transforme
时间: 2023-10-14 08:03:59 浏览: 100
transformation
Transformer是一种用于自然语言处理任务的模型架构,相较于CNN依靠卷积操作处理局部信息,Transformer能够直接获取全局信息。其中,Multi-head Attention是Transformer中的一个重要组件。Transformer的学习重点通常是理解Encoder和Decoder的工作原理。在一些具体的应用中,如GPT和Bert,Transformer被用于构建语言模型。GPT训练的是单向语言模型,主要应用了Transformer的Decoder部分,而Bert训练的是双向语言模型,除了使用了Transformer的Encoder部分,还进行了Masked操作。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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- *1* *3* [【神经网络】Transformer基础问答](https://blog.csdn.net/weixin_44750512/article/details/128896619)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
- *2* [transforme框架](https://blog.csdn.net/gldzkjdxwzs/article/details/126450563)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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