Instructions to use YakovElm/IntelDAOS5SetFitModel_clean_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YakovElm/IntelDAOS5SetFitModel_clean_data with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YakovElm/IntelDAOS5SetFitModel_clean_data") 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 YakovElm/IntelDAOS5SetFitModel_clean_data with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("YakovElm/IntelDAOS5SetFitModel_clean_data") 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 YakovElm/IntelDAOS5SetFitModel_clean_data: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS5SetFitModel_clean_data/resolve/86baecaee3528444ce5a5d8acbca9ffd22cf0a36/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/IntelDAOS5SetFitModel_clean_data@86baecaee3528444ce5a5d8acbca9ffd22cf0a36/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/IntelDAOS5SetFitModel_clean_data/resolve/86baecaee3528444ce5a5d8acbca9ffd22cf0a36/pytorch_model.bin
438 MB
- Xet hash:
- 62a904b44774eb321130838e22a0aec2ff56016ba0c6f5f6cf8d18edef0693d8
- Size of remote file:
- 438 MB
- SHA256:
- 7773238daef3c5f00e80ac16c749a37a5ab058f2c1e2569c02afff4a5ccd524d
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