docs: add fine-tune classifier design spec
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docs/superpowers/specs/2026-03-15-finetune-classifier-design.md
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docs/superpowers/specs/2026-03-15-finetune-classifier-design.md
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# Fine-tune Email Classifier — Design Spec
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**Date:** 2026-03-15
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**Status:** Approved
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**Scope:** Avocet — `scripts/`, `app/api.py`, `web/src/views/BenchmarkView.vue`
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---
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## Problem
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The benchmark baseline shows zero-shot macro-F1 of 0.366 for the best models (`deberta-zeroshot`, `deberta-base-anli`). Zero-shot inference cannot improve with more labeled data. Fine-tuning the fastest models (`deberta-small` at 111ms, `bge-m3` at 123ms) on the growing labeled dataset is the path to meaningful accuracy gains.
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---
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## Constraints
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- 501 labeled samples after dropping 2 non-canonical `profile_alert` rows
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- Heavy class imbalance: `digest` 29%, `neutral` 26%, `new_lead` 2.6%, `survey_received` 3%
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- 8.2 GB VRAM (shared with Peregrine vLLM during dev)
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- Target models: `cross-encoder/nli-deberta-v3-small` (100M params), `MoritzLaurer/bge-m3-zeroshot-v2.0` (600M params)
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- Output: local `models/avocet-{name}/` directory
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- UI-triggerable via web interface (SSE streaming log)
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---
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## Architecture
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### New file: `scripts/finetune_classifier.py`
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CLI entry point for fine-tuning. Designed so stdout is SSE-streamable (all prints use `flush=True`).
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```
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python scripts/finetune_classifier.py --model deberta-small [--epochs 5]
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```
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Supported `--model` values: `deberta-small`, `bge-m3`
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**Model registry** (internal to this script):
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| Key | Base model ID | Max tokens | Gradient checkpointing |
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|-----|--------------|------------|----------------------|
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| `deberta-small` | `cross-encoder/nli-deberta-v3-small` | 512 | No |
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| `bge-m3` | `MoritzLaurer/bge-m3-zeroshot-v2.0` | 512 | Yes |
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### Modified: `scripts/classifier_adapters.py`
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Add `FineTunedAdapter(ClassifierAdapter)`:
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- Takes `model_dir: str` (path to a `models/avocet-*/` checkpoint)
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- Loads via `pipeline("text-classification", model=model_dir)`
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- `classify()` returns the top predicted label directly (single forward pass — no per-label NLI scoring loop)
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- Expected inference speed: ~10–20ms/email vs 111–338ms for zero-shot
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### Modified: `scripts/benchmark_classifier.py`
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At startup, scan `models/` for subdirectories containing `training_info.json`. Register each as a dynamic entry in the model registry using `FineTunedAdapter`. Silently skips if `models/` does not exist. Existing CLI behaviour unchanged.
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### Modified: `app/api.py`
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Two new GET endpoints (GET required for `EventSource` compatibility):
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**`GET /api/finetune/status`**
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Scans `models/` for `training_info.json` files. Returns:
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```json
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[
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{
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"name": "avocet-deberta-small",
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"base_model": "cross-encoder/nli-deberta-v3-small",
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"val_macro_f1": 0.712,
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"timestamp": "2026-03-15T12:00:00Z",
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"sample_count": 401
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}
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]
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```
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Returns `[]` if no fine-tuned models exist.
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**`GET /api/finetune/run?model=deberta-small&epochs=5`**
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Spawns `finetune_classifier.py` via the `job-seeker-classifiers` Python binary. Streams stdout as SSE `{"type":"progress","message":"..."}` events. Emits `{"type":"complete"}` on clean exit, `{"type":"error","message":"..."}` on non-zero exit.
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### Modified: `web/src/views/BenchmarkView.vue`
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**Trained models badge row** (top of view, conditional on fine-tuned models existing):
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Shows each fine-tuned model name + val macro-F1 chip.
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**Fine-tune section** (collapsible, below benchmark charts):
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- Dropdown: `deberta-small` | `bge-m3`
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- Number input: epochs (default 5, range 1–20)
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- Run button → streams into existing log component
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- On `complete`: auto-triggers `/api/benchmark/run` (with `--save`) so charts update immediately
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---
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## Training Pipeline
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### Data preparation
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1. Load `data/email_score.jsonl`
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2. Drop rows where `label` not in canonical `LABELS` (removes `profile_alert` etc.)
