Hi, and welcome to this modern NLP course! Here is a quick summary of the different parts of the course and the links to the Google Colab file for each application. Enjoy!

1. Introduction

2. BERT's intuition

3. Application 1: apply BERT's tokenizer for data preprocessing (sentimental analyser):

        Google Colab file: https://colab.research.google.com/drive/1jMin0iXmW4ZrSlwjhj2xJAiCrUMOLsqg?usp=sharing
        Data link: http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip

4. Application 2: use BERT as an embedding layer for your personal NLP model (sentiment analyser):
        Google Colab file: https://colab.research.google.com/drive/15YIsJP3GsbLn5HVmmo6jehmcGxaafny-?usp=sharing
        Data link: http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip

5. Application3: fine-tune BERT to create a question answering system (SQuaD benchmark):
        Google Colab file: https://colab.research.google.com/drive/1_g8fJDwFXDNZATaK6Ub4-WOnhZOzXgBM?usp=sharing
        Data link: training set https://drive.google.com/drive/folders/1KsmLJhYtLKwc5Ox_wKAluaqVYnoJaO30?usp=sharing
                          dev set https://drive.google.com/drive/folders/1KsmLJhYtLKwc5Ox_wKAluaqVYnoJaO30?usp=sharing
                          vocab.txt https://drive.google.com/drive/folders/1KsmLJhYtLKwc5Ox_wKAluaqVYnoJaO30?usp=sharing
                          evaluation script https://drive.google.com/file/d/1Oa2kyOMWSitFAsNbZScZtUrvpbDZi9Kh/view?usp=sharing