Upload README.md

README.md

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+# 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.
+
+## Confusion Behavior
+- Strong performance on Service-related and Government delay cases
+- Moderate confusion between Quality disputes and Partial payment disputes
+- Balanced macro performance across all classes
+
+## 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())