Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use tstadel/answer-classification-setfit-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use tstadel/answer-classification-setfit-v2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("tstadel/answer-classification-setfit-v2") - sentence-transformers
How to use tstadel/answer-classification-setfit-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tstadel/answer-classification-setfit-v2") 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:
- e0a359cb8f8945ac85321b763465a6cdf749e97ede5a702238582cb38f91105f
- Size of remote file:
- 438 MB
- SHA256:
- 3605fe29e71bc28476e11cd8b9d0673a5c6142651639962a62f71aafcd2f00d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.