Feature Extraction
Transformers
Safetensors
Arabic
embedding_gemma2
arabic
ocr
document-ai
word-matching
multimodal-embedding
embeddinggemma
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("g3ida/ArabicWordsEmbedding")
model = AutoModel.from_pretrained("g3ida/ArabicWordsEmbedding", device_map="auto")Quick Links
ArabicWordsEmbedding
- Fine-tuned from google/embeddinggemma-2 (440M parameters with the audio encoder removed).
Training
- Real data: about 400k word crops from 3,000 scanned pages of classical Arabic printed books (seemorg/books-ocr, human-reviewed transcriptions). Word boxes come from Tesseract and are aligned to the transcription.
- Synthetic data: generated on the fly in about 50 fonts. Each word is rendered with full, partial or no tashkeel, attached punctuation and neighbouring words, then cropped like a word detector would and degraded like a scan (low resolution, blur, JPEG, binarisation, noise, rotation).
- Objective: contrastive (InfoNCE). Every batch contains lookalike words (same letters without dots), plus targeted pairs where tashkeel or punctuation imitates a dot.
- Method: LoRA on the vision tower and the transformer, merged into this checkpoint.
Limitations
- Trained and tested on printed Arabic book pages. Handwriting, scene text and decorative fonts were not evaluated.
- Real-data boxes came from Tesseract. A detector that cuts boxes very differently (much tighter, or several words per box) may need a quick check.
- Short words that differ by one prefix letter (ู / ุงู / ู) cause most of the remaining real errors.
- Matching inside one page is the intended use. Open-vocabulary recognition is weaker: 93.6% top-1 among 31.7k words.
License
Apache 2.0, the same as the base model google/embeddinggemma-2. Training pages come from seemorg/books-ocr (MIT).
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Model tree for g3ida/ArabicWordsEmbedding
Base model
google/embeddinggemma-2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="g3ida/ArabicWordsEmbedding")