Using Language Models on Low-end Hardware

• URL: http://arxiv.org/abs/2305.02350v2
• Date: Mon, 8 May 2023 15:43:52 GMT
• Title: Using Language Models on Low-end Hardware
• Authors: Fabian Ziegner, Janos Borst, Andreas Niekler, Martin Potthast
• Abstract summary: 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.
• Score: 17.33390660481404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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