Fine-Tuning BERT for Intent Recognition

Author(s): Sandani Fernando

Originally published on Towards AI.

Fine-Tuning BERT for Intent Recognition

In the field of natural language processing, intent recognition is a critical component of chatbots, virtual assistants, and customer support systems. It allows these systems to understand the user’s intentions from their input text and respond accordingly. In this article, we will explore how to fine-tune a BERT (Bidirectional Encoder Representations from Transformers) model for intent recognition using Python and the Transformers library.

This article explains the steps to fine-tune a BERT model for intent recognition, which is essential for various natural language understanding applications. It discusses data preprocessing, the creation of a BERT tokenizer, defining a custom dataset for training, and building a classification model using PyTorch. The training process is described in detail, showcasing the effectiveness of the model over multiple epochs, and concludes with how to use the trained model for intent recognition, emphasizing the importance of understanding user intent for enhancing chatbot functionality and user experience.

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Published via Towards AI