  Transfomer-based models have significantly advanced natural language
processing, in particular the performance in text classification tasks.
Nevertheless, these models face challenges in processing large files, primarily
due to their input constraints, which are generally restricted to hundreds or
thousands of tokens. Attempts to address this issue in existing models usually
consist in extracting only a fraction of the essential information from lengthy
inputs, while often incurring high computational costs due to their complex
architectures. In this work, we address the challenge of classifying large
files from the perspective of correlated multiple instance learning. We
introduce LaFiCMIL, a method specifically designed for large file
classification. LaFiCMIL is optimized for efficient operation on a single GPU,
making it a versatile solution for binary, multi-class, and multi-label
classification tasks. We conducted extensive experiments using seven diverse
and comprehensive benchmark datasets to assess LaFiCMIL's effectiveness. By
integrating BERT for feature extraction, LaFiCMIL demonstrates exceptional
performance, setting new benchmarks across all datasets. A notable achievement
of our approach is its ability to scale BERT to handle nearly 20,000 tokens
while operating on a single GPU with 32GB of memory. This efficiency, coupled
with its state-of-the-art performance, highlights LaFiCMIL's potential as a
groundbreaking approach in the field of large file classification.
