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.
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}}
}
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)
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/MAYAS3/deepparse.git@dev
License
deepparse is LGPLv3 licensed, as found in the LICENSE file.
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