Instructions to use mohamed2811/Muffakir_Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mohamed2811/Muffakir_Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mohamed2811/Muffakir_Embedding") sentences = [ "هذا شخص سعيد", "هذا كلب سعيد", "هذا شخص سعيد جدا", "اليوم هو يوم مشمس" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 229 Bytes
5db55c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.models.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.models.Pooling"
}
] |