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3. Input text: `f"{subject} [SEP] {body[:400]}"` — fits within 512 tokens for both target models
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4. Stratified 80/20 train/val split via `sklearn.model_selection.train_test_split(stratify=labels)`
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### Class weighting
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Compute per-class weights: `total_samples / (n_classes × class_count)`. Pass to a `WeightedTrainer` subclass that overrides `compute_loss`:
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```python
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class WeightedTrainer(Trainer):
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def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
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labels = inputs.pop("labels")
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outputs = model(**inputs)
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loss = F.cross_entropy(outputs.logits, labels, weight=self.class_weights)
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return (loss, outputs) if return_outputs else loss
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```
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### Model setup
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```python
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AutoModelForSequenceClassification.from_pretrained(
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base_model_id,
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num_labels=10,
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ignore_mismatched_sizes=True, # drops NLI head, initialises fresh 10-class head
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id2label=id2label,
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label2id=label2id,
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)
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```
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`ignore_mismatched_sizes=True` is required because the NLI head (3 classes) is being replaced with a 10-class head.
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### Training config
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| Hyperparameter | Value |
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|---------------|-------|
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| Epochs | 5 (default, CLI-overridable) |
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| Batch size | 16 |
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| Learning rate | 2e-5 |
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| LR schedule | Linear with 10% warmup |
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| Optimizer | AdamW |
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| Eval strategy | Every epoch |
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| Best checkpoint | By val macro-F1 |
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| Early stopping | 3 epochs without improvement |
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| Gradient checkpointing | bge-m3 only |
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### Output
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Saved to `models/avocet-{name}/`:
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- Model weights + tokenizer (standard HuggingFace format)
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- `training_info.json`:
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```json
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{
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"name": "avocet-deberta-small",
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"base_model_id": "cross-encoder/nli-deberta-v3-small",
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"timestamp": "2026-03-15T12:00:00Z",
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"epochs_run": 5,
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"val_macro_f1": 0.712,
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"val_accuracy": 0.798,
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"sample_count": 401,
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"label_counts": { "digest": 116, "neutral": 104, ... }
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}
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```
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---
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## Data Flow
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```
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email_score.jsonl
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│
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▼
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finetune_classifier.py
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├── drop non-canonical labels
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├── stratified 80/20 split
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├── tokenize (subject [SEP] body[:400])
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├── compute class weights
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├── WeightedTrainer (HuggingFace Trainer subclass)
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└── save → models/avocet-{name}/
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│
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├── FineTunedAdapter (classifier_adapters.py)
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│ └── pipeline("text-classification")
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│ └── ~10–20ms/email inference
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│
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└── training_info.json
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└── /api/finetune/status
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└── BenchmarkView badge row
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```
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---
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## Error Handling
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- **Insufficient data per class:** Warn and skip classes with < 2 samples in the training split (can't stratify). Log which classes were skipped.
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- **VRAM OOM:** Surface as a clear error message in the SSE stream. Suggest stopping Peregrine vLLM first.
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- **Missing score file:** Raise `FileNotFoundError` with actionable message (same pattern as `load_scoring_jsonl`).
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- **Model dir already exists:** Overwrite with a warning log line (re-running fine-tune should always produce a fresh checkpoint).
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---
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## Testing
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- Unit test `WeightedTrainer.compute_loss` with a mock model and known label distribution — verify loss differs from unweighted
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- Unit test `FineTunedAdapter.classify` with a mock pipeline — verify it returns a string from `LABELS`
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- Unit test auto-discovery in `benchmark_classifier.py` — mock `models/` dir with two `training_info.json` files, verify both appear in the active registry
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- Integration test: fine-tune on the `.example` JSONL (10 samples, 1 epoch) — verify `models/avocet-*/training_info.json` is written with correct keys
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---
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## Out of Scope
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- Pushing fine-tuned weights to HuggingFace Hub (future)
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- Cross-validation or k-fold evaluation (future — dataset too small to be meaningful now)
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- Hyperparameter search (future)
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- Fine-tuning models other than `deberta-small` and `bge-m3`
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