Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use tstadel/answer-classification-setfit-v2-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use tstadel/answer-classification-setfit-v2-binary with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("tstadel/answer-classification-setfit-v2-binary") - sentence-transformers
How to use tstadel/answer-classification-setfit-v2-binary with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tstadel/answer-classification-setfit-v2-binary") 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:
- e19fd7af7fc1ea66848500752941872987d1843a65bdd863fe24b82c20a48158
- Size of remote file:
- 438 MB
- SHA256:
- 752d8365803ed5e0838404b5972fae6848554b8a1e8d75089d5f3b95a1a03494
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