Introducing spaCy v3.5spaCy v3.5 introduces new CLI commands, fuzzy matching, improvements for entity linking and more.
Introducing spaCy v3.2spaCy v3.2 features usability improvements for custom training and scoring, improved performance and support for floret, our new fastText word vectors algorithm.
End-to-end Neural Coreference Resolution in spaCyCoreference resolution is the problem of resolving entities in texts to references such as pronouns. Even if you've never heard of it, it's something we all do constantly every day, and is a key to understanding natural language. We recently added an experimental implementation of an end-to-end neural coreference component to spaCy. This post explains the architecture of our model in detail.
Introducing spaCy v3.1It’s been great to see the adoption of spaCy v3, which introduced transformer-based pipelines, a new training system and more. Version 3.1 adds more on top of it, including the ability to use predicted annotations during training, a component for predicting arbitrary and overlapping spans and new pipelines for Catalan and Danish.
Introducing spaCy v3.4spaCy v3.4 brings typing and speed improvements along with new vectors for English CNN pipelines and new trained pipelines for Croatian.
Introducing spaCy v3.3spaCy v3.3 improves the speed of core pipeline components, adds a new trainable lemmatizer, and introduces trained pipelines for Finnish, Korean and Swedish.
