Instructions to use ernieg/setfit-beauty-multilabel-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ernieg/setfit-beauty-multilabel-example with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ernieg/setfit-beauty-multilabel-example") 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 ernieg/setfit-beauty-multilabel-example with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ernieg/setfit-beauty-multilabel-example") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ernieg/setfit-beauty-multilabel-example: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ernieg/setfit-beauty-multilabel-example/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ernieg/setfit-beauty-multilabel-example/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ernieg/setfit-beauty-multilabel-example/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- dcf7b4273bf713e412603e3e0d3f60baa2e65a7ea11f2a9b0810a19f0cd54043
- Size of remote file:
- 438 MB
- SHA256:
- 17debeec7aded90c2e1d95d9f6a3b60fefe71dbc6c7d1ab9ff5fb9aeaedb7998
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