Text Augmentation To Boost NLP Model Performance (Part 3/3)

Data augmentation techniques are used to generate additional, synthetic data using the data you have. Image augmentation has become a standard procedure in computer vision applications but text augmentation is relatively new to natural language processing (NLP) field. The nlpaug module implements a number of high-performance text augmentation algorithms that may boost performance of NLP models. In Part 1 of the tutorial (https://www.youtube.com/watch?v=lpWewl7y57o), we introduced some cool text augmentation functions in the nlpaug module. In Part 2 of the tutorial (https://www.youtube.com/watch?v=URLqjenW-2M), we used the nlpaug module to generate text augmentations to Twitter tweet data and evaluate bag-of-words model performances with and without text augmentations. In this last part--Part 3 of the tutorial, we used the nlpaug module to generate text augmentations to Twitter tweet data and evaluate deep neural network model performances with and without text augmentations. Topics include: How to install and import the dependences for running the nlpaug module successfully in Google Colab? How to download required models to be used by the nlpaug module? How to build a deep learning (neural network) model for tweet sentiment analysis using Hugging Face Transformers? How to manage tweet data using Pandas data frame and Apache Arrow dataset? How to do word tokenization using Hugging Face Transformers? How to calculate accuracy and F1 scores using Hugging Face Transformers? How to save and load models using Hugging Face Transformers? How to create pipelines using the saved models? How to create confusion matrix using model predictions? How to use synonym based text augmentation to augment tweet data? How to use contextual word embedding based text augmentation to augment tweet data? How to use back translation based text augmentation to augment tweet data? How to compared deep neural network model performances with and without text augmentation? Code used in this video can be downloaded from GitHub: https://github.com/DreamJarsAI/Apply-AI-like-a-Pro/blob/main/9%20DL%20Text%20Classification%20with%20Text%20Augmentation.ipynb.ipynb Hashtags: #nlp #naturallanguageprocessing #text #augmentation #ai #nlptraining #nlppractitioner #artificialintelligence #machinelearning #deeplearning #python #pythonprogramming #pythontutorial #aitutorial #coding