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Deepparse Address Dataset

This repository contains the address data used to train the models of our address parsing package deepparse.

The data present in this repository has been generated using data from the lipbostal project.

Content of the Project

  • The script to merge all the countries' datasets into a single one (make_dataset).
  • A script to decompress LZMA archive into normal pickle format for Python (lzma_decompress).

Download the Data

Our dataset is hosted on the address below in a zip format. You can manually download it by clicking here or you can use wget as follow:


About the Data

The data is split into two main categories:

  • clean data: addresses containing elements from at least four categories, namely a street name, a municipality, a province and a postal code

  • incomplete data: addresses missing at least one category amongst the aforementioned ones.

Clean data

The clean_data directory contains three subdirectory train, test and zero_shot. The number of data samples per country for the train & test dataset. Each directory contains data for 20 countries, as described in Figure 1.

Country Number of samples in train Number of samples in test Country Number of samples in train Number of samples in test
Italy 100,000 178,848 United States 100,000 8,000,000
Germany 100,000 1,576,059 Austria 100,000 335,800
South Korea 100,000 6,048,106 Canada 100,000 910,891
Mexico 100,000 4,853,349 Australia 100,000 5,428,043
Finland 100,000 280,219 Netherlands 100,000 1,202,173
France 100,000 20,050 United Kingdom 100,000 14,338
Russia 100,000 8,115 Norway 100,000 405,649
Switzerland 100,000 474,240 Poland 100,000 459,522
Brazil 100,000 8,000,000 Denmark 100,000 199,694
Spain 100,000 1,395,758 Czechia 100,000 195,269

Figure 1. The number of data samples per country in the train and test datasets for clean data.

The zero_shot dataset contains data for an additional 41 countries. The number of data samples per country is described in Figure 2.

Country Number of samples Country Number of samples
Latvia 1,325 Faroe Islands 2,982
Colombia 569 Singapore 968
Réunion 2,514 Indonesia 2,259
Japan 14,089 Portugal 4,637
Algeria 601 Belgium 66,182
Malaysia 2,043 Ukraine 9,554
Estonia 1,024 Bangladesh 888
Slovenia 9,773 Hungary 17,460
Bermuda 2,065 Romania 19,420
Philippines 10,471 Belarus 7,590
Bosnia and Herzegovina 681 Moldova, Republic of 2,376
Lithuania 3,126 Paraguay 839
Croatia 5,671 Argentina 27,692
Ireland 638 Kazakhstan 1,087
Greece 4,974 Bulgaria 3,715
Serbia 6,792 New Caledonia 1,036
Sweden 32,291 Venezuela, Bolivarian Republic of 10,696
New Zealand 4,678 Iceland 13,617
India 26,075 Uzbekistan 505
Cyprus 836 Slovakia 18,975
South Africa 1,388

Figure 2. The number of data samples per country in the zero shot dataset for clean data.

About the Incomplete Data

The incomplete directory contains two subdirectories, each containing address data for the 20 countries listed in Figure 1. The train subdirectory contains 50,000 addresses per country, and the test subdirectory contains 25,000 per country.

Some countries use a hyphen to split the unit from the street number (e.g. 3-305 a street name, where 3 is the unit and 305 is the street number), and none were properly parsed in the training data. We have created a new dataset with such cases. The dataset consists of 3 012 automatically parsed addresses using regex where the hyphen has been removed, and tags have been adjusted. Namely, before the address '3-305 a street name' tags would have been [StreetNumber, StreetName, StreetName, StreetName]. In this dataset, the address is '3 305 a street name', and the tags are [Unit, StreetNumber, StreetName, StreetName, StreetName].

About the make_dataset Script

The make_dataset script merges all the individual files in a particular folder into one dataset. This script merges all the data so as to allow for balanced batches during training (when the batch size is greater than the number of files).

Run the following

python3 -h

to get information about the arguments needed to run the script.

About the lzma_decompress Script

Since the dataset is pretty large (especially for Github hosting), we have used the LZMA compressing algorithm using Python to make our files as small as possible. To do so, we used the convenient compress_pickle wrapper library to compress the data. Since LZMA is really slow to decompress, we provide a script to decompress it into standard pickle format.

The script will decompress the data files into the path_to_save directory provided as an argument, and will create the same subdirectory as described earlier (clean_data and incomplete_data plus the subdirectories train, test and zero_shot).

This script takes a couple of minutes to execute.

Cite the Dataset

If you use the data provided with this repository, please cite us using the following:

    author = {Marouane Yassine and David Beauchemin},
    title  = {{Structured Multinational Address Data}},
    year   = {2020},
    note   = {\url{}}


This dataset is under MIT License

Dataset Metadata

The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.

property value
name Multinational Structured Address Dataset
alternateName Address Parser Dataset
alternateName deepparse-dataset
description The Multinational Structured Address Dataset is a collection of addresses of 61 different countries. The addresses can either be "complete" (all the usual address components) or "incomplete" (missing some usual address components). \n Example of addresses ![preview](\n
property value
name Marouane Yassine
name David Beauchemin
property value
name GRAIL
property value
name MIT