Instructions to use edwsiew/tech-sentiment-setfit-cpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use edwsiew/tech-sentiment-setfit-cpu with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("edwsiew/tech-sentiment-setfit-cpu") 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 edwsiew/tech-sentiment-setfit-cpu with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("edwsiew/tech-sentiment-setfit-cpu") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9511f28624c8ccd9a5562b1345e12f7aec444146ebb56ca90c6e305d41ca80d2
- Size of remote file:
- 19.3 kB
- SHA256:
- c1134f3253811374874b143c711309606b8f7642b7c8fee63d7a63ec9698e226
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.