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Question and answer generation (QAG) is a natural language processing (NLP) task that generates a question and an answer in the same time by using context information. The input context can be represented in form of structured information in a database or raw text. The outputs of QAG systems can be directly applied to several NLP applications...
Code and data for paper 'Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling'
Pre-Training and Fine-Tuning transformer models using PyTorch and the Hugging Face Transformers library. Whether you're delving into pre-training with custom datasets or fine-tuning for specific classification tasks, these notebooks offer explanations and code for implementation.