Retrain an attention mechanism model

import os

import poutyne

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

First, let’s download the train and test data with “new tags” from the public repository.

saving_dir = "./data"
file_extension = "p"
training_dataset_name = "sample_incomplete_data"
test_dataset_name = "test_sample_data"
download_from_url(training_dataset_name, saving_dir, file_extension=file_extension)
download_from_url(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 attention version of our pretrained model.

model = "bpemb"
address_parser = AddressParser(model_type=model, device=0, attention_mechanism=True)

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

logging_path = "./checkpoints"

address_parser.retrain(
    training_container,
    0.8,
    epochs=5,
    batch_size=8,
    num_workers=2,
    callbacks=[lr_scheduler],
    logging_path=logging_path
)

Now, let’s test our fine-tuned model using the best checkpoint (default parameter).

address_parser.test(test_container, batch_size=256)