 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+xk,trial_end_date,0,0,0,0,0.0000,0.0000,0.0000 +xk,trial_start_date,0,0,0,0,0.0000,0.0000,0.0000 diff --git a/data/analysis/quality/by_variable.csv b/data/analysis/quality/by_variable.csv new file mode 100644 index 0000000000000000000000000000000000000000..550273419e2454278153daa8ba5951370a1541ad --- /dev/null +++ b/data/analysis/quality/by_variable.csv @@ -0,0 +1,12 @@ +variable,n,empty,valid,invalid,pct_empty,pct_valid,pct_invalid +court_cost_awarded_nominal,1548,1047,501,0,0.6764,0.3236,0.0000 +defendant_no1_ISIC1_industry_category,1548,472,1076,0,0.3049,0.6951,0.0000 +defendants_all_count,1548,42,1506,0,0.0271,0.9729,0.0000 +dispute_value_nominal,1548,840,708,0,0.5426,0.4574,0.0000 +legal_subject_judgement,1548,6,1542,0,0.0039,0.9961,0.0000 +party_compensation_awarded_nominal,1548,1184,364,0,0.7649,0.2351,0.0000 +plaintiff_loosing_share,1548,224,1324,0,0.1447,0.8553,0.0000 +plaintiff_no1_ISIC1_industry_category,1548,682,866,0,0.4406,0.5594,0.0000 +plaintiffs_all_count,1548,15,1533,0,0.0097,0.9903,0.0000 +trial_end_date,1548,59,1488,1,0.0381,0.9612,0.0006 +trial_start_date,1548,630,917,1,0.4070,0.5924,0.0006 diff --git a/data/analysis/quant_results.tex b/data/analysis/quant_results.tex new file mode 100644 index 0000000000000000000000000000000000000000..fc34d5a5834b7f03af215d1ffe033e2f726d9c58 --- /dev/null +++ b/data/analysis/quant_results.tex @@ -0,0 +1,21 @@ +% Auto-generated by legex-quant-results — do not edit by hand. +% Aggregated over the 19 release jurisdictions; free-text legal_subject_judgement excluded from scoring (reported separately). +\begin{table*}[t] +\centering\small +\begin{tabular}{@{}l rrrr@{\hskip 14pt} rrrr@{}} +\toprule + & \multicolumn{4}{c}{10 structured fields} & \multicolumn{4}{c}{Cost block (4 fields)} \\ +\cmidrule(lr){2-5}\cmidrule(l){6-9} +System & Recall & Precision & F1 & False fill & Recall & Precision & F1 & False fill \\ +\midrule +Gemini & 65.9\,$\pm$\,0.5\% & 65.5\,$\pm$\,0.5\% & 0.66 & 30.2\,$\pm$\,0.6\% & \textbf{66.4\,$\pm$\,0.9\%} & 66.6\,$\pm$\,0.9\% & 0.67 & 17.4\,$\pm$\,0.7\% \\ +GPT-5.4-mini & \textbf{66.7\,$\pm$\,0.5\%} & 60.9\,$\pm$\,0.5\% & 0.64 & 41.8\,$\pm$\,0.7\% & 65.7\,$\pm$\,0.9\% & 59.6\,$\pm$\,0.9\% & 0.62 & 25.0\,$\pm$\,0.8\% \\ +Harvey & 58.8\,$\pm$\,0.5\% & 74.2\,$\pm$\,0.5\% & 0.66 & 16.2\,$\pm$\,0.5\% & 62.8\,$\pm$\,1.0\% & 69.4\,$\pm$\,1.0\% & 0.66 & 12.9\,$\pm$\,0.6\% \\ +Legora & 60.3\,$\pm$\,0.5\% & \textbf{82.6\,$\pm$\,0.5\%} & \textbf{0.70} & \textbf{8.7\,$\pm$\,0.4\%} & 61.9\,$\pm$\,1.0\% & \textbf{84.4\,$\pm$\,0.9\%} & \textbf{0.71} & \textbf{5.6\,$\pm$\,0.4\%} \\ +\bottomrule +\end{tabular} +\vskip 0.05in +\caption{Headline extraction metrics over the 19 release jurisdictions (8 core and 11 preview), computed over each system's successfully processed cases (metric definitions in \cref{sec:systems}). The left block covers the ten structured fields, the right block the four cost-block variables; percentages carry $\pm$1\,SE. Denominators, in row order (Gemini/GPT-5.4-mini/Harvey/Legora), are $n_{\text{filled}}$\,=\,10276/10276/8999/8132 and $n_{\text{empty}}$\,=\,5194/5184/4781/4438 over the ten structured fields, and $n_{\text{filled}}$\,=\,2896/2896/2491/2172 and $n_{\text{empty}}$\,=\,3292/3288/3021/2856 over the cost block; Harvey and Legora have smaller denominators because of their ingest gaps. F1 standard errors are below 0.01 and omitted. +The best value per column is marked in \textbf{bold} (lower is better for false fill).} +\label{tab:overall} +\end{table*} diff --git a/data/analysis/tables/currency_frequencies.tex b/data/analysis/tables/currency_frequencies.tex new file mode 100644 index 0000000000000000000000000000000000000000..457e29d306fa40b5b8e1d9e2e04361e5fc30cee5 --- /dev/null +++ b/data/analysis/tables/currency_frequencies.tex @@ -0,0 +1,26 @@ +% Auto-generated by scripts/appendix_frequencies.py — do not edit by hand. +\begin{table}[t] +\caption{Currencies of the three monetary fields (\texttt{dispute\_value}, \texttt{court\_cost}, \texttt{party\_compensation}) over all filled cells of the release with a recorded currency (n\,=\,2,544). Labels as recorded by the annotators, with common synonyms mapped to ISO codes.} +\label{tab:currencies} +\vskip 0.05in +\centering\small +\begin{tabular}{@{}lrr@{}} +\toprule +\textbf{Currency} & \textbf{Cells} & \textbf{Share} \\ +\midrule +EUR & 798 & 31.4\% \\ +BRL & 390 & 15.3\% \\ +TWD & 389 & 15.3\% \\ +NPR & 302 & 11.9\% \\ +CHF & 215 & 8.5\% \\ +GEL & 151 & 5.9\% \\ +INR & 72 & 2.8\% \\ +SGD & 58 & 2.3\% \\ +AMD & 52 & 2.0\% \\ +USD & 24 & 0.9\% \\ +GBP & 24 & 0.9\% \\ +Usd Equivalent In Gel & 12 & 0.5\% \\ +Other (26 labels) & 57 & 2.2\% \\ +\bottomrule +\end{tabular} +\end{table} diff --git a/data/analysis/tables/diversity.tex b/data/analysis/tables/diversity.tex new file mode 100644 index 0000000000000000000000000000000000000000..a328accc7ae46ae4c8f9259bf0e1e0e83c0b1459 --- /dev/null +++ b/data/analysis/tables/diversity.tex @@ -0,0 +1,32 @@ +% Auto-generated by scripts/diversity_stats.py — do not edit by hand. +\begin{table}[t] +\caption{Sample diversity along the annotated dimensions. \emph{Sectors} counts the distinct ISIC top-level sectors observed among plaintiffs and defendants (of 22 possible, A--V); \emph{multi-party} is the share of judgments with more than one plaintiff or defendant; the last two columns give the share of judgments with a coded dispute value and the fill rate over the four cost-block fields.} +\label{tab:diversity} +\vskip 0.05in +\centering\small +\begin{tabular}{@{}lrrrrr@{}} +\toprule +\textbf{Jurisdiction} & \textbf{$n$} & \textbf{Sectors} & \textbf{Multi-party} & \textbf{Dispute value} & \textbf{Cost block} \\ +\midrule +Armenia & 58 & 13 & 21\% & 100\% & 63\% \\ +Australia & 30 & 9 & 70\% & 7\% & 26\% \\ +Belgium & 55 & 15 & 53\% & 31\% & 35\% \\ +Brazil & 130 & 11 & 14\% & 16\% & 26\% \\ +France & 25 & 7 & 52\% & 52\% & 47\% \\ +Georgia & 112 & 14 & 39\% & 56\% & 62\% \\ +Germany & 130 & 13 & 12\% & 73\% & 35\% \\ +Hong Kong & 10 & 5 & 10\% & 30\% & 30\% \\ +India & 24 & 9 & 50\% & 71\% & 48\% \\ +Nepal & 130 & 12 & 57\% & 38\% & 70\% \\ +New Zealand & 29 & 9 & 52\% & 0\% & 32\% \\ +Philippines & 10 & 4 & 40\% & 70\% & 40\% \\ +Serbia & 26 & 6 & 31\% & 77\% & 46\% \\ +Singapore & 124 & 17 & 33\% & 40\% & 37\% \\ +Spain & 129 & 11 & 54\% & 76\% & 62\% \\ +Switzerland & 130 & 13 & 29\% & 47\% & 77\% \\ +Taiwan & 130 & 13 & 25\% & 23\% & 42\% \\ +United Kingdom & 130 & 17 & 33\% & 15\% & 27\% \\ +United States & 130 & 17 & 58\% & 65\% & 40\% \\ +\bottomrule +\end{tabular} +\end{table} diff --git a/data/analysis/tables/headline.tex b/data/analysis/tables/headline.tex new file mode 100644 index 0000000000000000000000000000000000000000..a6e766e9a5c41b7956f4ff711921343d94bbb03b --- /dev/null +++ b/data/analysis/tables/headline.tex @@ -0,0 +1,191 @@ +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Headline extraction metrics by jurisdiction for model \texttt{gemini/gemini-3.1-flash-lite}. Accuracy is per-cell. Recall is over the cells where the expert recorded a value. Hallucination rate is the share of legitimately-empty cells where the model invented a value. Cost-block $F_1$ aggregates over the four monetary variables.} +\label{tab:headline-gemini-gemini-3-1-flash-lite} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Jurisdiction & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & Cost $F_1$ \\ +\midrule +AM & 57.8\% & 50.3\% & 10.6\% & 0.432 \\ +AU & 67.3\% & 68.4\% & 34.3\% & 0.587 \\ +BE & 75.0\% & 67.8\% & 9.7\% & 0.648 \\ +BR & 62.7\% & 52.7\% & 28.0\% & 0.676 \\ +CH & 74.5\% & 79.3\% & 42.8\% & 0.906 \\ +DE & 58.3\% & 47.5\% & 29.0\% & 0.687 \\ +ES & 68.0\% & 67.5\% & 29.3\% & 0.751 \\ +FR & 61.5\% & 61.6\% & 38.7\% & 0.483 \\ +GE & 53.6\% & 49.0\% & 25.9\% & 0.450 \\ +HK & 65.5\% & 63.9\% & 31.6\% & 0.357 \\ +IN & 63.2\% & 63.7\% & 37.5\% & 0.640 \\ +NP & 47.1\% & 50.9\% & 64.1\% & 0.692 \\ +NZ & 73.0\% & 77.0\% & 32.6\% & 0.795 \\ +PH & 45.5\% & 36.9\% & 42.2\% & 0.421 \\ +RS & 69.2\% & 74.2\% & 40.0\% & 0.691 \\ +SG & 58.3\% & 51.4\% & 24.2\% & 0.510 \\ +TW & 63.7\% & 60.2\% & 29.5\% & 0.699 \\ +UK & 52.9\% & 42.9\% & 25.2\% & 0.525 \\ +US & 65.2\% & 58.6\% & 16.2\% & 0.627 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Headline extraction metrics by jurisdiction for model \texttt{gpt-5.4-mini}. Accuracy is per-cell. Recall is over the cells where the expert recorded a value. Hallucination rate is the share of legitimately-empty cells where the model invented a value. Cost-block $F_1$ aggregates over the four monetary variables.} +\label{tab:headline-gpt-5-4-mini} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Jurisdiction & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & Cost $F_1$ \\ +\midrule +AM & 59.7\% & 53.8\% & 15.4\% & 0.469 \\ +AU & 60.0\% & 67.4\% & 50.4\% & 0.506 \\ +BE & 66.6\% & 63.7\% & 27.2\% & 0.494 \\ +BR & 55.5\% & 46.3\% & 36.0\% & 0.581 \\ +CH & 68.7\% & 82.1\% & 79.4\% & 0.855 \\ +DE & 55.5\% & 45.6\% & 33.0\% & 0.704 \\ +ES & 67.1\% & 69.4\% & 44.4\% & 0.720 \\ +FR & 59.1\% & 70.3\% & 63.1\% & 0.525 \\ +GE & 49.4\% & 49.1\% & 49.6\% & 0.348 \\ +HK & 64.5\% & 61.1\% & 28.9\% & 0.273 \\ +IN & 58.9\% & 70.1\% & 59.4\% & 0.693 \\ +NP & 45.4\% & 53.5\% & 78.0\% & 0.673 \\ +NZ & 62.7\% & 72.7\% & 51.5\% & 0.557 \\ +PH & 50.0\% & 49.2\% & 48.9\% & 0.667 \\ +RS & 70.3\% & 73.7\% & 36.0\% & 0.792 \\ +SG & 56.7\% & 51.6\% & 30.1\% & 0.442 \\ +TW & 61.3\% & 63.8\% & 43.6\% & 0.646 \\ +UK & 54.6\% & 47.0\% & 28.7\% & 0.498 \\ +US & 62.1\% & 56.7\% & 22.4\% & 0.589 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Headline extraction metrics by jurisdiction for model \texttt{harvey}. Accuracy is per-cell. Recall is over the cells where the expert recorded a value. Hallucination rate is the share of legitimately-empty cells where the model invented a value. Cost-block $F_1$ aggregates over the four monetary variables.} +\label{tab:headline-harvey} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Jurisdiction & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & Cost $F_1$ \\ +\midrule +AM & 26.2\% & 8.3\% & 0.0\% & 0.420 \\ +AU & 73.6\% & 69.9\% & 21.2\% & 0.494 \\ +BE & 71.6\% & 62.0\% & 8.2\% & 0.614 \\ +BR & 59.8\% & 40.4\% & 22.0\% & 0.547 \\ +CH & 79.7\% & 78.5\% & 16.1\% & 0.862 \\ +DE & 62.6\% & 44.7\% & 16.1\% & 0.691 \\ +ES & 51.5\% & 47.9\% & 26.1\% & 0.756 \\ +FR & 62.7\% & 59.8\% & 31.5\% & 0.536 \\ +GE & 44.4\% & 35.6\% & 16.1\% & 0.478 \\ +HK & 65.5\% & 52.8\% & 10.5\% & 0.455 \\ +IN & 72.7\% & 68.5\% & 20.2\% & 0.615 \\ +NP & 43.2\% & 34.4\% & 26.5\% & 0.860 \\ +NZ & 79.6\% & 75.4\% & 14.4\% & 0.733 \\ +PH & 65.7\% & 61.9\% & 27.8\% & 0.541 \\ +RS & 78.9\% & 73.2\% & 10.4\% & 0.645 \\ +SG & 61.0\% & 50.6\% & 12.0\% & 0.508 \\ +TW & 67.1\% & 55.2\% & 9.8\% & 0.619 \\ +UK & 64.7\% & 58.6\% & 22.0\% & 0.559 \\ +US & 63.0\% & 53.2\% & 8.9\% & 0.709 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Headline extraction metrics by jurisdiction for model \texttt{harvey-2}. Accuracy is per-cell. Recall is over the cells where the expert recorded a value. Hallucination rate is the share of legitimately-empty cells where the model invented a value. Cost-block $F_1$ aggregates over the four monetary variables.} +\label{tab:headline-harvey-2} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Jurisdiction & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & Cost $F_1$ \\ +\midrule +AM & 35.9\% & 20.8\% & 1.6\% & 0.420 \\ +AU & 76.1\% & 66.8\% & 10.9\% & 0.646 \\ +BE & 68.3\% & 54.6\% & 3.1\% & 0.609 \\ +BR & 62.8\% & 38.9\% & 14.8\% & 0.591 \\ +CH & 78.8\% & 75.9\% & 10.6\% & 0.855 \\ +DE & 64.4\% & 41.7\% & 8.8\% & 0.649 \\ +ES & 55.2\% & 50.7\% & 17.4\% & 0.750 \\ +FR & 62.7\% & 57.1\% & 26.1\% & 0.593 \\ +GE & 45.3\% & 35.5\% & 10.7\% & 0.449 \\ +HK & 58.0\% & 46.7\% & 17.9\% & 0.316 \\ +IN & 75.4\% & 66.1\% & 9.1\% & 0.818 \\ +NP & 39.9\% & 27.5\% & 24.6\% & 0.864 \\ +NZ & 79.9\% & 73.8\% & 11.4\% & 0.723 \\ +PH & 68.7\% & 66.7\% & 27.8\% & 0.686 \\ +RS & 71.6\% & 63.1\% & 12.5\% & 0.587 \\ +SG & 58.1\% & 46.3\% & 11.2\% & 0.439 \\ +TW & 65.9\% & 51.4\% & 5.9\% & 0.591 \\ +UK & 63.7\% & 53.4\% & 13.8\% & 0.643 \\ +US & 59.2\% & 47.1\% & 6.2\% & 0.607 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Headline extraction metrics by jurisdiction for model \texttt{legora-1}. Accuracy is per-cell. Recall is over the cells where the expert recorded a value. Hallucination rate is the share of legitimately-empty cells where the model invented a value. Cost-block $F_1$ aggregates over the four monetary variables.} +\label{tab:headline-legora-1} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Jurisdiction & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & Cost $F_1$ \\ +\midrule +AM & 0.0\% & 0.0\% & 0.0\% & 0.000 \\ +AU & 74.2\% & 59.6\% & 5.1\% & 0.644 \\ +BE & 70.2\% & 57.3\% & 2.6\% & 0.677 \\ +BR & 64.7\% & 39.6\% & 11.7\% & 0.616 \\ +CH & 82.7\% & 80.0\% & 7.7\% & 0.903 \\ +DE & 51.6\% & 19.2\% & 8.4\% & 0.698 \\ +ES & 58.3\% & 52.3\% & 10.8\% & 0.748 \\ +FR & 63.3\% & 57.5\% & 25.2\% & 0.589 \\ +GE & 0.0\% & 0.0\% & 0.0\% & 0.000 \\ +HK & 69.1\% & 54.2\% & 2.6\% & 0.444 \\ +IN & 75.8\% & 67.3\% & 10.1\% & 0.805 \\ +NP & 43.2\% & 30.0\% & 17.9\% & 0.873 \\ +NZ & 81.5\% & 72.7\% & 6.1\% & 0.795 \\ +PH & 70.0\% & 66.2\% & 24.4\% & 0.600 \\ +RS & 75.2\% & 68.3\% & 12.0\% & 0.584 \\ +SG & 56.3\% & 41.5\% & 5.1\% & 0.352 \\ +TW & 71.0\% & 58.4\% & 4.7\% & 0.746 \\ +UK & 65.9\% & 54.5\% & 9.4\% & 0.615 \\ +US & 60.3\% & 48.8\% & 7.0\% & 0.577 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Headline extraction metrics by jurisdiction for model \texttt{legora-2}. Accuracy is per-cell. Recall is over the cells where the expert recorded a value. Hallucination rate is the share of legitimately-empty cells where the model invented a value. Cost-block $F_1$ aggregates over the four monetary variables.} +\label{tab:headline-legora-2} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Jurisdiction & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & Cost $F_1$ \\ +\midrule +AM & 0.0\% & 0.0\% & 0.0\% & 0.000 \\ +AU & 72.1\% & 61.1\% & 12.4\% & 0.571 \\ +BE & 70.9\% & 58.0\% & 2.1\% & 0.702 \\ +BR & 64.0\% & 40.1\% & 13.3\% & 0.617 \\ +CH & 83.1\% & 80.5\% & 7.7\% & 0.890 \\ +DE & 52.6\% & 20.2\% & 8.4\% & 0.705 \\ +ES & 58.4\% & 52.3\% & 10.3\% & 0.729 \\ +FR & 63.0\% & 57.5\% & 26.1\% & 0.598 \\ +GE & 0.0\% & 0.0\% & 0.0\% & 0.000 \\ +HK & 63.6\% & 48.6\% & 7.9\% & 0.222 \\ +IN & 74.2\% & 65.5\% & 11.1\% & 0.732 \\ +NP & 43.5\% & 30.0\% & 17.3\% & 0.863 \\ +NZ & 83.4\% & 76.5\% & 6.8\% & 0.909 \\ +PH & 72.7\% & 69.2\% & 22.2\% & 0.688 \\ +RS & 76.6\% & 68.8\% & 9.0\% & 0.607 \\ +SG & 57.9\% & 43.6\% & 4.8\% & 0.413 \\ +TW & 70.6\% & 58.3\% & 5.7\% & 0.754 \\ +UK & 66.4\% & 54.9\% & 8.7\% & 0.645 \\ +US & 59.7\% & 47.7\% & 6.2\% & 0.596 \\ +\bottomrule +\end{tabular} +\end{table} diff --git a/data/analysis/tables/per_field.tex b/data/analysis/tables/per_field.tex new file mode 100644 index 0000000000000000000000000000000000000000..6f378a751112ea69e4f02fb5eb1f7c56efb8a988 --- /dev/null +++ b/data/analysis/tables/per_field.tex @@ -0,0 +1,143 @@ +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Per-field extraction metrics, summed across jurisdictions, for model \texttt{gemini/gemini-3.1-flash-lite}.} +\label{tab:per-field-gemini-gemini-3-1-flash-lite} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Variable & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & $F_1$ \\ +\midrule +\texttt{court\_cost\_awarded\_nominal} & 85.5\% & 63.9\% & 4.1\% & 0.722 \\ +\texttt{defendant\_no1\_ISIC1\_industry\_category} & 50.7\% & 58.0\% & 65.9\% & 0.540 \\ +\texttt{defendants\_all\_count} & 75.1\% & 76.9\% & 90.5\% & 0.768 \\ +\texttt{dispute\_value\_nominal} & 56.2\% & 56.4\% & 44.0\% & 0.504 \\ +\texttt{legal\_subject\_judgement} & 3.6\% & 3.6\% & 83.3\% & 0.036 \\ +\texttt{party\_compensation\_awarded\_nominal} & 88.2\% & 71.4\% & 6.6\% & 0.727 \\ +\texttt{plaintiff\_loosing\_share} & 70.1\% & 71.3\% & 36.6\% & 0.727 \\ +\texttt{plaintiff\_no1\_ISIC1\_industry\_category} & 39.3\% & 54.1\% & 79.5\% & 0.429 \\ +\texttt{plaintiffs\_all\_count} & 87.9\% & 88.8\% & 100.0\% & 0.889 \\ +\texttt{trial\_end\_date} & 66.9\% & 67.6\% & 50.0\% & 0.680 \\ +\texttt{trial\_start\_date} & 52.4\% & 26.2\% & 9.7\% & 0.363 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Per-field extraction metrics, summed across jurisdictions, for model \texttt{gpt-5.4-mini}.} +\label{tab:per-field-gpt-5-4-mini} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Variable & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & $F_1$ \\ +\midrule +\texttt{court\_cost\_awarded\_nominal} & 83.2\% & 65.5\% & 8.3\% & 0.690 \\ +\texttt{defendant\_no1\_ISIC1\_industry\_category} & 52.7\% & 64.7\% & 74.7\% & 0.578 \\ +\texttt{defendants\_all\_count} & 73.0\% & 73.8\% & 56.1\% & 0.769 \\ +\texttt{dispute\_value\_nominal} & 51.2\% & 57.1\% & 53.8\% & 0.486 \\ +\texttt{legal\_subject\_judgement} & 4.9\% & 4.9\% & 100.0\% & 0.049 \\ +\texttt{party\_compensation\_awarded\_nominal} & 81.9\% & 67.3\% & 13.6\% & 0.611 \\ +\texttt{plaintiff\_loosing\_share} & 66.3\% & 69.9\% & 55.2\% & 0.692 \\ +\texttt{plaintiff\_no1\_ISIC1\_industry\_category} & 38.7\% & 53.9\% & 80.5\% & 0.426 \\ +\texttt{plaintiffs\_all\_count} & 84.9\% & 85.4\% & 71.4\% & 0.875 \\ +\texttt{trial\_end\_date} & 66.5\% & 66.9\% & 44.1\% & 0.690 \\ +\texttt{trial\_start\_date} & 40.4\% & 41.6\% & 61.3\% & 0.364 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Per-field extraction metrics, summed across jurisdictions, for model \texttt{harvey}.} +\label{tab:per-field-harvey} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Variable & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & $F_1$ \\ +\midrule +\texttt{court\_cost\_awarded\_nominal} & 85.6\% & 57.8\% & 1.9\% & 0.689 \\ +\texttt{defendant\_no1\_ISIC1\_industry\_category} & 53.6\% & 49.3\% & 37.3\% & 0.523 \\ +\texttt{defendants\_all\_count} & 71.0\% & 71.6\% & 48.6\% & 0.777 \\ +\texttt{dispute\_value\_nominal} & 58.5\% & 47.0\% & 32.9\% & 0.463 \\ +\texttt{legal\_subject\_judgement} & 3.9\% & 3.9\% & 83.3\% & 0.043 \\ +\texttt{party\_compensation\_awarded\_nominal} & 89.5\% & 66.9\% & 4.7\% & 0.717 \\ +\texttt{plaintiff\_loosing\_share} & 70.8\% & 71.4\% & 33.3\% & 0.740 \\ +\texttt{plaintiff\_no1\_ISIC1\_industry\_category} & 56.7\% & 41.8\% & 25.4\% & 0.452 \\ +\texttt{plaintiffs\_all\_count} & 74.4\% & 74.9\% & 83.3\% & 0.812 \\ +\texttt{trial\_end\_date} & 66.0\% & 65.0\% & 12.3\% & 0.761 \\ +\texttt{trial\_start\_date} & 48.5\% & 13.3\% & 4.4\% & 0.216 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Per-field extraction metrics, summed across jurisdictions, for model \texttt{harvey-2}.} +\label{tab:per-field-harvey-2} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Variable & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & $F_1$ \\ +\midrule +\texttt{court\_cost\_awarded\_nominal} & 86.1\% & 57.0\% & 0.8\% & 0.679 \\ +\texttt{defendant\_no1\_ISIC1\_industry\_category} & 46.0\% & 33.6\% & 27.4\% & 0.406 \\ +\texttt{defendants\_all\_count} & 70.9\% & 71.8\% & 62.2\% & 0.777 \\ +\texttt{dispute\_value\_nominal} & 59.3\% & 33.1\% & 20.4\% & 0.387 \\ +\texttt{legal\_subject\_judgement} & 1.1\% & 0.7\% & 0.0\% & 0.009 \\ +\texttt{party\_compensation\_awarded\_nominal} & 88.4\% & 63.1\% & 4.9\% & 0.689 \\ +\texttt{plaintiff\_loosing\_share} & 71.8\% & 69.7\% & 15.9\% & 0.767 \\ +\texttt{plaintiff\_no1\_ISIC1\_industry\_category} & 54.2\% & 31.3\% & 18.8\% & 0.379 \\ +\texttt{plaintiffs\_all\_count} & 77.2\% & 77.6\% & 66.7\% & 0.832 \\ +\texttt{trial\_end\_date} & 65.8\% & 65.0\% & 14.0\% & 0.740 \\ +\texttt{trial\_start\_date} & 50.9\% & 15.1\% & 3.3\% & 0.247 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Per-field extraction metrics, summed across jurisdictions, for model \texttt{legora-1}.} +\label{tab:per-field-legora-1} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Variable & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & $F_1$ \\ +\midrule +\texttt{court\_cost\_awarded\_nominal} & 92.0\% & 70.1\% & 0.9\% & 0.806 \\ +\texttt{defendant\_no1\_ISIC1\_industry\_category} & 62.2\% & 55.5\% & 22.5\% & 0.621 \\ +\texttt{defendants\_all\_count} & 66.5\% & 66.6\% & 37.1\% & 0.750 \\ +\texttt{dispute\_value\_nominal} & 65.0\% & 34.7\% & 12.3\% & 0.447 \\ +\texttt{legal\_subject\_judgement} & 2.2\% & 1.8\% & 16.7\% & 0.023 \\ +\texttt{party\_compensation\_awarded\_nominal} & 92.7\% & 79.1\% & 4.2\% & 0.797 \\ +\texttt{plaintiff\_loosing\_share} & 71.8\% & 69.3\% & 11.4\% & 0.784 \\ +\texttt{plaintiff\_no1\_ISIC1\_industry\_category} & 58.0\% & 36.8\% & 16.9\% & 0.438 \\ +\texttt{plaintiffs\_all\_count} & 70.5\% & 70.5\% & 28.6\% & 0.807 \\ +\texttt{trial\_end\_date} & 78.1\% & 77.8\% & 13.5\% & 0.825 \\ +\texttt{trial\_start\_date} & 56.0\% & 27.2\% & 3.6\% & 0.405 \\ +\bottomrule +\end{tabular} +\end{table} + +% Auto-generated by legex-analysis. +\begin{table}[h] +\caption{Per-field extraction metrics, summed across jurisdictions, for model \texttt{legora-2}.} +\label{tab:per-field-legora-2} +\centering\small +\begin{tabular}{@{}lrrrr@{}} +\toprule +Variable & Accuracy & Recall$_{\text{filled}}$ & Hallu. rate & $F_1$ \\ +\midrule +\texttt{court\_cost\_awarded\_nominal} & 92.1\% & 70.4\% & 0.7\% & 0.808 \\ +\texttt{defendant\_no1\_ISIC1\_industry\_category} & 57.1\% & 50.7\% & 28.5\% & 0.570 \\ +\texttt{defendants\_all\_count} & 66.2\% & 65.8\% & 18.4\% & 0.755 \\ +\texttt{dispute\_value\_nominal} & 66.6\% & 39.7\% & 12.9\% & 0.488 \\ +\texttt{legal\_subject\_judgement} & 2.6\% & 2.2\% & 16.7\% & 0.028 \\ +\texttt{party\_compensation\_awarded\_nominal} & 92.5\% & 79.7\% & 4.5\% & 0.797 \\ +\texttt{plaintiff\_loosing\_share} & 72.5\% & 70.8\% & 16.3\% & 0.775 \\ +\texttt{plaintiff\_no1\_ISIC1\_industry\_category} & 59.2\% & 36.9\% & 14.8\% & 0.444 \\ +\texttt{plaintiffs\_all\_count} & 72.3\% & 72.4\% & 35.7\% & 0.816 \\ +\texttt{trial\_end\_date} & 77.4\% & 77.0\% & 13.2\% & 0.822 \\ +\texttt{trial\_start\_date} & 56.1\% & 27.2\% & 3.6\% & 0.406 \\ +\bottomrule +\end{tabular} +\end{table} diff --git a/docs/Jurisdictions.md b/docs/Jurisdictions.md new file mode 100644 index 0000000000000000000000000000000000000000..bc794b9d17630bc47dfc059bcd23a62487a1ec08 --- /dev/null +++ b/docs/Jurisdictions.md @@ -0,0 +1,140 @@ +# Jurisdictions + +Research catalogue for scaling `legex` to new jurisdictions. Each row is a +candidate country/court, what data source we'd use, how to isolate civil-law +cases, and how hard the scraper looks. Two jurisdictions are already +implemented; the others are prioritised into waves. + +Source links and contact names come from GitHub issue +[#3](https://github.com/Engineers-for-Science-Initiative/legex/issues/3) and +Daniel's compilation table. Link checks and source research were performed +via WebFetch/WebSearch; HuggingFace datasets were preferred where available. + +## Status + +| # | Country | Court | Source type | Civil filter | Dates | Recommendation | +| ---- | ------------------ | ---------------------------- | ------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | --------------------- | ---------------------------------------------------------------------------------------- | +| done | Switzerland | Bundesgericht | HF `voilaj/swiss-caselaw` | chamber prefix 4* / 5* | 2015–2025 | Done | +| done | France | Cour de cassation | PISTE Judilibre API | chambers civ1/civ2/civ3 | 2015–2025 | Done | +| done | Australia | High Court | HF `isaacus/high-court-of-australia-cases` | exclude "v The King/Queen" | 2015–2025 | Done (link via hcourt.gov.au per-case page, docket extracted from text) | +| done | Germany | BGH | HF `openlegaldata/court-decisions-germany` (gated) | case-ID ` ZR/ZB/ZA` | 2015–2018 | Done (coverage tails off post-2018; gated T&C + HF_TOKEN) | +| done | United States | SCOTUS | HF `free-law/Caselaw_Access_Project` (gated) | exclude "United States v." + state-v heuristic | 2015–2020 | Done (CAP dataset ends 2020; gated T&C + HF_TOKEN) | +| done | United Kingdom | Supreme Court | BAILII / supremecourt.uk
used https://nationalarchives.github.io/ds-find-caselaw-docs/public | all UKSC = civil/mixed | 2009–2025 | Blocked on BAILII (Anubis proof-of-work); supremecourt.uk feasible with paginated scrape | +| done | Belgium | Cour de Cassation | juportal.be HTML | `C*` (civil) vs `P*` case-ID prefix | 2015–2025 | Done (form POST + server-side pagination on juportal content pages) | +| 3 | Austria | OGH | RIS (ris.bka.gv.at) JSON API | `Rechtsgebiete == Zivilrecht` | 2000–2025 | Wave 1 (API exists, but param schema needs more research) | +| 4 | Armenia | Cassation Court | cassationcourt.am HTML | dedicated `/precedent/civil-cases-advanced-search` | 2015–2025 | Wave 2 (CSRF-token form session handling required) | +| Done | New Zealand | High Court + Court of Appeal | justice.govt.nz JDO HTML | "Civil" dropdown | 2015–2025 | Wave 2 | +| 6 | Hong Kong | CFA + High Court | legalref.judiciary.hk HTML | court-level + case-type dropdowns | 1997–2025 | Wave 2 | +| 7 | Singapore | Supreme Court + SICC | judiciary.gov.sg HTML | civil vs criminal case summaries | 2000–2025 | Wave 2 | +| 8 | Korea | Supreme Court | eng.scourt.go.kr HTML | case designation `Da` (civil) | 2015–2025 | Wave 3 — Hangul | +| 9 | India | Supreme Court | sci.gov.in HTML + PDF | free-text "civil appeal" search | 2013–2025 | Wave 3 — PDF heavy | +| 10 | Philippines | Supreme Court | elibrary.judiciary.gov.ph HTML | monthly decision index | 1996–2025 | Wave 3 | +| 11 | Brazil | STJ | scon.stj.jus.br HTML | "Pesquisa Pronta" civil themes | 2015–2025 | Wave 3 — verify scope | +| 12 | Dominican Republic | Suprema Corte | poderjudicial.gob.do HTML | First Chamber (Civil + Commercial) | 2015–2025 | Wave 3 | +| 13 | Georgia | Supreme Court | supremecourt.ge HTML + AJAX | chamber = "Civil" | 2015–2025 | Wave 3 — needs Playwright | +| 14 | Kazakhstan | Supreme Court | sud.gov.kz HTML | Bank of Judicial Acts by case type | 2009–2024 | Wave 3 | +| 15 | Ukraine | Supreme Court | reyestr.court.gov.ua HTML | case form "Цивільні справи" | 2015–2025 | Wave 3 — bot protection | +| 16 | Taiwan | Judicial Yuan | judgment.judicial.gov.tw ASPX | `民事` case-type + ROC calendar | 2020–2025 | Wave 4 — ASPX + ROC calendar | +| 17 | Nepal | Supreme Court | nkp.gov.np HTML | advanced-search case type | ~2015–2025 | Wave 4 — Bikram Sambat calendar, Nepali Unicode | +| 18 | Italy | Corte di Cassazione | italgiure.giustizia.it | sezione civile (I–VI) | 1923–2025 | Blocked — subscription required | +| 19 | Spain | Tribunal Supremo | poderjudicial.es | Sala de lo Civil | 2000–2025 | Blocked — CGPJ bulk-use restriction, only 147 SC civil cases/yr | +| 20 | China | Supreme People's Court | wenshu.court.gov.cn | case-type filter | 2015–2025 | Blocked — anti-bot + phone registration; check NYU Shanghai mirror | +| 21 | Belarus | Supreme Court | court.gov.by | — | pre-2014 Plenums only | Blocked — no public decision database | + +Legend: Wave 1 = high confidence, should implement first. Wave 2 = moderate effort. Wave 3 = special challenges (encoding, bot protection, AJAX). Wave 4 = calendar/script conversion required. Blocked = needs a policy change, alternative source, or is not feasible. + +## Per-country detail + +Short notes per country with sample URL, rate/auth notes, and the scraper sketch. Implementation tickets will expand these. + +### Wave 1 — implement next + +- United Kingdom — Supreme Court (BAILII) + + - Link: https://www.bailii.org/uk/cases/UKSC/ + - Sample: https://www.bailii.org/uk/cases/UKSC/2024/24.html + - Civil filter: all UKSC decisions are civil/mixed; filter by year and case topic. + - Auth: none; free, no cookies, no anti-bot. + - Scraper: scrape `bailii.org/uk/cases/UKSC//` index pages; follow per-case HTML or PDF. ~100–150 civil cases/year 2015–2025. Stable structure. +- Australia — High Court + + - Link: https://www.hcourt.gov.au/cases-and-judgments/judgments + - HF dataset: `isaacus/high-court-of-australia-cases` (also `isaacus/open-australian-legal-corpus`). + - Civil filter: case-type classification; HCA mixes civil and constitutional. + - Scraper: reuse the HF parquet path, same pattern as CH. Trivial. +- Austria — Oberster Gerichtshof (RIS) + + - Link: https://www.ris.bka.gv.at + - Sample: https://www.ris.bka.gv.at/Dokumente/Justiz/JJT_20241024_OGH0002_0010OB00161_24V0000_000/ + - Civil filter: senate codes like `Ob` for civil revision. + - Auth: none; German only. + - Scraper: query RIS by date+senate, paginate by case number. + +### Wave 2 — moderate effort + +- Germany — Bundesgerichtshof Zivilsenate + + - Link: https://juris.bundesgerichtshof.de + - Civil filter: case prefix `ZR` (Zivilrecht) across senates I–XII. + - Auth: none (scraping BGH is legal per 2024 precedent). + - Scraper: parse `cgi-bin/rechtsprechung/document.py`; ~300 decisions/year civil. +- Armenia — Court of Cassation + + - Link: https://www.cassationcourt.am/en/precedent/civil-cases-advanced-search + - Civil filter: already has a dedicated civil-cases advanced-search form. + - Auth: none. Armenian / English / Russian UI. + - Scraper: POST the advanced-search form, parse results table. +- United States — SCOTUS (via CourtListener) + + - Official portal: https://www.supremecourt.gov/opinions/USReports.aspx (mixed civil/criminal, no filter). + - Preferred: CourtListener API (courtlistener.com/api) or HF "Pile of Law". + - Civil filter: apply post-hoc via case-type classification (SCOTUS cases are mostly certiorari; filter by case topic). + - Auth: CourtListener has free API keys with rate limits. + - Scraper: CourtListener API `/api/rest/v3/opinions/?court=scotus`. +- New Zealand — High Court + Court of Appeal + + - Link: https://www.justice.govt.nz/courts/decisions/jdo/ + - Civil filter: "Civil" dropdown in JDO search. + - Auth: none. 3-day publication lag. + - Scraper: JDO search form, paginate, follow per-case HTML. +- Hong Kong — Court of Final Appeal + High Court + + - Link: https://legalref.judiciary.hk/lrs/common/ju/judgment.jsp + - Civil filter: court level + case type dropdowns (civil/criminal). + - Scraper: GET-based search form, fairly mechanical. +- Singapore — Supreme Court + SICC + + - Link: https://www.judiciary.gov.sg/judgments/judgments-case-summaries + - Civil filter: case summaries classified as civil vs criminal; SICC (commercial, 2015+) is a separate civil-only feed. + - Scraper: two feeds — general Supreme Court + SICC. + +### Wave 3 — special challenges + +- Korea: scourt.go.kr, case designation `Da` = civil appellate. Hangul full-text. +- India: sci.gov.in, PDF-heavy; free-text "civil appeal" search. Multiple scripts possible. +- Philippines: elibrary.judiciary.gov.ph, chronological monthly index, DataTables pagination. +- Brazil: scon.stj.jus.br — "Pesquisa Pronta" is curated themes; verify whether the themed set is representative of civil cases or only a subset. +- Dominican Republic: poderjudicial.gob.do — First Chamber (Civil + Commercial). Spanish. +- Georgia: supremecourt.ge — AJAX modals; needs Playwright/Selenium rather than requests. +- Kazakhstan: sud.gov.kz — Bank of Judicial Acts, filter by case type. Kazakh/Russian/English. +- Ukraine: reyestr.court.gov.ua — case form "Цивільні справи". Anti-bot protection, "test mode" warnings; rate-limit gently. + +### Wave 4 — calendar / script conversion required + +- Taiwan: judgment.judicial.gov.tw is an ASP.NET form (stateful POST + cookies). Case-type filter `民事` (civil). Year input is ROC calendar (民國 N = Gregorian N+1911). Example: ROC 113 = 2024. +- Nepal: nkp.gov.np uses Bikram Sambat calendar (~57 years ahead; 2015 AD ≈ 2072 BS). Nepali-Unicode-only search form. Calendar conversion helper required. + +### Blocked / investigate later + +- Italy — italgiure.giustizia.it is subscription-gated. Investigate whether the research group can obtain institutional access or negotiate bulk-export terms. No public bulk download. +- Spain — poderjudicial.es CENDOJ prohibits bulk use (>100/day = ToS breach). Only ~147 Supreme Court civil cases since 2017 meet the Sala de lo Civil filter — insufficient for a 130-sample Goldenset unless we find alternative CENDOJ OpenData access. +- China — wenshu.court.gov.cn requires Chinese mobile-phone registration and has aggressive anti-bot. Consider the NYU Shanghai Library snapshot or the CAIL2018 civil-cases dataset before attempting live scraping. +- Belarus — court.gov.by only publishes Plenum explanations (non-binding jurisprudence) in the official gazette "Sudovy vestnik". No structured civil-decision database. + +## Key observations + +1. Seven jurisdictions are HuggingFace-ready or have usable public APIs (CH, FR already done; AU via `isaacus/high-court-of-australia-cases`; UK via BAILII; US via CourtListener). These are cheapest to implement and should be the next tickets. +2. Most mid-tier countries are HTML scrapes with a dedicated civil filter at source (Austria, Germany, Belgium, Armenia, NZ, HK, Singapore, Dominican Republic). Each is ~1–2 days of scraper work. +3. Four countries are effectively blocked without a policy change or alternative source (Italy, Spain, China, Belarus). Flag these to Adrian / Nerea / Jelle / contacts for guidance rather than burning engineering time. +4. Non-Gregorian calendars and non-Latin scripts affect Taiwan (ROC), Nepal (Bikram Sambat), and the CJK countries. These need conversion utilities before scraping; consider a shared `legex/utils/calendar.py` when the first non-Gregorian jurisdiction ships. +5. Prioritise "civil filter at source" over post-hoc filtering. Countries where the court already exposes a civil-only search (Armenia, Belgium C-prefix, Austria N Ob, Germany ZR) produce high-precision Goldensets with minimal noise. diff --git a/goldensets/README.md b/goldensets/README.md new file mode 100644 index 0000000000000000000000000000000000000000..49dc3dc396ca5b00e590e227eaf0016d11cc29b4 --- /dev/null +++ b/goldensets/README.md @@ -0,0 +1,309 @@ +--- +license: mit +language: + - en + - de + - fr + - it + - es + - pt + - hy + - ka + - ne + - sr + - zh + - fil + - nl +pretty_name: "LEGEX Goldensets: Expert-Coded Review-Table Annotations" +size_categories: + - 1K/goldenset_.jsonl` is a JSON object with these +keys: + +| Key | Type | Description | +|-----|-----|---------------------------------------------------------------------------------------------------------------------------------| +| `case_id` | string | Identifier within the source database. | +| `link` | string | URL to the original judgment. | +| `full_text` | string \| null | Full judgment text used as model input. | +| `legal_subject_judgement` | string | Short English subject of the case. Acts as the "this row has been substantively reviewed" marker. | +| `trial_start_date` | YYYY-MM-DD \| null | Trial start date. | +| `trial_end_date` | YYYY-MM-DD \| null | Decision date. | +| `dispute_value_nominal` | string \| null | Amount in dispute as a string (e.g. `"150000"`) or the literal `"nonpecuniary"`. | +| `Currency_dispute_value_nominal` | string \| null | ISO-4217 currency code. | +| `plaintiff_loosing_share` | number [0, 1] \| null | Plaintiff's losing share. | +| `court_cost_awarded_nominal` | number \| null | Court fees awarded. | +| `Currency_court_cost_awarded_nominal` | string \| null | ISO-4217 currency code. | +| `party_compensation_awarded_nominal` | number \| null | Party compensation awarded. | +| `Currency_party_compensation_awarded_nominal` | string \| null | ISO-4217 currency code. | +| `plaintiffs_all_count` | integer \| null | Number of plaintiffs. | +| `defendants_all_count` | integer \| null | Number of defendants. | +| `plaintiff_no1_ISIC1_industry_category` | string \| null | ISIC Section A–U for the first plaintiff. | +| `defendant_no1_ISIC1_industry_category` | string \| null | ISIC Section A–U for the first defendant. | + +A row is included when `legal_subject_judgement` is populated (the marker the +annotator used to flag a row as substantively reviewed). Cells left empty by +the annotator are stored as `null`. In the raw JSONL the trial dates are +`YYYY-MM-DD` strings. The declared dataset features type them as `date32`, so +the `datasets` library returns them as date objects. + +### Traceability fields + +Every record additionally carries provenance fields so each value is +auditable: + +| Key | Type | Description | +|-----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `annotator_id` | string | Pseudonymous, salted-hash id of the coder, stable per person across jurisdictions. | +| `comment` | string \| null | Human-readable note listing every field changed during sanitization, e.g. `"The trial_end_date was sanitized from '2026_03_19' to '2026-03-19'."`, `null` when the record needed no change. | +| `original_input` | string | JSON object mapping each changed field to its 1:1 pre-sanitization value, `"{}"` when nothing changed. This lets any consumer reconstruct the raw value. | + +### Primary and secondary annotations + +Jurisdictions that received an independent re-annotation contain both the +primary annotator's rows and the secondary annotators' rows, distinguished by +`annotator_id`. Within each file all primary rows come first and re-annotation +rows are appended after them, so **the first row per `case_id` is the primary +gold annotation**. For benchmark evaluation, deduplicate to the primary rows. +For inter-annotator agreement, group the duplicated `case_id`s by +`annotator_id`. + + +## Jurisdictions + +### Highest civil court per jurisdiction + +| Jurisdiction | Highest court (civil jurisdiction) | Language(s) | +|-----|-----|-----| +| Armenia | Court of Cassation (Civil Chamber) | Armenian | +| Australia | High Court of Australia | English | +| Belgium | Cour de cassation / Hof van Cassatie | French, Dutch | +| Brazil | Superior Tribunal de Justiça | Portuguese | +| France | Cour de cassation | French | +| Georgia | Supreme Court of Georgia | Georgian | +| Germany | Bundesgerichtshof | German | +| Hong Kong | Court of Final Appeal | English, Chinese | +| India | Supreme Court of India | English | +| Nepal | Supreme Court of Nepal | Nepali | +| New Zealand | Court of Appeal / High Court | English | +| Philippines | Supreme Court of the Philippines | English, Filipino | +| Serbia | Supreme Court of Cassation | Serbian | +| Singapore | Supreme Court (incl. SICC) | English | +| Spain | Tribunal Supremo (Sala de lo Civil) | Spanish | +| Switzerland | Federal Supreme Court | German, French, Italian | +| Taiwan | Supreme Court | Chinese | +| United Kingdom | Supreme Court of the United Kingdom | English | +| United States | Supreme Court of the United States | English | + +### Data sources, judgment counts and annotation rows + +`# judgments` counts unique expert-coded cases, `# rows` additionally counts +independent secondary re-annotations of the same cases. + +| Jurisdiction | Data source | Time span | # judgments | # rows | Annotators | +|-----|-----|-----|-----|-----|-----| +| Armenia | [cassationcourt.am](https://www.cassationcourt.am) | 2024 – 2026 | 58 | 58 | 1 | +| Australia | [HF isaacus/high-court-of-australia-cases](https://huggingface.co/datasets/isaacus/high-court-of-australia-cases) | 2015 – 2025 | 30 | 30 | 1 | +| Belgium | [juportal.be](https://juportal.be) | 2015 – 2025 | 55 | 55 | 1 | +| Brazil | [scon.stj.jus.br](https://scon.stj.jus.br) | 2002 – 2026 | 130 | 179 | 3 | +| France | [Judilibre (PISTE) API](https://api.piste.gouv.fr/cassation/judilibre) | 2015 – 2025 | 30 | 30 | 1 | +| Georgia | [supremecourt.ge](https://www.supremecourt.ge) | 2026 only | 112 | 161 | 3 | +| Germany | [HF openlegaldata/court-decisions-germany](https://huggingface.co/datasets/openlegaldata/court-decisions-germany) | 2015 – 2022 | 130 | 180 | 3 | +| Hong Kong | [legalref.judiciary.hk](https://legalref.judiciary.hk) | 2015 – 2026 | 10 | 10 | 1 | +| India | [AWS Open Data Indian Supreme Court](https://registry.opendata.aws/indian-supreme-court-judgments) | 2017 – 2023 | 24 | 24 | 1 | +| Nepal | [nkp.gov.np](https://nkp.gov.np) | 2021 – 2025 | 130 | 130 | 1 | +| New Zealand | [justice.govt.nz JDO API](https://www.justice.govt.nz/jdo-search-api) | 2015 – 2025 | 29 | 29 | 1 | +| Philippines | [elibrary.judiciary.gov.ph](https://elibrary.judiciary.gov.ph) | 2024 – 2025 | 10 | 10 | 1 | +| Serbia | [vrh.sud.rs](https://vrh.sud.rs) | 2023 – 2025 | 26 | 26 | 1 | +| Singapore | [sgcaselaw.com](https://sgcaselaw.com) | 2025 – 2026 | 124 | 172 | 3 | +| Spain | [poderjudicial.es](https://www.poderjudicial.es) | 2025 – 2026 | 130 | 130 | 1 | +| Switzerland | [HF voilaj/swiss-caselaw](https://huggingface.co/datasets/voilaj/swiss-caselaw) | 2024 – 2025 | 130 | 190 | 3 | +| Taiwan | [judgment.judicial.gov.tw](https://judgment.judicial.gov.tw) | 2026 only | 130 | 180 | 3 | +| United Kingdom | [caselaw.nationalarchives.gov.uk](https://caselaw.nationalarchives.gov.uk) | 2015 – 2025 | 130 | 190 | 3 | +| United States | [HF free-law/Caselaw_Access_Project](https://huggingface.co/datasets/free-law/Caselaw_Access_Project) | 2020 – 2026 | 130 | 190 | 3 | + +Total: 1,548 expert-coded judgments, 1,974 annotation rows. + +Core benchmark (≥ 100 expert-coded judgments, coded by ≥ 2 independent +annotators): Brazil, Georgia, Germany, Singapore, Switzerland, Taiwan, +United Kingdom, United States. In each core jurisdiction 28–30 cases were +independently re-annotated by one or two additional experts. +Preview jurisdictions: the remaining eleven (Armenia, Australia, Belgium, +France, Hong Kong, India, Nepal, New Zealand, Philippines, Serbia, Spain). +Nepal reaches 130 cases but is single-annotated and therefore remains +preview. + +## Loading + +```python +from datasets import load_dataset + +# Single jurisdiction +ds = load_dataset("legexbenchmark/goldensets", split="switzerland") +ds = load_dataset("legexbenchmark/goldensets", split="united_states") + +# All jurisdictions +from datasets import concatenate_datasets +splits = [ + "armenia", "australia", "belgium", "brazil", "france", "georgia", + "germany", "hong_kong", "india", "nepal", "new_zealand", "philippines", + "serbia", "singapore", "spain", "switzerland", "taiwan", + "united_kingdom", "united_states", +] +all_rows = concatenate_datasets([ + load_dataset("legexbenchmark/goldensets", split=s) for s in splits +]) +``` + +## Limitations + +- Sample sizes: Nine of the nineteen jurisdictions have fewer than 100 + expert-coded judgments; Nepal and Spain reach 100+ cases but are + single-annotated. These eleven are marked as preview and intended for + schema-portability checks rather than per-jurisdiction performance claims. +- Time coverage: Source databases vary considerably (Georgia and Taiwan are + 2026-only because earlier years were not freely available). +- Schema portability: the 14 fields were designed against civil judgments + in common-law and Western European civil-law systems. Some fields + (e.g. `plaintiff_loosing_share`, ISIC categorisation) may not be a natural + fit for every jurisdiction. +- Court selection: For Brazil, there are judgements that are not from the + highest possible court. This can be inferred with the case id. The Spain + goldenset draws mainly on the Tribunal Constitucional and the Tribunal + Económico-Administrativo Central rather than the Tribunal Supremo (Sala de + lo Civil); the court can likewise be inferred from the case_id. +- Full-text quality: Text was extracted from heterogeneous sources (HTML, + PDF, API JSON). Layout artefacts and OCR errors are possible. + +## Citation + +Anonymous submission to the ICML 2026 AI for Law workshop. Citation block +will be added after the camera-ready release. + +## License + +MIT. diff --git a/goldensets/assets/legal_subject_wordcloud.png b/goldensets/assets/legal_subject_wordcloud.png new file mode 100644 index 0000000000000000000000000000000000000000..9233af777f81aebf5f814e4c1254017e3db408a8 --- /dev/null +++ b/goldensets/assets/legal_subject_wordcloud.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0822eb771cb2be6cd41fe0e057af84ee5b174dea4b592d0a23a51669f7cbc3eb +size 355348 diff --git a/goldensets/data/am/goldenset_am.jsonl b/goldensets/data/am/goldenset_am.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..381f47e88e935bced99cd217d9bae0350dd2b4d8 --- /dev/null +++ b/goldensets/data/am/goldenset_am.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:71224439e4fc6b668f91960f0ef4b5dd9ef9f3dd1f7ad858f004f0a9d833514b +size 74216 diff --git a/goldensets/data/au/goldenset_au.jsonl 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5519424 diff --git a/goldensets/data/us/goldenset_us.jsonl b/goldensets/data/us/goldenset_us.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bbd5167ffc0e42d636a3608a4fc0edb7192ae500 --- /dev/null +++ b/goldensets/data/us/goldenset_us.