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The areas indicated in the previous section as GBK/1 and GBK/2, taken by themselves, is simply GB 2312-80 in its usual encoding, GBK/1 being the non-hanzi region and GBK/2 the hanzi region. GB 2312, or more properly the EUC-CN encoding thereof, takes a pair of bytes from the range A1 – FE , like any 94² ISO-2022 character set loaded into GR.
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.
The RNNsearch model introduced an attention mechanism to seq2seq for machine translation to solve the bottleneck problem (of the fixed-size output vector), allowing the model to process long-distance dependencies more easily. The name is because it "emulates searching through a source sentence during decoding a translation".
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).
The machine learning task for knowledge graph embedding that is more often used to evaluate the embedding accuracy of the models is the link prediction. [ 1 ] [ 3 ] [ 5 ] [ 6 ] [ 7 ] [ 18 ] Rossi et al. [ 5 ] produced an extensive benchmark of the models, but also other surveys produces similar results.
Two encoding schemes existed for GB 2312: a one-or-two byte 8-bit EUC-CN encoding commonly used, and a 7-bit encoding called HZ [1] for usenet posts. [2]: 94 A traditional variant called GB/T 12345 was published in 1990. The EUC-CN form was later extended into GBK to include all Unicode 1.1 CJK Ideographs in 1993, abandoning the ISO-2022 model.
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods.
Where two hexadecimal numbers are given, the value below 0x7F is used in a 7-bit encoding, [a] and the larger value (between 0xA1 and 0xFE) is used in an 8-bit EUC-style encoding. [17] The extended UHC -style 8-bit encodings defined by the 2003 edition onwards likewise use the larger byte values, between 0xA1 and 0xFE inclusive, for the main ...