# Fine-tuning the clause classifier (simple guide)

Goal: teach the DeBERTa model our exact clause-tagging task using the CUAD
dataset, raising accuracy from a measured **0.61** to an expected
**0.75–0.85** macro-F1.

You do NOT need to understand the internals. It's three commands.

## One-time setup

```bash
# from the repo root
python3 -m venv .venv
.venv/bin/pip install -r backend/requirements.txt
.venv/bin/pip install -r backend/requirements-ml.txt   # torch, transformers, ...
.venv/bin/python backend/scripts/download_cuad.py        # downloads CUAD (~once)
```

## The three steps

```bash
cd backend

# 1. Build training data from CUAD (QA spans -> labelled clauses)
../.venv/bin/python -m scripts.prepare_cuad
#   -> data/cuad/train.jsonl, val.jsonl, labels.json

# 2. Train (writes backend/models/clause-clf/)
../.venv/bin/python -m scripts.train_classifier --epochs 3
#   prints loss per epoch + a quick validation F1

# 3. Measure against CUAD, compare to the old number
../.venv/bin/python -m eval.run_eval --classifier finetuned --limit 50
```

## Use the fine-tuned model in the app

```bash
CLASSIFIER=finetuned ../.venv/bin/uvicorn app.main:app --port 8000
```
If no trained model exists yet, the app safely falls back — set
`CLASSIFIER=zeroshot` (DeBERTa, no training) or `rules` (no model at all).

## Tips

- **No GPU?** It still runs on a Mac, just slower. For a quick smoke test:
  `--device=cpu --batch=4 --max_len=256 --limit=800`.
- **Apple Silicon (MPS) out-of-memory?** DeBERTa at batch 8 / seq 512 can
  exhaust unified memory. Use `--device=cpu` (most reliable), or keep MPS but
  lower `--batch=4 --max_len=256`.
- **Fastest full run:** upload `prepare_cuad`'s output to Google Colab (free
  GPU) and run step 2 there, then copy `backend/models/clause-clf/` back.
- **Knobs:** `--model` (default `microsoft/deberta-v3-base`), `--epochs`,
  `--batch`, `--lr`. Defaults are sensible; only change if you know why.

## What gets produced

```
backend/models/clause-clf/      the trained model (loaded by app/finetuned.py)
data/cuad/train.jsonl, val.jsonl training data (git-ignored)
```

## Why not LegalBERT?

We deliberately fine-tune **DeBERTa**, not LegalBERT — same effort, better
result, cleaner licence. See [DECISIONS.md](DECISIONS.md) §2.