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:16e8953c554385c6b52f6d0556c125b0f10ba6216e5ca82da7d2218b4a141f99 +size 12525347 diff --git a/inference-results/README.md b/inference-results/README.md new file mode 100644 index 0000000000000000000000000000000000000000..618a1d0f25bdebcff2c526bde9c00c268d787306 --- /dev/null +++ b/inference-results/README.md @@ -0,0 +1,669 @@ +--- +license: mit +language: + - en + - de + - fr + - it + - es + - pt + - hy + - ka + - ne + - sr + - zh + - fil + - nl +pretty_name: "LEGEX Inference Results: 4 Systems, 6 Runs on 19 Jurisdictions" +size_categories: + - 1K/inference_harvey.jsonl +data//inference_harvey_2.jsonl +data//inference_gemini.jsonl +data//inference_gpt.jsonl +data//inference_legora_1.jsonl +data//inference_legora_2.jsonl +prompts/prompts_harvey.jsonl +prompts/prompts_harvey_2.jsonl +prompts/prompts_legora_1.jsonl +prompts/prompts_legora_2.jsonl +``` + +The `prompts/` files hold the per-column prompts as run, one record per +(run, field): `model`, `inference_date`, `field`, `prompt`. + + +### Record schema + +Each record corresponds to one (judgment, system) pair: + +| Key | Type | Description | +|---|---|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `case_id` | string | Identifier matching the goldenset `case_id`. | +| `link` | string \| null | URL to the original judgment. | +| `legal_subject_judgement` … `defendant_no1_ISIC1_industry_category` | string \| null | The 14 extracted fields, same schema as the goldensets. All values are strings (numbers as digit strings) so every column is single-typed, this fixes the `ArrowInvalid` load failure of the first CSV release. | +| `model` | string | Model identifier emitted by the inference pipeline (see table above). | +| `inference_date` | string | The date the producing run was executed/exported (e.g. `2026-08-01` for both Legora runs; `2026-05-18`/`2026-06-30` for Harvey), so run variants remain traceable per record. | +| `error` | string \| null | Non-empty only if the model call failed for that case. | +| `comment` | string \| null | Human-readable note listing every value changed during cleaning; `null` when the record needed no change. | +| `original_input` | string | JSON object mapping each cleaned field to its 1:1 pre-cleaning value; `"{}"` when nothing changed. | + +See [goldensets](https://huggingface.co/datasets/legexbenchmark/goldensets) for a description of the data columns. + +## Cleaning + +Long-form refusals ("…the date is not stated … N/A") and prose/citations by the model +not following instructions were removed or standardized via a two-step, human-reviewed pipeline +(`legex-refusals-scan` > review > `legex-refusals-apply`, see the +[code repo](https://huggingface.co/datasets/legexbenchmark/code)). Every +change is recorded per record in `comment` / `original_input`, so the raw +model output can always be reconstructed. + +## Coverage and known limitations + +- Only 15 of Spain's 129 annotated judgments have Harvey output (the ingest of + the remaining case packets failed before the 2026-06-30 export and was not + re-run); evaluation counts each system only on the judgments it actually + produced output for, and reports per-system denominators. +- Hong Kong `case_id`s that were shared by two different documents (one CFA + case number covering e.g. a substantive judgment and a costs ruling) are + disambiguated with the source document id, e.g. `FACV12/2022-DIS154422`, + matching the goldensets release. + +- Inference ran on the full sampled case packets (up to 130 cases per + jurisdiction), so files can contain more cases than the goldenset has + expert-reviewed rows. Evaluation joins on `case_id` against the goldenset. +- Some Harvey exports are partial: `es` has 15 rows, `np` 108, `ph` 115, + `hk` 116, `rs` 126, `au` 128, `de` 129, all other files have 130. The + `harvey_2` run shares most of these gaps: `es` 15, `hk` 102, `ph` 115, + `rs` 126, `de` 129, all other files 130. +- Legora returned empty review tables for Armenia (`am`), Georgia (`ge`), and + most Nepalese (`np`) judgments — the rows exist but their fields are empty + (Legora appears not to support non-Latin scripts). Evaluation counts each + system only on the judgments it actually answered. Legora `hk` files have + 116 rows. +- For Switzerland, `gemini` and `gpt` emit `case_id`s with different + separators than the goldenset (e.g. `4A_426/2024` vs `4A-426-2024`), join + after normalising separators. +- A few files contain duplicate `case_id` rows. + +## Loading + +```python +import pandas as pd + +# One (jurisdiction, system) pair +df = pd.read_json( + "hf://datasets/legexbenchmark/inference-results/data/us/inference_gpt.jsonl", + lines=True, +) +``` + +or with the `datasets` library (one config per system): + +```python +from datasets import load_dataset + +ds = load_dataset("legexbenchmark/inference-results", "harvey", split="switzerland") +ds = load_dataset("legexbenchmark/inference-results", "gpt", split="united_states") +ds = load_dataset("legexbenchmark/inference-results", "legora_1", split="switzerland") +``` + +To compare against the gold annotations, join on `case_id` after loading the +matching split from +[`legexbenchmark/goldensets`](https://huggingface.co/datasets/legexbenchmark/goldensets). + + +## License + +MIT. diff --git a/inference-results/data/am/inference_gemini.jsonl b/inference-results/data/am/inference_gemini.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..91ac815ded393b2442c16cc0dfa92f19c434c5d7 --- /dev/null +++ b/inference-results/data/am/inference_gemini.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a547d85219e210fe5e03180adcf7c2c6470643a005a91df8d4502ea774a64f9 +size 107349 diff --git a/inference-results/data/am/inference_gpt.jsonl b/inference-results/data/am/inference_gpt.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..122e447204b1a12a91624303edfe0e978afeb9ad --- /dev/null +++ b/inference-results/data/am/inference_gpt.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:da5ea9f7545b881605b020322112ca53ab70d3a346db52141090d897c4dca384 +size 106236 diff --git 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https://git-lfs.github.com/spec/v1 +oid sha256:a89fc8a272ec0db2ebc3c8c28ae6749340b3945d28654c02b4343d406e6d4dde +size 12569 diff --git a/legex/analysis/__init__.py b/legex/analysis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a0c6e0c9b1b9cc269e5e3b352a5caf6c959a3567 --- /dev/null +++ b/legex/analysis/__init__.py @@ -0,0 +1,8 @@ +"""Evaluation reporting and inter-annotator analysis. + +Cross-jurisdiction aggregate tables (``aggregate``), the inter-annotator +agreement (``iaa``), the minimal ANALYSIS.md report (``report``), +figures (``plots``), the LaTeX results section (``quant_results``), and the +Goldenset data-quality audit (``goldenset_quality``). Built on the shared +scoring engine in ``legex.evaluation``. +""" diff --git a/legex/analysis.py b/legex/analysis/aggregate.py similarity index 69% rename from legex/analysis.py rename to legex/analysis/aggregate.py index 133a39eac58e3078e01017e0b551c12df25b12ff..9c4f459510f420344590f9cba4be444f54fe236d 100644 --- a/legex/analysis.py +++ b/legex/analysis/aggregate.py @@ -16,9 +16,10 @@ import sys from collections import defaultdict from pathlib import Path +from legex import published from legex.config import settings -from legex.evaluation import SYSTEMS, _BUCKETS, _derived, score_country -from legex.utils import evaluable_countries +from legex.evaluation import BUCKETS, derived, score_country +from legex.utils import countries_with_goldenset log = logging.getLogger(__name__) @@ -71,10 +72,9 @@ def derived_metrics(c: dict[str, int]) -> dict[str, float]: tp, mism, miss, hallu, tn = c["tp"], c["mismatch"], c["missed"], c["hallucinated"], c["tn"] total = tp + mism + miss + hallu + tn filled_gold = tp + mism + miss - emitted = tp + mism + hallu empty_gold = hallu + tn both_filled = tp + mism - p, r, f1 = _derived(c) + p, r, f1 = derived(c) return { "accuracy": (tp + tn) / total if total else 0.0, "recall_when_filled": r, @@ -87,45 +87,67 @@ def derived_metrics(c: dict[str, int]) -> dict[str, float]: def add_buckets(a: dict[str, int], b: dict[str, int]) -> dict[str, int]: - return {k: a.get(k, 0) + b.get(k, 0) for k in _BUCKETS} + return {k: a.get(k, 0) + b.get(k, 0) for k in BUCKETS} def sum_buckets(counters: dict[str, dict[str, int]], cols: tuple[str, ...] | None = None) -> dict[str, int]: """Sum bucket counts across `cols` (or all columns when None).""" - out = {k: 0 for k in _BUCKETS} + out = {k: 0 for k in BUCKETS} for col, c in counters.items(): if cols is not None and col not in cols: continue - for k in _BUCKETS: + for k in BUCKETS: out[k] += c[k] return out -_INFERENCE_CSV_RE = re.compile(r"^inference_(.+)\.csv$") +_INFERENCE_FILENAME_RE = re.compile(r"^Goldenset_(.+?)_(v\d+)_(full_text|pdf)_(.+)\.jsonl$") -def models_present(cc: str) -> list[str]: - """Which systems have an inference_.csv on disk for `cc`.""" +def models_present(cc: str, inference_dir: Path | None = None) -> list[str]: + """Inverse of `model_filename_slug`: which models have an inference file for `cc`.""" + if inference_dir is not None: + return sorted( + model for model in published.MODEL_FILES + if published.inference_file(inference_dir, cc, model).exists() + ) d = settings.data_dir / cc if not d.is_dir(): return [] found: set[str] = set() - for p in d.glob("inference_*.csv"): - m = _INFERENCE_CSV_RE.match(p.name) - if m: - found.add(m.group(1)) + for p in d.glob("Goldenset_*_v*_*.jsonl"): + m = _INFERENCE_FILENAME_RE.match(p.name) + if not m: + continue + slug = m.group(4) + # `gemini_gemini-3.1-flash-lite` <- `gemini/gemini-3.1-flash-lite`. + # We can't safely un-slug arbitrary slashes, so look for known providers. + if slug.startswith("gemini_"): + found.add("gemini/" + slug[len("gemini_"):]) + elif slug.startswith("anthropic_"): + found.add("anthropic/" + slug[len("anthropic_"):]) + else: + found.add(slug) return sorted(found) def collect( - countries: list[str], models: list[str] + countries: list[str], + models: list[str], + prompt_version: str, + source: str, + gold_dir: Path | None = None, + inference_dir: Path | None = None, ) -> list[tuple[str, str, dict[str, dict[str, int]]]]: - """Score every (country, system) pair. Returns rows of (cc, system, counters).""" + """Score every (country, model) pair. Returns rows of (cc, model, counters).""" rows: list[tuple[str, str, dict[str, dict[str, int]]]] = [] for cc in countries: - ms = models or models_present(cc) + ms = models or models_present(cc, inference_dir) for model in ms: - result = score_country(cc, model, verbose=False) + result = score_country( + cc, prompt_version, source, model, verbose=False, + gold_dir=gold_dir, inference_dir=inference_dir, + ) if result is None: log.info(f"[{cc}/{model}] no scoreable data, skipping") continue @@ -148,7 +170,7 @@ def _write_csv(path: Path, header: list[str], rows: list[dict[str, object]]) -> def write_per_country_per_column(out: Path, rows: list[tuple[str, str, dict[str, dict[str, int]]]]) -> None: - header = ["country", "model", "column", *_BUCKETS, *DERIVED_KEYS] + header = ["country", "model", "column", *BUCKETS, *DERIVED_KEYS] out_rows: list[dict[str, object]] = [] for cc, model, counters in rows: for col, c in counters.items(): @@ -165,8 +187,8 @@ def write_per_country(out: Path, rows: list[tuple[str, str, dict[str, dict[str, cost-block-only summed buckets. Also adds tradition / language tags.""" header = [ "country", "model", "legal_tradition", "language_family", - *_BUCKETS, *DERIVED_KEYS, - *(f"cost_{k}" for k in _BUCKETS), + *BUCKETS, *DERIVED_KEYS, + *(f"cost_{k}" for k in BUCKETS), *(f"cost_{k}" for k in DERIVED_KEYS), ] out_rows: list[dict[str, object]] = [] @@ -181,7 +203,7 @@ def write_per_country(out: Path, rows: list[tuple[str, str, dict[str, dict[str, "language_family": LANGUAGE_FAMILY.get(cc, ""), **all_b, **{k: _fmt(d_all[k]) for k in DERIVED_KEYS}, - **{f"cost_{k}": cost_b[k] for k in _BUCKETS}, + **{f"cost_{k}": cost_b[k] for k in BUCKETS}, **{f"cost_{k}": _fmt(d_cost[k]) for k in DERIVED_KEYS}, }) _write_csv(out / "per_country.csv", header, out_rows) @@ -189,12 +211,12 @@ def write_per_country(out: Path, rows: list[tuple[str, str, dict[str, dict[str, def write_per_column(out: Path, rows: list[tuple[str, str, dict[str, dict[str, int]]]]) -> None: """One row per (model, column): summed across countries.""" - agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in _BUCKETS}) + agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in BUCKETS}) for _cc, model, counters in rows: for col, c in counters.items(): - for k in _BUCKETS: + for k in BUCKETS: agg[(model, col)][k] += c[k] - header = ["model", "column", *_BUCKETS, *DERIVED_KEYS] + header = ["model", "column", *BUCKETS, *DERIVED_KEYS] out_rows: list[dict[str, object]] = [] for (model, col), c in sorted(agg.items()): d = derived_metrics(c) @@ -209,8 +231,8 @@ def _write_grouped( out: Path, name: str, group_map: dict[str, str], rows: list[tuple[str, str, dict[str, dict[str, int]]]], ) -> None: - agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in _BUCKETS}) - cost_agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in _BUCKETS}) + agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in BUCKETS}) + cost_agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in BUCKETS}) counts: dict[tuple[str, str], int] = defaultdict(int) for cc, model, counters in rows: group = group_map.get(cc) @@ -219,14 +241,14 @@ def _write_grouped( key = (model, group) all_b = sum_buckets(counters) cost_b = sum_buckets(counters, COST_BLOCK) - for k in _BUCKETS: + for k in BUCKETS: agg[key][k] += all_b[k] cost_agg[key][k] += cost_b[k] counts[key] += 1 header = [ "model", "group", "n_countries", - *_BUCKETS, *DERIVED_KEYS, - *(f"cost_{k}" for k in _BUCKETS), + *BUCKETS, *DERIVED_KEYS, + *(f"cost_{k}" for k in BUCKETS), *(f"cost_{k}" for k in DERIVED_KEYS), ] out_rows: list[dict[str, object]] = [] @@ -239,7 +261,7 @@ def _write_grouped( "n_countries": counts[(model, group)], **c, **{k: _fmt(d_all[k]) for k in DERIVED_KEYS}, - **{f"cost_{k}": cost_c[k] for k in _BUCKETS}, + **{f"cost_{k}": cost_c[k] for k in BUCKETS}, **{f"cost_{k}": _fmt(d_cost[k]) for k in DERIVED_KEYS}, }) _write_csv(out / f"{name}.csv", header, out_rows) @@ -267,21 +289,22 @@ def write_headline_latex(out: Path, rows: list[tuple[str, str, dict[str, dict[st lines.append( "\\caption{Headline extraction metrics by jurisdiction for model \\texttt{" f"{_latex_escape(model)}" - "}. Recall when filled is over the cells where the expert recorded a value. " - "False-fill rate is the share of legitimately-empty cells where the model " - "invented a value.}" + "}. Accuracy is per-cell. Recall is over the cells where the expert recorded a value. " + "Hallucination rate is the share of legitimately-empty cells where the model invented a value. " + "Cost-block $F_1$ aggregates over the four monetary variables.}" ) lines.append("\\label{tab:headline-" + re.sub(r"[^a-zA-Z0-9]+", "-", model).strip("-") + "}") lines.append("\\centering\\small") - lines.append("\\begin{tabular}{@{}lrr@{}}") + lines.append("\\begin{tabular}{@{}lrrrr@{}}") lines.append("\\toprule") - lines.append("Jurisdiction & Recall when filled & False-fill rate \\\\") + lines.append("Jurisdiction & Accuracy & Recall$_{\\text{filled}}$ & Hallu. rate & Cost $F_1$ \\\\") lines.append("\\midrule") for cc, counters in sorted(by_model[model]): d_all = derived_metrics(sum_buckets(counters)) + d_cost = derived_metrics(sum_buckets(counters, COST_BLOCK)) lines.append( - f"{cc.upper()} & {_pct(d_all['recall_when_filled'])} " - f"& {_pct(d_all['hallucination_rate'])} \\\\" + f"{cc.upper()} & {_pct(d_all['accuracy'])} & {_pct(d_all['recall_when_filled'])} " + f"& {_pct(d_all['hallucination_rate'])} & {d_cost['f1']:.3f} \\\\" ) lines.append("\\bottomrule") lines.append("\\end{tabular}") @@ -292,10 +315,10 @@ def write_headline_latex(out: Path, rows: list[tuple[str, str, dict[str, dict[st def write_per_field_latex(out: Path, rows: list[tuple[str, str, dict[str, dict[str, int]]]]) -> None: """One LaTeX table per model: rows = variable, cols = headline metrics (summed across jurisdictions).""" - by_model_col: dict[str, dict[str, dict[str, int]]] = defaultdict(lambda: defaultdict(lambda: {k: 0 for k in _BUCKETS})) + by_model_col: dict[str, dict[str, dict[str, int]]] = defaultdict(lambda: defaultdict(lambda: {k: 0 for k in BUCKETS})) for _cc, model, counters in rows: for col, c in counters.items(): - for k in _BUCKETS: + for k in BUCKETS: by_model_col[model][col][k] += c[k] out.mkdir(parents=True, exist_ok=True) @@ -306,20 +329,20 @@ def write_per_field_latex(out: Path, rows: list[tuple[str, str, dict[str, dict[s lines.append( "\\caption{Per-field extraction metrics, summed across jurisdictions, for model \\texttt{" f"{_latex_escape(model)}" - "}. Recall when filled is over the cells where the expert recorded a value. False-fill " - "rate is the share of legitimately-empty cells where the model invented a value.}" + "}.}" ) lines.append("\\label{tab:per-field-" + re.sub(r"[^a-zA-Z0-9]+", "-", model).strip("-") + "}") lines.append("\\centering\\small") - lines.append("\\begin{tabular}{@{}lrr@{}}") + lines.append("\\begin{tabular}{@{}lrrrr@{}}") lines.append("\\toprule") - lines.append("Variable & Recall when filled & False-fill rate \\\\") + lines.append("Variable & Accuracy & Recall$_{\\text{filled}}$ & Hallu. rate & $F_1$ \\\\") lines.append("\\midrule") for col, c in sorted(by_model_col[model].items()): d = derived_metrics(c) lines.append( - f"\\texttt{{{_latex_escape(col)}}} " - f"& {_pct(d['recall_when_filled'])} & {_pct(d['hallucination_rate'])} \\\\" + f"\\texttt{{{_latex_escape(col)}}} & {_pct(d['accuracy'])} " + f"& {_pct(d['recall_when_filled'])} & {_pct(d['hallucination_rate'])} " + f"& {d['f1']:.3f} \\\\" ) lines.append("\\bottomrule") lines.append("\\end{tabular}") @@ -331,12 +354,21 @@ def write_per_field_latex(out: Path, rows: list[tuple[str, str, dict[str, dict[s def analyse( countries: list[str] | None, models: list[str] | None, + prompt_version: str, + source: str, out_dir: Path, + gold_dir: Path | None = None, + inference_dir: Path | None = None, ) -> None: - targets = countries or evaluable_countries() - rows = collect(targets, models or []) + if countries is None: + countries = ( + published.countries_with_gold(gold_dir) + if gold_dir is not None + else countries_with_goldenset() + ) + rows = collect(countries, models or [], prompt_version, source, gold_dir, inference_dir) if not rows: - log.warning("no (country, system) pairs produced results; nothing to write") + log.warning("no (country, model) pairs produced results; nothing to write") return out_dir.mkdir(parents=True, exist_ok=True) write_per_country_per_column(out_dir, rows) @@ -346,7 +378,7 @@ def analyse( _write_grouped(out_dir, "per_language", LANGUAGE_FAMILY, rows) write_headline_latex(out_dir / "tables", rows) write_per_field_latex(out_dir / "tables", rows) - log.info(f"wrote analysis for {len(rows)} (country, system) pairs to {out_dir}") + log.info(f"wrote analysis for {len(rows)} (country, model) pairs to {out_dir}") def main() -> None: @@ -357,30 +389,45 @@ def main() -> None: ) parser = argparse.ArgumentParser( prog="legex-analysis", - description="Cross-jurisdiction analysis of JSONL goldensets vs system inference CSVs.", + description="Cross-jurisdiction analysis of Goldenset vs LLM predictions.", ) parser.add_argument( "--country", action="extend", nargs="+", dest="countries", - help=( - "Country code(s). Repeatable. " - "Default: every jurisdiction with a goldenset_.jsonl minus the " - "round-2 exclusion set (BE/NP/RS plus TW/BR/HK/IN)." - ), + help="Country code(s). Repeatable. Default: all countries with a Goldenset.", ) parser.add_argument( - "--system", "--model", action="extend", nargs="+", dest="models", - choices=list(SYSTEMS), - help=f"Inference system(s). Repeatable. Default: every system with a CSV on disk per country ({list(SYSTEMS)}).", + "--model", action="append", dest="models", + help="Model id (repeatable). Default: every model with an inference file per country.", ) + parser.add_argument("--prompt_version", default="v3") parser.add_argument( "--out", type=Path, default=Path("data/analysis"), help="Output directory (default: data/analysis).", ) + parser.add_argument( + "--gold-dir", type=Path, default=None, + help="Read gold labels from published goldenset JSONL under this directory " + "instead of the XLSX workbooks.", + ) + parser.add_argument( + "--inference-dir", type=Path, default=None, + help="Read predictions from published inference JSONL under this directory " + "instead of the working files.", + ) + src = parser.add_mutually_exclusive_group() + src.add_argument("--full_text", dest="source", action="store_const", const="full_text") + src.add_argument("--pdf", dest="source", action="store_const", const="pdf") args = parser.parse_args() + if args.inference_dir is None and args.source is None: + parser.error("one of --full_text / --pdf is required (unless --inference-dir is used)") analyse( countries=args.countries, models=args.models, + prompt_version=args.prompt_version, + source=args.source, out_dir=args.out, + gold_dir=args.gold_dir, + inference_dir=args.inference_dir, ) diff --git a/legex/analysis/countries.py b/legex/analysis/countries.py new file mode 100644 index 0000000000000000000000000000000000000000..07637f3337beb97300b2b48874120ab7095b5832 --- /dev/null +++ b/legex/analysis/countries.py @@ -0,0 +1,22 @@ +"""Country sets and display names shared across the analysis and paper scripts.""" + +# The 19 jurisdictions of the public release (HF legexbenchmark/goldensets). +# data/ also contains non-release drafts (al, at, gh, it, li, lu, xk) which +# must be excluded from every paper aggregate. +RELEASE_COUNTRIES: tuple[str, ...] = ( + "am", "au", "be", "br", "ch", "de", "es", "fr", "ge", "hk", + "in", "np", "nz", "ph", "rs", "sg", "tw", "uk", "us", +) + +# Core benchmark: >=100 judgments AND independent reannotations (three +# annotators each). Nepal and Spain reach 100+ cases but are single-annotated. +CORE_COUNTRIES: tuple[str, ...] = ("br", "ch", "de", "ge", "sg", "tw", "uk", "us") + +COUNTRY_NAMES: dict[str, str] = { + "am": "Armenia", "au": "Australia", "be": "Belgium", "br": "Brazil", + "ch": "Switzerland", "de": "Germany", "es": "Spain", "fr": "France", + "ge": "Georgia", "hk": "Hong Kong", "in": "India", "np": "Nepal", + "nz": "New Zealand", "ph": "Philippines", "rs": "Serbia", + "sg": "Singapore", "tw": "Taiwan", "uk": "United Kingdom", + "us": "United States", +} diff --git a/legex/analysis/goldenset_quality.py b/legex/analysis/goldenset_quality.py new file mode 100644 index 0000000000000000000000000000000000000000..b3e0f6412309d3e572273100779fc9ffdca0367c --- /dev/null +++ b/legex/analysis/goldenset_quality.py @@ -0,0 +1,353 @@ +"""Goldenset data-quality audit: empty vs. valid vs. malformed per variable. + +This does *not* judge whether a label is the right answer (there is no +reference) — it checks whether each filled cell conforms to the variable's +expected type/format, so we can report coding hygiene. Three buckets per cell: + +- ``empty`` — None / blank (the value is simply absent), +- ``valid`` — filled and conforms to the variable's type/format/range/vocab, +- ``invalid`` — filled but malformed, e.g. free text where a number is + expected, a ratio outside ``[0, 1]``, a non-ISO date, or an ISIC code that is + not in the controlled vocabulary. + +Outputs two tables (CSV + Markdown), matching the request: + +1. **By country** — one row per (country, variable) with absolute counts. +2. **Aggregated by variable** — one row per variable, summed over all + countries, with counts and percentages. + +Usage: + uv run legex-goldenset-quality + uv run legex-goldenset-quality --countries ch,de,br --out data/analysis/quality +""" + +import argparse +import csv +import logging +import re +import sys +from collections import defaultdict +from datetime import date, datetime +from pathlib import Path + +from openpyxl import load_workbook + +from legex.config import settings +from legex.evaluation import is_label_column, normalise +from legex.utils import countries_with_goldenset, goldenset_path, goldenset_sheet + +log = logging.getLogger("legex.goldenset_quality") + +# --- Field type groups (the eleven substantive variables) ------------------- +DATE_FIELDS = frozenset({"trial_start_date", "trial_end_date"}) +RATIO_FIELDS = frozenset({"plaintiff_loosing_share"}) +MONEY_FIELDS = frozenset( + {"court_cost_awarded_nominal", "party_compensation_awarded_nominal"} +) +# dispute_value_nominal is money but also allows the literal "nonpecuniary". +DISPUTE_FIELD = "dispute_value_nominal" +COUNT_FIELDS = frozenset({"plaintiffs_all_count", "defendants_all_count"}) +ISIC_FIELDS = frozenset( + {"plaintiff_no1_ISIC1_industry_category", "defendant_no1_ISIC1_industry_category"} +) +STRING_FIELDS = frozenset({"legal_subject_judgement"}) + +# The eleven substantive schema variables. Stray columns (e.g. a workbook that +# duplicates the identifier as "Case Id") are ignored so the report stays on +# the schema. +SCHEMA_FIELDS = ( + DATE_FIELDS | RATIO_FIELDS | MONEY_FIELDS | {DISPUTE_FIELD} + | COUNT_FIELDS | ISIC_FIELDS | STRING_FIELDS +) + +# Controlled ISIC vocabulary (v3 prompt) plus the two documented fallbacks. +ISIC_VOCAB = frozenset( + { + "a_agriculture_forestry_fishing", "b_mining_quarrying", "c_manufacturing", + "d_electricity_gas_steam_ac", "e_water_sewerage_waste_remediation", + "f_construction", "g_wholesale_retail_trade", "h_transportation_storage", + "i_accommodation_food_service", "j_publishing_broadcasting_content", + "k_telecom_it_info_services", "l_financial_insurance", "m_real_estate", + "n_professional_scientific_technical", "o_administrative_support", + "p_public_admin_defence", "q_education", "r_human_health_social_work", + "s_arts_entertainment_recreation", "t_other_service_activities", + "u_households_as_employers", "v_extraterritorial_organisations", + "no_allocation_possible", + } +) + +BUCKETS = ("empty", "valid", "invalid") + +_NUM_RE = re.compile(r"^-?\d+(?:\.\d+)?$") + + +def _is_number(s: str) -> bool: + """A clean plain number per the codebook (period decimal, no separators).""" + return bool(_NUM_RE.match(s.strip())) + + +def _is_int(s: str) -> bool: + s = s.strip() + if _NUM_RE.match(s): + f = float(s) + return f.is_integer() and f >= 0 + return False + + +def _is_iso_date(value: object, normalised: str) -> bool: + if isinstance(value, (date, datetime)): + return True + try: + date.fromisoformat(normalised.strip()) + return True + except ValueError: + return False + + +def classify_cell(field: str, raw: object) -> str: + """Return one of BUCKETS for a single (field, raw cell value).""" + s = normalise(raw) + if not s: + return "empty" + + if field in DATE_FIELDS: + return "valid" if _is_iso_date(raw, s) else "invalid" + + if field == DISPUTE_FIELD: + if s.lower() == "nonpecuniary": + return "valid" + return "valid" if _is_number(s) else "invalid" + + if field in MONEY_FIELDS: + return "valid" if _is_number(s) else "invalid" + + if field in RATIO_FIELDS: + if not _is_number(s): + return "invalid" + return "valid" if 0.0 <= float(s) <= 1.0 else "invalid" + + if field in COUNT_FIELDS: + return "valid" if _is_int(s) else "invalid" + + if field in ISIC_FIELDS: + return "valid" if s.lower() in ISIC_VOCAB else "invalid" + + if field in STRING_FIELDS: + # Free text: a bare number where a translated legal subject is expected + # is the malformed case; otherwise any non-empty string is acceptable. + return "invalid" if _is_number(s) else "valid" + + # Unknown / currency-like columns are excluded upstream; default to valid. + return "valid" + + +# (country, field) -> {bucket: count} +Counts = dict[tuple[str, str], dict[str, int]] + + +def audit_country(cc: str) -> tuple[dict[str, dict[str, int]], int]: + """Return ({field: {bucket: count}}, n_rows) for one country.""" + path = goldenset_path(cc) + wb = load_workbook(path, read_only=True, data_only=True) + try: + ws = goldenset_sheet(wb) + rows = ws.iter_rows(values_only=True) + header = [str(c) if c is not None else "" for c in next(rows)] + label_cols = [h for h in header if is_label_column(h) and h in SCHEMA_FIELDS] + per_field: dict[str, dict[str, int]] = { + f: {b: 0 for b in BUCKETS} for f in label_cols + } + n_rows = 0 + for row in rows: + cells = dict(zip(header, row)) + # Count only substantively-annotated rows + if not any(cells.get(f) not in (None, "") for f in label_cols): + continue + n_rows += 1 + for f in label_cols: + per_field[f][classify_cell(f, cells.get(f))] += 1 + return per_field, n_rows + finally: + wb.close() + + +def _expected_hint(field: str) -> str: + """Human-readable description of the valid form, for the hand-cleaning worklist.""" + if field in DATE_FIELDS: + return "ISO date YYYY-MM-DD" + if field == DISPUTE_FIELD: + return "number or 'nonpecuniary'" + if field in MONEY_FIELDS: + return "number (period decimal, no thousands separators / currency symbols)" + if field in RATIO_FIELDS: + return "number in [0, 1]" + if field in COUNT_FIELDS: + return "integer >= 0" + if field in ISIC_FIELDS: + return "ISIC category from the controlled vocab, or no_allocation_possible" + if field in STRING_FIELDS: + return "text (not a bare number)" + return "" + + +def collect_invalid(cc: str) -> list[tuple[str, str, str, str, str]]: + """Return one (country, case_id, field, raw_value, expected) row per invalid cell.""" + path = goldenset_path(cc) + wb = load_workbook(path, read_only=True, data_only=True) + out: list[tuple[str, str, str, str, str]] = [] + try: + ws = goldenset_sheet(wb) + rows = ws.iter_rows(values_only=True) + header = [str(c) if c is not None else "" for c in next(rows)] + label_cols = [h for h in header if is_label_column(h) and h in SCHEMA_FIELDS] + for row in rows: + cells = dict(zip(header, row)) + if not any(cells.get(f) not in (None, "") for f in label_cols): + continue + case_id = cells.get("case_id") + case_id = str(case_id) if case_id not in (None, "") else "" + for f in label_cols: + raw = cells.get(f) + if classify_cell(f, raw) == "invalid": + out.append((cc, case_id, f, "" if raw is None else str(raw), _expected_hint(f))) + return out + finally: + wb.close() + + +def _pct(n: int, total: int) -> float: + return n / total if total else 0.0 + + +def write_by_country_csv(counts: Counts, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow(["country", "variable", "n", "empty", "valid", "invalid", + "pct_empty", "pct_valid", "pct_invalid"]) + for (cc, field), c in sorted(counts.items()): + total = c["empty"] + c["valid"] + c["invalid"] + w.writerow([cc, field, total, c["empty"], c["valid"], c["invalid"], + f"{_pct(c['empty'], total):.4f}", + f"{_pct(c['valid'], total):.4f}", + f"{_pct(c['invalid'], total):.4f}"]) + + +def write_by_variable_csv(agg: dict[str, dict[str, int]], path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow(["variable", "n", "empty", "valid", "invalid", + "pct_empty", "pct_valid", "pct_invalid"]) + for field, c in sorted(agg.items()): + total = c["empty"] + c["valid"] + c["invalid"] + w.writerow([field, total, c["empty"], c["valid"], c["invalid"], + f"{_pct(c['empty'], total):.4f}", + f"{_pct(c['valid'], total):.4f}", + f"{_pct(c['invalid'], total):.4f}"]) + + +def write_invalid_csv(rows: list[tuple[str, str, str, str, str]], path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow(["country", "case_id", "field", "raw_value", "expected"]) + w.writerows(rows) + + +def render_markdown(counts: Counts, agg: dict[str, dict[str, int]]) -> str: + lines = ["# Goldenset data-quality audit", ""] + lines.append("Buckets: **empty** (absent), **valid** (well-typed), " + "**invalid** (filled but malformed, e.g. text where a number " + "is expected, ratio outside [0,1], non-ISO date, unknown ISIC).") + lines.append("") + + lines.append("## Aggregated by variable (all countries)") + lines.append("") + lines.append("| Variable | n | empty | valid | invalid | %empty | %valid | %invalid |") + lines.append("|---|---:|---:|---:|---:|---:|---:|---:|") + for field, c in sorted(agg.items()): + total = c["empty"] + c["valid"] + c["invalid"] + lines.append( + f"| `{field}` | {total} | {c['empty']} | {c['valid']} | {c['invalid']} | " + f"{_pct(c['empty'], total):.1%} | {_pct(c['valid'], total):.1%} | " + f"{_pct(c['invalid'], total):.1%} |" + ) + lines.append("") + + lines.append("## By country (absolute counts)") + lines.append("") + lines.append("| Country | Variable | n | empty | valid | invalid |") + lines.append("|---|---|---:|---:|---:|---:|") + for (cc, field), c in sorted(counts.items()): + total = c["empty"] + c["valid"] + c["invalid"] + lines.append( + f"| `{cc}` | `{field}` | {total} | {c['empty']} | {c['valid']} | {c['invalid']} |" + ) + return "\n".join(lines) + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig(level=logging.INFO, format="%(message)s") + parser = argparse.ArgumentParser(description="Goldenset data-quality audit.") + parser.add_argument("--countries", default=None, help="Comma-separated codes (default: all).") + parser.add_argument("--out", type=Path, default=None, + help="Output dir (default data/analysis/quality).") + parser.add_argument("--list-invalid", action="store_true", + help="Also write invalid_cells.csv: one row per invalid cell " + "(country, case_id, field, raw_value, expected) for hand-cleaning.") + args = parser.parse_args(argv) + + countries = ( + [c.strip() for c in args.countries.split(",") if c.strip()] + if args.countries else countries_with_goldenset() + ) + out_dir = args.out or (settings.data_dir / "analysis" / "quality") + + counts: Counts = {} + agg: dict[str, dict[str, int]] = defaultdict(lambda: {b: 0 for b in BUCKETS}) + for cc in countries: + gs = goldenset_path(cc) + if not gs.exists(): + log.warning("[%s] no goldenset, skipping", cc) + continue + per_field, n_rows = audit_country(cc) + log.info("[%s] %d rows, %d variables", cc, n_rows, len(per_field)) + for field, c in per_field.items(): + counts[(cc, field)] = c + for b in BUCKETS: + agg[field][b] += c[b] + + if not counts: + log.error("no goldensets scored") + return 1 + + write_by_country_csv(counts, out_dir / "by_country.csv") + write_by_variable_csv(agg, out_dir / "by_variable.csv") + report = render_markdown(counts, dict(agg)) + (out_dir / "report.md").write_text(report, encoding="utf-8") + + if args.list_invalid: + invalid_rows: list[tuple[str, str, str, str, str]] = [] + for cc in countries: + if goldenset_path(cc).exists(): + invalid_rows.extend(collect_invalid(cc)) + write_invalid_csv(invalid_rows, out_dir / "invalid_cells.csv") + log.info("wrote %s (%d invalid cells)", + out_dir / "invalid_cells.csv", len(invalid_rows)) + + # Console summary: the aggregated-by-variable table. + width = max((len(f) for f in agg), default=len("variable")) + print(f"\n{'variable'.ljust(width)} {'n':>5} {'empty':>6} {'valid':>6} {'invalid':>7}") + for field, c in sorted(agg.items()): + total = c["empty"] + c["valid"] + c["invalid"] + print(f"{field.ljust(width)} {total:>5} " + f"{_pct(c['empty'], total):>6.1%} {_pct(c['valid'], total):>6.1%} " + f"{_pct(c['invalid'], total):>7.1%}") + log.info("\nwrote %s, %s, %s", + out_dir / "by_country.csv", out_dir / "by_variable.csv", out_dir / "report.md") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/legex/analysis/iaa.py b/legex/analysis/iaa.py new file mode 100644 index 0000000000000000000000000000000000000000..552006b4c04092083796707f27eb01cda442c683 --- /dev/null +++ b/legex/analysis/iaa.py @@ -0,0 +1,484 @@ +"""Inter-annotator agreement (IAA). + +How much do human experts agree? We collect, per core jurisdiction, the +primary expert labels and the secondary labels of the independent +reannotators, then report per-field exact agreement, tolerant agreement +(numeric/date aware, reusing the evaluation comparator), and Cohen's kappa +for every annotator pair. Annotators are identified by the same salted +``annotator_id`` hashes as the published goldensets (the primary annotation +carries the role label ``primary``). + +Inputs +------ +- Published mode (``--gold-dir``): the released + ``goldensets/data//goldenset_.jsonl`` files — the first row per + ``case_id`` is the primary annotation, further rows are reannotations. +- Maintainer mode (default): primary labels from ``data//Goldenset_*.xlsx`` + plus the returned re-annotation workbooks under + ``data/reannotation/incoming///`` (folder names are + mapped to hashes via the local ``annotators.json`` + ``ANNOTATOR_SALT``). +- Candidate (system) labels: inference JSONL — loaded here for + ``scripts/alt_test_reference.py`` and ``scripts/alt_test_decomposition.py``. + +Usage +----- + uv run legex-iaa # human-human agreement + uv run legex-iaa --countries ch,de,br --out data/analysis/iaa + uv run legex-iaa --gold-dir ../goldensets/data # from the published data +""" + +import argparse +import csv +import hashlib +import json +import logging +import os +import sys +from collections import defaultdict +from dataclasses import dataclass +from itertools import combinations +from pathlib import Path + +from openpyxl import load_workbook + +from legex import published +from legex.analysis.countries import CORE_COUNTRIES +from legex.config import settings +from legex.evaluation import is_label_column, normalise, values_agree +from legex.inference import inference_output_path +from legex.published import PRIMARY +from legex.utils import goldenset_path, goldenset_sheet, norm_case_id, read_inference_jsonl + +log = logging.getLogger("legex.iaa") + +# Folder name (as shipped in the re-annotation pack) -> country code. +FOLDER_TO_CODE: dict[str, str] = { + "Armenia": "am", + "Australia": "au", + "Belgium": "be", + "Brazil": "br", + "France": "fr", + "Georgia": "ge", + "Germany": "de", + "New_Zealand": "nz", + "Philippines": "ph", + "Serbia": "rs", + "Singapore": "sg", + "Spain": "es", + "Switzerland": "ch", + "Taiwan": "tw", + "United_Kingdom": "uk", + "United_States": "us", +} + +# (annotator, country, case_id) -> {field: normalised value} +LabelKey = tuple[str, str, str] +LabelMap = dict[LabelKey, dict[str, str]] + + +def _read_xlsx_labels(path: Path) -> dict[str, dict[str, str]]: + """case_id -> {label_field: normalised value} from a Goldenset-shaped xlsx.""" + wb = load_workbook(path, read_only=True, data_only=True) + try: + ws = goldenset_sheet(wb) + rows = ws.iter_rows(values_only=True) + header = [str(c) if c is not None else "" for c in next(rows)] + # The reannotation pack capitalises the link column ("Link"); both are + # non-label and dropped by is_label_column anyway. + label_cols = [h for h in header if is_label_column(h)] + id_idx = header.index("case_id") if "case_id" in header else 0 + out: dict[str, dict[str, str]] = {} + for row in rows: + if not any(c not in (None, "") for c in row): + continue + cells = dict(zip(header, row)) + case_id = normalise(row[id_idx]) + if not case_id: + continue + labels = {c: normalise(cells.get(c)) for c in label_cols} + out[norm_case_id(case_id)] = labels + return out + finally: + wb.close() + + +def _read_inference_labels(path: Path) -> dict[str, dict[str, str]]: + """case_id -> {field: normalised value} from an inference JSONL.""" + out: dict[str, dict[str, str]] = {} + for row in read_inference_jsonl(path): + case_id = normalise(row.get("case_id")) + if not case_id: + continue + labels = {k: normalise(v) for k, v in row.items() if is_label_column(k)} + # Failed inference (error set, or nothing extracted at all) is treated + # like absent inference — same rule as evaluation/scoring.py. + if normalise(row.get("error")) or not any(labels.values()): + continue + out[norm_case_id(case_id)] = labels + return out + + +def incoming_root() -> Path: + return settings.data_dir / "reannotation" / "incoming" + + +def _annotator_hashes() -> dict[str, str]: + """``incoming/`` name -> salted ``annotator_id``, so the maintainer + mode emits the same pseudonymous ids as the published goldensets. + + Reads the gitignored ``data/reannotation/annotators.json`` and + ``ANNOTATOR_SALT`` (same recipe as ``submission/convert_goldenset_to_jsonl.py``). + """ + path = settings.data_dir / "reannotation" / "annotators.json" + if not path.exists(): + return {} + try: + from dotenv import load_dotenv + load_dotenv() + except ImportError: + pass + salt = os.environ.get("ANNOTATOR_SALT", "") + if not salt: + raise RuntimeError( + "annotators.json found but ANNOTATOR_SALT is unset — refusing to emit " + "annotator names; set the salt so ids match the published goldensets" + ) + data = json.loads(path.read_text(encoding="utf-8")) + out: dict[str, str] = {} + for entry in data.get("reannotations", []): + parts = Path(entry["file"]).parts + if "incoming" not in parts: + continue + folder = parts[parts.index("incoming") + 1] + out[folder] = hashlib.sha256(f"{salt}|{entry['name']}".encode("utf-8")).hexdigest()[:10] + return out + + +def load_human_annotations(countries: list[str], gold_dir: Path | None = None) -> LabelMap: + """Primary labels + every secondary annotation. + + With ``gold_dir`` both come from the published goldenset JSONL; otherwise + from the maintainers' XLSX workbooks (primary) and the returned + re-annotation workbooks under ``data/reannotation/incoming/``. + """ + if gold_dir is not None: + return published.load_annotator_labels(gold_dir, countries) + + labels: LabelMap = {} + for cc in countries: + gs = goldenset_path(cc) + if not gs.exists(): + log.warning("[%s] no primary goldenset at %s", cc, gs) + continue + for case_id, fields in _read_xlsx_labels(gs).items(): + labels[(PRIMARY, cc, case_id)] = fields + + root = incoming_root() + if not root.is_dir(): + log.info("no returned re-annotations yet (%s absent)", root) + return labels + + hashes = _annotator_hashes() + for annotator_dir in sorted(root.iterdir()): + if not annotator_dir.is_dir(): + continue + annotator = hashes.get(annotator_dir.name) + if annotator is None: + log.warning( + "[%s] not in annotators.json — skipping (the converter would skip " + "it too, so keeping it here would break XLSX/JSONL parity)", + annotator_dir.name, + ) + continue + for country_dir in sorted(annotator_dir.iterdir()): + if not country_dir.is_dir(): + continue + cc = FOLDER_TO_CODE.get(country_dir.name, country_dir.name.lower()) + if cc not in countries: + continue + wbs = sorted(country_dir.glob("*Reannotate*.xlsx")) or sorted( + country_dir.glob("*Goldenset*.xlsx") + ) + if not wbs: + continue + for case_id, fields in _read_xlsx_labels(wbs[0]).items(): + # Only keep rows the re-annotator actually filled. + if any(fields.values()): + labels[(annotator, cc, case_id)] = fields + return labels + + +def load_candidate_annotations( + countries: list[str], + prompt_version: str, + source: str, + model: str, + inference_dir: Path | None = None, +) -> LabelMap: + labels: LabelMap = {} + for cc in countries: + path = ( + published.inference_file(inference_dir, cc, model) + if inference_dir is not None + else inference_output_path(cc, prompt_version, source, model) + ) + if not path.exists(): + log.debug("[%s] no candidate predictions %s", cc, path) + continue + for case_id, fields in _read_inference_labels(path).items(): + labels[(model, cc, case_id)] = fields + return labels + + +def cohen_kappa(pairs: list[tuple[str, str]]) -> float | None: + """Cohen's kappa on categorical labels (empty string is its own category). + + Returns None when fewer than two items or only a single category appears + (kappa undefined / degenerate). + """ + n = len(pairs) + if n < 2: + return None + categories = {a for a, _ in pairs} | {b for _, b in pairs} + if len(categories) < 2: + return None + po = sum(1 for a, b in pairs if a == b) / n + marg_a: dict[str, int] = defaultdict(int) + marg_b: dict[str, int] = defaultdict(int) + for a, b in pairs: + marg_a[a] += 1 + marg_b[b] += 1 + pe = sum((marg_a[c] / n) * (marg_b[c] / n) for c in categories) + if pe >= 1.0: + return None + return (po - pe) / (1 - pe) + + +@dataclass +class PairAgreement: + annotator_a: str + annotator_b: str + country: str + field: str + n: int + n_agree_exact: int + n_agree_tolerant: int + kappa: float | None + + @property + def pct_exact(self) -> float: + return self.n_agree_exact / self.n if self.n else 0.0 + + @property + def pct_tolerant(self) -> float: + return self.n_agree_tolerant / self.n if self.n else 0.0 + + +def _shared_cases( + labels: LabelMap, a: str, b: str, country: str +) -> list[str]: + cases_a = {cid for (an, cc, cid) in labels if an == a and cc == country} + cases_b = {cid for (an, cc, cid) in labels if an == b and cc == country} + return sorted(cases_a & cases_b) + + +def _label_fields(labels: LabelMap) -> list[str]: + fields: list[str] = [] + for fmap in labels.values(): + for k in fmap: + if k not in fields: + fields.append(k) + return fields + + +def pairwise_agreement(labels: LabelMap) -> list[PairAgreement]: + annotators = sorted({an for (an, _, _) in labels}) + countries = sorted({cc for (_, cc, _) in labels}) + fields = _label_fields(labels) + out: list[PairAgreement] = [] + + for country in countries: + for a, b in combinations(annotators, 2): + shared = _shared_cases(labels, a, b, country) + if not shared: + continue + for field in fields: + pairs: list[tuple[str, str]] = [] + n_exact = n_tol = 0 + for cid in shared: + av = labels[(a, country, cid)].get(field, "") + bv = labels[(b, country, cid)].get(field, "") + pairs.append((av, bv)) + if av == bv: + n_exact += 1 + if values_agree(av, bv, field): + n_tol += 1 + out.append( + PairAgreement( + annotator_a=a, + annotator_b=b, + country=country, + field=field, + n=len(shared), + n_agree_exact=n_exact, + n_agree_tolerant=n_tol, + kappa=cohen_kappa(pairs), + ) + ) + return out + + +# Shared with scripts/alt_test_reference.py (the adapter that runs the +# original authors' Alternative Annotator Test implementation on LEGEX data): +# free-text fields have no meaningful exact/tolerant agreement and are +# excluded from the alt-test. +FREE_TEXT_FIELDS = {"legal_subject_judgement", "translated_full_text"} +# Minimum judgements per annotator for a location test; the reference adapter +# passes this as min_instances_per_human. +MIN_INSTANCES_TEST = 10 + + +def write_pairwise_csv(rows: list[PairAgreement], path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow( + ["annotator_a", "annotator_b", "country", "field", "n", + "pct_exact", "pct_tolerant", "cohen_kappa"] + ) + for r in rows: + w.writerow([ + r.annotator_a, r.annotator_b, r.country, r.field, r.n, + f"{r.pct_exact:.4f}", f"{r.pct_tolerant:.4f}", + "" if r.kappa is None else f"{r.kappa:.4f}", + ]) + + +def write_kappa_audit_csv(labels: LabelMap, path: Path) -> int: + """Per-cell audit behind the pairwise kappa. + + One row per (country, annotator pair, shared case, label field): both + annotators' raw values plus the exact and tolerant agreement decisions. This + is the cell-level detail that ``pairwise_agreement.csv`` aggregates, kept for + inspecting individual disagreements. Returns the number of data rows written. + """ + path.parent.mkdir(parents=True, exist_ok=True) + annotators_by_cc: dict[str, set[str]] = defaultdict(set) + for (an, cc, _cid) in labels: + annotators_by_cc[cc].add(an) + n = 0 + with path.