Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Gopal2002/NASFUND_MODEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use Gopal2002/NASFUND_MODEL with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Gopal2002/NASFUND_MODEL") - sentence-transformers
How to use Gopal2002/NASFUND_MODEL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Gopal2002/NASFUND_MODEL") 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:
- ca3e34c4175373c547882e692346a6f2de1105694f22755971ad520a281b19ad
- Size of remote file:
- 133 MB
- SHA256:
- 76dd486c748faa620962c3c66134e4397764af0d23f90e6d33305a1a0191499f
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