📌 Model Card: LEGIT-BART Series

🏛️ Model Overview

The LEGIT-BART models are a family of pre-trained transformer-based models for Italian legal text processing. They build upon BART-IT (morenolq/bart-it) and are further pre-trained on Italian legal corpora.

💡 Key features:

• Extended context length with Local-Sparse-Global (LSG) Attention (up to 16,384 tokens) 📜
• Trained on legal documents such as statutes, case law, and contracts 📑
• Not fine-tuned for specific tasks (requires further adaptation)

📂 Available Models

Model	Description	Link
LEGIT-BART	Continued pre-training of morenolq/bart-it on Italian legal texts	🔗 Link
LEGIT-BART-LSG-4096	Continued pre-training of morenolq/bart-it, supporting 4,096 tokens	🔗 Link
LEGIT-BART-LSG-16384	Continued pre-training of morenolq/bart-it, supporting 16,384 tokens	🔗 Link
LEGIT-SCRATCH-BART	Trained from scratch on Italian legal texts	🔗 Link
LEGIT-SCRATCH-BART-LSG-4096	Trained from scratch with LSG attention, supporting 4,096 tokens	🔗 Link
LEGIT-SCRATCH-BART-LSG-16384	Trained from scratch with LSG attention, supporting 16,384 tokens	🔗 Link
BART-IT-LSG-4096	morenolq/bart-it with LSG attention, supporting 4,096 tokens (no legal adaptation)	🔗 Link
BART-IT-LSG-16384	morenolq/bart-it with LSG attention, supporting 16,384 tokens (no legal adaptation)	🔗 Link

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🛠️ Model Details

🔹 Architecture

• Base Model: morenolq/bart-it
• Transformer Encoder-Decoder
• LSG Attention for long documents
• Specific tokenizers for models trained from scratch (underperforming continual pre-training in our experiments).

🔹 Training Data

• Dataset: joelniklaus/Multi_Legal_Pile
• Types of legal texts used:
  • Legislation (laws, codes, amendments)
  • Case law (judicial decisions)
  • Contracts (public legal agreements)

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🚀 How to Use

from transformers import BartForConditionalGeneration, AutoTokenizer

# Load tokenizer and model
model_name = "morenolq/LEGIT-BART"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

# Example input
input_text = "<mask> 1234: Il contratto si intende concluso quando..."
inputs = tokenizer(input_text, return_tensors="pt", max_length=4096, truncation=True)

# Generate summary
summary_ids = model.generate(inputs.input_ids, max_length=150, num_beams=4, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("📝 Summary:", summary)

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⚠️ Limitations & Ethical Considerations

• Not fine-tuned for specific tasks: The models are pre-trained on legal texts and may require further adaptation for specific legal NLP tasks (e.g., summarization, question-answering).
• Bias and fairness: Legal texts may contain biases present in the legal system. Care should be taken to ensure fairness and ethical use of the models.
• Legal advice: The models are not a substitute for professional legal advice. Always consult a qualified legal professional for legal matters.

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📚 Reference

The paper presenting LEGIT-BART models is currently under review and will be updated here once published.

@article{benedetto2025legitbart,
    title        = {LegItBART: a summarization model for Italian legal documents},
    author       = {Benedetto, Irene and La Quatra, Moreno and Cagliero, Luca},
    year         = 2025,
    journal      = {Artificial Intelligence and Law},
    publisher    = {Springer},
    pages        = {1--31},
    doi          = {10.1007/s10506-025-09436-y},
    url          = {doi.org/10.1007/s10506-025-09436-y}
}

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