Instructions to use SahilGoel/indian-txn-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SahilGoel/indian-txn-classifier with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SahilGoel/indian-txn-classifier")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SahilGoel/indian-txn-classifier")
model = AutoModelForCausalLM.from_pretrained("SahilGoel/indian-txn-classifier", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SahilGoel/indian-txn-classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SahilGoel/indian-txn-classifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SahilGoel/indian-txn-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SahilGoel/indian-txn-classifier

SGLang

How to use SahilGoel/indian-txn-classifier with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SahilGoel/indian-txn-classifier" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SahilGoel/indian-txn-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SahilGoel/indian-txn-classifier" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SahilGoel/indian-txn-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SahilGoel/indian-txn-classifier with Docker Model Runner:
```
docker model run hf.co/SahilGoel/indian-txn-classifier
```

Indian Transaction Classifier

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

⚡ Try it now (Inference API — free, no setup)

Use the Inference API widget on the right side of this page. Type a transaction description in the text box and click Compute.

Or call it programmatically:

from huggingface_hub import InferenceClient

client = InferenceClient(model="SahilGoel/indian-txn-classifier")

system_prompt = 'You are a bank transaction classifier for Indian bank statements. Given a raw transaction description, infer both its category and the actual company when evidence exists. Respond with ONLY a JSON object: {"category": "<category>", "company_name": "<company_or_null>", "is_income": false, "confidence": 0.0}.'

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

result = client.text_generation(prompt, max_new_tokens=100, temperature=0.1)
print(result)
# {"category": "trading_deposit", "company_name": "Zerodha", "is_income": false, "confidence": 0.95}

Pipeline Architecture

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

Run locally

pip install transformers torch

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/swiggybengaluru@/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, temperature=0.1, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Run on Google Colab (free GPU)

Open app.py from the GitHub repo in a Colab notebook — it works as a Gradio app with a public share link.

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

Downloads last month: -

Model tree for SahilGoel/indian-txn-classifier

Base model

Qwen/Qwen2.5-0.5B

Finetuned

(683)

this model

SahilGoel
/

indian-txn-classifier