AraStyleTransfer-21

AraStyleTransfer-21 | 21 Arabic Author Styles. One Model.

🏆 First Place Winner at AraGenEval 2025 Competition

A state-of-the-art Arabic text style transfer model that transforms text into the writing style of 21 different Arabic authors using descriptive author tokens and prompt engineering.

🔗 Paper Link (ACL Anthology)

📘 ANLPers at AraGenEval Shared Task: Descriptive Author Tokens for Transparent Arabic Authorship Style Transfer [https://aclanthology.org/2025.arabicnlp-sharedtasks.8.pdf]

🏗️ Model Architecture

• Base Model: UBC-NLP/AraT5v2-base-1024
• Approach: Descriptive Author Tokens + Prompt Engineering
• Input Format: "اكتب النص التالي بأسلوب <author:name>: [text]"
• Training: Fine-tuned with author-specific tokens

🔬 Technical Details

Stylometric Analysis

The model incorporates comprehensive stylometric analysis including:

• Lexical Features: Sentence length, word length, vocabulary richness
• Syntactic Patterns: Definite articles, conjunctions, prepositions
• Author-Specific Vocabulary: TF-IDF based characteristic words
• Style Classification: Formality, complexity, emotional intensity

Prompt Engineering

• Format: "اكتب النص التالي بأسلوب <author:يوسف_إدريس>: [original_text]"
• Author Tokens: Descriptive tokens like <author:يوسف_إدريس>
• Target: Generated text in author's style

📚 Supported Authors

<p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/qDHUSa6ZvD1LjN9uJs-jp.png" width="600"/> </p>

📁 Input File Format

For batch processing, your input file should have the following format:

📊 Example Snippets from the Dataset

📊 Performance Metrics

• BLEU Score: 24.58
• chrF Score: 59.01
• Competition: First Place in AraGenEval 2024
• Supported Authors: 21 Arabic authors

Official results on the AraGenEval 2025 testset. Our prompt engineering system ranked first.

<p align="left"> <img src="https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/pCfAK4zefvXZ4YI1AvXIG.png" width="400"/> </p>

🚀 Quick Start: Style Transfer Example

from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

# Load model
model_name = "Omartificial-Intelligence-Space/AraStyleTransfer-21"

tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Input text and author
text = "لم أقم مطلقًا بالاحتفال بعيد ميلادي منذ طفولتي."
author = "يوسف إدريس"

# Prompt format
prompt = f"اكتب النص التالي بأسلوب <author:{author.replace(' ', '_')}>: {text}"

# Tokenize
inputs = tokenizer(prompt, return_tensors="pt").to(device)

# Generate
output_ids = model.generate(
    **inputs,
    max_length=256,
    num_beams=5,
    early_stopping=True
)

# Decode
generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print("Original:", text)
print("Author:", author)
print("Output:", generated_text)

🎯 Use Cases

• Content Creation: Generate text in specific author styles
• Educational Tools: Demonstrate different writing styles
• Research: Study Arabic literary styles and patterns
• Creative Writing: Inspire new content in classic styles

🤝 Contributing

This model was developed for the AraGenEval 2025 competition. For questions or contributions, please refer to the competition guidelines.

📄 License

This model is released under the same license as the base AraT5v2 model.

BibTeX Citation

@inproceedings{nacar2025anlpers,
  title={ANLPers at AraGenEval Shared Task: Descriptive Author Tokens for Transparent Arabic Authorship Style Transfer},
  author={Nacar, Omer and Reda, Mahmoud and Sibaee, Serry and Alhabashi, Yasser and Ammar, Adel and Boulila, Wadii},
  booktitle={Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks},
  pages={49--53},
  year={2025}
}

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🏆 First Place Winner at AraGenEval 2025 - Arabic Text Style Transfer Competition