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Related Questions
- How does masked language modeling contribute to the pre-training of BERT and other transformer-based language models?
- What is the objective of masked language modeling in BERT, and how does it improve language understanding?
- Can you explain the difference between masking and permutation-based training methods in transformer-based language models?
- How does the choice of masking scheme (e.g., span-based, word-level, or character-level) affect the performance of BERT and other transformer-based models?
- In addition to masked language modeling, what other pre-training tasks are commonly used in BERT and other transformer-based language models?
- How does the masking technique used in BERT enable the model to capture nuances of language, such as context-dependent relationships and inference?
- Can you discuss the implications of masked language modeling on the overall architecture and performance of BERT and other transformer-based language models?
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