Update README.md

README.md

@@ -1,108 +1,125 @@
-# 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())
+---
+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())