# Keyword Extractor

This project is a simple script for extracting keywords from text using Natural Language Processing (NLP).

## How it works
The script processes input text and extracts the most relevant keywords using a **pre-trained transformer model** (e.g., `bert-base-uncased` or a similar NLP model).  
It is designed to be lightweight, easy to run, and customizable.

## Requirements
- Python 3.8+
- NLP libraries (transformers, torch, etc.)
- Other utilities as listed in the requirements file

For exact versions of the libraries, please check the **`requirements.txt`** file.

## Usage
1. Clone the repository.
2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```
3. Run the script:
   ```bash
   python keyword_extractor.py
   ```

## Main Methods
- `load_model()`: Loads the pre-trained transformer model for text processing. This is the main method for model initialization.
- `preprocess_text(text)`: Cleans and prepares the input text (e.g., lowercasing, removing stopwords, etc.).
- `extract_keywords(text, top_n=10)`: The core method that applies the model and retrieves the top keywords from the input text.
- `display_results(keywords)`: Prints or saves the extracted keywords for further use.

## Model
The script uses a **transformer-based model** for keyword extraction. The exact model can be changed in the code if needed.

## Notes
- Works with English (and potentially other languages, depending on the model).
- Results may vary based on the model and input text.