Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1][2] It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture.

2018年10月11日 · We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers.

To solve this problem, we propose the Double-Level BERT-style model (DL-BERT). Considering EHR’s hierarchical structure, the model contains a code-level and a visit-level representation layer which can learn the relationship between medical codes and temporal influence respectively.

BERT (Bidirectional Encoder Representation from Transformers)是2018年10月由Google AI研究院提出的一种预训练模型，该模型在机器阅读理解顶级水平测试 SQuAD1.1 中表现出惊人的成绩: 全部两个衡量指标上全面超越人类，并且在11种不同NLP测试中创出SOTA表现，包括将GLUE基准推高至80.4% (绝对改进7.6%)， MultiNLI 准确度达到86.7% (绝对改进5.6%)，成为NLP发展史上的里程碑式的模型成就。 BERT的网络架构使用的是 《Attention is all you need》 中提出的多 …

2024年12月10日 · BERT is an open-source machine learning framework developed by Google AI Language for natural language processing, utilizing a bidirectional transformer architecture to enhance understanding of context in text through pre …

Bert 是 NLP 领域（甚至是在 DL 领域）最近几年最重要的论文了，其将预训练任务、 attention 发扬光大，开辟了一个非常有趣的研究放方向，甚至后续的很多 cv 网络中（如 vit、 vilbert、mae）都可以看到它的身影。

We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models.

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2020年6月21日 · BERT的主模型是BERT中最重要组件，BERT通过预训练（pre-training），具体来说，就是在主模型后再接个专门的模块计算预训练的损失（loss），预训练后就得到了主模型的参数（parameter），当应用到下游任务时，就在主模型后接个跟下游任务配套的模块，然后主模 …

2023年8月17日 · BERT (Bidirectional Encoder Representations from Transformers) 模型 在论文 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 中提出，BERT即 双向的 Transformer 的Encoder表示。 BERT是基于上下文的预训练模型， Bert模型 的训练分为两步： fine-tuning：通过监督的方式在具体语言任务上进行fine-tuning。 如图所示，BERT首先在大规模无监督语料上进行预训练，然后在预训练好的参数基础上增加一个与任 …