Process of Adapting a Supervised Pre-trained Model

Advantages of Supervised Pre-training

Disadvantages of Supervised Pre-training

Example of a Supervised Pre-training Task

A startup is building a system to automatically categorize legal contracts into specific sub-types (e.g., 'lease agreement', 'employment contract', 'non-disclosure agreement'). They have a very small, private dataset of 500 labeled contracts. Their proposed strategy is to first train a large neural network on a massive, publicly available dataset of millions of labeled news articles, classifying them by topic (e.g., 'sports', 'politics', 'technology'). After this initial training, they plan to adapt the model to their legal contract categorization task. What is the most significant weakness of this proposed pre-training approach for their specific goal?

A machine learning engineer wants to use a supervised pre-training approach to build a model that can detect toxic comments online. Arrange the following steps in the correct chronological order to reflect this process.

Evaluating a Pre-training Strategy for Scientific Text

Assumption of Supervised Pre-training