  Segmenting an address into meaningful components, also known as address
parsing, is an essential step in many applications from record linkage to
geocoding and package delivery. Consequently, a lot of work has been dedicated
to develop accurate address parsing techniques, with machine learning and
neural network methods leading the state-of-the-art scoreboard. However, most
of the work on address parsing has been confined to academic endeavours with
little availability of free and easy-to-use open-source solutions.
  This paper presents Deepparse, a Python open-source, extendable, fine-tunable
address parsing solution under LGPL-3.0 licence to parse multinational
addresses using state-of-the-art deep learning algorithms and evaluated on over
60 countries. It can parse addresses written in any language and use any
address standard. The pre-trained model achieves average $99~\%$ parsing
accuracies on the countries used for training with no pre-processing nor
post-processing needed. Moreover, the library supports fine-tuning with new
data to generate a custom address parser.
