Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use abehandler/setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use abehandler/setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("abehandler/setfit") - sentence-transformers
How to use abehandler/setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("abehandler/setfit") 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:
- 83bba43616c0d8a63a09bd932e816ec424f62da92a2ca1ce571dcad20214c1f4
- Size of remote file:
- 133 MB
- SHA256:
- daa3dd0b9c1cf8067cc8f93083ca7cd9835014a42bc3fd1b611993a2c01c765f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.