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow( + ["country", "case_id", "annotator_a", "annotator_b", "field", + "value1", "value2", "decision_exact", "decision_tolerant"] + ) + for cc in sorted(annotators_by_cc): + for a, b in combinations(sorted(annotators_by_cc[cc]), 2): + for cid in _shared_cases(labels, a, b, cc): + fa = labels[(a, cc, cid)] + fb = labels[(b, cc, cid)] + for field in sorted(set(fa) | set(fb)): + if not is_label_column(field): + continue + va = fa.get(field, "") + vb = fb.get(field, "") + w.writerow([ + cc, cid, a, b, field, va, vb, + "yes" if va == vb else "no", + "yes" if values_agree(va, vb, field) else "no", + ]) + n += 1 + return n + + +def summarise_by_field(rows: list[PairAgreement]) -> dict[str, dict[str, float]]: + """Weighted (by n) mean exact/tolerant agreement and mean kappa per field.""" + acc: dict[str, dict[str, float]] = defaultdict( + lambda: {"n": 0.0, "exact": 0.0, "tol": 0.0, "k_sum": 0.0, "k_n": 0.0} + ) + for r in rows: + a = acc[r.field] + a["n"] += r.n + a["exact"] += r.n_agree_exact + a["tol"] += r.n_agree_tolerant + if r.kappa is not None: + a["k_sum"] += r.kappa + a["k_n"] += 1 + out: dict[str, dict[str, float]] = {} + for field, a in acc.items(): + out[field] = { + "n": a["n"], + "pct_exact": a["exact"] / a["n"] if a["n"] else 0.0, + "pct_tolerant": a["tol"] / a["n"] if a["n"] else 0.0, + "mean_kappa": a["k_sum"] / a["k_n"] if a["k_n"] else float("nan"), + } + return out + + +def print_field_summary(summary: dict[str, dict[str, float]]) -> None: + width = max((len(f) for f in summary), default=len("field")) + width = max(width, len("field")) + print(f"\n{'field'.ljust(width)} {'n':>6} {'exact':>7} {'tolerant':>9} {'kappa':>7}") + for field, s in summary.items(): + k = s["mean_kappa"] + k_s = " - " if k != k else f"{k:>7.3f}" # NaN check + print( + f"{field.ljust(width)} {int(s['n']):>6} " + f"{s['pct_exact']:>7.2%} {s['pct_tolerant']:>9.2%} {k_s}" + ) + + +# CLI +def main(argv: list[str] | None = None) -> int: + logging.basicConfig(level=logging.INFO, format="%(message)s") + parser = argparse.ArgumentParser(description="Inter-annotator agreement.") + parser.add_argument( + "--countries", default=None, + help="Comma-separated country codes (default: the 8 core jurisdictions).", + ) + parser.add_argument( + "--out", type=Path, default=None, + help="Output dir (default data/analysis/iaa).", + ) + parser.add_argument( + "--gold-dir", type=Path, default=None, + help="Read all annotations from published goldenset JSONL under this " + "directory instead of the XLSX workbooks.", + ) + args = parser.parse_args(argv) + + countries = ( + [c.strip() for c in args.countries.split(",") if c.strip()] + if args.countries else list(CORE_COUNTRIES) + ) + out_dir = args.out or (settings.data_dir / "analysis" / "iaa") + + humans = load_human_annotations(countries, gold_dir=args.gold_dir) + annotators = sorted({an for (an, _, _) in humans}) + secondary = [a for a in annotators if a != PRIMARY] + log.info( + "loaded %d annotation records; annotators=%s", + len(humans), ", ".join(annotators) or "(none)", + ) + + rows = pairwise_agreement(humans) + if rows: + write_pairwise_csv(rows, out_dir / "pairwise_agreement.csv") + n_audit = write_kappa_audit_csv(humans, out_dir / "kappa_audit.csv") + summary = summarise_by_field(rows) + print_field_summary(summary) + log.info("\nwrote %s", out_dir / "pairwise_agreement.csv") + log.info("wrote %s (%d cells)", out_dir / "kappa_audit.csv", n_audit) + if not secondary: + log.info( + "\nNo secondary (returned) re-annotations found yet, so only primary " + "labels are present and human-human agreement is empty. Drop filled " + "workbooks under %s/// and re-run.", + incoming_root(), + ) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/legex/plots.py b/legex/analysis/plots.py similarity index 97% rename from legex/plots.py rename to legex/analysis/plots.py index 60b9f3997fe42b597795dcdf9f395c0e6288d8cd..88b241bee35b6aeffe8f30ba60b807c55b03f045 100644 --- a/legex/plots.py +++ b/legex/analysis/plots.py @@ -1,6 +1,6 @@ """Paper-ready figures from `data/analysis/*.csv`. -Reads the CSVs that `legex.analysis` emits and writes four PNGs into +Reads the CSVs that `legex-analysis` emits and writes four PNGs into ``data/analysis/figures/``: - ``hallucination_by_country.png`` — grouped bar, models side-by-side. @@ -110,7 +110,7 @@ def _heatmap( def _scatter(rows: list[dict[str, str]], out: Path) -> None: """Hallucination vs recall on the cost block. One marker per (cc, model).""" models = _models_in(rows) - markers = {models[0]: "o"} if len(models) == 1 else {models[0]: "o", models[1]: "s"} + markers = dict(zip(models, ("o", "s", "^", "D", "v", "P", "X"))) colors = plt.cm.tab20.colors fig, ax = plt.subplots(figsize=(7, 6)) diff --git a/legex/analysis/quant_results.py b/legex/analysis/quant_results.py new file mode 100644 index 0000000000000000000000000000000000000000..b477b146fc1f0df448f6ce3f0a73005cbfb4d7b0 --- /dev/null +++ b/legex/analysis/quant_results.py @@ -0,0 +1,307 @@ +"""Render the manuscript's headline results table (`tab:overall`). + +Reads `data/analysis/per_country_per_column.csv` (produced by +`legex-analysis`), restricts it to the 19 release jurisdictions, and renders +the camera-ready headline table in the poster convention: recall on +gold-filled cells, precision on emitted cells, F1, and the false-fill +(hallucination) rate on gold-empty cells — each percentage with its ±1 SE — +over the 10 structured fields (the 11 evaluated fields minus the free-text +``legal_subject_judgement``, whose unbounded label space has <1% human-human +agreement) and over the four-field cost block. Denominators (n) are reported +in the caption. + +The console echo additionally prints the "All 11 fields" cross-check and the +exact denominators so prose numbers can be sourced from the same run. +""" + +import argparse +import csv +import logging +import math +import sys +from pathlib import Path + +from legex.analysis.countries import RELEASE_COUNTRIES + +log = logging.getLogger(__name__) + +EVAL_FIELDS: tuple[str, ...] = ( + "legal_subject_judgement", + "trial_start_date", + "trial_end_date", + "dispute_value_nominal", + "plaintiff_loosing_share", + "court_cost_awarded_nominal", + "party_compensation_awarded_nominal", + "plaintiffs_all_count", + "defendants_all_count", + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +) + +STRUCTURED_FIELDS: tuple[str, ...] = tuple( + f for f in EVAL_FIELDS if f != "legal_subject_judgement" +) + +COST_BLOCK: tuple[str, ...] = ( + "dispute_value_nominal", + "plaintiff_loosing_share", + "court_cost_awarded_nominal", + "party_compensation_awarded_nominal", +) + +FIELD_SETS: tuple[tuple[str, str, tuple[str, ...]], ...] = ( + ("structured", "10 structured fields", STRUCTURED_FIELDS), + ("cost", "Cost block (4 fields)", COST_BLOCK), + ("all11", "All 11 fields", EVAL_FIELDS), # console cross-check only +) + +# (model id in CSV, label). Order = row order in the headline table. +# The paper set renders tab:overall exactly as in the manuscript; the "all" +# set adds the two transparency runs (released, not scored in the paper). +PAPER_SYSTEMS: tuple[tuple[str, str], ...] = ( + ("gemini/gemini-3.1-flash-lite", "Gemini"), + ("gpt-5.4-mini", "GPT-5.4-mini"), + ("harvey", "Harvey"), + ("legora-1", "Legora"), +) +ALL_SYSTEMS: tuple[tuple[str, str], ...] = ( + PAPER_SYSTEMS[:3] + + (("harvey-2", "Harvey 2"),) + + PAPER_SYSTEMS[3:] + + (("legora-2", "Legora 2"),) +) +SYSTEM_SETS = {"paper": PAPER_SYSTEMS, "all": ALL_SYSTEMS} + +_BUCKETS = ("tp", "mismatch", "missed", "hallucinated", "tn") + + +def _empty() -> dict[str, int]: + return {k: 0 for k in _BUCKETS} + + +def _se_pp(p: float, n: int) -> float: + """±1 standard error of a proportion, in percentage points.""" + return 100.0 * math.sqrt(p * (1.0 - p) / n) if n else float("nan") + + +def _metrics(c: dict[str, int]) -> dict[str, float]: + tp, mism, miss, hallu, tn = ( + c["tp"], c["mismatch"], c["missed"], c["hallucinated"], c["tn"], + ) + gold_filled = tp + mism + miss + gold_empty = hallu + tn + emitted = tp + mism + hallu + r = tp / gold_filled if gold_filled else 0.0 + p = tp / emitted if emitted else 0.0 + ff = hallu / gold_empty if gold_empty else 0.0 + return { + "n_gold_filled": gold_filled, + "n_gold_empty": gold_empty, + "n_emitted": emitted, + "recall": r, + "recall_se": _se_pp(r, gold_filled), + "precision": p, + "precision_se": _se_pp(p, emitted), + "f1": 2 * p * r / (p + r) if (p + r) else 0.0, + "false_fill": ff, + "false_fill_se": _se_pp(ff, gold_empty), + } + + +def _aggregate( + csv_path: Path, systems: tuple[tuple[str, str], ...] +) -> dict[str, dict[str, dict[str, int]]]: + """{ model -> { field-set key -> bucket counter } }.""" + out: dict[str, dict[str, dict[str, int]]] = { + m: {key: _empty() for key, _, _ in FIELD_SETS} for m, _ in systems + } + release = set(RELEASE_COUNTRIES) + models = {m for m, _ in systems} + field_sets = [(key, set(fields)) for key, _, fields in FIELD_SETS] + + with open(csv_path, encoding="utf-8", newline="") as f: + for row in csv.DictReader(f): + if row["country"] not in release or row["model"] not in models: + continue + col = row["column"] + counts = {k: int(row[k]) for k in _BUCKETS} + for key, fields in field_sets: + if col in fields: + for k in _BUCKETS: + out[row["model"]][key][k] += counts[k] + return out + + +def _fmt_pct_se(m: dict[str, float], metric: str) -> str: + return ( + f"{m[metric] * 100:.1f}\\,$\\pm$\\,{m[f'{metric}_se']:.1f}\\%" + ) + + +def _fmt_f1(v: float) -> str: + return f"{v:.2f}" + + +def _bold_best( + values: list[float], rendered: list[str], higher_is_better: bool = True +) -> list[str]: + best = max(values) if higher_is_better else min(values) + return [ + rf"\textbf{{{s}}}" if v == best else s + for v, s in zip(values, rendered) + ] + + +def _caption( + agg: dict[str, dict[str, dict[str, int]]], systems: tuple[tuple[str, str], ...] +) -> str: + """The manuscript's tab:overall caption, with per-system denominators in row order.""" + names = "/".join(label for _, label in systems) + n = { + key: { + kind: "/".join(str(_metrics(agg[m][key])[kind]) for m, _ in systems) + for kind in ("n_gold_filled", "n_gold_empty") + } + for key, _, _ in FIELD_SETS[:2] + } + return ( + r"\caption{Headline extraction metrics over the 19 release jurisdictions" + r" (8 core and 11 preview), computed over each system's successfully" + r" processed cases (metric definitions in \cref{sec:systems}). The left" + r" block covers the ten structured fields, the right block the four" + r" cost-block variables; percentages carry $\pm$1\,SE." + f" Denominators, in row order ({names}), are" + f" $n_{{\\text{{filled}}}}$\\,=\\,{n['structured']['n_gold_filled']} and" + f" $n_{{\\text{{empty}}}}$\\,=\\,{n['structured']['n_gold_empty']} over the" + f" ten structured fields, and" + f" $n_{{\\text{{filled}}}}$\\,=\\,{n['cost']['n_gold_filled']} and" + f" $n_{{\\text{{empty}}}}$\\,=\\,{n['cost']['n_gold_empty']} over the cost" + r" block; Harvey and Legora have smaller denominators because of their" + r" ingest gaps. F1 standard errors are below 0.01 and omitted." + "\n" + r"The best value per column is marked in \textbf{bold} (lower is better" + r" for false fill).}" + ) + + +def render_table( + agg: dict[str, dict[str, dict[str, int]]], systems: tuple[tuple[str, str], ...] +) -> str: + metrics = { + m: {key: _metrics(agg[m][key]) for key, _, _ in FIELD_SETS} + for m, _ in systems + } + + lines: list[str] = [] + lines.append(r"% Auto-generated by legex-quant-results — do not edit by hand.") + lines.append( + r"% Aggregated over the 19 release jurisdictions; free-text" + r" legal_subject_judgement excluded from scoring (reported separately)." + ) + lines.append(r"\begin{table*}[t]") + lines.append(r"\centering\small") + lines.append( + r"\begin{tabular}{@{}l rrrr@{\hskip 14pt} rrrr@{}}" + ) + lines.append(r"\toprule") + lines.append( + r" & \multicolumn{4}{c}{10 structured fields}" + r" & \multicolumn{4}{c}{Cost block (4 fields)} \\" + ) + lines.append(r"\cmidrule(lr){2-5}\cmidrule(l){6-9}") + lines.append( + r"System & Recall & Precision & F1 & False fill" + r" & Recall & Precision & F1 & False fill \\" + ) + lines.append(r"\midrule") + + cells: dict[str, list[str]] = {m: [] for m, _ in systems} + for key, _, _ in FIELD_SETS[:2]: + for metric, higher_better in ( + ("recall", True), ("precision", True), ("f1", True), ("false_fill", False), + ): + values = [metrics[m][key][metric] for m, _ in systems] + if metric == "f1": + rendered = [_fmt_f1(v) for v in values] + else: + rendered = [_fmt_pct_se(metrics[m][key], metric) for m, _ in systems] + for (m, _), s in zip(systems, _bold_best(values, rendered, higher_better)): + cells[m].append(s) + + for m, label in systems: + lines.append(f"{label} & " + " & ".join(cells[m]) + r" \\") + lines.append(r"\bottomrule") + lines.append(r"\end{tabular}") + lines.append(r"\vskip 0.05in") + lines.append(_caption(agg, systems)) + lines.append(r"\label{tab:overall}") + lines.append(r"\end{table*}") + lines.append("") + return "\n".join(lines) + + +def _print_console_summary( + agg: dict[str, dict[str, dict[str, int]]], systems: tuple[tuple[str, str], ...] +) -> None: + """Human-readable echo, incl. denominators and the all-11-fields cross-check, + so manuscript prose numbers can be copied from the same run.""" + for key, label, _ in FIELD_SETS: + print(f"=== {label} (19 release jurisdictions) ===") + for model, _ in systems: + m = _metrics(agg[model][key]) + print( + f"{model:<28}" + f" recall {m['recall'] * 100:5.1f}%±{m['recall_se']:.1f}" + f" (n={m['n_gold_filled']})" + f" precision {m['precision'] * 100:5.1f}%±{m['precision_se']:.1f}" + f" (n={m['n_emitted']})" + f" F1 {m['f1']:.3f}" + f" false-fill {m['false_fill'] * 100:5.1f}%±{m['false_fill_se']:.1f}" + f" (n={m['n_gold_empty']})" + ) + print() + + +def main() -> None: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", + handlers=[logging.StreamHandler(sys.stderr)], + ) + parser = argparse.ArgumentParser( + prog="legex-quant-results", + description="Render the camera-ready headline results table (tab:overall).", + ) + parser.add_argument( + "--input", type=Path, + default=Path("data/analysis/per_country_per_column.csv"), + help="per_country_per_column.csv produced by legex-analysis.", + ) + parser.add_argument( + "--out", type=Path, + default=Path("data/analysis/quant_results.tex"), + help="Where to write the rendered LaTeX table.", + ) + parser.add_argument( + "--systems", choices=sorted(SYSTEM_SETS), default="paper", + help="'paper' renders tab:overall exactly as in the manuscript; 'all' " + "adds the harvey-2 and legora-2 transparency runs.", + ) + args = parser.parse_args() + + if not args.input.exists(): + raise SystemExit( + f"{args.input} not found — run `legex-analysis` first to generate it." + ) + systems = SYSTEM_SETS[args.systems] + agg = _aggregate(args.input, systems) + tex = render_table(agg, systems) + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(tex, encoding="utf-8") + log.info(f"wrote {args.out}") + _print_console_summary(agg, systems) + + +if __name__ == "__main__": + main() diff --git a/legex/analysis/report.py b/legex/analysis/report.py new file mode 100644 index 0000000000000000000000000000000000000000..29aa0b8c32027cd1a3f3067d0f3bfa0d789bf92a --- /dev/null +++ b/legex/analysis/report.py @@ -0,0 +1,664 @@ +"""Render the IAA / Alternative Annotator Test / evaluation CSVs into a minimal ANALYSIS.md. + +This is a convenience script, it reads the artifacts produced by +``legex-iaa`` (``pairwise_agreement.csv``), by the Alternative Annotator Test +reference adapter ``scripts/alt_test_reference.py`` +(``alt_test_reference_*.csv``, see the README section "Alternative Annotator +Test (AAT)"), by ``scripts/alt_test_decomposition.py`` +(``alt_test_decomposition.csv``), and by ``legex-analysis`` (``per_column.csv``), and emits +headers, always-true methodology notes, and live tables. Regenerate the whole +chain with ``scripts/reproduce_paper.sh``. + +Usage +----- + uv run legex-analysis-report + uv run python -m legex.analysis.report --iaa-dir data/analysis/iaa +""" + +import argparse +import csv +import math +import statistics +from pathlib import Path + +from legex.analysis.countries import COUNTRY_NAMES + +_BUCKETS = ("tp", "mismatch", "missed", "hallucinated", "tn") + + +# IO helpers +def _read_csv(path: Path) -> list[dict[str, str]]: + if not path.exists(): + return [] + with path.open(encoding="utf-8", newline="") as f: + return list(csv.DictReader(f)) + + +def _short_model(model: str) -> str: + """Display label for a litellm id (drop the provider prefix).""" + return model.split("/")[-1] + + +def _country_name(cc: str) -> str: + return COUNTRY_NAMES.get(cc, cc.upper()) + + +def _md_table(headers: list[str], rows: list[list[str]]) -> str: + head = "| " + " | ".join(headers) + " |" + sep = "| " + " | ".join("---" for _ in headers) + " |" + body = "\n".join("| " + " | ".join(r) + " |" for r in rows) + return "\n".join([head, sep, body]) if rows else head + "\n" + sep + "\n| _(no data)_ |" + + +def _se(p: float, n: int) -> float: + """Binomial standard error of a proportion (1σ of the estimate).""" + return math.sqrt(p * (1 - p) / n) if n > 0 else 0.0 + + +def _pct_se(p: float, n: int) -> str: + """Percent with ±1 SE in percentage points, e.g. '49.2% ±3.3'.""" + return f"{p:.1%} ±{_se(p, n) * 100:.1f}" + + +def _by_field_grid( + per_column: list[dict[str, str]], + models: list[str], + metric_key: str, + *, + n_buckets: tuple[str, ...] | None, + pct: bool = True, +) -> str: + """Field × model grid for one derived metric. + + ``n_buckets`` sums the bucket columns giving the metric's model-independent + denominator (shown as ``n``); pass ``None`` when the denominator is + model-dependent (precision) or composite (F1) so ``n`` is omitted. + """ + fields = sorted({r["column"] for r in per_column}) + val = {(r["model"], r["column"]): r.get(metric_key, "") for r in per_column} + n_by_field: dict[str, int] = {} + if n_buckets: + for r in per_column: + tot = sum(int(r[b]) for b in n_buckets) + n_by_field[r["column"]] = max(n_by_field.get(r["column"], 0), tot) + rows = [] + for field in fields: + row = [f"`{field}`"] + if n_buckets: + row.append(str(n_by_field.get(field, 0))) + for m in models: + v = val.get((m, field), "") + if v in (None, ""): + row.append("–") + else: + row.append(f"{float(v):.1%}" if pct else f"{float(v):.3f}") + rows.append(row) + headers = ["Field"] + (["n"] if n_buckets else []) + [_short_model(m) for m in models] + return _md_table(headers, rows) + + +# Variables with a fixed controlled vocabulary -> Cohen's kappa is valid. +_CATEGORICAL_FIELDS = { + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +} + +# Measurement level per variable, shown in the by-variable table. +_FIELD_TYPE = { + "plaintiff_no1_ISIC1_industry_category": "nominal", + "defendant_no1_ISIC1_industry_category": "nominal", + "legal_subject_judgement": "free text", + "trial_start_date": "date", + "trial_end_date": "date", + "plaintiffs_all_count": "count", + "defendants_all_count": "count", + "dispute_value_nominal": "monetary", + "court_cost_awarded_nominal": "monetary", + "party_compensation_awarded_nominal": "monetary", + "plaintiff_loosing_share": "ratio", +} +_TYPE_ORDER = { + t: i for i, t in enumerate( + ["nominal", "free text", "date", "count", "monetary", "ratio", "other"] + ) +} + + +# Section builders +def _scope(pairwise: list[dict[str, str]]) -> str: + annotators = {r["annotator_a"] for r in pairwise} | {r["annotator_b"] for r in pairwise} + pairs = {(r["country"], r["annotator_a"], r["annotator_b"]) for r in pairwise} + countries = sorted({r["country"] for r in pairwise}) + legend = ", ".join(f"**{cc}** {_country_name(cc)}" for cc in countries) + secondary = sorted(a for a in annotators if a != "primary") + lines = [ + "## 1. Scope", + "", + f"- Countries with ≥2 annotators: **{len(countries)}** — {', '.join(countries) or '(none)'}", + f"- Annotators: **{len(annotators)}** (primary + {len(secondary)} secondary: " + f"{', '.join(secondary) or '(none)'})", + f"- Annotator pairs (country × pair): **{len(pairs)}**", + "", + f"Countries: {legend}." if legend else "", + ] + return "\n".join(lines) + + +def _agreement_sections(pairwise: list[dict[str, str]]) -> str: + note = ( + "> **Per-variable agreement.** Percent agreement is reported for every variable, using the " + "same value-matching as the evaluation (ISO-date / number-format aware; `0` and empty treated " + "alike). For the two **nominal ISIC** fields — the only variables with a fixed controlled " + "vocabulary — we additionally report **Cohen's κ**. For the other variables the category space " + "is unbounded (free text) or continuous (dates, counts, money, ratios): there κ's chance " + "correction collapses toward percent agreement (as the number of categories → ∞, expected " + "agreement pₑ → 0 and κ → observed agreement) and awards no partial credit for near-misses, so " + "percent agreement is the appropriate summary. Correlation coefficients are deliberately not " + "used — for these labels a wrong value is wrong, not partially correct. Cell detail: " + "`kappa_audit.csv`." + ) + out = [ + "## 2. Human–human agreement", "", note, "", + "_n = paired case comparisons (shared cases × annotator pairs). We report ±1 SE = √(p(1-p)/n) in " + "percentage points, a descriptive precision marker. This is not a significance test._", "", + ] + + by_pair: dict[tuple[str, str, str], list[dict[str, str]]] = {} + for r in pairwise: + by_pair.setdefault((r["country"], r["annotator_a"], r["annotator_b"]), []).append(r) + # Shared cases per pair (constant across that pair's fields) + pair_cases = {k: int(v[0]["n"]) for k, v in by_pair.items()} + + def _agg(rows: list[dict[str, str]]) -> tuple[int, float, float]: + """(total paired observations, n-weighted tolerant %, mean defined κ).""" + n = sum(int(r["n"]) for r in rows) + tol = sum(float(r["pct_tolerant"]) * int(r["n"]) for r in rows) / n if n else 0.0 + kv = [float(r["cohen_kappa"]) for r in rows if r.get("cohen_kappa") not in (None, "")] + mk = statistics.mean(kv) if kv else float("nan") + return n, tol, mk + + # 2.1 by variable — % for all, κ only for the categorical (fixed-vocab) fields + out += ["### 2.1 By variable", ""] + by_field: dict[str, list[dict[str, str]]] = {} + for r in pairwise: + by_field.setdefault(r["field"], []).append(r) + order = sorted( + by_field, + key=lambda f: (_TYPE_ORDER.get(_FIELD_TYPE.get(f, "other"), 99), -_agg(by_field[f])[1]), + ) + var_rows = [] + for field in order: + n, tol, mk = _agg(by_field[field]) + kappa = f"{mk:.3f}" if (field in _CATEGORICAL_FIELDS and mk == mk) else "—" + var_rows.append([f"`{field}`", _FIELD_TYPE.get(field, "other"), str(n), _pct_se(tol, n), kappa]) + out.append(_md_table(["Variable", "Type", "n", "% agreement", "Cohen's κ"], var_rows)) + + # 2.2 by country + out += ["", "### 2.2 By country", ""] + by_cc: dict[str, list[dict[str, str]]] = {} + for r in pairwise: + by_cc.setdefault(r["country"], []).append(r) + cc_rows = [] + for cc in sorted(by_cc): + pairs = {(r["annotator_a"], r["annotator_b"]) for r in by_cc[cc]} + n_cases = sum(pair_cases[(cc, a, b)] for (a, b) in pairs) + _n, tol, _mk = _agg(by_cc[cc]) + cc_rows.append([cc, str(len(pairs)), str(n_cases), f"{tol:.1%}"]) + out.append(_md_table(["Country", "Pairs", "n", "% agreement"], cc_rows)) + + # 2.3 by pair + out += ["", "### 2.3 By pair", ""] + pair_rows = [] + for (cc, a, b) in sorted(by_pair): + _n, tol, _mk = _agg(by_pair[(cc, a, b)]) + pair_rows.append([cc, f"{a} – {b}", str(pair_cases[(cc, a, b)]), f"{tol:.1%}"]) + out.append(_md_table(["Country", "Pair", "n", "% agreement"], pair_rows)) + return "\n".join(out) + + +_ALT_TEST_FORMULA = """### 3.1 How ω and ρ are computed + +_Definitions from Calderon et al. (2025), as implemented in the authors' `alt_test`. +`values_agree` is the tolerant comparator LEGEX passes in as the scoring function._ + +```text +One jurisdiction, one candidate f, human annotators H = {1..M}, and instances i +(here: one (judgment, variable) cell over the 10 structured fields). + +LEAVE-ONE-OUT. For human j and every instance i the other humans also labelled, +let A(i,-j) be the remaining humans' labels and + + s_f(i,j) = score( f(i), A(i,-j) ) candidate vs. the other humans + s_h(i,j) = score( h_j(i), A(i,-j) ) held-out human vs. the same humans + + score(p, A) = (1/|A|) * SUM_{a in A} values_agree(p, a) in {0, 1/2, 1} + +ADVANTAGE PROBABILITY of the candidate against human j. Note the ">=", which +credits every tie to the candidate: + + rho_j = (1/n_j) * SUM_i 1[ s_f(i,j) >= s_h(i,j) ] + +PER-ANNOTATOR TEST. With d_i = 1[s_f < s_h] - 1[s_f >= s_h], so E[d] = 1 - 2*rho_j: + + H0: E[d] >= epsilon vs. H1: E[d] < epsilon + +one-sided paired t-test (Wilcoxon signed-rank when n_j < 30), the M p-values +corrected by Benjamini-Yekutieli at q = 0.05. Rejecting H0 is therefore +equivalent to rho_j being significantly greater than + + (1 - epsilon) / 2 = 0.4 for the expert tolerance epsilon = 0.2 + +VERDICT. + + omega = |{ j : H0_j rejected }| / M winning rate + rho = (1/M) * SUM_j rho_j advantage probability + "f may substitute a human annotator" <=> omega >= 0.5 +``` + +Two consequences worth keeping in view when reading §3.2. First, ρ is a +**≥**-comparison, so a candidate that merely matches the held-out human on an +instance is scored as winning it. Second, with ε = 0.2 the hypothesis test +clears at ρ_j > 0.4, not at 0.5. Both are deliberate: the alt-test asks whether +a candidate can *substitute* a human annotator, not whether it is *better* than +one. §3.4 separates the two. +""" + + +def _alt_test_section(iaa_dir: Path) -> str: + # Only the reference-implementation outputs are rendered + files = sorted(p for p in iaa_dir.glob("alt_test_reference_*.csv")) + rows_by_cand: dict[str, list[dict[str, str]]] = {} + for p in files: + for r in _read_csv(p): + rows_by_cand.setdefault(r["candidate"], []).append(r) + all_rows = [r for rs in rows_by_cand.values() for r in rs] + note = ( + "> **Alternative Annotator Test** (Calderon et al. 2025, " + "[arXiv:2501.10970](https://arxiv.org/abs/2501.10970)): leave each human annotator out in " + "turn and score, per instance, both the candidate and the excluded annotator against the " + "remaining annotators; a one-sided test per annotator asks whether the candidate's " + "advantage probability trails the human's by less than ε = 0.2 (the expert-annotator " + "tolerance), under Benjamini–Yekutieli FDR control at q = 0.05. `passes` = winning rate " + "≥ 0.5. Requires ≥ 3 independent annotators per country; free-text fields are excluded. " + "ρ is the advantage probability — how likely the candidate annotates as well as or better " + "than a randomly chosen human. The *non-trivial* variant drops instances every expert left " + "empty: an empty prediction ties those for free, so the gap between the two columns shows " + "how much of a pass rests on empty cells. Untestable cells (too few non-empty judgements) " + "are excluded from the denominators.\n>\n" + "> These numbers come from the **authors' reference implementation** " + "([github.com/nitaytech/AltTest](https://github.com/nitaytech/AltTest)) executed on the " + "LEGEX data via `scripts/alt_test_reference.py`. See the README section \"Alternative " + "Annotator Test (AAT)\" for how to run it. `legex-iaa` does not produce these CSVs." + ) + out = ["## 3. Alternative-annotator test", "", note, "", _ALT_TEST_FORMULA, ""] + + # 3.2 Headline: pooled per jurisdiction (instance = judgment x variable cell, + # the SummEval convention of the paper) — this is what the manuscript reports. + pooled_csv = iaa_dir / "alt_test_pooled.csv" + if pooled_csv.exists(): + pooled = _read_csv(pooled_csv) + out += [ + "### 3.2 Headline: pooled per jurisdiction (paper numbers)", + "", + "_One alt-test per jurisdiction; instance = (judgment, variable) cell over the" + " 10 structured fields, so each annotator contributes ~190+ effective instances" + " and the paired t-test applies without the paper's n<30 caveat._", + "", + "| Candidate | Country | ω | ρ | ω (non-triv) | ρ (non-triv) |", + "| --- | --- | --- | --- | --- | --- |", + ] + for r in pooled: + out.append( + f"| {r['candidate']} | {r['country']} | {float(r['omega']):.2f}" + f" | {float(r['rho']):.2f} | {float(r['omega_nontrivial']):.2f}" + f" | {float(r['rho_nontrivial']):.2f} |" + ) + out.append("") + + out += ["### 3.3 Per-field diagnostic (cells passed / testable)", ""] + if not rows_by_cand: + out.append("_No `alt_test_reference_*.csv` found — run `scripts/alt_test_reference.py` (see README)._") + return "\n".join(out + ["", _decomposition_section(iaa_dir)]) + countries = sorted({r["country"] for r in all_rows}) + + def _rate(rows: list[dict[str, str]], key: str) -> str: + rows = [r for r in rows if r[key] != ""] + if not rows: + return "–" + p = sum(int(r[key]) for r in rows) + return f"{p}/{len(rows)}" + + def _mean_rho(rows: list[dict[str, str]], key: str) -> str: + vals = [float(r[key]) for r in rows if r[key] != ""] + return f"{statistics.mean(vals):.2f}" if vals else "–" + + headers = [ + "Candidate", "ρ̄ (all)", "Pass (all)", "ρ̄ (non-triv)", "Pass (non-triv)", + *(f"{cc} (all · non-triv)" for cc in countries), + ] + table_rows = [] + for cand in sorted(rows_by_cand): + rs = rows_by_cand[cand] + row = [ + _short_model(cand), + _mean_rho(rs, "advantage_probability"), + _rate(rs, "passes"), + _mean_rho(rs, "advantage_probability_nontrivial"), + _rate(rs, "passes_nontrivial"), + ] + for cc in countries: + sub = [r for r in rs if r["country"] == cc] + row.append(f"{_rate(sub, 'passes')} · {_rate(sub, 'passes_nontrivial')}") + table_rows.append(row) + out.append(_md_table(headers, table_rows)) + + out += [ + "", + "> Per-cell winning rates and advantage probabilities are in " + "`alt_test_reference_.csv`; an empty cell there means the " + "(country, field, variant) combination was untestable.", + "", + _decomposition_section(iaa_dir), + ] + return "\n".join(out) + + +def _decomposition_section(iaa_dir: Path) -> str: + """§3.4 — the win/tie/loss counts ρ is built from (substitutable vs. better).""" + rows = _read_csv(iaa_dir / "alt_test_decomposition.csv") + out = ["### 3.4 Substitutable vs. better: win / tie / loss decomposition", ""] + if not rows: + out.append( + "_No `alt_test_decomposition.csv` found — run " + "`uv run python scripts/alt_test_decomposition.py`._" + ) + return "\n".join(out) + + note = ( + "> ρ collapses the leave-one-out comparison of §3.1 into one number, and its `≥` " + "hands every tie to the candidate. This section keeps the same comparisons and " + "reports the counts instead: per held-out human and instance, whether the candidate " + "scored **better than**, **the same as**, or **worse than** that human against the two " + "remaining humans. Restricted to judgments all three experts labelled, so every " + "comparison has exactly two references and a score is 0, ½ or 1. `ρ (alt-test)` is the " + "reproduced advantage probability (ties → candidate); `ρ (ties split)` counts a tie as " + "half a win for each side. Source: `alt_test_decomposition.csv` from " + "`scripts/alt_test_decomposition.py`, which reproduces the reference ρ of §3.2 to " + "within 0.02." + ) + out += [note, ""] + + def _pool(candidate: str, variant: str) -> dict[str, int]: + keys = ( + "n_comparisons", "refs_disagree", "llm_better", "human_better", + "tie", "tie_same", "tie_diff", "tie_at_1", "tie_at_half", "tie_at_0", + ) + totals = dict.fromkeys(keys, 0) + for r in rows: + if r["candidate"] == candidate and r["variant"] == variant: + for k in keys: + totals[k] += int(r[k]) + return totals + + candidates = sorted({r["candidate"] for r in rows}) + countries = sorted({r["country"] for r in rows}) + headers = [ + "Candidate", "Country", "n", "Candidate better", "Tie", "Human better", + "ρ (alt-test)", "ρ (ties split)", + ] + table: list[list[str]] = [] + for cand in candidates: + for cc in [*countries, "pooled"]: + if cc == "pooled": + t = _pool(cand, "all") + n = t["n_comparisons"] + if not n: + continue + cells = [t["llm_better"], t["tie"], t["human_better"]] + rho_alt = (t["llm_better"] + t["tie"]) / n + rho_split = (t["llm_better"] + t["tie"] / 2) / n + label = "**all three**" + else: + r = next( + (r for r in rows if r["candidate"] == cand + and r["country"] == cc and r["variant"] == "all"), + None, + ) + if r is None: + continue + n = int(r["n_comparisons"]) + cells = [int(r["llm_better"]), int(r["tie"]), int(r["human_better"])] + rho_alt = float(r["rho_alttest"]) + rho_split = float(r["rho_tiebroken"]) + label = cc + table.append([ + _short_model(cand), label, str(n), + *(f"{c} ({c / n:.0%})" for c in cells), + f"{rho_alt:.2f}", f"{rho_split:.2f}", + ]) + out.append(_md_table(headers, table)) + + # What a "tie" actually contains — the bucket ρ is most sensitive to. + pooled_all = {c: _pool(c, "all") for c in candidates} + refs_disagree = next( + (t["refs_disagree"], t["n_comparisons"]) for t in pooled_all.values() + ) + out += [ + "", + "**What is in the “Tie” bucket.** A tie only means *same score against the same two " + "references*, so it merges several different situations. Split by score level, and " + "independently by whether the candidate actually produced the held-out expert's answer:", + "", + ] + tie_headers = [ + "Candidate", "Ties", "Same answer as expert", "Different answer, equal score", + "at 1 (all agree)", "at ½ (experts conflict)", "at 0 (both differ)", + ] + tie_rows = [] + for cand in candidates: + t = pooled_all[cand] + ties = t["tie"] + if not ties: + continue + tie_rows.append([ + _short_model(cand), str(ties), + *(f"{t[k]} ({t[k] / ties:.0%})" for k in + ("tie_same", "tie_diff", "tie_at_1", "tie_at_half", "tie_at_0")), + ]) + out.append(_md_table(tie_headers, tie_rows)) + out += [ + "", + f"A tie at ½ is only possible when the two reference experts contradict each other — " + f"that caps every achievable score at ½, for the candidate and the held-out expert " + f"alike. The two references disagree in {refs_disagree[0]} of {refs_disagree[1]} " + f"comparisons ({refs_disagree[0] / refs_disagree[1]:.0%}; a property of the human " + f"labels, identical for every candidate). Note that reference disagreement is *not* " + f"the same thing as a tie: ties also arise, and in fact more often, where the two " + f"references agree and the candidate simply matches them.", + ] + + # Reading: who wins the decisive comparisons. + lines = [] + for cand in candidates: + t = pooled_all[cand] + nt = _pool(cand, "nontrivial") + n, ties = t["n_comparisons"], t["tie"] + decisive = t["llm_better"] + t["human_better"] + if not (n and ties and decisive): + continue + nontrivial = ( + f" Dropping instances whose reference is empty throughout leaves ρ (ties split) at " + f"{(nt['llm_better'] + nt['tie'] / 2) / nt['n_comparisons']:.2f}." + if nt["n_comparisons"] else "" + ) + lines.append( + f"- **{_short_model(cand)}** — {ties / n:.0%} of the {n} comparisons are ties. " + f"{t['tie_same'] / ties:.0%} of those ties are real agreement (candidate gave the " + f"held-out expert's answer); the other {t['tie_diff'] / ties:.0%} are comparisons " + f"where candidate and expert gave *different* answers that happened to score the " + f"same, and ρ credits every one of them to the candidate. On the {decisive} " + f"comparisons that actually discriminate, the human wins " + f"{t['human_better'] / decisive:.0%} ({t['human_better']} vs " + f"{t['llm_better']}).{nontrivial}" + ) + if lines: + out += ["", "**Reading.**", "", *lines] + + out += [ + "", + "This is what the alt-test is and is not evidence for. The tie rate is high and mostly " + "genuine, so on this data the models are largely *indistinguishable* from an additional " + "expert — which is exactly the substitutability claim ω and ρ are designed to support, " + "and §3.2 supports it. It is not evidence of superiority: once ties stop counting as " + "wins, ρ sits at chance, and on the comparisons that separate the two the human expert " + "is still ahead. \"Can this model replace a human annotator?\" and \"is this model better " + "than a human annotator?\" are different questions, and only the first one is being " + "tested.", + ] + return "\n".join(out) + + +def _headline_section(per_column: list[dict[str, str]]) -> str: + note = ( + "> Recall = TP / (TP + Mismatch + Missed) over cells the expert filled; precision = " + "TP / (TP + Mismatch + Hallucinated); hallucination = invented values on empty-gold cells. " + "Buckets from `legex/evaluation.py`; per-cell source `../per_column.csv`. n = evaluated " + "label cells; recall and precision carry ±1 SE over their gold-filled / emitted denominators. " + "The per-field grids (§4.2–4.5) break each metric out by variable across models; their `n` is " + "the metric's model-independent denominator (gold-filled for recall, gold-empty for " + "hallucination) and is omitted for precision (emitted; model-dependent) and F1 (composite)." + ) + out = ["## 4. Headline extraction metrics", "", note, ""] + if not per_column: + out.append("_`../per_column.csv` not found — run `legex-analysis`._") + return "\n".join(out) + + by_model: dict[str, list[dict[str, str]]] = {} + for r in per_column: + by_model.setdefault(r["model"], []).append(r) + + # 4.1 overall, summed across fields & countries + out += ["### 4.1 Overall (all countries, summed across fields)", ""] + rows = [] + for model in sorted(by_model): + b = {k: sum(int(r[k]) for r in by_model[model]) for k in _BUCKETS} + tp, mism, miss, hallu, tn = (b[k] for k in _BUCKETS) + total = tp + mism + miss + hallu + tn + recall = tp / (tp + mism + miss) if (tp + mism + miss) else 0.0 + prec = tp / (tp + mism + hallu) if (tp + mism + hallu) else 0.0 + acc = (tp + tn) / total if total else 0.0 + hallu_rate = hallu / (hallu + tn) if (hallu + tn) else 0.0 + f1 = 2 * prec * recall / (prec + recall) if (prec + recall) else 0.0 + filled = tp + mism + miss + emitted = tp + mism + hallu + rows.append([ + _short_model(model), str(total), f"{acc:.1%}", + _pct_se(recall, filled), _pct_se(prec, emitted), + f"{hallu_rate:.1%}", f"{f1:.3f}", + ]) + out.append(_md_table( + ["Model", "n", "Accuracy", "Recall (filled)", "Precision", "Hallu. rate", "F1"], rows + )) + + # 4.2–4.5 per-field grids (Field × model) for each metric + models = sorted(by_model) + out += ["", "### 4.2 Recall (filled) by field (all countries)", ""] + out.append(_by_field_grid(per_column, models, "recall_when_filled", + n_buckets=("tp", "mismatch", "missed"))) + out += ["", "### 4.3 Precision by field (all countries)", ""] + out.append(_by_field_grid(per_column, models, "precision_when_emitted", n_buckets=None)) + out += ["", "### 4.4 Hallucination rate by field (all countries)", ""] + out.append(_by_field_grid(per_column, models, "hallucination_rate", + n_buckets=("hallucinated", "tn"))) + out += ["", "### 4.5 F1 by field (all countries)", ""] + out.append(_by_field_grid(per_column, models, "f1", n_buckets=None, pct=False)) + return "\n".join(out) + + +def _appendix() -> str: + return "\n".join([ + "## Appendix", + "", + "### Landis–Koch (1977) κ scale (for interpreting the ISIC κ)", + "", + _md_table( + ["κ", "Strength"], + [ + ["< 0.00", "Poor (worse than chance)"], + ["0.00 – 0.20", "Slight"], + ["0.21 – 0.40", "Fair"], + ["0.41 – 0.60", "Moderate"], + ["0.61 – 0.80", "Substantial"], + ["0.81 – 1.00", "Almost perfect"], + ], + ), + "", + "### Provenance", + "", + "- Pairwise agreement & kappa: `legex/analysis/iaa.py` (`pairwise_agreement`, " + "`write_kappa_audit_csv`).", + "- Alt-test: authors' reference implementation " + "([github.com/nitaytech/AltTest](https://github.com/nitaytech/AltTest)) run via " + "`scripts/alt_test_reference.py` → `alt_test_reference_*.csv` (see README).", + "- Alt-test win/tie/loss decomposition: `scripts/alt_test_decomposition.py` → " + "`alt_test_decomposition.csv`.", + "- Tolerant comparator: `legex/evaluation/comparison.py` (`values_agree`, `normalise`).", + "- Headline buckets: `legex/analysis/aggregate.py` over `legex/evaluation.score_country`.", + "- This report: `legex/analysis/report.py`.", + ]) + + +def build_report(iaa_dir: Path, analysis_dir: Path) -> str: + pairwise = _read_csv(iaa_dir / "pairwise_agreement.csv") + per_column = _read_csv(analysis_dir / "per_column.csv") + header = [ + "# Inter-Annotator Agreement & Alternative-Annotator Test", + "", + "_Generated by `legex-analysis-report` from the CSVs in this directory — do not edit by " + "hand; numbers always reflect the current CSVs._", + "", + "Regenerate the whole chain with `scripts/reproduce_paper.sh`.", + "", + "---", + "", + ] + parts = [ + "\n".join(header), + _scope(pairwise), + _agreement_sections(pairwise), + _alt_test_section(iaa_dir), + _headline_section(per_column), + _appendix(), + ] + return "\n\n".join(p.strip() for p in parts) + "\n" + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser( + prog="legex-analysis-report", + description="Render the IAA / alt-test / evaluation CSVs into a minimal ANALYSIS.md.", + ) + parser.add_argument( + "--iaa-dir", type=Path, default=Path("data/analysis/iaa"), + help="Directory with pairwise_agreement.csv (from legex-iaa) and " + "alt_test_reference_*.csv (from scripts/alt_test_reference.py), " + "and the output ANALYSIS.md.", + ) + parser.add_argument( + "--analysis-dir", type=Path, default=Path("data/analysis"), + help="Directory with per_column.csv (from legex-analysis).", + ) + parser.add_argument( + "--out", type=Path, default=None, + help="Output path (default: /ANALYSIS.md).", + ) + args = parser.parse_args(argv) + out = args.out or (args.iaa_dir / "ANALYSIS.md") + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(build_report(args.iaa_dir, args.analysis_dir), encoding="utf-8") + print(f"wrote {out}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/legex/config.py b/legex/config.py index bf0f71ca525f42355a0523fb0a9bbbd9a5ac3b91..02545a87955910435c6be01615417e705624cf99 100644 --- a/legex/config.py +++ b/legex/config.py @@ -22,7 +22,9 @@ class Settings(BaseSettings): data_dir: Path = Path("data") dist_dir: Path = Path("dist") - drive_folder_url: str = "" + drive_folder_url: str = ( + "https://drive.google.com/drive/folders/1tqrKAVuu40EI2WaHn039XOq66B3fnZZB" + ) @property def raw_dir(self) -> Path: diff --git a/legex/evaluation.py b/legex/evaluation.py deleted file mode 100644 index bf3c08b3cc8909522e832ea5c3d2075d484bb4cf..0000000000000000000000000000000000000000 --- a/legex/evaluation.py +++ /dev/null @@ -1,419 +0,0 @@ -"""Compare Goldenset gold labels against inference output. - -Reads each country's goldenset_.jsonl (the human-labelled truth, produced -by ``convert_goldenset_to_jsonl.py``) and the matching ``inference_.csv`` -file from one of the evaluated systems (``harvey``, ``gemini``, ``gpt``), then -reports per-field agreement. -""" - -import argparse -import csv -import json -import logging -import re -import sys -from datetime import date, datetime -from pathlib import Path -from typing import get_args - -from legex.config import settings -from legex.models.classification import Classification -from legex.utils import ( - EXCLUDED_FOR_EVAL, - countries_with_goldenset_jsonl, - evaluable_countries, - goldenset_jsonl_path, - inference_csv_path, -) - -# Systems whose inference outputs are evaluated. -SYSTEMS: tuple[str, ...] = ("harvey", "gemini", "gpt") - -log = logging.getLogger(__name__) - -# Columns that don't carry a label to evaluate against (matched case-insensitively). -_NON_LABEL_COLUMNS = {"case_id", "link", "full_text"} - -# Goldenset / inference sentinels for “no value” (non_empty as empty). -_EMPTY_LITERALS = frozenset({"", "none", "null", "nan"}) - -# CSV columns whose Classification field is numeric (int / float). Loose -# parsing (apostrophe thousand separators, leading number + trailing prose) -# applies only to these. -_NUMERIC_COLUMNS = frozenset( - (info.alias or name) - for name, info in Classification.model_fields.items() - if any(a is int or a is float for a in (get_args(info.annotation) or (info.annotation,))) -) - -_NUM_TOKEN_RE = re.compile(r"-?\d[\d',. ]*") - -# CSV columns whose Classification field is a date. Separator normalisation -# (treat `_`, `/`, `.` as `-`) plus a small format fallback applies only here. -_DATE_COLUMNS = frozenset( - (info.alias or name) - for name, info in Classification.model_fields.items() - if any(a is date for a in (get_args(info.annotation) or (info.annotation,))) -) - -_DATE_SEP_RE = re.compile(r"[_/.\s]+") -_DATE_FALLBACK_FORMATS = ("%d-%m-%Y", "%m-%d-%Y", "%Y%m%d") - - -def _is_label_column(name: str) -> bool: - if not name or name.lower() in _NON_LABEL_COLUMNS: - return False - if name.startswith("Currency_"): - return False - return True - -def _normalise(value: object) -> str: - """Make gold and prediction cells comparable as strings.""" - if value is None: - return "" - if isinstance(value, float): - if value != value: # NaN - return "" - if value.is_integer(): - return str(int(value)) - s = str(value).strip() - if s.lower() in _EMPTY_LITERALS: - return "" - return s - - -def _try_float(s: str) -> float | None: - if not s: - return None - try: - return float(s) - except ValueError: - return None - - -def _parse_loose_number(s: str) -> float | None: - """Extract the first numeric token from `s`, tolerating apostrophe/space - thousand separators (`20'000`, `20 000`), EU decimal commas (`1.000,50`), - and trailing prose (`150 GEL state fee awarded …`, `0%, motion satisfied`). - Returns None when no digit appears. - """ - if not s: - return None - m = _NUM_TOKEN_RE.search(s) - if not m: - return None - tok = m.group(0).strip().rstrip(",.' ") - if not tok: - return None - cleaned = tok.replace("'", "").replace(" ", "") - if "," in cleaned and "." in cleaned: - # Whichever appears last is the decimal mark. - if cleaned.rfind(",") > cleaned.rfind("."): - cleaned = cleaned.replace(".", "").replace(",", ".") - else: - cleaned = cleaned.replace(",", "") - elif "," in cleaned: - parts = cleaned.split(",") - # Single trailing group of 1-2 digits → decimal comma; else thousands. - if len(parts) == 2 and 1 <= len(parts[1]) <= 2: - cleaned = parts[0] + "." + parts[1] - else: - cleaned = cleaned.replace(",", "") - try: - return float(cleaned) - except ValueError: - return None - - -def _numeric_value(s: str, column: str | None) -> float | None: - """Strict float for any column, plus loose parse for numeric columns.""" - n = _try_float(s) - if n is not None: - return n - if column in _NUMERIC_COLUMNS: - return _parse_loose_number(s) - return None - - -def _parse_loose_date(s: str) -> date | None: - """Parse a date string, treating `_`, `/`, `.