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)

# 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 / src /__init__.py

Ranjit Behera

FinEE v1.0 - Finance Entity Extractor

dcc24f8 7 months ago

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1.13 kB

	"""
	LLM Mail Trainer - Finance Entity Extraction Package.

	A production-grade system for extracting financial entities from emails
	using fine-tuned LLMs on Apple Silicon (MLX).

	Features:
	- Multi-bank email parsing (HDFC, ICICI, SBI, Axis, Kotak)
	- UPI/NEFT/IMPS transaction detection
	- Merchant and category classification
	- REST API for inference
	- LoRA fine-tuning support

	Example:
	>>> from src.data import EntityExtractor
	>>> extractor = EntityExtractor()
	>>> result = extractor.extract("Rs.500 debited from account 1234")
	>>> print(result.amount)
	'500'

	Author: Ranjit Behera
	License: MIT
	Version: 0.3.0
	"""

	__version__ = "0.3.0"
	__author__ = "Ranjit Behera"
	__email__ = "ranjit@example.com"
	__license__ = "MIT"

	# Package-level imports for convenience
	from src.data.extractor import EntityExtractor, FinancialEntity
	from src.data.classifier import EmailClassifier, ClassificationResult
	from src.data.parser import EmailParser

	__all__ = [
	"EntityExtractor",
	"FinancialEntity",
	"EmailClassifier",
	"ClassificationResult",
	"EmailParser",
	"__version__",
	]