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 >= 3.8.

Countries and Results

We evaluate our models on two forms of address data

• clean data which refers to addresses containing elements from four categories, namely a street name, a municipality, a province and a postal code,

• incomplete data which is made up of addresses missing at least one category amongst the aforementioned ones.

You can get our dataset here.

Clean Data

The following table presents the accuracy on the 20 countries (using clean data) we used during training for both our models. Attention mechanisms improve performance by around 0.5% for all countries.

Country	FastText (%)	BPEmb (%)	Country	FastText (%)	BPEmb (%)
Norway	99.06	98.3	Austria	99.21	97.82
Italy	99.65	98.93	Mexico	99.49	98.9
United Kingdom	99.58	97.62	Switzerland	98.9	98.38
Germany	99.72	99.4	Denmark	99.71	99.55
France	99.6	98.18	Brazil	99.31	97.69
Netherlands	99.47	99.54	Australia	99.68	98.44
Poland	99.64	99.52	Czechia	99.48	99.03
United States	99.56	97.69	Canada	99.76	99.03
South Korea	99.97	99.99	Russia	98.9	96.97
Spain	99.73	99.4	Finland	99.77	99.76

We have also made a zero-shot evaluation of our models using clean data from 41 other countries; the results are shown in the next table.

Country	FastText (%)	BPEmb (%)	Country	FastText (%)	BPEmb (%)
Latvia	89.29	68.31	Faroe Islands	71.22	64.74
Colombia	85.96	68.09	Singapore	86.03	67.19
Réunion	84.3	78.65	Indonesia	62.38	63.04
Japan	36.26	34.97	Portugal	93.09	72.01
Algeria	86.32	70.59	Belgium	93.14	86.06
Malaysia	83.14	89.64	Ukraine	93.34	89.42
Estonia	87.62	70.08	Bangladesh	72.28	65.63
Slovenia	89.01	83.96	Hungary	51.52	37.87
Bermuda	83.19	59.16	Romania	90.04	82.9
Philippines	63.91	57.36	Belarus	93.25	78.59
Bosnia	88.54	67.46	Moldova	89.22	57.48
Lithuania	93.28	69.97	Paraguay	96.02	87.07
Croatia	95.8	81.76	Argentina	81.68	71.2
Ireland	80.16	54.44	Kazakhstan	89.04	76.13
Greece	87.08	38.95	Bulgaria	91.16	65.76
Serbia	92.87	76.79	New Caledonia	94.45	94.46
Sweden	73.13	86.85	Venezuela	79.23	70.88
New Zealand	91.25	75.57	Iceland	83.7	77.09
India	70.3	63.68	Uzbekistan	85.85	70.1
Cyprus	89.64	89.47	Slovakia	78.34	68.96
South Africa	95.68	74.829

Moreover, we also tested the performance when using attention mechanism to further improve zero-shot performance on those countries; the result are shown in the next table.

Country	FastText (%)	FastTextAtt (%)	BPEmb (%)	BPEmbAtt (%)	Country	FastText (%)	FastTextAtt (%)	BPEmb (%)	BPEmbAtt (%)
Ireland	80.16	89.11	54.44	81.84	Serbia	92.87	95.88	76.79	91.4
Uzbekistan	85.85	87.24	70.1	76.71	Ukraine	93.34	94.58	89.42	92.65
South Africa	95.68	97.25	74.82	97.95	Paraguay	96.02	97.08	87.07	97.36
Greece	87.08	86.04	38.95	58.79	Algeria	86.32	87.3	70.59	84.56
Belarus	93.25	97.4	78.59	97.49	Sweden	73.13	89.24	86.85	93.53
Portugal	93.09	94.92	72.01	93.76	Hungary	51.52	51.08	37.87	24.48
Iceland	83.7	96.54	77.09	96.63	Colombia	85.96	90.08	68.09	88.52
Latvia	89.29	93.14	68.31	73.79	Malaysia	83.14	74.62	89.64	91.14
Bosnia	88.54	87.27	67.46	89.02	India	70.3	75.31	63.68	80.56
Réunion	84.3	97.74	78.65	94.27	Croatia	95.8	95.32	81.76	85.99
Estonia	87.62	88.2	70.08	77.32	New Caledonia	94.45	99.61	94.46	99.77
Japan	36.26	46.91	34.97	49.48	New Zealand	91.25	97.0	75.57	95.7
Singapore	86.03	89.92	67.19	88.17	Romania	90.04	95.38	82.9	93.41
Bangladesh	72.28	78.21	65.63	77.09	Slovakia	78.34	82.29	68.96	96.0
Argentina	81.68	88.59	71.2	86.8	Kazakhstan	89.04	92.37	76.13	96.08
Venezuela	79.23	95.47	70.88	96.38	Indonesia	62.38	66.87	63.04	71.17
Bulgaria	91.16	91.73	65.76	93.28	Cyprus	89.64	97.44	89.47	98.01
Bermuda	83.19	93.25	59.16	93.8	Moldova	89.22	92.07	57.48	89.08
Slovenia	89.01	95.08	83.96	96.73	Lithuania	93.28	87.74	69.97	78.67
Philippines	63.91	81.94	57.36	83.42	Belgium	93.14	90.72	86.06	89.85
Faroe Islands	71.22	73.23	64.74	85.39

Incomplete Data

The following table presents the accuracy on the 20 countries we used during training for both our models but for incomplete data. We didn’t test on the other 41 countries since we did not train on them and therefore do not expect to achieve an interesting performance. Attention mechanisms improve performance by around 0.5% for all countries.

Country	FastText (%)	BPEmb (%)	Country	FastText (%)	BPEmb (%)
Norway	99.52	99.75	Austria	99.55	98.94
Italy	99.16	98.88	Mexico	97.24	95.93
United Kingdom	97.85	95.2	Switzerland	99.2	99.47
Germany	99.41	99.38	Denmark	97.86	97.9
France	99.51	98.49	Brazil	98.96	97.12
Netherlands	98.74	99.46	Australia	99.34	98.7
Poland	99.43	99.41	Czechia	98.78	98.88
United States	98.49	96.5	Canada	98.96	96.98
South Korea	91.1	99.89	Russia	97.18	96.01
Spain	99.07	98.35	Finland	99.04	99.52

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 LGPLv3 licensed, as found in the LICENSE file.

Installation

• Installation

Get Started

• Getting Started

API

• Parser
• Pre-Processors
• Dataset Container
• Comparer
• CLI
• Parse Address With Our Out-Of-The-Box API

Examples

• Parse Addresses
• Parse Addresses Using A URI
• Parse Addresses Using Our CLI
• Use a Retrained Model to Parse Addresses
• Retrain a Pretrained Model
• Retrain a Pretrained Model Using A URI
• Retrain a Pretrained Model Using a CSV Dataset
• Retrain an Attention Mechanism Model
• Retrain With New Prediction Tags
• Retrain With New Seq2Seq Parameters
• Retrain an Address Parser for Single Country Uses

Model training

• Training Guide

Indices and Tables

• Index

• Module Index