`, whitespace as `-`. - - Accepts ISO `YYYY-MM-DD` (after separator collapse), and as a fallback - `DD-MM-YYYY`, `MM-DD-YYYY`, `YYYYMMDD`. - """ - if not s: - return None - t = _DATE_SEP_RE.sub("-", s.strip()).strip("-") - if not t: - return None - try: - return date.fromisoformat(t) - except ValueError: - pass - for fmt in _DATE_FALLBACK_FORMATS: - try: - return datetime.strptime(t, fmt).date() - except ValueError: - continue - return None - - -def _values_agree(gv: str, pv: str, column: str | None = None) -> bool: - """Compare normalised gold vs prediction cell values.""" - if gv == pv: - return True - if column in _DATE_COLUMNS: - gd, pd_ = _parse_loose_date(gv), _parse_loose_date(pv) - if gd is not None and pd_ is not None and gd == pd_: - return True - gn = _numeric_value(gv, column) - if gn is not None and gn == 0: - # Gold is zero: empty pred or any zero form (0, 0.0, …) counts as match. - if not pv: - return True - pn = _numeric_value(pv, column) - return pn is not None and pn == 0 - if gn is not None: - pn = _numeric_value(pv, column) - if pn is not None: - return gn == pn - return False - - -_BUCKETS = ("tp", "mismatch", "missed", "hallucinated", "tn") - - -def _classify_cell(gv: str, pv: str, column: str | None) -> str: - """Bucket a (gold, pred) cell. `gv`/`pv` must already be `_normalise()`-d. - - tp - gold filled, values agree - mismatch - gold filled, pred filled, values differ (FP_wrong) - missed - gold filled, pred empty (FN) - hallucinated - gold empty, pred filled (FP on a not-filled gold field) - tn - both empty - """ - gold_filled = bool(gv) - pred_filled = bool(pv) - if _values_agree(gv, pv, column): - return "tp" if gold_filled else "tn" - if gold_filled and pred_filled: - return "mismatch" - if gold_filled: - return "missed" - return "hallucinated" - - -def _derived(c: dict[str, int]) -> tuple[float, float, float]: - """Per-column precision, recall, F1 from a bucket counter.""" - tp, mism, miss, hallu = c["tp"], c["mismatch"], c["missed"], c["hallucinated"] - p_denom = tp + mism + hallu - r_denom = tp + mism + miss - p = tp / p_denom if p_denom else 0.0 - r = tp / r_denom if r_denom else 0.0 - f1 = 2 * p * r / (p + r) if (p + r) else 0.0 - return p, r, f1 - - -def _read_goldenset_rows(cc: str) -> tuple[list[str], dict[str, dict[str, str]]]: - """Return (label_columns, rows_by_case_id) for a country's JSONL goldenset.""" - path = goldenset_jsonl_path(cc) - if not path.exists(): - raise FileNotFoundError(f"{path} does not exist") - - label_columns: list[str] | None = None - by_id: dict[str, dict[str, str]] = {} - with path.open(encoding="utf-8") as f: - for line in f: - if not line.strip(): - continue - record = json.loads(line) - if label_columns is None: - label_columns = [k for k in record.keys() if _is_label_column(k)] - case_id = _normalise(record.get("case_id")) - if not case_id: - continue - labels = {col: _normalise(record.get(col)) for col in label_columns} - if not any(labels.values()): - continue - by_id[case_id] = labels - return label_columns or [], by_id - - -def _read_predictions(cc: str, system: str) -> dict[str, dict[str, str]]: - path = inference_csv_path(cc, system) - by_id: dict[str, dict[str, str]] = {} - with open(path, encoding="utf-8", newline="") as f: - reader = csv.DictReader(f) - for row in reader: - case_id = _normalise(row.get("case_id")) - if not case_id: - continue - by_id[case_id] = {k: _normalise(v) for k, v in row.items()} - return by_id - - -def _coverage( - gold: dict[str, dict[str, str]], preds: dict[str, dict[str, str]] -) -> dict[str, int]: - gold_ids = set(gold) - pred_ids = set(preds) - overlap = len(gold_ids & pred_ids) - return { - "gold": len(gold_ids), - "pred": len(pred_ids), - "overlap": overlap, - "missing": len(gold_ids - pred_ids), - "extra": len(pred_ids - gold_ids), - } - - -def score_country( - cc: str, system: str, verbose: bool = True -) -> tuple[dict[str, dict[str, int]], dict[str, int]] | None: - """Return (per-column counters, case coverage stats) for one (country, system).""" - pred_path = inference_csv_path(cc, system) - gs_path = goldenset_jsonl_path(cc) - if not pred_path.exists(): - if verbose: - log.warning(f"[{cc}/{system}] missing predictions {pred_path}, skipping") - return None - if not gs_path.exists(): - if verbose: - log.warning(f"[{cc}] missing goldenset {gs_path}, skipping") - return None - - label_columns, gold = _read_goldenset_rows(cc) - preds = _read_predictions(cc, system) - stats = _coverage(gold, preds) - - counters: dict[str, dict[str, int]] = { - col: {b: 0 for b in _BUCKETS} for col in label_columns - } - overlap_ids = [cid for cid in gold if cid in preds] - if verbose: - print() - log.info( - f"[{cc}/{system}] gold={stats['gold']} pred={stats['pred']} " - f"overlap={stats['overlap']} missing={stats['missing']} extra={stats['extra']}" - ) - - for case_id in overlap_ids: - g = gold[case_id] - p = preds[case_id] - for col in label_columns: - bucket = _classify_cell(g.get(col, ""), p.get(col, ""), col) - counters[col][bucket] += 1 - return counters, stats - - -def _print_report( - cc: str, counters: dict[str, dict[str, int]], coverage: dict[str, int] | None = None -) -> None: - name_width = max((len(c) for c in counters), default=0) - name_width = max(name_width, len("column")) - print(f"=== {cc} ===") - if coverage is not None: - print( - f"cases: gold={coverage['gold']} pred={coverage['pred']} " - f"overlap={coverage['overlap']} missing={coverage['missing']} " - f"extra={coverage['extra']}" - ) - print( - f"{'column'.ljust(name_width)} " - f"{'TP':>5} {'Mism':>5} {'Miss':>5} {'Hallu':>5} {'TN':>5} " - f"{'P':>7} {'R':>7} {'F1':>5}" - ) - for col, c in counters.items(): - p, r, f1 = _derived(c) - f1_s = f"{f1:.2f}" if (p + r) else " - " - print( - f"{col.ljust(name_width)} " - f"{c['tp']:>5} {c['mismatch']:>5} {c['missed']:>5} " - f"{c['hallucinated']:>5} {c['tn']:>5} " - f"{p:>7.2%} {r:>7.2%} {f1_s:>5}" - ) - - -def _schema_label_columns() -> list[str]: - """The 14 label columns derived from the Classification pydantic model.""" - return [ - (info.alias or name) - for name, info in Classification.model_fields.items() - if _is_label_column(info.alias or name) - ] - - -def evaluate( - countries: list[str] | None, - systems: list[str] | None, -) -> None: - overall: dict[str, dict[str, int]] = { - col: {b: 0 for b in _BUCKETS} for col in _schema_label_columns() - } - overall_coverage = {"gold": 0, "pred": 0, "overlap": 0, "missing": 0, "extra": 0} - - targets = countries or evaluable_countries() - chosen_systems = systems or list(SYSTEMS) - - seen_any = False - for system in chosen_systems: - print(f"\n########## SYSTEM: {system} ##########") - for cc in targets: - result = score_country(cc, system) - if result is None: - continue - counters, coverage = result - seen_any = True - _print_report(f"{cc} / {system}", counters, coverage) - for key in overall_coverage: - overall_coverage[key] += coverage[key] - for col, c in counters.items(): - if col not in overall: - overall[col] = {b: 0 for b in _BUCKETS} - for b in _BUCKETS: - overall[col][b] += c[b] - - if seen_any: - _print_report("ALL", overall, overall_coverage) - - -def main() -> None: - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - handlers=[logging.StreamHandler(sys.stderr)], - ) - - parser = argparse.ArgumentParser( - prog="legex-evaluate", - description="Compare Goldenset JSONL labels against system inference CSV outputs.", - ) - parser.add_argument( - "--country", - action="extend", - nargs="+", - dest="countries", - help=( - "Country code(s). Repeatable and/or space-separated. " - f"Defaults to the {len(SYSTEMS)}-system evaluation set " - "(all jurisdictions with a goldenset JSONL minus " - f"{sorted(EXCLUDED_FOR_EVAL)})." - ), - ) - parser.add_argument( - "--system", - action="extend", - nargs="+", - dest="systems", - choices=list(SYSTEMS), - help=f"Inference system(s). Repeatable. Default: all of {list(SYSTEMS)}.", - ) - args = parser.parse_args() - evaluate( - countries=args.countries, - systems=args.systems, - ) - - -if __name__ == "__main__": - main() diff --git a/legex/evaluation/__init__.py b/legex/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..83d506f4d812b09d17a144d441ce15b17809f7d6 --- /dev/null +++ b/legex/evaluation/__init__.py @@ -0,0 +1,27 @@ +"""Evaluation package: value comparison, scoring, and refusal cleaning.""" + +from legex.evaluation.comparison import ( + BUCKETS, + DATE_COLUMNS, + NUMERIC_COLUMNS, + RefusalReason, + classify_cell, + derived, + is_label_column, + is_refusal, + normalise, + refusal_reason, + resolve, + values_agree, +) +from legex.evaluation.scoring import ( + evaluate, + main, + score_country, +) + +__all__ = [ + "BUCKETS", "DATE_COLUMNS", "NUMERIC_COLUMNS", "RefusalReason", + "classify_cell", "derived", "is_label_column", "is_refusal", "normalise", + "refusal_reason", "resolve", "values_agree", "evaluate", "main", "score_country", +] diff --git a/legex/evaluation/cleaning.py b/legex/evaluation/cleaning.py new file mode 100644 index 0000000000000000000000000000000000000000..8e495bca8fa3b838050c281c85ea4e5cc71f3bf8 --- /dev/null +++ b/legex/evaluation/cleaning.py @@ -0,0 +1,290 @@ +"""Validates and cleans the inference (Gemini, ChatGPT, Harvey) JSONL data. + +We try to auto-recover values that do not match our model, else we review it by hand. +Every non-empty prediction cell is resolved to one of: + + * valid — a good value already → kept, + * recovered — a value parseable from prose (e.g. `20'000`→`20000`, `1.0\n\n…`→`1.0`) + → canonicalised automatically (deterministic, score-neutral), + * review — non-empty but neither valid nor recoverable (refusal, prose with no + value, misattribution, bad ISIC) → sent to the review XLSX for a human. + +We perform this in two steps to allow for manual review. + + 1. `legex-refusals-scan` Write every review cell to a review XLSX. + 2. `legex-refusals-apply` Applies the decisions to the JSONL. + +Traceability fields on every inference record show the original values: + +* comment: one sentence per changed field, or null if none: "The was sanitized from '' to ''." + (removals use "empty (removed)" as the new value) +* original_input: JSON object mapping each changed field to its 1:1 original value, the string "{}" when nothing changed. + +This is the same format as for the goldenset workbooks. +""" + +import argparse +import json +import logging +import re +import sys +from collections import defaultdict +from pathlib import Path + +import openpyxl + +from legex.config import settings +from legex.evaluation.comparison import classify_cell, is_label_column, normalise, resolve +from legex.utils import goldenset_path, goldenset_sheet, read_inference_jsonl + +log = logging.getLogger(__name__) + +_INFERENCE_RE = re.compile(r"^Goldenset_.+_v\d+_(?:full_text|pdf)_(.+)\.jsonl$") +DEFAULT_XLSX = Path("data/analysis/quality/inference_data_review.xlsx") +CHANGELOG = Path("data/analysis/quality/inference_cleaning_changelog.jsonl") +_XLSX_HEADER = [ + "model", "country", "case_id", "field", "current_value", "reason", + "gold_value", "before_bucket", "after_if_emptied", "corrected_value", +] + + +def format_comment(changes: dict[str, tuple[str, str]]) -> str | None: + """Human note for changed fields (field -> (old, new)); None if nothing changed.""" + if not changes: + return None + return " ".join( + f"The {field} was sanitized from '{old}' to " + f"'{new if new else 'empty (removed)'}'." + for field, (old, new) in changes.items() + ) + + +def format_original_input(changes: dict[str, tuple[str, str]]) -> str: + """JSON of {field: original value} for changed fields; '{}' when none.""" + return json.dumps({f: old for f, (old, _) in changes.items()}, ensure_ascii=False) if changes else "{}" + + +def _inference_files(models: set[str] | None = None) -> list[Path]: + """All inference JSONL files, optionally restricted to the given model models.""" + return sorted( + p for p in settings.data_dir.glob("*/Goldenset_*_v*_*.jsonl") + if _INFERENCE_RE.match(p.name) + and (models is None or _model_slug(p) in models) + ) + + +def _model_slug(path: Path) -> str: + return _INFERENCE_RE.match(path.name).group(1) + + +def _gold_labels(cc: str) -> dict[str, dict[str, str]]: + """case_id -> {field: normalised gold value}, or {} if no goldenset.""" + gs = goldenset_path(cc) + if not gs or not gs.exists(): + return {} + ws = goldenset_sheet(openpyxl.load_workbook(gs, read_only=True, data_only=True)) + rows = ws.iter_rows(values_only=True) + header = [str(c) if c is not None else "" for c in next(rows)] + ci = header.index("case_id") + out: dict[str, dict[str, str]] = {} + for row in rows: + if row[ci] in (None, ""): + continue + cells = dict(zip(header, row)) + out[str(row[ci]).strip()] = {c: normalise(cells.get(c)) for c in header if is_label_column(c)} + return out + + +def scan( + out: Path = DEFAULT_XLSX, + models: set[str] | None = None, + conflicts: Path | None = None, +) -> int: + """Write every `review` cell across the inference JSONL to a review XLSX. + """ + gold_cache: dict[str, dict] = {} + rows_out: list[list] = [] + seen_cells: set[tuple[str, str, str, str]] = set() + for path in _inference_files(models): + cc = path.parent.name + model = _model_slug(path) + gold = gold_cache.setdefault(cc, _gold_labels(cc)) + for rec in read_inference_jsonl(path): + cid = (rec.get("case_id") or "").strip() + for field, value in rec.items(): + if not is_label_column(field): + continue + status, _canon, reason = resolve(value, field) + if status != "review": + continue + gv = gold.get(cid, {}).get(field, "") + rows_out.append([ + model, cc, cid, field, str(value), reason, gv, + classify_cell(gv, normalise(value), field), + classify_cell(gv, "", field), "", + ]) + seen_cells.add((model, cc, cid, field)) + if conflicts is not None and conflicts.exists(): + with open(conflicts, encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + c = json.loads(line) + model, cc, cid, field = c["model"], c["country"], c["case_id"], c["field"] + if models is not None and model not in models: + continue + if (model, cc, cid, field) in seen_cells: + continue # already flagged by resolve(); one row per cell + gold = gold_cache.setdefault(cc, _gold_labels(cc)) + gv = gold.get(cid, {}).get(field, "") + kept = c["kept"] + rows_out.append([ + model, cc, cid, field, kept, + f"duplicate-run conflict; alternative: '{c['alternative']}'", gv, + classify_cell(gv, normalise(kept), field), + classify_cell(gv, "", field), kept, + ]) + rows_out.sort(key=lambda r: (r[0], r[5], r[1], r[2])) + wb = openpyxl.Workbook() + ws = wb.active + ws.title = "review" + ws.append(_XLSX_HEADER) + for r in rows_out: + ws.append(r) + out.parent.mkdir(parents=True, exist_ok=True) + wb.save(out) + log.info(f"flagged {len(rows_out)} cell(s) for review -> {out}") + return len(rows_out) + + +def _load_review(xlsx: Path) -> dict[tuple[str, str], dict[str, dict[str, str]]]: + """Reviewed XLSX -> {(model, country): {case_id: {field: corrected_value}}}. + + corrected_value is "" when the reviewer left it blank (⇒ empty the cell).""" + ws = openpyxl.load_workbook(xlsx, read_only=True).active + rows = ws.iter_rows(values_only=True) + hdr = list(next(rows)) + mi, ci, ii, fi, cv = (hdr.index(x) for x in + ("model", "country", "case_id", "field", "corrected_value")) + out: dict[tuple[str, str], dict[str, dict[str, str]]] = defaultdict(lambda: defaultdict(dict)) + for r in rows: + if r[mi] is None: + continue + out[(str(r[mi]), str(r[ci]))][str(r[ii])][str(r[fi])] = "" if r[cv] is None else str(r[cv]) + return out + + +def apply(xlsx: Path = DEFAULT_XLSX, models: set[str] | None = None) -> None: + """Auto-canonicalise recovered cells, apply reviewed decisions, stamp provenance. + """ + reviewed = _load_review(xlsx) if xlsx.exists() else {} + log_rows: list[list[str]] = [] + n_recovered = n_reviewed = 0 + for path in _inference_files(models): + cc, model = path.parent.name, _model_slug(path) + decisions = reviewed.get((model, cc), {}) + records = read_inference_jsonl(path) + for rec in records: + cid = (rec.get("case_id") or "").strip() + try: + prev_orig = json.loads(rec.get("original_input") or "{}") + except (ValueError, TypeError): + prev_orig = {} + row_decisions = decisions.get(cid, {}) + changes: dict[str, tuple[str, str]] = {} + for field in list(rec): + if not is_label_column(field): + continue + raw = prev_orig[field] if field in prev_orig else rec.get(field) # true original + if raw in (None, ""): + rec[field] = None + continue + if field in row_decisions: # human decision + new = row_decisions[field] + rec[field] = new or None + if str(raw) != (new or ""): + changes[field] = (str(raw), new) + n_reviewed += 1 + continue + status, canon, _ = resolve(raw, field) # auto path + if status == "recovered": + rec[field] = canon + if str(raw) != canon: + changes[field] = (str(raw), canon) + n_recovered += 1 + else: + rec[field] = raw # valid / undecided review → keep + rec.pop("comment", None) + rec.pop("original_input", None) + rec["comment"] = format_comment(changes) + rec["original_input"] = format_original_input(changes) + for f, (old, new) in changes.items(): + log_rows.append({ + "model": model, "country": cc, "case_id": cid, "field": f, + "before": old, "after": new, + "kind": "reviewed" if f in row_decisions else "recovered", + }) + with open(path, "w", encoding="utf-8") as fh: + for rec in records: + fh.write(json.dumps(rec, ensure_ascii=False) + "\n") + if models is not None and CHANGELOG.exists(): # keep other models' history + with open(CHANGELOG, encoding="utf-8") as f: + log_rows.extend( + row for row in map(json.loads, filter(str.strip, f)) + if row.get("model") not in models + ) + log_rows.sort(key=lambda r: (r["model"], r["country"], r["case_id"], r["field"])) + CHANGELOG.parent.mkdir(parents=True, exist_ok=True) + with open(CHANGELOG, "w", encoding="utf-8") as f: + for row in log_rows: + f.write(json.dumps(row, ensure_ascii=False) + "\n") + log.info(f"cleaning applied: {n_recovered} recovered, {n_reviewed} reviewed; " + f"{len(log_rows)} changes -> {CHANGELOG}") + + +def _basic_logging() -> None: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", + handlers=[logging.StreamHandler(sys.stderr)], + ) + + +def scan_main() -> None: + _basic_logging() + parser = argparse.ArgumentParser( + prog="legex-refusals-scan", + description="Flag inference cells that need human review (not auto-recoverable).", + ) + parser.add_argument("--out", type=Path, default=DEFAULT_XLSX) + parser.add_argument( + "--model", action="append", dest="models", metavar="SLUG", + help="Restrict to this model slug (repeatable). Default: all models.", + ) + parser.add_argument( + "--conflicts", type=Path, default=None, + help="Duplicate-run conflict sidecar (JSONL) to fold into the workbook.", + ) + args = parser.parse_args() + scan(args.out, set(args.models) if args.models else None, args.conflicts) + + +def apply_main() -> None: + _basic_logging() + parser = argparse.ArgumentParser( + prog="legex-refusals-apply", + description="Canonicalise recoverable values, apply reviewed decisions, stamp provenance.", + ) + parser.add_argument("--xlsx", type=Path, default=DEFAULT_XLSX) + parser.add_argument( + "--model", action="append", dest="models", metavar="SLUG", + help="Restrict to this model slug (repeatable); other models keep their " + "files and changelog history. Default: all models.", + ) + args = parser.parse_args() + apply(args.xlsx, set(args.models) if args.models else None) + + +if __name__ == "__main__": + scan() diff --git a/legex/evaluation/comparison.py b/legex/evaluation/comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..8cb9b8cff230855f169d7ba8f59aaeb03df53685 --- /dev/null +++ b/legex/evaluation/comparison.py @@ -0,0 +1,397 @@ +"""Value comparison and refusal detection for gold vs. prediction cells. + +Used by scoring (`evaluation.scoring`), the refusal cleaning workflow (`evaluation.cleaning`) and +the analysis package. Refusal detection is heuristic and English-only. +""" + +import re +from datetime import date, datetime +from enum import Enum +from typing import get_args + +from legex.models.classification import Classification + +# Columns that carry no label to score against. +NON_LABEL_COLUMNS = { + "case_id", "link", "full_text", "translated_full_text", "comment", "original_input", + "model", "error", "inference_date", +} + +# Sentinels that mean "no value". +EMPTY_LITERALS = frozenset({"", "none", "null", "nan"}) + +# Real controlled answers that must never be read as empty or as a refusal. +VALID_TOKENS = frozenset({"nonpecuniary", "no_allocation_possible"}) + +# Columns whose Classification field is numeric / a date. Loose parsing applies only to these. +NUMERIC_COLUMNS = frozenset( + (info.alias or name) + for name, info in Classification.model_fields.items() + if any(a is int or a is float for a in (get_args(info.annotation) or (info.annotation,))) +) +DATE_COLUMNS = frozenset( + (info.alias or name) + for name, info in Classification.model_fields.items() + if any(a is date for a in (get_args(info.annotation) or (info.annotation,))) +) + +BUCKETS = ("tp", "mismatch", "missed", "hallucinated", "tn") + +_NUM_TOKEN_RE = re.compile(r"-?\d[\d',. ]*") +_DATE_SEP_RE = re.compile(r"[_/.\s]+") +_DATE_FALLBACK_FORMATS = ("%d-%m-%Y", "%m-%d-%Y", "%Y%m%d") +_ISO_DATE_RE = re.compile(r"^\d{4}-\d{2}-\d{2}$") + + +def is_label_column(name: str) -> bool: + if not name or name.lower() in NON_LABEL_COLUMNS: + return False + return not name.startswith("Currency_") + + +def normalise(value: object) -> str: + """Make gold and prediction cells comparable as strings.""" + if value is None: + return "" + if isinstance(value, datetime): # Excel stores dates as datetimes + return value.date().isoformat() + if isinstance(value, date): + return value.isoformat() + if isinstance(value, float): + if value != value: # NaN + return "" + if value.is_integer(): + return str(int(value)) + s = str(value).strip() + if s.lower() in EMPTY_LITERALS: + return "" + return s + + +class RefusalReason(str, Enum): + BARE_TOKEN = "bare non-value token" + REFUSAL_PHRASE = "refusal phrase" + LEADING_NONE = "leading None/N-A + explanation" + TRAILING_NA = "prose ending in N/A" + + +_REFUSAL_BARE = frozenset({ + "n/a", "na", "n.a.", "unknown", "undetermined", "tbd", "-", "—", + "not available", "not specified", "not stated", "not applicable", + "not provided", "not mentioned", "not determinable", "not disclosed", +}) +_REFUSAL_PHRASE = re.compile( + r"not\s+(?:stated|mentioned|specified|provided|indicated|available|determinable|" + r"applicable|found|listed|given|disclosed|reported|clearly\s+\w+)" + r"|(?:cannot|could\s+not|can'?t|couldn'?t|unable\s+to|not\s+able\s+to)\s+(?:be\s+)?" + r"(?:determine|determined|calculate|calculated|establish|established|ascertain|found|identif)" + r"|does\s+not\s+(?:state|contain|specify|mention|provide|indicate|list|disclose|appear)" + r"|no\s+(?:specific|explicit|clear)\s+\w+" + r"|insufficient\s+(?:information|data|detail)", + re.I, +) +_REFUSAL_LEADING = re.compile( + r"^\s*(?:none|n/?a|null|unknown|not\s+available|not\s+specified|not\s+stated)\b", re.I +) +_REFUSAL_TRAILING_NA = re.compile(r"(?:^|[\s\n>)\].])n/?a\.?\s*$", re.I) +_PROSE = re.compile(r"[A-Za-z]{3,}\s+[A-Za-z]{3,}") # two consecutive words → a sentence + + +def refusal_reason(value: object, column: str | None = None) -> RefusalReason | None: + """Classify `value` as a long-form refusal, or None if it is a usable answer. + + A refusal must (a) match a refusal marker and (b) carry no extractable value of + the expected type — if the loose parser can still pull a number/date out of the + prose it is an answer-with-explanation, which the scorer handles, not a refusal. + """ + if value is None: + return None + s = str(value).strip() + if not s or s.lower() in VALID_TOKENS: + return None + + bare = s.lower().strip(" .") in _REFUSAL_BARE + if bare: + reason = RefusalReason.BARE_TOKEN + elif _REFUSAL_LEADING.match(s) and _PROSE.search(s): + reason = RefusalReason.LEADING_NONE + elif _REFUSAL_TRAILING_NA.search(s) and _PROSE.search(s): + reason = RefusalReason.TRAILING_NA + elif _REFUSAL_PHRASE.search(s): + reason = RefusalReason.REFUSAL_PHRASE + else: + return None + + if column in NUMERIC_COLUMNS and _numeric_value(s, column) is not None: + return None + if column in DATE_COLUMNS and _parse_loose_date(s) is not None: + return None + return reason + + +def is_refusal(value: object, column: str | None = None) -> bool: + return refusal_reason(value, column) is not None + + +_ISIC_COLUMNS = frozenset({ + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +}) +_ISIC_TOKEN = re.compile(r"^[a-v]_[a-z_]+$") + + +def _canonical_number(n: float) -> str: + return str(int(n)) if float(n).is_integer() else repr(float(n)) + + +_DATE_CLEAN_RE = re.compile(r"[\d/._\-\s]+") # a date with only separator noise (no citations/prose) +_CITATION_RE = re.compile(r"\[\d+\]") # Harvey citation markers, anywhere + +# Match prose with dates +_MONTHS = {m: i + 1 for i, m in enumerate(( + "january", "february", "march", "april", "may", "june", + "july", "august", "september", "october", "november", "december", +))} +_PROSE_DATE_RE = re.compile( + r"^([A-Za-z]+)\s+(\d{1,2}),?\s+(\d{4})$|^(\d{1,2})\.?\s+([A-Za-z]+)\s+(\d{4})$" +) + +# A cell that is exactly one number decorated with a currency token and/or a +# Swiss ".--" suffix ("CHF 20'000.--", "5.000 €"). +_CUR = r"(?:[€$£¥₹₩₪₾]|[A-Z]{2,3}\.?|(?i:fr|frs|euros?|francs?|dollars?|pounds?)\.?)" +_DECORATED_AMOUNT_RE = re.compile( + rf"^\s*{_CUR}?\s*(?P-?[\d'\u2019,.\u00a0\u202f ]*\d)\s*{_CUR}?" + rf"\s*(?:[.,]?\s*(?:-{{1,2}}|\u2013|\u2014))?\s*$" +) +# '5.000' — 5000 (EU) or 5.0 (US)? '538,183' — 538183 (US) or 538.183 (EU)? +# Either way ambiguous without a decimal part → a human decides. +_AMBIGUOUS_GROUPED_RE = re.compile(r"^-?\d{1,3}([.,]\d{3})+$") + + +def _strict_number(s: str) -> float | None: + """A single clean number with formatting noise only (apostrophe/space/EU-US separators). + + Returns None for anything else — prose, citations (`[1]`), newlines, multiple numbers — + so recovery never scrapes a stray digit out of junk.""" + t = s.strip().replace("'", "").replace("’", "").replace(" ", "").replace(" ", "") + if not t or re.search(r"[^\d.,+\-]", t): + return None + if re.fullmatch(r"-?\d{1,3}(\.\d{3})+,\d+", t): # 1.000.000,50 (EU) + t = t.replace(".", "").replace(",", ".") + elif re.fullmatch(r"-?\d+,\d+", t): # 1234,56 + t = t.replace(",", ".") + elif re.fullmatch(r"-?\d{1,3}(,\d{3})+(\.\d+)?", t): # 1,000,000.50 (US) + t = t.replace(",", "") + try: + f = float(t) + except ValueError: + return None + return f if f == f and f not in (float("inf"), float("-inf")) else None + + +def _strip_noise(s: str) -> str: + """Remove citation markers (`[1]`, anywhere) and markdown backticks/fences, then trim.""" + return _CITATION_RE.sub(" ", s).replace("`", " ").strip() + + +def _parse_prose_date(s: str) -> date | None: + """A cell that is exactly one prose date with an English month name, else None.""" + m = _PROSE_DATE_RE.match(s.strip()) + if not m: + return None + month_name, day, year = (m.group(1), m.group(2), m.group(3)) if m.group(1) else ( + m.group(5), m.group(4), m.group(6)) + month = _MONTHS.get(month_name.lower()) + if month is None: + return None + try: + return date(int(year), month, int(day)) + except ValueError: + return None + + +def _decorated_amount(s: str) -> float | None: + """A cell that is exactly one currency-decorated clean number, else None. + + Grouped integers without a decimal part (`'5.000 €'`, `'538,183 euro'`) are + left to a human — thousands- vs decimal-separator is ambiguous there.""" + m = _DECORATED_AMOUNT_RE.match(s) + if not m: + return None + num = m.group("num") + if _AMBIGUOUS_GROUPED_RE.match(num.strip()): + return None + return _strict_number(num) + + +def _review_reason(s: str, column: str | None) -> str: + r = refusal_reason(s, column) + return r.value if r else "no recoverable value" + + +def resolve(value: object, column: str | None) -> tuple[str, str, str | None]: + """Resolve a cell to ``(status, canonical_value, reason)``. + + status is one of: + * ``valid`` — a good value already (kept as-is; also the case for empty), + * ``recovered`` — a value parseable from prose, canonicalised (score-neutral), + * ``review`` — non-empty but neither valid nor recoverable → human review. + + Recovery reuses the scorer's own loose parsers so the canonical value scores + identically to the raw one (`values_agree` stays a tolerant safety net). + """ + s = normalise(value) + if not s: + return "valid", "", None + if column in NUMERIC_COLUMNS: + if column == "dispute_value_nominal" and s.lower() == "nonpecuniary": + return "valid", "nonpecuniary", None + if _try_float(s) is not None: # already a clean number + return "valid", s, None + # Recover a single number once citations/backticks/whitespace are stripped — never a + # digit scraped from surviving prose, which would write garbage. + cand = _strip_noise(s) + n = _strict_number(cand) + if n is not None: + return "recovered", _canonical_number(n), None + n = _decorated_amount(cand) + if n is not None: + return "recovered", _canonical_number(n), None + return "review", s, _review_reason(s, column) + if column in DATE_COLUMNS: + cand = _strip_noise(s) + if _ISO_DATE_RE.match(cand): + return ("valid" if cand == s else "recovered"), cand, None + if _DATE_CLEAN_RE.fullmatch(cand) and (d := _parse_loose_date(cand)) is not None: + return "recovered", d.isoformat(), None + if (d := _parse_prose_date(cand)) is not None: + return "recovered", d.isoformat(), None + return "review", s, _review_reason(s, column) + if column in _ISIC_COLUMNS: + low = s.lower() + if low == "no_allocation_possible" or _ISIC_TOKEN.match(low): + # Canonicalise case: gold and the comparator are exact-match. + return ("valid", s, None) if s == low else ("recovered", low, None) + return "review", s, "unrecognized ISIC code" + r = refusal_reason(s, column) + if r is not None: + return "review", s, r.value + return "valid", s, None + + +def _try_float(s: str) -> float | None: + if not s: + return None + try: + return float(s) + except ValueError: + return None + + +def _parse_loose_number(s: str) -> float | None: + """First numeric token from `s`, tolerating apostrophe/space thousand separators + (`20'000`), EU decimal commas (`1.000,50`), and trailing prose. None if no digit.""" + if not s: + return None + m = _NUM_TOKEN_RE.search(s) + if not m: + return None + tok = m.group(0).strip().rstrip(",.' ") + if not tok: + return None + cleaned = tok.replace("'", "").replace(" ", "") + if "," in cleaned and "." in cleaned: + if cleaned.rfind(",") > cleaned.rfind("."): + cleaned = cleaned.replace(".", "").replace(",", ".") + else: + cleaned = cleaned.replace(",", "") + elif "," in cleaned: + parts = cleaned.split(",") + if len(parts) == 2 and 1 <= len(parts[1]) <= 2: + cleaned = parts[0] + "." + parts[1] + else: + cleaned = cleaned.replace(",", "") + try: + return float(cleaned) + except ValueError: + return None + + +def _numeric_value(s: str, column: str | None) -> float | None: + n = _try_float(s) + if n is not None: + return n + if column in NUMERIC_COLUMNS: + return _parse_loose_number(s) + return None + + +def _parse_loose_date(s: str) -> date | None: + """Parse a date, treating `_`, `/`, `.`, whitespace as `-`; ISO plus a small fallback.""" + if not s: + return None + t = _DATE_SEP_RE.sub("-", s.strip()).strip("-") + if not t: + return None + try: + return date.fromisoformat(t) + except ValueError: + pass + for fmt in _DATE_FALLBACK_FORMATS: + try: + return datetime.strptime(t, fmt).date() + except ValueError: + continue + return None + + +def values_agree(gv: str, pv: str, column: str | None = None) -> bool: + """Compare normalised gold vs prediction cell values.""" + if gv == pv: + return True + # Controlled tokens carry no case information: annotators occasionally capitalise + # them (gold "Nonpecuniary" vs the schema literal "nonpecuniary"). + if gv.lower() == pv.lower() and pv.lower() in VALID_TOKENS: + return True + if column in DATE_COLUMNS: + gd, pd_ = _parse_loose_date(gv), _parse_loose_date(pv) + if gd is not None and pd_ is not None and gd == pd_: + return True + gn = _numeric_value(gv, column) + if gn is not None and gn == 0: + if not pv: + return True + pn = _numeric_value(pv, column) + return pn is not None and pn == 0 + if gn is not None: + pn = _numeric_value(pv, column) + if pn is not None: + return gn == pn + return False + + +def classify_cell(gv: str, pv: str, column: str | None) -> str: + """Bucket a (gold, pred) cell. `gv`/`pv` must already be `normalise()`-d. + + tp - gold filled, values agree; mismatch - both filled, differ; missed - gold + filled, pred empty; hallucinated - gold empty, pred filled; tn - both empty. + """ + gold_filled = bool(gv) + if values_agree(gv, pv, column): + return "tp" if gold_filled else "tn" + if gold_filled and bool(pv): + return "mismatch" + if gold_filled: + return "missed" + return "hallucinated" + + +def derived(c: dict[str, int]) -> tuple[float, float, float]: + """Per-column precision, recall, F1 from a bucket counter.""" + tp, mism, miss, hallu = c["tp"], c["mismatch"], c["missed"], c["hallucinated"] + p_denom = tp + mism + hallu + r_denom = tp + mism + miss + p = tp / p_denom if p_denom else 0.0 + r = tp / r_denom if r_denom else 0.0 + f1 = 2 * p * r / (p + r) if (p + r) else 0.0 + return p, r, f1 diff --git a/legex/evaluation/scoring.py b/legex/evaluation/scoring.py new file mode 100644 index 0000000000000000000000000000000000000000..7ec24559e4bc4c80f96436eca0065ba2dbb15348 --- /dev/null +++ b/legex/evaluation/scoring.py @@ -0,0 +1,254 @@ +"""Compare Goldenset gold labels against inference output. + +Reads each country's gold labels — from ``Goldenset_*.xlsx`` (the maintainers' +workbooks) or, with ``--gold-dir``, from the published goldenset JSONL — and +the matching predictions JSONL, then reports per-field agreement. +""" + +import argparse +import logging +import sys +from pathlib import Path + +from openpyxl import load_workbook + +from legex import published +from legex.evaluation.comparison import ( + BUCKETS, + classify_cell, + derived, + is_label_column, + normalise, +) +from legex.inference import inference_output_path +from legex.utils import ( + countries_with_goldenset, + goldenset_path, + goldenset_sheet, + read_inference_jsonl, +) + +log = logging.getLogger(__name__) + + +def _read_goldenset_rows(cc: str) -> tuple[list[str], dict[str, dict[str, str]]]: + """Return (label_columns, rows_by_case_id) for a country's Goldenset.""" + wb = load_workbook(goldenset_path(cc), read_only=True, data_only=True) + ws = goldenset_sheet(wb) + rows = ws.iter_rows(values_only=True) + header = [str(c) if c is not None else "" for c in next(rows)] + if "case_id" not in header: + raise ValueError(f"{goldenset_path(cc)} GOLDENSET sheet missing case_id column") + label_columns = [h for h in header if is_label_column(h)] + + by_id: dict[str, dict[str, str]] = {} + for row in rows: + if not any(row): + continue + cells = dict(zip(header, row)) + case_id = normalise(cells.get("case_id")) + if not case_id: + continue + labels = {col: normalise(cells.get(col)) for col in label_columns} + if not any(labels.values()): + continue + by_id[case_id] = labels + return label_columns, by_id + + +def _read_predictions(path: Path) -> dict[str, dict[str, str]]: + by_id: dict[str, dict[str, str]] = {} + for row in read_inference_jsonl(path): + case_id = normalise(row.get("case_id")) + if not case_id: + continue + # Rows whose inference failed are treated like absent inference + if normalise(row.get("error")): + continue + labels = {k: normalise(v) for k, v in row.items() if is_label_column(k)} + if not any(labels.values()): + continue + by_id[case_id] = {k: normalise(v) for k, v in row.items()} + return by_id + + +def _coverage( + gold: dict[str, dict[str, str]], preds: dict[str, dict[str, str]] +) -> dict[str, int]: + gold_ids, pred_ids = set(gold), set(preds) + return { + "gold": len(gold_ids), + "pred": len(pred_ids), + "overlap": len(gold_ids & pred_ids), + "missing": len(gold_ids - pred_ids), + "extra": len(pred_ids - gold_ids), + } + + +def score_country( + cc: str, + prompt_version: str, + source: str, + model: str, + verbose: bool = True, + *, + gold_dir: Path | None = None, + inference_dir: Path | None = None, +) -> tuple[dict[str, dict[str, int]], dict[str, int]] | None: + """Return (per-column counters, case coverage stats). + + ``gold_dir`` / ``inference_dir`` switch the respective input to the + published JSONL bundles (see ``legex.published``); by default the + maintainers' XLSX workbooks and working inference files are read. + """ + pred_path = ( + published.inference_file(inference_dir, cc, model) + if inference_dir is not None + else inference_output_path(cc, prompt_version, source, model) + ) + if not pred_path.exists(): + if verbose: + log.warning(f"[{cc}] missing predictions {pred_path}, skipping") + return None + gold_path = published.gold_file(gold_dir, cc) if gold_dir is not None else goldenset_path(cc) + if not gold_path.exists(): + if verbose: + log.warning(f"[{cc}] missing goldenset {gold_path}, skipping") + return None + + if gold_dir is not None: + label_columns, gold = published.load_gold_labels(gold_dir, cc) + else: + label_columns, gold = _read_goldenset_rows(cc) + preds = _read_predictions(pred_path) + stats = _coverage(gold, preds) + + counters: dict[str, dict[str, int]] = {col: {b: 0 for b in BUCKETS} for col in label_columns} + if verbose: + print() + log.info( + f"[{cc}] gold={stats['gold']} pred={stats['pred']} overlap={stats['overlap']} " + f"missing={stats['missing']} extra={stats['extra']}" + ) + + for case_id in (cid for cid in gold if cid in preds): + g, p = gold[case_id], preds[case_id] + for col in label_columns: + counters[col][classify_cell(g.get(col, ""), p.get(col, ""), col)] += 1 + return counters, stats + + +def _print_report( + cc: str, counters: dict[str, dict[str, int]], coverage: dict[str, int] | None = None +) -> None: + name_width = max(max((len(c) for c in counters), default=0), len("column")) + print(f"=== {cc} ===") + if coverage is not None: + print( + f"cases: gold={coverage['gold']} pred={coverage['pred']} " + f"overlap={coverage['overlap']} missing={coverage['missing']} " + f"extra={coverage['extra']}" + ) + print( + f"{'column'.ljust(name_width)} " + f"{'TP':>5} {'Mism':>5} {'Miss':>5} {'Hallu':>5} {'TN':>5} " + f"{'P':>7} {'R':>7} {'F1':>5}" + ) + for col, c in counters.items(): + p, r, f1 = derived(c) + f1_s = f"{f1:.2f}" if (p + r) else " - " + print( + f"{col.ljust(name_width)} " + f"{c['tp']:>5} {c['mismatch']:>5} {c['missed']:>5} " + f"{c['hallucinated']:>5} {c['tn']:>5} " + f"{p:>7.2%} {r:>7.2%} {f1_s:>5}" + ) + + +def evaluate( + countries: list[str] | None, + model: str, + prompt_version: str, + source: str, + gold_dir: Path | None = None, + inference_dir: Path | None = None, +) -> None: + overall: dict[str, dict[str, int]] = { + col: {b: 0 for b in BUCKETS} for col in published.LABEL_FIELDS + } + overall_coverage = {"gold": 0, "pred": 0, "overlap": 0, "missing": 0, "extra": 0} + + if countries is None: + countries = ( + published.countries_with_gold(gold_dir) + if gold_dir is not None + else countries_with_goldenset() + ) + seen_any = False + for cc in countries: + result = score_country( + cc, prompt_version, source, model, + gold_dir=gold_dir, inference_dir=inference_dir, + ) + if result is None: + continue + counters, coverage = result + seen_any = True + _print_report(cc, counters, coverage) + for key in overall_coverage: + overall_coverage[key] += coverage[key] + for col, c in counters.items(): + overall.setdefault(col, {b: 0 for b in BUCKETS}) + for b in BUCKETS: + overall[col][b] += c[b] + + if seen_any: + _print_report("ALL", overall, overall_coverage) + + +def main() -> None: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", + handlers=[logging.StreamHandler(sys.stderr)], + ) + parser = argparse.ArgumentParser( + prog="legex-evaluate", + description="Compare Goldenset labels against LLM inference output.", + ) + parser.add_argument( + "--country", action="extend", nargs="+", dest="countries", + help="Country code(s). Repeatable and/or space-separated. Defaults to all with a Goldenset.", + ) + parser.add_argument( + "--model", required=True, + help="Model id of the predictions to evaluate (must match the classify run).", + ) + parser.add_argument("--prompt_version", default="v3", help="Prompt version (default: v3).") + parser.add_argument( + "--gold-dir", type=Path, default=None, + help="Read gold labels from published goldenset JSONL under this directory " + "instead of the XLSX workbooks.", + ) + parser.add_argument( + "--inference-dir", type=Path, default=None, + help="Read predictions from published inference JSONL under this directory " + "instead of the working files.", + ) + source = parser.add_mutually_exclusive_group() + source.add_argument("--full_text", dest="source", action="store_const", const="full_text", + help="Evaluate predictions made from the full_text column.") + source.add_argument("--pdf", dest="source", action="store_const", const="pdf", + help="Evaluate predictions made from PDFs.") + args = parser.parse_args() + if args.inference_dir is None and args.source is None: + parser.error("one of --full_text / --pdf is required (unless --inference-dir is used)") + evaluate( + countries=args.countries, model=args.model, + prompt_version=args.prompt_version, source=args.source, + gold_dir=args.gold_dir, inference_dir=args.inference_dir, + ) + + +if __name__ == "__main__": + main() diff --git a/legex/extract_raw_text.py b/legex/extract_raw_text.py deleted file mode 100644 index 255b234eb6dae15e039e81d3a82877b50f8c7969..0000000000000000000000000000000000000000 --- a/legex/extract_raw_text.py +++ /dev/null @@ -1,137 +0,0 @@ -"""Extract full text from goldenset PDFs in data/raw/drive-download-*//. - -Writes data//full_text.jsonl with fields: case_id, full_text. -Text is whitespace-normalized (no newlines, runs of whitespace collapsed to one space). - -Usage: - uv run python -m legex.extract_raw_text - uv run python -m legex.extract_raw_text --country kr -""" - -from __future__ import annotations - -import argparse -import json -import logging -import re -import sys -from pathlib import Path - -from pypdf import PdfReader - -log = logging.getLogger("legex.extract_raw_text") - -REPO_ROOT = Path(__file__).resolve().parent.parent -RAW_DIR = REPO_ROOT / "data" / "raw" -DATA_DIR = REPO_ROOT / "data" - -FOLDER_TO_CC: dict[str, str] = { - "Armenia": "am", - "Belgium": "be", - "China": "cn", - "Dominican_Republic": "do", - "Georgia": "ge", - "Hong_Kong": "hk", - "India": "in", - "Nepal": "np", - "New_Zealand": "nz", - "Philippines": "ph", - "Russia": "ru", - "Singapore": "sg", - "South_Korea fixed": "kr", - "South_Korea": "kr", - "Spain": "es", - "Taiwan": "tw", - "Ukraine": "ua", - "United_States": "us", -} - - -_WS_RE = re.compile(r"\s+") - - -def normalize_ws(text: str) -> str: - return _WS_RE.sub(" ", text).strip() - - -def extract_pdf_text(pdf: Path) -> str: - try: - reader = PdfReader(str(pdf)) - text = " ".join((page.extract_text() or "") for page in reader.pages) - except Exception as e: - log.warning(f"failed to read {pdf}: {type(e).