重写下面这段文本：“预训练模型发展简史” 

随着深度学习的飞速发展，预训练模型在自然语言处理领域取得了重大进展。本文简要介绍了自然语言处理中预训练模型的发展历程以及目前主流的预训练模型。

早期的自然语言处理任务通常需要手工设计特征和规则来提取文本中的信息，这种方法的性能依赖于任务和语言，且需要大量的专业知识和时间。近年来，深度学习的兴起极大地推动了自然语言处理领域的发展，尤其是预训练模型的出现。

预训练模型最早出现在计算机视觉领域，其中最著名的是AlexNet和VGGNet。在自然语言处理领域，Mikolov等人提出了word2vec模型，其可以将词向量空间映射为连续向量，这大大简化了文本分类等任务的特征提取。近年来，预训练模型在自然语言处理领域的应用也在不断发展。其中最著名的是BERT、GPT、XLNet等模型，它们都是基于Transformer模型进行改进和扩展的。这些预训练模型在多个自然语言处理任务上都有很好的表现，例如文本分类、命名实体识别、关系提取等。

总的来说，预训练模型在自然语言处理领域取得了巨大的成功，为自然语言处理任务的解决带来了新的思路和方法。
"Pre-training model development history" 

With the rapid development of deep learning, pre-training models have made significant progress in the field of natural language processing. This article briefly introduces the development history of pre-training models in natural language processing and the current mainstream pre-training models.

Early natural language processing tasks usually required manually designed features and rules to extract information from text. This method's performance depends on the task and language, and requires a lot of expertise and time. In recent years, the rise of deep learning has greatly pushed the development of natural language processing, especially the emergence of pre-training models.

Pre-training models first appeared in the computer vision field, with the most famous being AlexNet and VGGNet. In natural language processing, Mikolov et al. proposed the word2vec model, which can map the word vector space to continuous vectors, greatly simplifying feature extraction for tasks such as text classification. In recent years, the application of pre-training models in natural language processing has also been constantly developed. The most famous are BERT, GPT, XLNet and other models, which are all based on the Transformer model for improvement and extension. These pre-training models perform well in many natural language processing tasks, such as text classification, named entity recognition, and relationship extraction.

Overall, pre-training models have achieved great success in the field of natural language processing, bringing new ideas and methods for solving natural language processing tasks.