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:
- f3f80b83e8c138049ab5ef31c2c10b88b30eff408f68b1f5bc07e5b2724de0ed
- Size of remote file:
- 38 kB
- SHA256:
- 7d3a70f7ca14d3173bb97c07c6a6577393e9ce1b1eb2355b955e0b168ea8853b
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