train_example.py

model_name = "tf2_gpt2_naver_movie"

earlystop_callback = EarlyStopping(monitor='val_accuracy', min_delta=0.0001, patience=2)

checkpoint_path = os.path.join(DATA_OUT_PATH, model_name, 'weights.h5')
checkpoint_dir = os.path.dirname(checkpoint_path)

if os.path.exists(checkpoint_dir):
    print("{} -- Folder already exists \n".format(checkpoint_dir))
else:
    os.makedirs(checkpoint_dir, exist_ok=True)
    print("{} -- Folder create complete \n".format(checkpoint_dir))
    
cp_callback = ModelCheckpoint(
    checkpoint_path, monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=True)

history = cls_model.fit(train_data_sents, train_data_labels, 
                        epochs=NUM_EPOCHS, 
                        batch_size=BATCH_SIZE,
                        validation_split=VALID_SPLIT, 
                        callbacks=[earlystop_callback, cp_callback])