Use a Retrained Model to Parse Addresses

import os

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

Here is an example on how to parse multiple addresses using a retrained model. First, let’s download the train and test data from the public repository.

data_saving_dir = "./data"
file_extension = "p"
test_dataset_name = "predict"
download_from_public_repository(test_dataset_name, data_saving_dir, file_extension=file_extension)

Now let’s load the dataset using one of our dataset container.

addresses_to_parse = PickleDatasetContainer("./data/predict.p", is_training_container=False)

Let’s download a BPEmb retrained model create just for this example, but you can also use one of yours.

model_saving_dir = "./retrained_models"
retrained_model_name = "retrained_light_bpemb_address_parser"
model_file_extension = "ckpt"
download_from_public_repository(retrained_model_name, model_saving_dir, file_extension=model_file_extension)

address_parser = AddressParser(
    model_type="bpemb", device=0,
    path_to_retrained_model=os.path.join(model_saving_dir, retrained_model_name + "." + model_file_extension)

We can now parse some addresses

parsed_addresses = address_parser(addresses_to_parse[0:300])