“英特尔创新大师杯”深度学习挑战赛 赛道3：CCKS2021中文NLP地址相关性任务

Related tags

Deep Learningccks2021-track3-top1
Overview

ccks2021-track3

CCKS2021中文NLP地址相关性任务-赛道三-冠军方案

团队：我的加菲鱼- wodejiafeiyu

初赛第二/复赛第一/决赛第一

前言

19年开始，陆陆续续参加了一些比赛，拿到过一些top，比较懒一直都没分享过，这次比较幸运又拿了top1，打算分享下

分类的任务用这个框架基本都能top10，之前个人参加的《全球人工智能大赛-赛道一》和这个也差不多，只是有些具体任务的trick不同

比赛主页

https://tianchi.aliyun.com/competition/entrance/531901/introduction

环境

  • torch==1.6.0
  • transformers=3.0.2

预训练模型

- nezha-base 
- nezha-wwm
- macbert
  • 下载预训练模型，放到文件夹user_data/model_param/pretrain_model_param/下，文件夹和模型名字一一对应

全流程脚本

sh run.sh

核心思路

  • 预训练mlm中的mask策略使用ngram-mask，相比原始的动态mask，提升了预训练难度
  • 标签也包含语义信息，预训练部分融入标签信息，提升预训练效果
  • 混合精度预训练，损失一部分精度，提升整体的训练速度，实际测试结果精度损失不大，速度提升明显
  • 对抗训练，已经是一个比较常用的trick了
  • 后12层加上embedding的cls动态加权平均
  • multi-sample dropout
  • 推理的时候，阈值搜索
  • 三折交叉验证，每折使用不同的随机数种子使用dynamic pad
  • 加权平均，模型融合

总结

模块 提分点分析 提升
ngram-mask 相比于单个字的遮蔽，ngram-mask加大了预训练任务的难度，从而提升效果 2.6个千分点
融入标签信息预训练 标签也含有语义信息，模型学习的更多 1个千分点
后12层加上embedding的cls动态加权平均+multi-sample dropout 加权平均能增强向量的语义表征能力从而提升效果。multi-sample dropout能加速训练，增强泛化能力 3.3个千分点
对抗训练fgm 生成对抗样本，对抗样本的训练，增加模型泛化能力 2.2个千分点
多分类阈值搜索 缩放系数使得f1最优 0.7个千分点
模型融合 提升模型的鲁棒性和泛化能力 1.5个千分点
动态pad和预训练混合精度训练和每折使用不同的随机数种子 提升模型训练速度，不同的随机数种子加大了模型的差异，提升融合的效果
Owner
shaochenjie
shaochenjie
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