README: list data/ and repair_pipeline_package/, with usage

README.md

@@ -14,7 +14,9 @@ pretty_name: ProseOnlyRepair — Evaluation Analysis Scripts
 # ProseOnlyRepair — Evaluation Analysis Scripts
 
 Anonymized evaluation/analysis scripts that reproduce the tables and
-figures in the *Repair-First* paper (under double-blind review).
+figures in the *Repair-First* paper (under double-blind review), the
+per-task results behind them (`data/`), and the implementation of the
+ten-stage repair pipeline (`repair_pipeline_package/`).
 
 ## Contents
 
@@ -25,6 +27,10 @@ figures in the *Repair-First* paper (under double-blind review).
 | `build_12variant_analysis.py` | 12-variant superset including POST-only (used in appendix) |
 | `make_paper_figures.py` | Regenerates Figures 1 and 2 from eval JSONs |
 | `requirements.txt` | Python dependencies (lm-evaluation-harness 0.4.12, datasets <4.0, etc.) |
+| `data/results.py` | Per-task results: 27 downstream tasks and 11 Paloma corpora for each tier (HQ, MQ, LQ) and configuration (V1: surface repair; V2: surface + linguistic repair) against Raw (mean over three pre-training seeds), with noise bands, deltas and z-scores; corpus statistics, recovery yield and repair-operation shares |
+| `data/make_tables.py` | Writes the appendix LaTeX tables (`appendix_tables.tex`, `table_anatomy.tex`, `table_summary.tex`, `appendix_analysis_tables.tex`) from `data/results.py` |
+| `data/make_figures.py` | Writes the result figures (`figures/fig1_teaser*.pdf`, `fig3_heatmaps*.pdf`, `fig4_paloma_split.pdf`, `fig5_ops.pdf`) from `data/results.py` |
+| `repair_pipeline_package/` | Implementation of the ten-stage repair pipeline (see below) |
 
 ## Variant naming
 
@@ -51,12 +57,58 @@ python3 build_12variant_analysis.py
 python3 make_paper_figures.py
 ```
 
+The scripts in `data/` need no evaluation JSONs (the per-task results are in
+`data/results.py`); run them from the repository root, and they write to it:
+
+```bash
+python3 data/results.py        # win/loss tallies per tier and configuration
+python3 data/make_tables.py    # ./appendix_tables.tex, ./table_anatomy.tex, ./table_summary.tex, ./appendix_analysis_tables.tex
+python3 data/make_figures.py   # ./figures/*.pdf
+```
+
 The accompanying evaluation result JSONs (12 variants × prose + paloma
 suites) are released in the paper's supplementary materials zip.
 
+## Repair pipeline (`repair_pipeline_package/`)
+
+The ten-stage repair pipeline in its masking-first form: mixed-content
+masking runs before the ftfy repair.
+
+| Path | Role |
+|---|---|
+| `pipeline.py` | `RepairPipeline`: runs the stages in order and keeps an audit trail of every change |
+| `stages/stage01_encoding.py` … `stage10_restore.py` | encoding detection, masking, ftfy repair, artifact removal, line-level repair, character normalization, whitespace, OCR repair, quality validation, restoring the masked segments |
+| `masking/` | masking of non-prose segments (DOM path, plain-text path, sentinels) |
+| `typed_repair/` | type-specific repair of the masked code, math and structured segments |
+| `quality/ensemble.py` | the six-tool quality ensemble |
+| `config.py` | model paths (under `$REPAIR_MODELS_ROOT`, default `/data/models`) and thresholds |
+| `document.py`, `run.py` | the document and audit data model; the command-line entry point |
+
+From the repository root:
+
+```bash
+python -m repair_pipeline_package.run input.html -o out.jsonl                      # one file
+python -m repair_pipeline_package.run ./corpus_dir -o out.jsonl                    # every file under a directory
+python -m repair_pipeline_package.run scan.txt --source-hint ocr --http-charset utf-8
+```
+
+```python
+from repair_pipeline_package import RepairPipeline
+doc = RepairPipeline().run(raw_bytes)      # doc.text, doc.quality_passed, doc.audit, ...
+```
+
+Output is JSONL, one document per line: `path`, `encoding`, `quality_passed`,
+`quality_score`, `discarded`, `discard_reason`, `n_segments`, `audit`, `text`.
+Every model and library a stage uses (fastText, LanguageTool, NLTK, SymSpell,
+ByT5, KenLM, Magika, the masking classifiers, BeautifulSoup, ftfy) is optional:
+when one is missing, the stage logs a warning and falls back to a heuristic or
+skips that step. Model files go under `$REPAIR_MODELS_ROOT` at the paths in
+`config.py`. The docstrings call the package `repair_pipeline`; use the folder
+name as above, or rename the folder.
+
 ## Notes
 
-- All scripts are CPU-only Python and require no GPU.
+- The analysis scripts are CPU-only Python and require no GPU.
 - Scripts are deterministic: identical inputs → identical outputs.
 - The 28-task prose suite + 11-corpus Paloma BPB panel are evaluated
   with `lm-evaluation-harness==0.4.12` (vLLM backend).