Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use promforge/so_mpnet-base_question_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use promforge/so_mpnet-base_question_classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("promforge/so_mpnet-base_question_classifier") - sentence-transformers
How to use promforge/so_mpnet-base_question_classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("promforge/so_mpnet-base_question_classifier") 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:
- 13f621eb29324bc1a655dc5e0318d49b46f5bc7e7d90a38fb8a91e3ce02accb9
- Size of remote file:
- 438 MB
- SHA256:
- d2f525cfb8e8b3946018793494ac6a26049d8ed198c5d20c983cf73ec90efb45
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