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The only literal byte pair left occurs only once, and the encoding might stop here. Alternatively, the process could continue with recursive byte pair encoding, replacing "ZY" with "X": XdXac X=ZY Y=ab Z=aa This data cannot be compressed further by byte pair encoding because there are no pairs of bytes that occur more than once.
[citation needed] EJS was inspired by templating systems like ERB ( also known as Embedded Ruby) used in Ruby on Rails, which also allows code embedding within HTML. [4] ELS was created for JavaScript developers to create server-rendered HTML pages in an easy and familiar way, likely other templating engines available in other programming ...
Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.
BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [3]
GPT-J is a GPT-3-like model with 6 billion parameters. [3] Like GPT-3, it is an autoregressive, decoder-only transformer model designed to solve natural language processing (NLP) tasks by predicting how a piece of text will continue.
Nashorn is a JavaScript engine developed in the Java programming language originally by Oracle and later by the OpenJDK Community. It relies on the support for dynamically typed languages on the Java Platform (JSR 292) (a concept first realized in the experimental Da Vinci Machine and a standard part of Java 7 and later.)
The loss function is defined using triplets of training points of the form (,,).In each triplet, (called an "anchor point") denotes a reference point of a particular identity, (called a "positive point") denotes another point of the same identity in point , and (called a "negative point") denotes an point of an identity different from the identity in point and .
An embedding, or a smooth embedding, is defined to be an immersion that is an embedding in the topological sense mentioned above (i.e. homeomorphism onto its image). [ 4 ] In other words, the domain of an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold .