anonymous-md/ProseOnlyRepair_Codes

ProseOnlyRepair — Evaluation Analysis Scripts

Anonymized evaluation/analysis scripts that reproduce the tables and 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

File	Purpose
build_paper_tables.py	Headline tables (per-task accuracy, Paloma BPB, repair deltas)
build_8variant_analysis.py	8-variant repair-effect-by-tier analysis (POST-only excluded)
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

Each evaluation result directory follows {cluster}_{tier}_new[_repaired[_upsampled]]_results:

Token	Meaning
clusterA_	Pretrained on Cluster A (anonymous identifier)
clusterB_	Pretrained on Cluster B (anonymous identifier)
_lq / _mq / _hq	Low / Medium / High quality CommonCrawl tier
_new	PRE: pre-repair baseline
_new_repaired	POST: post-repair, token budget held by truncation
_new_repaired_upsampled	POST+UP: post-repair + upsampled to original token budget

How to use

pip install -r requirements.txt
# Place evaluation result JSONs under ./eval_results/{prose,paloma}/
python3 build_paper_tables.py
python3 build_8variant_analysis.py
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:

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:

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

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

• 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).
• Decoding: greedy, max-len 256 except CoQA/SQuADv2 (512).

This release is anonymous for double-blind review and will be re-released with author and affiliation metadata after the review period.