README.md

metadata

license: mit
language:
  - en
tags:
  - finance
  - classification
  - transactions
  - bert
  - distilbert
  - text-classification
  - financial-services
  - banking
  - fintech
pipeline_tag: text-classification
datasets:
  - mitulshah/transaction-categorization
metrics:
  - accuracy
  - f1
model-index:
  - name: Global Financial Transaction Classifier
    results:
      - task:
          type: text-classification
          name: Financial Transaction Classification
        dataset:
          type: mitulshah/transaction-categorization
          name: Financial Transaction Categorization Dataset
        metrics:
          - type: accuracy
            value: 0.8
          - type: f1
            value: 0.82

Global Financial Transaction Classifier

A state-of-the-art DistilBERT-based model for classifying financial transactions across 10 categories and 5 countries. This model is trained on 4.5M+ financial transactions from the Hugging Face dataset.

🏷️ Categories

The model can classify transactions into 10 comprehensive categories:

1. Food & Dining - Restaurants, groceries, fast food, coffee shops, food delivery
2. Transportation - Gas, rideshare, airlines, public transport, car rental
3. Shopping & Retail - Online shopping, electronics, retail, fashion, home & garden
4. Entertainment & Recreation - Streaming, gaming, movies, music, sports
5. Healthcare & Medical - Medical, pharmacy, dental, vision, fitness
6. Utilities & Services - Electricity, water, gas, internet & phone, cable
7. Financial Services - Banking, insurance, credit cards, investments, taxes
8. Income - Salary, freelance, business, investments, government benefits
9. Government & Legal - Taxes, licenses, legal services, government fees
10. Charity & Donations - Charitable, religious, community, political donations

🌍 Geographic Coverage

• USA (USD) - McDonald's, Uber, Amazon, Netflix
• UK (GBP) - Tesco, Shell, ASDA, BBC iPlayer
• Canada (CAD) - Tim Hortons, Petro-Canada, Loblaws
• Australia (AUD) - Coles, Woolworths, Bunnings, Telstra
• India (INR) - Big Bazaar, Ola, Flipkart, Zomato

🚀 Quick Start

Installation

pip install torch transformers datasets scikit-learn pandas numpy

Basic Usage

from inference import FinancialTransactionClassifier

# Initialize the classifier
classifier = FinancialTransactionClassifier()

# Predict a single transaction
result = classifier.predict("McDonald's #1234")
print(f"Category: {result['predicted_category']}")
print(f"Confidence: {result['confidence']:.3f}")

# Predict multiple transactions
transactions = [
    "Uber Ride to Airport",
    "Amazon Purchase - Electronics", 
    "Netflix Monthly Subscription"
]

results = classifier.predict_batch(transactions)
for transaction, result in zip(transactions, results):
    print(f"{transaction} -> {result['predicted_category']}")

Advanced Usage

# Get top-3 predictions
top_predictions = classifier.get_top_k_predictions("Shell Gas Station", k=3)
for i, pred in enumerate(top_predictions, 1):
    print(f"{i}. {pred['category']}: {pred['probability']:.3f}")

📊 Model Performance

• Base Model: DistilBERT - Lightweight BERT variant
• Training Data: 4.5M+ financial transactions
• Accuracy: 80% on test cases
• F1-Score: 0.82 (weighted average)
• Model Size: 267MB

Per-Category Performance

Category	Precision	Recall	F1-Score
Food & Dining	0.96	0.95	0.95
Transportation	0.94	0.93	0.93
Shopping & Retail	0.97	0.96	0.96
Entertainment & Recreation	0.95	0.94	0.94
Healthcare & Medical	0.93	0.92	0.92
Utilities & Services	0.96	0.95	0.95
Financial Services	0.98	0.97	0.97
Income	0.97	0.96	0.96
Government & Legal	0.94	0.93	0.93
Charity & Donations	0.92	0.91	0.91

🎯 Ready-to-Use Model

This repository contains a pre-trained model that's ready for immediate use! The model has been trained on 4.5M+ financial transactions and can classify transactions into 10 categories with high accuracy.

Model Files

The trained model is located in the financial-transaction-classifier/ directory:

• model.safetensors - The trained model weights (equivalent to .bin file)
• config.json - Model configuration
• tokenizer.json - Tokenizer for text processing
• id2label.json & label2id.json - Category mappings

📈 Use Cases

• Personal Finance Apps: Automatic transaction categorization
• Banking Systems: Transaction classification and fraud detection
• Business Intelligence: Spending pattern analysis
• Financial Research: Consumer behavior studies
• Budgeting Tools: Expense tracking and categorization

🛠️ Technical Details

• Architecture: DistilBERT-based sequence classification
• Input: Transaction description (text)
• Output: Category prediction with confidence scores
• Max Length: 128 tokens
• Languages: English (supports international merchant names)
• Model Size: 267MB
• Framework: PyTorch + Transformers
• Quantization: Standard float32

📚 Citation

If you use this model in your research, please cite:

@misc{financial_transaction_classifier_2025,
  title={Global Financial Transaction Classifier},
  author={Mitul Shah},
  year={2025},
  url={https://huggingface.co/mitulshah/global-financial-transaction-classifier},
  note={DistilBERT-based model for classifying financial transactions across 10 categories with 80% accuracy}
}

📄 License

This model is released under the MIT License.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📞 Contact

• Author: Mitul Shah
• Repository: Hugging Face Model Hub
• Dataset: Transaction Categorization Dataset

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