---
language: en
license: apache-2.0
tags:
  - text-classification
  - legal
  - longformer
  - document-classification
  - multi-class-classification
  - msme
  - dispute-resolution
  - indian-legal
pipeline_tag: text-classification
library_name: transformers
model_name: MSME Legal Dispute Classifier (Longformer, 6-Class)
---

# MSME Legal Dispute Classifier (Longformer, 6-Class)

## Model Overview
This model is a multi-class legal document classifier designed to categorize MSME-related dispute cases into six statutory dispute categories. It is fine-tuned from `allenai/longformer-base-4096` and optimized for long-form legal documents up to 1200 tokens. The system is intended for automated dispute categorization, legal triage, and decision-support applications in MSME dispute resolution workflows.

## Problem Statement
MSME dispute cases often involve lengthy legal narratives including:

- Statement of claim
- Buyer response
- Case summary
- Contractual and payment details

Manual classification is time-consuming and error-prone. This model automates dispute categorization into predefined legal classes.

## Classification Labels
The model predicts one of the following six categories:

| Label ID | Category                       |
|----------|--------------------------------|
| 0        | Delayed payment (no dispute)   |
| 1        | Quality dispute                |
| 2        | No formal contract             |
| 3        | Partial payment dispute        |
| 4        | Government procurement delay   |
| 5        | Service-related dispute        |

Label mapping is included in `label_mapping.json`.

## Model Architecture
- **Base Model**: Longformer  
- **Checkpoint**: `allenai/longformer-base-4096`  
- **Max Sequence Length**: 1200 tokens  
- **Hidden Size**: 768  
- **Number of Layers**: 12  
- **Attention Type**: Local attention (CLS token classification)  
- **Classification Head**: Linear layer (6 outputs)  

Longformer was selected due to the long-document nature of legal dispute texts.

## Dataset Information
- Final Dataset Size (after cleaning): 2152 samples  
- Duplicates removed  
- Label conflicts resolved  
- Stratified 80–20 train/test split  
- 5-fold stratified cross-validation  

Class imbalance handled using weighted cross-entropy loss.

## Training Configuration
- Optimizer: AdamW  
- Learning Rate: 2e-5  
- Batch Size: 2  
- Gradient Accumulation Steps: 4  
- Effective Batch Size: 8  
- Epochs: 3  
- Warmup Steps: 200  
- Mixed Precision (FP16): Enabled  
- Loss Function: Weighted Cross Entropy  

## Evaluation Results (Held-Out Test Set)

Test Set Size: 431 samples

| Metric                  | Score |
|-------------------------|-------|
| Accuracy                | 0.77  |
| Macro Precision         | 0.76  |
| Macro Recall            | 0.74  |
| Macro F1 Score          | 0.75  |
| Macro AUC-ROC (OvR)     | 0.948 |

These results indicate strong class separability and balanced performance across all categories.

## Intended Use
This model is suitable for:

- Automated legal dispute classification  
- MSME case triage systems  
- Online Dispute Resolution (ODR) platforms  
- Legal analytics systems  
- Case routing and prioritization tools  

## Limitations
- Performance may degrade for documents significantly exceeding 1200 tokens.  
- Domain-specific to MSME dispute scenarios.  
- Not designed for general legal classification tasks.  
- Should not be used as a substitute for legal judgment.  

## Ethical Considerations
This model is intended as a decision-support tool. Human oversight is recommended for legal decision-making applications. It does not provide legal advice.

## Usage Example

```python
from transformers import LongformerForSequenceClassification, AutoTokenizer
import torch

model = LongformerForSequenceClassification.from_pretrained("YOUR_USERNAME/msme-legal-dispute-classifier-longformer")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/msme-legal-dispute-classifier-longformer")

text = "The buyer failed to release payment within the agreed 45-day period."

inputs = tokenizer(text, truncation=True, max_length=1200, return_tensors="pt")
outputs = model(**inputs)

predicted_class = torch.argmax(outputs.logits, dim=1)
print("Predicted Label:", predicted_class.item())