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arXiv · 2305.02350

Using Language Models on Low-end Hardware

Fabian Ziegner·Janos Borst·Andreas Niekler·Martin Potthast
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Abstract

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.

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Fabian Ziegner, Janos Borst, Andreas Niekler, Martin Potthast. 2023-05-03. Using Language Models on Low-end Hardware. https://arxiv.org/abs/2305.02350

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