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Using Language Models on Low-end Hardware by Ziegner, Fabian; Borst, Janos; Niekler, Andreas; Potthast, Martin is a scholarly article available to read on EtoBox.

What is Using Language Models on Low-end Hardware about?

This paper evaluates the viability of using fixed language models for training text classification networks on low-end hardware. We combine language models with a CNN architecture and put together a comprehensive benchmark with 8 datasets covering single-label and multi-label classification of topic, sentiment, and genre. Our observations are distilled into a list of trade-offs, concluding that there are scenarios, where not fine-tuning a language model yields competitive effectiveness at faster training, requiring only a quarter of the memory compared to fine-tuning.

Author

Ziegner, Fabian; Borst, Janos; Niekler, Andreas; Potthast, Martin

Published

2023

Language

EN