- 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
Final Loss after 12 Epoches of training:
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