Feature Extraction
sentence-transformers
Safetensors
Arabic
bert
sentence-similarity
dense
Generated from Trainer
dataset_size:2964
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use AzeerDev/SA-STS-Embeddings-0.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AzeerDev/SA-STS-Embeddings-0.2B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AzeerDev/SA-STS-Embeddings-0.2B") sentences = [ "كم تكلفة رحلة بحرية ليوم؟", "الباحثين يحلّلوا تأثير البيئة على اختلاف العادات بين المناطق.", "أبي محلات فيها بضاعة عالمية مشهورة.", "بكم أسعار الجولات البحرية اليومية؟" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from AzeerDev/SA-STS-Embeddings-0.2B: direct link, hf CLI and curl.
- Browser
- Download file 2.69 MB
-
https://huggingface.co/AzeerDev/SA-STS-Embeddings-0.2B/resolve/main/tokenizer.json
- Command line
-
hf download hf://AzeerDev/SA-STS-Embeddings-0.2B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/AzeerDev/SA-STS-Embeddings-0.2B/resolve/main/tokenizer.json
2.69 MB
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