Instructions to use mahaswec/setfit_ostrom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mahaswec/setfit_ostrom with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mahaswec/setfit_ostrom") 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 mahaswec/setfit_ostrom with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("mahaswec/setfit_ostrom") 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 mahaswec/setfit_ostrom: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/mahaswec/setfit_ostrom/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mahaswec/setfit_ostrom/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mahaswec/setfit_ostrom/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 2f05f1c5f9ba60c9816ac36bff3602aaeb39102a55d124d68c1fa8554a3724f3
- Size of remote file:
- 438 MB
- SHA256:
- e920604aec94126332fa02c81fa959fd3b18df9796c314d79d6090fcdbb1e268
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.