BIGBIRD-ITC:在内部transformer构造(ITC)中,我们将一些现有token“全局”化,并在整个序列中使用。 具体来说,我们选择索引的子集G(具有g:=|G|),对于所有i \in G,A(i,:)=1和A(:,i)=1。 BIGBIRD-ETC:在扩展transformer(ETC)构造中,我们包括其他“全局”token,例如CLS。 具体来说,我们添加g个全局token,这...
The t5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. In the paper, we demonstrate how to achieve state-of-the-art results on multiple NLP tasks using a text-to-text transformer pre-trained...
简介:Paper:2017年的Google机器翻译团队《Transformer:Attention Is All You Need》翻译并解读 6.2、Model Variations To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the...
The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively.Transformer 遵循这个总体架构,使用堆叠的自关注层和点方式的完全连接层,分别用于编码器和解...
allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. 在这项工作中,我们提出了Transformer,这是一种避免重复出现的模型架构,而完全依赖于注意机制来绘制输入和输出之间的全局依赖关系...
Paper:2017年的Google机器翻译团队《Transformer:Attention Is All You Need》翻译并解读 目录 论文评价 1、Motivation: 2、创新点: Abstract 1、Introduction 2、Background ...
1. 科学论文「Attention Is All You Need」如今已经到达了传奇地位,其中所提及的 Transformer 架构正在影响着所有人的生活。 2. Transformer 的故事始于 Uszkoreit,递归神经网络在解析较长文本时遇到困难, 因此从 2014 年起他开始构思一种不同的方法:自注意力(self-attention)。
Open source Scaling Transformer Paper to Google Research Github. Feb 1, 2022 scann Internal changes Sep 26, 2024 schema_guided_dst Open-sourcing the code for "CLIP as RNN: Segment Countless Visual Con… Jan 23, 2024 schptm_benchmark [scipy] Add pytype suppressions to fix new pytype errors...
In the new paperMaking Transformers Solve Compositional Tasks, a Google Research team explores the design space of transformer models in an effort to enable deep learning architectures to solve natural language compositional tasks. The proposed approach provides models with inductive biase...
虽然这个任务很难,但我们认为 transformer 的潜力很强大。这其实很像人类思考过程,我们思考时并不是所有过程都需要显式的文字表达。在 reasoning 这个过程中,即使你在思考,有时答案会在某个瞬间突然出现,这个过程更类似于直觉,是难以用逻辑解释的。 最近我发现一些有趣的现象,如果我们把 reasoning 看作与传统任务...