BERT-NEXT: Exploring Contextual Sentence Understanding
The advent of advanced natural language processing (NLP) techniques has revolutionized the way we handle textual data. This project presents the implementation of exploring contextual sentence understanding on the Quora Insincere Questions dataset using the pretrained BERT architecture. In this study, we explore the application of BERT, a bidirectional transformer model, for text classification tasks. The goal is to classify if a question contains hateful, disrespectful or toxic content. BERT represents the state-of-the-art in language representation models and has shown strong performance on various natural language processing tasks. In this project, the pretrained BERT base model is fine-tuned on a sample of the Quora dataset for next sentence prediction. Results show that with just 1% of the data (around 13,000 examples), the fine-tuned model achieves over 90% validation accuracy in identifying insincere questions after 4 epochs of training. This demonstrates the effectiveness of leveraging BERT for text classification tasks with minimal labeled data requirements. Being able to automatically detect toxic, hateful or disrespectful content is important to maintain healthy online discussions. However, the nuances of human language make this a challenging natural language processing problem. Insincere questions may contain offensive language, hate speech, or misinformation, making their identification crucial for maintaining a positive and safe online environment. In this project, we explore using the pretrained Bidirectional Encoder Representations from Transformers (BERT) model for next sentence prediction on the task of identifying insincere questions.