This Repo is for Kaggle - Automated Essay Scoring 2.0

Python Environment

1. Install Packages

pip install -r requirements.txt

Prepare Data

1. Set Kaggle Api

export KAGGLE_USERNAME="your_kaggle_username"
export KAGGLE_KEY="your_api_key"

2. Install unzip

sudo apt install unzip

2. Download Dataset

cd dataset
kaggle competitions download -c learning-agency-lab-automated-essay-scoring-2
unzip learning-agency-lab-automated-essay-scoring-2.zip
kaggle datasets download -d lizhecheng/aes2-0-train-dataset
unzip aes2-0-train-dataset.zip

Run Deberta Regression

1. If you want to run with specific pooling method (no awp)

cd deberta
cd xxxPooling
chmod +x ./regression.sh
./regression.sh

2. If you want to run with specific pooling method (awp)

cd deberta-awp
cd xxxPooling
chmod +x ./regression.sh
./regression.sh

3. If you want the flexibility to set the parameters of the model

cd src
(change the settings in config.py)
python train.py

Run LLM

1. Classification

cd llm
chmod +x ./classification.sh
./classification.sh

2. Regression

cd llm
chmod +x ./regression.sh
./regression.sh

Run Tree Models

cd tree
(change the settings in config.py)
python full_cv_main.py / python out_of_fold_cv_main.py

Some Tricks (Replace "\n\n" ...)

cd replace
chmod +x ./regression.sh
./regression.sh

You can also combine tricks into the codes under src directory

Conclusion

1. It is very important to use a tree model as a two-stage correction in this competition.

2. You don't need to submit your entry for the Learning Agency Lab competition in the final month.

3. A meaningless competition, a meaningless bronze medal.