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:
- 6b9b0d60e0701a9f833d5cde253db23696e88a07129de047fd1e08054c036b92
- Size of remote file:
- 151 kB
- SHA256:
- f4e06d7316a0d6e38c66a511b652f7d2e2687b56da81f3146dcd9cc1cda4642f
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