retrained_model_parsing.py

# pylint: skip-file
###################
"""
IMPORTANT:
THE EXAMPLE IN THIS FILE IS CURRENTLY NOT FUNCTIONAL
BECAUSE THE `download_from_public_repository` FUNCTION
NO LONGER EXISTS. WE HAD TO MAKE A QUICK RELEASE TO
REMEDIATE AN ISSUE IN OUR PREVIOUS STORAGE SOLUTION.
THIS WILL BE FIXED IN A FUTURE RELEASE.

IN THE MEAN TIME IF YOU NEED ANY CLARIFICATION
REGARDING THE PACKAGE PLEASE FEEL FREE TO OPEN AN ISSUE.
"""
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
# 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])