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.

Deepparse is compatible with the latest version of PyTorch and Python >= 3.6.

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.

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

Clean data

The following table presents the accuracy on the 20 countries (using clean data) we used during training for both our models.

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

Noisy data

The following table presents the accuracy on the 20 countries we used during training for both our models but for noisy and 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.

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

Getting started

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)

The predictions tags are the following

  • “StreetNumber”: for the street number

  • “StreetName”: for the name of the street

  • “Unit”: for the unit (such as apartment)

  • “Municipality”: for the municipality

  • “Province”: for the province or local region

  • “PostalCode”: for the postal code

  • “Orientation”: for the street orientation (e.g. west, east)

  • “GeneralDelivery”: for other delivery information

Retrain a model

see here for a complete example.

# We will retrain the fasttext version of our pretrained model.
address_parser = AddressParser(model_type="fasttext", device=0)

address_parser.retrain(training_container, 0.8, epochs=5, batch_size=8)

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/GRAAL-Research/deepparse.git@dev
    

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}}
}

License

Deepparse is LGPLv3 licensed, as found in the LICENSE file.

Indices and tables