🤯 Did You Know (click to read)
BERT randomly masks 15% of input tokens during pretraining and learns to predict them using the context of both left and right tokens.
BERT is pretrained on large text corpora using masked language modeling (MLM), where a random subset of words is masked and the model predicts them based on surrounding context. This forces the model to learn bidirectional representations of language. MLM pretraining enables BERT to capture syntax, semantics, and context, which can then be fine-tuned for specific NLP tasks with limited labeled data.
💥 Impact (click to read)
Masked language modeling allowed BERT to generalize to multiple downstream tasks efficiently. It reduced the need for large labeled datasets, enabling developers to achieve high performance with minimal task-specific data.
For users, MLM pretraining translates into AI that understands words in context, producing more accurate translations, summaries, and responses. The irony is that the model predicts missing words statistically without comprehension.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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