Instructions to use yjlee1011/ncodeR_data_setfit_multilabel_32_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_32_samples with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yjlee1011/ncodeR_data_setfit_multilabel_32_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_32_samples with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("yjlee1011/ncodeR_data_setfit_multilabel_32_samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f4f10cbcb6024420c15ab6c535f257912db3ad64853f7dad494b636707b2451
- Size of remote file:
- 438 MB
- SHA256:
- 595733ff4d2a01357b4631c6e3ad1a191d6a32888465920151579492b139fc0f
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