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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.
Three levels of view are defined in IDEF1X: entity relationship (ER), key-based (KB), and fully attributed (FA). They differ in level of abstraction. The ER level is the most abstract. It models the most fundamental elements of the subject area - the entities and their relationships. It is usually broader in scope than the other levels.
An entity–relationship model (or ER model) describes interrelated things of interest in a specific domain of knowledge. A basic ER model is composed of entity types (which classify the things of interest) and specifies relationships that can exist between entities (instances of those entity types).
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
Project management office: The Project management office in a business or professional enterprise is the department or group that defines and maintains the standards of process, generally related to project management, within the organization. The PMO strives to standardize and introduce economies of repetition in the execution of projects.
A diagram of a sinusoidal positional encoding with parameters =, = A positional encoding is a fixed-size vector representation of the relative positions of tokens within a sequence: it provides the transformer model with information about where the words are in the input sequence.
The enhanced entity–relationship (EER) model (or extended entity–relationship model) in computer science is a high-level or conceptual data model incorporating extensions to the original entity–relationship (ER) model, used in the design of databases.
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]