Kaggle competition : Build a model to predict whether a text is produced by a human or a machine
Team Name : HSZ
Team Members :
train_set.json: This file contains 4000 paragraphs for various subjects (in the field text of the json file) and labels (in the field label of the json file). The dataset is divided as follows: 2016 human written text and 1984 text generated from different text generation models.
test_set.json: This file contains 4000 text in total, divided as follows: 2020 human written text and 1980 text generated using the same models used in the train_set. This dataset is distributed equally between the public and private leaderboards on kaggle.
DeBERTa+lgbm.ipynb
python roberta.py # support roberta/deberta training
python basic_transformer.py # support bert/xlnet training
python gpt2.py # support gpt2 training
python inference.py # template for writing results to submission.csv
xlnet.py # train xlnet (poor performance)
EDA1.ipynb / EDA2.ipynb # Exploratory Data Analysis
logistiv_regression_baseline.py # baseline with logistic regression / adaboost / random forest
large_transformer.py # bert large (poor performance)
semi-supervised # enhance the dataset base on leaderboard results. (not useful)
Some of the best weights can be downloaded here :
https://drive.google.com/drive/folders/1_38fv85i-WXuSAbz0ZwMnY5lVoMO_H8Y?usp=sharing
Once Downloaded, each model folder should be placed in parallel at the root of the project folder.