Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

It’s printing the training and validation losses, and the validation accuracy, every
three hundred mini-batches as configured. To check the final validation figures,
though, we need to call the evaluate() method, which, guess what, actually runs a
validation loop! Thank you, HuggingFace :-)
trainer.evaluate()
Output
{'eval_loss': 0.142591193318367,
'eval_accuracy': 0.9610894941634242,
'eval_runtime': 1.6634,
'eval_samples_per_second': 463.51,
'epoch': 1.0,
'eval_mem_cpu_alloc_delta': 0,
'eval_mem_gpu_alloc_delta': 0,
'eval_mem_cpu_peaked_delta': 0,
'eval_mem_gpu_peaked_delta': 8132096}
That’s 96.11% accuracy on the validation set after one epoch, roughly the same as
our own implementation ("Model IV"). Nice!
Once the model is trained, we can save it to disk using the save_model() method
from Trainer:
trainer.save_model('bert_alice_vs_wizard')
os.listdir('bert_alice_vs_wizard')
Output
['training_args.bin', 'config.json', 'pytorch_model.bin']
It creates a folder with the provided name, and it stores the trained model
(pytorch_model.bin) along with its configuration (config.json) and training
arguments (training_args.bin).
Later on, we can easily load the trained model using the from_pretrained()
method from the corresponding AutoModel:
998 | Chapter 11: Down the Yellow Brick Rabbit Hole