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:
- 790acfe9ed22fe5ce908f1f073eaa54f4e9399f1c154386b5da41ad06b9cced3
- Size of remote file:
- 25.6 kB
- SHA256:
- 997c8cb6395841b75b502d811d3151392313529db6da426d09843f0d25b78faf
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