Instructions to use YakovElm/IntelDAOS10SetFitModel_clean_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YakovElm/IntelDAOS10SetFitModel_clean_data with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YakovElm/IntelDAOS10SetFitModel_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/IntelDAOS10SetFitModel_clean_data with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("YakovElm/IntelDAOS10SetFitModel_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/IntelDAOS10SetFitModel_clean_data: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS10SetFitModel_clean_data/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/IntelDAOS10SetFitModel_clean_data/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/IntelDAOS10SetFitModel_clean_data/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 2cab55aa44fc73f4cd33e28e94a0dfc17df5e8a9dde1f4cddf74127def5a0de7
- Size of remote file:
- 438 MB
- SHA256:
- 79aa2469ca9c288d5d0daafcfe23946cbf680af46c190529e45bf865c8399237
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