【36论文泛读】DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter 小z呀 凭君莫话封侯事, 一将功成万骨枯。摘要 随着迁移学习从大规模的预训练模型开始在自然语言处理(NLP)变得更加普遍,在有限的计算训练和推理压力的端设备具有挑战性。在本次工作中,我们提出一种方法来预训练一...
本文是对论文DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter的一个回顾。 论文地址: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterarxiv.org/abs/1910.01108 官方代码地址: https://github.com/huggingface/transformersgithub.com/huggingf...
《DistilBERT,adistilledversionofBERT:smaller, faster,cheaperandlighter》阅读心得 该文主要采用“知识蒸馏”方法对BERT(预训练语言模型)精简、优化,将较大模型压缩成较小的模型,最终目的是:提高模型推理的效率,让运行在智能手机等硬件设备上的深度学习模型具有轻量级、响应快及能源利用率高等特性。 在2019年的NLP领域...
1、BERT(句子中有15%的词汇被随机mask掉,预测两个句子是否应该连在一起) 2、ALBERT(A Lite BERT,轻量级的BERT,共享的方法有很多,ALBERT选择了全部共享,FFN和ATTENTION的都共享) 3、RoBERTa(基本就是说训练过程可以再优化优化,最核心的就是如何在语言模型中设计mask) 4、DistilBE(A distilled version of BERT: ...
The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine In the sentiment analysis of labeling with positive and negative categories, a distilled version of BERT (DistilBERT) was used in this study. With a ... AA Ajhari - 《Ap...
论文地址:DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter 图1: 几个预训练模型的参数量统计 近年来,大规模预训练语言模型成为NLP任务的基本工具,虽然这些模型带来了显著的改进,但它们通常拥有数亿个参数(如图1所示),而这会引起两个问题。首先,大型预训练模型需要的计算成本很高...
论文地址:DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter 图1: 几个预训练模型的参数量统计 近年来,大规模预训练语言模型成为NLP任务的基本工具,虽然这些模型带来了显著的改进,但它们通常拥有数亿个参数(如图1所示),而这会引起两个问题。首先,大型预训练模型需要的计算成本很高。
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter All images unless otherwise noted are by the authorMachine Learning NLP Large Language Models Bert Distilbert-- 1Written by Vyacheslav Efimov 1.91K Followers ·Writer for Towards Data Science BSc in Software Engineering....
[1] Victor SANH, Lysandre DEBUT, Julien CHAUMOND, Thomas WOLF, DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (2019), Hugging Face [2] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi...
The head of the teacher is also copied. """ # Get teacher configuration as a dictionnary configuration = teacher_model.config.to_dict() # Half the number of hidden layer configuration['num_hidden_layers'] //= 2 # Convert the dictionnary to the student configuration ...