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.
- 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