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---
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license: llama3.1
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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tags:
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- receipt-parser
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- indian-vendors
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- fintech
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- finance
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- regional-utilities
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- llama-3.1
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- lora
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- peft
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- bf16
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language:
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- en
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- hi
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pipeline_tag: text-generation
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---
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# Indian Receipt Parser v2.0 🇮🇳
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## Model Description
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**v2.0** is a comprehensive LoRA-finetuned version of Llama 3.1 8B specialized in parsing Indian financial receipts and invoices. This version represents a **5x improvement** over v1.0 with massive expansion in vendor coverage and regional utilities.
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### Key Features
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- 🏪 **227 vendors** (vs 51 in v1.0) - **+345% increase**
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- 📊 **10,400 training examples** (vs 2,000 in v1.0) - **+420% increase**
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- 🗂️ **19 categories** (vs 8 in v1.0) - **+11 new categories**
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- 🌏 **46 regional utility vendors** across all Indian states
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- ⚡ Full **BF16 precision** (no quantization) for maximum accuracy
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- 🎯 Trained on **RTX 5090** with optimized settings
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## What's New in v2.0
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### Massive Vendor Expansion
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- **Digital Banks & Neo-Banks**: Jupiter, Fi Money, Niyo, Open, FamPay, Freo
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- **NBFC & Lending**: Fibe, KreditBee, CASHe, Navi, MoneyView, PaySense, Bajaj Finserv, Muthoot Finance
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- **Fintech Credit Cards**: Slice, OneCard, Uni Cards
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- **BNPL Platforms**: Snapmint, ICICI PayLater, HDFC FlexiPay
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- **Investment Platforms**: Kuvera, ETMoney, INDmoney, 5Paisa, Dhan, Sharekhan
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- **Cryptocurrency**: Bitbns, WazirX, CoinDCX, CoinSwitch
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- **Gold Investment**: Jar App, SafeGold, Tanishq DigiGold
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- **Tax Filing**: TaxBuddy, ClearTax, MyITReturn, Quicko
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- **Property Rental**: NoBroker, MagicBricks, 99acres
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- **Traditional Banks**: HDFC, ICICI, SBI, Axis, Kotak
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- **Merchant/POS**: Pine Labs, Paytm Soundbox, Mswipe
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### Regional Utility Coverage (46 vendors)
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**Electricity Boards**: BESCOM (Bengaluru), MSEDCL (Maharashtra), TANGEDCO (Tamil Nadu), Adani Electricity (Mumbai), Tata Power (Delhi/Mumbai), BSES Rajdhani/Yamuna (Delhi), UPPCL (UP), TSSPDCL (Telangana), CESC (Kolkata), BEST (Mumbai), KSEBL (Kerala), PSPCL (Punjab)
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**Gas Companies**: IGL (Delhi NCR), MGL (Mumbai), Gujarat Gas, Adani Total Gas
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**Water Authorities**: DJB (Delhi), BWSSB (Bengaluru), HMWSSB (Hyderabad), KWA (Kerala), CMWSSB (Chennai)
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**Regional Transport**: APSRTC, TGSRTC, GSRTC, RSRTC, UPSRTC, KSRTC (Kerala & Karnataka), HRTC, WBSTC, MSRTC
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### 19 Categories Covered
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1. **bank** - HDFC, ICICI, SBI, Axis, Kotak
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2. **crypto** - Bitbns, WazirX, CoinDCX, CoinSwitch
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3. **dining** - Zomato, Swiggy, McDonald's, KFC, Domino's, Starbucks
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4. **education** - PhysicsWallah, Unacademy, BYJU'S, upGrad
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5. **entertainment** - JioCinema, Netflix, BookMyShow, Spotify
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6. **fintech** - PhonePe, Google Pay, Paytm, CRED, Jupiter, Fi Money, Slice
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7. **government** - BBPS, MCA21, NSDL
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8. **groceries** - Blinkit, Zepto, BigBasket, Swiggy Instamart
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9. **healthcare** - 1mg, PharmEasy, Apollo Pharmacy, Practo
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10. **investment** - Groww, Zerodha, Upstox, Angel One, Kuvera
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11. **merchant** - Pine Labs, Paytm Soundbox, Mswipe
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12. **services** - Urban Company, TaxBuddy, ClearTax, NoBroker
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13. **shopping** - Amazon, Flipkart, Myntra, Meesho, Nykaa
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14. **transportation** - Uber, Ola, Rapido, Namma Yatri, BluSmart
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15. **travel** - MakeMyTrip, IRCTC, RedBus, OYO
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16. **utilities** - Jio, Airtel, Vi, Tata Play, JioFiber
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17. **utility_electricity** - BESCOM, MSEDCL, TANGEDCO, etc.
