39 lines
1.5 KiB
Markdown
39 lines
1.5 KiB
Markdown
# Keyword Extractor
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This project is a simple script for extracting keywords from text using Natural Language Processing (NLP).
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## How it works
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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).
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It is designed to be lightweight, easy to run, and customizable.
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## Requirements
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- Python 3.8+
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- NLP libraries (transformers, torch, etc.)
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- Other utilities as listed in the requirements file
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For exact versions of the libraries, please check the **`requirements.txt`** file.
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## Usage
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1. Clone the repository.
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Run the script:
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```bash
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python keyword_extractor.py
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```
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## Main Methods
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- `load_model()`: Loads the pre-trained transformer model for text processing. This is the main method for model initialization.
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- `preprocess_text(text)`: Cleans and prepares the input text (e.g., lowercasing, removing stopwords, etc.).
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- `extract_keywords(text, top_n=10)`: The core method that applies the model and retrieves the top keywords from the input text.
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- `display_results(keywords)`: Prints or saves the extracted keywords for further use.
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## Model
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The script uses a **transformer-based model** for keyword extraction. The exact model can be changed in the code if needed.
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## Notes
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- Works with English (and potentially other languages, depending on the model).
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- Results may vary based on the model and input text.
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