Instructions to use ernieg/setfit-all-multilabel-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ernieg/setfit-all-multilabel-example with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ernieg/setfit-all-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-all-multilabel-example with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ernieg/setfit-all-multilabel-example") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download model_head.pkl from ernieg/setfit-all-multilabel-example: direct link, hf CLI and curl.
- Browser
- Download file 513 kB
-
https://huggingface.co/ernieg/setfit-all-multilabel-example/resolve/main/model_head.pkl
- Command line
-
hf download hf://ernieg/setfit-all-multilabel-example/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/ernieg/setfit-all-multilabel-example/resolve/main/model_head.pkl
513 kB
- Xet hash:
- 5188853b78bc1ec49708ce060c6d80fee9dadf8d3d0e6d1a5ae448d5966eb0d3
- Size of remote file:
- 513 kB
- SHA256:
- b1ac0ee0cdc44b835de153725bebb2823c64213dbaa5eeffa61bce2ec5c64b38
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.