Update Readme file

README.md

@@ -1,22 +1,82 @@
----
-base_model: unsloth/qwen2.5-vl-3b-instruct-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2_5_vl
-- trl
-license: apache-2.0
-language:
-- en
----
-
-# Uploaded  model
-
-- **Developed by:** OussamaBenSlama
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-vl-3b-instruct-bnb-4bit
-
-This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# Alef-OCR-Image2Html
+
+An Arabic OCR model that transforms document images,including historical texts, scanned pages, and handwritten materials—into structured and semantic HTML.
+
+### Key Features
+
+- **Semantic HTML Output:** Generates structured HTML with semantic tags (section, header, main, footer, table, etc.)
+- **Multi-format Support:** Handles various document types including historical manuscripts, newspaper articles, scientific papers, invoices, and more
+- **Arabic-Optimized:** Fine-tuned specifically for Arabic text recognition and structure extraction
+- **Zero-cost Training:** Developed using Kaggle's free tier computational resources
+
+## Model Architecture
+
+- **Base Model:** Qwen2.5-VL-Instruct
+- **Fine-tuning Method:** QLoRA with 4-bit quantization
+- **LoRA Configuration:** Rank 16 applied to all modules
+- **Optimization:** Unsloth for memory efficiency and training speed
+
+## Training Data
+
+The model was trained on a custom dataset of **28K image-HTML pairs** consisting of:
+
+- **46% Web-scraped content** (~13K samples): Arabic Wikipedia articles with cleaned semantic HTML
+- **54% Synthetic data** (~15K samples): Generated documents mimicking ~13 real-world formats with diverse layouts and styles
+
+For more details, see the [arabic-image2html dataset](https://huggingface.co/datasets/OussamaBenSlama/arabic-image2html).
+
+## Training Procedure
+
+Training was performed in two stages:
+
+**Stage 1:**
+- Data: 40% of training dataset
+- Learning rate: 5e-5
+- LR scheduler: Linear
+
+**Stage 2:**
+- Data: 30% of training dataset (different split)
+- Learning rate: 1e-5
+- LR scheduler: Cosine
+
+
+## Performance
+
+Evaluated by the NAMAA community on an anonymous benchmark dataset:
+
+| Model | WER | CER | BLEU |
+|-------|-----|-----|------|
+| Alef-OCR-Image2Html | 0.92 | **0.72** | **0.19** |
+| Qari-OCR-v0.3 (baseline) | **0.84** | 0.73 | 0.17 |
+
+**Key Results:**
+- Better Character Error Rate (CER): 0.72 vs 0.73
+- Better BLEU Score: 0.19 vs 0.17
+- Higher Word Error Rate (WER) due to limited diacritics handling in training data
+
+
+## Related Resources
+
+- **Dataset:** [arabic-image2html](https://huggingface.co/datasets/OussamaBenSlama/arabic-image2html)
+- **Training and Inference Notebooks:** [Available in the repository](https://github.com/OussamaBenSlama/Alef-OCR-Image2Html)
+
+## Citation
+
+```bibtex
+@misc{alef_ocr_image2html_2025,
+  title={Alef-OCR-Image2Html: Arabic OCR to Semantic HTML},
+  author={Oussama Ben Slama},
+  year={2025},
+  howpublished={Hugging Face Models},
+  url={https://huggingface.co/OussamaBenSlama/Alef-OCR-Image2Html}
+}
+```
+
+## Acknowledgments
+
+This work builds upon:
+- The NAMAA community's state-of-the-art Qari-OCR model
+
+## License
+
+Apache2.0