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:
- 9847f0ee2feaca922acd41a14e2362c9c6a6e3821e585a963199eb8be2e8c6fe
- Size of remote file:
- 438 MB
- SHA256:
- 22ce976e2f1f9af7ee6775403060529594e363778605bf0db0a6adca3f608e39
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