---
license: apache-2.0
language:
- en
tags:
- pretraining
- data-curation
- evaluation
- llm
- common-crawl
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).

## 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.) |

## 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

```bash
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 accompanying evaluation result JSONs (12 variants × prose + paloma
suites) are released in the paper's supplementary materials zip.

## Notes

- All 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.