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
| """ | |
| Data Module - Email Processing and Entity Extraction. | |
| This module provides production-grade tools for processing financial emails, | |
| extracting structured data, and classifying email content. | |
| Components: | |
| - EmailParser: Parse emails from MBOX files | |
| - EntityExtractor: Extract financial entities from text | |
| - EmailClassifier: Classify emails into categories | |
| - PDFExtractor: Extract transactions from bank statement PDFs | |
| - BankEmailGenerator: Generate synthetic training data | |
| Example: | |
| >>> from src.data import EntityExtractor, EmailClassifier | |
| >>> | |
| >>> extractor = EntityExtractor() | |
| >>> result = extractor.extract("Rs.500 debited from account 1234") | |
| >>> print(result.to_dict()) | |
| {'amount': '500', 'type': 'debit', 'account': '1234'} | |
| >>> | |
| >>> classifier = EmailClassifier() | |
| >>> result = classifier.classify(subject="Transaction Alert", ...) | |
| >>> print(result.category) | |
| 'finance' | |
| Author: Ranjit Behera | |
| License: MIT | |
| """ | |
| from __future__ import annotations | |
| # Core exports | |
| from src.data.extractor import ( | |
| EntityExtractor, | |
| FinancialEntity, | |
| extract_entities, | |
| ) | |
| from src.data.classifier import ( | |
| EmailClassifier, | |
| EmailCategory, | |
| ClassificationResult, | |
| classify_email, | |
| ) | |
| from src.data.parser import EmailParser | |
| # Optional exports (may not be installed) | |
| try: | |
| from src.data.pdf_extractor import PDFExtractor, Transaction | |
| except ImportError: | |
| PDFExtractor = None # type: ignore | |
| Transaction = None # type: ignore | |
| try: | |
| from src.data.bank_templates import BankEmailGenerator | |
| except ImportError: | |
| BankEmailGenerator = None # type: ignore | |
| try: | |
| from src.data.labeling import LabelingPipeline, LabeledExample | |
| except ImportError: | |
| LabelingPipeline = None # type: ignore | |
| LabeledExample = None # type: ignore | |
| # Public API | |
| __all__ = [ | |
| # Core classes | |
| "EntityExtractor", | |
| "FinancialEntity", | |
| "EmailClassifier", | |
| "EmailCategory", | |
| "ClassificationResult", | |
| "EmailParser", | |
| # Convenience functions | |
| "extract_entities", | |
| "classify_email", | |
| # Optional classes | |
| "PDFExtractor", | |
| "Transaction", | |
| "BankEmailGenerator", | |
| "LabelingPipeline", | |
| "LabeledExample", | |
| ] | |
| def get_version() -> str: | |
| """Get the package version.""" | |
| from src import __version__ | |
| return __version__ | |