fine_tuning.py

import os
import poutyne

from deepparse import download_from_public_repository
from deepparse.dataset_container import PickleDatasetContainer
from deepparse.parser import AddressParser

# First, let's download the train and test data from the public repository.
saving_dir = "./data"
file_extension = "p"
training_dataset_name = "sample_incomplete_data"
test_dataset_name = "test_sample_data"
download_from_public_repository(training_dataset_name, saving_dir, file_extension=file_extension)
download_from_public_repository(test_dataset_name, saving_dir, file_extension=file_extension)

# Now let's create a training and test container.
training_container = PickleDatasetContainer(os.path.join(saving_dir, training_dataset_name + "." + file_extension))
test_container = PickleDatasetContainer(os.path.join(saving_dir, test_dataset_name + "." + file_extension))

# We will retrain the FastText version of our pretrained model.
address_parser = AddressParser(model_type="fasttext", device=0)

# Now, let's retrain for 5 epochs using a batch size of 8 since the data is really small for the example.
# Let's start with the default learning rate of 0.01 and use a learning rate scheduler to lower the learning rate
# as we progress.
lr_scheduler = poutyne.StepLR(step_size=1, gamma=0.1)  # reduce LR by a factor of 10 each epoch

# The checkpoints (ckpt) are saved in the default "./checkpoints" directory, so if you wish to retrain
# another model (let's say BPEmb), you need to change the `logging_path` directory; otherwise, you will get
# an error when retraining since Poutyne will try to use the last checkpoint.
address_parser.retrain(
    training_container,
    train_ratio=0.8,
    epochs=5,
    batch_size=8,
    num_workers=2,
    callbacks=[lr_scheduler],
)

# Now, let's test our fine-tuned model using the best checkpoint (default parameter).
address_parser.test(test_container, batch_size=256)

# Now let's retrain the FastText version but with an attention mechanism.
address_parser = AddressParser(model_type="fasttext", device=0, attention_mechanism=True)

# Since the previous checkpoints were saved in the default "./checkpoints" directory, we need to use a new one.
# Otherwise, poutyne will try to reload the previous checkpoints, and our model has changed.
address_parser.retrain(
    training_container,
    train_ratio=0.8,
    epochs=5,
    batch_size=8,
    num_workers=2,
    callbacks=[lr_scheduler],
    logging_path="checkpoints_attention",
)

# Now, let's test our fine-tuned model using the best checkpoint (default parameter).
address_parser.test(test_container, batch_size=256)