KeyBERT
Generate keywords from text
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What is KeyBERT ?
KeyBERT is a state-of-the-art keyword extraction model developed using BERT and other transformer-based architectures. It is designed to generate high-quality keywords from textual data, enabling efficient text analysis and summarization. By leveraging advanced NLP techniques, KeyBERT provides accurate and relevant keyword extraction, making it a valuable tool for researchers, analysts, and developers.
Features
- Transformer-Based Architecture: Built on top of BERT and similar models, ensuring high accuracy in keyword extraction.
- Multi-Language Support: Capable of processing and extracting keywords from text in multiple languages.
- Customizable Models: Allows users to fine-tune models for specific domains or use cases.
- Efficient Processing: Optimized for fast keyword extraction, even for large volumes of text.
- Integration Capabilities: Easily integrates with existing NLP pipelines and workflows.
How to use KeyBERT ?
- Install the Library: Use pip to install the KeyBERT package.
pip install keybert - Import the Model: Import KeyBERT from the keybert library.
from keybert import KeyBERT - Load Pre-trained Model: Load a pre-trained model based on your specific needs.
model = KeyBERT() - Extract Keywords: Provide the text input and extract keywords.
text = "Your input text here." keywords = model.extract_keywords(text) - Review Results: Analyze the extracted keywords for further processing or analysis.
Frequently Asked Questions
What languages does KeyBERT support?
KeyBERT supports a wide range of languages, including but not limited to English, Spanish, French, German, Dutch, and many more. It leverages the multilingual capabilities of transformer models.
Can I use KeyBERT for long documents?
Yes, KeyBERT is designed to handle long documents and large volumes of text efficiently. It processes text quickly while maintaining high accuracy.
How can I customize KeyBERT for my specific use case?
You can customize KeyBERT by fine-tuning pre-trained models on your specific dataset. This allows the model to adapt to your domain-specific terminology and requirements.