GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and T5 (Text-to-Text Transfer Transformer) are all state-of-the-art transformers models for natural language processing tasks, but each has a different approach:
GPT: It's designed to generate sequences using a unidirectional transformer. This allows the model to employ the full context of the input by predicting what word comes next during training. It's mostly used for text generation tasks like machine translation, summary, dialogue systems and also for tasks like sentiment classification.
BERT: BERT is a bidirectional transformer that uses a masked language model for training. The model is trained to predict missing words from input sentences. It learns context representation by both looking at tokens to the left and right of the given token. BERT is often used for tasks that require understanding of context such as Question-Answering, Sentence Pair Classification and Named Entity Recognition.
T5: T5 treats all NLP tasks as a text-to-text problem: it transforms every task, whether translation, classification, or summarization, into a problem of translating from one sequence of text to another. It's based on the transformer model but has its own unique method for pre-training and fine-tuning, which tends to perform very well on a wide range of tasks.
In summary, GPT is great for predicting and generating sequences, BERT is excellent at extracting the meaning of language from context, and T5 aims to be a "universal" transformer model that doesn't differentiate between different types of language-based tasks. They all have unique strengths and use-cases. The choice between them often comes down to the specific needs and requirements of a task or project.
