Solution for GAIIC-Track1

Introduction

Here is a basic part of codes for GAIIC-Track1. Other parts will be released after final.

Data Structure

├── config.yaml
├── data
│   ├── preliminary_testA.txt
│   ├── preliminary_testB.txt
│   └── train
│       ├── attr_to_attrvals.json
│       ├── train_coarse.txt
│       └── train_fine.txt
├── README.md
├── Solver.py
├── Inferencer.py
├── data.ipynb
└── src
    ├── dataset.py
    ├── __init__.py
    ├── metrics.py
    ├── model.py
    └── optimizer.py

Solution Idea

1. Visual-Bert was used to integrated multi-modality infomation, followed by a multi head MLP classifier for 13 binary classification task

2. Multiple augmentations were designed for robust training, including

3. Model ensembling gave a good result

Run

1. Run data.ipynb cell by cell, and make sure you specify "preliminary_test(A or B).txt" correctly for test set of different rounds.

2. Write your own configs in config.yaml:

   • NOTE

   • The augmentation functions (./src/dataset.py: JointDataset.aug(...)) are not shown in codes, but replaced by place holders in comments.

   • Normal augmentations like replacing, removing, and shuffling can get a comparable score.

   • Multiple models ensemble is the key to get a better score.

   • Hyperarameters in config.yaml is the same as my final solution, except those augmentation args.

3. Run python Solver.py --config config.yaml to train models

   • GPU ids are specified at the first line of Solver.py, and multi-GPU training is supported (e.g. gpus = "0,1")

4. Run python Inferencer.py to inference on test set. Before that, you should:

   • Set model checkpoints paths in Inferencer.py. group_id isn't necessary

   • Specify test set path of X_test = np.load("./data/test(A or B).npy") and df_test = pd.read_csv("./data/test(A or B)_text.csv") in ./src/dataset.py

Results

Rounds	Place	Score
Preliminary-A	1st	0.95823330
Preliminary-B	1st	0.95555683
Semi-final-A	2nd	0.93149024
Semi-final-B	2nd	0.95485400