Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use ITOCJ/SciGenSetfit3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use ITOCJ/SciGenSetfit3 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ITOCJ/SciGenSetfit3") - sentence-transformers
How to use ITOCJ/SciGenSetfit3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ITOCJ/SciGenSetfit3", 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:
- e77fc1811f4aa32dc05d5f1f56f55d8796b34c6a4d71e0a5403b221276e598ad
- Size of remote file:
- 55 kB
- SHA256:
- f033529ac4cb485d155c9a7f4466798a188186c7c1c599327842835c47c9c7a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.