Instructions to use yjlee1011/ncodeR_data_multilabel_32samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yjlee1011/ncodeR_data_multilabel_32samples with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yjlee1011/ncodeR_data_multilabel_32samples") 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_multilabel_32samples with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("yjlee1011/ncodeR_data_multilabel_32samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d77a07f27f62c5baddc74c4abb8bc0c55c6c7f220e1e1b01a8677204ec85551f
- Size of remote file:
- 438 MB
- SHA256:
- adc542368ca86a79e6dae9b702b80450f62b87dc2022585966129959e1076e07
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