Instructions to use Ranjit0034/finance-entity-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ranjit0034/finance-entity-extractor with Transformers:

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

pipe = pipeline("text-generation", model="Ranjit0034/finance-entity-extractor", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ranjit0034/finance-entity-extractor", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Ranjit0034/finance-entity-extractor", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ranjit0034/finance-entity-extractor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ranjit0034/finance-entity-extractor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ranjit0034/finance-entity-extractor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ranjit0034/finance-entity-extractor

SGLang

How to use Ranjit0034/finance-entity-extractor 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 "Ranjit0034/finance-entity-extractor" \
    --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": "Ranjit0034/finance-entity-extractor",
		"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 "Ranjit0034/finance-entity-extractor" \
        --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": "Ranjit0034/finance-entity-extractor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ranjit0034/finance-entity-extractor with Docker Model Runner:
```
docker model run hf.co/Ranjit0034/finance-entity-extractor
```

finance-entity-extractor / docs /banking_email_formats.md

Ranjit Behera

FinEE v1.0 - Finance Entity Extractor

dcc24f8 9 months ago

preview code

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Indian Banking Email Samples for ML Training

A comprehensive collection of sample banking emails covering all transaction types across major Indian banks. Designed for training finance entity extraction models.

Banks Covered

HDFC Bank
ICICI Bank
SBI (State Bank of India)
Axis Bank
Kotak Mahindra Bank
PNB (Punjab National Bank)

Transaction Types

Credit Types

Type	Keywords
UPI_CREDIT	credited, UPI, received
NEFT_CREDIT	NEFT, credited, transfer
RTGS_CREDIT	RTGS, credited
IMPS_CREDIT	IMPS, credited
SALARY_CREDIT	salary, credited
INTEREST_CREDIT	interest, credited
REFUND_CREDIT	refund, credited
CASH_DEPOSIT	cash, deposit, CDM
DIVIDEND_CREDIT	dividend, credited

Debit Types

Type	Keywords
UPI_DEBIT	debited, UPI, payment
NEFT_DEBIT	NEFT, debited, transfer
ATM_WITHDRAWAL	ATM, withdrawn, cash
POS_DEBIT	POS, merchant, debit card
BILL_PAYMENT	bill, payment, recharge
EMI_DEBIT	EMI, loan, deducted
SIP_DEBIT	SIP, mutual fund

Entity Extraction Fields

{
  "date": "2026-01-10",
  "amount": 5000.00,
  "type": "credit|debit",
  "account": "4521",
  "bank": "hdfc",
  "reference": "503421789456",
  "merchant": "swiggy",
  "category": "food"
}

VPA Patterns

HDFC: @hdfcbank, @okhdfc
ICICI: @icici, @okicici
SBI: @sbi, @oksbi
Axis: @axisbank, @okaxis
Kotak: @kotak
PayTM: @paytm
PhonePe: @ybl