Transformers are a type of Neural Network architecture that were introduced in 2017 in the paper "Attention Is All You Need (opens in a new tab)" by Vaswani et al. Transformers are designed to handle sequential data, such as natural language sentences or time series data, and have been shown to achieve state-of-the-art performance on a wide range of Natural Language Processing tasks.
The key innovation of transformers is the use of self-attention mechanisms, which allow the network to selectively attend to different parts of the input sequence when making predictions. This is in contrast to traditional recurrent Neural Networks, which process the input sequence in a sequential manner and can suffer from vanishing gradients or exploding gradients.
The basic architecture of a transformer consists of an encoder network and a decoder network, with the encoder network used to process the input sequence and the decoder network used to generate the output sequence. Each layer of the encoder and decoder network consists of multiple self-attention sub-layers, followed by feed-forward sub-layers, with residual connections and layer normalization applied between each sub-layer.
Transformers are particularly well-suited for tasks such as machine translation, sentiment analysis, and question answering, where the input data consists of sequential data, such as sentences or paragraphs of text. By using self-attention mechanisms to selectively attend to different parts of the input sequence, transformers are able to capture long-range dependencies and relationships in the data, making them a powerful tool for Natural Language Processing tasks.
An example of a transformer is the BERT (Bidirectional Encoder Representations from Transformers) model, which was introduced in 2018 by Devlin et al. BERT is a transformer-based model that has achieved state-of-the-art performance on a wide range of Natural Language Processing tasks, including question answering, sentiment analysis, and named entity recognition.
The BERT model consists of a transformer encoder network that is pre-trained on a large corpus of text, such as Wikipedia and BookCorpus. During pre-training, the model is trained to predict missing words in a sentence, similar to a fill-in-the-blank task. This enables the model to learn a general representation of language that can be fine-tuned for specific tasks.
After pre-training, the BERT model can be fine-tuned for specific Natural Language Processing tasks by adding a task-specific output layer and fine-tuning the entire model on a smaller labeled dataset. For example, the BERT model can be fine-tuned for question answering by adding a task-specific output layer that predicts the answer to a given question.
The key innovation of the BERT model is the use of bidirectional self-attention mechanisms, which allow the model to capture long-range dependencies and relationships in the input text. By considering both the left and right context of each word, the BERT model is able to achieve state-of-the-art performance on a wide range of Natural Language Processing tasks.
Overall, the BERT model is a powerful example of a transformer-based model that has revolutionized the field of Natural Language Processing, enabling intelligent systems to perform complex language tasks with human-level accuracy.