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 sequence. It also employs next sentence prediction to understand relationships between text segments and utilizes shared parameter multilingualism to maintain a unified structure across diverse languages.
Beyond these core capabilities, the toolkit provides utilities for subword-based tokenization to manage vocabulary and punctuation, as well as functionality for generating high-dimensional contextual embeddings. It supports the development of question answering systems by identifying specific start and end positions for text segments within a document.
