Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:17702
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use mohsayed/para_tr_enar_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mohsayed/para_tr_enar_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mohsayed/para_tr_enar_1") sentences = [ "zonivan 100 mg 20 caps.", "fuci-top 2% cream 15 gm", "فاستاتينال 10/40مجم 7 قرص", "زونيفان 100 مجم 20 كبسولة" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle