What are Transformers

Transformers are a type of neural network architecture that was introduced in the paper “Attention is All You Need” by Vaswani et al. in 2017. Since then, it has become one of the most popular and successful models in natural language processing (NLP) tasks such as language translation, summarization, and text classification. Furthermore it is the foundation for Language Models and their application.

The key innovation of the Transformer architecture is the use of attention mechanisms. In a traditional neural network, each input is processed independently, without any information about the relationships between the inputs. In contrast, the Transformer model uses attention mechanisms to weight the inputs based on their relevance to the output.

For example, in a language translation task, the Transformer model might pay more attention to the words at the beginning and end of a sentence, as these are typically more important for determining the overall meaning of the sentence. On the other hand, it might pay less attention to words that are less important or less relevant to the translation.

Another key advantage of the Transformer architecture is its ability to process input sequences in parallel. Traditional recurrent neural networks (RNNs), which are commonly used in NLP tasks, process input sequences one element at a time, making them slow and inefficient. In contrast, the Transformer model processes all elements of the input sequence at the same time, allowing it to run much faster and more efficiently.

Attention

At a high level, the Transformer model is based on the idea of self-attention, which allows the model to weight the input elements based on their relevance to the output. Mathematically, self-attention can be computed using the following formula

$$ Attention(Q, K, V) = softmax(\frac{QK^T}{\sqrt{d_k}})V$$

Here, \(Q\), \(K\), and \(V\) are matrices of query, key, and value vectors, respectively. \(d_k\) is the dimensionality of the key vectors. The dot product of \(Q\) and \(K^T\) is divided by the square root of \(d_k\) to ensure that the dot products do not become too large and blow up the softmax function. The output of the self-attention layer is a weighted sum of the value vectors, with the weights determined by the dot products of the query and key vectors. In addition to self-attention, the Transformer model also includes feed-forward layers and residual connections. The feed-forward layers consist of a linear transformation followed by a non-linear activation function, such as ReLU. The output of the feed-forward layers is then added to the output of the self-attention layers using residual connections.

Overall, the Transformer model can be described using the following pseudo-code:

for each input sequence:
encode input sequence using self-attention and feed-forward layers
add the output to the original input using residual connections
apply layer normalization

apply final self-attention and feed-forward layers to obtain output

WORK IN PROGRESS

How to set up a Transformer Model?

Here is an example of how you can set up a Transformer model in Python using the Transformers library:

import transformers

# Set up the Transformer model and tokenizer
model_name = 'bert-base-cased' # choose a pre-trained model
model = transformers.BertModel.from_pretrained(model_name)
tokenizer = transformers.BertTokenizer.from_pretrained(model_name)

# Tokenize input text
text = "This is some input text that I want to feed into the Transformer model."
input_ids = tokenizer.encode(text, return_tensors='pt') # convert text to numerical input

# Run input through the model
output = model(input_ids)

This code sets up a BertModel from the Transformers library, which is a type of Transformer model developed by Google. It also sets up a BertTokenizer, which is used to convert the input text into a numerical representation that can be fed into the model.

Finally, the code tokenizes the input text and passes it through the model to obtain the output. The output of the model will be a tensor containing the encoded representation of the input text.