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Transformer由论文 《Attention is All You Need》 提出，现在是谷歌云TPU推荐的参考模型。 论文相关的 Tensorflow 的代码可以从GitHub获取，其作为Tensor2Tensor包的一部分。 通过本章，相信你已经对 Transformer 模型的定义和发展有了大概的了解，接下来就可以根据自己的需要对感兴趣的 Transformer 模型进行更深入地探索。 Transformer模型是2017年谷歌团队在《AttentionIsAllYouNeed》论文中提出的革命性深度学习架构。 它完全基于注意力机制，解决了RNN无法并行计算和CNN难以捕捉长距离依赖的问题，成为NLP、CV等领域的核心模型。
Transformer 彻底改变了自然语言处理（NLP）领域，并逐渐扩展到计算机视觉（CV）等领域。 Transformer 的核心思想是完全摒弃传统的循环神经网络（RNN）结构，仅依赖注意力机制来处理序列数据，从而实现更高的并行性和更快的训练速度。 本文旨在通过可视化方式深入浅出地解释 Transformer 模型的工作原理。 该文章详细剖析了 Transformer 的编码器和解码器结构，以及自注意力机制的核心概念。 transformer （直译为“ 变换器 ”）是一种采用 注意力机制 的 深度学习 架构，这一机制可以按输入数据各部分重要性的不同而分配不同的权重。
Transformers documentation 🤗 transformers简介 transformers join the hugging face community and get access to the augmented documentation experience collaborate on models, datasets and spaces faster examples with accelerated inference switch between documentation themes to get started copy page
Transformer的核心原理基于自注意力机制，通过编码器-解码器架构实现序列到序列的转换，其核心创新在于并行处理能力和长距离依赖捕捉技术。 本文旨在为初学者提供一个全面的Transformers入门指南，介绍Transformer模型的基本概念、结构组成及其相对于传统RNN和CNN模型的优势。 Transformer是谷歌在2017年的论文《Attention Is All You Need》中提出的，用于NLP的各项任务，现在是谷歌云TPU推荐的参考模型。 网上有关Transformer原理的介绍很多，在本文中我们将尽量模型简化，让普通读者也能轻松理解。
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