Toxic-Comment-Classification-Challenge

Kaggle Toxic Comment Classification Challenge: (https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)

Single LSTM + GRU Model with 10 fold CV yields a ROC-AUC score of 0.9871 against Public LB highest of 0.9890 with current solution ranked 300^th on Public LB

Additional Details:

1. Embedding Vectors - fastText & GloVe Twitter (200d)
2. Implementation Libraries - Pytorch (Model) & Keras (Text Pre-processing)

Potential Areas of Improvement:

1. Modifying model architecture with focus on better regularization
2. Ensembling (though ensembling with NB-SVM baseline did not help improve the score)

Note - Did not use BERT baseline since it wasn't released at the time of competition