Model details
模型介绍 中文整理 孟子中文BERT金融行业预训练模型
mengzi-bert-base-fin是一个针对金融场景的领域语言模型。本模型在mengzi-bert-base模型基础上，
继续使用20G财经新闻和研究报告进行微调。
详细的技术报告请参考：Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
模型描述
孟子中文Bert预训练模型与Bert结构相同，不包含下游任务，需要在特定任务上 Finetune 后使用。
Bert模型的详细介绍见：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
在预训练技术上，不同于Bert，孟子采用了：
语言学驱动的目标 - 在原始MLM和NSP目标的基础上，额外使用POS-Tagging和NER两个序列标注任务作为辅助目标。POS和NE标签使用spaCy标注。
序列关系目标 - 为了更好地拟合句子间的关系信息，孟子加入了Sentence Order Prediction (SOP)作为训练目标。
动态梯度修正 - 使用了一系列动态梯度修正技术提高模型的容量和鲁棒性，改善MLM导致的对原始句子结构的干扰。
期望模型使用方式以及适用范围
本模型主要用于中文相关下游任务微调，也可以直接使用于完形填空任务。用户可以基于自有训练数据进行微调，具体调用方式请参考代码示例。
如何使用
在安装完成Modelscope-lib之后即可进行下游任务finetune，下面给了一个直接使用本模型进行完形填空的范例。
代码范例
模型局限性以及可能的偏差
本模型基于20G财经新闻和研究报告（金融领域）进行微调，在其他垂直领域文本上的效果会有降低，请用户自行评测后决定如何使用。
相关论文以及引用信息
如果我们的模型对您有帮助，请您引用我们的文章：
优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。
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完整仓库可能包含多个权重格式，因此需要的磁盘空间可能大于单一格式的模型大小。下载文件不等于完成模型部署。
使用前须知 运行环境 运行模型需要的设备、工具与依赖，以原作者说明为准。
授权范围 是否允许商用、修改和分发，请查看模型许可证。
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