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

 

- 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