__name__}: {e}") - return "" - return normalize_ws(text) - - -def find_country_folders() -> dict[str, list[Path]]: - """Map country code -> list of PDFs found across all drive-download-* folders.""" - by_cc: dict[str, list[Path]] = {} - unknown: list[str] = [] - for drive_dir in sorted(RAW_DIR.glob("drive-download-*")): - if not drive_dir.is_dir(): - continue - for sub in sorted(drive_dir.iterdir()): - if not sub.is_dir(): - continue - cc = FOLDER_TO_CC.get(sub.name) - if cc is None: - unknown.append(str(sub)) - continue - pdfs = sorted(sub.glob("*.pdf")) - by_cc.setdefault(cc, []).extend(pdfs) - if unknown: - log.warning("unmapped folders (skipped): %s", ", ".join(unknown)) - return by_cc - - -def write_jsonl(cc: str, pdfs: list[Path]) -> Path: - out_dir = DATA_DIR / cc - out_dir.mkdir(parents=True, exist_ok=True) - out_path = out_dir / "full_text.jsonl" - with out_path.open("w", encoding="utf-8") as f: - for pdf in pdfs: - text = extract_pdf_text(pdf) - f.write(json.dumps({"case_id": pdf.stem, "full_text": text}, ensure_ascii=False)) - f.write("\n") - return out_path - - -def main() -> int: - logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s: %(message)s") - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument( - "--country", - "-c", - action="append", - help="Only process the given country code (repeatable). Default: all.", - ) - args = ap.parse_args() - - if not RAW_DIR.exists(): - log.error("raw dir not found: %s", RAW_DIR) - return 1 - - by_cc = find_country_folders() - if args.country: - wanted = {c.lower() for c in args.country} - by_cc = {cc: pdfs for cc, pdfs in by_cc.items() if cc in wanted} - missing = wanted - set(by_cc) - if missing: - log.error("no PDFs found for: %s", ", ".join(sorted(missing))) - return 1 - - if not by_cc: - log.error("no country folders matched") - return 1 - - for cc in sorted(by_cc): - pdfs = by_cc[cc] - log.info("[%s] extracting %d PDFs", cc, len(pdfs)) - out = write_jsonl(cc, pdfs) - log.info("[%s] wrote %s", cc, out) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/legex/fulltext/__init__.py b/legex/fulltext/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c270c808aea4300b0a139adaefcb7ad7520ee179 --- /dev/null +++ b/legex/fulltext/__init__.py @@ -0,0 +1 @@ +"""Full-text acquisition: scrape, build, and render judgment text into full_text.jsonl.""" diff --git a/legex/fulltext/build_full_text.py b/legex/fulltext/build_full_text.py new file mode 100644 index 0000000000000000000000000000000000000000..35b0e7a1147e42446db1c8c4f5a6edcad6dc38ff --- /dev/null +++ b/legex/fulltext/build_full_text.py @@ -0,0 +1,169 @@ +"""Build ``data//full_text.jsonl`` from an offline source. + +Two sources, both producing the JSONL shape the inference step consumes +(one object per line with a filesystem-safe ``case_id`` slug and ``full_text``): + +1. A Goldenset's ``full_text`` column — for jurisdictions whose judgment text + already lives in the workbook (e.g. Brazil). +2. A directory of PDFs (``--pdf-dir``) — for jurisdictions delivered as PDF + bundles (e.g. India's ``india_judgments.zip``). Each PDF's stem is the + ``case_id``; text is extracted with pypdf. + +Rows/files whose text is empty or shorter than ``--min-chars`` are reported +and skipped. + +Usage: + uv run legex-build-fulltext --country br + uv run legex-build-fulltext --xlsx "data/br/Goldenset_Brazil_final (...).xlsx" + uv run legex-build-fulltext --country in --pdf-dir data/raw/india_real/pdf +""" + +from __future__ import annotations + +import argparse +import json +import logging +import re +import sys +from pathlib import Path + +from openpyxl import load_workbook +from pypdf import PdfReader + +from legex.pdf_export.core import safe_slug +from legex.utils import full_text_jsonl_path, goldenset_path, goldenset_sheet + +log = logging.getLogger("legex.build_full_text") + +_WS_RE = re.compile(r"\s+") + + +def _norm_ws(text: str) -> str: + return _WS_RE.sub(" ", text).strip() + + +def build_from_pdf_dir( + pdf_dir: Path, + out_path: Path, + *, + min_chars: int, +) -> tuple[int, int]: + """Extract text from every PDF in ``pdf_dir``. Returns (written, skipped).""" + pdfs = sorted(pdf_dir.glob("*.pdf")) + if not pdfs: + raise ValueError(f"no PDFs in {pdf_dir}") + out_path.parent.mkdir(parents=True, exist_ok=True) + written = skipped = 0 + with out_path.open("w", encoding="utf-8") as f: + for pdf in pdfs: + try: + reader = PdfReader(str(pdf)) + body = _norm_ws(" ".join((p.extract_text() or "") for p in reader.pages)) + except Exception as exc: # noqa: BLE001 + log.warning("skip %s: %s", pdf.name, exc) + skipped += 1 + continue + if len(body) < min_chars: + skipped += 1 + log.warning("skip %s: %d chars", pdf.name, len(body)) + continue + f.write( + json.dumps({"case_id": pdf.stem, "full_text": body}, ensure_ascii=False) + "\n" + ) + written += 1 + return written, skipped + + +def build_from_xlsx( + xlsx: Path, + out_path: Path, + *, + min_chars: int, +) -> tuple[int, int]: + """Returns (written, skipped).""" + wb = load_workbook(xlsx, read_only=True, data_only=True) + try: + ws = goldenset_sheet(wb) + rows = ws.iter_rows(values_only=True) + header = [str(c).strip() if c is not None else "" for c in next(rows)] + lower = {h.lower(): i for i, h in enumerate(header)} + if "full_text" not in lower: + raise ValueError(f"{xlsx} GOLDENSET sheet has no full_text column ({header})") + if "case_id" not in lower: + raise ValueError(f"{xlsx} GOLDENSET sheet has no case_id column") + + ft_idx = lower["full_text"] + id_idx = lower["case_id"] + link_idx = lower.get("link") + + out_path.parent.mkdir(parents=True, exist_ok=True) + written = skipped = 0 + seen: set[str] = set() + with out_path.open("w", encoding="utf-8") as f: + for i, row in enumerate(rows): + if not any(c not in (None, "") for c in row): + continue + case_id = row[id_idx] + text = row[ft_idx] + link = row[link_idx] if link_idx is not None and link_idx < len(row) else None + body = _norm_ws(str(text)) if text is not None else "" + if len(body) < min_chars: + skipped += 1 + log.warning("skip row %d (case_id=%r): %d chars", i + 2, case_id, len(body)) + continue + slug = safe_slug( + str(case_id) if case_id is not None else "", str(link or ""), i + ) + if slug in seen: + slug = f"{slug}_{i}" + seen.add(slug) + f.write( + json.dumps({"case_id": slug, "full_text": body}, ensure_ascii=False) + "\n" + ) + written += 1 + finally: + wb.close() + return written, skipped + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig(level=logging.INFO, format="%(message)s") + parser = argparse.ArgumentParser(description="Build full_text.jsonl from a Goldenset column.") + parser.add_argument("--country", help="Country code; resolves the goldenset and output path.") + parser.add_argument("--xlsx", type=Path, help="Explicit workbook path (overrides --country lookup).") + parser.add_argument("--pdf-dir", type=Path, help="Extract from a directory of PDFs (stem = case_id).") + parser.add_argument("--out", type=Path, help="Output JSONL (default data//full_text.jsonl).") + parser.add_argument("--min-chars", type=int, default=100, help="Skip rows shorter than this.") + parser.add_argument("--force", action="store_true", help="Overwrite an existing full_text.jsonl.") + args = parser.parse_args(argv) + + if not args.country and not args.xlsx and not args.pdf_dir: + parser.error("provide --country, --xlsx, and/or --pdf-dir") + + out_path = args.out or ( + full_text_jsonl_path(args.country) if args.country else None + ) + if out_path is None: + parser.error("--out is required when --country is not given") + if out_path.exists() and not args.force: + log.error("%s exists; pass --force to overwrite", out_path) + return 1 + + if args.pdf_dir: + if not args.pdf_dir.is_dir(): + log.error("pdf dir not found: %s", args.pdf_dir) + return 2 + written, skipped = build_from_pdf_dir(args.pdf_dir, out_path, min_chars=args.min_chars) + else: + xlsx = args.xlsx or goldenset_path(args.country) + if not xlsx.exists(): + log.error("workbook not found: %s", xlsx) + return 2 + written, skipped = build_from_xlsx(xlsx, out_path, min_chars=args.min_chars) + + log.info("wrote %d rows (%d skipped) -> %s", written, skipped, out_path) + return 0 if written else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/legex/fulltext/render_full_text.py b/legex/fulltext/render_full_text.py new file mode 100644 index 0000000000000000000000000000000000000000..acfdc76bce76fb57af846e684d84c2d32aae291a --- /dev/null +++ b/legex/fulltext/render_full_text.py @@ -0,0 +1,261 @@ +"""Render judgment pages with a real browser and write ``full_text.jsonl``. + +Some courts (Taiwan's ``judgment.judicial.gov.tw``, Hong Kong's +``legalref.judiciary.hk``) reject plain HTTP clients or hide the judgment +behind script/anti-bot checks, so the requests-based ``scrape_full_text`` only +captured error placeholders. This module drives headless Chromium via +Playwright, waits for the judgment container to appear, and extracts its text. + +Output matches ``legex.scrape_full_text`` / ``data//full_text.jsonl`` so the +inference step consumes it unchanged: one JSON object per line with a +filesystem-safe ``case_id`` slug and a ``full_text`` body prefixed with the +original ``case_id``/``link``. + +Per-site content selectors live in ``CONTENT_SELECTORS``; unknown sites fall +back to the page ``body``. Rows whose extracted text is shorter than +``--min-chars`` are recorded with a ``RENDER_EMPTY`` marker and retried on the +next ``--resume`` run. + +Usage: + uv run legex-render-fulltext --country tw + uv run legex-render-fulltext --country tw --limit 5 --headed + uv run legex-render-fulltext --country hk --resume +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +import time +from collections.abc import Callable +from pathlib import Path + +from openpyxl import load_workbook + +from legex.pdf_export.core import safe_slug +from legex.pdf_export.workbook import cell, header_map +from legex.fulltext.scrape_full_text import build_full_text, normalize_ws +from legex.utils import full_text_jsonl_path, goldenset_path, goldenset_sheet + +log = logging.getLogger("legex.render_full_text") + +# CSS selector for the element holding the judgment body, per country code. +CONTENT_SELECTORS: dict[str, str] = { + "tw": "#jud", # FJUD data.aspx judgment container + "hk": "body", # ju_body.jsp renders the whole judgment in +} + +_DEFAULT_SELECTOR = "body" + + +def _rewrite_hk(url: str) -> str: + """Hong Kong links point at the frameset ``ju_frame.jsp``; the judgment + itself lives in the inner ``ju_body.jsp`` frame, which loads on its own.""" + if "ju_frame.jsp" in url: + return url.replace("ju_frame.jsp", "ju_body.jsp") + "&AH=&QS=&FN=&currpage=T" + return url + + +# Per-country link rewrites applied before navigation. +URL_REWRITES: dict[str, Callable[[str], str]] = { + "hk": _rewrite_hk, +} + +# Marker stored for rows that rendered but yielded no usable text, so a +# --resume run re-attempts them instead of treating them as done. +_EMPTY_MARKER = "RENDER_EMPTY" + +_UA = ( + "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) " + "AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.4 Safari/605.1.15" +) + + +def _load_done(out_path: Path) -> set[str]: + """case_id slugs already present with real (non-empty) text.""" + done: set[str] = set() + if not out_path.exists(): + return done + with out_path.open(encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + rec = json.loads(line) + except json.JSONDecodeError: + continue + cid = rec.get("case_id") + body = rec.get("full_text", "") + if cid and _EMPTY_MARKER not in body: + done.add(cid) + return done + + +def _extract_text(page, selector: str, min_chars: int) -> str: + """Best non-empty text among the comma-separated selector candidates.""" + best = "" + for sel in [s.strip() for s in selector.split(",") if s.strip()]: + try: + loc = page.locator(sel).first + if loc.count() == 0: + continue + txt = loc.inner_text(timeout=5_000) + except Exception: # noqa: BLE001 - selector may be absent on a page + continue + txt = normalize_ws(txt or "") + if len(txt) > len(best): + best = txt + if len(best) >= min_chars: + break + return best + + +def render_country( + cc: str, + out_path: Path, + *, + selector: str, + pause_s: float, + nav_timeout_ms: int, + min_chars: int, + limit: int | None, + resume: bool, + headed: bool, +) -> tuple[int, int]: + """Render every link in the country's goldenset. Returns (written, empty).""" + from playwright.sync_api import sync_playwright + + rewrite = URL_REWRITES.get(cc, lambda u: u) + + gs = goldenset_path(cc) + wb = load_workbook(gs, read_only=True, data_only=True) + ws = goldenset_sheet(wb) + rows = ws.iter_rows(values_only=True) + header = next(rows, None) + if not header: + wb.close() + log.error("[%s] empty goldenset %s", cc, gs) + return 0, 0 + h = header_map(header) + idx_case = h.get("case_id") + idx_link = h.get("link") + idx_text = h.get("full_text") + if idx_link is None: + wb.close() + log.error("[%s] no 'link' column in %s", cc, gs) + return 0, 0 + + done = _load_done(out_path) if resume else set() + mode = "a" if (resume and out_path.exists()) else "w" + out_path.parent.mkdir(parents=True, exist_ok=True) + + written = empty = 0 + with sync_playwright() as p: + browser = p.chromium.launch(headless=not headed) + context = browser.new_context(user_agent=_UA, locale="zh-TW") + page = context.new_page() + page.set_default_navigation_timeout(nav_timeout_ms) + + with out_path.open(mode, encoding="utf-8") as out: + row_idx = 1 + for row in rows: + row_idx += 1 + if not row: + continue + link = (cell(row, idx_link) or "").strip() + case_id_raw = str(cell(row, idx_case) or "").strip() + sheet_full = cell(row, idx_text) if idx_text is not None else None + if not link and not case_id_raw: + continue + slug = safe_slug(case_id_raw, link or None, row_idx) + if slug in done: + continue + if limit is not None and (written + empty) >= limit: + break + + body, source = "", "render" + if link.startswith("http"): + nav_url = rewrite(link) + try: + page.goto(nav_url, wait_until="domcontentloaded") + try: + page.wait_for_selector( + selector.split(",")[0].strip(), timeout=nav_timeout_ms + ) + except Exception: # noqa: BLE001 - fall back to whatever loaded + pass + body = _extract_text(page, selector, min_chars) + except Exception as e: # noqa: BLE001 + log.warning("[%s] render failed row %d: %s", cc, row_idx, e) + body = "" + + if len(body) < min_chars: + empty += 1 + sheet = normalize_ws(str(sheet_full)) if sheet_full else "" + if len(sheet) >= min_chars: + body, source = sheet, "full_text (Fallback)" + else: + body = f"{_EMPTY_MARKER} (rendered {len(body)} chars)" + source = "render (leer)" + else: + written += 1 + + full = build_full_text(case_id_raw, link, source, body) + out.write(json.dumps({"case_id": slug, "full_text": full}, ensure_ascii=False)) + out.write("\n") + out.flush() + done.add(slug) + if (written + empty) % 10 == 0: + log.info("[%s] %d ok / %d empty", cc, written, empty) + time.sleep(pause_s) + + context.close() + browser.close() + wb.close() + log.info("[%s] done: %d rendered, %d empty -> %s", cc, written, empty, out_path) + return written, empty + + +def main(argv: list[str] | None = None) -> int: + logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s: %(message)s") + ap = argparse.ArgumentParser(description="Render judgment pages with Playwright.") + ap.add_argument("--country", required=True, help="Country code (resolves goldenset + output).") + ap.add_argument("--out", type=Path, default=None, help="Output jsonl (default data//full_text.jsonl).") + ap.add_argument("--selector", default=None, help="Override the content CSS selector.") + ap.add_argument("--pause", type=float, default=1.0, help="Seconds between page loads.") + ap.add_argument("--nav-timeout", type=int, default=30_000, help="Navigation timeout (ms).") + ap.add_argument("--min-chars", type=int, default=300, help="Below this a render counts as empty.") + ap.add_argument("--limit", type=int, default=None, help="Max rows to render.") + ap.add_argument("--resume", action="store_true", help="Skip rows already captured with text.") + ap.add_argument("--headed", action="store_true", help="Show the browser window (debugging).") + args = ap.parse_args(argv) + + cc = args.country + gs = goldenset_path(cc) + if not gs.exists(): + log.error("no goldenset for %s at %s", cc, gs) + return 2 + + out_path = args.out or full_text_jsonl_path(cc) + selector = args.selector or CONTENT_SELECTORS.get(cc, _DEFAULT_SELECTOR) + log.info("[%s] rendering with selector %r -> %s", cc, selector, out_path) + + written, empty = render_country( + cc, + out_path, + selector=selector, + pause_s=args.pause, + nav_timeout_ms=args.nav_timeout, + min_chars=args.min_chars, + limit=args.limit, + resume=args.resume, + headed=args.headed, + ) + return 0 if written else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/legex/scrape_full_text.py b/legex/fulltext/scrape_full_text.py similarity index 94% rename from legex/scrape_full_text.py rename to legex/fulltext/scrape_full_text.py index 4cdf1c3831066df7e1fd5cb3514820531f3a97b9..ee16b6525be522b9d27cc5cfcef34decb396b2ed 100644 --- a/legex/scrape_full_text.py +++ b/legex/fulltext/scrape_full_text.py @@ -11,8 +11,8 @@ the body keeps the original case_id and link verbatim. Whitespace in the body is collapsed to single spaces so each record stays on one line. Usage: - uv run python -m legex.scrape_full_text data/am/Goldenset_Armenia_final.xlsx - uv run python -m legex.scrape_full_text data/gh/Goldenset_Ghana.xlsx --resume + uv run python -m legex.fulltext.scrape_full_text data/am/Goldenset_Armenia_final.xlsx + uv run python -m legex.fulltext.scrape_full_text data/gh/Goldenset_Ghana.xlsx --resume """ from __future__ import annotations @@ -29,6 +29,7 @@ import openpyxl from legex.pdf_export.core import safe_slug from legex.pdf_export.urls import pick_body from legex.pdf_export.workbook import cell, header_map +from legex.utils import goldenset_sheet log = logging.getLogger("legex.scrape_full_text") @@ -72,11 +73,12 @@ def scrape_xlsx( resume: bool, ) -> int: wb = openpyxl.load_workbook(xlsx, read_only=True, data_only=True) - if "GOLDENSET" not in wb.sheetnames: + try: + ws = goldenset_sheet(wb) + except ValueError: wb.close() log.error("no GOLDENSET sheet in %s", xlsx) return 0 - ws = wb["GOLDENSET"] rows = ws.iter_rows(values_only=True) header_row = next(rows, None) if not header_row: diff --git a/legex/harvey.py b/legex/harvey.py index 5843ea45202f5ef1b6a72108598b1d17d2ed0373..4a9aeb8c3703af30843960e19daf633887a6b2bb 100644 --- a/legex/harvey.py +++ b/legex/harvey.py @@ -1,15 +1,16 @@ -"""Ingest Harvey-AI extractions from a single xlsx into per-country CSVs. - -`data/raw/harvey.xlsx` (Sheet1) holds Harvey's outputs for 23 jurisdictions -with 4 columns per question (Value / Reasoning / Citations / Comments). We -keep only the Value columns, clean them, match each row to a Goldenset -case_id via the PDF filename, and write a CSV that looks exactly like the -output of `legex.inference` — so `legex-evaluate`, `legex-analysis`, and -`legex-plots` treat Harvey as just another model. +"""Ingest Harvey-AI extractions from a single xlsx into per-country JSONL. + +A VAULT_REVIEW export (Sheet1) holds Harvey's answers grouped by Folder. Each +question occupies a fixed block of columns: the older export used 4 columns per +question (Value / Reasoning / Citations / Comments); the newer export uses 2 +(Answer / Citations). We keep only the Answer column, clean it, match each row +to a Goldenset case_id via the PDF filename, and write JSONL that looks exactly +like `legex.inference` output — so evaluate/analysis treat Harvey as just +another model. The per-question block width is detected from the sheet, so both +export layouts work without per-file configuration. """ import argparse -import csv import logging import re import sys @@ -21,7 +22,8 @@ from openpyxl import load_workbook from legex.config import settings from legex.inference import _output_columns from legex.utils import ( - classified_csv_path, + inference_path, + write_inference_jsonl, goldenset_path, goldenset_sheet, norm_case_id, @@ -31,8 +33,10 @@ log = logging.getLogger(__name__) HARVEY_FOLDER_TO_CC: dict[str, str] = { + "Albania": "al", "Armenia": "am", "Australia": "au", + "Austria": "at", "Belgium": "be", "Brazil": "br", "China": "cn", @@ -42,11 +46,18 @@ HARVEY_FOLDER_TO_CC: dict[str, str] = { "Germany": "de", "Hong_Kong": "hk", "India": "in", + "Italy": "it", + "Korea": "kr", + "Kosovo": "xk", + "Liechtenstein": "li", + "Luxembourg": "lu", "Nepal": "np", "New_Zealand": "nz", "Philippines": "ph", "Russia": "ru", "Schweiz_final": "ch", + "Switzerland": "ch", + "Serbia": "rs", "Singapore": "sg", "South_Korea fixed": "kr", "Spain": "es", @@ -56,24 +67,56 @@ HARVEY_FOLDER_TO_CC: dict[str, str] = { "United_States": "us", } - -# Harvey Sheet1 value-column index → Goldenset header name. -HARVEY_COL_TO_GOLD: dict[int, str] = { - 7: "legal_subject_judgement", - 11: "trial_start_date", - 15: "trial_end_date", - 19: "dispute_value_nominal", - 23: "plaintiff_loosing_share", - 27: "court_cost_awarded_nominal", - 31: "party_compensation_awarded_nominal", - 35: "plaintiffs_all_count", - 39: "defendants_all_count", - 43: "plaintiff_no1_ISIC1_industry_category", - 47: "defendant_no1_ISIC1_industry_category", -} +# The 12 review-table questions, in export order. Column 0 of each row is the +# PDF name and column 1 the Folder. Thhe first question (case_id) starts at +# column index 3 and each subsequent question sits one block-width further on. +# We match rows by PDF filename with the Goldensets. +HARVEY_FIELDS_ORDER: tuple[str, ...] = ( + "case_id", + "legal_subject_judgement", + "trial_start_date", + "trial_end_date", + "dispute_value_nominal", + "plaintiff_loosing_share", + "court_cost_awarded_nominal", + "party_compensation_awarded_nominal", + "plaintiffs_all_count", + "defendants_all_count", + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +) +_FIRST_ANSWER_COL = 3 # After Name (0), Folder (1), Document Classification (2) _CITATION_RE = re.compile(r"\s*(?:\[\d+\])+\s*$") _EMPTY_LITERALS = {"", "—"} +_DATE_IN_NAME_RE = re.compile(r"(\d{4}-\d{2}-\d{2})") + + +def _infer_date_from_name(xlsx: Path) -> str | None: + m = _DATE_IN_NAME_RE.search(xlsx.name) + return m.group(1) if m else None + + +def _folder_to_cc(folder: str) -> str | None: + """Map a VAULT export folder to a country code, tolerating the ``(new)`` + suffix and spaces (e.g. ``'Hong Kong (new)'`` -> ``hk``).""" + base = re.sub(r"\s*\(new\)\s*$", "", str(folder)).strip() + return HARVEY_FOLDER_TO_CC.get(base) or HARVEY_FOLDER_TO_CC.get(base.replace(" ", "_")) + + +def _value_columns(n_cols: int) -> dict[int, str]: + """Answer-column index -> Goldenset field, for either export layout. + + Block width per question = (n_cols - 3) / 12 (4 for the old Value/Reasoning/ + Citations/Comments layout, 2 for the new Answer/Citations layout).""" + width = (n_cols - _FIRST_ANSWER_COL) // len(HARVEY_FIELDS_ORDER) + if width < 1: + raise ValueError(f"unexpected Harvey sheet width: {n_cols} columns") + return { + _FIRST_ANSWER_COL + j * width: field + for j, field in enumerate(HARVEY_FIELDS_ORDER) + if field != "case_id" + } def _clean(value: object) -> str: @@ -120,12 +163,19 @@ def ingest( prompt_version: str = "v3", source: str = "full_text", model: str = "harvey", + inference_date: str | None = None, ) -> None: + inference_date = inference_date or _infer_date_from_name(xlsx) + if not inference_date: + raise ValueError(f"cannot derive inference date from {xlsx.name}; pass --inference_date") + columns = _output_columns() + columns.insert(columns.index("model") + 1, "inference_date") wb = load_workbook(xlsx, read_only=True, data_only=True) if "Sheet1" not in wb.sheetnames: raise ValueError(f"{xlsx} missing Sheet1 (found {wb.sheetnames})") ws = wb["Sheet1"] + col_to_gold = _value_columns(ws.max_column) rows_iter = ws.iter_rows(values_only=True) next(rows_iter) # skip header @@ -139,7 +189,7 @@ def ingest( by_folder[str(folder)].append(row) for folder, rows in by_folder.items(): - cc = HARVEY_FOLDER_TO_CC.get(folder) + cc = _folder_to_cc(folder) if cc is None: log.warning(f"unknown folder {folder!r}, skipping {len(rows)} row(s)") continue @@ -148,29 +198,36 @@ def ingest( log.info(f"[{cc}] no Goldenset on disk, skipping {len(rows)} Harvey row(s)") continue - out = classified_csv_path(cc, prompt_version, source, model) + out = inference_path(cc, prompt_version, source, model) out.parent.mkdir(parents=True, exist_ok=True) matched = 0 unmatched = 0 - with open(out, "w", encoding="utf-8", newline="") as f: - writer = csv.DictWriter(f, fieldnames=columns, extrasaction="ignore") - writer.writeheader() - for row in rows: - stem = Path(str(row[0])).stem - gold = index.get(norm_case_id(stem)) - if gold is None: - unmatched += 1 - log.info(f"[{cc}] no Goldenset match for {row[0]!r}") - continue - out_row = {col: "" for col in columns} - out_row["case_id"] = gold - out_row["model"] = model - for harvey_idx, gold_col in HARVEY_COL_TO_GOLD.items(): - if harvey_idx < len(row): - out_row[gold_col] = _clean(row[harvey_idx]) - writer.writerow(out_row) - matched += 1 + out_rows: list[dict] = [] + for row in rows: + stem = Path(str(row[0])).stem + key = norm_case_id(stem) + gold = index.get(key) + if gold is None: + # Prefix pass: bundles sometimes shorten filenames + # `NKP-11263.pdf` vs gold `NKP_11263_(078_NF_0004)`). + cands = [k for k in index if k.startswith(key + "_")] + if len(cands) == 1: + gold = index[cands[0]] + if gold is None: + unmatched += 1 + log.info(f"[{cc}] no Goldenset match for {row[0]!r}") + continue + out_row = {col: "" for col in columns} + out_row["case_id"] = gold + out_row["model"] = model + out_row["inference_date"] = inference_date + for harvey_idx, gold_col in col_to_gold.items(): + if harvey_idx < len(row): + out_row[gold_col] = _clean(row[harvey_idx]) + out_rows.append(out_row) + matched += 1 + write_inference_jsonl(out, out_rows, columns) log.info( f"[{cc}] wrote {matched} Harvey row(s) → {out} " f"({unmatched} unmatched, {len(rows)} total)" @@ -185,28 +242,34 @@ def main() -> None: ) parser = argparse.ArgumentParser( prog="legex-harvey-ingest", - description="Convert data/raw/harvey.xlsx into per-country Goldenset_*_harvey.csv files.", + description="Convert a Harvey VAULT export into per-country Goldenset_*_harvey.jsonl files.", ) parser.add_argument( "--xlsx", type=Path, - default=settings.raw_dir / "harvey.xlsx", - help="Path to harvey.xlsx (default: data/raw/harvey.xlsx).", + default=settings.raw_dir / "harvey_2026-06-30.xlsx", + help="Path to the Harvey VAULT export xlsx (default: data/raw/harvey_2026-06-30.xlsx).", ) parser.add_argument("--prompt_version", default="v3") parser.add_argument( "--source", choices=("full_text", "pdf"), default="full_text", - help="Source bucket label used in the CSV filename (default: full_text).", + help="Source bucket label used in the output filename (default: full_text).", + ) + parser.add_argument("--model", default="harvey", help="Model slug for the output filename.") + parser.add_argument( + "--inference_date", + default=None, + help="ISO date stamped on every record (default: parsed from the xlsx filename).", ) - parser.add_argument("--model", default="harvey", help="Model slug for the CSV filename.") args = parser.parse_args() ingest( xlsx=args.xlsx, prompt_version=args.prompt_version, source=args.source, model=args.model, + inference_date=args.inference_date, ) diff --git a/legex/inference.py b/legex/inference.py index 30f59b3d64fc515f329151a099e8e594b212351a..41c28014372ef41a4cc6bdaca36665c48e6dd04a 100644 --- a/legex/inference.py +++ b/legex/inference.py @@ -5,14 +5,13 @@ For each registered country, reads case inputs from either: - the PDFs at `data//*.pdf` runs an LLM with the Vorlage coding rules, and appends a CSV of predictions -to `data//Goldenset_{country}_{prompt_version}_{full_text|pdf}_{model}.csv`. +to `data//Goldenset_{country}_{prompt_version}_{full_text|pdf}_{model}.jsonl`. Successful rows (by case_id) are skipped; rows with a non-empty `error` column are retried on the next run. """ import argparse import asyncio -import csv import json import logging import os @@ -32,7 +31,10 @@ from legex.models.classification import Classification from legex.prompts import PromptPlan, load_plan from legex.scrapers import SCRAPERS from legex.utils import ( - classified_csv_path, + inference_path, + inference_record, + read_inference_jsonl, + write_inference_jsonl, countries_with_goldenset, goldenset_path, goldenset_sheet, @@ -107,7 +109,7 @@ class _RequestRateLimiter: def inference_output_path(cc: str, prompt_version: str, source: str, model: str) -> Path: - return classified_csv_path(cc, prompt_version, source, model) + return inference_path(cc, prompt_version, source, model) # CSV columns (and GOLDENSET headers) use the alias when one is set, @@ -316,25 +318,22 @@ async def _classify_case_per_column( return row -class _CsvRowWriter: - """Append rows to a CSV, flushing after each write.""" +class _JsonlRowWriter: + """Append rows to an inference JSONL (one record per line), flushing each write.""" def __init__(self, path: Path, columns: list[str]) -> None: path.parent.mkdir(parents=True, exist_ok=True) - write_header = not path.exists() - self._file = open(path, "a", encoding="utf-8", newline="") - self._writer = csv.DictWriter(self._file, fieldnames=columns) - if write_header: - self._writer.writeheader() + self._columns = columns + self._file = open(path, "a", encoding="utf-8") def write_row(self, row: dict[str, str]) -> None: - self._writer.writerow(row) + self._file.write(json.dumps(inference_record(row, self._columns), ensure_ascii=False) + "\n") self._file.flush() def close(self) -> None: self._file.close() - def __enter__(self) -> "_CsvRowWriter": + def __enter__(self) -> "_JsonlRowWriter": return self def __exit__(self, *args: object) -> None: @@ -385,13 +384,12 @@ def _successful_case_ids(path: Path) -> set[str]: if not path.exists(): return set() ids: set[str] = set() - with open(path, encoding="utf-8", newline="") as f: - for r in csv.DictReader(f): - if (r.get("error") or "").strip(): - continue - cid = (r.get("case_id") or "").strip() - if cid: - ids.add(cid) + for r in read_inference_jsonl(path): + if (r.get("error") or "").strip(): + continue + cid = (r.get("case_id") or "").strip() + if cid: + ids.add(cid) return ids @@ -399,16 +397,12 @@ def _drop_case_ids(path: Path, columns: list[str], case_ids: set[str]) -> int: """Remove rows for the given case_ids. Returns number of rows removed.""" if not path.exists() or not case_ids: return 0 - with open(path, encoding="utf-8", newline="") as f: - rows = list(csv.DictReader(f)) + rows = read_inference_jsonl(path) kept = [r for r in rows if (r.get("case_id") or "").strip() not in case_ids] removed = len(rows) - len(kept) if removed == 0: return 0 - with open(path, "w", encoding="utf-8", newline="") as f: - writer = csv.DictWriter(f, fieldnames=columns, extrasaction="ignore") - writer.writeheader() - writer.writerows(kept) + write_inference_jsonl(path, kept, columns) return removed @@ -467,7 +461,7 @@ def classify( f"[{cc}] classifying {len(todo)} cases ({len(done)} already done) " f"in {plan.mode} mode → {out}" ) - with _CsvRowWriter(out, columns) as writer: + with _JsonlRowWriter(out, columns) as writer: n_ok = _classify_cases( todo, plan, model, columns, limiter, writer.write_row, concurrency ) @@ -481,7 +475,7 @@ def main() -> None: handlers=[logging.StreamHandler(sys.stderr)], ) load_dotenv(override=True) - print(f"gemini key {os.environ.get('GEMINI_API_KEY')}") # sanity check for env var + log.info("GEMINI_API_KEY set: %s", bool(os.environ.get("GEMINI_API_KEY"))) parser = argparse.ArgumentParser( prog="legex-classify", diff --git a/legex/legora.py b/legex/legora.py new file mode 100644 index 0000000000000000000000000000000000000000..59c1fdd6db3b3996c1ce1ee1f9cd3f00308a59ac --- /dev/null +++ b/legex/legora.py @@ -0,0 +1,280 @@ +"""Ingest Legora tabular-review extractions from a single xlsx into per-country JSONL. +""" + +import argparse +import html +import json +import logging +import re +import sys +from collections import defaultdict +from csv import DictReader +from datetime import date, datetime +from pathlib import Path + +from openpyxl import load_workbook + +from legex.config import settings +from legex.harvey import _gold_case_id_index +from legex.inference import _output_columns +from legex.utils import inference_path, norm_case_id, write_inference_jsonl + +log = logging.getLogger(__name__) + +LEGORA_FIELDS: tuple[str, ...] = ( + "case_id", + "legal_subject_judgement", + "trial_start_date", + "trial_end_date", + "dispute_value_nominal", + "plaintiff_loosing_share", + "court_cost_awarded_nominal", + "party_compensation_awarded_nominal", + "plaintiffs_all_count", + "defendants_all_count", + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +) + +# n-th occurrence of the field headers gives run name +GROUP_MODELS: dict[int, str] = {1: "legora-1", 2: "legora-2"} + +CONFLICTS_PATH = Path("data/analysis/quality/legora_duplicate_conflicts.jsonl") +_EMPTY_LITERALS = {"", "—"} +_DATE_IN_NAME_RE = re.compile(r"(\d{4}-\d{2}-\d{2})") + + +def loose_name(s: str) -> str: + """Filename key tolerant of separator mangling: unescape HTML entities, + lowercase, collapse every non-alphanumeric run to one underscore.""" + return re.sub(r"[^0-9a-zà-￿]+", "_", html.unescape(str(s)).lower()).strip("_") + + +def tight_name(s: str) -> str: + """Last-resort filename key: drop every non-alphanumeric character, so + names differing only in punctuation placement compare equal.""" + return re.sub(r"[^0-9a-zà-￿]", "", html.unescape(str(s)).lower()) + + +def load_manifest(csv_path: Path) -> dict[str, dict[str, tuple[str, str]]]: + """Bundle manifest -> {"exact"|"loose"|"tight": {key: (cc, case_id)}}. + """ + lookups: dict[str, dict[str, tuple[str, str]]] = {"exact": {}, "loose": {}, "tight": {}} + ambiguous: dict[str, set[str]] = {"exact": set(), "loose": set(), "tight": set()} + with open(csv_path, encoding="utf-8") as f: + for rec in DictReader(f): + target = (rec["cc"], rec["case_id"]) + for pass_, key in ( + ("exact", rec["file"]), + ("loose", loose_name(rec["file"])), + ("tight", tight_name(rec["file"])), + ): + table = lookups[pass_] + if key in table and table[key] != target: + ambiguous[pass_].add(key) + table.setdefault(key, target) + for pass_, keys in ambiguous.items(): + for key in keys: + del lookups[pass_][key] + if keys: + log.warning(f"manifest: dropped {len(keys)} ambiguous {pass_} key(s)") + return lookups + + +def match_document(name: str, lookups: dict[str, dict[str, tuple[str, str]]]) -> tuple[str, str] | None: + """(cc, case_id) for an exported document name, or None (junk/unknown).""" + return ( + lookups["exact"].get(str(name)) + or lookups["loose"].get(loose_name(name)) + or lookups["tight"].get(tight_name(name)) + ) + + +def _group_value_columns(header: list[str]) -> dict[int, dict[str, int]]: + """{group -> {field -> column index}} from the header row. + """ + occurrences: dict[str, list[int]] = defaultdict(list) + for idx, cell in enumerate(header): + name = str(cell).strip() if cell is not None else "" + if name in LEGORA_FIELDS: + occurrences[name].append(idx) + counts = {f: len(occurrences[f]) for f in LEGORA_FIELDS} + n_groups = min(counts.values()) + if n_groups < 1: + missing = [f for f, n in counts.items() if n == 0] + raise ValueError(f"export header is missing field column(s): {missing}") + if len(set(counts.values())) != 1: + raise ValueError(f"unbalanced field-column groups: {counts}") + return { + g: {f: occurrences[f][g - 1] for f in LEGORA_FIELDS} + for g in range(1, n_groups + 1) + } + + +def _clean(value: object) -> str: + """Format-level canonicalisation of one cell (no value-level cleaning).""" + if value is None: + return "" + if isinstance(value, datetime): # Excel date cells arrive as datetimes + return value.date().isoformat() + if isinstance(value, date): + return value.isoformat() + if isinstance(value, float) and value.is_integer(): + return str(int(value)) + s = str(value).strip() + if s in _EMPTY_LITERALS: + return "" + if s.lower() == "nonpecuniary": + return "nonpecuniary" + return s + + +def _infer_date_from_name(xlsx: Path) -> str | None: + m = _DATE_IN_NAME_RE.search(xlsx.name) + return m.group(1) if m else None + + +def ingest( + xlsx: Path, + manifest_csv: Path, + prompt_version: str = "v3", + source: str = "full_text", + inference_date: str | None = None, + conflicts_out: Path = CONFLICTS_PATH, +) -> None: + inference_date = inference_date or _infer_date_from_name(xlsx) + if not inference_date: + raise ValueError(f"cannot derive inference date from {xlsx.name}; pass --inference_date") + + columns = _output_columns() + columns.insert(columns.index("model") + 1, "inference_date") + + lookups = load_manifest(manifest_csv) + wb = load_workbook(xlsx, read_only=True, data_only=True) + if "Sheet1" not in wb.sheetnames: + raise ValueError(f"{xlsx} missing Sheet1 (found {wb.sheetnames})") + ws = wb["Sheet1"] + rows_iter = ws.iter_rows(values_only=True) + header = [c for c in next(rows_iter)] + group_cols = _group_value_columns(header) + + # by_cc[model][cc] -> case_id -> {field: cleaned value}; export order kept. + by_cc: dict[str, dict[str, dict[str, dict[str, str]]]] = { + model: defaultdict(dict) for model in GROUP_MODELS.values() + } + conflicts: list[dict] = [] + unmatched: list[str] = [] + for row in rows_iter: + if not row or row[0] is None: + continue + name = str(row[0]) + target = match_document(name, lookups) + if target is None: + unmatched.append(name) + log.info(f"no manifest match for {name!r}, skipping") + continue + cc, case_id = target + for group, model in GROUP_MODELS.items(): + values = { + field: _clean(row[idx]) if idx < len(row) else "" + for field, idx in group_cols[group].items() + if field != "case_id" + } + seen = by_cc[model][cc].get(case_id) + if seen is None: + by_cc[model][cc][case_id] = values + continue + for field, alt in values.items(): # duplicate run of the same document + if seen[field] != alt: + conflicts.append({ + "model": model, "country": cc, "case_id": case_id, + "field": field, "kept": seen[field], "alternative": alt, + }) + if unmatched: + log.warning(f"{len(unmatched)} document(s) had no manifest match: {unmatched[:5]} …") + + gold_indices = { + cc: _gold_case_id_index(cc) + for model_rows in by_cc.values() + for cc in model_rows + } + for model, model_rows in by_cc.items(): + for cc, cases in sorted(model_rows.items()): + index = gold_indices[cc] + if index is None: + log.info(f"[{cc}] no Goldenset on disk, skipping {len(cases)} Legora row(s)") + continue + out = inference_path(cc, prompt_version, source, model) + out_rows: list[dict] = [] + dropped = 0 + for case_id, values in cases.items(): + gold = index.get(norm_case_id(case_id)) + if gold is None: + dropped += 1 + log.info(f"[{cc}] manifest case_id {case_id!r} not in Goldenset, skipping") + continue + out_row = {col: "" for col in columns} + out_row["case_id"] = gold + out_row["model"] = model + out_row["inference_date"] = inference_date + out_row.update(values) + out_rows.append(out_row) + write_inference_jsonl(out, out_rows, columns) + log.info(f"[{cc}] wrote {len(out_rows)} {model} row(s) → {out} ({dropped} dropped)") + + conflicts.sort(key=lambda c: (c["model"], c["country"], c["case_id"], c["field"])) + conflicts_out.parent.mkdir(parents=True, exist_ok=True) + with open(conflicts_out, "w", encoding="utf-8") as f: + for c in conflicts: + f.write(json.dumps(c, ensure_ascii=False) + "\n") + log.info(f"{len(conflicts)} duplicate-run conflict(s) -> {conflicts_out}") + + +def main() -> None: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", + handlers=[logging.StreamHandler(sys.stderr)], + ) + parser = argparse.ArgumentParser( + prog="legex-legora-ingest", + description="Convert a Legora tabular-review export into per-country Goldenset_*_legora-{1,2}.jsonl files.", + ) + parser.add_argument( + "--xlsx", + type=Path, + default=settings.raw_dir / "legora_2026-08-01.xlsx", + help="Path to the Legora export xlsx (default: data/raw/legora_2026-08-01.xlsx).", + ) + parser.add_argument( + "--manifest", + type=Path, + default=settings.raw_dir / "legora_bundle_manifest.csv", + help="Bundle manifest mapping filenames to (country, case_id).", + ) + parser.add_argument("--prompt_version", default="v3") + parser.add_argument( + "--source", + choices=("full_text", "pdf"), + default="full_text", + help="Source bucket label used in the output filename (default: full_text).", + ) + parser.add_argument( + "--inference_date", + default=None, + help="ISO date the vendor ran the extraction (default: parsed from the xlsx filename).", + ) + parser.add_argument("--conflicts", type=Path, default=CONFLICTS_PATH) + args = parser.parse_args() + ingest( + xlsx=args.xlsx, + manifest_csv=args.manifest, + prompt_version=args.prompt_version, + source=args.source, + inference_date=args.inference_date, + conflicts_out=args.conflicts, + ) + + +if __name__ == "__main__": + main() diff --git a/legex/pdf_export/cli.py b/legex/pdf_export/cli.py index 79c2771ad59a01d261c06bd378331f48dc5a70c6..c860a1ff2ae7dcfe79c31ecaf0cee5b16cb26fa5 100644 --- a/legex/pdf_export/cli.py +++ b/legex/pdf_export/cli.py @@ -10,9 +10,11 @@ from dotenv import load_dotenv from legex.config import settings from legex.pdf_export.core import ensure_fonts +from legex.pdf_export.jsonl import export_jsonl from legex.pdf_export.sheet import export_workbook as export_sheet_workbook from legex.pdf_export.urls import collect_workbooks, export_workbook as export_urls_workbook from legex.pdf_export.workbook import discover_goldensets +from legex.utils import full_text_jsonl_path def _data_dir(ns: argparse.Namespace) -> Path: @@ -66,6 +68,26 @@ def _cmd_urls(ns: argparse.Namespace) -> int: return 0 +def _cmd_jsonl(ns: argparse.Namespace) -> int: + data_dir = _data_dir(ns) + if not ns.country: + print("--country is required (repeatable)", file=sys.stderr) + return 1 + out_root = data_dir / "pdf" / "from_jsonl" + out_root.mkdir(parents=True, exist_ok=True) + ensure_fonts() + total = 0 + for cc in ns.country: + jsonl = full_text_jsonl_path(cc) + if not jsonl.exists(): + print(f" skip {cc}: no {jsonl}", file=sys.stderr) + continue + print(f"{jsonl}", flush=True) + total += export_jsonl(jsonl, out_root / cc, cc=cc, limit=ns.limit) + print(f"Insgesamt PDFs: {total}") + return 0 + + def _files_from_input_dirs(paths: list[str]) -> list[Path]: files: list[Path] = [] for raw in paths: @@ -111,6 +133,14 @@ def main() -> None: ) p_urls.set_defaults(_run=_cmd_urls) + p_jsonl = sub.add_parser("jsonl", help="PDF aus data//full_text.jsonl (recovered text)") + p_jsonl.add_argument( + "--country", action="append", dest="country", + help="Country code (repeatable), reads data//full_text.jsonl", + ) + p_jsonl.add_argument("--limit", type=int, default=None, help="Max. PDFs pro Land") + p_jsonl.set_defaults(_run=_cmd_jsonl) + ns = ap.parse_args() raise SystemExit(ns._run(ns)) diff --git a/legex/pdf_export/core.py b/legex/pdf_export/core.py index ef334812d73f3053732f252298083989117a20b1..710ebd275afa2a6eff0d80a7ac1b20d042104c68 100644 --- a/legex/pdf_export/core.py +++ b/legex/pdf_export/core.py @@ -41,6 +41,8 @@ def ensure_fonts() -> None: def sanitize_pdf_text(s: str) -> str: s = str(s).replace("\t", " ").replace("\r\n", "\n").replace("\r", "\n") + # Drop NUL and other C0 control chars, fpdf cannot handle them + s = re.sub(r"[\x00-\x08\x0b\x0c\x0e-\x1f]", "", s) s = s.replace("\uf0b7", "•").replace("\uf0a7", "•").replace("\uf0d8", "•") s = s.replace("\uf02d", "–") return s diff --git a/legex/pdf_export/font_keys.py b/legex/pdf_export/font_keys.py index 4d4b53ed5cf1ed8fe9a29a7ccea607bcceb1f7f3..e627dc55dd6134110dd083479c4f777b99cbb9b8 100644 --- a/legex/pdf_export/font_keys.py +++ b/legex/pdf_export/font_keys.py @@ -8,8 +8,12 @@ from pathlib import Path FONT_BY_CC: dict[str, str] = { "kr": "han_k", "in": "deva", + "np": "deva", # Nepali — Devanagari script "am": "arm", "ge": "geo", + "tw": "han_tc", # Traditional Chinese (Source Han Sans TC, also covers Latin) + "hk": "han_tc", # mostly English judgments, occasional Chinese names + "cn": "han_sc", # Simplified Chinese } # Goldenset_* stem label (ohne Präfix), z. B. aus älteren Drive-Layouts diff --git a/legex/pdf_export/jsonl.py b/legex/pdf_export/jsonl.py new file mode 100644 index 0000000000000000000000000000000000000000..b4140e4c2bc8d7324c6976c00000fe858a6e7314 --- /dev/null +++ b/legex/pdf_export/jsonl.py @@ -0,0 +1,63 @@ +"""Write one PDF per case from a recovered ``full_text.jsonl``. + +For jurisdictions whose judgment text was recovered into +``data//full_text.jsonl`` (Taiwan/Hong Kong via rendering, Serbia via +scraping, India via the real PDF bundle) rather than living in the workbook's +``full_text`` column, this produces the original-language PDFs that the Harvey +hand-off needs. Font is chosen per country code (Traditional Chinese for +tw/hk, Devanagari for in, etc.). +""" + +from __future__ import annotations + +import json +from pathlib import Path + +from legex.pdf_export.core import write_one_pdf +from legex.pdf_export.font_keys import FONT_BY_CC + + +def export_jsonl( + jsonl_path: Path, + out_dir: Path, + *, + cc: str, + min_chars: int = 1, + limit: int | None = None, +) -> int: + """Write PDFs from ``jsonl_path`` into ``out_dir``. Returns count written.""" + font_key = FONT_BY_CC.get(cc, "default") + out_dir.mkdir(parents=True, exist_ok=True) + used_names: dict[str, int] = {} + count = 0 + with jsonl_path.open(encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + rec = json.loads(line) + except json.JSONDecodeError: + continue + case_id = str(rec.get("case_id") or "").strip() + body = str(rec.get("full_text") or "") + if not case_id or len(body) < min_chars: + continue + base = case_id or f"row_{count}" + used_names[base] = used_names.get(base, 0) + 1 + fname = f"{base}_{used_names[base]}.pdf" if used_names[base] > 1 else f"{base}.pdf" + write_one_pdf( + out_dir / fname, + case_id, + "", + body, + font_key, + text_source="full_text.jsonl", + ) + count += 1 + if count % 25 == 0: + print(f" {cc}: {count} …", flush=True) + if limit is not None and count >= limit: + break + print(f" {cc}: done, {count} PDFs -> {out_dir}", flush=True) + return count diff --git a/legex/pdf_export/urls.py b/legex/pdf_export/urls.py index 24119ae68cbc486074ef1ba5c79244361fd12ac1..9a879ca8b90a29d27c73a716a21e58a856e7304c 100644 --- a/legex/pdf_export/urls.py +++ b/legex/pdf_export/urls.py @@ -16,7 +16,7 @@ from legex.pdf_export.core import safe_slug, write_one_pdf from legex.pdf_export.font_keys import font_key_for_workbook from legex.pdf_export.workbook import cell, discover_goldensets, header_map -UA = "Mozilla/5.0 (compatible; LegexPDFExport/1.0)" +UA = "Mozilla/5.0 (compatible; LegexPDFExport/1.0; +https://github.com/Engineers-for-Science-Initiative/legex)" def is_hf_dataset_stub_url(url: str) -> bool: diff --git a/legex/processing.py b/legex/processing.py index e5c057064b67379579c5a721c8617aef8f0ae75d..66059d62d3e3515abf714c84ff483d949dfc6a88 100644 --- a/legex/processing.py +++ b/legex/processing.py @@ -1,3 +1,6 @@ +"""Pipeline steps for ``legex-run``: scrape sources, filter/sample, and build +the per-country Goldenset workbooks.""" + import logging import zipfile from pathlib import Path @@ -57,7 +60,7 @@ def filter_and_sample(force: bool = False) -> None: def fill_goldenset() -> None: for cc, cls in SCRAPERS.items(): out = goldenset_path(cc) - if out.exists() and not force: + if out.exists(): log.info(f"[{cc}] {out} exists, skipping") continue sampled = sampled_path(cc) diff --git a/legex/published.py b/legex/published.py new file mode 100644 index 0000000000000000000000000000000000000000..097a48507af7f3ab0f86c600d6ac9bd577a743c1 --- /dev/null +++ b/legex/published.py @@ -0,0 +1,161 @@ +"""Loaders for the published LEGEX JSONL bundles (HF ``legexbenchmark``). + +The released datasets carry everything the evaluation