ines-bettaieb / mlproject

Key Extractor tool with KeyBert and Streamlit

• 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 🤗 to create keywords/keyphrases that are most similar to a document.

Installation

1. Create a project repository:

mkdir key-word-extractor

2. Navigate to the project directory:

cd key-word-extractor

3. Create and activate a virtual environment (optional but recommended):

python3 -m venv venv source venv/bin/activate

4. Install the dependencies:

pip install -r requirements.txt

Usage

1. Start the application:

streamlit run main.py

2. Access the app in your browser at http://localhost:8501.

3. You will see a text input field where you can copy/paste your text.

4. Utilize the left-hand panel to experiment with settings and visualize dynamic result changes.

5. To exit the app, press Ctrl+C in the terminal.

Screenshots