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

pip install transformers torch peft

Basic Usage

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

{
  "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:

@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.

Contact

For issues, questions, or suggestions:

GitHub Issues: [url]
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!

Downloads last month: 17

Model tree for manaspros/indian-receipt-parser-v1

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Adapter

(1340)

this model