.. role:: hidden :class: hidden-section Use a Retrained Model to Parse Addresses **************************************** .. code-block:: python 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. .. code-block:: python 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. .. code-block:: python 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. .. code-block:: python 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 .. code-block:: python parsed_addresses = address_parser(addresses_to_parse[0:300])