transformers pytorch bart text2text-generation summarization legal-ai italian-law it dataset:joelniklaus/Multi_Legal_Pile base_model:morenolq/bart-it base_model:finetune:morenolq/bart-it license:mit autotrain_compatible endpoints_compatible region:us
---
base_model:
- morenolq/bart-it
datasets:
- joelniklaus/Multi_Legal_Pile
language:
- it
library_name: transformers
license: mit
pipeline_tag: text2text-generation
tags:
- text2text-generation
- summarization
- legal-ai
- italian-law
widget:
- text: '<mask> 1234: Il contratto si intende concluso quando...'
---

# 📌 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`](https://huggingface.co/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](https://huggingface.co/morenolq/LEGIT-BART) |
| **LEGIT-BART-LSG-4096** | Continued pre-training of `morenolq/bart-it`, supporting **4,096 tokens** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-BART-LSG-4096) |
| **LEGIT-BART-LSG-16384** | Continued pre-training of `morenolq/bart-it`, supporting **16,384 tokens** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-BART-LSG-16384) |
| **LEGIT-SCRATCH-BART** | Trained from scratch on **Italian legal texts** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-SCRATCH-BART) |
| **LEGIT-SCRATCH-BART-LSG-4096** | Trained from scratch with **LSG attention**, supporting **4,096 tokens** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-SCRATCH-BART-LSG-4096) |
| **LEGIT-SCRATCH-BART-LSG-16384** | Trained from scratch with **LSG attention**, supporting **16,384 tokens** | [🔗 Link](https://huggingface.co/morenolq/LEGIT-SCRATCH-BART-LSG-16384) |
| **BART-IT-LSG-4096** | `morenolq/bart-it` with **LSG attention**, supporting **4,096 tokens** (no legal adaptation) | [🔗 Link](https://huggingface.co/morenolq/BART-IT-LSG-4096) 
| **BART-IT-LSG-16384** | `morenolq/bart-it` with **LSG attention**, supporting **16,384 tokens** (no legal adaptation) | [🔗 Link](https://huggingface.co/morenolq/BART-IT-LSG-16384) |

---

## 🛠️ Model Details

🔹 **Architecture**
  - Base Model: [`morenolq/bart-it`](https://huggingface.co/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`](https://huggingface.co/datasets/joelniklaus/Multi_Legal_Pile)
  - Types of legal texts used:
    - **Legislation** (laws, codes, amendments)
    - **Case law** (judicial decisions)
    - **Contracts** (public legal agreements)

---

## 🚀 How to Use

```python
from transformers import BartForConditionalGeneration, AutoTokenizer

# Load tokenizer and model
model_name = "morenolq/LEGIT-SCRATCH-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=512, truncation=True)

# Pre-trained model fill the mask
output_ids = model.generate(inputs.input_ids, max_length=150, num_beams=4, early_stopping=True)
output_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("📝:", output_text)
```

---

⚠️ 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.

---

## 📚 Reference

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

```bibtex
@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}
}
```

---