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Utilities for generating vector representations that account for word context.
Distinguishing note: Focuses on the generation of embeddings rather than the underlying model architecture.
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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
Creates numerical vector representations for words by processing input text through deep learning layers.