Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use CabraVC/emb_classifier_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use CabraVC/emb_classifier_model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("CabraVC/emb_classifier_model") - sentence-transformers
How to use CabraVC/emb_classifier_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CabraVC/emb_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:
- 014a39fc334370ad623fb039219c9a82a3d9a9ff85d4c6e6885c6e615557ac7a
- Size of remote file:
- 799 kB
- SHA256:
- d25580b23d1c52463a4911b84bf4dd4b6bf008b02e9c9b60d17f7c018e021375
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