作者将这种策略称为混合深度(Mixture-of-Depths,MoD)。 作者还提到,MoD技术还允许在性能和速度之间进行权衡。一方面,可以训练一个MoD transformer在最终的对数概率训练目标上比普通transformer提高1.5%,并且训练所需的挂钟时间(wall-clock time)相当。另一方面,可以将一个MoD transformer训练达到与isoFLOP最佳的传统...
《Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping》阅读笔记 窥探 nlp算法工程师5 人赞同了该文章 背景 基于transformer架构的预训练语言模型展现出了明显的优越性,刷新了众多nlp下游任务的效果。但是预训练语言模型是从海量无监督数据集中学习知识,且模型规模一般都比较大(base...
These results suggest that the propensity of larger Transformer-based models to 'memorize' sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pre-trained language models to study human language processing....
近日，Google DeepMind 研究了这个问题，他们希望使用更低的计算预算来缩减 Transformer 使用的计算量。论文标题：Mixture-of-Depths: Dynamically allocating compute in transformer-based language models论文地址：https://arxiv.org/pdf/2404.02258.pdf 他们设想：在每一层中，网络必须学会为每个 token 做决策，从而...
The surge of pre-trained language models has begun a new era in the field of Natural Language Processing (NLP) by allowing us to build powerful language models. Among these models, Transformer-based models such as BERT have become increasingly popular due to their state-of-the-art performance...
1. Language Model 语言模型来辅助NLP任务已经得到了学术界较为广泛的探讨,通常有两种方式: 1.1 Feature-based方法 Feature-based指利用语言模型的中间结果也就是LM embedding, 将其作为额外的特征,引入到原任务的模型中,例如在下图中,采用了两个单向RNN构成的语言模型,将语言模型的中间结果 ...
in optimizing these models for smaller datasets and using transfer learning to solve new problems. This allows certain activities to be performed more effectively while using fewer data. Various parameters of the transformer-based language model are shown in Figure 1....
标题:Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context 文章链接:Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context 代码:https://github.com/kimiyoung/transformer-xl 发表:ACL 2019 领域:Transformer (decoder) 改进 ...
,分别作为content-based key vectors和location-based key vectors。 综上,对于一个N层的,只有一个head的模型,计算公式如下: 评价: 这篇论文是在transformer的变体中很有名的一个了。综合来说,它提出了一种新的相对位置编码,性能略有提升,但参数量增大。提出使用跨相邻两个segment的attention来建模长程依赖,在长...
Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0). - jalammar/ecco