Text Generation
Transformers
English
phi3
finance
entity-extraction
ner
phi-3
production
indian-banking
custom_code
4-bit precision
Instructions to use Ranjit0034/finance-entity-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| """ | |
| FinEE CLI - Command-line interface for financial entity extraction. | |
| Usage: | |
| finee extract "Rs.500 debited from A/c 1234 on 01-01-25" | |
| finee extract --file transactions.txt | |
| finee stats | |
| finee backends | |
| """ | |
| import argparse | |
| import json | |
| import sys | |
| from typing import Optional | |
| import logging | |
| from .extractor import FinEE, extract, get_extractor | |
| from .schema import ExtractionConfig | |
| from .backends import get_available_backends | |
| def setup_logging(verbose: bool = False): | |
| """Configure logging.""" | |
| level = logging.DEBUG if verbose else logging.WARNING | |
| logging.basicConfig( | |
| level=level, | |
| format='%(levelname)s: %(message)s' | |
| ) | |
| def cmd_extract(args): | |
| """Handle extract command.""" | |
| # Get text from argument or file | |
| if args.file: | |
| with open(args.file, 'r') as f: | |
| texts = [line.strip() for line in f if line.strip()] | |
| else: | |
| texts = [args.text] | |
| # Configure extractor | |
| config = ExtractionConfig( | |
| use_llm=not args.no_llm, | |
| cache_enabled=not args.no_cache, | |
| ) | |
| extractor = FinEE(config) | |
| # Extract | |
| for text in texts: | |
| result = extractor.extract(text) | |
| if args.json: | |
| print(result.to_json()) | |
| else: | |
| print(f"\n{'='*60}") | |
| print(f"Input: {text[:80]}{'...' if len(text) > 80 else ''}") | |
| print(f"{'='*60}") | |
| # Core fields | |
| print(f"Amount: {result.amount}") | |
| print(f"Type: {result.type.value if result.type else 'N/A'}") | |
| print(f"Date: {result.date or 'N/A'}") | |
| print(f"Account: {result.account or 'N/A'}") | |
| print(f"Reference: {result.reference or 'N/A'}") | |
| # Enrichment | |
| print(f"Merchant: {result.merchant or 'N/A'}") | |
| print(f"Category: {result.category.value if result.category else 'N/A'}") | |
| # Metadata | |
| print(f"\nConfidence: {result.confidence.value} ({result.confidence_score:.0%})") | |
| print(f"Time: {result.processing_time_ms:.2f}ms") | |
| print(f"Cached: {result.from_cache}") | |
| def cmd_stats(args): | |
| """Handle stats command.""" | |
| extractor = get_extractor() | |
| stats = extractor.get_stats() | |
| print("\nFinEE Statistics") | |
| print("="*40) | |
| print(json.dumps(stats, indent=2)) | |
| def cmd_backends(args): | |
| """Handle backends command.""" | |
| backends = get_available_backends() | |
| print("\nAvailable Backends") | |
| print("="*40) | |
| if backends: | |
| for backend in backends: | |
| print(f" ✅ {backend}") | |
| else: | |
| print(" ⚠️ No LLM backends available") | |
| print("\nInstall a backend:") | |
| print(" pip install finee[metal] # Apple Silicon") | |
| print(" pip install finee[cuda] # NVIDIA GPU") | |
| print(" pip install finee[cpu] # CPU (llama.cpp)") | |
| def cmd_version(args): | |
| """Handle version command.""" | |
| from . import __version__ | |
| print(f"finee {__version__}") | |
| def main(): | |
| """Main CLI entry point.""" | |
| parser = argparse.ArgumentParser( | |
| prog='finee', | |
| description='Extract structured financial entities from Indian banking messages' | |
| ) | |
| parser.add_argument('-v', '--verbose', action='store_true', help='Verbose output') | |
| parser.add_argument('--version', action='store_true', help='Show version') | |
| subparsers = parser.add_subparsers(dest='command', help='Commands') | |
| # Extract command | |
| extract_parser = subparsers.add_parser('extract', help='Extract entities from text') | |
| extract_parser.add_argument('text', nargs='?', help='Transaction text') | |
| extract_parser.add_argument('-f', '--file', help='Read from file (one per line)') | |
| extract_parser.add_argument('--json', action='store_true', help='Output as JSON') | |
| extract_parser.add_argument('--no-llm', action='store_true', help='Disable LLM (regex only)') | |
| extract_parser.add_argument('--no-cache', action='store_true', help='Disable caching') | |
| extract_parser.set_defaults(func=cmd_extract) | |
| # Stats command | |
| stats_parser = subparsers.add_parser('stats', help='Show extraction statistics') | |
| stats_parser.set_defaults(func=cmd_stats) | |
| # Backends command | |
| backends_parser = subparsers.add_parser('backends', help='List available backends') | |
| backends_parser.set_defaults(func=cmd_backends) | |
| # Parse arguments | |
| args = parser.parse_args() | |
| # Setup logging | |
| setup_logging(args.verbose) | |
| # Handle version | |
| if args.version: | |
| cmd_version(args) | |
| return | |
| # Handle commands | |
| if hasattr(args, 'func'): | |
| # Validate extract command | |
| if args.command == 'extract': | |
| if not args.text and not args.file: | |
| extract_parser.error("Either TEXT or --file is required") | |
| args.func(args) | |
| else: | |
| parser.print_help() | |
| if __name__ == '__main__': | |
| main() | |