How to Do Twitter Sentiment Analysis with a Pre-Trained Language Model

Thus, the winning strategy has been to first pre-train a transformer-based model with vast amounts of unlabelled and, consequentially, fine-tune the model to make it perform better at a specific task. This second step is usually accomplished with labeled data -- though much fewer learning examples are required in comparison to training the model from scratch. Natural Language Processing (NLP) has a large variety of tasks and applications, including Automatic, or Machine Translation, Text Summarization, Text Generation, Text Classification, Question Answering, and Named Entity Recognition (NER). The ability to develop and improve these very different types of tasks have wide-reaching possibilities for developing NLP. Recurrent Neural Networks (RNNs) got very popular in sequence modeling for supervised NLP tasks like classification and regression.