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 pytorch_model.bin from kimnt93/test_cls: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/kimnt93/test_cls/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kimnt93/test_cls/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kimnt93/test_cls/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 42dc5511129fef0e40006cdc8af3a559736eead0cc2fed1d80636909d2a43902
- Size of remote file:
- 1.11 GB
- SHA256:
- 9846ce43e2ef6bd5e427c51140499d0c4ead650eed300fd5e2aa9026e13761b1
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