Token

In the context of GPT-J, a "token" refers to a unit of text that the model uses to make predictions and generate text. A token can be a word, a punctuation mark, or a number, and it is the basic unit of input and output for GPT-J.

Before being processed by GPT-J, input text is split into tokens, which are then converted into numerical representations that can be used as input to the neural network. Each token is assigned a unique numerical value called an "embedding", which represents its meaning and relationship to other tokens in the input text.

During the generation of text, GPT-J predicts the most likely next token or sequence of tokens given the previous input tokens. The predicted tokens are then converted back into human-readable text, which forms the output of the model.

GPT-J uses a technique called "byte pair encoding" to split text into tokens. This technique splits text into sub-word units based on the frequency of their occurrence in the training data, which enables GPT-J to handle rare and out-of-vocabulary words.

In summary, tokens are the basic units of input and output for GPT-J, and they are used to represent and process text data in a numerical form that can be used by the neural network.