DeepLearning---ATIS-dataset

In this notebook I will perform intent classification using three different models to see which one is better for this task. I will be using PyTorch. the data I used for this notebook is provided. The names are testlabel, testseq.in, train_label, trainseq.in

In this notebook you can expect to see:

• Uploading the data and understanding it
• Preprocessing the data and some feature engineering
• Creating my own tokenization algorithm
• Creating my own accuracy, confusion matrix and training functions
• Feeding the data into the models and performing statistical inference

The models I used for intent classification are:

• Simple ANN model
• BiDirectional LSTM model based off of Yu Wang, Yilin Shen & Hongxia Jin
• BERT transformer model (transfer learning). I used a pretrained model and changed it up a bit to work for my own problem.

I think Natural Language Processing is very interesting and hope to work on more similar projects.