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How to Use Hugging Face Transformer Models in MATLAB

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Learn how to use Hugging Face transformer models for natural language processing (NLP) tasks in MATLAB®.
Transformer models have been shown to achieve superior results in many NLP tasks. You can use transformer models such as BERT and GPT-2 in MATLAB for text-based applications with Text Analytics Toolbox™ and Deep Learning Toolbox™. You can also use models from external frameworks, such as Hugging Face transformers with text data in MATLAB.
We walk through the steps of using a Hugging Face transformer model to perform machine translation and analyze the text data using Text Analytics Toolbox, creating a complete NLP pipeline.
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© 2022 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
Transformer models have been shown to achieve superior results in many NLP tasks. You can use transformer models such as BERT and GPT-2 in MATLAB for text-based applications with Text Analytics Toolbox™ and Deep Learning Toolbox™. You can also use models from external frameworks, such as Hugging Face transformers with text data in MATLAB.
We walk through the steps of using a Hugging Face transformer model to perform machine translation and analyze the text data using Text Analytics Toolbox, creating a complete NLP pipeline.
Additional resources:
Related Products:
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© 2022 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
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