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:
- 38aefab728ed8afd39f023e6a54188c82b2c8b27f7fd4617eb75ffc0dfa95a4a
- Size of remote file:
- 328 MB
- SHA256:
- 62937138265b78d1dc60d3bd3d4bf2939317223c02e21b6281187015cc7acb9a
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