Supervised Text Classification: A Framework for AIGenerated Text

Prophet Net and Transformer models have both made significant advancements in the domain of text generation toward producing language that is comparable to human-authored content. For text generation on several platforms, including English, Chinese, and others, Prophet Net is an effective approach. A branch of natural language processing (NLP) termed text creation combines computational linguistics and artificial intelligence to create original material. A sophisticated deep learning model Bert, GPT models are excellent for expressing the idea and following fundamental grammatical norms. In our work, the method we suggested for automatically producing new text includes a language model that, given input data, generates new text pertinent to that data's domain. The fundamental basis for the idea of text generation is the Synthetic text Generator structure, which is comparable to Prophet Net and GPT. The generated data is cleansed in order to determine whether the text was produced by a machine or by a human author. The results of this study show the proportion of text that was categorized as human and machine-generated text produced by machine learning models