- This Streamlit application uses KeyBert to extract meaningful keywords from text documents.
- KeyBert can be an alternative to bag of words techniques (e.g. Count or Tfidf vectorizers) that might suffer from noisy results.
- KeyBert uses a minimal keyword extraction technique that leverages multiple NLP embeddings and relies on Transformers ๐ค to create keywords/keyphrases that are most similar to a document.
- Create a project repository:
mkdir key-word-extractor
- Navigate to the project directory:
cd key-word-extractor
- Create and activate a virtual environment (optional but recommended):
python3 -m venv venv source venv/bin/activate
- Install the dependencies:
pip install -r requirements.txt
- Start the application:
streamlit run main.py
-
Access the app in your browser at http://localhost:8501.
-
You will see a text input field where you can copy/paste your text.
-
Utilize the left-hand panel to experiment with settings and visualize dynamic result changes.
-
To exit the app, press
Ctrl+C
in the terminal.