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
this https URL We hope this will be useful for
practitioners and researchers working on financial NLP tasks.

本文旨在满足金融领域中对预训练语言模型的需求，提出了一种基于大量金融通信语料库的金融领域特定 BERT 模型（FinBERT），在三个金融情感分类任务上的实验结果证明了 FinBERT 相比于一般领域的 BERT 模型具有优势。

FinBERT：金融通信预训练语言模型

FinBERT: A Pretrained Language Model for Financial Communications

We propose a simple definition of an explanation for the outcome of a
classifier based on concepts from causality. We compare it with previously
proposed notions of explanation, and study their complexity. We conduct an
experimental evaluation with two real datasets from the financial domain.

本研究提出了一种基于因果关系概念的分类器结果解释定义，并与以前提出的解释概念进行了比较和研究，同时在金融领域使用两个真实数据集进行了实验评估。