Pretraining is a foundational concept in machine learning and natural language processing where a model is initially trained on a large, diverse dataset before being fine-tuned for specific tasks. This approach allows the model to learn general features and patterns inherent in the data, thereby enhancing its ability to perform specialized tasks such as sentiment analysis, machine translation, or question answering. By leveraging the insights gained during pretraining, models can achieve improved accuracy and efficiency when applied to targeted applications.
