FinBERT: A Pretrained Language Model for Financial Communications

• URL: http://arxiv.org/abs/2006.08097v2
• Date: Thu, 9 Jul 2020 02:50:04 GMT
• Title: FinBERT: A Pretrained Language Model for Financial Communications
• Authors: Yi Yang, Mark Christopher Siy UY, Allen Huang
• Abstract summary: There is no pretrained finance specific language models available. We address the need by pretraining a financial domain specific BERT models, FinBERT, using a large scale of financial communication corpora. Experiments on three financial sentiment classification tasks confirm the advantage of FinBERT over generic domain BERT model.
• Score: 25.900063840368347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contextual pretrained language models, such as BERT (Devlin et al., 2019), have made significant breakthrough in various NLP tasks by training on large scale of unlabeled text re-sources.Financial sector also accumulates large amount of financial communication text.However, there is no pretrained finance specific language models available. In this work,we address the need by pretraining a financial domain specific BERT models, FinBERT, using a large scale of financial communication corpora. Experiments on three financial sentiment classification tasks confirm the advantage of FinBERT over generic domain BERT model. The code and pretrained models are available at https://github.com/yya518/FinBERT. We hope this will be useful for practitioners and researchers working on financial NLP tasks.

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