Instructions to use yjlee1011/ncodeR_data_multilabel_16samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yjlee1011/ncodeR_data_multilabel_16samples with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yjlee1011/ncodeR_data_multilabel_16samples") 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_16samples with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("yjlee1011/ncodeR_data_multilabel_16samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b66cab950d5d51eba22193f3cc77d0600c780d617adb31fe32feb312c922a71f
- Size of remote file:
- 438 MB
- SHA256:
- 0a31e0c5fa41438e2937dc7dae977ed17d7fe4dcb2606fc72a0469df0d4c9b08
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