Fill Mask
term_id: fill_mask
Category: basic_concepts
Definition
Fill Mask is a fundamental pre-training objective used in transformer-based models like BERT. The process involves masking random tokens in a text sequence and training the model to predict the original values of those masked words. This self-supervised learning approach helps the model understand bidirectional context and semantic relationships between words, forming the basis for many downstream NLP applications such as question answering and text completion.
Summary
A natural language processing task where a model predicts missing tokens within a sentence based on surrounding context.
Key Concepts
- Masked Language Modeling
- Contextual Understanding
- Self-Supervised Learning
- Token Prediction
Use Cases
- Text completion
- Semantic role labeling
- Pre-training foundation