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
File size: 5,003 Bytes
dcc24f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | """
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()
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