Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
Generated from Trainer
dataset_size:166507
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Abdelkareem/Abjd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Abdelkareem/Abjd with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Abdelkareem/Abjd") sentences = [ "التطبيق الثاني، النادر، هو عندما يتم التشكيك في تأكيد عام أو عالمي للغاية، ونحن قادرون على اختباره من خلال فحص حالة واحدة.", "هناك على الأقل تطبيقان يمكن استخدامهما.", "كلية الثالوث تأسست كمركز للتعلم البروتستانتي.", "التطبيق الثاني ليس نادرًا على الإطلاق ويتم استخدامه عادة." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Abdelkareem/Abjd: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Abdelkareem/Abjd/resolve/main/tokenizer.json
- Command line
-
hf download hf://Abdelkareem/Abjd/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Abdelkareem/Abjd/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- d41600527dabf13a4df6c25c6fc6c2982fee612c6b7a58976dd550c5bb7aa5af
- Size of remote file:
- 11.4 MB
- SHA256:
- 7a5b90ffcbd8fe896c9ee9fe56c5dd84116f876ad5cdbe0d1424fbe150f41ca6
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