Ensembling Finetuned Language Models for Text Classification
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arXiv
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| Format: | Preprint |
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2024
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| author | Arango, Sebastian Pineda Janowski, Maciej Purucker, Lennart Zela, Arber Hutter, Frank Grabocka, Josif |
| author_facet | Arango, Sebastian Pineda Janowski, Maciej Purucker, Lennart Zela, Arber Hutter, Frank Grabocka, Josif |
| contents | Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks. Text classification is one of these tasks for which many pretrained models are available. On the other hand, ensembles of neural networks are typically used to boost performance and provide reliable uncertainty estimates. However, ensembling pretrained models for text classification is not a well-studied avenue. In this paper, we present a metadataset with predictions from five large finetuned models on six datasets, and report results of different ensembling strategies from these predictions. Our results shed light on how ensembling can improve the performance of finetuned text classifiers and incentivize future adoption of ensembles in such tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19889 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ensembling Finetuned Language Models for Text Classification Arango, Sebastian Pineda Janowski, Maciej Purucker, Lennart Zela, Arber Hutter, Frank Grabocka, Josif Computation and Language Machine Learning Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks. Text classification is one of these tasks for which many pretrained models are available. On the other hand, ensembles of neural networks are typically used to boost performance and provide reliable uncertainty estimates. However, ensembling pretrained models for text classification is not a well-studied avenue. In this paper, we present a metadataset with predictions from five large finetuned models on six datasets, and report results of different ensembling strategies from these predictions. Our results shed light on how ensembling can improve the performance of finetuned text classifiers and incentivize future adoption of ensembles in such tasks. |
| title | Ensembling Finetuned Language Models for Text Classification |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.19889 |