What is a transformer and how does it work
Introduction:
The transformer is a deep learning model introduced in 2017, which revolutionized the field of natural language processing (NLP). It is a neural network architecture that employs a self-attention mechanism to process sequential data, such as sentences or documents. This answer will explain the inner workings of the transformer architecture in detail.
I. Background:
Before the transformer, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) were widely used in NLP tasks. However, RNNs have a limitation that they process sequential data one token at a time, making them computationally expensive, while CNNs lack the ability to capture the context of a word in a sentence. This led to the development of the transformer, which can process entire sentences at once, making it more efficient than RNNs, and is capable of capturing the context of a word in a sentence, making it more effective than CNNs.
II. Architecture:
The transformer architecture consists of an encoder and a decoder. The encoder takes in an input sequence and generates a fixed-size representation of the sequence, while the decoder takes in the representation generated by the encoder and generates an output sequence.
A. Encoder:
The encoder is composed of multiple layers of identical submodules, (leobet register) called encoder layers. Each encoder layer consists of two sublayers: a self-attention layer and a feedforward layer.
- Self-attention layer:
The self-attention layer is responsible for capturing the dependencies between different words in a sentence. It computes a weighted sum of all the words in a sentence, where the weights are determined by the relevance of each word to the other words in the sentence. This is achieved through the use of attention scores, which are computed using the dot product between query, key, and value matrices.
- Feedforward layer:
The feedforward layer applies a non-linear function to the outputs of the self-attention layer, providing a learned representation of the input sequence.
B. Decoder:
The decoder is similar to the encoder, but it also has a third sublayer, called the masked self-attention layer, which prevents the decoder from attending to future tokens during training. This is important because the decoder must generate the output sequence one token at a time.
III. Training:
The transformer is trained using a variant of the backpropagation algorithm called the transformer algorithm. The transformer algorithm computes gradients for all the parameters in the model, which are then used to update the parameters during training.
A. Loss function:
The loss function used to train the transformer depends on the task being performed. For example, for machine translation, the loss function is typically the cross-entropy loss between the predicted and actual translations.
B. Optimization:
The transformer is optimized using the Adam optimizer, which is a stochastic gradient descent algorithm that uses adaptive learning rates.
IV. Conclusion:
In conclusion, the transformer is a neural network architecture that has revolutionized the field of NLP by allowing for more efficient and effective processing of sequential data. Its unique self-attention mechanism allows it to capture the context of a word in a sentence, making it a powerful tool for a wide range of NLP tasks.
