Instructions to use kimnt93/test_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kimnt93/test_cls with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kimnt93/test_cls") 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 kimnt93/test_cls with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("kimnt93/test_cls") 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 kimnt93/test_cls: direct link, hf CLI and curl.
- Browser
- Download file 19.3 kB
-
https://huggingface.co/kimnt93/test_cls/resolve/main/model_head.pkl
- Command line
-
hf download hf://kimnt93/test_cls/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/kimnt93/test_cls/resolve/main/model_head.pkl
19.3 kB
- Xet hash:
- b8c9b14b14590df805bc32578054e818bb147f6ce34b6cbfb285472a9c589248
- Size of remote file:
- 19.3 kB
- SHA256:
- b844035edbac4647c2e8a83a4c167e24ae5fe8186a16274c49c28bbb6750551e
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