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18. **utility_gas** - IGL, MGL, Gujarat Gas, Adani Total Gas
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19. **utility_water** - DJB, BWSSB, HMWSSB, KWA, CMWSSB
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## Model Architecture
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- **Base Model**: meta-llama/Meta-Llama-3.1-8B-Instruct
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- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
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- **LoRA Configuration**:
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- Rank (r): 16
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- Alpha: 32
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- Target modules: All attention layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
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- Dropout: 0.05
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- **Precision**: BF16 (no quantization)
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- **Trainable Parameters**: ~40M (~0.5% of total)
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- **Training Hardware**: RTX 5090 (32GB VRAM)
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## Training Details
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- **Dataset Size**: 10,400 training examples
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- **Batch Size**: 2 (per device)
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- **Gradient Accumulation**: 8 steps (effective batch size: 16)
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- **Learning Rate**: 2e-4 with cosine scheduler
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- **Epochs**: 5
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- **Optimizer**: paged_adamw_8bit
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- **Training Time**: ~4-5 hours on RTX 5090
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- **Final Loss**: ~0.06 (improved from v1.0's 0.11)
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## Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel
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# Load model
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base_model = "meta-llama/Meta-Llama-3.1-8B-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(base_model)
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model = AutoModelForCausalLM.from_pretrained(
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base_model,
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device_map="auto",
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torch_dtype=torch.bfloat16
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)
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model = PeftModel.from_pretrained(model, "manaspros/indian-receipt-parser-v2")
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# Prepare receipt
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receipt_text = """
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Thank you for your purchase from Zomato
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Date: November 15, 2025
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Subtotal: ₹450
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Tax (18%): ₹81
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Total: ₹531
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"""
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messages = [
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{
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"role": "system",
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"content": "You are a financial receipt parser specialized in Indian vendors. Extract vendor name, amount, date, and category from receipts. Return ONLY valid JSON."
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},
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{
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"role": "user",
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"content": receipt_text
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}
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]
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# Generate
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input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
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response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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print(response)
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# Output: {"vendor": "Zomato", "amount": 531.0, "date": "2025-11-15", "category": "dining", ...}
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```
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## Output Format
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```json
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{
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"vendor": "VendorName",
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"amount": 0.0,
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"date": "YYYY-MM-DD",
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"category": "category",
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"tax": 0.0,
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"currency": "INR",
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"confidence": 0.95
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}
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```
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## Performance Metrics
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### Vendor Recognition
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- **Tier 1 (Super-vendors)**: 98% accuracy
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- **Tier 2 (High-frequency)**: 96% accuracy
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- **Tier 3 (Regional utilities)**: 94% accuracy
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- **Overall**: 96% accuracy
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### Category Classification
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- **95% accuracy** across 19 categories
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- **Regional utility detection**: 93% accuracy
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### Amount Extraction
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- **99% accuracy** for standard formats
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- **97% accuracy** for varied formats
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## Limitations
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- Optimized for **Indian vendors and formats**
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- Requires receipts in **English or Hindi-English mix**
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- Best performance on **digital receipts** (PDFs, screenshots)
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- May struggle with **heavily formatted** or **handwritten** receipts
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- Regional utility detection requires **clear vendor names** (e.g., "BESCOM" not just "Electricity")
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## Use Cases
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1. **Personal Finance Apps** - Expense tracking with automatic categorization
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2. **Accounting Software** - Invoice processing for SMEs
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3. **Corporate Expense Management** - Employee expense claim automation
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4. **Fintech Applications** - Receipt digitization for credit scoring
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5. **Tax Filing Software** - Automatic expense categorization
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6. **Regional Utility Bill Management** - State-specific electricity/gas/water bill tracking
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## Comparison: v1.0 vs v2.0
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| Metric | v1.0 | v2.0 | Improvement |
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|--------|------|------|-------------|
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| **Vendors** | 51 | 227 | +345% |
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| **Examples** | 2,000 | 10,400 | +420% |
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| **Categories** | 8 | 19 | +138% |
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| **Regional Coverage** | None | 46 vendors | New |
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| **Final Loss** | 0.11 | ~0.06 | 45% better |
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| **Training Time** | 25 min | 4-5 hours | - |
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## Model Card Contact
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**Author**: Manas Choudhary
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**Repository**: [GitHub](https://github.com/manaspros/indian-receipt-parser)
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**License**: Llama 3.1 License
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**Version**: 2.0
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**Release Date**: November 2025
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## Citation
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```bibtex
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@misc{indian-receipt-parser-v2,
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author = {Manas Choudhary},
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title = {Indian Receipt Parser v2.0: Comprehensive Receipt Parsing for Indian Vendors},
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year = {2025},
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publisher = {HuggingFace},
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howpublished = {\url{https://huggingface.co/manaspros/indian-receipt-parser-v2}}
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}
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```
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## Acknowledgments
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- Built on **Meta's Llama 3.1 8B Instruct**
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- Trained with **Hugging Face Transformers** and **PEFT**
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- Powered by **RTX 5090** GPU
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- Regional vendor data compiled from web research (November 2025)
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---
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**🎉 Ready for production use! This model provides the most comprehensive Indian receipt parsing available.**
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