Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use BenPhan/demo-sentence-bert1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use BenPhan/demo-sentence-bert1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("BenPhan/demo-sentence-bert1") - sentence-transformers
How to use BenPhan/demo-sentence-bert1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BenPhan/demo-sentence-bert1") 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:
- 505cee1488d554ea017681eb338929f6fd1028df00e8e42923e4ea11b7d92b6c
- Size of remote file:
- 37.8 kB
- SHA256:
- c0b2605216f9ff0319cad005dc9b27a55028fbf6c48870bf796c7cf2a589ceaf
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