Transformer performances versus Human Baselines

Transformers, like humans, can be fine-tuned to perform downstream tasks by inheriting the properties of a pretrained model. The pretrained model provides its architecture and language representations through its parameters.

A pretrained model trains on key tasks to enable it to acquire a general knowledge of the language. A fine-tuned model trains on downstream tasks. Not every transformer model uses the same tasks for pretraining. Potentially, tasks can all be pretrained or fine-tuned tasks.

Every NLP model needs to be evaluated with a standard method.

In this section, we will first go through some of the key measurement methods. Then, we will go through some of the main benchmark tasks and datasets.

Let's start by going through some of the key metric methods.

Evaluating models with metrics

It is impossible to compare one transformer model to another transformer model (or any other NLP model) without a...