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

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{% for message in messages %}{% if message['role'] == 'user' %}<start_of_turn>user
{{ message['content'] }}<end_of_turn>
{% elif message['role'] == 'model' %}<start_of_turn>model
{{ message['content'] }}<end_of_turn>
{% endif %}{% endfor %}

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{
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],
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"hidden_size": 1152,
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"model_type": "gemma3_text",
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"sliding_window": 512,
"sliding_window_pattern": 6,
"torch_dtype": "bfloat16",
"transformers_version": "4.52.4",
"unsloth_fixed": true,
"unsloth_version": "2025.5.9",
"use_cache": true,
"vocab_size": 262144
}

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