SiD - Simple Deep Model

Vectorwise Interpretable Attentions for Multimodal Tabular Data

Winning Solution for a Competition

This repository is an official implementation of a model which won first place in the dacon competition. You can see the final result on this post. If you want to reproduce the score of the competition, please check out this documentation which is used to verify by the competition hosts.

Introduction

SiD is a vectorwise interpretable attention model for multimodal tabular data. It is designed and considered to handle data as vectors so that multimodal data (e.g. text, image and audio) can be encoded into the vectors and used with the the tabular data.

Requirements

The requirements of this project is as follows:

• numpy
• omegaconf
• pandas
• pytorch_lightning
• scikit_learn
• torch==1.10.1
• transformers
• wandb

Instead, you can simply install the libraries at once:

$ pip install -r requirements.txt

Architecture

Model Architecture	Residual Block

As mentioned above, SiD is considered to extend TabNet with vectorwise approach. Because many multimodal data (e.g. text, image and audio) are encoded into the vectors, it is important to merge the tabular data with the vectors. However, the attention mechanism (attentive transformer) of TabNet does not consider the vectorized features. Therefore we propose the vectorwise interpretable attention model.

Experiments

Hyperparameter Settings
Experimental Results
Ablation Studies

Interpretability

2017
2018
2019
2020

Importance Mask
Question Dialogs

License

This repository is released under the Apache License 2.0. License can be found in LICENSE file.