├── README.md ├── bert-cased-k-fold.sh ├── bert-uncased-k-fold.sh ├── dataset ├── __pycache__ │ └── dataset.cpython-36.pyc ├── back_translation.py ├── back_translation_continue.py └── dataset.py ├── inference_kernel └── models-with-optimization-v5.ipynb ├── model ├── __pycache__ │ └── model_bert.cpython-36.pyc ├── check_model.ipynb └── model_bert.py ├── oof-bert-cased-k-fold.sh ├── oof-bert-uncased-k-fold.sh ├── oof-k-fold.py ├── oof-roberta-base.sh ├── oof-xlnet-cased-k-fold.sh ├── oof.txt ├── postprocessing_optimization ├── __pycache__ │ ├── file.cpython-36.pyc │ ├── include.cpython-36.pyc │ └── metric.cpython-36.pyc ├── apply_optimization.py ├── file.py ├── include.py ├── metric.py ├── oof_after_postprocessing.csv ├── run_binning_opt.py ├── stacking_pipeline.py └── test_postprocessing.py ├── roberta-base-k-fold.sh ├── start_kernel_kaggle ├── distilbert-use-features-oof.ipynb ├── pytorch-bert-baseline.ipynb └── use-features-oof.ipynb ├── swa-bert-base-cased-kfold.sh ├── swa-bert-base-uncased-kfold.sh ├── swa-k-fold.py ├── swa-roberta-base-k-fold.sh ├── swa-xlnet-cased-kfold.sh ├── training-k-fold.py ├── translation_data ├── Processing_Translated_xlsx.ipynb ├── train_answer.xlsx ├── train_augment_final_with_clean.csv ├── train_body.xlsx ├── train_title.xlsx └── translation_augmentation.py ├── utils ├── Radam.py ├── __pycache__ │ ├── file.cpython-36.pyc │ ├── functions.cpython-36.pyc │ ├── include.cpython-36.pyc │ ├── loss_function.cpython-36.pyc │ ├── lrs_scheduler.cpython-36.pyc │ ├── metric.cpython-36.pyc │ └── ranger.cpython-36.pyc ├── file.py ├── functions.py ├── include.py ├── loss_function.py ├── lrs_scheduler.py ├── metric.py └── ranger.py └── xlnet-cased-k-fold.sh /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jionie/Google-Quest-Answer/HEAD/README.md -------------------------------------------------------------------------------- /bert-cased-k-fold.sh: 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