Package · PyPI
PyPI · v2.4.3 · MIT
This tool provides the state-of-the-art models for aspect term extraction (ATE), aspect polarity classification (APC), and text classification (TC).
Tentative categories were assigned with moderate confidence (0.5 to 0.75): they are shown, but they do not count toward a category’s momentum. How categories work
pip install pyabsaPyPI publishes no adoption figure, so there is no growth to derive. Nothing is estimated in its place.
Adoption history
PyPI publishes no adoption count, so there is no history to chart. That is a fact about the registry, not a gap in ours: the package’s releases and maintenance are measured instead.
In the Crest catalog
Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;
Linked by the package’s own registry metadata.GitHub (opens in a new tab)
>=0.16>=2.3.5>=2.0.0>=0.9.6>=1.0.0<4.30.0No published news event mentions this package or its repository yet.