Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Saliltrehan7/setfit-bge-small-v1.5-sst2-10-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use Saliltrehan7/setfit-bge-small-v1.5-sst2-10-shot with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Saliltrehan7/setfit-bge-small-v1.5-sst2-10-shot") - sentence-transformers
How to use Saliltrehan7/setfit-bge-small-v1.5-sst2-10-shot with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Saliltrehan7/setfit-bge-small-v1.5-sst2-10-shot") 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:
- 1e386b67fbe2d9b233bef0cc7f1ececaeb29b3ee140c50d9eb213148f6216966
- Size of remote file:
- 3.94 kB
- SHA256:
- 932a4bdffe3743cc83a64ccb9a642e87c0de387c8995d392cc8a6f5103ff3251
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