Instructions to use YakovElm/Apache15SetFitModel_clean_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YakovElm/Apache15SetFitModel_clean_data with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YakovElm/Apache15SetFitModel_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/Apache15SetFitModel_clean_data with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("YakovElm/Apache15SetFitModel_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/Apache15SetFitModel_clean_data: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Apache15SetFitModel_clean_data/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/Apache15SetFitModel_clean_data/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/Apache15SetFitModel_clean_data/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 4c43b9b35ee5d0f09ca605a4486d37f57b667e42e692d94151326e860b327c33
- Size of remote file:
- 438 MB
- SHA256:
- d41a088d8d550e975542e7af9fccc2b32e1f4687eb7bfa0b9f1e346728fbc0c1
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