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:
- e89ec27aa65828e6cb78c0b43411d91c7bb0ce2fafe286466714df8b0623e3c0
- Size of remote file:
- 438 MB
- SHA256:
- 4472458f2bd39954ada83d8bdab621284a049e952e2b8f8a6a8e08aad89f5b69
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