Text Classification
setfit
Safetensors
sentence-transformers
mpnet
absa
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use Andyrasika/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use Andyrasika/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Andyrasika/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity") - sentence-transformers
How to use Andyrasika/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Andyrasika/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e683e7fba19250f3e2d40bb395f599538255bafa38fb97b39615d3bfc1517608
- Size of remote file:
- 25.6 kB
- SHA256:
- fc2f018ee681229c22775db9833da0264ce307c4effe78742efadf55cc4df198
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