Masked language modeling

Also written: masked language model, MLM

Training by hiding some tokens and asking the model to guess them from the words on both sides. It's how BERT learned.

Other terms from the same lesson

• BERT: Google's 2018 encoder model, trained by hiding words and guessing them from both sides. Its descendants still power much search and classification.
• bidirectional: Reading in both directions: each token can look at the words before and after it, as encoders like BERT do.
• contrastive learning: Training that pulls the embeddings of matching pairs (like a question and its answer) together and pushes mismatched pairs apart.
• cross-attention: Attention where one sequence looks at another, such as a translation model's decoder reading the encoder's output.
• cross-encoder: A model that reads a query and a document together and gives one relevance score. More accurate than comparing embeddings but slower, so it reranks a short list.
• encoder: A transformer that reads the whole text at once to understand it, used for embeddings and classification.
• mean pooling: Turning a text's token vectors into one vector for the whole text by averaging them.