---
library_name: transformers
tags:
- legal-nlp
- document-classification
- pict
- longformer
- pytorch
license: apache-2.0
language:
- en
base_model:
- Saibo-creator/legal-longformer-base-4096
pipeline_tag: text-classification
metrics:
- loss
---

# Intelligent Legal Document Analysis Classifier (Longformer)

This model is a fine-tuned version of the **Legal-Longformer-Base-4096**, developed at the **Pune Institute of Computer Technology (PICT)** as part of the research paper: *"Intelligent Legal Document Analysis using NLP"*. 

The framework leverages NLP and Information Retrieval (IR) to classify unstructured documents into three primary domains: **Criminal Law**, **Contract Law**, and **Education Law**.

## Model Details

### Model Description

Traditional NLP often struggles with the complexity of legal discourse and domain-specific jargon. This model addresses these challenges by focusing on a tractable subset of twelve small-scale laws. It utilizes the **Vector Space Model (VSM)** for clause-level representation and the Longformer's sliding window attention to process documents spanning thousands of tokens.

- **Developed by:** Tanishq Shinde, Nilakshi Sonawane, Sarang Joshi, Mansi Jangle, and Vaishnavi Madavi
- **Institution:** Pune Institute of Computer Technology (PICT), Pune, India
- **Model type:** Transformer-based Sequence Classifier
- **Finetuned from model:** Saibo-creator/legal-longformer-base-4096

### Model Sources

- **Research Project:** Intelligent Legal Document Analysis using NLP (Paper ID 685)
- **Repository:** [Hugging Face Model Hub](https://huggingface.co/Tanishq77/legal-classifier-v1)

## Uses

### Direct Use

The model is optimized to categorize legal clauses into the following twelve sub-domains:

* **Criminal Law**: Traffic signal violations, drunk driving penalties, petty theft, and noise pollution laws.
* **Contract Law**: House rent agreements, lease termination, small loan disputes, and consumer redressal rules.
* **Education Law**: Teacher appointments, service statutes, wage rules, and leave regulations.

### Engineering-Inspired Features

The classifier is designed to support several high-level analytical components described in the paper:
* **Knapsack-based Term Selection**: Selecting the most informative terms to maximize relevance within a "scope budget".
* **Fuzzy Word Identification**: Flagging ambiguous expressions (e.g., "reasonable time") to highlight legal uncertainties for human review.
* **Finite State Machines (FSM)**: Modeling legal procedural flows, such as the transition from a "contract active" state to a "penalty imposed" state.

## Training Details

### Training Data

The training utilized a balanced dataset of **30,000 legal rows** (10,000 per primary domain).
* **Preprocessing**: Text normalization, tokenization, and retention of critical legal abbreviations and Latin expressions.
* **Representation**: Documents were segmented into individual clauses, treating each as a mathematical point in high-dimensional space.

### Training Procedure

* **Hardware**: Single NVIDIA P100 GPU (Kaggle).
* **Precision**: FP16 Mixed Precision for accelerated computation.
* **Epochs**: 1.0.
* **Effective Batch Size**: 32 (Batch Size 16 with Gradient Accumulation steps: 2).
* **Final Training Loss**: **0.0842**.

## Evaluation

### Results

The model achieved a **Global Training Loss of 0.084**, demonstrating high accuracy in differentiating between the structured yet dense terminologies of Criminal, Contract, and Education law.



## How to Get Started with the Model

```python
from transformers import pipeline

# Load the fine-tuned legal classifier
classifier = pipeline("text-classification", model="Tanishq77/legal-classifier-v1")

# Test on a Contract Law clause
text = "The lessee shall be responsible for all utility payments during the lease term."

result = classifier(text)
print(f"Domain: {result[0]['label']} | Confidence: {result[0]['score']:.4f}")
```

## Ethical Considerations

As outlined in the research methodology, this model is intended for **educational and analytical purposes only**. It is not a substitute for professional legal advice. The framework assumes human oversight at every stage, with legal experts expected to validate outputs to prevent misinterpretation and misuse.



## Citation

If you use this model or refer to the intelligent legal document analysis framework in your research, please cite it as follows:

### BibTeX

```bibtex
@article{shinde2026intelligent,
  title={Intelligent Legal Document Analysis using NLP},
  author={Shinde, Tanishq and Sonawane, Nilakshi and Joshi, Sarang and Jangle, Mansi and Madavi, Vaishnavi},
  journal={Dept. of Computer Engineering, Pune Institute of Computer Technology},
  year={2026}
}
```