What are the components of GPT?
The components of GPT (Generative Pre-trained Transformer) are:
Pre-trained model: This is a large neural network which is trained on a large-scale dataset, such as the Google Billion Word Corpus. The model is pre-trained with a specific task, such as language modeling or text generation, to help it understand the language better.
Attention mechanism: This is a mechanism that allows the model to focus on specific parts of the input to understand the context better. The attention mechanism helps the model to learn the relationships between words and phrases in the input.
Tokenizer: This is a tool which helps in transforming the input text into tokens which can be used by the model. The tokenizer is responsible for generating the tokens which are then fed into the model.
Optimizer: This is a tool which helps the model to learn from the data. The optimizer helps in adjusting the weights of the model in order to improve its accuracy.
Output layer: This layer is responsible for producing the output of the model. The output layer can produce either a single output or a set of outputs.