The Entity Recognition serves as a library for identifying technological entities within texts and suggesting relevant technologies using advanced NLP techniques.
Integrate our library into your project by installing it directly from PyPI:
pip install entity-recognition-lib
The required spaCy model (en_core_web_sm
) will be automatically downloaded and installed if not already present on
your system.
Here's how to use the Entity Recognition library in your Python scripts:
from entity_recognition_lib import EntityRecognizer
recognizer = EntityRecognizer()
texts = ["Your text here"]
results = recognizer.process_texts(texts)
print(results)
The library processes text to identify and categorize technology entities, delivering structured output that includes identified entities and contextual recommendations.
Example output:
[
{
"input_text": "Example text with technology entities",
"predicted_topic_name": "topic_name",
"extracted_entities": [
{
"entity_name": "entity_1",
"score": 0.9,
"category": "Category 1"
},
{
"entity_name": "entity_2",
"score": 0.8,
"category": "Category 1"
}
],
"recommendations": [
{
"category": "Category 1",
"recommendation": "entity_1"
}
]
}
]
For detailed usage examples and code snippets, please refer to the examples directory in the repository. The examples cover various scenarios, including:
- Basic usage of the library for entity recognition and recommendation generation
- Advanced features such as result analysis and visualization
- Integration samples with popular frameworks like Flask and Streamlit
We recommend exploring the examples to understand how to effectively utilize the Entity-Recognition library in your projects.
If you consider contributing to the Entity Recognition library, make sure to check out our Contributing Guide and our Development Guide, for more information on how to run and test the project locally. You are welcome to expand the technology entities corpus or add a new feature.
This project is licensed under the MIT License - see the LICENSE file for details.