Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use ThomBors/NLBSE2026-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use ThomBors/NLBSE2026-python with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ThomBors/NLBSE2026-python") - sentence-transformers
How to use ThomBors/NLBSE2026-python with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ThomBors/NLBSE2026-python") 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:
- 586f05fb00ede44879d2ef4dd5f84cfd6a5c7f27e3abe0066fc7dd457b40745f
- Size of remote file:
- 90.9 MB
- SHA256:
- 4ef52908a5f72ac2f3cdef0d7a47b24f5438844d2884097af0c97c16f180b19a
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