Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:5000000
loss:combine_dstilationLoss_studentLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use hatemestinbejaia/R3mmarco-Arabic-mMiniLML-bi-encoder-KD-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hatemestinbejaia/R3mmarco-Arabic-mMiniLML-bi-encoder-KD-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hatemestinbejaia/R3mmarco-Arabic-mMiniLML-bi-encoder-KD-v1") sentences = [ "ما هو كور يعني", "تعريف cor . : وحدة قياس سعة عبرية وفينيقية قديمة .", "رفعت منظمة صندوق ادخار الموظفين ( EPFO ) الحد الأدنى للراتب إلى 15000 روبية من 6500 روبية في وقت سابق .", "شكل الجمع بين الجلطة - o يعني _ _ . صيغة الجمع embol - o تعني _ _ . اللاحقة التي تعني أداة قياس الضغط هي _ _ . لاحقة غير كلها تعني صغيرة هي _ _ . اللاحقة التي تعني التصلب هي _ _ ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 742 Bytes
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