---
title: "Session: model-training-loop — 2026-02-02"
type: source
tags: [claude-code, session-transcript, demo]
date: 2026-02-02
source_file: raw/sessions/demo-ml-pipeline/2026-02-02-model-training-loop.md
sessionId: demo-000000000000000000000000000022
slug: model-training-loop
project: demo-ml-pipeline
started: 2026-02-02T11:00:00+00:00
ended: 2026-02-02T15:30:00+00:00
cwd: /Users/demo/code/demo-ml-pipeline
gitBranch: feat/train-loop
permissionMode: default
model: claude-opus-4-6
user_messages: 9
tool_calls: 31
tools_used: [Read, Write, Edit, Bash, Grep, WebFetch]
tool_counts: {"Edit": 12, "Read": 9, "Write": 5, "Bash": 3, "Grep": 1, "WebFetch": 1}
token_totals: {"input": 18000, "cache_creation": 28000, "cache_read": 71000, "output": 8200}
turn_count: 9
hour_buckets: {"2026-02-02T11": 6, "2026-02-02T12": 10, "2026-02-02T13": 8, "2026-02-02T14": 5, "2026-02-02T15": 2}
duration_seconds: 16200
is_subagent: false
---

# Session: model-training-loop — 2026-02-02

**Project:** `demo-ml-pipeline` · **Branch:** `feat/train-loop` · **Mode:** `default` · **Model:** `claude-opus-4-6`

## Summary

Built the training loop on top of `transformers.Trainer`. Fine-tuned `distilbert-base-uncased` on the shards from the previous session. Added W&B logging, early-stopping, and a checkpoint-on-best-val-f1 callback.

## Conversation

### Turn 1 — User

Write the training loop. Fine-tune `distilbert-base-uncased` with the shards we prepped last session.

### Turn 1 — Assistant

Going with `transformers.Trainer` — it gives us logging, checkpointing, and mixed precision out of the box.

Key choices:
- `learning_rate=2e-5` (the canonical BERT fine-tune LR)
- `per_device_train_batch_size=16`
- `num_train_epochs=3` (data is small)
- `load_best_model_at_end=True` with `metric_for_best_model="f1"`
- `report_to=["wandb"]` — will pick up `WANDB_PROJECT` from env

**Tools used in this turn:**
- `Write`: `train.py`

```python
from pathlib import Path
from datasets import load_from_disk
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainingArguments,
    EarlyStoppingCallback,
)
from sklearn.metrics import f1_score, accuracy_score
import numpy as np


MODEL_ID = "distilbert-base-uncased"


def compute_metrics(eval_pred):
    logits, labels = eval_pred
    preds = np.argmax(logits, axis=1)
    return {
        "accuracy": accuracy_score(labels, preds),
        "f1": f1_score(labels, preds, average="weighted"),
    }


def main(data_dir: str, out_dir: str) -> None:
    ds = load_from_disk(data_dir)
    tok = AutoTokenizer.from_pretrained(MODEL_ID)
    model = AutoModelForSequenceClassification.from_pretrained(
        MODEL_ID, num_labels=len(ds["train"].features["label"].names)
    )

    args = TrainingArguments(
        output_dir=out_dir,
        learning_rate=2e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=32,
        num_train_epochs=3,
        evaluation_strategy="epoch",
        save_strategy="epoch",
        load_best_model_at_end=True,
        metric_for_best_model="f1",
        greater_is_better=True,
        report_to=["wandb"],
        logging_steps=25,
    )

    trainer = Trainer(
        model=model,
        args=args,
        train_dataset=ds["train"],
        eval_dataset=ds["validation"],
        data_collator=DataCollatorWithPadding(tokenizer=tok),
        compute_metrics=compute_metrics,
        callbacks=[EarlyStoppingCallback(early_stopping_patience=2)],
    )
    trainer.train()
    trainer.save_model(out_dir)


if __name__ == "__main__":
    main(data_dir="data/processed", out_dir="checkpoints/dev")
```

- `Bash`: `python train.py --config configs/dev.yaml` — exit 0

### Turn 2 — User

What F1 did we get?

### Turn 2 — Assistant

Best checkpoint landed at **0.87 F1** on the val set at epoch 2. Loss was still trending down so we might squeeze more out of epoch 3+ with a bigger dataset, but for a dev smoke test this is fine.

## Connections

- [[demo-ml-pipeline]] — project
- [[DistilBERT]] — model
- [[HuggingFaceTrainer]] — library
- [[FineTuning]] — concept