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.

2024年12月10日 · BERT (Bidirectional Encoder Representations from Transformers) leverages a transformer-based neural network to understand and generate human-like language. BERT employs an encoder-only architecture. In the original Transformer architecture, there are both encoder and decoder modules.

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.

2022年3月2日 · BERT, short for Bidirectional Encoder Representations from Transformers, is a Machine Learning (ML) model for natural language processing. It was developed in 2018 by researchers at Google AI Language and serves as a swiss army knife solution to 11+ of the most common language tasks, such as sentiment analysis and named entity recognition.

第一部分主要介绍bert的网络结构原理以及mlm和nsp这两种任务的具体原理；第二部分将主要介绍如何实现bert以及bert预训练模型在下游任务中的使用；第三部分则是介绍如何利用mlm和nsp这两个任务来训练bert模型（可以是从头开始，也可以是基于开源的bert预训练 ...

2018年12月3日 · The paper presents two model sizes for BERT: BERT BASE – Comparable in size to the OpenAI Transformer in order to compare performance; BERT LARGE – A ridiculously huge model which achieved the state of the art results reported in the paper; BERT is basically a trained Transformer Encoder stack.

4 天之前 · 掩蔽语言模型（Masked Language Model，MLM） BERT的训练方法与传统语言模型不同，它通过掩蔽某些词并让模型预测这些被掩蔽的词来进行训练。 具体来说，BERT会随机将输入句子中的15%的词进行掩蔽，并让模型预测这些掩蔽的词是什么。

2023年1月6日 · BERT is a stack of many encoder blocks. The input text is separated into tokens as in the transformer model, and each token will be transformed into a vector at the output of BERT. What Can BERT Do? A BERT model is trained using the masked language model (MLM) and next sentence prediction (NSP) simultaneously.

Masked Language Model (MLM): Unlike traditional language models that predict the next word in a sequence, MLM randomly masks some words in the input and predicts them based on their surrounding context. For instance, in the sentence “The cat [MASK] on the mat,” BERT attempts to predict “sat” based on the context provided by the other words.

2025年2月4日 · In the following, we’ll explore BERT models from the ground up — understanding what they are, how they work, and most importantly, how to use them practically in your projects. We’ll focus on using pre-trained models through the Hugging Face Transformers library, making advanced NLP accessible without requiring deep learning expertise.