---
license: mit
library_name: xgboost
tags:
  - text-classification
  - document-analysis
  - ocr
  - legal-tech
  - msme
  - binary-classification
  - tabular-text
pipeline_tag: text-classification
model-index:
  - name: MSME Document Presence Detection Model
    results:
      - task:
          type: text-classification
        dataset:
          name: MSME Document Presence Dataset
          type: custom
        metrics:
          - name: Precision (avg)
            type: precision
            value: 0.992
          - name: Recall (avg)
            type: recall
            value: 0.987
          - name: F1 Score (avg)
            type: f1
            value: 0.990
          - name: ROC-AUC (avg)
            type: roc_auc
            value: 0.999
---

# MSME Document Presence Detection Model

## Overview

This repository contains production-grade XGBoost models designed to detect the presence of mandatory documents in MSME arbitration cases based on OCR-extracted text.

The system performs binary classification for the following documents:

- Invoice
- Purchase Order
- Delivery Proof
- GST Certificate
- Contract

Each document is modeled independently as a separate classifier.

---

## Model Architecture

- Algorithm: XGBoost (Gradient Boosted Trees)
- Feature Extraction: TF-IDF (1–2 n-grams)
- Max Features per model: 3000
- Independent model per document type
- Stratified train-test split
- Hard negative augmentation included
- Severe OCR corruption simulation included

---

## Training Data

The model was trained on a synthetic and augmented dataset consisting of:

- 5,000 LLM-generated structured OCR samples
- OCR distortion simulation
- Keyword masking
- Partial truncation
- Cross-document contamination
- Line shuffling
- Hard negative construction
- Class imbalance simulation

Final training dataset size: approximately 10,000 samples.

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## Performance

Average performance across all document classifiers:

- Precision: 0.992
- Recall: 0.987
- F1 Score: 0.990
- ROC-AUC: 0.999
- False Negative Rate: < 2%

Performance evaluated using stratified 80/20 split.

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## Inference

Each model expects OCR-extracted raw text for a specific document type.

Output per document:

- Binary prediction (0 = Missing, 1 = Present)
- Probability score
- Optional SHAP-based explainability (external implementation)

Completeness Score can be computed as:

completeness = (documents_present / required_documents) × 100

---

## Intended Use

This model is suitable for:

- MSME arbitration automation
- Legal document validation pipelines
- OCR post-processing systems
- Document completeness scoring engines
- Hybrid rule + ML legal systems

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## Limitations

- Trained primarily on synthetic and augmented OCR data
- Real-world scanned PDFs may introduce unseen distortions
- Extreme low-quality scans may reduce recall
- Contract optionality logic must be implemented externally
- Not intended for semantic contract analysis

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## Ethical Considerations

The model was trained exclusively on synthetic data.
No real personal, financial, or legal records were used.

---

## Future Work

- Fine-tuning on real arbitration case documents
- Probability calibration
- Threshold optimization per document type
- Model drift monitoring
- Ensemble rule + ML integration
- ONNX export for optimized inference

---

## License

This project is released under the MIT License.