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

Clone the repository.
Install dependencies:
```
pip install -r requirements.txt
```
Run the script:
```
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.

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