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Model: alnnahwi/gemma-3-1b-arabic-gec-v1 Source: Original Platform
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README.md
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README.md
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---
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license: gemma
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library_name: transformers
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language:
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- ar
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base_model:
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- google/gemma-3-1b-pt
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pipeline_tag: text-generation
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tags:
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- arabic
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- grammatical-error-correction
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- gemma
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- unsloth
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- arabic-nlp
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---
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# Gemma 3 1B Arabic Grammatical Error Correction v1
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## Model Description
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This model is a fine-tuned version of Google's Gemma 3 1B model, specifically trained for Arabic Grammatical Error Correction (GEC) by Alnnahwi. The model takes Arabic sentences as input and outputs their grammatically corrected versions.
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**Developed by**: Bahjat Al Mostafa (Alnnahwi)
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**Base Model:** google/gemma-3-1b
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**Task:** Grammatical Error Correction
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**Language:** Arabic
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**Version:** 1.0.0
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**Organization**: [Alnnahwi](https://alnnahwi.com/)
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## Quick Start
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### Installation
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```bash
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pip install transformers torch
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```
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### Basic Usage
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```python
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from transformers import pipeline, AutoTokenizer
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import torch
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MODEL_NAME = "alnnahwi/gemma-3-1b-arabic-gec-v1"
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def extract_model_response(generated_text):
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"""Extract just the model's response from the full generated text."""
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# Find the position after "model" marker
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model_marker = "\nmodel\n"
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if model_marker in generated_text:
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response_start = generated_text.find(model_marker) + len(model_marker)
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return generated_text[response_start:].strip()
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# Alternative format (in case formatting changes)
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alt_marker = "model\n"
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if alt_marker in generated_text:
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response_start = generated_text.find(alt_marker) + len(alt_marker)
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return generated_text[response_start:].strip()
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# If markers not found, return the original text
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return generated_text
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# Initialize the tokenizer
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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# Add Gemma chat template manually
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tokenizer.chat_template = """{% for message in messages %}{{'<start_of_turn>' + message['role'] + '\n' + message['content'] + '<end_of_turn>\n'}}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}"""
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# Device selection
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if torch.backends.mps.is_available():
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device = "mps"
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elif torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# Create pipeline
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pipe = pipeline(
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"text-generation",
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model=MODEL_NAME,
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tokenizer=tokenizer,
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device=device,
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)
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def correct_arabic_text(text):
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"""Correct Arabic text using the fine-tuned model."""
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messages = [{"role": "user", "content": text}]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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outputs = pipe(
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prompt,
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max_new_tokens=512,
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do_sample=False, # Use greedy decoding for evaluation consistency
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temperature=None,
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top_p=None,
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top_k=None,
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)
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full_text = outputs[0]["generated_text"]
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return extract_model_response(full_text)
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# Example usage with real outputs
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test_inputs = [
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"كيف حالكي اليوم؟",
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"وجدنا سبعون حالة",
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"جاء في تسعة و سبعين سورة.",
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"لاكن ما رايكم",
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]
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for text in test_inputs:
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corrected = correct_arabic_text(text)
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print(f"Original: {text}")
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print(f"Corrected: {corrected}")
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print("-" * 50)
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# Expected output:
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# Original: كيف حالكي اليوم؟
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# Corrected: كيف حالك اليوم؟
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# --------------------------------------------------
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# Original: وجدنا سبعون حالة
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# Corrected: وجدنا سبعين حالة
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# --------------------------------------------------
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# Original: جاء في تسعة و سبعين سورة.
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# Corrected: جاء في تسع وسبعين سورة.
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# --------------------------------------------------
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# Original: لاكن ما رايكم
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# Corrected: لكن ما رأيكم؟
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# --------------------------------------------------
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```
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### Example Corrections
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| Input (Incorrect) | Output (Corrected) | Error Type |
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|---|---|---|
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| كيف حالكي اليوم؟ | كيف حالك اليوم؟ | Gender agreement |
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| وجدنا سبعون حالة | وجدنا سبعين حالة | Number declension |
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| جاء في تسعة و سبعين سورة. | جاء في تسع وسبعين سورة. | Number gender + spacing |
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| لاكن ما رايكم | لكن ما رأيكم؟ | Spelling + punctuation |
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## Model Details
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### Training Data
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- **Dataset**: Custom Arabic GEC dataset
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- **Training Epochs**: 7
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- **Base Architecture**: Gemma 3 1B parameters
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### Performance
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- Designed for Modern Standard Arabic (MSA).
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- Handles common grammatical errors.
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### Limitations
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- Primarily trained on Modern Standard Arabic
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- May not handle dialectical Arabic variations optimally
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- Performance may vary with very long texts (>512 tokens)
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- Context-dependent corrections may sometimes be imperfect
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## Use Cases
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- **Educational Tools**: Helping Arabic learners with gender agreement and number declension
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- **Content Creation**: Proofreading Arabic content for grammatical accuracy
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- **Text Processing**: Preprocessing Arabic text for downstream NLP tasks
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- **Writing Assistance**: Supporting writers with:
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- Proper number-noun agreement
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- Correct case declensions
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- Spelling standardization
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- Punctuation normalization
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- **Academic Writing**: Ensuring grammatical correctness in formal Arabic texts
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## Training Details
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- **Fine-tuning Framework**: Unsloth
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- **Base Model**: Gemma 3 1B
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- **Training Epochs**: 7
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- **Optimization**: Memory-efficient fine-tuning techniques
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## Citation
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If you use this model in your research or applications, please cite:
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```bibtex
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@misc{gemma3-arabic-gec-v1,
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title={Gemma 3 1B Arabic Grammatical Error Correction v1},
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author={Bahjat Al Mostafa},
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organization={Alnnahwi},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/alnnahwi/gemma-3-1b-arabic-gec-v1},
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website={https://alnnahwi.com/}
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}
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```
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## License
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This model is released under the same license as the base Gemma model. Please refer to Google's Gemma license for usage terms and conditions.
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**Important**: This model is based on Google's Gemma and is subject to Google's AI Principles and licensing terms.
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## Acknowledgments
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- Built upon Google's Gemma 3 1B model
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- Fine-tuned using Unsloth framework
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- Trained for Arabic Grammatical Error Correction
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- Developed by Bahjat Al Mostafa at Alnnahwi
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- Visit [Alnnahwi](https://alnnahwi.com/) for more Arabic NLP resources
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## Contact
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**Author**: Bahjat Al Mostafa [@Bahjat](https://x.com/bahjat/)
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**Email**: <almostafa.bahjat@gmail.com>
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**Organization**: Alnnahwi
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**Website**: [https://alnnahwi.com/](https://alnnahwi.com/)
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For questions, issues, or collaboration opportunities, please open an issue in this repository or visit our website.
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---
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**Model Version**: v1.0.0
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**Last Updated**: May 2025
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**Model Size**: ~2.0GB
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