Omartificial-Intelligence-Space / AraGemma-Embedding-300m

AraGemma-Embedding-300m

Model Page : AraGemma-Embedding (Hugging Face)

Authors : Google DeepMind (base model), fine-tuned by Omartificial-Intelligence-Space

Find More About : Arabic Semantic Embeddings Models

Simple RAG and Other NLP Tasks Example:

RAG & NLP Tasks Notebook

Model Overview

AraGemma-Embedding-300m is a fine-tuned version of EmbeddingGemma-300M , optimized for Arabic semantic understanding .
This model was fine-tuned using 1 million Arabic triplet pairs (anchor, positive, negative) with Matryoshka Representation Learning (MRL) to enhance semantic similarity, clustering, classification, and retrieval for Arabic texts.

It builds on Google’s Gemma 3 research, making it lightweight, efficient, and deployable on-device (mobile, laptops, desktops) while achieving state-of-the-art Arabic semantic embedding performance .

Model Information

Input

• Text string (Arabic or multilingual)
• Maximum context length: 2048 tokens

Output

• Dense vector representation of size 768
• Supports MRL truncation to 512, 256, or 128 dimensions with re-normalization

Performance

Benchmark Results

Significant improvements show stronger semantic Arabic understanding .

Usage

This model is compatible with Sentence Transformers and Hugging Face Transformers .

from sentence_transformers import SentenceTransformer

# Load the Arabic-optimized embedding model
model = SentenceTransformer("Omartificial-Intelligence-Space/AraGemma-Embedding-300m")

# Example: Arabic semantic similarity
query = "ما هو الكوكب الأحمر؟"
documents = [
    "الزهرة تشبه الأرض في الحجم والقرب.",
    "المريخ يسمى بالكوكب الأحمر بسبب لونه المميز.",
    "المشتري أكبر كواكب المجموعة الشمسية.",
    "زحل يتميز بحلقاته الشهيرة."
]

query_embedding = model.encode(query)
doc_embeddings = model.encode(documents)

# Compute cosine similarities
from torch import cosine_similarity
import torch

query_tensor = torch.tensor(query_embedding)
doc_tensors = torch.tensor(doc_embeddings)
similarities = cosine_similarity(query_tensor.unsqueeze(0), doc_tensors)

print(similarities)

Applications

• Semantic Chunking for RAG (Retrieval-Augmented Generation)
• Semantic Search & Retrieval (Arabic focus)
• Clustering and Classification of Arabic documents
• Cross-lingual retrieval (multilingual data supported)

Limitations

• Embedding activations do not support float16 – use float32 or bfloat16.

Citation

If you use this model in your work, please cite:

@misc{AraGemmaEmbedding2025,
  title={AraGemma-Embedding: Fine-tuned EmbeddingGemma for Arabic Semantic Understanding},
  author={Omartificial-Intelligence-Space},
  year={2025},
  url={https://huggingface.co/Omartificial-Intelligence-Space/AraGemma-Embedding-300m}
}