Chapter 5: Positional Encoding in Transformers

After generating the positional encoding, the next step is to add it to the word embeddings. The word embeddings and positional encodings have the same dimension $d_{model}$, so they can be summed element-wise. This is done for both the encoder and decoder.

In fact, the positional encoding is a crucial component of the Transformer model, which was introduced in the seminal paper "Attention is All You Need" by Vaswani et al. (2017). The Transformer model is a neural network architecture that has revolutionized natural language processing (NLP) tasks such as machine translation, text classification, and language modeling.

To understand why the positional encoding is necessary, it's important to note that traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have limitations when it comes to modeling long-range dependencies in sequences. The Transformer model solves this problem by using a self-attention mechanism, which allows it to attend to all positions in the input sequence, regardless of their distance from the current position.

However, in order to make use of this self-attention mechanism, the Transformer model needs a way to distinguish between the positions of different words in the input sequence. This is where the positional encoding comes in: by adding a vector that encodes the position of each word to its embedding, the Transformer model can take into account the relative positions of words and use this information to perform better at NLP tasks.

So, while the addition of the positional encoding to the word embeddings may seem like a small detail, it is actually a crucial step in the Transformer model that allows it to outperform previous state-of-the-art models on a wide range of NLP tasks.

Here's how you might apply positional encoding in Python, assuming we have a batch of word embeddings word_emb and a positional_encoding function as implemented in the previous section:

This code snippet assumes that word_emb is a tensor with shape (batch_size, sequence_length, d_model). The positional encodings are added to each word embedding in the batch.

After generating the positional encoding, the next step is to add it to the word embeddings. The word embeddings and positional encodings have the same dimension $d_{model}$, so they can be summed element-wise. This is done for both the encoder and decoder.

In fact, the positional encoding is a crucial component of the Transformer model, which was introduced in the seminal paper "Attention is All You Need" by Vaswani et al. (2017). The Transformer model is a neural network architecture that has revolutionized natural language processing (NLP) tasks such as machine translation, text classification, and language modeling.

To understand why the positional encoding is necessary, it's important to note that traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have limitations when it comes to modeling long-range dependencies in sequences. The Transformer model solves this problem by using a self-attention mechanism, which allows it to attend to all positions in the input sequence, regardless of their distance from the current position.

However, in order to make use of this self-attention mechanism, the Transformer model needs a way to distinguish between the positions of different words in the input sequence. This is where the positional encoding comes in: by adding a vector that encodes the position of each word to its embedding, the Transformer model can take into account the relative positions of words and use this information to perform better at NLP tasks.

So, while the addition of the positional encoding to the word embeddings may seem like a small detail, it is actually a crucial step in the Transformer model that allows it to outperform previous state-of-the-art models on a wide range of NLP tasks.

Here's how you might apply positional encoding in Python, assuming we have a batch of word embeddings word_emb and a positional_encoding function as implemented in the previous section:

This code snippet assumes that word_emb is a tensor with shape (batch_size, sequence_length, d_model). The positional encodings are added to each word embedding in the batch.

After generating the positional encoding, the next step is to add it to the word embeddings. The word embeddings and positional encodings have the same dimension $d_{model}$, so they can be summed element-wise. This is done for both the encoder and decoder.

In fact, the positional encoding is a crucial component of the Transformer model, which was introduced in the seminal paper "Attention is All You Need" by Vaswani et al. (2017). The Transformer model is a neural network architecture that has revolutionized natural language processing (NLP) tasks such as machine translation, text classification, and language modeling.

To understand why the positional encoding is necessary, it's important to note that traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have limitations when it comes to modeling long-range dependencies in sequences. The Transformer model solves this problem by using a self-attention mechanism, which allows it to attend to all positions in the input sequence, regardless of their distance from the current position.

However, in order to make use of this self-attention mechanism, the Transformer model needs a way to distinguish between the positions of different words in the input sequence. This is where the positional encoding comes in: by adding a vector that encodes the position of each word to its embedding, the Transformer model can take into account the relative positions of words and use this information to perform better at NLP tasks.

So, while the addition of the positional encoding to the word embeddings may seem like a small detail, it is actually a crucial step in the Transformer model that allows it to outperform previous state-of-the-art models on a wide range of NLP tasks.

Here's how you might apply positional encoding in Python, assuming we have a batch of word embeddings word_emb and a positional_encoding function as implemented in the previous section:

This code snippet assumes that word_emb is a tensor with shape (batch_size, sequence_length, d_model). The positional encodings are added to each word embedding in the batch.

After generating the positional encoding, the next step is to add it to the word embeddings. The word embeddings and positional encodings have the same dimension $d_{model}$, so they can be summed element-wise. This is done for both the encoder and decoder.

In fact, the positional encoding is a crucial component of the Transformer model, which was introduced in the seminal paper "Attention is All You Need" by Vaswani et al. (2017). The Transformer model is a neural network architecture that has revolutionized natural language processing (NLP) tasks such as machine translation, text classification, and language modeling.

To understand why the positional encoding is necessary, it's important to note that traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have limitations when it comes to modeling long-range dependencies in sequences. The Transformer model solves this problem by using a self-attention mechanism, which allows it to attend to all positions in the input sequence, regardless of their distance from the current position.

However, in order to make use of this self-attention mechanism, the Transformer model needs a way to distinguish between the positions of different words in the input sequence. This is where the positional encoding comes in: by adding a vector that encodes the position of each word to its embedding, the Transformer model can take into account the relative positions of words and use this information to perform better at NLP tasks.

So, while the addition of the positional encoding to the word embeddings may seem like a small detail, it is actually a crucial step in the Transformer model that allows it to outperform previous state-of-the-art models on a wide range of NLP tasks.

Here's how you might apply positional encoding in Python, assuming we have a batch of word embeddings word_emb and a positional_encoding function as implemented in the previous section:

This code snippet assumes that word_emb is a tensor with shape (batch_size, sequence_length, d_model). The positional encodings are added to each word embedding in the batch.