BERT uses self-supervised learning by training on two tasks: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). These tasks enable the model to learn contextual relationships in text without requiring manually labeled data. Instead, BERT generates its own “labels” by manipulating input sentences—for example, masking words or pairing sentences—and learns to predict the original content or relationships. This approach allows BERT to build a general understanding of language structure, which can later be fine-tuned for specific NLP tasks like classification or question answering.
In Masked Language Modeling (MLM), BERT randomly masks 15% of tokens in a sentence and learns to predict the masked words using their surrounding context. For instance, given the input sentence “The [MASK] sat on the mat,” BERT processes all tokens bidirectionally (using both left and right context) to predict the masked word, which might be “cat.” Unlike earlier models that processed text left-to-right or right-to-left, BERT’s bidirectional Transformer architecture allows it to consider the full context of each word. This is critical for understanding nuances, such as distinguishing between “bank” in “river bank” versus “bank account.” The masking ensures the model doesn’t overly rely on specific words and instead learns robust contextual patterns.
For Next Sentence Prediction (NSP), BERT learns to determine whether two sentences logically follow each other. During training, 50% of input pairs are consecutive sentences (e.g., “The cat sat. It was hungry.”), and 50% are random pairs from unrelated text. The model classifies whether the second sentence is a valid continuation. For example, given “He opened the door.” followed by “A cold breeze entered,” BERT learns to recognize valid sequences. This task helps the model understand relationships between sentences, which is essential for tasks like question answering or document summarization. After pre-training on MLM and NSP, BERT’s parameters are fine-tuned on smaller labeled datasets for specific tasks, leveraging its pre-trained knowledge while adapting to new objectives. This combination of self-supervised pre-training and task-specific fine-tuning makes BERT highly effective across diverse NLP applications.