nlp_debertav2_fill-mask_chinese-base

iic/nlp_debertav2_fill-mask_chinese-base

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基于DeBERTaV2的中文Base预训练模型介绍（文档更新中） DeBERTaV2的中文Base预训练模型是使用wikipedia数据集、wudao数据集及中文网页数据，以masked language model任务训练的中文自然语言理解预训练模型。 DeBERTaV2在BERT/RoBERTa的基础之上，通过解耦注意力机制、增强的掩码解码机制等方面进行改进，从而更好的对语言理解任务进行建模。详见论文Deberta: Decoding-enhanced bert with

Text Processing

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Model details

模型介绍

中文整理

基于DeBERTaV2的中文Base预训练模型介绍（文档更新中）

DeBERTaV2的中文Base预训练模型是使用wikipedia数据集、wudao数据集及中文网页数据，以masked language model任务训练的中文自然语言理解预训练模型。

模型描述

DeBERTaV2在BERT/RoBERTa的基础之上，通过解耦注意力机制、增强的掩码解码机制等方面进行改进，从而更好的对语言理解任务进行建模。详见论文Deberta: Decoding-enhanced bert with disentangled attention，主要改进点如下：

解耦注意力机制：在通用Transformer自注意力机制基础之上，将每个token的编码解耦为内容编码（content embedding）和位置编码（position embbeding），并通过两者的cross attention进行建模

增强的掩码解码机制：MLM任务预训练中在softmax层之前加入专门的解码层，并在解码层中引入绝对位置信息辅助MLM任务训练

nGram Induced Input Encoding：在输入层增加char级别的卷积层，以进一步丰富输入token的相信信息
128K vocabulary：结合训练数据，通过中文分词及SentencePiece，构建了包含128K个词语的词典

本模型为Lite规模（Layer-12 / Hidden-768 / Head-12)，参数规模约为137M。

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。用户可以基于自有训练数据进行微调，具体微调训练方式请参考文档。

如何使用

在安装完成ModelScope-lib之后即可基于nlp debertav2 fill-mask chinese-lite进行下游任务finetune

代码范例

模型局限性以及可能的偏差

基于中文数据进行训练，模型训练数据有限，效果可能存在一定偏差。

训练数据介绍

数据来源于维基数据、WuDao数据和中文网页数据等

模型训练流程

在中文wiki等无监督数据上，通过MLM任务训练得到。

数据评估及结果

暂无

你将获得什么

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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。

闲社提供资料与镜像下载方法，不代表已运行模型或完成安全审计。下载的代码应先检查，再执行。

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nlp_debertav2_fill-mask_chinese-base model download

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