“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

Last update: Dec 28, 2021

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Deep Learning ccks2021_track3_baseline

Overview

基于 bert4keras 的一个baseline

不作任何 数据trick 单模 线上 最高可到 0.7891


# 基础 版
train.py         0.7769

# transformer 各层 cls concat   明神的trick https://xv44586.github.io/2021/01/20/ccf-qa-2/
train_concat.py  0.7786

# 使用那个FGM对抗
train_FGM.py     0.7891

# 苏神提出的 PET
train_PET.py     0.7812


预训练模型使用 nezha

使用说明：
	数据放在 data下
	下载预训练模型解压
	模型会保存在 train中

优化方向：
 1，融合
  🙃
 

bert4keras >= 0.10.0

nezha的预训练模型
链接: https://pan.baidu.com/s/1lURyXs39PYnsb4imCjVkfQ  密码: 1eqq
--来自百度网盘超级会员V4的分享

Owner

孙永松

机器学习，数据挖掘，神经网络，NLP

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“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

Overview

Owner

孙永松

Trafffic prediction analysis using hybrid models - Machine Learning

Brain tumor detection using Convolution-Neural Network (CNN)

Framework for training options with different attention mechanism and using them to solve downstream tasks.

Tools for manipulating UVs in the Blender viewport.

Character-Input - Create a program that asks the user to enter their name and their age

Transfer Learning for Pose Estimation of Illustrated Characters

Alphabetical Letter Recognition

The code release of paper Low-Light Image Enhancement with Normalizing Flow

Learning Modified Indicator Functions for Surface Reconstruction

Model-based Reinforcement Learning Improves Autonomous Racing Performance

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Codes for the ICCV'21 paper "FREE: Feature Refinement for Generalized Zero-Shot Learning"

Dataset Condensation with Contrastive Signals

Build Graph Nets in Tensorflow

Training Very Deep Neural Networks Without Skip-Connections

Chess reinforcement learning by AlphaGo Zero methods.

Implementation of the Paper: "Parameterized Hypercomplex Graph Neural Networks for Graph Classification" by Tuan Le, Marco Bertolini, Frank Noé and Djork-Arné Clevert

Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training".

PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Representation

The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

“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

Overview

Owner

孙永松

Trafffic prediction analysis using hybrid models - Machine Learning

Brain tumor detection using Convolution-Neural Network (CNN)

Framework for training options with different attention mechanism and using them to solve downstream tasks.

Tools for manipulating UVs in the Blender viewport.

Character-Input - Create a program that asks the user to enter their name and their age

Transfer Learning for Pose Estimation of Illustrated Characters

Alphabetical Letter Recognition

The code release of paper Low-Light Image Enhancement with Normalizing Flow

Learning Modified Indicator Functions for Surface Reconstruction

Model-based Reinforcement Learning Improves Autonomous Racing Performance

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Codes for the ICCV'21 paper "FREE: Feature Refinement for Generalized Zero-Shot Learning"

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Chess reinforcement learning by AlphaGo Zero methods.

Implementation of the Paper: "Parameterized Hypercomplex Graph Neural Networks for Graph Classification" by Tuan Le, Marco Bertolini, Frank Noé and Djork-Arné Clevert

Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training".

PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Representation

The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

“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