Retrain a Pretrained Model Using a CSV Dataset

import poutyne

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

First, let’s download the train and test data from the public repository.

saving_dir = "./data"
file_extension = "csv"
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 = CSVDatasetContainer(os.path.join(saving_dir, training_dataset_name + "." + file_extension),
                                         column_names=['Address', 'Tags'], separator=',')
test_container = CSVDatasetContainer(os.path.join(saving_dir, test_dataset_name + "." + file_extension),
                                     column_names=['Address', 'Tags'], separator=',')

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.

# Reduce LR by a factor of 10 each epoch
lr_scheduler = poutyne.StepLR(step_size=1, gamma=0.1)

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, 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,
                       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)