Instructions to use MSG3/setfit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MSG3/setfit_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MSG3/setfit_model") 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 MSG3/setfit_model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("MSG3/setfit_model") 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 MSG3/setfit_model: direct link, hf CLI and curl.
- Browser
- Download file 106 kB
-
https://huggingface.co/MSG3/setfit_model/resolve/main/model_head.pkl
- Command line
-
hf download hf://MSG3/setfit_model/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/MSG3/setfit_model/resolve/main/model_head.pkl
106 kB
- Xet hash:
- 2084b3feeb0dc8cc50c04adc4955355b04984f59b9e0869a95a80ce3928df56d
- Size of remote file:
- 106 kB
- SHA256:
- 28021e3b116e48adb148a571755f82bf142edf95c0eea84d8d53a63d535e4c62
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