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Methods for breaking text into subword units to handle out-of-vocabulary words and maintain consistent representations.
Distinguishing note: Focuses on the subword-level granularity of input processing.
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This project is a transformer-based language model and natural language processing toolkit designed to generate deep contextual representations of text. By utilizing a transformer-based encoder architecture, the system processes input sequences through stacked self-attention layers to capture the semantic meaning of tokens based on their surrounding sentence structure. The model distinguishes itself through bidirectional contextual processing, which analyzes text in both directions simultaneously, and masked language modeling, which trains the system by predicting hidden tokens within a seque
Breaks raw text into smaller units using a frequency-based vocabulary to handle out-of-vocabulary words.