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- 读懂BERT，看这一篇就够了 - 知乎
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
- 万字长文，带你搞懂什么是BERT模型（非常详细）看这一篇就够了！-CSDN博客
问：BERT 有什么用？ BERT 用于执行 NLP 任务，如文本表示、命名实体识别、文本分类、问答系统、机器翻译、文本摘要等。
- BERT 系列模型 | 菜鸟教程
BERT (Bidirectional Encoder Representations from Transformers)是2018年由Google提出的革命性自然语言处理模型，它彻底改变了NLP领域的研究和应用范式。
- BERT: Pre-training of Deep Bidirectional Transformers for Language . . .
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
- BERT (language model) - Wikipedia
Next sentence prediction (NSP): In this task, BERT is trained to predict whether one sentence logically follows another For example, given two sentences, "The cat sat on the mat" and "It was a sunny day", BERT has to decide if the second sentence is a valid continuation of the first one
- 【BERT】详解BERT - 彼得虫 - 博客园
BERT，全称Bidirectional Encoder Representation of Transformer，首次提出于《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》一文中。
- 什么是BERT？一文读懂谷歌推出的预训练语言模型 | AI铺子
2018年，谷歌推出的BERT（Bidirectional Encoder Representations from Transformers）模型，以双向语境理解能力和大规模无监督预训练为核心，彻底改变了NLP的技术范式。
- BERT - Hugging Face
Bert Model with two heads on top as done during the pretraining: a masked language modeling head and a next sentence prediction (classification) head This model inherits from PreTrainedModel
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