Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use HelgeKn/Swag-multi-class-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use HelgeKn/Swag-multi-class-6 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("HelgeKn/Swag-multi-class-6") - sentence-transformers
How to use HelgeKn/Swag-multi-class-6 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HelgeKn/Swag-multi-class-6") 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:
- e1d4dedfd6144761dbb8e077c3e6eb42887157039f79a0f28e49242ff86c48e9
- Size of remote file:
- 29.2 kB
- SHA256:
- 11ebf0c0dd91e7e3eb4b819b9834c23a4be814d9c83128fff91c40f58f120b30
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