Instructions to use ronenlap/restaurants-48samples-PolarityDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ronenlap/restaurants-48samples-PolarityDetection with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ronenlap/restaurants-48samples-PolarityDetection") 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 ronenlap/restaurants-48samples-PolarityDetection with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ronenlap/restaurants-48samples-PolarityDetection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e426d8110f56d89023553021a9ccb58420b705f192cf8b8cbb93da44f416e462
- Size of remote file:
- 438 MB
- SHA256:
- 444821230d882b483a1c7213d1b3a8769c3ad2ed466e66516fb16450e04ae18f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.