| --- |
| license: apache-2.0 |
| language: |
| - ar |
| base_model: google/embeddinggemma-2 |
| library_name: transformers |
| pipeline_tag: feature-extraction |
| tags: |
| - arabic |
| - ocr |
| - document-ai |
| - word-matching |
| - multimodal-embedding |
| - embeddinggemma |
| datasets: |
| - seemorg/books-ocr |
| --- |
| |
| # ArabicWordsEmbedding |
|
|
| - Fine-tuned from [google/embeddinggemma-2](https://huggingface.co/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](https://huggingface.co/datasets/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 (ل / ال / و) 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](https://huggingface.co/google/embeddinggemma-2). |
| Training pages come from [seemorg/books-ocr](https://huggingface.co/datasets/seemorg/books-ocr) (MIT). |
|
|
