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Transformer Model

The Transformer model was launched by a team at Google Brain in 2017 and has gradually replaced RNN models such as Long Short-Term Memory (LSTM) to become the model of choice for NLP problems. The parallelization advantage allows it to be trained on larger datasets. This has also led to the development of pre-trained models such as BERT and GPT. These systems are trained using large corpora such as Wikipedia and Common Crawl, and can be fine-tuned for specific tasks.

The Transformer model is a deep learning model that uses a self-attention mechanism, which assigns different weights to different parts of the input data according to their importance. This model is mainly used in the fields of natural language processing (NLP) and computer vision (CV).

Like recurrent neural networks (RNNs), the Transformer model is designed to process sequential input data such as natural language and can be applied to tasks such as translation and text summarization. Unlike RNNs, the Transformer model can process all input data at once. The attention mechanism can provide context for any position in the input sequence. If the input data is natural language, the Transformer does not have to process only one word at a time like RNN. This architecture allows more parallel computing and reduces training time.

train

Transformer models are usually self-supervised learning, including unsupervised pre-training and supervised fine-tuning. Since the labeled training data used in supervised fine-tuning is generally limited, pre-training is usually done on a larger dataset than that used for fine-tuning. The tasks of pre-training and fine-tuning usually include:

  • Language Modeling
  • Next sentence prediction
  • Question answering system
  • Reading comprehension
  • Text Sentiment Analysis
  • Text rewriting

application

The Transformer model has achieved great success in the field of natural language processing (NLP), such as machine translation and time series prediction tasks. Many pre-trained models including GPT-2, GPT-3, BERT, XLNet and RoBERTa demonstrate the ability of the Transformer model to perform various NLP-related tasks and have many potential practical applications. These applications include:

  • Machine Translation
  • Text Summarization
  • Text Generation
  • Named Entity Recognition
  • Biological sequence analysis
  • Video Understanding

In 2020, the Transformer architecture (more specifically GPT-2) was shown to be able to perform chess-playing tasks with fine-tuning. Transformer models have also been applied to image processing with results comparable to convolutional neural networks.

References

【1】https://zh.wikipedia.org/wiki/Transformer%E6%A8%A1%E5%9E%8B#cite_note-:6-4