Testing and Validation

Testing and Validation

Verified on the supplied source in this package build

• Python syntax: python -m py_compile transformer_from_scratch.py — passed.
• Offline smoke-test: python transformer_from_scratch.py --smoke-test --output <temporary-dir> — passed on CPU with PyTorch 2.10.0+cpu.
• Execution path: train for 2 epochs; select and save best checkpoint; reload checkpoint; perform final held-out synthetic test evaluation — completed.
• Observed model size: 56,738 trainable parameters in smoke-test configuration.

Example observed synthetic metrics (not a Yelp benchmark)

Metric	Observed value
Dataset sizes	train 64 / validation 8 / test 8
Epochs	2
Best validation accuracy	0.6250
Held-out synthetic test accuracy	0.5000

These metrics are tiny-sample diagnostic results, not meaningful model-quality or generalization claims.

Not verified in this package build

• Hugging Face dataset and MiniLM model downloads / real-data training.
• CUDA GPU execution and SDPA backend performance.
• Multi-GPU, distributed, or mixed-precision execution (not implemented).
• torchview / Graphviz architecture PNG rendering (optional).

Reproducing the check

python -m py_compile transformer_from_scratch.py
python transformer_from_scratch.py --smoke-test

Optional manual robustness checks include testing --help, running --train with a small batch on an internet-connected machine, and inspecting the generated .pt file metadata. Keep .pt files out of public Git commits unless intentionally publishing trained artifacts.