Why we switched from Spacy to Flair to anonymize French legal cases

Why we switched from Spacy to Flair to anonymize French legal cases

  • November 9, 2020
Table of Contents

Why we switched from Spacy to Flair to anonymize French legal cases

This article details a work we did in collaboration with the French administration (DINSIC) and a French supreme court (Cour de cassation) around 2 well-known Named Entity Recognition (NER below) libraries, Spacy and Zalando Flair. Spacy accuracy was too limited for our needs, and Flair was too slow. At the end we optimized Flair up to a point where inference time has been divided by 10, making it fast enough to anonymize a large inventory of French case law.

Major ideas behind our approach are described below.

Source: towardsdatascience.com

Tags :
Share :
comments powered by Disqus

Related Posts

New advances in natural language processing

New advances in natural language processing

Natural language understanding (NLU) and language translation are key to a range of important applications, including identifying and removing harmful content at scale and connecting people across different languages worldwide. Although deep learning–based methods have accelerated progress in language processing in recent years, current systems are still limited when it comes to tasks for which large volumes of labeled training data are not readily available. Recently, Facebook AI has achieved impressive breakthroughs in NLP using semi-supervised and self-supervised learning techniques, which leverage unlabeled data to improve performance beyond purely supervised systems.

Read More
NLP with a fashion twist: Zalando’s natural language processing framework

NLP with a fashion twist: Zalando’s natural language processing framework

Read More
More-Efficient “Kernel Methods” Dramatically Reduce Training Time for Natural-Language-Understanding Systems

More-Efficient “Kernel Methods” Dramatically Reduce Training Time for Natural-Language-Understanding Systems

Read More