| --- |
| 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!** |