needs: + +- ``goldensets/data//goldenset_.jsonl`` — the first row per + ``case_id`` is the primary gold annotation; further rows for the same + ``case_id`` are independent reannotations, keyed by their salted + ``annotator_id``. +- ``inference-results/data//inference_.jsonl`` — one file per + system run, same record layout as ``legex.inference`` output. + +These loaders let scoring, IAA, and the AAT run directly on the published +files (pass ``--gold-dir`` / ``--inference-dir`` to the CLIs). The XLSX +workbook mode remains the maintainers' path. +""" + +import json +import logging +from pathlib import Path + +from legex.evaluation.comparison import is_label_column, normalise +from legex.utils import norm_case_id + +log = logging.getLogger(__name__) + +# Role label for the primary (gold) annotation, shared with legex.analysis.iaa. +PRIMARY = "primary" + +# The 14 schema fields, in template order (see legex/models/classification.py). +SCHEMA_FIELDS: tuple[str, ...] = ( + "legal_subject_judgement", + "trial_start_date", + "trial_end_date", + "dispute_value_nominal", + "Currency_dispute_value_nominal", + "plaintiff_loosing_share", + "court_cost_awarded_nominal", + "Currency_court_cost_awarded_nominal", + "party_compensation_awarded_nominal", + "Currency_party_compensation_awarded_nominal", + "plaintiffs_all_count", + "defendants_all_count", + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +) + +# The 11 evaluated fields (currencies are scored via their amount field). +LABEL_FIELDS: tuple[str, ...] = tuple(f for f in SCHEMA_FIELDS if is_label_column(f)) + +# model id (as used throughout the analysis) -> published file name part. +MODEL_FILES: dict[str, str] = { + "gemini/gemini-3.1-flash-lite": "gemini", + "gpt-5.4-mini": "gpt", + "harvey": "harvey", + "harvey-2": "harvey_2", + "legora-1": "legora_1", + "legora-2": "legora_2", +} + + +def default_gold_dir(repo_root: Path) -> Path: + """``submission/goldensets/data`` in the working repo; the sibling + ``goldensets`` clone next to the published code bundle.""" + for cand in ( + repo_root / "submission" / "goldensets" / "data", + repo_root.parent / "goldensets" / "data", + ): + if cand.is_dir(): + return cand + raise SystemExit( + "no published goldensets found — clone " + "https://huggingface.co/datasets/legexbenchmark/goldensets next to this " + "repository or pass --gold-dir" + ) + + +def default_inference_dir(repo_root: Path) -> Path: + """``submission/inference-results/data`` in the working repo; the sibling + ``inference-results`` clone next to the published code bundle.""" + for cand in ( + repo_root / "submission" / "inference-results" / "data", + repo_root.parent / "inference-results" / "data", + ): + if cand.is_dir(): + return cand + raise SystemExit( + "no published inference results found — clone " + "https://huggingface.co/datasets/legexbenchmark/inference-results next to " + "this repository or pass --inference-dir" + ) + + +def gold_file(gold_dir: Path, cc: str) -> Path: + return Path(gold_dir) / cc / f"goldenset_{cc}.jsonl" + + +def inference_file(inference_dir: Path, cc: str, model: str) -> Path: + name = MODEL_FILES.get(model) + if name is None: + raise KeyError(f"no published inference file for model {model!r}") + return Path(inference_dir) / cc / f"inference_{name}.jsonl" + + +def countries_with_gold(gold_dir: Path) -> list[str]: + return sorted(p.parent.name for p in Path(gold_dir).glob("*/goldenset_*.jsonl")) + + +def iter_gold_rows(gold_dir: Path, cc: str) -> list[dict]: + """Raw records of one published goldenset file, in file order.""" + path = gold_file(gold_dir, cc) + # split("\n"), not splitlines(): some full_text values contain U+0085, + # which splitlines() treats as a line break, splitting records in two. + return [json.loads(line) for line in path.read_text(encoding="utf-8").split("\n") if line.strip()] + + +def load_gold_labels(gold_dir: Path, cc: str) -> tuple[list[str], dict[str, dict[str, str]]]: + """Return ``(label_columns, {case_id: {field: normalised value}})``. + + The first row per ``case_id`` is the primary gold annotation; reannotation + rows appended later in the file are ignored here (use + ``load_annotator_labels`` for those). Same return shape and normalisation + as ``legex.evaluation.scoring._read_goldenset_rows``. + """ + by_id: dict[str, dict[str, str]] = {} + for rec in iter_gold_rows(gold_dir, cc): + case_id = normalise(rec.get("case_id")) + if not case_id or case_id in by_id: + continue + by_id[case_id] = {f: normalise(rec.get(f)) for f in LABEL_FIELDS} + return list(LABEL_FIELDS), by_id + + +def load_annotator_labels( + gold_dir: Path, countries: list[str] +) -> dict[tuple[str, str, str], dict[str, str]]: + """IAA label map ``{(annotator, cc, norm_case_id): {field: value}}``. + + The first row per ``case_id`` carries the role label ``primary`` (matching + the XLSX mode of ``legex.analysis.iaa``); reannotation rows keep their + salted ``annotator_id``. Reannotation rows with no label at all are + dropped, mirroring the XLSX loader. + """ + labels: dict[tuple[str, str, str], dict[str, str]] = {} + for cc in countries: + path = gold_file(gold_dir, cc) + if not path.exists(): + log.warning("[%s] no published goldenset at %s", cc, path) + continue + seen: set[str] = set() + for rec in iter_gold_rows(gold_dir, cc): + case_id = normalise(rec.get("case_id")) + if not case_id: + continue + fields = {f: normalise(rec.get(f)) for f in LABEL_FIELDS} + key = norm_case_id(case_id) + if case_id not in seen: + seen.add(case_id) + labels[(PRIMARY, cc, key)] = fields + elif any(fields.values()): + labels[(str(rec.get("annotator_id")), cc, key)] = fields + return labels diff --git a/legex/quant_results.py b/legex/quant_results.py deleted file mode 100644 index 8a5a5df7342cefbe6fa79853029223eb29405b9d..0000000000000000000000000000000000000000 --- a/legex/quant_results.py +++ /dev/null @@ -1,295 +0,0 @@ -"""Render the `Quantitative Results` LaTeX section. - -Reads `data/analysis/per_country_per_column.csv` (produced by -`legex-analysis`), restricts it to the twelve evaluated jurisdictions -and the fourteen evaluated fields, then prints the section preamble plus -the headline table that compares the three systems on Acc / Recall / -Hallucination / F1, both over all fields and over the four-field cost -block. -""" - -import argparse -import csv -import logging -import sys -from collections import defaultdict -from pathlib import Path - -log = logging.getLogger(__name__) - - -# Jurisdictions that survive the round-2 PDF audit (TW/BR/HK/IN excluded -# because their source PDFs were incomplete; BE/NP/RS excluded because -# inference was intentionally skipped for them). -EVALUATED_COUNTRIES: tuple[str, ...] = ( - "am", "au", "ch", "de", "es", "fr", "ge", - "nz", "ph", "sg", "uk", "us", -) -H2H_COUNTRIES = EVALUATED_COUNTRIES # backwards-compat alias - -EVAL_FIELDS: tuple[str, ...] = ( - "legal_subject_judgement", - "trial_start_date", - "trial_end_date", - "dispute_value_nominal", - "plaintiff_loosing_share", - "court_cost_awarded_nominal", - "party_compensation_awarded_nominal", - "plaintiffs_all_count", - "defendants_all_count", - "plaintiff_no1_ISIC1_industry_category", - "defendant_no1_ISIC1_industry_category", -) - -COST_BLOCK: tuple[str, ...] = ( - "dispute_value_nominal", - "plaintiff_loosing_share", - "court_cost_awarded_nominal", - "party_compensation_awarded_nominal", -) - -# (system slug as written by legex-analysis, LaTeX label). Order = row order -# in the headline table. -SYSTEMS: tuple[tuple[str, str], ...] = ( - ("gemini", r"\texttt{gemini-3.1-flash-lite}"), - ("gpt", r"\texttt{gpt-5.4-mini} "), - ("harvey", r"\textsc{Harvey} "), -) - -_BUCKETS = ("tp", "mismatch", "missed", "hallucinated", "tn") - - -def _empty() -> dict[str, int]: - return {k: 0 for k in _BUCKETS} - - -def _metrics(c: dict[str, int]) -> dict[str, float]: - tp, mism, miss, hallu, tn = c["tp"], c["mismatch"], c["missed"], c["hallucinated"], c["tn"] - total = tp + mism + miss + hallu + tn - filled_gold = tp + mism + miss - empty_gold = hallu + tn - p_denom = tp + mism + hallu - p = tp / p_denom if p_denom else 0.0 - r = tp / filled_gold if filled_gold else 0.0 - f1 = 2 * p * r / (p + r) if (p + r) else 0.0 - return { - "accuracy": (tp + tn) / total if total else 0.0, - "recall_when_filled": r, - "hallucination_rate": hallu / empty_gold if empty_gold else 0.0, - "f1": f1, - } - - -def _aggregate( - csv_path: Path, -) -> dict[str, dict[str, dict[str, int]]]: - """{ model -> { 'all' | 'cost' -> bucket counter } }.""" - out: dict[str, dict[str, dict[str, int]]] = { - m: {"all": _empty(), "cost": _empty()} for m, _ in SYSTEMS - } - h2h = set(H2H_COUNTRIES) - eval_fields = set(EVAL_FIELDS) - cost_fields = set(COST_BLOCK) - models = {m for m, _ in SYSTEMS} - - with open(csv_path, encoding="utf-8", newline="") as f: - for row in csv.DictReader(f): - if row["country"] not in h2h or row["model"] not in models: - continue - col = row["column"] - if col not in eval_fields: - continue - counts = {k: int(row[k]) for k in _BUCKETS} - for k in _BUCKETS: - out[row["model"]]["all"][k] += counts[k] - if col in cost_fields: - for k in _BUCKETS: - out[row["model"]]["cost"][k] += counts[k] - return out - - -def _fmt_pct(v: float) -> str: - return f"{v * 100:.1f}\\%" - - -def _fmt_f1(v: float) -> str: - return f"{v:.3f}" - - -def _bold_best(values: list[float], formatter, higher_is_better: bool = True) -> list[str]: - best = max(values) if higher_is_better else min(values) - return [ - rf"\textbf{{{formatter(v)}}}" if v == best else formatter(v) - for v in values - ] - - -def render_section(agg: dict[str, dict[str, dict[str, int]]]) -> str: - lines: list[str] = [] - lines.append(r"% Auto-generated by legex-quant-results — do not edit by hand.") - lines.append(r"\section{Quantitative Results}") - lines.append("") - lines.append( - r"We score the three systems against the human coded dataset: a commercial" - ) - lines.append( - r"review-table product by \textsc{Harvey} and two schema-constrained LLM" - ) - lines.append( - r"pipelines (\texttt{gpt-5.4-mini} and \texttt{gemini-3.1-flash-lite})." - ) - lines.append( - r"Since the dataset contains missing fields, e.g.\ where a court judgement" - ) - lines.append( - r"does not issue costs we evaluate our systems against two metrics:" - ) - lines.append(r"\begin{itemize}") - lines.append( - r" \item \textbf{Accuracy when filled}: how often the system extracts the" - r" correct value given that the human expert has classified it." - ) - lines.append( - r" \item \textbf{Hallucination rate}: how often the system extracts a" - r" value given that the human expert has left the field empty." - ) - lines.append(r"\end{itemize}") - lines.append("") - - metrics_all = {m: _metrics(agg[m]["all"]) for m, _ in SYSTEMS} - metrics_cost = {m: _metrics(agg[m]["cost"]) for m, _ in SYSTEMS} - - def _p(model: str, panel: str, key: str) -> str: - src = metrics_all if panel == "all" else metrics_cost - return f"{src[model][key] * 100:.1f}" - - lines.append( - r"\Cref{tab:overall} shows the same trade-off on both panels: the two" - r" LLM pipelines are more eager extractors, while \textsc{Harvey} is" - r" more conservative. Across all " + str(len(EVAL_FIELDS)) + r" evaluated" - r" fields \texttt{gpt-5.4-mini} and \texttt{gemini-3.1-flash-lite}" - r" reach accuracy when filled of " - + _p("gpt", "all", "recall_when_filled") + r"\,\% and " - + _p("gemini", "all", "recall_when_filled") - + r"\,\%, alongside \textsc{Harvey} at " - + _p("harvey", "all", "recall_when_filled") + r"\,\%, but the LLM" - r" pipelines pay for that recall with hallucination rates of " - + _p("gpt", "all", "hallucination_rate") + r"\,\% and " - + _p("gemini", "all", "hallucination_rate") + r"\,\% against \textsc{Harvey}'s " - + _p("harvey", "all", "hallucination_rate") + r"\,\%." - r" Narrowing to the four cost-block variables, the three systems" - r" converge on accuracy (" - + _p("harvey", "cost", "recall_when_filled") + r"--" - + _p("gemini", "cost", "recall_when_filled") + r"\,\%), and the" - r" hallucination rates land at " - + _p("gemini", "cost", "hallucination_rate") + r"\,\% (Gemini), " - + _p("harvey", "cost", "hallucination_rate") + r"\,\% (Harvey), and " - + _p("gpt", "cost", "hallucination_rate") + r"\,\% (GPT)." - r" No single system dominates: the LLM pipelines are preferable when" - r" the downstream task tolerates noisy extractions in exchange for" - r" coverage, whereas \textsc{Harvey} is preferable when emitted values" - r" must be trusted on the non-cost variables." - ) - lines.append("") - - acc_all = [metrics_all[m]["recall_when_filled"] for m, _ in SYSTEMS] - hal_all = [metrics_all[m]["hallucination_rate"] for m, _ in SYSTEMS] - acc_cost = [metrics_cost[m]["recall_when_filled"] for m, _ in SYSTEMS] - hal_cost = [metrics_cost[m]["hallucination_rate"] for m, _ in SYSTEMS] - - acc_all_s = _bold_best(acc_all, _fmt_pct, True) - hal_all_s = _bold_best(hal_all, _fmt_pct, False) - acc_cost_s = _bold_best(acc_cost, _fmt_pct, True) - hal_cost_s = _bold_best(hal_cost, _fmt_pct, False) - - lines.append(r"\begin{table}[h]") - lines.append( - r"\caption{Overall metrics on the " - + str(len(EVALUATED_COUNTRIES)) - + r" head-to-head jurisdictions, golden-set rows with no" - r" expert-filled field excluded." - r" \emph{Acc.\ when filled} is the share of expert-filled cells the" - r" system extracts correctly; \emph{Hallu.\ rate} is the share of" - r" expert-empty cells where the system invented a value." - r" The right block restricts the same metrics to the four cost-block" - r" variables. Best per column in \textbf{bold} (lower is better for" - r" Hallu.\ rate)." - r"}" - ) - lines.append(r"\label{tab:overall}") - lines.append(r"\centering\small") - lines.append(r"\begin{tabular}{@{}lrr@{\hskip 12pt}rr@{}}") - lines.append(r"\toprule") - lines.append( - r" & \multicolumn{2}{c}{All " - + str(len(EVAL_FIELDS)) - + r" evaluated fields} & \multicolumn{2}{c}{Cost block (" - + str(len(COST_BLOCK)) - + r" fields)} \\" - ) - lines.append(r"\cmidrule(lr){2-3}\cmidrule(l){4-5}") - lines.append( - r"System & Acc.\ when filled & Hallu.\ rate" - r" & Acc.\ when filled & Hallu.\ rate \\" - ) - lines.append(r"\midrule") - for i, (_, label) in enumerate(SYSTEMS): - lines.append( - f"{label} & {acc_all_s[i]} & {hal_all_s[i]}" - f" & {acc_cost_s[i]} & {hal_cost_s[i]} \\\\" - ) - lines.append(r"\bottomrule") - lines.append(r"\end{tabular}") - lines.append(r"\end{table}") - lines.append("") - return "\n".join(lines) - - -def _print_console_summary(agg: dict[str, dict[str, dict[str, int]]]) -> None: - """Quick human-readable echo so the user sees the numbers in the terminal too.""" - print(f"{'system':<28} {'set':<5} {'Acc.filled':>11} {'Hallu':>6}") - for model, _ in SYSTEMS: - for tag in ("all", "cost"): - m = _metrics(agg[model][tag]) - print( - f"{model:<28} {tag:<5} " - f"{m['recall_when_filled']:>11.1%} {m['hallucination_rate']:>6.1%}" - ) - - -def main() -> None: - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - handlers=[logging.StreamHandler(sys.stderr)], - ) - parser = argparse.ArgumentParser( - prog="legex-quant-results", - description="Render the Quantitative Results section + headline table.", - ) - parser.add_argument( - "--input", type=Path, - default=Path("data/analysis/per_country_per_column.csv"), - help="per_country_per_column.csv produced by legex-analysis.", - ) - parser.add_argument( - "--out", type=Path, - default=Path("data/analysis/quant_results.tex"), - help="Where to write the rendered LaTeX section.", - ) - args = parser.parse_args() - - if not args.input.exists(): - raise SystemExit( - f"{args.input} not found — run `legex-analysis` first to generate it." - ) - agg = _aggregate(args.input) - tex = render_section(agg) - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(tex, encoding="utf-8") - log.info(f"wrote {args.out}") - _print_console_summary(agg) - - -if __name__ == "__main__": - main() diff --git a/legex/scrapers/ge.py b/legex/scrapers/ge.py index 441c497e441fe41c12bc3145eb78b6034d6228ec..533657a7d0a7b4b91ff3f95094da99e95002c6a7 100644 --- a/legex/scrapers/ge.py +++ b/legex/scrapers/ge.py @@ -14,8 +14,8 @@ USER_AGENT = "FriendyResearcher" DELAY_SECONDS = 0.3 URL_TEMPLATE = "https://www.supremecourt.ge/ka/fullcase/{nid}/1" -# Note: the sample is not random; it was assembled from manual searches across -# multiple supreme-court listings. See the paper for sampling caveats. +# Not a random sample: these ids come from several manual searches during the +# initial OpenClaw-assisted collection (documented in the paper's limitations). _EXISTING_29 = {75795, 76240, 76234, 76198, 76209, 76215, 76205, 76201, 76238, 76202, 76203, 76204, 76211, 76218, 76219, 76220, 76221, 76225, 76188, 76190, 76187, 76182, 76185, 76186, 76177, 76178, 76191, 76199, 76200} diff --git a/legex/scrapers/in_.py b/legex/scrapers/in_.py index 56599b0b2e91833f7fbf517e677b037291fb0455..b47932c4045a23994cbc242326e8d9785b7cca06 100644 --- a/legex/scrapers/in_.py +++ b/legex/scrapers/in_.py @@ -37,7 +37,7 @@ SAMPLE_YEARS = [ 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, ] -USER_AGENT_METADATA = "LegEx/1.0 (legal research project; CC-BY-4.0 dataset)" +USER_AGENT_METADATA = "OpenClaw/1.0 (legal research project; CC-BY-4.0 dataset)" USER_AGENT_EXTRACT = "LegEx/1.0 (selected AWS open-data extraction)" _CIVIL_CASE_NO_RE = re.compile( diff --git a/legex/scrapers/ph.py b/legex/scrapers/ph.py index 12cf464b4ba6bd5aa3a90ca1b8423d5370a7000e..3ff7c6537c1a543899e7acd5486d97ff38e1b624 100644 --- a/legex/scrapers/ph.py +++ b/legex/scrapers/ph.py @@ -15,6 +15,7 @@ HEADERS = {"User-Agent": "Mozilla/5.0 (compatible; LegalResearch/1.0)"} MONTHS = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] DEFAULT_YEARS = list(range(2026, 2004, -1)) +# Politeness cap inherited from the original OpenClaw script. MAX_CANDIDATES = 500 DELAY_SECONDS = 0.3 diff --git a/legex/utils.py b/legex/utils.py index 18593ef7af4f318d46db5d1a574cc32a65105359..483df9ac3f0461b4ec6a551b6d94785e913f8b57 100644 --- a/legex/utils.py +++ b/legex/utils.py @@ -82,40 +82,6 @@ def countries_with_goldenset() -> list[str]: ) -# Round-2 submission layout: JSONL gold + inference_.csv per jurisdiction. -EXCLUDED_FOR_EVAL: frozenset[str] = frozenset( - {"tw", "br", "hk", "in", "rs", "np", "be"} -) - - -def goldenset_jsonl_path(cc: str) -> Path: - """Submission-style gold path: data//goldenset_.jsonl.""" - return settings.data_dir / cc / f"goldenset_{cc}.jsonl" - - -def inference_csv_path(cc: str, system: str) -> Path: - """Submission-style prediction path: data//inference_.csv. - - `system` is one of `harvey`, `gemini`, `gpt`. - """ - return settings.data_dir / cc / f"inference_{system}.csv" - - -def countries_with_goldenset_jsonl() -> list[str]: - """Country codes that have a submission-style goldenset_.jsonl on disk.""" - if not settings.data_dir.is_dir(): - return [] - return sorted( - d.name for d in settings.data_dir.iterdir() - if d.is_dir() and goldenset_jsonl_path(d.name).exists() - ) - - -def evaluable_countries() -> list[str]: - """countries_with_goldenset_jsonl() minus the round-2 exclusion set.""" - return [cc for cc in countries_with_goldenset_jsonl() if cc not in EXCLUDED_FOR_EVAL] - - def goldenset_path(cc: str) -> Path: f = _preferred_goldenset_file(cc) if f is not None: @@ -192,11 +158,49 @@ def model_filename_slug(model: str) -> str: return model.replace("/", "_").replace("\\", "_").replace(":", "_") -def classified_csv_path(cc: str, prompt_version: str, source: str, model: str) -> Path: - """Per-country LLM output path. `source` ∈ {"full_text", "pdf"}.""" +def inference_path(cc: str, prompt_version: str, source: str, model: str) -> Path: + """Per-country LLM output path. `source` ∈ {"full_text", "pdf"}. + + Inference is stored as JSONL (one case per line, every field a string or null) + so mixed date/number/refusal content can't break a column's dtype the way it + does for CSV (which breaks pyarrow/HF loading). + """ country = _country_for(cc) slug = model_filename_slug(model) - return settings.data_dir / cc / f"Goldenset_{country}_{prompt_version}_{source}_{slug}.csv" + return settings.data_dir / cc / f"Goldenset_{country}_{prompt_version}_{source}_{slug}.jsonl" + + +def inference_record(row: dict, columns: list[str]) -> dict: + """One inference JSONL record: every column a string, or null when empty. + + Faithful to the model's raw output (no type coercion), this is exactly what + the CSV held, so scoring is unchanged; storing it as JSONL just keeps each + column single-typed (string|null) for reliable loading. + """ + rec: dict[str, str | None] = {} + for c in columns: + v = row.get(c, "") + rec[c] = None if v is None or str(v) == "" else str(v) + return rec + + +def read_inference_jsonl(path: Path) -> list[dict]: + """Read an inference JSONL file into a list of {column: str|None} records.""" + rows: list[dict] = [] + with open(path, encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + rows.append(json.loads(line)) + return rows + + +def write_inference_jsonl(path: Path, rows: list[dict], columns: list[str]) -> None: + """Write inference rows as JSONL (one record per line).""" + path.parent.mkdir(parents=True, exist_ok=True) + with open(path, "w", encoding="utf-8") as f: + for row in rows: + f.write(json.dumps(inference_record(row, columns), ensure_ascii=False) + "\n") def pdf_paths(cc: str) -> list[Path]: diff --git a/pyproject.toml b/pyproject.toml index 735fe531c5b01e10e0ee4cd51b80903c2d60788b..05c1417e29f26bb6c162d166e7700b0fdcd2dafd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,9 @@ dependencies = [ "fpdf2>=2.8", "beautifulsoup4>=4.12", "lxml>=5.0", + "pyyaml>=6.0", + "playwright>=1.60.0", + "scipy>=1.11", ] [project.optional-dependencies] @@ -31,17 +34,34 @@ dev = [ "pytest-asyncio>=0.23", "ruff>=0.4", ] +# Figures: legex-plots and scripts/appendix_frequencies.py need matplotlib, +# scripts/diversity_stats.py additionally needs wordcloud +# (or run with `uv run --with wordcloud ...`). +plots = [ + "matplotlib>=3.8", + "wordcloud>=1.9", +] [project.scripts] legex-run = "legex.main:main" -legex-india-extract = "legex.scrapers.in_:_extract_cli" legex-classify = "legex.inference:main" legex-evaluate = "legex.evaluation:main" -legex-analysis = "legex.analysis:main" -legex-plots = "legex.plots:main" -legex-pdf = "legex.pdf_export.cli:main" legex-harvey-ingest = "legex.harvey:main" -legex-quant-results = "legex.quant_results:main" +legex-india-extract = "legex.scrapers.in_:_extract_cli" + +# Full-text acquisition +legex-build-fulltext = "legex.fulltext.build_full_text:main" +legex-render-fulltext = "legex.fulltext.render_full_text:main" +# Evaluation & analysis +legex-analysis = "legex.analysis.aggregate:main" +legex-analysis-report = "legex.analysis.report:main" +legex-iaa = "legex.analysis.iaa:main" +legex-quant-results = "legex.analysis.quant_results:main" +legex-plots = "legex.analysis.plots:main" +legex-goldenset-quality = "legex.analysis.goldenset_quality:main" +# Refusal cleaning +legex-refusals-scan = "legex.evaluation.cleaning:scan_main" +legex-refusals-apply = "legex.evaluation.cleaning:apply_main" [tool.setuptools.packages.find] include = ["legex*"] diff --git a/scripts/alt_test_decomposition.py b/scripts/alt_test_decomposition.py new file mode 100644 index 0000000000000000000000000000000000000000..50a4f12b7a7d77cf71e0712e787a12e2db7990cf --- /dev/null +++ b/scripts/alt_test_decomposition.py @@ -0,0 +1,230 @@ +"""Win / tie / loss decomposition of the Alternative Annotator Test (AAT). + +The AAT headline (``alt_test_pooled.csv``, ANALYSIS.md §3.2) reports the winning +rate ω and the advantage probability ρ. Inside the test, an instance is credited +to the candidate whenever it scores *at least as well* as the held-out human +(the indicator is ``1[s_llm >= s_human]``), so ties count for the candidate. + +That is the correct convention for the question the AAT asks — *can this model +stand in for a human annotator?* — but it makes ρ unusable for the different +question *is this model better than a human?*. This script keeps the AAT's +leave-one-out comparison and, instead of collapsing it into ρ, writes out the +raw counts ρ is built from: + + for each held-out human j and each instance i = (judgment, variable) + s_llm = mean tolerant agreement of the candidate with the 2 remaining humans + s_human = mean tolerant agreement of human j with the same 2 humans + -> llm_better (s_llm > s_human) + tie (s_llm = s_human) + human_better(s_llm < s_human) + + rho_alttest = (llm_better + tie) / n <- the AAT's own definition + rho_tiebroken = (llm_better + tie / 2) / n <- ties split evenly + +A tie only means "same score against the same two references", so it is worth +being explicit about what it contains. Ties are split two independent ways: + + by score level (with two references a score is 0, ½ or 1) + tie_at_1 both matched both references — everybody agrees + tie_at_half each matched exactly one reference; this can only happen + when the two reference experts contradict each other, which + caps every possible score at ½ + tie_at_0 neither matched either reference — equally wrong, and still + credited to the candidate by ρ + + by whether the candidate actually gave the held-out human's answer + tie_same candidate ≈ held-out human (they really do agree) + tie_diff candidate ≉ held-out human — they gave *different* answers + that happen to be equally close to the references, so ρ + records a candidate win on a genuine disagreement + +``refs_disagree`` counts, for context, the comparisons whose two reference +experts do not agree with each other in the first place. + +This does not need the upstream AltTest clone: it is a direct, auditable +re-implementation of the comparison the reference implementation performs, and +it reproduces the reference ρ to within 0.02 on every jurisdiction. + +Usage +----- + uv run python scripts/alt_test_decomposition.py \ + [--countries ge,sg,tw] [--out data/analysis/iaa] +""" + +import argparse +import csv +import sys +from collections import defaultdict +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from legex.analysis.countries import CORE_COUNTRIES +from legex.analysis.iaa import ( + FREE_TEXT_FIELDS, + load_candidate_annotations, + load_human_annotations, +) +from legex.evaluation import values_agree + +# The five released candidates the paper's AAT covers. The AAT was frozen +# before the harvey-2 ingest; adding it would change the shipped CSVs and +# ANALYSIS.md, so it stays out deliberately. +MODELS = ("gpt-5.4-mini", "gemini/gemini-3.1-flash-lite", "harvey", "legora-1", "legora-2") + +COUNTS = ( + "refs_disagree", "llm_better", "human_better", + "tie", "tie_same", "tie_diff", "tie_at_1", "tie_at_half", "tie_at_0", +) +COLUMNS = [ + "candidate", "country", "variant", "n_comparisons", + *COUNTS, + "rho_alttest", "rho_tiebroken", +] + + +def score(prediction: str, references: list[str], field: str) -> float: + """Mean tolerant agreement — the AAT scoring function used in LEGEX.""" + return sum(values_agree(prediction, r, field) for r in references) / len(references) + + +def decompose( + countries: list[str], + model: str, + gold_dir: Path | None = None, + inference_dir: Path | None = None, +) -> list[dict]: + """One row per (country, variant) with the win/tie/loss counts.""" + humans = load_human_annotations(countries, gold_dir=gold_dir) + candidate = load_candidate_annotations( + countries, "v3", "full_text", model, inference_dir=inference_dir + ) + fields = sorted( + {f for fmap in humans.values() for f in fmap if f not in FREE_TEXT_FIELDS} + ) + + # country -> case_id -> annotator -> {field: value} + by_case: dict[str, dict[str, dict[str, dict[str, str]]]] = defaultdict( + lambda: defaultdict(dict) + ) + for (annotator, cc, case_id), fmap in humans.items(): + by_case[cc][case_id][annotator] = fmap + candidate_labels = {(cc, case_id): fmap for (_, cc, case_id), fmap in candidate.items()} + + rows: list[dict] = [] + for cc in countries: + annotators = sorted({an for (an, c, _) in humans if c == cc}) + if len(annotators) < 3: + print(f"[{cc}] only {len(annotators)} annotators — skipped", file=sys.stderr) + continue + for variant in ("all", "nontrivial"): + counts: dict[str, int] = defaultdict(int) + for case_id, case_annotators in by_case[cc].items(): + # Keep only judgments all three experts labelled, so every + # leave-one-out comparison has exactly two reference annotators + # (the reference implementation's min_humans_per_instance=2). + if len(case_annotators) < 3: + continue + names = sorted(case_annotators) + llm_labels = candidate_labels.get((cc, case_id), {}) + # No candidate output for this judgment (e.g. Legora's empty + # Georgia export): skip, mirroring the pooled AAT runner. + if not llm_labels: + continue + for field in fields: + human_values = {n: case_annotators[n].get(field, "") for n in names} + llm_value = llm_labels.get(field, "") + for held_out in names: + references = [human_values[n] for n in names if n != held_out] + # Non-trivial: drop comparisons whose reference is empty + # throughout — there is nothing to be right or wrong about. + if variant == "nontrivial" and not any(references): + continue + if not values_agree(references[0], references[1], field): + counts["refs_disagree"] += 1 + s_human = score(human_values[held_out], references, field) + s_llm = score(llm_value, references, field) + if s_llm > s_human: + counts["llm_better"] += 1 + elif s_llm < s_human: + counts["human_better"] += 1 + else: + counts["tie"] += 1 + counts[{1.0: "tie_at_1", 0.5: "tie_at_half"}.get(s_llm, "tie_at_0")] += 1 + same = values_agree(llm_value, human_values[held_out], field) + counts["tie_same" if same else "tie_diff"] += 1 + n = counts["llm_better"] + counts["tie"] + counts["human_better"] + if not n: + continue + rows.append({ + "candidate": model, + "country": cc, + "variant": variant, + "n_comparisons": n, + **{c: counts[c] for c in COUNTS}, + "rho_alttest": round((counts["llm_better"] + counts["tie"]) / n, 4), + "rho_tiebroken": round((counts["llm_better"] + counts["tie"] / 2) / n, 4), + }) + return rows + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("--countries", default=",".join(CORE_COUNTRIES), + help="comma-separated country codes (default: the 8 core " + "jurisdictions, all with 3 independent annotators)") + ap.add_argument("--out", type=Path, default=Path("data/analysis/iaa"), + help="output directory for alt_test_decomposition.csv") + ap.add_argument("--gold-dir", type=Path, default=None, + help="read annotations from published goldenset JSONL under " + "this directory instead of the XLSX workbooks") + ap.add_argument("--inference-dir", type=Path, default=None, + help="read candidate predictions from published inference " + "JSONL under this directory instead of the working files") + args = ap.parse_args() + + countries = [c.strip() for c in args.countries.split(",") if c.strip()] + rows = [ + r for model in MODELS + for r in decompose(countries, model, args.gold_dir, args.inference_dir) + ] + if not rows: + raise SystemExit("no comparisons — need 3+ annotators in at least one country") + + out_csv = args.out / "alt_test_decomposition.csv" + out_csv.parent.mkdir(parents=True, exist_ok=True) + with out_csv.open("w", encoding="utf-8", newline="") as f: + writer = csv.DictWriter(f, fieldnames=COLUMNS) + writer.writeheader() + writer.writerows(rows) + + for model in MODELS: + pooled: dict[str, int] = defaultdict(int) + for r in rows: + if r["candidate"] == model and r["variant"] == "all": + pooled["n_comparisons"] += r["n_comparisons"] + for key in COUNTS: + pooled[key] += r[key] + n = pooled["n_comparisons"] + if not n: + continue + print( + f"{model:<30} n={n:<5} " + f"better {pooled['llm_better']:>4} ({pooled['llm_better'] / n:.0%}) " + f"tie {pooled['tie']:>4} ({pooled['tie'] / n:.0%}) " + f"worse {pooled['human_better']:>4} ({pooled['human_better'] / n:.0%}) " + f"rho={(pooled['llm_better'] + pooled['tie']) / n:.2f} " + f"(ties split {(pooled['llm_better'] + pooled['tie'] / 2) / n:.2f})\n" + f"{'':<30} ties: same answer as expert {pooled['tie_same']} " + f"({pooled['tie_same'] / pooled['tie']:.0%}), different answer " + f"{pooled['tie_diff']} ({pooled['tie_diff'] / pooled['tie']:.0%}); " + f"at 1 {pooled['tie_at_1']}, at ½ {pooled['tie_at_half']}, at 0 {pooled['tie_at_0']}; " + f"references conflict in {pooled['refs_disagree']} of {n} comparisons " + f"({pooled['refs_disagree'] / n:.0%})" + ) + print(f"decomposition -> {out_csv}") + + +if __name__ == "__main__": + main() diff --git a/scripts/alt_test_reference.py b/scripts/alt_test_reference.py new file mode 100644 index 0000000000000000000000000000000000000000..74b8ca09c13bf77b8e578cfef0e3eb219c54c209 --- /dev/null +++ b/scripts/alt_test_reference.py @@ -0,0 +1,355 @@ +"""Run the Alternative Annotator Test (Calderon et al., ACL 2025) on LEGEX +using the authors' original implementation (https://github.com/nitaytech/AltTest), +""" + +import argparse +import contextlib +import csv +import io +import json +import sys +import warnings +from pathlib import Path + +warnings.filterwarnings("ignore", category=RuntimeWarning) + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from legex.analysis.countries import CORE_COUNTRIES +from legex.analysis.iaa import ( + FREE_TEXT_FIELDS, + MIN_INSTANCES_TEST, + load_candidate_annotations, + load_human_annotations, +) +from legex.evaluation import values_agree + +# The five released candidates the paper's AAT covers. The AAT was frozen +# before the harvey-2 ingest; adding it would change the shipped CSVs and +# ANALYSIS.md, so it stays out deliberately. +MODELS = ("gpt-5.4-mini", "gemini/gemini-3.1-flash-lite", "harvey", "legora-1", "legora-2") + +def load_reference_alt_test(alttest_dir: Path): + """Extract ``alt_test`` and its helpers from the authors' notebook. + """ + nb_path = alttest_dir / "alt_test_example.ipynb" + if not nb_path.exists(): + raise SystemExit( + f"{nb_path} not found, clone https://github.com/nitaytech/AltTest " + "and pass its path via --alttest." + ) + nb = json.loads(nb_path.read_text(encoding="utf-8")) + cells = ["".join(c["source"]) for c in nb["cells"] if c["cell_type"] == "code"] + ns: dict = {} + ran = 0 + for src in cells: + if src.lstrip().startswith("import ") or "def alt_test(" in src: + exec(compile(src, str(nb_path), "exec"), ns) + ran += 1 + if "alt_test" not in ns: + raise SystemExit(f"could not find alt_test() in {nb_path} ({ran} cells run)") + return ns["alt_test"] + + +def field_scoring_function(field: str): + """Mean tolerant agreement of one prediction against remaining annotators + """ + def score(pred, annotations) -> float: + return sum(values_agree(pred, ann, field) for ann in annotations) / len(annotations) + return score + + +def run_reference( + alt_test, + countries: list[str], + model: str, + epsilon: float, + prompt_version: str = "v3", + source: str = "full_text", + gold_dir: Path | None = None, + inference_dir: Path | None = None, +) -> list[dict]: + humans = load_human_annotations(countries, gold_dir=gold_dir) + candidate = load_candidate_annotations( + countries, prompt_version, source, model, inference_dir=inference_dir + ) + + fields = sorted( + { + f + for fmap in humans.values() + for f in fmap + if f not in FREE_TEXT_FIELDS + } + ) + + rows: list[dict] = [] + for cc in countries: + annotators = sorted({an for (an, c, _) in humans if c == cc}) + if len(annotators) < 3: + print(f"[{cc}] only {len(annotators)} annotators — skipped", file=sys.stderr) + continue + for field in fields: + humans_annotations = { + an: { + cid: fmap.get(field, "") + for (a, c, cid), fmap in humans.items() + if a == an and c == cc + } + for an in annotators + } + llm_annotations = { + cid: fmap.get(field, "") + for (m, c, cid), fmap in candidate.items() + if c == cc + } + # Non-trivial replay (legex-iaa convention): drop instances every + # human left empty — an empty prediction ties those for free. + nontrivial_ids = { + cid + for cid in llm_annotations + if any( + humans_annotations[an].get(cid, "") + for an in annotators + if cid in humans_annotations[an] + ) + } + result: dict = {"candidate": model, "country": cc, "field": field} + for variant, keep in (("", None), ("_nontrivial", nontrivial_ids)): + h = humans_annotations + llm = llm_annotations + if keep is not None: + h = { + an: {cid: v for cid, v in anns.items() if cid in keep} + for an, anns in humans_annotations.items() + } + llm = {cid: v for cid, v in llm_annotations.items() if cid in keep} + buf = io.StringIO() + try: + with contextlib.redirect_stdout(buf): + winning_rate, advantage_prob = alt_test( + llm_annotations=llm, + humans_annotations=h, + scoring_function=field_scoring_function(field), + epsilon=epsilon, + q_fdr=0.05, + min_humans_per_instance=2, + min_instances_per_human=MIN_INSTANCES_TEST, + ) + except ZeroDivisionError: + # Every annotator fell below min_instances_per_human + result[f"winning_rate{variant}"] = "" + result[f"advantage_probability{variant}"] = "" + result[f"passes{variant}"] = "" + continue + result[f"winning_rate{variant}"] = round(winning_rate, 4) + result[f"advantage_probability{variant}"] = round(advantage_prob, 4) + result[f"passes{variant}"] = int(winning_rate >= 0.5) + rows.append(result) + return rows + + +def model_slug(model: str) -> str: + return model.replace("/", "_") + + +def write_csv(rows: list[dict], path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as f: + w = csv.DictWriter(f, fieldnames=list(rows[0].keys())) + w.writeheader() + w.writerows(rows) + + +def compare(rows: list[dict], ours_csv: Path) -> int: + """Print cell-level pass/fail agreement with legex-iaa's CSV and return the + number of disagreeing cells (pass/fail decision, either variant).""" + if not ours_csv.exists(): + print(f" (no {ours_csv} to compare against)", file=sys.stderr) + return 0 + ours: dict[tuple[str, str], dict] = {} + with ours_csv.open(newline="") as f: + for r in csv.DictReader(f): + ours[(r["country"], r["field"])] = r + + disagreements = 0 + for row in rows: + key = (row["country"], row["field"]) + mine = ours.get(key) + if mine is None: + continue + for variant, ours_col in (("passes", "passes"), ("passes_nontrivial", "passes_nontrivial")): + ref_pass = row[variant] + our_pass = mine.get(ours_col, "") + if our_pass == "" or ref_pass == "": + continue + if int(our_pass) != ref_pass: + disagreements += 1 + print( + f" DIFF {key[0]}/{key[1]} [{variant}]: " + f"reference={'pass' if ref_pass else 'fail'} " + f"(wr={row[variant.replace('passes', 'winning_rate')]}), " + f"legex-iaa={'pass' if int(our_pass) else 'fail'} " + f"(wr={mine.get(variant.replace('passes', 'winning_rate'), '?')})" + ) + return disagreements + + +def pooled_scoring(pred, annotations) -> float: + """pred/annotations are (field, value) tuples, mean tolerant agreement.""" + field, value = pred + return sum(values_agree(value, ann[1], field) for ann in annotations) / len(annotations) + + +def run_pooled( + alt_test, countries, model, epsilon, nontrivial=False, + gold_dir: Path | None = None, inference_dir: Path | None = None, +): + """One alt-test per jurisdiction; instance = (judgment, variable) cell. + + This is the SummEval convention of Calderon et al. (each summary x aspect + pair is one instance) and the paper's headline design: with 10 structured + fields x 19-30 shared judgments, every annotator contributes n >= 190 + effective instances, so the paired t-test applies without the n<30 caveat. + """ + humans = load_human_annotations(countries, gold_dir=gold_dir) + candidate = load_candidate_annotations( + countries, "v3", "full_text", model, inference_dir=inference_dir + ) + fields = sorted( + {f for fmap in humans.values() for f in fmap if f not in FREE_TEXT_FIELDS} + ) + + results = [] + for cc in countries: + annotators = sorted({an for (an, c, _) in humans if c == cc}) + if len(annotators) < 3: + print(f"[{cc}] only {len(annotators)} annotators — skipped", file=sys.stderr) + continue + humans_annotations = { + an: { + (cid, f): (f, fmap.get(f, "")) + for (a, c, cid), fmap in humans.items() + if a == an and c == cc + for f in fields + } + for an in annotators + } + llm_annotations = { + (cid, f): (f, fmap.get(f, "")) + for (m, c, cid), fmap in candidate.items() + if c == cc + for f in fields + } + if nontrivial: + keep = { + iid + for iid in llm_annotations + if any( + humans_annotations[an][iid][1] + for an in annotators + if iid in humans_annotations[an] + ) + } + llm_annotations = {k: v for k, v in llm_annotations.items() if k in keep} + humans_annotations = { + an: {k: v for k, v in anns.items() if k in keep} + for an, anns in humans_annotations.items() + } + if not llm_annotations: + print(f"[{cc}] {model}: No candidate annotations, we skip it", file=sys.stderr) + continue + buf = io.StringIO() + with contextlib.redirect_stdout(buf): + try: + winning_rate, advantage_prob = alt_test( + llm_annotations=llm_annotations, + humans_annotations=humans_annotations, + scoring_function=pooled_scoring, + epsilon=epsilon, + q_fdr=0.05, + min_humans_per_instance=2, + min_instances_per_human=30, + ) + except ZeroDivisionError: + print(f"[{cc}] {model}: too few paired instances, we skip", file=sys.stderr) + continue + results.append((cc, winning_rate, advantage_prob)) + return results + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("--alttest", type=Path, required=True, + help="path to a clone of https://github.com/nitaytech/AltTest") + ap.add_argument("--countries", default=",".join(CORE_COUNTRIES), + help="comma-separated country codes (default: the 8 core " + "jurisdictions, all with 3 independent annotators)") + ap.add_argument("--epsilon", type=float, default=0.2, + help="cost-benefit tolerance (0.2 = expert annotators)") + ap.add_argument("--out", type=Path, default=Path("data/analysis/iaa"), + help="output directory for the CSVs") + ap.add_argument("--gold-dir", type=Path, default=None, + help="read annotations from published goldenset JSONL under " + "this directory instead of the XLSX workbooks") + ap.add_argument("--inference-dir", type=Path, default=None, + help="read candidate predictions from published inference " + "JSONL under this directory instead of the working files") + ap.add_argument("--per-field", action="store_true", + help="additionally run the fine-grained per-(jurisdiction, field) " + "variant (diagnostic; 19-30 instances per test)") + args = ap.parse_args() + + alt_test = load_reference_alt_test(args.alttest) + countries = [c.strip() for c in args.countries.split(",") if c.strip()] + + # Paper headline, pooled per jurisdiction. + pooled_rows: list[dict] = [] + for model in MODELS: + for variant, nt in (("", False), ("_nontrivial", True)): + for cc, wr, rho in run_pooled( + alt_test, countries, model, args.epsilon, nontrivial=nt, + gold_dir=args.gold_dir, inference_dir=args.inference_dir, + ): + row = next( + (r for r in pooled_rows if r["candidate"] == model and r["country"] == cc), + None, + ) + if row is None: + row = {"candidate": model, "country": cc} + pooled_rows.append(row) + row[f"omega{variant}"] = round(wr, 4) + row[f"rho{variant}"] = round(rho, 4) + row[f"passes{variant}"] = int(wr >= 0.5) + out_csv = args.out / "alt_test_pooled.csv" + write_csv(pooled_rows, out_csv) + for model in MODELS: + rows = [r for r in pooled_rows if r["candidate"] == model] + cells = " ".join( + f"{r['country']}: omega={r['omega']:.2f} rho={r['rho']:.2f}" + f" (non-triv {r['omega_nontrivial']:.2f}/{r['rho_nontrivial']:.2f})" + for r in rows + ) + print(f"{model:<30} {cells}") + print(f"pooled results -> {out_csv}") + + # Per-(jurisdiction, field) cells + if args.per_field: + for model in MODELS: + rows = run_reference( + alt_test, countries, model, args.epsilon, + gold_dir=args.gold_dir, inference_dir=args.inference_dir, + ) + if not rows: + print(f"{model}: no per-field results", file=sys.stderr) + continue + csv_path = args.out / f"alt_test_reference_{model_slug(model)}.csv" + write_csv(rows, csv_path) + n_pass = sum(r["passes"] for r in rows if r["passes"] != "") + n_test = sum(1 for r in rows if r["passes"] != "") + print(f"{model}: per-field {n_pass}/{n_test} cells pass -> {csv_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/appendix_frequencies.py b/scripts/appendix_frequencies.py new file mode 100644 index 0000000000000000000000000000000000000000..c3ffcd5548b28cc759b2a32529092001020122ae --- /dev/null +++ b/scripts/appendix_frequencies.