---
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
library_name: peft
tags:
- india
- receipt-parsing
- llama
- lora
- peft
- fintech
- expense-tracking
- indian-vendors
---
# Indian Receipt Parser v1.0

## Model Description

**Indian Receipt Parser** is a fine-tuned Llama 3.1 8B model specialized in parsing receipts from 50+ popular Indian vendors. It extracts vendor name, amount, date, and category from receipt text and returns structured JSON output.

### Model Details

- **Base Model:** meta-llama/Meta-Llama-3.1-8B-Instruct
- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **Training Data:** 2000+ synthetic receipt examples
- **Vendors Covered:** 50+ popular Indian brands
- **Categories:** 8 (dining, groceries, shopping, transportation, healthcare, entertainment, utilities, travel)
- **Output Format:** Structured JSON

### Covered Vendors

**Food Delivery & Dining (9):**
Zomato, Swiggy, EatSure, Dominos, McDonald's, KFC, Burger King, Pizza Hut, Foodpanda

**Quick Commerce & Groceries (7):**
BigBasket, Blinkit, Zepto, Swiggy Instamart, Dunzo, JioMart, DMart Ready

**E-commerce & Shopping (8):**
Amazon India, Flipkart, Myntra, Meesho, Ajio, Nykaa, Snapdeal, Tata CLiQ

**Transportation (6):**
Ola, Uber, Rapido, Namma Yatri, BluSmart, InDrive

**FinTech & Payments (7):**
Paytm, PhonePe, GooglePay, MobiKwik, Freecharge, Amazon Pay, CRED

**Healthcare (5):**
Apollo Pharmacy, 1mg, PharmEasy, Netmeds, MedLife

**Entertainment (8):**
BookMyShow, PVR INOX, Netflix, Amazon Prime, Disney+ Hotstar, Jio Cinema, ZEE5, Sony LIV

**Travel (5):**
MakeMyTrip, Goibibo, IRCTC, OYO, EaseMyTrip

**Telecom (3):**
Jio, Airtel, Vi (Vodafone Idea)

## Intended Use

### Primary Use Cases

- **Expense Tracking Apps:** Automatically categorize and extract receipt information
- **Financial Management:** Parse receipts for budgeting and analysis
- **Receipt Digitization:** Convert physical/digital receipts to structured data
- **Business Expense Reports:** Automate expense report generation

### Out-of-Scope Use Cases

- Non-Indian vendors (model is optimized for Indian brands)
- Complex multi-item receipts requiring line-item extraction
- Handwritten receipts (OCR required first)
- Non-receipt financial documents

## How to Use

### Installation

```bash
pip install transformers torch peft
```

### Basic Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load model
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3.1-8B-Instruct",
    device_map="auto",
    torch_dtype=torch.bfloat16
)
model = PeftModel.from_pretrained(base_model, "path/to/indian-receipt-parser-v1/final")

# Parse receipt
receipt_text = """
Thank you for ordering from Zomato

Order Date: 15/11/2024

Subtotal: ₹450
Tax: ₹81
Total: ₹531
"""

messages = [
    {
        "role": "system",
        "content": "You are a financial receipt parser specialized in Indian vendors. Extract vendor name, amount, date, and category from receipts. Return ONLY valid JSON in this exact format: {\"vendor\": \"VendorName\", \"amount\": 0.0, \"date\": \"YYYY-MM-DD\", \"category\": \"category\", \"tax\": 0.0, \"currency\": \"INR\", \"confidence\": 0.0}"
    },
    {
        "role": "user",
        "content": receipt_text
    }
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1)

response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)
```

### Expected Output

```json
{
  "vendor": "Zomato",
  "amount": 531.0,
  "date": "2024-11-15",
  "category": "dining",
  "tax": 81.0,
  "currency": "INR",
  "confidence": 0.95
}
```

## Training Details

### Training Data

- **Total Examples:** 2000+ synthetic receipt examples
- **Vendors:** 50+ popular Indian brands
- **Data Distribution:**
  - Dining: 360 examples
  - Groceries: 280 examples
  - Shopping: 320 examples
  - Transportation: 240 examples
  - Healthcare: 200 examples
  - Entertainment: 320 examples
  - Utilities: 200 examples
  - Travel: 200 examples

### Training Procedure

- **Fine-tuning Method:** LoRA (r=16, alpha=32)
- **Trainable Parameters:** 0.52% of total parameters
- **Precision:** BF16 (bfloat16) - no quantization
- **Epochs:** 5
- **Batch Size:** 16 (effective, with gradient accumulation)
- **Learning Rate:** 2e-4 with cosine schedule
- **Optimizer:** AdamW
- **Hardware:** NVIDIA RTX 5090 (32GB VRAM)
- **Training Time:** ~60-90 minutes

### Training Loss

```
Initial loss: 2.64
Final loss: 0.13
Improvement: 95%
```

## Evaluation

### Performance Metrics

- **Vendor Recognition Accuracy:** ~95%
- **Amount Extraction Accuracy:** ~98%
- **Category Classification:** ~92%
- **Date Parsing Accuracy:** ~90%
- **Valid JSON Output:** ~85%

### Limitations

- **Indian Vendors Only:** Optimized for brands listed above, may not recognize international brands
- **Date Format Variations:** Performs best with common Indian date formats (DD/MM/YYYY, DD-MM-YYYY)
- **Currency:** Optimized for INR (₹), may struggle with other currencies
- **Receipt Complexity:** Works best with simple receipt formats, complex multi-page receipts may need preprocessing
- **Hallucination:** May occasionally generate plausible but incorrect values for missing fields

## Ethical Considerations

### Privacy

- Model does not store or transmit any receipt data
- All processing happens locally
- Users should be aware of sensitive financial information in receipts

### Bias

- Model is trained primarily on synthetic data
- May perform differently on real-world receipts
- Vendor coverage is limited to 50+ popular brands
- Performance may vary for regional/local vendors not in training data

### Responsible Use

- Intended for personal finance management and expense tracking
- Should not be used as sole source of truth for financial/tax reporting
- Users should verify extracted information for critical applications
- Not recommended for legal/compliance purposes without human verification

## Citation

If you use this model, please cite:

```bibtex
@misc{indian-receipt-parser-v1,
  title={Indian Receipt Parser v1.0},
  author={Your Name},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/your-username/indian-receipt-parser-v1}}
}
```

## License

This model is released under the [Llama 3.1 Community License](https://ai.meta.com/llama/license/).

## Contact

For issues, questions, or suggestions:
- GitHub Issues: [[url](https://huggingface.co/manaspros/indian-receipt-parser-v1)]
- Email: [manas24102@iiitnr.edu.in]

---

## Acknowledgments

Built with:
- 🦙 Meta's Llama 3.1 8B
- 🔧 Hugging Face Transformers & PEFT
- ⚡ NVIDIA RTX 5090
- 🇮🇳 Inspired by India's digital payment ecosystem

**Made with ❤️ for India's fintech community!**