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
| """ | |
| Tests for the EmailParser class. | |
| Run with: pytest tests/test_parser.py -v | |
| """ | |
| import pytest | |
| import sys | |
| from pathlib import Path | |
| # Add src to path for imports | |
| sys.path.insert(0, str(Path(__file__).parent.parent / "src")) | |
| class TestEmailParserImport: | |
| """Test that EmailParser can be imported.""" | |
| def test_import_parser(self): | |
| """Test that parser module can be imported.""" | |
| try: | |
| from data.parser import EmailParser | |
| assert EmailParser is not None | |
| except ImportError as e: | |
| pytest.skip(f"Could not import EmailParser: {e}") | |
| class TestEmailParserMethods: | |
| """Test EmailParser methods with mock data.""" | |
| def test_clean_text_removes_urls(self): | |
| """Test URL removal in _clean_text.""" | |
| from data.parser import EmailParser | |
| # Create a mock parser (won't actually load MBOX) | |
| class MockParser(EmailParser): | |
| def __init__(self): | |
| # Skip file check in init | |
| pass | |
| parser = MockParser() | |
| text = "Visit https://example.com for more info" | |
| result = parser._clean_text(text) | |
| assert "https://example.com" not in result | |
| def test_clean_text_normalizes_whitespace(self): | |
| """Test whitespace normalization.""" | |
| from data.parser import EmailParser | |
| class MockParser(EmailParser): | |
| def __init__(self): | |
| pass | |
| parser = MockParser() | |
| text = "Hello World\n\nTest" | |
| result = parser._clean_text(text) | |
| assert " " not in result # No double spaces | |
| def test_decode_header_none(self): | |
| """Test handling of None header.""" | |
| from data.parser import EmailParser | |
| class MockParser(EmailParser): | |
| def __init__(self): | |
| pass | |
| parser = MockParser() | |
| result = parser._decode_header(None) | |
| assert result == "" | |
| def test_decode_header_plain_string(self): | |
| """Test decoding plain string header.""" | |
| from data.parser import EmailParser | |
| class MockParser(EmailParser): | |
| def __init__(self): | |
| pass | |
| parser = MockParser() | |
| result = parser._decode_header("Simple Subject") | |
| assert result == "Simple Subject" | |
| class TestEmailParserIntegration: | |
| """Integration tests that require actual files.""" | |
| def test_parser_file_not_found(self): | |
| """Test that missing file raises error.""" | |
| from data.parser import EmailParser | |
| with pytest.raises(FileNotFoundError): | |
| EmailParser(Path("/nonexistent/path/file.mbox")) | |
| if __name__ == "__main__": | |
| pytest.main([__file__, "-v"]) | |