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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.
pip install keybert
from keybert import KeyBERT
model = KeyBERT()
text = "Your input text here."
keywords = model.extract_keywords(text)
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.