Metadata-Version: 2.1
Name: deepparse
Version: 0.2.1
Summary: A library for parsing multinational street addresses using deep learning.
Home-page: https://deepparse.org/
Author: Marouane Yassine, David Beauchemin
Author-email: marouane.yassine.1@ulaval.ca, david.beauchemin.5@ulaval.ca
License: LGPLv3
Download-URL: https://github.com/GRAAL-Research/deepparse/archive/v0.2.1.zip
Description: <img src="https://raw.githubusercontent.com/GRAAL-Research/deepparse/master/docs/source/_static/logos/logo.png" width="150" height="135"/>
        
        [![License: LGPL v3](https://img.shields.io/badge/License-LGPL%20v3-blue.svg)](http://www.gnu.org/licenses/lgpl-3.0)
        [![Continuous Integration](https://github.com/GRAAL-Research/deepparse/workflows/Continuous%20Integration/badge.svg)](https://github.com/GRAAL-Research/deepparse/actions?query=workflow%3A%22Continuous+Integration%22+branch%3Amaster)
        
        ## 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](https://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: 
        
        ```python
        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)
        ```
        
        ------------------
        
        ## Installation
        
        Before installing deepparse, you must have the latest version of [PyTorch](https://pytorch.org/) in your environment.
        
        - **Install the stable version of deepparse:**
        
        ```sh
        pip install deepparse
        ```
        
        - **Install the latest development version of deepparse:**
        
        ```sh
        pip install -U git+https://github.com/GRAAL-Research/deepparse.git@dev
        ```
        
        ------------------
        
        ## 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](https://github.com/GRAAL-Research/deepparse/blob/master/CONTRIBUTING.md) for more details on this matter.
        
        ## License
        
        Deepparse is LGPLv3 licensed, as found in the [LICENSE file](https://github.com/GRAAL-Research/deepparse/blob/master/LICENSE).
        
        ------------------
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.1
Description-Content-Type: text/markdown
Provides-Extra: colorama
