Text Classification
setfit
Safetensors
sentence-transformers
mpnet
absa
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity") - sentence-transformers
How to use joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-polarity with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("joshuasundance/setfit-absa-all-mpnet-base-v2-laptops-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:
- 706fa0437e3d8b98afe46b7b04def62dd20aa14ed83646b21dec8122698aa9da
- Size of remote file:
- 438 MB
- SHA256:
- 044528001fa520cf8b424c5e6d3f59cdd18e4eb3c0ecd4f34ef59ffc491d8c75
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