Instructions to use YakovElm/IntelDAOS10SetFitModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YakovElm/IntelDAOS10SetFitModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YakovElm/IntelDAOS10SetFitModel") 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/IntelDAOS10SetFitModel with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("YakovElm/IntelDAOS10SetFitModel") 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/IntelDAOS10SetFitModel: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS10SetFitModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/IntelDAOS10SetFitModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/IntelDAOS10SetFitModel/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 3ede83bb6ca7ec5b87a78fea7d714b6c3a2543488e0f71cdf3060dfe0b79b267
- Size of remote file:
- 438 MB
- SHA256:
- d94c3b3dddf1415bf47cc028e3a5e9de0bd332d8360f3e142555b1872ca20dc7
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