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)

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Ranjit0034 commited on Jan 13

Commit

fd25f34

verified ·

1 Parent(s): 656419f

Upload src/finee/init.py with huggingface_hub

Browse files

Files changed (1) hide show

src/finee/__init__.py +16 -1

src/finee/__init__.py CHANGED Viewed

@@ -4,6 +4,13 @@ FinEE - Finance Entity Extractor
 A production-ready library for extracting structured financial entities
 from Indian banking messages (SMS, email, statements).
 Example:
     >>> from finee import extract
     >>> result = extract("Rs.500 debited from A/c 1234 on 01-01-25")
@@ -11,9 +18,17 @@ Example:
     500.0
     >>> print(result.to_json())
     {"amount": 500.0, "type": "debit", "date": "01-01-2025", ...}
 """
-__version__ = "1.0.0"
 __author__ = "Ranjit Behera"
 from .schema import (

 A production-ready library for extracting structured financial entities
 from Indian banking messages (SMS, email, statements).
+Features:
+    - 🏦 Multi-Bank Support: 25+ Indian banks
+    - 💳 All Transaction Types: UPI, NEFT, IMPS, Card, EMI
+    - 🌐 Multilingual: English, Hindi, Tamil, Telugu, Bengali, Kannada
+    - 🔍 RAG Enhanced: Context-aware extraction
+    - ⚡ Fast: <1ms with regex, <100ms with LLM
 Example:
     >>> from finee import extract
     >>> result = extract("Rs.500 debited from A/c 1234 on 01-01-25")
     500.0
     >>> print(result.to_json())
     {"amount": 500.0, "type": "debit", "date": "01-01-2025", ...}
+Web UI:
+    >>> from finee.ui import launch
+    >>> launch()  # Opens Gradio UI at http://localhost:7860
+API Server:
+    >>> from finee.api import start_server
+    >>> start_server()  # Starts FastAPI at http://localhost:8000
 """
+__version__ = "2.0.0"
 __author__ = "Ranjit Behera"
 from .schema import (

Upload src/finee/__init__.py with huggingface_hub

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