I am learning deep learning and I want to get into NLP. I have done LSTM, and now I am learning about vectorisation and transformers. Can you please tell me, which algorithm is more effective and accurate?
They are meant for different purposes and they are hardly comparable.
RoBERTa is meant for text classification and tagging tasks. The idea is that you take a pretrained RoBERTa model and finetune it on your (potentially small) classification or tagging dataset. Some examples of tasks where RoBERTa is useful are sentiment classification, part-of-speech (POS) tagging and named entity recognition (NER).
GPT-3 is meant for text generation tasks. Its paradigm is very different, normally referred to as "priming". You basically take GPT-3, give it some text as context and let it generate more text. The context should give GPT-3 the "pattern" of what it must generate. You don't finetune it, just give it some example of acceptable text generation pattern and then let it generate more alike.