Moryjj/simplification-legal-text

🧾 Persian Legal Text Simplification with ParsT5 + Unlimiformer

This model is part of the first benchmark for Persian legal text simplification. It fine-tunes the ParsT5-base encoder-decoder model with Unlimiformer extension to handle long legal documents efficiently—without truncation.

🔗 Project GitHub: mrjoneidi/Simplification-Legal-Texts

---

🧠 Model Description

• Base Model: Ahmad/parsT5-base
• Extended with: Unlimiformer for long-input attention
• Training Data: 5000+ Persian legal rulings, simplified using GPT-4o (ChatGPT) prompts
• Max Input Length: ~16,000 tokens (with Unlimiformer)
• Fine-tuned on: Simplified dataset generated from judicial rulings
• Task: Text simplification (formal → public-friendly)

---

✨ Example Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("mrjoneidi/parsT5-legal-simplification")
tokenizer = AutoTokenizer.from_pretrained("mrjoneidi/parsT5-legal-simplification")

input_text = "دادگاه با بررسی مدارک موجود، حکم به رد دعوی صادر می‌نماید..."
inputs = tokenizer(input_text, return_tensors="pt", truncation=True)

output_ids = model.generate(**inputs, max_new_tokens=512)
simplified = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print(simplified)

🛠 Training Details

• Platform: Kaggle P100 GPU
• Optimizer: AdamW (best performance among AdamW, LAMB, SGD)
• Configurations:
  • 1 vs. 3 unfrozen encoder-decoder blocks
  • Best results with 3-block configuration

---

More detail on GitHub