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You can use this template to include a timeline in an article page. Type {{subst:Include timeline}} where you want the timeline to appear. Click "Preview" In the box that appears, follow the link to create a timeline; Fill in the blanks using the instructions that appear; Once you've saved your timeline, return to the article page and press "save".
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.
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]
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
This template constructs a vertically arranged timeline. The editor defines 2D rectangles (bars) and optional annotations (notes). The header is customizable. A scale appears on the left, and annotations appear on the right. An optional legend appears at the foot. Has built-in compatibility for geological divisions.
Template documentation This template may have no transclusions , because this page is used as a preload or an edit intro in Module:Include timeline . Editors can experiment in this template's sandbox ( create | mirror ) and testcases ( create ) pages.
[[Category:Timeline templates]] to the <includeonly> section at the bottom of that page. Otherwise, add <noinclude>[[Category:Timeline templates]]</noinclude> to the end of the template code, making sure it starts on the same line as the code's last character.
The idea of skip-gram is that the vector of a word should be close to the vector of each of its neighbors. The idea of CBOW is that the vector-sum of a word's neighbors should be close to the vector of the word. In the original publication, "closeness" is measured by softmax, but the framework allows other ways to measure closeness.