Instructions to use scroobiustrip/sov-model-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use scroobiustrip/sov-model-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("scroobiustrip/sov-model-v1") 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 scroobiustrip/sov-model-v1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("scroobiustrip/sov-model-v1") 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 scroobiustrip/sov-model-v1: direct link, hf CLI and curl.
- Browser
- Download file 155 kB
-
https://huggingface.co/scroobiustrip/sov-model-v1/resolve/main/model_head.pkl
- Command line
-
hf download hf://scroobiustrip/sov-model-v1/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/scroobiustrip/sov-model-v1/resolve/main/model_head.pkl
155 kB
- Xet hash:
- c53dd35a5435ea19026999277bed15a2ba6c73e30e7a7d42642b73c538689f1c
- Size of remote file:
- 155 kB
- SHA256:
- 96078bd48253789078c41747490cf37e10beb832232db632c1301669ee22ae5e
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