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Techniques for identifying significant structural elements and hierarchies within long documents.
Distinct from Document Classification: Focuses on the internal structural analysis of a document via attention, whereas Document Classification focuses on assigning a label to the whole document.
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This project is a deep learning text classification framework and neural text analysis library. It provides tools for categorizing textual data, adapting large language models through fine-tuning, and treating classification tasks as sequence generation problems using transformer architectures. The framework distinguishes itself through the implementation of ensemble learning, using boosting to combine predictions from multiple architectures to increase accuracy. It also includes a toolkit for fine-tuning pre-trained models via layer updates and the ability to restore model sessions for real-
Implements attention mechanisms and bidirectional units to identify significant words and sentences within long documents.