We’re pleased to publish v3.4 of the spaCy Natural Language Processing library. spaCy v3.4 brings typing and speed improvements along with new vectors for English pipelines and new trained pipelines for Croatian. This release also includes prebuilt linux aarch64 wheels for all spaCy dependencies distributed by Explosion.
spaCy v3.4 supports pydantic v1.9 and mypy 0.950+ through extensive updates to types in Thinc v8.1.
- For the parser, use C
sgemmprovided by the
Opsimplementation in order to use Accelerate through
- Improved speed of vector lookups.
- Improved speed for
v3.4 introduces new CPU/CNN pipelines for Croatian, which use the trainable lemmatizer and floret vectors. Due to the use of Bloom embeddings and subwords, the pipelines have compact vectors with no out-of-vocabulary words.
New Trained Pipelines
|Package||UPOS||Parser LAS||NER F|
All CNN pipelines have been extended with whitespace augmentation.
The English CNN pipelines have new word vectors, which improve the NER performance and update the vectors with words like “AirTags”, “Brexit”, “covid” and “doomscrolling”:
New English Vectors
|Package||Model Version||TAG||Parser LAS||NER F|
Many cool new plugins, extensions, pipelines and tutorials have been added to the spaCy universe since v3.3:
|Aim-spacy||An Aim-based spaCy experiment tracker.|
|Asent||Fast, flexible and transparent sentiment analysis.|
|spaCy fishing||Named entity disambiguation and linking on Wikidata in spaCy with Entity-Fishing.|
|spacy-report||Generates interactive reports for spaCy models.|