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Frequency material for the paper appendix: ISIC sectors and currencies. + +Reads the 19 published goldensets (primary rows with a coded legal subject) +and writes + * data/analysis/figures/isic_frequencies.pdf — horizontal bar chart of the + ISIC top-level sectors observed for plaintiffs and defendants; + * data/analysis/tables/currency_frequencies.tex — frequency of the currency + labels over the three monetary fields. + +Usage: uv run python scripts/appendix_frequencies.py +""" + +import argparse +import sys +from collections import Counter +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from legex import published # noqa: E402 +from legex.analysis.countries import RELEASE_COUNTRIES # noqa: E402 + +ISIC_FIELDS = ( + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +) +CURRENCY_FIELDS = ( + "Currency_dispute_value_nominal", + "Currency_court_cost_awarded_nominal", + "Currency_party_compensation_awarded_nominal", +) +FIG_OUT = REPO_ROOT / "data/analysis/figures/isic_frequencies.pdf" +TAB_OUT = REPO_ROOT / "data/analysis/tables/currency_frequencies.tex" + + +def _norm(v) -> str: + return str(v).strip() if v is not None else "" + + +def collect(gold_dir: Path) -> tuple[Counter, Counter, Counter, int]: + isic_p, isic_d, currencies = Counter(), Counter(), Counter() + n_judgments = 0 + for cc in RELEASE_COUNTRIES: + seen: set[str] = set() + for rec in published.iter_gold_rows(gold_dir, cc): + cid = _norm(rec.get("case_id")) + if not cid or cid in seen: # first row per case_id = primary + continue + seen.add(cid) + if not _norm(rec.get("legal_subject_judgement")): + continue # released but not fully coded + n_judgments += 1 + p = _norm(rec.get(ISIC_FIELDS[0])).lower() + d = _norm(rec.get(ISIC_FIELDS[1])).lower() + if p: + isic_p[p] += 1 + if d: + isic_d[d] += 1 + for f in CURRENCY_FIELDS: + c = _norm(rec.get(f)).upper() + if c: + currencies[c] += 1 + return isic_p, isic_d, currencies, n_judgments + + +def _pretty(sector: str) -> str: + # e.g. "m_real_estate" -> "M — Real estate"; fallback codes stay verbatim. + if sector in ("none", "no_allocation_possible"): + return sector.replace("_", r"\_") + if len(sector) > 2 and sector[1] == "_": + letter, rest = sector[0].upper(), sector[2:] + return f"{letter} — {rest.replace('_', ' ').capitalize()}" + return sector.replace("_", " ") + + +def write_isic_chart(isic_p: Counter, isic_d: Counter) -> None: + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + sectors = sorted(set(isic_p) | set(isic_d), key=lambda s: -(isic_p[s] + isic_d[s])) + y = range(len(sectors)) + p_vals = [isic_p[s] for s in sectors] + d_vals = [isic_d[s] for s in sectors] + + fig, ax = plt.subplots(figsize=(7.0, 0.34 * len(sectors) + 1.2)) + h = 0.38 + bars_p = ax.barh( + [i - h / 2 for i in y], p_vals, height=h, + color="#1F4E79", edgecolor="black", linewidth=0.5, label="Plaintiff", + ) + bars_d = ax.barh( + [i + h / 2 for i in y], d_vals, height=h, + color="#DD6E6E", edgecolor="black", linewidth=0.5, label="Defendant", + ) + for bars in (bars_p, bars_d): + ax.bar_label(bars, fontsize=6.5, padding=2, color="#374151") + ax.set_yticks(list(y)) + ax.set_yticklabels([_pretty(s).replace(r"\_", "_") for s in sectors], fontsize=8) + ax.invert_yaxis() + ax.set_xlabel("Judgments with the sector assigned", fontsize=9) + ax.set_xlim(0, max(max(p_vals), max(d_vals)) * 1.08) + ax.grid(axis="x", linestyle=":", linewidth=0.6, color="#9CA3AF", alpha=0.7) + ax.set_axisbelow(True) + ax.legend(title="Party", frameon=True, edgecolor="#6B7280", fontsize=9, title_fontsize=9) + ax.tick_params(axis="x", labelsize=8) + fig.tight_layout() + FIG_OUT.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(FIG_OUT) + plt.close(fig) + print(f"wrote {FIG_OUT} ({len(sectors)} sectors)") + + +# The currency columns are auxiliary audit fields filled in free text; map the +# common synonyms to ISO codes, drop the explicit no-currency markers. +_CURRENCY_SYNONYMS = {"RUPEES": "INR", "EURO": "EUR", "EUROS": "EUR"} +_NO_CURRENCY = {"NONE", "N/A", "NA", "-"} +_TOP_N = 12 + + +def write_currency_table(currencies: Counter) -> None: + cleaned: Counter = Counter() + for cur, n in currencies.items(): + if cur in _NO_CURRENCY: + continue + cleaned[_CURRENCY_SYNONYMS.get(cur, cur)] += n + total = sum(cleaned.values()) + top = cleaned.most_common(_TOP_N) + rest = cleaned.most_common()[_TOP_N:] + lines = [ + "% Auto-generated by scripts/appendix_frequencies.py — do not edit by hand.", + r"\begin{table}[t]", + r"\caption{Currencies of the three monetary fields" + r" (\texttt{dispute\_value}, \texttt{court\_cost}, \texttt{party\_compensation})" + f" over all filled cells of the release with a recorded currency (n\\,=\\,{total:,})." + r" Labels as recorded by the annotators, with common synonyms mapped to ISO codes.}", + r"\label{tab:currencies}", + r"\vskip 0.05in", + r"\centering\small", + r"\begin{tabular}{@{}lrr@{}}", + r"\toprule", + r"\textbf{Currency} & \textbf{Cells} & \textbf{Share} \\", + r"\midrule", + ] + for cur, n in top: + label = cur.title() if len(cur) > 3 else cur + lines.append(f"{label} & {n:,} & {100 * n / total:.1f}\\% \\\\") + if rest: + n_rest = sum(n for _, n in rest) + lines.append( + f"Other ({len(rest)} labels) & {n_rest:,} & {100 * n_rest / total:.1f}\\% \\\\" + ) + lines += [r"\bottomrule", r"\end{tabular}", r"\end{table}", ""] + TAB_OUT.parent.mkdir(parents=True, exist_ok=True) + TAB_OUT.write_text("\n".join(lines), encoding="utf-8") + print(f"wrote {TAB_OUT} ({len(cleaned)} currencies after cleaning)") + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("--gold-dir", type=Path, default=None, + help="published goldenset data directory (default: " + "submission/goldensets/data, else ../goldensets/data)") + args = ap.parse_args() + gold_dir = args.gold_dir or published.default_gold_dir(REPO_ROOT) + + isic_p, isic_d, currencies, n = collect(gold_dir) + print(f"{n} annotated judgments scanned") + write_isic_chart(isic_p, isic_d) + write_currency_table(currencies) + + +if __name__ == "__main__": + main() diff --git a/scripts/compare_legora_prompts.py b/scripts/compare_legora_prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..795058955b9c20ad38637fc2ba071d04ab548585 --- /dev/null +++ b/scripts/compare_legora_prompts.py @@ -0,0 +1,232 @@ +"""Compare the per-column prompts of the two Legora runs against prompts v3. + +The 2026-08-05 Legora export (``data/raw/legora_2026-08-05_prompts.xlsx``) is a +re-export of the 2026-08-01 tabular review with the "with prompt" option +enabled: the data rows are byte-identical to ``legora_2026-08-01.xlsx``; row 2 +additionally carries the question text of every column. As with the values +(see ``scripts/compare_legora_runs.py``), the left column group belongs to +``legora-1`` and the right one to ``legora-2``. + +This script extracts both prompt sets — from the raw export, or with +``--prompts-dir`` from the published ``prompts_legora_{1,2}.jsonl`` — aligns +them with the per-variable sections of ``legex/prompts/v3.py``, and writes a +markdown report with word-level diffs (``[-deleted-]`` / ``{+inserted+}``). + + uv run python scripts/compare_legora_prompts.py \ + [--xlsx data/raw/legora_2026-08-05_prompts.xlsx] \ + [--prompts-dir ../inference-results/prompts] \ + [--out data/analysis/legora_prompt_comparison.md] +""" + +import argparse +import difflib +import json +import re +import sys +from pathlib import Path + +import openpyxl + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from legex.prompts import v3 # noqa: E402 + +MODEL_A, MODEL_B = "legora-1", "legora-2" +DEFAULT_XLSX = Path("data/raw/legora_2026-08-05_prompts.xlsx") +DEFAULT_OUT = Path("data/analysis/legora_prompt_comparison.md") +PROMPT_SUFFIX = " (with prompt)" +# Equal stretches longer than this many words are elided in the diffs. +CONTEXT_WORDS = 6 + +FINDINGS = """\ +## Findings + +* Both column groups are per-column adaptations of the v3 coding rules, + rewritten for Legora's tabular-review UI. Structural v3 material that cannot + exist per column is gone in both runs: the JSON-typing preamble, the + JSON-`null` semantics (the columns ask for the literal string `None` + instead), and the companion `Currency_` fields — the export has no + currency columns at all. +* **legora-2 is the cleaner, complete set**: all 12 prompts follow one template + ("Where to find it: … Formatting rules: … Permissible values: …") and carry + the full substance of the matching v3 section. +* **legora-1 deviates from the v3 content twice.** + 1. `legal_subject_judgement` is not our rule at all but Legora's + auto-generated question ("What legal subjects or issues are addressed …"), + an essay-style prompt without the underscore/slash coding format. This + explains the essay-like answers of legora-1 on that column and the low + inter-run agreement (39.9 % when both filled, see + `legora_run_comparison.md`). + 2. `case_id` has an ad-hoc tail appended ("Put here only the number (with + slahs, dots etc) no more information. No dates or text", note the typo) + that legora-2 lacks. + The other ten legora-1 prompts match legora-2 in substance and differ only + in scaffolding (flowing text vs. labelled sections, "None (the literal + string)" vs. "None"). +* **Both runs extend v3** with material that is not in the repo prompt: + a U.S. federal-court hint for `trial_start_date`; Swiss search anchors + ("Mit Beschwerde vom", "Lausanne, [date]", "Erwägungen — Eintreten / + recevabilité", "Die Gerichtskosten von Fr. …", "… à titre de dépens"; + legora-2 also "Gegenstand/Objet/Oggetto"); "typically item 2"/"item 3" + locations in the operative part; an explicit sum-to-1.0 constraint for + `plaintiff_loosing_share`; and, for the ISIC columns, a dominant-activity + rule for multi-sector entities, extra `no_allocation_possible` examples + (tenant, consumer, patient, unemployed) and anonymized-party examples for + `None`. The wrong-example currency changed from v3's `CHF 150000` to + `USD 150000`. +* **The ISIC category list is handled differently per run**: legora-1 inlines + all 24 permitted values in the prompt; legora-2 only asserts "You MUST enter + exactly one of the 24 permitted coded values" without listing them and + instead adds coding examples (law firm → n_professional_scientific_technical, + hospital → r_human_health_social_work, tradesperson → f_construction). Both + runs nevertheless answered in coded values. Notably, + `defendant_no1_ISIC1_industry_category` is the one field where legora-2 is + clearly worse than legora-1 against the Goldensets (acc 0.522 vs 0.563). +""" + + +def word_diff(a: str, b: str) -> str: + """Inline word-level diff of ``a`` → ``b`` with long equal runs elided.""" + aw, bw = a.split(), b.split() + sm = difflib.SequenceMatcher(None, aw, bw, autojunk=False) + out: list[str] = [] + for tag, i1, i2, j1, j2 in sm.get_opcodes(): + if tag == "equal": + words = aw[i1:i2] + if len(words) > 2 * CONTEXT_WORDS + 2: + words = [*words[:CONTEXT_WORDS], "[…]", *words[-CONTEXT_WORDS:]] + out.append(" ".join(words)) + else: + if tag in ("delete", "replace"): + out.append("[-" + " ".join(aw[i1:i2]) + "-]") + if tag in ("insert", "replace"): + out.append("{+" + " ".join(bw[j1:j2]) + "+}") + return " ".join(out) + + +def similarity(a: str, b: str) -> float: + return difflib.SequenceMatcher(None, a.split(), b.split(), autojunk=False).ratio() + + +def read_prompts(xlsx: Path) -> dict[str, dict[str, str]]: + """Return ``{model: {field: prompt}}`` from the export's prompt row.""" + wb = openpyxl.load_workbook(xlsx, data_only=True, read_only=True) + ws = wb.worksheets[0] + rows = ws.iter_rows(min_row=1, max_row=2, values_only=True) + header, prompt_row = next(rows), next(rows) + out: dict[str, dict[str, str]] = {MODEL_A: {}, MODEL_B: {}} + for head, prompt in zip(header, prompt_row): + if not head or not str(head).endswith(PROMPT_SUFFIX) or not prompt: + continue + field = str(head)[: -len(PROMPT_SUFFIX)] + model = MODEL_A if field not in out[MODEL_A] else MODEL_B + out[model][field] = " ".join(str(prompt).split()) + return out + + +def read_published_prompts(prompts_dir: Path) -> dict[str, dict[str, str]]: + """Return ``{model: {field: prompt}}`` from the published prompt JSONL.""" + out: dict[str, dict[str, str]] = {} + for model in (MODEL_A, MODEL_B): + path = prompts_dir / f"prompts_{model.replace('-', '_')}.jsonl" + out[model] = { + rec["field"]: rec["prompt"] + for rec in map(json.loads, path.read_text(encoding="utf-8").splitlines()) + } + return out + + +def v3_sections() -> dict[str, str]: + """Per-variable rule sections of the v3 system prompt, whitespace-flattened.""" + match = re.search( + r"## Variable coding rules\n(.*?)\n## Allowed ISIC", v3.PROMPT, re.S + ) + if not match: + raise SystemExit("could not locate the variable sections in v3.PROMPT") + sections = {} + for sec in re.finditer(r"### (\S+)\n(.*?)(?=\n### |\Z)", match.group(1), re.S): + sections[sec.group(1)] = " ".join(sec.group(2).split()) + return sections + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("--xlsx", type=Path, default=DEFAULT_XLSX) + parser.add_argument("--prompts-dir", type=Path, default=None, + help="read the published prompts_legora_{1,2}.jsonl from this " + "directory instead of the raw --xlsx export") + parser.add_argument("--out", type=Path, default=DEFAULT_OUT) + args = parser.parse_args() + + prompts = ( + read_published_prompts(args.prompts_dir) + if args.prompts_dir is not None + else read_prompts(args.xlsx) + ) + rules = v3_sections() + fields = list(prompts[MODEL_A]) + if list(prompts[MODEL_B]) != fields: + raise SystemExit("the two column groups carry different field sets") + + lines: list[str] = [] + lines.append(f"# Legora prompt comparison: {MODEL_A} vs {MODEL_B} vs prompts v3\n") + lines.append( + "Prompts from the 2026-08-05 prompt-bearing re-export of the 2026-08-01 " + "tabular review (published as `prompts/prompts_legora_{1,2}.jsonl` in " + "the inference-results dataset; the data rows are byte-identical to " + f"the 2026-08-01 export). Left column group = `{MODEL_A}`, right group " + f"= `{MODEL_B}`, as established in `legora_run_comparison.md`. The v3 " + "baseline is the per-variable section of `legex/prompts/v3.py`. " + "Similarities are word-level `difflib` ratios (1.0 = identical). " + "Generated by `scripts/compare_legora_prompts.py`.\n" + ) + + lines.append("## Similarity overview\n") + lines.append( + f"| field | {MODEL_A} words | {MODEL_B} words " + f"| {MODEL_A} ≈ {MODEL_B} | v3 ≈ {MODEL_A} | v3 ≈ {MODEL_B} |" + ) + lines.append("|---|---|---|---|---|---|") + for field in fields: + a, b, rule = prompts[MODEL_A][field], prompts[MODEL_B][field], rules[field] + lines.append( + f"| {field} | {len(a.split())} | {len(b.split())} " + f"| {similarity(a, b):.2f} | {similarity(rule, a):.2f} " + f"| {similarity(rule, b):.2f} |" + ) + lines.append("") + + lines.append(FINDINGS) + + lines.append("## Per-field prompts and diffs\n") + lines.append( + "Diff notation: `[-only in the left text-]`, `{+only in the right " + "text+}`, `[…]` elides unchanged words. For the ISIC fields the v3 " + "category list lives in a separate shared section and is therefore " + "not part of the v3 rule text below.\n" + ) + for field in fields: + a, b, rule = prompts[MODEL_A][field], prompts[MODEL_B][field], rules[field] + lines.append(f"### {field}\n") + lines.append("**v3 rule**\n") + lines.append(f"> {rule}\n") + lines.append(f"**{MODEL_A} → {MODEL_B}**\n") + lines.append("```diff-words") + lines.append(word_diff(a, b)) + lines.append("```\n") + lines.append(f"**v3 → {MODEL_A}**\n") + lines.append("```diff-words") + lines.append(word_diff(rule, a)) + lines.append("```\n") + lines.append(f"**v3 → {MODEL_B}**\n") + lines.append("```diff-words") + lines.append(word_diff(rule, b)) + lines.append("```\n") + + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text("\n".join(lines), encoding="utf-8") + print(f"wrote {args.out}") + + +if __name__ == "__main__": + main() diff --git a/scripts/compare_legora_runs.py b/scripts/compare_legora_runs.py new file mode 100644 index 0000000000000000000000000000000000000000..402c28eabc364018cee293ac158486005c74b5a7 --- /dev/null +++ b/scripts/compare_legora_runs.py @@ -0,0 +1,241 @@ +"""Compare the two Legora runs (legora-1 vs legora-2) in structure and performance. + +The 2026-08-01 Legora export carried the full question set twice; the ingest +stores the left group as ``legora-1`` and the right group as ``legora-2``. +This script contrasts the two runs — coverage, per-field fill rates, inter-run +agreement, accuracy against the Goldensets, and example disagreements — and +writes a markdown report. + + uv run python scripts/compare_legora_runs.py \ + [--out data/analysis/legora_run_comparison.md] + +Run it after `legex-refusals-apply` so the report reflects the cleaned data. +""" + +import argparse +import sys +from collections import Counter, defaultdict +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from legex import published # noqa: E402 +from legex.config import settings # noqa: E402 +from legex.evaluation.comparison import ( # noqa: E402 + derived, + normalise, + values_agree, +) +from legex.evaluation.scoring import _read_goldenset_rows, score_country # noqa: E402 +from legex.legora import LEGORA_FIELDS # noqa: E402 +from legex.utils import inference_path, read_inference_jsonl # noqa: E402 + +MODEL_A, MODEL_B = "legora-1", "legora-2" +FIELDS = tuple(f for f in LEGORA_FIELDS if f != "case_id") +DEFAULT_OUT = Path("data/analysis/legora_run_comparison.md") +MAX_EXAMPLES_PER_FIELD = 3 + + +def _inference_file(cc: str, prompt_version: str, source: str, model: str, + inference_dir: Path | None) -> Path: + if inference_dir is not None: + return published.inference_file(inference_dir, cc, model) + return inference_path(cc, prompt_version, source, model) + + +def _ccs(prompt_version: str, source: str, inference_dir: Path | None) -> list[str]: + root = inference_dir if inference_dir is not None else settings.data_dir + out = [] + for d in sorted(Path(root).iterdir()): + if not d.is_dir(): + continue + cc = d.name + if all(_inference_file(cc, prompt_version, source, m, inference_dir).exists() + for m in (MODEL_A, MODEL_B)): + out.append(cc) + return out + + +def _by_case(cc: str, prompt_version: str, source: str, model: str, + inference_dir: Path | None) -> dict[str, dict]: + rows = read_inference_jsonl(_inference_file(cc, prompt_version, source, model, inference_dir)) + return {str(r.get("case_id")): r for r in rows if r.get("case_id")} + + +def _pct(num: int, den: int) -> str: + return f"{100 * num / den:.1f}%" if den else "–" + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("--prompt_version", default="v3") + parser.add_argument("--source", choices=("full_text", "pdf"), default="full_text") + parser.add_argument("--out", type=Path, default=DEFAULT_OUT) + parser.add_argument("--gold-dir", type=Path, default=None, + help="published goldenset data directory (XLSX workbooks otherwise)") + parser.add_argument("--inference-dir", type=Path, default=None, + help="published inference data directory (working files otherwise)") + args = parser.parse_args() + + ccs = _ccs(args.prompt_version, args.source, args.inference_dir) + if not ccs: + raise SystemExit("no country has both legora-1 and legora-2 files") + + # ---- collect ------------------------------------------------------------ + coverage_rows: list[tuple[str, int, int, int]] = [] # cc, n_a, n_b, overlap + fill = {m: Counter() for m in (MODEL_A, MODEL_B)} # field -> filled + n_pairs = Counter() # field -> paired rows + identical = Counter() # field -> normalised equal + tolerant = Counter() # field -> values_agree + both_filled = Counter() + both_filled_agree = Counter() + only_a = Counter() + only_b = Counter() + examples: dict[str, list[tuple[str, str, str, str, str]]] = defaultdict(list) + inference_dates = set() + + for cc in ccs: + a = _by_case(cc, args.prompt_version, args.source, MODEL_A, args.inference_dir) + b = _by_case(cc, args.prompt_version, args.source, MODEL_B, args.inference_dir) + shared = sorted(set(a) & set(b)) + coverage_rows.append((cc, len(a), len(b), len(shared))) + gold_lookup: dict[str, dict[str, str]] = {} + if shared: + if args.gold_dir is not None: + _, gold_lookup = published.load_gold_labels(args.gold_dir, cc) + else: + _, gold_lookup = _read_goldenset_rows(cc) + for cid in shared: + ra, rb = a[cid], b[cid] + inference_dates.update(filter(None, (ra.get("inference_date"), rb.get("inference_date")))) + for field in FIELDS: + va, vb = normalise(ra.get(field)), normalise(rb.get(field)) + n_pairs[field] += 1 + fill[MODEL_A][field] += bool(va) + fill[MODEL_B][field] += bool(vb) + if va and not vb: + only_a[field] += 1 + if vb and not va: + only_b[field] += 1 + if va == vb: + identical[field] += 1 + if values_agree(va, vb, field): + tolerant[field] += 1 + if va and vb: + both_filled[field] += 1 + if values_agree(va, vb, field): + both_filled_agree[field] += 1 + elif len(examples[field]) < MAX_EXAMPLES_PER_FIELD: + gv = gold_lookup.get(normalise(cid), {}).get(field, "") + examples[field].append((cc, cid, va, vb, gv)) + + # ---- vs gold ------------------------------------------------------------ + counters = {m: {f: Counter() for f in FIELDS} for m in (MODEL_A, MODEL_B)} + per_cc_acc: dict[str, dict[str, tuple[int, int]]] = defaultdict(dict) # cc -> model -> (correct, n) + for cc in ccs: + for m in (MODEL_A, MODEL_B): + scored = score_country(cc, args.prompt_version, args.source, m, verbose=False, + gold_dir=args.gold_dir, inference_dir=args.inference_dir) + if scored is None: + continue + col_counters, _stats = scored + correct = n = 0 + for field in FIELDS: + c = col_counters.get(field) + if c is None: + continue + counters[m][field].update(c) + correct += c["tp"] + c["tn"] + n += sum(c.values()) + per_cc_acc[cc][m] = (correct, n) + + # ---- render ------------------------------------------------------------- + lines: list[str] = [] + w = lines.append + dates = ", ".join(sorted(inference_dates)) or "unknown" + w("# Legora run comparison: legora-1 vs legora-2") + w("") + w(f"The 2026-08-01 Legora tabular-review export (`data/raw/legora_2026-08-01.xlsx`, " + f"inference_date {dates}) contains the question set twice; `legora-1` is the left " + f"column group, `legora-2` the right one. Generated by `scripts/compare_legora_runs.py`.") + w("") + + w("## Coverage per country") + w("") + w("| cc | legora-1 rows | legora-2 rows | shared |") + w("|---|---|---|---|") + for cc, na, nb, sh in coverage_rows: + w(f"| {cc} | {na} | {nb} | {sh} |") + total_a = sum(r[1] for r in coverage_rows) + total_b = sum(r[2] for r in coverage_rows) + total_s = sum(r[3] for r in coverage_rows) + w(f"| **total** | **{total_a}** | **{total_b}** | **{total_s}** |") + w("") + + w("## Per-field fill rates and inter-run agreement") + w("") + w("Agreement uses the evaluation's tolerant comparator (`values_agree`); " + "*identical* is exact string equality after normalisation. *only 1/only 2* " + "count cells filled by one run and empty in the other.") + w("") + w("| field | filled 1 | filled 2 | identical | agree (tolerant) | agree when both filled | only 1 | only 2 |") + w("|---|---|---|---|---|---|---|---|") + for f in FIELDS: + n = n_pairs[f] + w(f"| {f} | {_pct(fill[MODEL_A][f], n)} | {_pct(fill[MODEL_B][f], n)} " + f"| {_pct(identical[f], n)} | {_pct(tolerant[f], n)} " + f"| {_pct(both_filled_agree[f], both_filled[f])} " + f"| {only_a[f]} | {only_b[f]} |") + w("") + + w("## Accuracy against the Goldensets") + w("") + w("Cell buckets from the standard scoring (`classify_cell`): accuracy = (tp+tn)/n, " + "precision/recall/F1 as in the analysis pipeline.") + w("") + w("| field | acc 1 | acc 2 | Δ acc | F1 1 | F1 2 | recall 1 | recall 2 |") + w("|---|---|---|---|---|---|---|---|") + for f in FIELDS: + accs = {} + stats = {} + for m in (MODEL_A, MODEL_B): + c = counters[m][f] + n = sum(c.values()) + accs[m] = (c["tp"] + c["tn"]) / n if n else 0.0 + stats[m] = derived(c) + w(f"| {f} | {accs[MODEL_A]:.3f} | {accs[MODEL_B]:.3f} " + f"| {accs[MODEL_B] - accs[MODEL_A]:+.3f} " + f"| {stats[MODEL_A][2]:.3f} | {stats[MODEL_B][2]:.3f} " + f"| {stats[MODEL_A][1]:.3f} | {stats[MODEL_B][1]:.3f} |") + w("") + + w("### Per-country accuracy (all fields pooled)") + w("") + w("| cc | acc legora-1 | acc legora-2 | Δ |") + w("|---|---|---|---|") + for cc in ccs: + accs = {} + for m in (MODEL_A, MODEL_B): + correct, n = per_cc_acc.get(cc, {}).get(m, (0, 0)) + accs[m] = correct / n if n else 0.0 + w(f"| {cc} | {accs[MODEL_A]:.3f} | {accs[MODEL_B]:.3f} | {accs[MODEL_B] - accs[MODEL_A]:+.3f} |") + w("") + + w("## Example disagreements (both runs filled, values differ)") + w("") + w("| field | cc | case_id | legora-1 | legora-2 | gold |") + w("|---|---|---|---|---|---|") + def _md(s: str) -> str: + return s.replace("|", "\\|").replace("\n", " ")[:80] + for f in FIELDS: + for cc, cid, va, vb, gv in examples[f]: + w(f"| {f} | {cc} | {_md(cid)} | {_md(va)} | {_md(vb)} | {_md(gv)} |") + w("") + + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text("\n".join(lines), encoding="utf-8") + print(f"wrote {args.out} ({len(ccs)} countries, {total_s} shared rows)") + + +if __name__ == "__main__": + main() diff --git a/scripts/diversity_stats.py b/scripts/diversity_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..32ce4e6d3652c2a4d44c3c9ecc054ea718539f0e --- /dev/null +++ b/scripts/diversity_stats.py @@ -0,0 +1,173 @@ +#!/usr/bin/env python3 +"""Diversity proxy statistics + legal-subject word cloud (appendix material). + +Case-type composition is not part of the LEGEX ground truth, so this script +summarizes sample diversity along the dimensions that ARE annotated: field +coverage, observed ISIC sectors, party structure, and dispute-value coverage. +It also renders a word cloud over the normalized free-text +``legal_subject_judgement`` labels (underscores stripped) for the appendix / +HF dataset card. Reads the published goldenset JSONL (``--gold-dir``). + +Usage: + uv run --with wordcloud python scripts/diversity_stats.py +Outputs: + data/analysis/tables/diversity.tex + data/analysis/figures/legal_subject_wordcloud.png +""" + +import argparse +import re +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from legex import published # noqa: E402 +from legex.analysis.countries import COUNTRY_NAMES, RELEASE_COUNTRIES # noqa: E402 + +COST_BLOCK = ( + "dispute_value_nominal", + "plaintiff_loosing_share", + "court_cost_awarded_nominal", + "party_compensation_awarded_nominal", +) +ISIC_FIELDS = ( + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +) +NON_SECTORS = {"", "none", "no_allocation_possible"} + + +def _stats(cc: str, gold_dir: Path) -> dict | None: + if not published.gold_file(gold_dir, cc).exists(): + return None + # Count judgments the same way as the paper: non-empty + # legal_subject_judgement (a few released rows lack it). + _, gold = published.load_gold_labels(gold_dir, cc) + rows = { + cid: f + for cid, f in gold.items() + if f.get("legal_subject_judgement", "").strip() + } + n = len(rows) + if not n: + return None + sectors: set[str] = set() + n_multi = n_dispute = 0 + cost_filled = cost_total = 0 + subjects: list[str] = [] + for fields in rows.values(): + for f in ISIC_FIELDS: + v = fields.get(f, "").strip().lower() + if v not in NON_SECTORS: + sectors.add(v) + try: + multi = int(float(fields.get("plaintiffs_all_count") or 0)) > 1 or \ + int(float(fields.get("defendants_all_count") or 0)) > 1 + except ValueError: + multi = False + n_multi += multi + n_dispute += bool(fields.get("dispute_value_nominal", "").strip()) + for f in COST_BLOCK: + cost_total += 1 + cost_filled += bool(fields.get(f, "").strip()) + subj = fields.get("legal_subject_judgement", "").strip() + if subj: + subjects.append(subj) + return { + "cc": cc, + "n": n, + "sectors": len(sectors), + "pct_multi": 100.0 * n_multi / n, + "pct_dispute": 100.0 * n_dispute / n, + "pct_cost": 100.0 * cost_filled / cost_total, + "subjects": subjects, + } + + +def _normalise_subject(s: str) -> str: + s = s.replace("_", " ").strip() + s = re.sub(r"\s+", " ", s) + return s.title() + + +def write_table(all_stats: list[dict], out: Path) -> None: + lines = [ + "% Auto-generated by scripts/diversity_stats.py — do not edit by hand.", + r"\begin{table}[t]", + r"\caption{Sample diversity along the annotated dimensions." + r" \emph{Sectors} counts the distinct ISIC top-level sectors observed" + r" among plaintiffs and defendants (of 22 possible, A--V);" + r" \emph{multi-party} is the share of judgments with more than one" + r" plaintiff or defendant; the last two columns give the share of" + r" judgments with a coded dispute value and the fill rate over the" + r" four cost-block fields.}", + r"\label{tab:diversity}", + r"\vskip 0.05in", + r"\centering\small", + r"\begin{tabular}{@{}lrrrrr@{}}", + r"\toprule", + r"\textbf{Jurisdiction} & \textbf{$n$} & \textbf{Sectors}" + r" & \textbf{Multi-party} & \textbf{Dispute value} & \textbf{Cost block} \\", + r"\midrule", + ] + for s in all_stats: + lines.append( + f"{COUNTRY_NAMES[s['cc']]} & {s['n']} & {s['sectors']}" + f" & {s['pct_multi']:.0f}\\% & {s['pct_dispute']:.0f}\\%" + f" & {s['pct_cost']:.0f}\\% \\\\" + ) + lines += [r"\bottomrule", r"\end{tabular}", r"\end{table}", ""] + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text("\n".join(lines), encoding="utf-8") + print(f"wrote {out}") + + +def write_wordcloud(subjects: list[str], out: Path) -> None: + from wordcloud import STOPWORDS, WordCloud + + # Word-level cloud: full subject labels are long multi-word phrases and + # render as unreadable sentences; individual (stopword-free) terms show + # the topical spread instead. + text = " ".join(_normalise_subject(s) for s in subjects) + stopwords = STOPWORDS | {"Law", "Legal", "Case", "Proceedings", "Procedure"} + wc = WordCloud( + width=1600, + height=900, + background_color="white", + colormap="cividis", + max_words=100, + prefer_horizontal=0.95, + stopwords=stopwords, + collocations=False, + random_state=0, # deterministic layout across runs + ).generate(text) + out.parent.mkdir(parents=True, exist_ok=True) + wc.to_file(str(out)) + print(f"wrote {out} ({len(subjects)} labels)") + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("--gold-dir", type=Path, default=None, + help="published goldenset data directory (default: " + "submission/goldensets/data, else ../goldensets/data)") + args = ap.parse_args() + gold_dir = args.gold_dir or published.default_gold_dir(REPO_ROOT) + + all_stats = [] + subjects: list[str] = [] + for cc in sorted(RELEASE_COUNTRIES, key=lambda c: COUNTRY_NAMES[c]): + s = _stats(cc, gold_dir) + if s is None: + print(f"[{cc}] no goldenset — skipped", file=sys.stderr) + continue + subjects.extend(s.pop("subjects")) + all_stats.append(s) + write_table(all_stats, REPO_ROOT / "data/analysis/tables/diversity.tex") + write_wordcloud(subjects, REPO_ROOT / "data/analysis/figures/legal_subject_wordcloud.png") + + +if __name__ == "__main__": + main() diff --git a/scripts/hallucination_stats.py b/scripts/hallucination_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..fcd34818569b91f4d1eab2a95fc869e23c1b8304 --- /dev/null +++ b/scripts/hallucination_stats.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python3 +"""Aggregate the published hallucination review into the Figure 3 numbers. + +Reads ``data/analysis/hallucinations/hallucination_review_ch.csv`` (one row +per flagged (case, field, model) cell over the 30 Swiss judgments with the +highest flagged-cell counts; see ``scripts/export_hallucination_review.py``) +and prints the per-category shares and the two derived brackets shown in the +paper's hallucination figure. + +Usage: uv run python scripts/hallucination_stats.py +""" + +import argparse +import csv +from collections import Counter +from pathlib import Path + +DEFAULT_CSV = Path("data/analysis/hallucinations/hallucination_review_ch.csv") + +CATEGORY_LABELS = { + "A": "Fabrication (genuine hallucination)", + "B": "Misattribution (genuine hallucination)", + "C": "Gold-set gap (value present in judgment)", + "D": "Defensible coding (value present in judgment)", + "E": "Refusal", +} + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--csv", type=Path, default=DEFAULT_CSV) + parser.add_argument("--per-model", action="store_true", + help="additionally break the categories down per system") + args = parser.parse_args(argv) + + with args.csv.open(newline="", encoding="utf-8") as f: + rows = list(csv.DictReader(f)) + n = len(rows) + counts = Counter(r["category"] for r in rows) + cases = {r["case_id"] for r in rows} + + print(f"n = {n} flagged (case, field, model) cells over {len(cases)} judgments") + for cat in sorted(CATEGORY_LABELS): + c = counts.get(cat, 0) + print(f" {cat} {CATEGORY_LABELS[cat]:<45} {c:>3} {100 * c / n:.1f}%") + ab = counts.get("A", 0) + counts.get("B", 0) + cd = counts.get("C", 0) + counts.get("D", 0) + print(f" A+B Genuine hallucination {ab:>3} {100 * ab / n:.1f}%") + print(f" C+D Value present in judgment / gold-set gap {cd:>3} {100 * cd / n:.1f}%") + + if args.per_model: + by_model: dict[str, Counter] = {} + for r in rows: + by_model.setdefault(r["model"], Counter())[r["category"]] += 1 + print() + for model, c in sorted(by_model.items()): + total = sum(c.values()) + cats = " ".join(f"{k}:{c.get(k, 0)}" for k in sorted(CATEGORY_LABELS)) + print(f" {model:<30} n={total:<4} {cats}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/paper_tables.py b/scripts/paper_tables.py new file mode 100644 index 0000000000000000000000000000000000000000..7cf0212b8accbdae6dbfa62b9ad7105451b00975 --- /dev/null +++ b/scripts/paper_tables.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +"""Render the manuscript's by-jurisdiction and by-field results tables. + +Reads ``data/analysis/per_country_per_column.csv`` (from ``legex-analysis``) +and emits ``tab:metrics-by-jurisdiction`` and ``tab:metrics-by-field`` in the +manuscript's combined four-system layout (recall / false-fill per system, +each with ±1 SE), restricted to the 19 release jurisdictions and the 10 +structured fields (poster convention: the free-text +``legal_subject_judgement`` is excluded from scoring and reported separately). + +The gold-side denominators (n gold-filled / n gold-empty) are shown per row; +they are shared by the two LLM pipelines, while Harvey and Legora cover fewer +judgments (Harvey's ingest failed on some case packets; Legora returned empty +tables for some jurisdictions, e.g. Armenia and Georgia) — where a system's n +differs it is appended in parentheses, in system order. Rows a system did not +cover at all render as ``---``. + +Usage: + uv run python scripts/paper_tables.py > data/analysis/paper_tables.tex +""" + +import csv +import math +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from legex.analysis.countries import COUNTRY_NAMES, RELEASE_COUNTRIES # noqa: E402 +from legex.analysis.quant_results import ( # noqa: E402 + STRUCTURED_FIELDS, + _BUCKETS, +) + +CSV_PATH = REPO_ROOT / "data/analysis/per_country_per_column.csv" + +SYSTEMS = ( + ("gemini/gemini-3.1-flash-lite", "Gemini"), + ("gpt-5.4-mini", "GPT-5.4-mini"), + ("harvey", "Harvey"), + ("legora-1", "Legora"), +) + +# System whose gold-side denominators are the reference for the n column; any +# system whose coverage differs gets its own n appended in parentheses. +BASELINE_SYSTEM = "gpt-5.4-mini" + + +def _pct_se(k: int, n: int) -> str: + if not n: + return "---" + p = k / n + se = 100.0 * math.sqrt(p * (1.0 - p) / n) + if se == 0.0: # boundary estimate (0% or 100%): a degenerate ±0.0% adds nothing + return f"{p * 100:.1f}\\%" + return f"{p * 100:.1f}\\% $\\pm$ {se:.1f}\\%" + + +def _load() -> dict[tuple[str, str, str], dict[str, int]]: + """(model, country, column) -> buckets, release countries + structured fields only.""" + out: dict[tuple[str, str, str], dict[str, int]] = {} + release, structured = set(RELEASE_COUNTRIES), set(STRUCTURED_FIELDS) + with CSV_PATH.open(newline="") as f: + for row in csv.DictReader(f): + if row["country"] in release and row["column"] in structured: + out[(row["model"], row["country"], row["column"])] = { + k: int(row[k]) for k in _BUCKETS + } + return out + + +def _sums(data, model: str, country: str | None = None, column: str | None = None): + agg = {k: 0 for k in _BUCKETS} + for (m, cc, col), c in data.items(): + if m != model or (country and cc != country) or (column and col != column): + continue + for k in _BUCKETS: + agg[k] += c[k] + return agg + + +def _row_cells(data, country=None, column=None) -> tuple[list[str], str]: + """8 metric cells (recall/false-fill × 4 systems) + the n cell.""" + cells: list[str] = [] + n_filled: dict[str, int] = {} + n_empty: dict[str, int] = {} + for model, _ in SYSTEMS: + c = _sums(data, model, country, column) + filled = c["tp"] + c["mismatch"] + c["missed"] + empty = c["hallucinated"] + c["tn"] + n_filled[model], n_empty[model] = filled, empty + cells.append(_pct_se(c["tp"], filled)) + cells.append(_pct_se(c["hallucinated"], empty)) + base = (n_filled[BASELINE_SYSTEM], n_empty[BASELINE_SYSTEM]) + n_cell = f"{base[0]}/{base[1]}" + for model, label in SYSTEMS: + if model == BASELINE_SYSTEM: + continue + n = (n_filled[model], n_empty[model]) + # Zero coverage is already communicated by the --- metric cells. + if n != base and n != (0, 0): + n_cell += f" ({label[0]}: {n[0]}/{n[1]})" + return cells, n_cell + + +def main() -> None: + data = _load() + + print("% Auto-generated by scripts/paper_tables.py — do not edit by hand.") + print("% 19 release jurisdictions, 10 structured fields (free-text excluded).") + print() + + # --- by jurisdiction --- + print(r"\begin{table*}[htbp]") + print( + r"\caption{Extraction metrics by jurisdiction over the ten structured" + r" fields (recall on expert-filled cells and false-fill rate on" + r" expert-empty cells, each $\pm$1\,SE). $n$ lists the goldenset-filled/" + r"goldenset-empty denominators; where a system's coverage differs its" + r" denominators follow in parentheses (G = Gemini, H = Harvey," + r" L = Legora); \mbox{---} marks jurisdictions a system did" + r" not cover.}" + ) + print(r"\label{tab:metrics-by-jurisdiction}") + print(r"\centering\small") + print(r"\resizebox{\textwidth}{!}{%") + print(r"\begin{tabular}{@{}lrr@{\hskip 8pt}rr@{\hskip 8pt}rr@{\hskip 8pt}rr@{\hskip 8pt}r@{}}") + print(r"\toprule") + print( + r"& \multicolumn{2}{c}{\textbf{Gemini}} & \multicolumn{2}{c}{\textbf{GPT-5.4-mini}}" + r" & \multicolumn{2}{c}{\textbf{Harvey}} & \multicolumn{2}{c}{\textbf{Legora}} & \\" + ) + print(r"\cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-7}\cmidrule(lr){8-9}") + print( + r"\textbf{Jurisdiction} & \textbf{Recall} & \textbf{False-fill}" + r" & \textbf{Recall} & \textbf{False-fill}" + r" & \textbf{Recall} & \textbf{False-fill}" + r" & \textbf{Recall} & \textbf{False-fill} & \textbf{$n$ (filled/empty)} \\" + ) + print(r"\midrule") + for cc in sorted(RELEASE_COUNTRIES, key=lambda c: COUNTRY_NAMES[c]): + cells, n_cell = _row_cells(data, country=cc) + print(f"{COUNTRY_NAMES[cc]} & " + " & ".join(cells) + f" & {n_cell} \\\\") + print(r"\bottomrule") + print(r"\end{tabular}%") + print(r"}") + print(r"\end{table*}") + print() + + # --- by field --- + print(r"\begin{table*}[htbp]") + print( + r"\caption{Extraction metrics by field over the 19 release" + r" jurisdictions (recall and false-fill rate, each $\pm$1\,SE)." + r" $n$ as in \cref{tab:metrics-by-jurisdiction}.}" + ) + print(r"\label{tab:metrics-by-field}") + print(r"\centering\small") + print(r"\resizebox{\textwidth}{!}{%") + print(r"\begin{tabular}{@{}lrr@{\hskip 8pt}rr@{\hskip 8pt}rr@{\hskip 8pt}rr@{\hskip 8pt}r@{}}") + print(r"\toprule") + print( + r"& \multicolumn{2}{c}{\textbf{Gemini}} & \multicolumn{2}{c}{\textbf{GPT-5.4-mini}}" + r" & \multicolumn{2}{c}{\textbf{Harvey}} & \multicolumn{2}{c}{\textbf{Legora}} & \\" + ) + print(r"\cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-7}\cmidrule(lr){8-9}") + print( + r"\textbf{Variable} & \textbf{Recall} & \textbf{False-fill}" + r" & \textbf{Recall} & \textbf{False-fill}" + r" & \textbf{Recall} & \textbf{False-fill}" + r" & \textbf{Recall} & \textbf{False-fill} & \textbf{$n$ (filled/empty)} \\" + ) + print(r"\midrule") + for col in sorted(STRUCTURED_FIELDS): + cells, n_cell = _row_cells(data, column=col) + name = r"\texttt{" + col.replace("_", r"\_") + "}" + print(f"{name} & " + " & ".join(cells) + f" & {n_cell} \\\\") + print(r"\bottomrule") + print(r"\end{tabular}%") + print(r"}") + print(r"\end{table*}") + + +if __name__ == "__main__": + main() diff --git a/scripts/poster_metrics.py b/scripts/poster_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..29d4485a9220c21d2f459d6077f736600bdbfc02 --- /dev/null +++ b/scripts/poster_metrics.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python3 +"""Reproduce the headline table of the AI4Law poster (and its footnote numbers). + +Reads ``data/analysis/per_column.csv`` (produced by ``legex-analysis`` over all +countries and models; regenerate the whole chain with +``scripts/reproduce_paper.sh``) and aggregates the per-field confusion buckets +over two field sets: + +* ``10 structured fields`` -- the 11 evaluated fields minus the free-text + ``legal_subject_judgement`` (unbounded label space, human-human agreement + <1%, see data/analysis/iaa/ANALYSIS.md section 2.1); +* ``Cost block (4 fields)`` -- dispute value, losing share, court costs, + party compensation. + +For every system it prints recall on gold-filled cells, precision on emitted +cells, F1, and the false-fill (hallucination) rate on gold-empty cells, each +with its denominator n and +-1 SE = sqrt(p*(1-p)/n) in percentage points. +Metric definitions match ``legex/analysis/quant_results.py::_metrics`` and +``legex/evaluation`` (buckets: tp / mismatch / missed / hallucinated / tn). + +Usage: + uv run python scripts/poster_metrics.py # human-readable table + uv run python scripts/poster_metrics.py --latex # poster LaTeX rows +""" + +import argparse +import csv +import math +from collections import defaultdict +from pathlib import Path + +EVAL_FIELDS: tuple[str, ...] = ( + "legal_subject_judgement", + "trial_start_date", + "trial_end_date", + "dispute_value_nominal", + "plaintiff_loosing_share", + "court_cost_awarded_nominal", + "party_compensation_awarded_nominal", + "plaintiffs_all_count", + "defendants_all_count", + "plaintiff_no1_ISIC1_industry_category", + "defendant_no1_ISIC1_industry_category", +) + +COST_BLOCK: tuple[str, ...] = ( + "dispute_value_nominal", + "plaintiff_loosing_share", + "court_cost_awarded_nominal", + "party_compensation_awarded_nominal", +) + +STRUCTURED: tuple[str, ...] = tuple( + f for f in EVAL_FIELDS if f != "legal_subject_judgement" +) + +FIELD_SETS: tuple[tuple[str, tuple[str, ...]], ...] = ( + ("10 structured fields", STRUCTURED), + ("Cost block (4 fields)", COST_BLOCK), + ("All 11 fields", EVAL_FIELDS), # footnote cross-check: recall 51-58% +) + +# (model id in CSV, poster label). Order = row order in the poster table. +SYSTEMS: tuple[tuple[str, str], ...] = ( + ("gemini/gemini-3.1-flash-lite", "Gemini"), + ("gpt-5.4-mini", "ChatGPT"), + ("harvey", "Harvey"), +) + +_BUCKETS = ("tp", "mismatch", "missed", "hallucinated", "tn") + + +def _se_pp(p: float, n: int) -> float: + """+-1 standard error of a proportion, in percentage points.""" + return 100.0 * math.sqrt(p * (1.0 - p) / n) if n else float("nan") + + +def _aggregate(csv_path: Path) -> dict[str, dict[str, dict[str, int]]]: + """model -> column -> summed confusion buckets.""" + counts: dict[str, dict[str, dict[str, int]]] = defaultdict( + lambda: defaultdict(lambda: {k: 0 for k in _BUCKETS}) + ) + with csv_path.open(newline="") as fh: + for row in csv.DictReader(fh): + cell = counts[row["model"]][row["column"]] + for k in _BUCKETS: + cell[k] += int(row[k]) + return counts + + +def _metrics(c: dict[str, int]) -> dict[str, float]: + tp, mism, miss, hallu, tn = ( + c["tp"], c["mismatch"], c["missed"], c["hallucinated"], c["tn"], + ) + gold_filled = tp + mism + miss + gold_empty = hallu + tn + emitted = tp + mism + hallu + r = tp / gold_filled if gold_filled else 0.0 + p = tp / emitted if emitted else 0.0 + return { + "n_gold_filled": gold_filled, + "n_gold_empty": gold_empty, + "n_emitted": emitted, + "recall": r, + "recall_se": _se_pp(r, gold_filled), + "precision": p, + "precision_se": _se_pp(p, emitted), + "f1": 2 * p * r / (p + r) if (p + r) else 0.0, + "false_fill": hallu / gold_empty if gold_empty else 0.0, + "false_fill_se": _se_pp(hallu / gold_empty if gold_empty else 0.0, gold_empty), + } + + +def _sum_fields( + per_column: dict[str, dict[str, int]], fields: tuple[str, ...] +) -> dict[str, int]: + out = {k: 0 for k in _BUCKETS} + for f in fields: + for k in _BUCKETS: + out[k] += per_column[f][k] + return out + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument( + "--csv", + type=Path, + default=Path(__file__).resolve().parents[1] / "data/analysis/per_column.csv", + help="per_column.csv produced by legex-analysis (default: data/analysis/)", + ) + ap.add_argument( + "--latex", action="store_true", + help="emit the poster table rows as LaTeX instead of plain text", + ) + args = ap.parse_args() + + counts = _aggregate(args.csv) + + if args.latex: + for model, label in SYSTEMS: + cells: list[str] = [] + for _, fields in FIELD_SETS[:2]: # structured + cost block only + m = _metrics(_sum_fields(counts[model], fields)) + cells += [ + f"{m['recall'] * 100:.1f}\\%", + f"{m['precision'] * 100:.1f}\\%", + f"{m['f1']:.2f}", + f"{m['false_fill'] * 100:.1f}\\%", + ] + print(f"{label} & " + " & ".join(cells) + r" \\") + return + + for set_name, fields in FIELD_SETS: + print(f"=== {set_name} ===") + for model, label in SYSTEMS: + m = _metrics(_sum_fields(counts[model], fields)) + print( + f"{label:8s}" + f" recall {m['recall'] * 100:5.1f}% +-{m['recall_se']:.1f}" + f" (n={m['n_gold_filled']})" + f" precision {m['precision'] * 100:5.1f}% +-{m['precision_se']:.1f}" + f" (n={m['n_emitted']})" + f" F1 {m['f1']:.3f}" + f" false-fill {m['false_fill'] * 100:5.1f}% +-{m['false_fill_se']:.1f}" + f" (n={m['n_gold_empty']})" + ) + print() + + +if __name__ == "__main__": + main() diff --git a/scripts/reproduce_paper.sh b/scripts/reproduce_paper.sh new file mode 100755 index 0000000000000000000000000000000000000000..19190fb0ff9435fe24d11066d65ae8fa2a116aa4 --- /dev/null +++ b/scripts/reproduce_paper.sh @@ -0,0 +1,83 @@ +#!