Notice: This is only For the convinience of Chineses reader who cannot read English version directly
完整中文版BERT模型分析请移步我的博客:NLP自然语言处理-谷歌BERT模型深度解析
Author-作者 Junseong Kim, Scatter Lab ([email protected] / [email protected]) License This project following Apache 2.0 License as written in LICENSE file
Copyright 2018 Junseong Kim, Scatter Lab, respective BERT contributors
Copyright (c) 2018 Alexander Rush : The Annotated Trasnformer
Environment require:
-tqdm
-numpy
-torch>=0.4.0
-python3.6+
This version has based on the version in https://github.com/codertimo/BERT-pytorch
此中文版本仅基于原作者Junseong Kim与原Google项目的pytorch版本代码作为分享,如有其他用途请与原作者联系
Pytorch implementation of Google AI's 2018 BERT, with simple annotation
BERT 2018 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Paper URL : https://arxiv.org/abs/1810.04805
最近谷歌搞了个大新闻,公司AI团队新发布的BERT模型,在机器阅读理解顶级水平测试SQuAD1.1中表现出惊人的成绩:全部两个衡量指标上全面超越人类,并且还在11种不同NLP测试中创出最佳成绩,包括将GLUE基准推至80.4%(绝对改进7.6%),MultiNLI准确度达到86.7% (绝对改进率5.6%)等。可以预见的是,BERT将为NLP带来里程碑式的改变,也是NLP领域近期最重要的进展。
第一,是这个模型非常的深,12层,并不宽(wide),中间层只有1024,而之前的Transformer模型中间层有2048。这似乎又印证了计算机图像处理的一个观点——深而窄 比 浅而宽 的模型更好。
第二,MLM(Masked Language Model),同时利用左侧和右侧的词语,这个在ELMo上已经出现了,绝对不是原创。其次,对于Mask(遮挡)在语言模型上的应用,已经被Ziang Xie提出了(我很有幸的也参与到了这篇论文中):[1703.02573] Data Noising as Smoothing in Neural Network Language Models。这也是篇巨星云集的论文:Sida Wang,Jiwei Li(香侬科技的创始人兼CEO兼史上发文最多的NLP学者),Andrew Ng,Dan Jurafsky都是Coauthor。但很可惜的是他们没有关注到这篇论文。用这篇论文的方法去做Masking,相信BRET的能力说不定还会有提升。
部分代码基于 The Annotated Transformer 目前此项目仍在开发中,后续会有不断更新,欢迎FORK
pip install bert-pytorch
注意 : 你的语料如果位于同一行,则应该由制表符\t所分割
Welcome to the \t the jungle\n
I can stay \t here all night\n
or tokenized corpus (tokenization is not in package)
Wel_ _come _to _the \t _the _jungle\n
_I _can _stay \t _here _all _night\n
bert-vocab -c data/corpus.small -o data/vocab.small
bert -c data/corpus.small -v data/vocab.small -o output/bert.model
在原论文中,作者展示了新的语言训练模型,称为编码语言模型与次一句预测 In the paper, authors shows the new language model training methods, which are "masked language model" and "predict next sentence".
Original Paper : 3.3.1 Task #1: Masked LM
Input Sequence : The man went to [MASK] store with [MASK] dog
Target Sequence : the his
会有15%的随机输入被改变,这些改变基于以下规则: Randomly 15% of input token will be changed into something, based on under sub-rules:
- 80%的tokens会成为‘掩码’token
- 10%的tokens会称为‘随机’token
- 10%的tokens会保持不变但需要被预测
Original Paper : 3.3.2 Task #2: Next Sentence Prediction
Input : [CLS] the man went to the store [SEP] he bought a gallon of milk [SEP]
Label : Is Next
Input = [CLS] the man heading to the store [SEP] penguin [MASK] are flight ##less birds [SEP]
Label = NotNext
"Is this sentence can be continuously connected?"
- 50%的下一句会(随机)成为连续句子
- 50%的下一句会(随机)成为不关联句子
Junseong Kim, Scatter Lab ([email protected] / [email protected])
This project following Apache 2.0 License as written in LICENSE file
Copyright 2018 Junseong Kim, Scatter Lab, respective BERT contributors
Copyright (c) 2018 Alexander Rush : The Annotated Trasnformer