| --- |
| 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()) |
| |
