Instructions to use YakovElm/IntelDAOS10SetFitModel_Train_balance_ratio_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YakovElm/IntelDAOS10SetFitModel_Train_balance_ratio_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YakovElm/IntelDAOS10SetFitModel_Train_balance_ratio_1") 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_Train_balance_ratio_1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("YakovElm/IntelDAOS10SetFitModel_Train_balance_ratio_1") 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/IntelDAOS10SetFitModel_Train_balance_ratio_1: direct link, hf CLI and curl.
- Browser
- Download file 6.99 kB
-
https://huggingface.co/YakovElm/IntelDAOS10SetFitModel_Train_balance_ratio_1/resolve/main/model_head.pkl
- Command line
-
hf download hf://YakovElm/IntelDAOS10SetFitModel_Train_balance_ratio_1/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/YakovElm/IntelDAOS10SetFitModel_Train_balance_ratio_1/resolve/main/model_head.pkl
6.99 kB
- Xet hash:
- 4e99a9c4b30f15f71ad8f839767cd8bc10f74d4f07a926ffe84ec6ffd33f1772
- Size of remote file:
- 6.99 kB
- SHA256:
- 8f34406c602190d8a0e54c457b478213d1c05afc9b4a4248c1a1ef7cb7912566
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