Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use Kurrant/RevenueStreamJP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use Kurrant/RevenueStreamJP with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Kurrant/RevenueStreamJP") - sentence-transformers
How to use Kurrant/RevenueStreamJP with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Kurrant/RevenueStreamJP") 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:
- e7463c1c6b7c46ae0de79b004f3d7318bf4307ef0bef4de3be2e38ae80fab160
- Size of remote file:
- 792 kB
- SHA256:
- d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
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