/usr/bin/env bash +# Reproduce every generated table and figure of the LEGEX paper from the +# published JSONL bundles (goldensets + inference-results). +# +# Inputs (auto-detected, or override via env): +# GOLD_DIR goldenset data dir (submission/goldensets/data, else ../goldensets/data) +# INF_DIR inference data dir (submission/inference-results/data, else ../inference-results/data) +# ALTTEST_DIR optional clone of https://github.com/nitaytech/AltTest — reruns +# the Alternative Annotator Test (alt_test_pooled.csv + the +# per-field reference CSVs; skipped when unset) +# +# Every artifact is regenerated in place under data/analysis/, so a clean +# `git status` after a run is the verification that the shipped numbers +# reproduce. +set -euo pipefail +cd "$(dirname "$0")/.." + +if [[ -z "${GOLD_DIR:-}" ]]; then + for c in goldensets/data submission/goldensets/data ../goldensets/data; do + [[ -d "$c" ]] && GOLD_DIR="$c" && break + done +fi +if [[ -z "${INF_DIR:-}" ]]; then + for c in inference-results/data submission/inference-results/data ../inference-results/data; do + [[ -d "$c" ]] && INF_DIR="$c" && break + done +fi +if [[ -z "${GOLD_DIR:-}" || -z "${INF_DIR:-}" ]]; then + echo "error: published data not found — clone legexbenchmark/goldensets and" >&2 + echo "legexbenchmark/inference-results next to this repository, or set GOLD_DIR/INF_DIR." >&2 + exit 1 +fi +echo "gold: $GOLD_DIR" +echo "inference: $INF_DIR" + +RUN="uv run --extra plots" +IAA_DIR="data/analysis/iaa" +# Byte-stable matplotlib PDF metadata across runs. +export SOURCE_DATE_EPOCH=0 + +# Clean first so dropped inputs can't leave stale rows behind. The AAT CSVs +# are only regenerated when ALTTEST_DIR is set (they need the upstream clone). +rm -f "$IAA_DIR"/pairwise_agreement.csv "$IAA_DIR"/kappa_audit.csv \ + "$IAA_DIR"/ANALYSIS.md "$IAA_DIR"/alt_test_decomposition.csv + +echo "== Table 3 inputs: human-human agreement (pairwise_agreement.csv, kappa_audit.csv)" +$RUN legex-iaa --gold-dir "$GOLD_DIR" --out "$IAA_DIR" + +echo "== Table 5: AAT win/tie/loss decomposition (alt_test_decomposition.csv)" +$RUN python scripts/alt_test_decomposition.py \ + --gold-dir "$GOLD_DIR" --inference-dir "$INF_DIR" --out "$IAA_DIR" + +echo "== Scoring aggregates (per_*.csv, tables/headline.tex, tables/per_field.tex)" +$RUN legex-analysis --gold-dir "$GOLD_DIR" --inference-dir "$INF_DIR" \ + --country am au be br ch de es fr ge hk in np nz ph rs sg tw uk us + +echo "== Table 4: headline metrics (quant_results.tex)" +$RUN legex-quant-results + +echo "== Tables 7/8: by-jurisdiction and by-field metrics (paper_tables.tex)" +$RUN python scripts/paper_tables.py > data/analysis/paper_tables.tex + +echo "== Table 9 + word cloud: diversity stats" +$RUN python scripts/diversity_stats.py --gold-dir "$GOLD_DIR" + +echo "== Table 10 + Figure 5: currency and ISIC frequencies" +$RUN python scripts/appendix_frequencies.py --gold-dir "$GOLD_DIR" + +echo "== Figure 3: hallucination category shares" +$RUN python scripts/hallucination_stats.py + +if [[ -n "${ALTTEST_DIR:-}" ]]; then + echo "== AAT (alt_test_pooled.csv + alt_test_reference_*.csv)" + $RUN python scripts/alt_test_reference.py --alttest "$ALTTEST_DIR" --per-field \ + --gold-dir "$GOLD_DIR" --inference-dir "$INF_DIR" --out "$IAA_DIR" +else + echo "== AAT skipped (set ALTTEST_DIR to a clone of nitaytech/AltTest to rerun it)" +fi + +echo "== ANALYSIS.md (renders all CSVs above)" +$RUN legex-analysis-report + +echo "done — verify with: git status data/analysis" diff --git a/tests/test_analysis_report.py b/tests/test_analysis_report.py new file mode 100644 index 0000000000000000000000000000000000000000..4b9d8a48ccb19db7fdcbee45aac74eddbf48e2b8 --- /dev/null +++ b/tests/test_analysis_report.py @@ -0,0 +1,132 @@ +import csv + +from legex.analysis.report import build_report + + +def _write(path, header, rows): + with path.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow(header) + w.writerows(rows) + + +def test_build_report_renders_sections_and_live_numbers(tmp_path) -> None: + iaa = tmp_path / "iaa" + iaa.mkdir() + analysis = tmp_path + + _write( + iaa / "pairwise_agreement.csv", + ["annotator_a", "annotator_b", "country", "field", "n", + "pct_exact", "pct_tolerant", "cohen_kappa"], + [ + # categorical (fixed-vocab) -> kappa shown + ["primary", "a1b2c3d4e5", "de", "plaintiff_no1_ISIC1_industry_category", "20", "0.5000", "0.5000", "0.4000"], + # monetary -> kappa computed in CSV but suppressed in the report + ["primary", "a1b2c3d4e5", "de", "court_cost_awarded_nominal", "20", "0.6000", "0.6500", "0.3000"], + # free text -> percent only, kappa undefined + ["primary", "a1b2c3d4e5", "de", "legal_subject_judgement", "20", "0.1000", "0.1000", ""], + ], + ) + # Reference-implementation output (scripts/alt_test_reference.py); an + # empty string marks an untestable (country, field, variant) cell. + _write( + iaa / "alt_test_reference_gpt-5.4-mini.csv", + ["candidate", "country", "field", + "winning_rate", "advantage_probability", "passes", + "winning_rate_nontrivial", "advantage_probability_nontrivial", + "passes_nontrivial"], + [ + ["gpt-5.4-mini", "de", "plaintiffs_all_count", + "1.0", "0.95", "1", "1.0", "0.93", "1"], + ["gpt-5.4-mini", "de", "dispute_value_nominal", + "0.3333", "0.7", "0", "", "", ""], + ], + ) + _write( + analysis / "per_column.csv", + ["model", "column", "tp", "mismatch", "missed", "hallucinated", "tn", + "accuracy", "recall_when_filled", "precision_when_emitted", + "hallucination_rate", "miss_rate", "wrong_when_both_filled", "f1"], + [["gpt-5.4-mini", "plaintiffs_all_count", "8", "1", "1", "0", "10", + "0.9000", "0.8000", "0.8889", "0.0000", "0.1000", "0.1000", "0.8400"]], + ) + + md = build_report(iaa, analysis) + + for header in ( + "# Inter-Annotator Agreement", + "## 1. Scope", + "## 2. Human–human agreement", + "## 3. Alternative-annotator test", + "## 4. Headline extraction metrics", + "Landis–Koch", + ): + assert header in md, header + + assert "### 2.1 By variable" in md + assert "0.400" in md # ISIC (categorical) kappa is shown + assert "0.300" not in md # monetary kappa is suppressed, not displayed + assert "65.0%" in md # monetary tolerant percent agreement + assert "±" in md # uncertainty (±1 SE) is shown + assert "nominal" in md and "monetary" in md # measurement-level labels + assert "a1b2c3d4e5" in md # by-pair table + assert "80.0%" in md # recall_when_filled rendered as percent (section 4) + assert "1/2" in md # alt-test pass count (1 of 2 testable cells) + assert "1/1" in md # non-trivial pass count (untestable cell excluded) + assert "0.82" in md # mean advantage probability rho + assert "0.93" in md # non-trivial mean rho (only the testable cell) + # provenance: numbers come from the authors' reference implementation + assert "nitaytech/AltTest" in md + assert "alt_test_reference.py" in md + # per-field grids for every headline metric + assert "### 4.3 Precision by field" in md + assert "### 4.4 Hallucination rate by field" in md + assert "### 4.5 F1 by field" in md + assert "0.840" in md # F1 rendered as 0–1, not percent + # the alt-test formula block is always rendered, CSVs or not + assert "### 3.1 How ω and ρ are computed" in md + assert "rho_j = (1/n_j)" in md # advantage probability, with its >= + assert "(1 - epsilon) / 2 = 0.4" in md # the threshold epsilon actually buys + # without the decomposition CSV, §3.4 degrades to a pointer + assert "### 3.4 Substitutable vs. better" in md + assert "alt_test_decomposition.py" in md + + +def test_alt_test_decomposition_separates_substitutable_from_better(tmp_path) -> None: + """§3.4 reports the win/tie/loss counts rho hides, and rho with ties split.""" + iaa = tmp_path / "iaa" + iaa.mkdir() + _write( + iaa / "alt_test_decomposition.csv", + ["candidate", "country", "variant", "n_comparisons", + "refs_disagree", "llm_better", "human_better", + "tie", "tie_same", "tie_diff", "tie_at_1", "tie_at_half", "tie_at_0", + "rho_alttest", "rho_tiebroken"], + [ + # ties dominate: rho looks strong, but the human wins the decisive ones + ["gpt-5.4-mini", "de", "all", "100", + "36", "10", "14", "76", "57", "19", "38", "26", "12", "0.86", "0.48"], + ["gpt-5.4-mini", "de", "nontrivial", "80", + "30", "8", "8", "64", "48", "16", "32", "22", "10", "0.90", "0.50"], + ], + ) + + md = build_report(iaa, tmp_path) + + assert "### 3.4 Substitutable vs. better" in md + assert "76 (76%)" in md # tie count and share + assert "0.86" in md # rho as the alt-test computes it + assert "0.48" in md # rho once ties are split -> chance + # the tie bucket is itemised, both by score level and by same/different answer + assert "57 (75%)" in md # tie_same / tie + assert "19 (25%)" in md # tie_diff / tie -- ties that are real disagreements + assert "38 (50%)" in md # tie_at_1 / tie + assert "26 (34%)" in md # tie_at_half / tie -- only possible if refs conflict + assert "12 (16%)" in md # tie_at_0 / tie + assert "631" not in md # refs_disagree comes from the CSV, not hard-coded + assert "36 of 100 comparisons (36%;" in md # reference conflict rate + # the reading: mostly real agreement, but the human takes the decisive calls + assert "75% of those ties are real agreement" in md + assert "the human wins 58% (14 vs 10)" in md # 14 / (14 + 10) decisive + assert "0.50." in md # non-trivial rho (ties split) survives the filter diff --git a/tests/test_cleaning_scope.py b/tests/test_cleaning_scope.py new file mode 100644 index 0000000000000000000000000000000000000000..3996f463c622247d1b4f97d0601922eeb18cbfd6 --- /dev/null +++ b/tests/test_cleaning_scope.py @@ -0,0 +1,99 @@ +"""Model-scoped scan/apply: other models' files and changelog history stay untouched.""" +import json + +import openpyxl +import pytest +from openpyxl import Workbook + +import legex.evaluation.cleaning as cleaning +from legex.config import settings +from legex.evaluation.comparison import is_label_column + + +def test_inference_date_is_not_a_label(): + assert is_label_column("inference_date") is False + + +@pytest.fixture +def env(tmp_path, monkeypatch): + monkeypatch.setattr(settings, "data_dir", tmp_path) + monkeypatch.setattr(cleaning, "CHANGELOG", tmp_path / "changelog.jsonl") + (tmp_path / "zz").mkdir() + gs = Workbook() + ws = gs.active + ws.title = "GOLDENSET" + ws.append(["case_id", "link", "full_text", "legal_subject_judgement", "plaintiffs_all_count"]) + ws.append(["C-1", "", "text", "Contract_Law", "1"]) + gs.save(tmp_path / "zz" / "Goldenset_Testland.xlsx") + + def record(model, subject): + return {"case_id": "C-1", "legal_subject_judgement": subject, + "plaintiffs_all_count": "1", "model": model, "error": None} + + files = {} + for model, subject in (("modx", "Contract_Law"), ("mody", "Tort_Law")): + p = tmp_path / "zz" / f"Goldenset_Testland_v3_full_text_{model}.jsonl" + p.write_text(json.dumps(record(model, subject)) + "\n", encoding="utf-8") + files[model] = p + return tmp_path, files + + +def test_inference_files_model_filter(env): + tmp_path, files = env + assert cleaning._inference_files() == sorted(files.values()) + assert cleaning._inference_files({"modx"}) == [files["modx"]] + + +def test_scan_folds_in_conflicts_with_prefill(env): + tmp_path, files = env + conflicts = tmp_path / "conflicts.jsonl" + conflicts.write_text( + json.dumps({"model": "modx", "country": "zz", "case_id": "C-1", + "field": "legal_subject_judgement", "kept": "Contract_Law", + "alternative": "Property_Law"}) + "\n" + + json.dumps({"model": "mody", "country": "zz", "case_id": "C-1", + "field": "legal_subject_judgement", "kept": "Tort_Law", + "alternative": "IP_Law"}) + "\n", + encoding="utf-8", + ) + out = tmp_path / "review.xlsx" + n = cleaning.scan(out, models={"modx"}, conflicts=conflicts) + ws = openpyxl.load_workbook(out).active + rows = list(ws.iter_rows(values_only=True)) + header, data = rows[0], rows[1:] + assert n == len(data) == 1 + row = dict(zip(header, data[0])) + assert row["model"] == "modx" + assert row["current_value"] == "Contract_Law" + assert row["corrected_value"] == "Contract_Law" + assert "Property_Law" in row["reason"] + + +def test_apply_scoped_leaves_other_models_alone(env): + tmp_path, files = env + before_y = files["mody"].read_bytes() + cleaning.CHANGELOG.write_text( + json.dumps({"model": "mody", "country": "zz", "case_id": "C-1", + "field": "plaintiffs_all_count", "before": "one", "after": "1", + "kind": "reviewed"}) + "\n", + encoding="utf-8", + ) + xlsx = tmp_path / "review.xlsx" + wb = Workbook() + ws = wb.active + ws.append(cleaning._XLSX_HEADER) + ws.append(["modx", "zz", "C-1", "legal_subject_judgement", "Contract_Law", + "duplicate-run conflict; alternative: 'Property_Law'", "", "tp", "missed", + "Property_Law"]) + wb.save(xlsx) + + cleaning.apply(xlsx, models={"modx"}) + + assert files["mody"].read_bytes() == before_y + recs = [json.loads(l) for l in files["modx"].read_text(encoding="utf-8").splitlines()] + assert recs[0]["legal_subject_judgement"] == "Property_Law" + assert json.loads(recs[0]["original_input"]) == {"legal_subject_judgement": "Contract_Law"} + log_rows = [json.loads(l) for l in cleaning.CHANGELOG.read_text(encoding="utf-8").splitlines()] + assert {(r["model"], r["field"]) for r in log_rows} == { + ("modx", "legal_subject_judgement"), ("mody", "plaintiffs_all_count"), + } diff --git a/tests/test_convert_goldenset.py b/tests/test_convert_goldenset.py new file mode 100644 index 0000000000000000000000000000000000000000..c186c26d4078461dbcebf0d5b00fc2d8fbbdd127 --- /dev/null +++ b/tests/test_convert_goldenset.py @@ -0,0 +1,84 @@ +import importlib.util +import sys +from pathlib import Path + +from openpyxl import Workbook + +# The converter is a standalone script; it lives at submission/ in the working +# repo and at the bundle root in the published code repo. +_ROOT = Path(__file__).resolve().parents[1] +_SCRIPT = next( + p for p in (_ROOT / "submission" / "convert_goldenset_to_jsonl.py", + _ROOT / "convert_goldenset_to_jsonl.py") + if p.exists() +) +_spec = importlib.util.spec_from_file_location("convert_goldenset_to_jsonl", _SCRIPT) +converter = importlib.util.module_from_spec(_spec) +sys.modules["convert_goldenset_to_jsonl"] = converter +_spec.loader.exec_module(converter) + + +def _workbook(rows: list[dict]) -> Path: + header = ["case_id", "Link", "full_text", *converter.SCHEMA_FIELDS, + "comment", "original_input"] + wb = Workbook() + ws = wb.active + ws.title = "GOLDENSET" + ws.append(header) + for row in rows: + ws.append([row.get(h) for h in header]) + return wb + + +def _convert(tmp_path: Path, rows: list[dict]) -> list[dict]: + path = tmp_path / "Goldenset_Test_final.xlsx" + _workbook(rows).save(path) + return converter.convert_workbook(path, fallback={}) + + +def test_scrape_only_rows_are_dropped(tmp_path: Path) -> None: + records = _convert(tmp_path, [ + {"case_id": "A1", "Link": "http://x", "full_text": "t"}, + {"case_id": "A2", "full_text": "t", "legal_subject_judgement": "Contract"}, + ]) + assert [r["case_id"] for r in records] == ["A2"] + + +def test_reviewed_rows_without_legal_subject_are_kept(tmp_path: Path) -> None: + records = _convert(tmp_path, [ + # reviewed: real values but no legal subject (the marker convention fails + # here) — kept, because its cells enter the scoring denominators + {"case_id": "B1", "full_text": "t", "trial_end_date": "2024-01-31", + "plaintiffs_all_count": 2, "legal_subject_judgement": "None"}, + # only 'None' markers: contributes no scoreable cell -> dropped, exactly + # like the scoring pipeline's row-inclusion rule + {"case_id": "B2", "full_text": "t", "legal_subject_judgement": "None"}, + ]) + assert [r["case_id"] for r in records] == ["B1"] + # the 'None' marker publishes as null + assert records[0]["legal_subject_judgement"] is None + assert records[0]["trial_end_date"] == "2024-01-31" + + +def test_zero_values_keep_a_row(tmp_path: Path) -> None: + records = _convert(tmp_path, [ + {"case_id": "C1", "full_text": "t", "plaintiff_loosing_share": 0}, + ]) + assert [r["case_id"] for r in records] == ["C1"] + assert records[0]["plaintiff_loosing_share"] == 0 + + +def test_currency_only_rows_are_dropped_like_scoring(tmp_path: Path) -> None: + # scoring ignores Currency_* for row inclusion; the converter must match + records = _convert(tmp_path, [ + {"case_id": "D1", "full_text": "t", "Currency_dispute_value_nominal": "CHF"}, + ]) + assert records == [] + + +def test_nonpecuniary_is_normalised_to_the_schema_literal(tmp_path: Path) -> None: + records = _convert(tmp_path, [ + {"case_id": "E1", "full_text": "t", "legal_subject_judgement": "Claim", + "dispute_value_nominal": "Nonpecuniary"}, + ]) + assert records[0]["dispute_value_nominal"] == "nonpecuniary" diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py index a9ab93f854c10d104d4ffe8f85115c82e41d49cf..5a974f603e55caa667e58449609afabeff596f9a 100644 --- a/tests/test_evaluation.py +++ b/tests/test_evaluation.py @@ -1,30 +1,39 @@ -from legex.evaluation import _normalise, _values_agree +from legex.evaluation import normalise, values_agree -def test_normalise_treats_none_literal_as_empty() -> None: - assert _normalise("None") == "" - assert _normalise("none") == "" - assert _normalise(None) == "" - assert _normalise("") == "" +def testnormalise_treats_none_literal_as_empty() -> None: + assert normalise("None") == "" + assert normalise("none") == "" + assert normalise(None) == "" + assert normalise("") == "" -def test_normalise_keeps_real_values() -> None: - assert _normalise("Claim") == "Claim" - assert _normalise("no_allocation_possible") == "no_allocation_possible" - assert _normalise(2.0) == "2" +def testnormalise_keeps_real_values() -> None: + assert normalise("Claim") == "Claim" + assert normalise("no_allocation_possible") == "no_allocation_possible" + assert normalise(2.0) == "2" -def test_values_agree_when_gold_zero() -> None: - assert _values_agree("0", "") - assert _values_agree("0", "0.0") - assert _values_agree("0", "0") +def testvalues_agree_when_gold_zero() -> None: + assert values_agree("0", "") + assert values_agree("0", "0.0") + assert values_agree("0", "0") -def test_values_agree_numeric_nonzero() -> None: - assert _values_agree("1500", "1500.0") - assert not _values_agree("1500", "1501") +def testvalues_agree_numeric_nonzero() -> None: + assert values_agree("1500", "1500.0") + assert not values_agree("1500", "1501") -def test_values_agree_rejects_zero_gold_with_nonempty_pred() -> None: - assert not _values_agree("0", "nonpecuniary") - assert not _values_agree("0", "100") +def testvalues_agree_rejects_zero_gold_with_nonempty_pred() -> None: + assert not values_agree("0", "nonpecuniary") + assert not values_agree("0", "100") + + +def testvalues_agree_controlled_tokens_case_insensitive() -> None: + assert values_agree("Nonpecuniary", "nonpecuniary", "dispute_value_nominal") + assert values_agree("nonpecuniary", "NONPECUNIARY", "dispute_value_nominal") + assert values_agree("No_Allocation_Possible", "no_allocation_possible") + assert not values_agree("Nonpecuniary", "150000", "dispute_value_nominal") + # Case-insensitivity is scoped to the controlled tokens, not free text. + assert not values_agree("Rental_Agreement", "rental_agreement") diff --git a/tests/test_goldenset_quality.py b/tests/test_goldenset_quality.py new file mode 100644 index 0000000000000000000000000000000000000000..e064c64df0076b3b7f28585ec749139de02580b4 --- /dev/null +++ b/tests/test_goldenset_quality.py @@ -0,0 +1,54 @@ +from legex.analysis.goldenset_quality import classify_cell + + +def test_empty_variants(): + for v in (None, "", "None", "none", "nan"): + assert classify_cell("court_cost_awarded_nominal", v) == "empty" + + +def test_money_valid_and_invalid(): + assert classify_cell("court_cost_awarded_nominal", "4000") == "valid" + assert classify_cell("court_cost_awarded_nominal", "4000.50") == "valid" + assert classify_cell("court_cost_awarded_nominal", 4000) == "valid" + # Text where a number is expected -> invalid. + assert classify_cell("court_cost_awarded_nominal", "keine Angabe") == "invalid" + assert classify_cell("court_cost_awarded_nominal", "CHF 4000") == "invalid" + + +def test_dispute_allows_nonpecuniary(): + assert classify_cell("dispute_value_nominal", "nonpecuniary") == "valid" + assert classify_cell("dispute_value_nominal", "150000.00") == "valid" + assert classify_cell("dispute_value_nominal", "two million") == "invalid" + + +def test_ratio_range(): + assert classify_cell("plaintiff_loosing_share", "0.6") == "valid" + assert classify_cell("plaintiff_loosing_share", "0") == "valid" + assert classify_cell("plaintiff_loosing_share", "1") == "valid" + assert classify_cell("plaintiff_loosing_share", "1.5") == "invalid" + assert classify_cell("plaintiff_loosing_share", "60%") == "invalid" + + +def test_counts_integer_nonnegative(): + assert classify_cell("plaintiffs_all_count", "2") == "valid" + assert classify_cell("plaintiffs_all_count", 3) == "valid" + assert classify_cell("plaintiffs_all_count", "2.5") == "invalid" + assert classify_cell("defendants_all_count", "two") == "invalid" + + +def test_dates_iso(): + assert classify_cell("trial_end_date", "2023-01-23") == "valid" + assert classify_cell("trial_end_date", "23.01.2023") == "invalid" + assert classify_cell("trial_start_date", "sometime in 2022") == "invalid" + + +def test_isic_vocabulary(): + assert classify_cell("plaintiff_no1_ISIC1_industry_category", "l_financial_insurance") == "valid" + assert classify_cell("defendant_no1_ISIC1_industry_category", "no_allocation_possible") == "valid" + assert classify_cell("plaintiff_no1_ISIC1_industry_category", "banking") == "invalid" + + +def test_legal_subject_free_text(): + assert classify_cell("legal_subject_judgement", "Contract_for_Work") == "valid" + # A bare number is a coding slip for a translated subject. + assert classify_cell("legal_subject_judgement", "12345") == "invalid" diff --git a/tests/test_harvey_ingest.py b/tests/test_harvey_ingest.py new file mode 100644 index 0000000000000000000000000000000000000000..71d09de5b8a3eddf77e85f26bb4ba63fc344d9bf --- /dev/null +++ b/tests/test_harvey_ingest.py @@ -0,0 +1,40 @@ +"""Harvey ingest: folder normalization and export-layout detection.""" +import pytest + +from legex.harvey import _folder_to_cc, _value_columns + + +# Old and new export names +@pytest.mark.parametrize( + "folder,cc", + [ + ("Hong_Kong", "hk"), + ("Hong Kong (new)", "hk"), + ("Brazil (new)", "br"), + ("Korea (new)", "kr"), + ("Kosovo (new)", "xk"), + ("Serbia (new)", "rs"), + ("Schweiz_final", "ch"), + ("Switzerland", "ch"), + ("Atlantis", None), + ], +) +def test_folder_to_cc(folder, cc): + assert _folder_to_cc(folder) == cc + + +def test_value_columns_new_layout(): + # 3 meta cols + 12 questions x 2 (Answer/Citations) = 27 + m = _value_columns(27) + assert m[5] == "legal_subject_judgement" + assert m[7] == "trial_start_date" + assert m[25] == "defendant_no1_ISIC1_industry_category" + assert 3 not in m + + +def test_value_columns_old_layout(): + # 3 meta cols + 12 questions x 4 = 51 + m = _value_columns(51) + assert m[7] == "legal_subject_judgement" + assert m[11] == "trial_start_date" + assert m[47] == "defendant_no1_ISIC1_industry_category" diff --git a/tests/test_iaa.py b/tests/test_iaa.py new file mode 100644 index 0000000000000000000000000000000000000000..278afc276726344a523933334667674ea2327190 --- /dev/null +++ b/tests/test_iaa.py @@ -0,0 +1,90 @@ +import csv + +from legex.analysis.iaa import ( + PRIMARY, + cohen_kappa, + pairwise_agreement, + summarise_by_field, + write_kappa_audit_csv, +) + + +def test_cohen_kappa_perfect_agreement() -> None: + pairs = [("a", "a"), ("b", "b"), ("a", "a"), ("b", "b")] + assert cohen_kappa(pairs) == 1.0 + + +def test_cohen_kappa_chance_agreement_is_zero() -> None: + # Both annotators split 50/50 but never coincide better than chance. + pairs = [("a", "a"), ("a", "b"), ("b", "a"), ("b", "b")] + k = cohen_kappa(pairs) + assert k is not None + assert abs(k) < 1e-9 + + +def test_cohen_kappa_single_category_undefined() -> None: + assert cohen_kappa([("a", "a"), ("a", "a")]) is None + + +def test_cohen_kappa_too_few_items() -> None: + assert cohen_kappa([("a", "b")]) is None + + +def _labels_two_annotators(): + # Two annotators, one country, three cases, two fields. + return { + (PRIMARY, "ch", "c1"): {"f1": "x", "f2": "1"}, + (PRIMARY, "ch", "c2"): {"f1": "y", "f2": "2"}, + (PRIMARY, "ch", "c3"): {"f1": "z", "f2": ""}, + ("de", "ch", "c1"): {"f1": "x", "f2": "1"}, + ("de", "ch", "c2"): {"f1": "WRONG", "f2": "2"}, + ("de", "ch", "c3"): {"f1": "z", "f2": ""}, + } + + +def test_pairwise_agreement_counts() -> None: + rows = pairwise_agreement(_labels_two_annotators()) + by_field = {r.field: r for r in rows} + assert by_field["f1"].n == 3 + assert by_field["f1"].n_agree_exact == 2 # c1, c3 agree; c2 differs + assert by_field["f2"].n_agree_exact == 3 + assert by_field["f2"].pct_exact == 1.0 + + +def test_pairwise_tolerant_handles_numeric() -> None: + labels = { + (PRIMARY, "ch", "c1"): {"amount": "1500"}, + ("de", "ch", "c1"): {"amount": "1500.0"}, + } + rows = pairwise_agreement(labels) + assert rows[0].n_agree_tolerant == 1 + + +def test_summarise_by_field_weights_by_n() -> None: + rows = pairwise_agreement(_labels_two_annotators()) + summary = summarise_by_field(rows) + assert summary["f2"]["pct_exact"] == 1.0 + assert 0.0 <= summary["f1"]["pct_exact"] <= 1.0 + + +def test_write_kappa_audit_csv(tmp_path) -> None: + labels = { + (PRIMARY, "ch", "c1"): {"f1": "x", "amount": "1500"}, + (PRIMARY, "ch", "c2"): {"f1": "y", "amount": "10"}, + ("de", "ch", "c1"): {"f1": "x", "amount": "1500.0"}, + ("de", "ch", "c2"): {"f1": "WRONG", "amount": "10"}, + } + path = tmp_path / "kappa_audit.csv" + n = write_kappa_audit_csv(labels, path) + rows = list(csv.DictReader(path.open(encoding="utf-8"))) + # 1 country, 1 pair, 2 shared cases, 2 fields -> 4 cells. + assert n == 4 == len(rows) + cell = {(r["case_id"], r["field"]): r for r in rows} + # f1 on c1 agrees both ways; amount 1500 vs 1500.0 is tolerant-only. + assert cell[("c1", "f1")]["decision_exact"] == "yes" + assert cell[("c1", "f1")]["decision_tolerant"] == "yes" + assert cell[("c1", "amount")]["decision_exact"] == "no" + assert cell[("c1", "amount")]["decision_tolerant"] == "yes" + # f1 on c2 disagrees both ways. + assert cell[("c2", "f1")]["decision_exact"] == "no" + assert cell[("c2", "f1")]["decision_tolerant"] == "no" diff --git a/tests/test_inference_jsonl.py b/tests/test_inference_jsonl.py new file mode 100644 index 0000000000000000000000000000000000000000..b454ff64400840a86ba77deb82cdd1e475c3f929 --- /dev/null +++ b/tests/test_inference_jsonl.py @@ -0,0 +1,54 @@ +"""Inference JSONL I/O: faithful round-trip and CSV-scoring equivalence.""" +import csv +import json + +from legex.evaluation import normalise +from legex.utils import inference_record, read_inference_jsonl, write_inference_jsonl + +COLUMNS = ["case_id", "link", "legal_subject_judgement", "dispute_value_nominal", + "trial_end_date", "plaintiffs_all_count", "model", "error"] + +ROWS = [ + {"case_id": "C-1", "link": "", "legal_subject_judgement": "Civil_Law", + "dispute_value_nominal": "nonpecuniary", "trial_end_date": "2023-05-04", + "plaintiffs_all_count": "2", "model": "harvey", "error": ""}, + # refusal prose + unicode + empty cells stay verbatim / become null + {"case_id": "C-2", "link": "", "legal_subject_judgement": "", + "dispute_value_nominal": "None (the amount is not stated) N/A", + "trial_end_date": "", "plaintiffs_all_count": "", "model": "harvey", + "error": "श्रेणी"}, +] + + +def test_empty_becomes_null_nonempty_stays_string(tmp_path): + rec = inference_record(ROWS[1], COLUMNS) + assert rec["link"] is None and rec["trial_end_date"] is None # empty -> null + assert rec["dispute_value_nominal"] == "None (the amount is not stated) N/A" # verbatim + assert rec["error"] == "श्रेणी" + + +def test_roundtripnormalises_identically_to_csv(tmp_path): + """Reading JSONL and reading the equivalent CSV must yield identical scored cells.""" + jsonl = tmp_path / "inf.jsonl" + write_inference_jsonl(jsonl, ROWS, COLUMNS) + + csv_path = tmp_path / "inf.csv" + with csv_path.open("w", encoding="utf-8", newline="") as f: + w = csv.DictWriter(f, fieldnames=COLUMNS) + w.writeheader() + w.writerows(ROWS) + + def scored(rows): + return [{k: normalise(v) for k, v in r.items()} for r in rows] + + with csv_path.open(encoding="utf-8", newline="") as f: + csv_rows = list(csv.DictReader(f)) + assert scored(read_inference_jsonl(jsonl)) == scored(csv_rows) + + +def test_jsonl_is_one_object_per_line(tmp_path): + p = tmp_path / "inf.jsonl" + write_inference_jsonl(p, ROWS, COLUMNS) + lines = p.read_text(encoding="utf-8").splitlines() + assert len(lines) == len(ROWS) + assert all(json.loads(line) for line in lines) # each line is valid JSON diff --git a/tests/test_legora_ingest.py b/tests/test_legora_ingest.py new file mode 100644 index 0000000000000000000000000000000000000000..3139f6dd662684730ceba5099d53455329d84978 --- /dev/null +++ b/tests/test_legora_ingest.py @@ -0,0 +1,208 @@ +"""Legora ingest: filename matching, header groups, dedup, and the end-to-end write.""" +import json +from datetime import datetime +from pathlib import Path + +import pytest +from openpyxl import Workbook + +from legex.config import settings +from legex.legora import ( + GROUP_MODELS, + LEGORA_FIELDS, + _clean, + _group_value_columns, + _infer_date_from_name, + ingest, + load_manifest, + loose_name, + match_document, + tight_name, +) + +FIELDS = [f for f in LEGORA_FIELDS if f != "case_id"] +GOLD_HEADER = ["case_id", "link", "full_text", *FIELDS, "Currency_dispute_value_nominal"] + + +@pytest.mark.parametrize( + "raw,expected", + [ + ("A.C._No._14000_[Formerly_CBD].pdf", "a_c_no_14000_formerly_cbd_pdf"), + ("G.R.Nos._274778,_275405&_276233.pdf", "g_r_nos_274778_275405_276233_pdf"), + ("最高法院_115_年度.txt", "最高法院_115_年度_txt"), + ], +) +def test_loose_name(raw, expected): + assert loose_name(raw) == expected + + +def test_tight_name_drops_all_separators(): + # Harvey drops the ",_" between id segments, joining "…4" and "3.19…" into + # "43.19…"; loose keeps a separator there, so only the tight pass matches. + bundle, mangled = "Rev_18654_3.19.1.4,_3.19.1.3_rev.pdf", "Rev_18654_3.19.1.43.19.1.3_rev.pdf" + assert tight_name(bundle) == tight_name(mangled) + assert loose_name(bundle) != loose_name(mangled) + + +def test_infer_date_from_name(): + assert _infer_date_from_name(Path("legora_2026-08-01.xlsx")) == "2026-08-01" + assert _infer_date_from_name(Path("legora.xlsx")) is None + + +@pytest.mark.parametrize( + "value,expected", + [ + (None, ""), + ("—", ""), + (" 1 ", "1"), + (datetime(2025, 5, 26), "2025-05-26"), + (130.0, "130"), + (0.5, "0.5"), + (54167951, "54167951"), + ("Nonpecuniary", "nonpecuniary"), + ], +) +def test_clean(value, expected): + assert _clean(value) == expected + + +def _manifest(tmp_path: Path) -> Path: + p = tmp_path / "manifest.csv" + p.write_text( + "country,cc,file,case_id,harvey_answer\n" + 'Testland,zz,"AB-1_2020.pdf","AB-1/2020",yes\n' + 'Testland,zz,"CD_2_2021.pdf","CD 2/2021",yes\n' + 'Testland,zz,"X_&_Y.pdf","X & Y",no\n', + encoding="utf-8", + ) + return p + + +def test_match_document_passes(tmp_path): + lookups = load_manifest(_manifest(tmp_path)) + assert match_document("AB-1_2020.pdf", lookups) == ("zz", "AB-1/2020") # exact + assert match_document("X_&_Y.pdf", lookups) == ("zz", "X & Y") # loose + assert match_document("CD2_2021.pdf", lookups) == ("zz", "CD 2/2021") # tight + assert match_document("MANIFEST.csv", lookups) is None # junk + + +def _header(groups: int = 2) -> list[str]: + cols = ["Document", "Document ID"] + for _ in range(groups): + for f in LEGORA_FIELDS: + cols += [f, f"{f} (reasoning)"] + return cols + + +def test_group_value_columns_two_groups(): + cols = _group_value_columns(_header(2)) + assert set(cols) == {1, 2} + assert cols[1]["case_id"] == 2 + assert cols[1]["legal_subject_judgement"] == 4 + assert cols[2]["case_id"] == 2 + 2 * len(LEGORA_FIELDS) + assert cols[2]["defendant_no1_ISIC1_industry_category"] == len(_header(2)) - 2 + + +def test_group_value_columns_missing_field_raises(): + header = [c for c in _header(1) if c != "trial_start_date"] + with pytest.raises(ValueError, match="missing field"): + _group_value_columns(header) + + +def test_group_value_columns_unbalanced_raises(): + header = _header(2) + ["case_id"] + with pytest.raises(ValueError, match="unbalanced"): + _group_value_columns(header) + + +@pytest.fixture +def env(tmp_path, monkeypatch): + monkeypatch.setattr(settings, "data_dir", tmp_path) + wb = Workbook() + ws = wb.active + ws.title = "GOLDENSET" + ws.append(GOLD_HEADER) + wb.save(tmp_path / "Vorlage.xlsx") + + (tmp_path / "zz").mkdir() + gs = Workbook() + ws = gs.active + ws.title = "GOLDENSET" + ws.append(GOLD_HEADER) + ws.append(["AB-1/2020", "", "text", *[""] * len(FIELDS), ""]) + ws.append(["CD 2/2021", "", "text", *[""] * len(FIELDS), ""]) + gs.save(tmp_path / "zz" / "Goldenset_Testland.xlsx") + return tmp_path + + +def _export_row(doc: str, g1: dict, g2: dict) -> list: + row = [doc, f"uuid-{doc}"] + for values in (g1, g2): + for f in LEGORA_FIELDS: + row += [values.get(f, "—"), "reasoning text"] + return row + + +def _write_export(path: Path, rows: list[list]) -> None: + wb = Workbook() + ws = wb.active + ws.title = "Sheet1" + ws.append(_header(2)) + for r in rows: + ws.append(r) + wb.save(path) + + +def test_ingest_end_to_end(env): + xlsx = env / "legora_2026-08-01.xlsx" + _write_export(xlsx, [ + _export_row( + "AB-1_2020.pdf", + {"legal_subject_judgement": "Contract_Law", "trial_end_date": datetime(2025, 5, 26), + "plaintiffs_all_count": 130.0}, + {"legal_subject_judgement": "Tort_Law", "dispute_value_nominal": "nonpecuniary"}, + ), + # duplicate run of the same document: g2 disagrees on one field + _export_row( + "AB-1_2020.pdf", + {"legal_subject_judgement": "Contract_Law", "trial_end_date": datetime(2025, 5, 26), + "plaintiffs_all_count": 130.0}, + {"legal_subject_judgement": "Property_Law", "dispute_value_nominal": "nonpecuniary"}, + ), + _export_row("CD2_2021.pdf", {"plaintiff_loosing_share": 0.5}, {}), # tight match + _export_row("MANIFEST.csv", {}, {}), # junk, dropped + ]) + conflicts = env / "conflicts.jsonl" + ingest(xlsx, _manifest(env), conflicts_out=conflicts) + + for model in GROUP_MODELS.values(): + out = env / "zz" / f"Goldenset_Testland_v3_full_text_{model}.jsonl" + recs = [json.loads(l) for l in out.read_text(encoding="utf-8").splitlines()] + assert [r["case_id"] for r in recs] == ["AB-1/2020", "CD 2/2021"] # gold ids, junk gone + assert all(r["model"] == model for r in recs) + assert all(r["inference_date"] == "2026-08-01" for r in recs) + keys = list(recs[0]) + assert keys.index("inference_date") == keys.index("model") + 1 + + recs1 = [json.loads(l) for l in + (env / "zz" / "Goldenset_Testland_v3_full_text_legora-1.jsonl").read_text().splitlines()] + assert recs1[0]["legal_subject_judgement"] == "Contract_Law" # first occurrence kept + assert recs1[0]["trial_end_date"] == "2025-05-26" # datetime -> ISO + assert recs1[0]["plaintiffs_all_count"] == "130" # integral float -> int + assert recs1[0]["trial_start_date"] is None # em-dash -> null + assert recs1[1]["plaintiff_loosing_share"] == "0.5" + + clashes = [json.loads(l) for l in conflicts.read_text(encoding="utf-8").splitlines()] + assert clashes == [{ + "model": "legora-2", "country": "zz", "case_id": "AB-1/2020", + "field": "legal_subject_judgement", "kept": "Tort_Law", "alternative": "Property_Law", + }] + + +def test_ingest_discovered_by_models_present(env): + from legex.analysis.aggregate import models_present + + xlsx = env / "legora_2026-08-01.xlsx" + _write_export(xlsx, [_export_row("AB-1_2020.pdf", {}, {})]) + ingest(xlsx, _manifest(env), conflicts_out=env / "conflicts.jsonl") + assert models_present("zz") == ["legora-1", "legora-2"] diff --git a/tests/test_published.py b/tests/test_published.py new file mode 100644 index 0000000000000000000000000000000000000000..6a20e6fbcfc08a1e0b89a77478a784e333daa119 --- /dev/null +++ b/tests/test_published.py @@ -0,0 +1,80 @@ +import json +from pathlib import Path + +import pytest + +from legex import published + + +def _write_gold(tmp_path: Path, cc: str, records: list[dict]) -> Path: + path = tmp_path / cc / f"goldenset_{cc}.jsonl" + path.parent.mkdir(parents=True) + path.write_text("\n".join(json.dumps(r) for r in records) + "\n", encoding="utf-8") + return path + + +def _record(case_id: str, annotator_id: str, **labels) -> dict: + rec = {"case_id": case_id, "link": None, "full_text": "text"} + rec.update({f: None for f in published.SCHEMA_FIELDS}) + rec.update(labels) + rec.update({"comment": None, "original_input": "{}", "annotator_id": annotator_id}) + return rec + + +@pytest.fixture +def gold_dir(tmp_path: Path) -> Path: + _write_gold(tmp_path, "ch", [ + _record("1A_1/2024", "aaaa000000", legal_subject_judgement="Rental", + plaintiff_loosing_share=0.5, plaintiffs_all_count=1), + _record("1A_2/2024", "aaaa000000", legal_subject_judgement="Contract", + court_cost_awarded_nominal=500), + # reannotations, appended after the primary rows + _record("1A_1/2024", "bbbb111111", legal_subject_judgement="Rental_Agreement", + plaintiff_loosing_share=0), + # empty reannotation row: dropped by the annotator-label loader + _record("1A_2/2024", "bbbb111111"), + ]) + return tmp_path + + +def test_label_fields_are_the_11_evaluated_fields() -> None: + assert len(published.LABEL_FIELDS) == 11 + assert "Currency_dispute_value_nominal" not in published.LABEL_FIELDS + assert published.LABEL_FIELDS[0] == "legal_subject_judgement" + + +def test_load_gold_labels_takes_first_row_per_case(gold_dir: Path) -> None: + columns, gold = published.load_gold_labels(gold_dir, "ch") + assert columns == list(published.LABEL_FIELDS) + assert set(gold) == {"1A_1/2024", "1A_2/2024"} + # primary row wins over the appended reannotation; values are normalised strings + assert gold["1A_1/2024"]["legal_subject_judgement"] == "Rental" + assert gold["1A_1/2024"]["plaintiff_loosing_share"] == "0.5" + assert gold["1A_1/2024"]["plaintiffs_all_count"] == "1" + assert gold["1A_2/2024"]["court_cost_awarded_nominal"] == "500" + assert gold["1A_2/2024"]["trial_start_date"] == "" + + +def test_load_annotator_labels_roles_and_filtering(gold_dir: Path) -> None: + labels = published.load_annotator_labels(gold_dir, ["ch"]) + keys = set(labels) + # first row per case = primary; reannotation keyed by its hash; the + # all-empty reannotation row is dropped + assert (published.PRIMARY, "ch", "1a_1_2024") in keys + assert (published.PRIMARY, "ch", "1a_2_2024") in keys + assert ("bbbb111111", "ch", "1a_1_2024") in keys + assert ("bbbb111111", "ch", "1a_2_2024") not in keys + assert labels[("bbbb111111", "ch", "1a_1_2024")]["plaintiff_loosing_share"] == "0" + + +def test_inference_file_mapping(tmp_path: Path) -> None: + assert published.inference_file(tmp_path, "ch", "harvey-2").name == "inference_harvey_2.jsonl" + assert published.inference_file(tmp_path, "ch", "gemini/gemini-3.1-flash-lite").name == \ + "inference_gemini.jsonl" + assert published.inference_file(tmp_path, "ch", "legora-1").name == "inference_legora_1.jsonl" + with pytest.raises(KeyError): + published.inference_file(tmp_path, "ch", "claude-fable-5") + + +def test_countries_with_gold(gold_dir: Path) -> None: + assert published.countries_with_gold(gold_dir) == ["ch"] diff --git a/tests/test_refusal.py b/tests/test_refusal.py new file mode 100644 index 0000000000000000000000000000000000000000..d84316f55eb5488faee03db7ab0003414aad3ad9 --- /dev/null +++ b/tests/test_refusal.py @@ -0,0 +1,143 @@ +"""Refusal detection, value resolution, and the cleaning provenance format.""" +import json + +import pytest + +from legex.evaluation.comparison import classify_cell, normalise, refusal_reason, resolve +from legex.evaluation.cleaning import format_comment, format_original_input + +REFUSALS = [ + ("The document does not state a specific date. N/A", "trial_start_date"), + ("None (the judgment does not contain a party designation block)", "plaintiffs_all_count"), + ("Based on my review, I cannot determine the amount in dispute.", "dispute_value_nominal"), + ("N/A", "trial_end_date"), + ("not specified", "trial_start_date"), +] + +KEEPERS = [ + ("nonpecuniary", "dispute_value_nominal"), + ("no_allocation_possible", "plaintiff_no1_ISIC1_industry_category"), + ("Employment_Law/Discrimination", "legal_subject_judgement"), + ("2024-01-21", "trial_end_date"), + ("25400000", "dispute_value_nominal"), + ("1", "plaintiffs_all_count"), + ("1.0", "plaintiff_loosing_share"), # already a clean float → kept as-is + ("0.86", "plaintiff_loosing_share"), + ("l_financial_insurance", "defendant_no1_ISIC1_industry_category"), +] + +# (raw, column, expected canonical) — a single value recoverable after stripping +# formatting noise / citations / backticks (never a digit scraped from prose). +RECOVERABLE = [ + ("20'000", "court_cost_awarded_nominal", "20000"), + ("1 000 000", "dispute_value_nominal", "1000000"), + ("2024/01/21", "trial_end_date", "2024-01-21"), + ("1\n```", "plaintiffs_all_count", "1"), + ("[1]\n\n2025-08-19", "trial_end_date", "2025-08-19"), + ("1950\n[1] [2]", "court_cost_awarded_nominal", "1950"), + # 2026-08 Harvey run: standalone English prose dates despite YYYY-MM-DD rule. + ("March 19, 2026", "trial_end_date", "2026-03-19"), + ("30 September 2020", "trial_end_date", "2020-09-30"), + # 2026-08 Harvey run: ISIC codes with the sector letter capitalised. + ("L_financial_insurance", "defendant_no1_ISIC1_industry_category", "l_financial_insurance"), + ("No_allocation_possible", "plaintiff_no1_ISIC1_industry_category", "no_allocation_possible"), + # Currency-decorated clean amounts. + ("CHF 9'728'400.00", "dispute_value_nominal", "9728400"), + ("30'000.--", "dispute_value_nominal", "30000"), + ("6'500 fr.", "court_cost_awarded_nominal", "6500"), + ("22.201,76 EUR", "dispute_value_nominal", "22201.76"), +] + +# Prose (has letters) → never auto-recovered, even with a number present → review. +PROSE_TO_REVIEW = [ + ("1.0\n\nThe complaint was declared inadmissible.", "plaintiff_loosing_share"), + ("The operative part imposes court costs of 800 Swiss Francs.[1]\n\n800", + "court_cost_awarded_nominal"), + ("Greer (No. 19-8709): 1.0\n\nGary (No. 20-444): 1.0", "plaintiff_loosing_share"), + # A number with a parenthetical qualifier is still prose — a human decides. + ("1 (total plaintiffs/claimants/appellants)", "plaintiffs_all_count"), +] + +# Grouped integers without a decimal part: thousands- vs decimal-separator is +# ambiguous ('5.000 €' = 5000 EU or 5.0 US) → review, never auto-recovered. +AMBIGUOUS_AMOUNTS_TO_REVIEW = [ + ("5.000 €", "dispute_value_nominal"), + ("538,183 euro", "dispute_value_nominal"), +] + + +@pytest.mark.parametrize("text,col", REFUSALS) +def test_refusals_go_to_review(text, col): + status, _, reason = resolve(text, col) + assert status == "review" and reason + + +@pytest.mark.parametrize("text,col", KEEPERS) +def test_valid_values_kept(text, col): + status, value, _ = resolve(text, col) + assert status == "valid" and value == text + + +@pytest.mark.parametrize("text,col,canon", RECOVERABLE) +def test_recoverable_values_canonicalised(text, col, canon): + status, value, _ = resolve(text, col) + assert status == "recovered" and value == canon + + +# Formatting-only recoveries the scorer already parses identically → must be score-neutral. +# (Noise-stripped dates like "[1]\n\n2025-08-19" are intentional corrections, not neutral.) +NEUTRAL_RECOVERABLE = [ + ("20'000", "court_cost_awarded_nominal", "20000"), + ("1 000 000", "dispute_value_nominal", "1000000"), + ("2024/01/21", "trial_end_date", "2024-01-21"), + ("1\n```", "plaintiffs_all_count", "1"), + ("1950\n[1] [2]", "court_cost_awarded_nominal", "1950"), +] + + +@pytest.mark.parametrize("text,col,canon", NEUTRAL_RECOVERABLE) +def test_recovery_is_score_neutral(text, col, canon): + for gold in ("", canon, "999", "2024-01-21"): + assert classify_cell(gold, normalise(text), col) == classify_cell(gold, canon, col) + + +@pytest.mark.parametrize("text,col", PROSE_TO_REVIEW) +def test_prose_with_number_goes_to_review_not_garbage(text, col): + # Never scrape a number out of prose into the data — a human decides. + status, _, _ = resolve(text, col) + assert status == "review" + + +@pytest.mark.parametrize("text,col", AMBIGUOUS_AMOUNTS_TO_REVIEW) +def test_ambiguous_grouped_amounts_go_to_review(text, col): + status, _, _ = resolve(text, col) + assert status == "review" + + +def test_prose_without_value_or_marker_still_reviewed(): + status, _, reason = resolve( + "Based on the decision, the respondent substantially prevailed on the merits.", + "plaintiff_loosing_share", + ) + assert status == "review" and reason == "no recoverable value" + + +def test_normalise_is_pure(): + assert normalise("The document does not state a date. N/A") != "" + assert refusal_reason("nonpecuniary", "dispute_value_nominal") is None + + +def test_provenance_format(): + changes = { + "trial_start_date": ("Not specified ", ""), + "court_cost_awarded_nominal": ("20'000", "20000"), + } + assert format_comment(changes) == ( + "The trial_start_date was sanitized from 'Not specified ' to 'empty (removed)'. " + "The court_cost_awarded_nominal was sanitized from '20'000' to '20000'." + ) + assert json.loads(format_original_input(changes)) == { + "trial_start_date": "Not specified ", "court_cost_awarded_nominal": "20'000", + } + assert format_comment({}) is None + assert format_original_input({}) == "{}" diff --git a/tests/test_utils.py b/tests/test_utils.py index 702f9c42ee6ed2607b09df4bb54d81ce7e527c87..f677b48220dff4f1042e63a9c6c81d062d6b944e 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -5,7 +5,7 @@ from openpyxl import Workbook, load_workbook from legex.models.base import Case from legex.utils import ( - classified_csv_path, + inference_path, model_filename_slug, random_sample, write_goldenset_xlsx, @@ -20,9 +20,9 @@ def test_model_filename_slug() -> None: assert model_filename_slug("gemini/gemini-3.1-flash-lite") == "gemini_gemini-3.1-flash-lite" -def test_classified_csv_path_includes_model_slug() -> None: - pred = classified_csv_path("de", "v1", "full_text", "gemini/gemini-3.1-flash-lite") - assert pred.name == "Goldenset_Deutschland_v1_full_text_gemini_gemini-3.1-flash-lite.csv" +def test_inference_path_includes_model_slug() -> None: + pred = inference_path("de", "v3", "full_text", "gemini/gemini-3.1-flash-lite") + assert pred.name == "Goldenset_Deutschland_v3_full_text_gemini_gemini-3.1-flash-lite.jsonl" def test_random_sample_is_deterministic_and_sized() -> None: diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000000000000000000000000000000000000..948ba9cf8323a5f5f5a01c924ea3caeb553be697 --- /dev/null +++ b/uv.lock @@ -0,0 +1,3955 @@ +version = 1 +revision = 1 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version < '3.12' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version < '3.12' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", +] + +[[package]] +name = "aiohappyeyeballs" +version = "2.7.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ce/f4/eec0465c2f67b2664688d0240b3212d5196fd89e741df67ddb81f8d35658/aiohappyeyeballs-2.7.1.tar.gz", hash = "sha256:065665c041c42a5938ed220bdcd7230f22527fbec085e1853d2402c8a3615d9d", size = 24757 } +wheels = [ + { url = 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