deepparse 0.1.2

A library for parsing multinational street addresses using deep learning.

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.

Read the documentation at deepparse.org.

Deepparse is compatible with the latest version of PyTorch and Python >= 3.6.

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}}
}

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Getting started:

from deepparse.parser import AddressParser

address_parser = AddressParser(model="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)

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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

License

deepparse is LGPLv3 licensed, as found in the LICENSE file.

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