Here is deepparse.

Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning.

Use deepparse to

  • Use the pre-trained models to parse multinational addresses,
  • retrain our pre-trained models on new data to parse multinational addresses,
  • retrain our pre-trained models with your own prediction tags easily.

Read the documentation at deepparse.org.

Deepparse is compatible with the latest version of PyTorch and Python >= 3.7.

Countries and Results

We evaluate our models on two forms of address data

  • clean data which refers to addresses containing elements from four categories, namely a street name, a
    municipality, a province and a postal code,
  • incomplete data which is made up of addresses missing at least one category amongst the aforementioned ones.

You can get our dataset here.

Clean Data

The following table presents the accuracy (using clean data) on the 20 countries we used during training for both our
models.

Country Fasttext (%) BPEmb (%) Country Fasttext (%) BPEmb (%)
Norway 99.06 98.3 Austria 99.21 97.82
Italy 99.65 98.93 Mexico 99.49 98.9
United Kingdom 99.58 97.62 Switzerland 98.9 98.38
Germany 99.72 99.4 Denmark 99.71 99.55
France 99.6 98.18 Brazil 99.31 97.69
Netherlands 99.47 99.54 Australia 99.68 98.44
Poland 99.64 99.52 Czechia 99.48 99.03
United States 99.56 97.69 Canada 99.76 99.03
South Korea 99.97 99.99 Russia 98.9 96.97
Spain 99.73 99.4 Finland 99.77 99.76

We have also made a zero-shot evaluation of our models using clean data from 41 other countries; the results are shown
in the next table.

Country Fasttext (%) BPEmb (%) Country Fasttext (%) BPEmb (%)
Latvia 89.29 68.31 Faroe Islands 71.22 64.74
Colombia 85.96 68.09 Singapore 86.03 67.19
Réunion 84.3 78.65 Indonesia 62.38 63.04
Japan 36.26 34.97 Portugal 93.09 72.01
Algeria 86.32 70.59 Belgium 93.14 86.06
Malaysia 83.14 89.64 Ukraine 93.34 89.42
Estonia 87.62 70.08 Bangladesh 72.28 65.63
Slovenia 89.01 83.96 Hungary 51.52 37.87
Bermuda 83.19 59.16 Romania 90.04 82.9
Philippines 63.91 57.36 Belarus 93.25 78.59
Bosnia 88.54 67.46 Moldova 89.22 57.48
Lithuania 93.28 69.97 Paraguay 96.02 87.07
Croatia 95.8 81.76 Argentina 81.68 71.2
Ireland 80.16 54.44 Kazakhstan 89.04 76.13
Greece 87.08 38.95 Bulgaria 91.16 65.76
Serbia 92.87 76.79 New Caledonia 94.45 94.46
Sweden 73.13 86.85 Venezuela 79.23 70.88
New Zealand 91.25 75.57 Iceland 83.7 77.09
India 70.3 63.68 Uzbekistan 85.85 70.1
Cyprus 89.64 89.47 Slovakia 78.34 68.96
South Africa 95.68 74.82

Incomplete Data

The following table presents the accuracy on the 20 countries we used during training for both our models but for
incomplete data. We didn't test on the other 41 countries since we did not train on them and therefore do not expect
to achieve an interesting performance.

Country Fasttext (%) BPEmb (%) Country Fasttext (%) BPEmb (%)
Norway 99.52 99.75 Austria 99.55 98.94
Italy 99.16 98.88 Mexico 97.24 95.93
United Kingdom 97.85 95.2 Switzerland 99.2 99.47
Germany 99.41 99.38 Denmark 97.86 97.9
France 99.51 98.49 Brazil 98.96 97.12
Netherlands 98.74 99.46 Australia 99.34 98.7
Poland 99.43 99.41 Czechia 98.78 98.88
United States 98.49 96.5 Canada 98.96 96.98
South Korea 91.1 99.89 Russia 97.18 96.01
Spain 99.07 98.35 Finland 99.04 99.52

Getting Started:

from deepparse.parser import AddressParser

address_parser = AddressParser(model_type="bpemb", device=0)

# you can parse one address
parsed_address = address_parser("350 rue des Lilas Ouest Québec Québec G1L 1B6")

# or multiple addresses
parsed_address = address_parser(
    ["350 rue des Lilas Ouest Québec Québec G1L 1B6", "350 rue des Lilas Ouest Québec Québec G1L 1B6"])

# or multinational addresses
# Canada, US, Germany, UK and South Korea
parsed_address = address_parser(
    ["350 rue des Lilas Ouest Québec Québec G1L 1B6", "777 Brockton Avenue, Abington MA 2351",
     "Ansgarstr. 4, Wallenhorst, 49134", "221 B Baker Street", "서울특별시 종로구 사직로3길 23"])

# you can also get the probability of the predicted tags
parsed_address = address_parser("350 rue des Lilas Ouest Québec Québec G1L 1B6", with_prob=True)

The predictions tags are the following

  • "StreetNumber": for the street number,
  • "StreetName": for the name of the street,
  • "Unit": for the unit (such as apartment),
  • "Municipality": for the municipality,
  • "Province": for the province or local region,
  • "PostalCode": for the postal code,
  • "Orientation": for the street orientation (e.g. west, east),
  • "GeneralDelivery": for other delivery information.

Retrain a Model

see here for a complete example.

# We will retrain the fasttext version of our pretrained model.
address_parser = AddressParser(model_type="fasttext", device=0)

address_parser.retrain(training_container, 0.8, epochs=5, batch_size=8)

Retrain a Model With New Tags

See here for a complete example.


address_components = {"ATag":0, "AnotherTag": 1, "EOS": 2}
address_parser.retrain(training_container, 0.8, epochs=1, batch_size=128, prediction_tags=address_components)

Download our Models

Here are the URLs to download our pre-trained models directly


Installation

Before installing deepparse, you must have the latest version of PyTorch in your environment.

  • Install the stable version of deepparse:
pip install deepparse
  • Install the latest development version of deepparse:
pip install -U git+https://github.com/GRAAL-Research/[email protected]

Cite

Use the following for the article;

@misc{yassine2020leveraging,
    title={{Leveraging Subword Embeddings for Multinational Address Parsing}},
    author={Marouane Yassine and David Beauchemin and François Laviolette and Luc Lamontagne},
    year={2020},
    eprint={2006.16152},
    archivePrefix={arXiv}
}

and this one for the package;

@misc{deepparse,
    author = {Marouane Yassine and David Beauchemin},
    title  = {{Deepparse: A state-of-the-art deep learning multinational addresses parser}},
    year   = {2020},
    note   = {\url{https://deepparse.org}}
}

GitHub

https://github.com/GRAAL-Research/deepparse