Instructions to use yjlee1011/ncodeR_data_setfit_multilabel_64_samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yjlee1011/ncodeR_data_setfit_multilabel_64_samples with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yjlee1011/ncodeR_data_setfit_multilabel_64_samples") 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] - setfit
How to use yjlee1011/ncodeR_data_setfit_multilabel_64_samples with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("yjlee1011/ncodeR_data_setfit_multilabel_64_samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08232c600885f7e6dada8939960a0581c2bbe30678c9649c6c834d8e6954fc00
- Size of remote file:
- 438 MB
- SHA256:
- a5ac96514303b515812b034aaf1b31012ea71dadd6b12c12be00ef5dcd1d0067
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