Instructions to use SarahALo/Arabic_embed_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use SarahALo/Arabic_embed_model with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("SarahALo/Arabic_embed_model") - sentence-transformers
How to use SarahALo/Arabic_embed_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SarahALo/Arabic_embed_model") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d696bba366c746c13ed162109fd03efda65668a55a6e664487b83350a7a0f9b2
- Size of remote file:
- 34.8 MB
- SHA256:
- 2ac80f57e7805d968c8e03c2a937b0d0d51124288c837d9969f205a23ef3183a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.