MaskedLanguageModeling
Masked language modeling is a training technique widely used in natural language processing for teaching models to understand and generate human language. The core concept involves randomly masking or hiding portions of input text, such as replacing some words with a special token like [MASK], and training the model to predict the original words based on the surrounding context.
This method is considered a form of self-supervised learning because it creates a training objective from the
The most prominent application of this technique is in transformer-based architectures like BERT (Bidirectional Encoder Representations
While highly effective, the method has a limitation. The [MASK] token is only present during training and