Explain BERT Model Architecture and It differs form GPT

Question

Accepted Answer

BERT is a bidirectional transformer model for pre-training language representations, while GPT is a generative model.

BERT is a pre-training model that learns contextual representations of words by considering both left and right context.
GPT is a generative model that uses a transformer decoder to generate text based on the context.
BERT is bidirectional, meaning it can understand the context of a word by looking at both preceding and following words.
GPT is unidirectional, meaning it generates text in a left-to-right manner without considering future context.
BERT is trained on masked language modeling and next sentence prediction tasks.
GPT is trained on autoregressive language modeling, predicting the next word in a sequence.
BERT is commonly used for tasks like text classification, named entity recognition, and question answering.
GPT is commonly used for tasks like text generation, language translation, and summarization.

Top Accenture Senior Data Scientist interview questions & answers