Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use COURSEMO/physics-classifier-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use COURSEMO/physics-classifier-model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("COURSEMO/physics-classifier-model") - sentence-transformers
How to use COURSEMO/physics-classifier-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("COURSEMO/physics-classifier-model") 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:
- b59317bc17cfc8667020e2f7a5c367d9eebe817d0ae36f2cd8ca14b68a37b452
- Size of remote file:
- 438 MB
- SHA256:
- 4fab6b6ed375c11c622327aa5c41c99bfc5e1b7d6410c182c231bb0492fe35d8
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