Legal documents are often lengthy and contain complex legal language. Traditional machine learning methods struggle to capture contextual relationships in such texts.

This project fine-tunes InLegalBERT, a legal-domain transformer model, to perform legal outcome prediction using Indian legal datasets.

The workflow includes:

text

Input Legal Document
          ↓
PDF/Text Extraction
          ↓
Tokenization
          ↓
Chunk Generation (512 tokens)
          ↓
InLegalBERT Prediction
          ↓
Chunk Aggregation
          ↓
Final Outcome
(REJECT / ACCEPT)

🧠 Model Information

Property	Value
Base Model	InLegalBERT
Architecture	BertForSequenceClassification
Task	Binary Classification
Classes	REJECT / ACCEPT
Max Tokens	512
Prediction Method	Chunk Aggregation
Domain	Legal NLP

📊 Model Performance

Evaluation was performed using chunk-based document prediction.

Metric	Score
Accuracy	0.70
Precision	0.71
Recall	0.71
F1 Score	0.71

⚙️ Features

✅ Legal outcome prediction

✅ Fine-tuned InLegalBERT model

✅ Long document support

✅ Chunk-based inference

✅ PDF processing pipeline

✅ FastAPI deployment support

✅ Confidence scoring

📂 Labels

python

0 → REJECT
1 → ACCEPT

Model mapping:

python

id2label = {
    0: "REJECT",
    1: "ACCEPT"
}

🚀 Usage

Install dependencies:

pip install transformers torch

Load model:

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="vikas-maurya/justifi-inlegalbert-outcome-predictor"
)

text = """
Legal case description text here
"""

result = classifier(text)

print(result)

Example output:

python

[
 {
   'label':'ACCEPT',
   'score':0.74
 }
]

🔬 Training Configuration

Parameter	Value
Learning Rate	2e-5
Batch Size	4
Gradient Accumulation	4
Optimizer	AdamW
Loss Function	Cross Entropy
Mixed Precision	FP16
Training Steps	~4500

📈 Evaluation Methods

The project includes:

Accuracy vs Epoch
Precision–Recall Curve
Confusion Matrix
Confidence Distribution
Response Time Analysis

🏗 Deployment

The model was integrated into:

FastAPI backend
PDF upload pipeline
Chunk-based prediction system

Output format:

json

{
    "prediction":"ACCEPT",
    "confidence":0.64,
    "num_chunks":7
}

⚠ Limitations

Trained on Indian legal documents only
Limited generalization to other jurisdictions
Long documents require chunk aggregation
Similar case retrieval not included in current version

🔮 Future Improvements

Legal summarization
Explainability module
Similar case retrieval
Legal chatbot
Multilingual support
Outcome reasoning

👨‍💻 Author

Vikas Maurya

Final Year Computer Engineering Student

Focus Areas:

NLP
Legal AI
Deep Learning
Transformer Models
Generative AI

Project:

JustiFi – AI Powered Legal Intelligence System

📜 Citation

If using this model in academic work:

bibtex

@misc{justifi_inlegalbert,
title={JustiFi: InLegalBERT Legal Outcome Predictor},
author={Vikas Maurya},
year={2026}
}