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Here is Deepparse

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

Use deepparse to

  • parse multinational address using one of our pretrained models with or without attention mechanism,

  • parse addresses directly from the command line without code to write,

  • parse addresses with our out-of-the-box FastAPI parser,

  • retrain our pretrained models on new data to improve parsing on specific country address patterns,

  • retrain our pretrained models with new prediction tags easily,

  • retrain our pretrained models with or without freezing some layers,

  • train a new Seq2Seq addresses parsing models easily using a new model configuration.

Deepparse is compatible with the latest version of PyTorch and Python >= 3.10, <= 3.14.

This documentation covers the latest stable release. The documentation of the development version (dev branch) is available at deepparse.org/dev.

Quick Example

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")

See Installation to install Deepparse and Getting Started for a complete tour.

Where to Go Next

  • Choose a Model: compare the pretrained models (memory, disk, speed, multilingual coverage) to pick one.

  • Pretrained Models Results: the accuracy of our pretrained models on 20 training countries and 41 zero-shot countries, with clean and incomplete data.

  • Parser: the AddressParser API, with the memory usage and inference time benchmarks.

  • Training Guide and the examples: retrain a model on your own data.

Cite

@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 MIT licensed, as found in the LICENSE file.

Installation

Get Started

API

Examples

Model training

Project

Indices and Tables