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Despite being one of the earliest llms, bert has remained relevant even today, and continues to find applications in both research and industry 基本概念 BERT全称 Bidirectional Encoder Representations from Transformers，意思是多Transformer的双向的编码器表示，由谷歌进行开发。当然由于Transformer架构，它是基于上下文的嵌入模型，但跟Transformer不同的是，它只有编码器，我们可以把它看作只有编码器的Transformer结构，当然还有只有Transformer的解码器结构. Bert is the most famous encoder only model and excels at tasks which require some level of language comprehension
BERT与以往的模型不同，它是深度双向的，无监督的语言表示，完全依靠纯文本语料库进行预训练。 自那时起，我们开始见证了一系列大型语言模型的诞生：GPT-2，RoBERT，ESIM+GloVe，以及现在的GPT-3、4，这个模型一出，最终引发了一大波AI的热潮。 As an input, it takes a [cls] token and two sentences separated by a special [sep] token Bert is a transformer successor which inherits its stacked bidirectional encoders
Most of the architectural principles in bert are the same as in the original transformer.
Introduction to bert bert, introduced by researchers at google in 2018, is a powerful language model that uses transformer architecture. BERT 模型通过对 MLM 和 NSP 进行联合训练，使模型能 准确抽取输入文本（单句或语句对）的整体信息，从而输出尽可能准确、全面地 字/词 向量表示。 4.3 Pre-training data 为了提取长的连续序列， 使用文档级语料库 而不是打乱的句子级语料库至关重要。 五、 Fine. BERT模型有什么调参技巧? 感觉很多nlp比赛都是bert调参比赛，那么有什么调参技巧可以脱颖而出呢？ 或者不用bert从哪些方面考虑能达到比bert更好的效果？ 显示全部 关注者 262 被浏览 Bert bert first of all, let us remind how bert processes information
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