(training_file, dev_file,
trained_models_dir,
trained_critique_dir,
per_gpu_train_batch_size,
learning_rate,
epochs,
language_model,
grad_acc,
sequence_length,
optimizer_algorithm='adam',
noisy_file=None,)
| 10 | DATA_FOLDER = 'data' |
| 11 | |
| 12 | def training(training_file, dev_file, |
| 13 | trained_models_dir, |
| 14 | trained_critique_dir, |
| 15 | per_gpu_train_batch_size, |
| 16 | learning_rate, |
| 17 | epochs, |
| 18 | language_model, |
| 19 | grad_acc, |
| 20 | sequence_length, |
| 21 | optimizer_algorithm='adam', |
| 22 | noisy_file=None,): |
| 23 | |
| 24 | if not os.path.exists(trained_models_dir): |
| 25 | os.mkdir(trained_models_dir) |
| 26 | classifier = T5LMClassifier( |
| 27 | max_seq_length=sequence_length, |
| 28 | output_model_dir=trained_models_dir, |
| 29 | output_critique_model=trained_critique_dir, |
| 30 | cache_dir=os.path.join(DATA_FOLDER, 'pretrained'), |
| 31 | pretrained_model_name_or_path=language_model |
| 32 | ) |
| 33 | classifier.train(training_file, dev_file, |
| 34 | per_gpu_train_batch_size=per_gpu_train_batch_size, |
| 35 | learning_rate=learning_rate, |
| 36 | optimizer_algorithm=optimizer_algorithm, |
| 37 | num_train_epochs=epochs, |
| 38 | noisy_file=noisy_file, |
| 39 | gradient_accumulation_steps=grad_acc) |
| 40 | |
| 41 | |
| 42 | def evaluate(test_file, trained_models_dir, trained_critique_dir, sequence_length, |
no test coverage detected