Text Classification
setfit
Safetensors
sentence-transformers
new
generated_from_setfit_trainer
custom_code
text-embeddings-inference
Instructions to use tmp-org/tmp_cv_model_2025_09_29_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use tmp-org/tmp_cv_model_2025_09_29_0 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("tmp-org/tmp_cv_model_2025_09_29_0") - sentence-transformers
How to use tmp-org/tmp_cv_model_2025_09_29_0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tmp-org/tmp_cv_model_2025_09_29_0", trust_remote_code=True) 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:
- a1453bd808efc0cdedc35bb2d7073bfcd8d3d70bca1658f310d33c7dbd2ee345
- Size of remote file:
- 17.1 MB
- SHA256:
- aa7a6ad87a7ce8fe196787355f6af7d03aee94d19c54a5eb1392ed18c8ef451a
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