/Kaggle-Quora-Insincere-Questions-Classification

59th place solution (top 2%)

Primary LanguagePython

Kaggle: Quora Insincere Questions Classification.

This is the 59th place solution for the Kaggle competition Quora: Insincere Questions Classification.

The Problem

This was a binary classification problem where we were required to predict whether the question contains abusive content or not. More info here: https://www.kaggle.com/c/quora-insincere-questions-classification

Model

The code of the model is located in models\model.py.

The model architecture consists of a few layers:
I have averaged GloVe and PARAGRAM embeddings and loaded this vector to the torch.nn.Embedding layer. The concatenation of LSTM with Attention, GRU with Attention, GRU with MaxPooling, GRU with AveragePooling, and Capsule layer (taken from here https://github.com/binzhouchn/capsule-pytorch) over GRU layer. Head output is composed of the Linear layer with ReLU activation and a Linear layer.


This model reaches an F1-score of 0.69764 in Public and 0.70489 in Private leaderboards

Code

To test the algorithm place train.csv, test.csv, embeddings in folder data. Afterwards run the following code:

python3 main.py --train_data_path='data/train.csv' \
                --test_data_path='data/test.csv' \
                --text_column='question_text' \
                --target_column='target' \
                --embeddings_folder='data/embeddings'