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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.
A use case diagram [1] is a graphical depiction of a user's possible interactions with a system. A use case diagram shows various use cases and different types of users the system has and will often be accompanied by other types of diagrams as well. The use cases are represented by either circles or ellipses. The actors are often shown as stick ...
Use case analysis usually starts by drawing use case diagrams. For agile development, a requirement model of many UML diagrams depicting use cases plus some textual descriptions, notes, or use case briefs would be very lightweight and just enough for small or easy project use. As good complements to use case texts, the visual diagram ...
Use-case modelling to describe system environments, user scenarios, and test cases. UML has support for object-oriented system specification, design and modelling. Growing interest in UML from the embedded systems and realtime community. Support for state-machine semantics which can be used for modelling and synthesis.
Provides management of actors, use cases, user stories, declarative requirements, and test scenarios. Includes glossary, data dictionary, and issue tracking. Supports use case diagrams, auto-generated flow diagrams, screen mock-ups, and free-form diagrams. clang-uml: Unknown Unknown Unknown Unknown No C++ PlantUML, Mermaid.js
Use case analysis is a technique used to identify the requirements of a system (normally associated with software/process design) and the information used to both define processes used and classes (which are a collection of actors and processes) which will be used both in the use case diagram and the overall use case in the development or redesign of a software system or program.
GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5] GPT-2 was created as a "direct scale-up" of GPT-1 [6] with a ten-fold increase in both its parameter count and the size of its training dataset. [5]
Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020.. Like its predecessor, GPT-2, it is a decoder-only [2] transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". [3]