Sentence Similarity
sentence-transformers
ONNX
Safetensors
Arabic
English
xlm-roberta
passage-retrieval
pruned
text-embeddings-inference
Instructions to use sayed0am/arabic-english-bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sayed0am/arabic-english-bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sayed0am/arabic-english-bge-m3") sentences = [ "هذا شخص سعيد", "هذا كلب سعيد", "هذا شخص سعيد جدا", "اليوم هو يوم مشمس" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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RANKED #3 in general and #1 Open source @ [The-Arabic-Rag-Leaderboard](https://huggingface.co/spaces/Navid-AI/The-Arabic-Rag-Leaderboard)
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The ONNX quantized version is
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This pruned model should perform similarly to the original model for Arabic language tasks with a much smaller
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memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not
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RANKED #3 in general and #1 Open source @ [The-Arabic-Rag-Leaderboard](https://huggingface.co/spaces/Navid-AI/The-Arabic-Rag-Leaderboard)
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The ONNX quantized version is around 75% smaller (363 MB) than the pruned model, while retaining approximately 98% of the original model's quality.
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This pruned model should perform similarly to the original model for Arabic language tasks with a much smaller
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memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not
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