Instructions to use YakovElm/IntelDAOS15SetFitModel_Train_balance_ratio_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YakovElm/IntelDAOS15SetFitModel_Train_balance_ratio_2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YakovElm/IntelDAOS15SetFitModel_Train_balance_ratio_2") 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/IntelDAOS15SetFitModel_Train_balance_ratio_2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("YakovElm/IntelDAOS15SetFitModel_Train_balance_ratio_2") 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 YakovElm/IntelDAOS15SetFitModel_Train_balance_ratio_2: direct link, hf CLI and curl.
- Browser
- Download file 6.99 kB
-
https://huggingface.co/YakovElm/IntelDAOS15SetFitModel_Train_balance_ratio_2/resolve/main/model_head.pkl
- Command line
-
hf download hf://YakovElm/IntelDAOS15SetFitModel_Train_balance_ratio_2/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/YakovElm/IntelDAOS15SetFitModel_Train_balance_ratio_2/resolve/main/model_head.pkl
6.99 kB
- Xet hash:
- 567aa8e872c08e81fe28d70e0e80626ee14e5b44acd311f59de28cfb78466891
- Size of remote file:
- 6.99 kB
- SHA256:
- 1ec3c0aaec3176989f4289da735791355e52a4e60f9dd9201c56651918fe9308
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