Text Classification
setfit
PyTorch
sentence-transformers
English
bert
absa
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity") - sentence-transformers
How to use tomaarsen/setfit-absa-bge-small-en-v1.5-restaurants-polarity with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/setfit-absa-bge-small-en-v1.5-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
Adding `safetensors` variant of this model
#1 opened almost 2 years ago
by
SFconvertbot