---
language: en
license: mit
library_name: transformers
tags:
  - finance
  - banking
  - indian
  - upi
  - transaction-classification
  - qwen
  - fine-tuned
base_model: Qwen/Qwen2.5-0.5B
---

# Indian Transaction Classifier

Fine-tuned Qwen2.5-0.5B for classifying Indian bank transactions (UPI, NEFT, IMPS, RTGS) into 30+ categories with merchant identification.

## Model Description

This model is fine-tuned on 1,700 privacy-sanitized Indian bank transaction descriptions. It classifies transactions into categories like salary, food, grocery, trading, bills, etc. and identifies the merchant/company when possible.

## Categories

**Income:** salary, dividend, interest, rental, capital_gains, other_income

**Expenses:** food, grocery, shopping, bills, medical, insurance, tax_payment, credit_card, personal_transfer, investment, trading_deposit, trading_credit, education, travel, entertainment, donation, loan_emi, loan_repayment, cash_withdrawal

**Special:** friends, family, flat_deposit, trading_fees, vehicle_purchase, staff_salary, health_fitness, transfer, unclassified

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SahilGoel/indian-txn-classifier")
tokenizer = AutoTokenizer.from_pretrained("SahilGoel/indian-txn-classifier")

system_prompt = "You are a bank transaction classifier for Indian bank statements..."

input_text = "UPI/zerodhabroking@/HDFC BANK LTD"
prompt = f"### System:\n{system_prompt}\n\n### Input:\n{input_text}\n\n### Output:\n"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Data

1,700 privacy-sanitized transaction descriptions. PII (phone numbers, personal UPI handles, reference numbers) has been redacted.

## Pipeline Architecture

1. **Rule engine** (70-80% coverage) - regex patterns for known merchants and UPI handles
2. **Recurring pattern detector** (10% more) - identifies repeating transactions
3. **Fine-tuned Qwen 0.5B** (remaining) - LLM fallback for uncertain transactions

## Supported Banks

ICICI, HDFC, SBI, Axis, Kotak, Yes Bank, Federal Bank, IDFC First, IndusInd, Bank of Baroda, Punjab National, Canara, Union Bank, Unity SFB

## GitHub

Code and training pipeline: [the-great-one/indian-txn-classifier](https://github.com/The-Great-One/indian-txn-classifier)