Pre-training is a technique used to train an AI model on a large dataset before fine-tuning it for a specific task. It is the 'P' in ChatGPT, which stands for Genralised Pre-Trained Transformer. Pre-training is required so that the Neural Network can 'understand' the data it is given. Typically neural networks can process arrays of numbers called Vectors and these normally have to be in a specific range, so that they can be compared to other inputs. This means that an image, a paragraph of text or any other type of data will have to be converted into vectors before the neural network can make sense of the data.
These are the steps that might typically be performed in pre-training, though any case might require substatial changes.
- Choose a pre-training algorithm: There are many pre-training algorithms available, depending on the type of data and the machine learning task. For example, autoencoders can be used for image data, while language models can be used for text data. Tools like LangChain can help with this.
- Prepare the data: The data must be prepared for pre-training by cleaning, normalizing, and transforming it into a format that can be used by the pre-training algorithm. This may involve techniques such as tokenization, stemming, and vectorization.
- Train the model: Once the data is prepared, the pre-training algorithm is applied to the data to train the model. The goal of pre-training is to learn general features or patterns in the data that can be applied to a wide range of tasks.
- Evaluate the model: After the model has been pre-trained, it is evaluated to determine its performance on the pre-training task. This may involve measuring metrics such as accuracy, precision, and recall.
- Fine-tune the model: Once the model has been pre-trained, it can be fine-tuned for a specific task. This involves adjusting the weights of the pre-trained model to optimize its performance on the specific task.