Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use promforge/sbert-questionclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use promforge/sbert-questionclassifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("promforge/sbert-questionclassifier") - sentence-transformers
How to use promforge/sbert-questionclassifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("promforge/sbert-questionclassifier") 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:
- 78702331476b1dbefb4324c4a93c5c6c801b1eb3656fadace9603a156bc2a7df
- Size of remote file:
- 7.01 kB
- SHA256:
- ab1ee698a2785bc53d5baec1ab0eba85b444eb4f03d64615c5853b19caee6ee8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.