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[1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text. T5 models are usually pretrained on a massive dataset of text and code, after which they can perform the text-based tasks that are similar to their pretrained tasks.
Anchor modeling is an agile database modeling technique suited for information that changes over time both in structure and content. It provides a graphical notation used for conceptual modeling similar to that of entity-relationship modeling , with extensions for working with temporal data .
Golden Age of Science Fiction — a period of the 1940s during which the science fiction genre gained wide public attention and many classic science fiction stories were published. New Wave science fiction — characterised by a high degree of experimentation, both in form and in content.
Novum (Latin for new thing) is a term used by science fiction scholar Darko Suvin and others to describe the scientifically plausible innovations used by science fiction narratives. [ 1 ] Frequently used science fictional nova include aliens , time travel , the technological singularity , artificial intelligence , and psychic powers.
Science fiction – genre of fiction dealing with the impact of imagined innovations in science or technology, often in a futuristic setting. [2] [3] [4] Exploring the consequences of such innovations is the traditional purpose of science fiction, making it a "literature of ideas". [5] Pornography
Sometimes the far future genre moves from science fiction to fantasy, showing a society where civilization has regressed to the point where older technologies are no longer understood and are seen as magic. This subgenre is sometimes known as the "far future fantasy" [2] and partially overlaps with the science fantasy genre. [3]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Unlike the works about the far future, set thousands or more years in the future and often tackling philosophical concepts such as the ultimate fate of the universe, fiction set in the near future, roughly defined as within the next few years or decades, α has been described as more realistic and containing themes that have been described as more socially relevant.