Instructions to use rawsun00001/banking-sms-json-parser-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rawsun00001/banking-sms-json-parser-v8 with Transformers:

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

pipe = pipeline("text-generation", model="rawsun00001/banking-sms-json-parser-v8")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rawsun00001/banking-sms-json-parser-v8")
model = AutoModelForCausalLM.from_pretrained("rawsun00001/banking-sms-json-parser-v8", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rawsun00001/banking-sms-json-parser-v8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rawsun00001/banking-sms-json-parser-v8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rawsun00001/banking-sms-json-parser-v8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rawsun00001/banking-sms-json-parser-v8

SGLang

How to use rawsun00001/banking-sms-json-parser-v8 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 "rawsun00001/banking-sms-json-parser-v8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rawsun00001/banking-sms-json-parser-v8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "rawsun00001/banking-sms-json-parser-v8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rawsun00001/banking-sms-json-parser-v8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rawsun00001/banking-sms-json-parser-v8 with Docker Model Runner:
```
docker model run hf.co/rawsun00001/banking-sms-json-parser-v8
```

Banking SMS JSON Parser V8

Advanced AI model that converts Indian banking SMS messages into structured JSON format.

Features

✅ Detects transaction vs non-transaction messages
✅ Extracts amount, date, transaction type, last 4 digits
✅ Categorizes transactions into 32+ categories
✅ Handles unknown merchants with "Other" category
✅ Supports UPI, NEFT, RTGS, Card transactions
✅ 60,000+ training samples with realistic Indian banking patterns

Usage

Training Data

60,000 training samples
6,000 validation samples
75% transaction, 25% non-transaction messages
Realistic Indian banking SMS patterns
Major Indian banks: ICICI, HDFC, SBI, Kotak, Axis, BOB, YES, etc.

Performance

Optimized for high accuracy on real-world Indian banking SMS messages with proper category classification and transaction detection.

Downloads last month: 28

Safetensors

Model size

81.9M params

Tensor type

F16