Introduction

Introduction

Natural Language Processing (NLP) has evolved tremendously, thanks to the advent of transformer-based models. Two of the most notable models in this space are BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). These models have set new benchmarks in various NLP tasks, but what makes them different? In this blog, we'll dive deep into BERT and GPT, exploring their architectures, training methodologies, performance, and practical applications.

Comments

Post a Comment

Popular posts from this blog

Conclusion and References

  Conclusion Both BERT and GPT have revolutionized the field of NLP, each excelling in different areas. BERT's strength lies in understanding and analyzing text, making it suitable for tasks requiring deep comprehension. On the other hand, GPT's prowess is in generating text, making it ideal for creative and conversational applications. The choice between BERT and GPT depends on the specific requirements of the task at hand. As NLP continues to evolve, we can expect further advancements and refinements in these models, pushing the boundaries of what machines can understand and generate in human language. References Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 . Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI . Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., C...
Read more

What is BERT

  What is BERT? Architecture BERT, developed by Google, is designed to understand the context of words in a sentence by looking at both directions (left and right). It uses the encoder part of the transformer model, which makes it highly effective for tasks that require comprehension of text. Training Methodology BERT undergoes two main stages of training: Pre-Training : During this stage, BERT is trained on a large corpus of text using two objectives: Masked Language Model (MLM) and Next Sentence Prediction (NSP). In MLM, 15% of the words in each input sequence are masked, and the model learns to predict these masked words. NSP helps the model understand the relationship between sentence pairs. Fine-Tuning : After pre-training, BERT is fine-tuned on specific tasks using labeled data. This stage involves minimal changes to the model architecture, making it adaptable to various NLP tasks like question answering, named entity recognition, and text classification. Performance BERT has...
Read more