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.
Typing improvements
spaCy v3.4 supports pydantic v1.9 and mypy 0.950+ through extensive updates to
types in Thinc v8.1.
Speed improvements
- For the parser, use C
saxpy/sgemmprovided by theOpsimplementation in
order to use Accelerate throughthinc-apple-ops. - Improved speed of vector lookups.
- Improved speed for
Example.get_aligned_parseandExample.get_aligned.
New trained pipelines
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.
Pipeline updates
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”:
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. |