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.

Read the documentation at deepparse.org.

Deepparse is compatible with the latest version of PyTorch and Python >= 3.10, <= 3.13.

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

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.

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.

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.

Getting Started:

```python from deepparse.parser import AddressParser from deepparse.dataset_container import CSVDatasetContainer

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", ] )

or multinational addresses

Canada, US, Germany, UK and South Korea

parsed_address = address_parser( [ "350 rue des Lilas Ouest Québec Québec G1L 1B6", "777 Brockton Avenue, Abington MA 2351", "Ansgarstr. 4, Wallenhorst, 49134", "221 B Baker Street", "서울특별시 종로구 사직로3길 23", ] )

you can also get the probability of the predicted tags

parsed_address = address_parser( "350 rue des Lilas Ouest Québec Québec G1L 1B