Sentence Similarity
sentence-transformers
Safetensors
Arabic
modernbert
feature-extraction
Generated from Trainer
loss:MultipleNegativesRankingLoss
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use NAMAA-Space/AraModernBert-Base-STS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NAMAA-Space/AraModernBert-Base-STS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NAMAA-Space/AraModernBert-Base-STS") sentences = [ "الذكاء الاصطناعي يغير طريقة تفاعلنا مع التكنولوجيا.", "التكنولوجيا تتطور بسرعة بفضل الذكاء الاصطناعي.", "الذكاء الاصطناعي يسهم في تطوير التطبيقات الذكية.", "تحديات الذكاء الاصطناعي تشمل الحفاظ على الأمان والأخلاقيات." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from NAMAA-Space/AraModernBert-Base-STS: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/NAMAA-Space/AraModernBert-Base-STS/resolve/main/modules.json
- Command line
-
hf download hf://NAMAA-Space/AraModernBert-Base-STS/modules.json
-
curl -L -o modules.json https://huggingface.co/NAMAA-Space/AraModernBert-Base-STS/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |