A library for parsing multinational street addresses using deep learning.
Project description
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.
Read the documentation at deepparse.org.
Deepparse is compatible with the latest version of PyTorch and Python >= 3.6.
Countries and Results
The following table presents the accuracy on the 20 countries we used during training for both our models.
| Country | Fasttext (%) | BPEmb (%) | Country | Fasttext (%) | BPEmb (%) |
|---|---|---|---|---|---|
| Italy | 99.66 | 99.73 | United States | 99.56 | 99.53 |
| Germany | 99.72 | 99.84 | Austria | 99.19 | 99.03 |
| South Korea | 99.96 | 100.00 | Canada | 99.76 | 99.80 |
| Mexico | 99.54 | 99.60 | Australia | 99.62 | 99.74 |
| Finland | 99.75 | 99.87 | Netherlands | 99.50 | 99.84 |
| France | 99.54 | 99.50 | United Kingdom | 99.54 | 99.62 |
| Russia | 98.71 | 99.49 | Norway | 99.40 | 98.71 |
| Switzerland | 99.48 | 99.61 | Poland | 99.64 | 99.83 |
| Brazil | 99.33 | 99.24 | Denmark | 99.65 | 99.84 |
| Spain | 99.70 | 99.79 | Czechia | 99.46 | 99.83 |
We have also made a zero-shot evaluation of our models using data from 41 other countries; the results are shown in the next table.
| Country | Fasttext (%) | BPEmb (%) | Country | Fasttext (%) | BPEmb (%) |
|---|---|---|---|---|---|
| Philippines | 81.56 | 83.73 | South Africa | 92.69 | 95.03 |
| Colombia | 85.92 | 87.50 | Venezuela | 95.36 | 89.67 |
| Bermuda | 91.30 | 93.66 | Lithuania | 89.21 | 76.60 |
| Moldova | 88.51 | 89.13 | India | 66.91 | 77.26 |
| Malaysia | 81.31 | 92.78 | Bosnia | 88.91 | 84.33 |
| Belgium | 89.57 | 86.41 | Ukraine | 91.80 | 92.73 |
| Greece | 83.42 | 39.82 | Algeria | 86.93 | 80.62 |
| Slovakia | 81.00 | 91.28 | Bangladesh | 74.49 | 79.29 |
| Latvia | 93.80 | 80.18 | Reunion | 96.48 | 93.40 |
| Romania | 93.23 | 91.83 | Singapore | 84.55 | 81.68 |
| Indonesia | 63.15 | 67.97 | Cyprus | 97.69 | 98.30 |
| Portugal | 93.39 | 93.20 | Serbia | 95.62 | 94.69 |
| Croatia | 96.63 | 86.24 | Japan | 44.33 | 35.77 |
| New Caledonia | 99.42 | 99.01 | New Zealand | 97.04 | 98.86 |
| Uzbekistan | 87.63 | 71.93 | Faroe Islands | 71.73 | 85.46 |
| Hungary | 47.00 | 24.05 | Slovenia | 96.27 | 97.28 |
| Paraguay | 97.00 | 97.15 | Iceland | 95.76 | 98.01 |
| Estonia | 90.61 | 76.45 | Argentina | 89.47 | 88.55 |
| Bulgaria | 92.70 | 95.87 | Sweden | 77.29 | 87.77 |
| Belarus | 88.77 | 93.00 | Kazakhstan | 87.24 | 91.23 |
| Ireland | 86.35 | 87.49 |
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"])
# 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)
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)
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/deepparse.git@dev
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}}
}
Contributing to Deepparse
We welcome user input, whether it is regarding bugs found in the library or feature propositions ! Make sure to have a look at our contributing guidelines for more details on this matter.
License
Deepparse is LGPLv3 licensed, as found in the LICENSE file.
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