pre-training

Pre-training is a concept often used in machine learning and natural language processing, involving training a model on a large dataset before fine-tuning it on a more specific task. This process helps the model to learn general features and patterns in the data, which can then be adapted to perform better on the specific task. Pre-training is particularly beneficial for tasks such as sentiment analysis, named entity recognition, and machine translation, and is widely used in models like BERT, GPT, and RoBERTa.