Instructions to use ronenlap/restaurants-48samples-AspectsExtraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ronenlap/restaurants-48samples-AspectsExtraction with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ronenlap/restaurants-48samples-AspectsExtraction") 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-AspectsExtraction with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ronenlap/restaurants-48samples-AspectsExtraction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db5a98a7692920ffcd3150840eb15829e27a8144d2983472fab50b9f5a43f9c7
- Size of remote file:
- 438 MB
- SHA256:
- 360ff98f9fbdd76f69e42c2b7c47d7dd25c56cdb8cb4c03d377556ea5d44b146
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.