• It converts the labels in training data in one hot encoding
• Splits the train_data into train.csv and validation.csv and saves the csvs in the Data folder

• I have used torchtext to tokenize the titles, creating the vocabulary and creating the trainloader, validationloader and testloader

• It contain the Bi-lstm network architecture

• This script is used for training the network and then saving the checkpoints of the network

• This script is used to test the network on the unseen data.
• In the Results folder -> test_results.csv is generated using test.py ( Data used is test.csv which is saved inside Data folder )

• I attempted to solve the multi-label classification problem using 2 layer Bi-LSTM . ( For the full network configuration see model.py)
• For the Loss Function I have used BCEWithLogitsLoss
• Optimizer used : Adam

Training Loss: 0.0049 Validation Loss: 0.0028

Model_Checkpoints folder contains the weight files. Final result csv is saved in the Results folder -> test_results.csv. It contains the titles and probability scores for each of the labels.

PyTorch, Sklearn, Torchtext, Pandas, Numpy