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Moryjj/
simplification-legal-text

Name: simplification-legal-text
Availability: InStock

Moryjj/Iranian-Courts-Ruling

2309.12254

Ahmad/parsT5-base

finetune

text-generation-inference

endpoints_compatible

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🧾 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

python

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

Moryjj/simplification-legal-text

🧾 Persian Legal Text Simplification with ParsT5 + Unlimiformer

🧠 Model Description

✨ Example Usage

🛠 Training Details

More detail on GitHub

Moryjj/
simplification-legal-text