从最初的绝对位置编码,与单词嵌入相加作为第一层的输入,再到 RPR 提出直接在注意力分数的计算中引入相对位置信息,并学习相对距离的表示矩阵(长度固定),再到 Transformer-XL 中引入偏置信息,并重新使用 Transformer 中的编码公式生成相对距离的表示矩阵,使长度可泛化。 需要注意的是,后两文中均有一些矩阵计算上的优化...
「方法简述:」Transformer-based models处理长序列时存在困难,因为它们的自注意力操作与序列长度呈二次方关系。Longformer通过引入一个与序列长度呈线性关系的注意力机制解决了这个问题,使其能够轻松处理数千个标记或更长的文档。Longformer在字符级语言建模方面表现优秀,并在各种下游任务上取得了最先进的结果。此外,Longform...
[1].Transformer-based models and hardware acceleration analysis in autonomous driving: A survey.
字节跳动LightSeq2-Transformer-based模型训练加速 本文是对LightSeq2: Accelerated Training for Transformer-based Models on GPUs一个简单概括,具体实现细节等接下来一两周我啃一啃源码再更。 LightSeq2优化基本架构图 论文里对Transformer的优化集中在上图的四个部分,前两个为算子层面的优化,后两个为内存层面的优化。
This paper provides a comprehensive review of transformer-based models applied to the primary modalities of text, vision, and speech, discussing their architectures, conversion methodologies, and applications. By synthesizing the literature on modality conversion, this survey aims to underline the ...
[13] Brown, E. S., & King, M. (2020). Language models are unsupervised multitask learners: A new perspective on transfer learning. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5610-5622). ...
An important feature of RNN-based encoder-decoder models is the definition ofspecialvectors, such as theEOSEOSandBOSBOSvector. TheEOSEOSvector often represents the final input vectorxnxnto "cue" the encoder that the input sequence has ended and also defines the end of the target sequence. As ...
Encoder-decoder models 或者 sequence-to-sequence models: 适用于需要根据输入进行生成的任务,如翻译或摘要。 三 理解Transformer中的Token 因为模型是无法直接处理文本的,只能处理数字,就跟ASCII码表、Unicode码表一样,计算机在处理文字时也是先将文字转成对应的字码,然后为每个字码编写一个对应的数字记录在表中,最后...
“GPT” seen in the tool’s various versions (e.g. GPT-2, GPT-3) stands for “generative pre-trained transformer.” Text-based generative AI tools such as ChatGPT benefit from transformer models because they can more readily predict the next word in a sequence of text, based on a ...
Transformer Based Models 论文标题: On the Importance of Local Information in Transformer Based Models 论文链接: https://arxiv.org/abs/2008.05828 目前已经有很多文章研究多头注意力机制取得成功的本质原因,他们有一些共性的发现:少部分关注于局部信息或者语义信息的注意力比其他 head 更加重要。本文是一篇实验性...