架构

BERT

定义

Bidirectional Encoder Representations from Transformers—Google's 2018 encoder-only transformer model that set new benchmarks across NLP tasks. BERT-family models remain widely used for classification, named-entity recognition, and information retrieval in enterprise settings.

相关术语

Transformer

The dominant neural network architecture for language, vision, and multimodal AI, introduced in the 2017 "Attention Is All You Need" paper. Transformers use self-attention to process all tokens in parallel, enabling training on internet-scale data and powering every major LLM in use today.

Encoder-Decoder

A two-part neural network architecture where the encoder compresses an input into a latent representation and the decoder generates an output from it. Used in translation, summarisation, and speech recognition systems.

Fine-tuning

The process of taking a pre-trained AI model and further training it on a specific dataset to adapt it for a particular task or domain. Fine-tuning can deliver significantly better performance than prompting alone for specialised enterprise workflows.

了解术语只是第一步，将其落地应用才是第二步。

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