---
license: apache-2.0
language:
- en

library_name: transformers

pipeline_tag: text-classification

tags:
- legal-ai
- inlegalbert
- nlp
- law
- legal-outcome-prediction
- text-classification
- bert
- indian-legal

base_model:
- law-ai/InLegalBERT

metrics:
- accuracy
- precision
- recall
- f1
---

# ⚖️ JustiFi – InLegalBERT Legal Outcome Predictor

Fine-tuned **InLegalBERT** model for **legal outcome prediction** on Indian legal documents.

This model predicts whether a legal case is:

- **REJECT (0)**
- **ACCEPT (1)**

The system is designed for long legal documents and uses **chunk-based processing** to overcome the 512-token limitation of BERT models.

---

# 📌 Project Overview

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:

```bash
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}
}
```

---