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Deep Learning model to classify addresses into their respective countries. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: Jul 25, 2022 Item updated: Jan 2, 2025 Number of downloads: 2,395

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Description

Accurate locations of people or places of interest is important to drive businesses and improve governement services. For accurate location, correctly geocoding addresses becomes important. Street addresses may sometimes be missing the country information and geocoding such incomplete addresses often results in poor accuracy. Geocoding accuracy and performance increases when the country is specified. This model categorizes incomplete addresses by automatically assigning the country they belong to.


This deep learning model is trained on address dataset provided by openaddresses.io and can be used to classify addresses from 18 different countries in the world.


Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Fine-tuning the model
This model can be fine-tuned using the Train Text Classification Model tool available in the GeoAI toolbox in ArcGIS Pro.. Follow the guide to fine-tune this model.

Input
Text on which country classification will be performed. Text should include street number or apartment number, street name, city or state.

Output
Text (classified country)

Supported countries
This model supports addresses from the following countries:

  • AR – Argentina
  • AT – Austria
  • AU – Australia
  • BE – Belgium
  • CA – Canada
  • CH – Switzerland
  • DE – Germany
  • DK – Denmark
  • ES – Spain
  • FI – Finland
  • FR – France
  • IS – Iceland
  • IT – Italy
  • KR – South Korea
  • LU – Luxemburg
  • NZ – New Zealand
  • SI – Slovenia
  • US – USA

Model architecture
This model uses the xlm-roberta architecture implemented in Hugging Face Transformers.

Accuracy metrics
The table below summarizes the precision, recall and F1-score of the model on the validation dataset.



Training data
The model has been trained on openly licensed data from openaddresses.io. 

Sample results
Here are a few results from the model.

An in-depth description of the item is not available.

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          LivingAtlasDLPK, country classifier, nlp, text classifier, text analysis

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