Explaining Transformer Embeddings with Easy Animations

Transformer embeddings convert words into mathematical vectors, allowing us to represent language in a way computers can understand.Let's start with some simple words like 'cat' and 'dog', along with related words 'kitten' and 'puppy'.Each word gets transformed into a vector of numbers. These numbers represent different features or characteristics of the word.In reality, transformer embeddings use hundreds of dimensions to capture the full complexity of language.To visualize these relationships, we can project the high-dimensional vectors onto a two-dimensional space.Notice how similar words cluster together. 'Cat' and 'kitten' are close to each other, as are 'dog' and 'puppy'.The distances between words in this space represent their semantic relationships. Related words appear closer together.The mathematical distance between word vectors helps us measure how similar or different words are.These relationships extend to many types of word similarities, including age relationships like we see between adult animals and their young.Now that we understand how words become vectors, let's see how transformers handle word order and position.Token embeddings represent the meaning of each word in our sentence 'The cat sat'.Each word is converted into a numerical vector that captures its semantic meaning.But transformers also need to know the position of each word in the sentence. This is where positional encodings come in.The final embedding combines both the token meaning and the position information.Now, let's look at how word order affects the final embeddings. Here's our second sentence: 'The sat cat'.Even though we're using the same words, their positions have changed.This demonstrates how transformers can distinguish between sentences that use the same words in different orders.By combining positional and token embeddings, transformers can understand both the meaning of words and their relationships within a sentence.In transformer models, the meaning of a word changes based on its context through self-attention.When the word 'bank' appears with 'river', the attention mechanism focuses on the nature-related context.This creates an embedding that emphasizes the geographical meaning of bank.However, when 'bank' appears with 'money', the attention shifts to focus on the financial context.This creates a different embedding that captures the financial institution meaning of bank.Through self-attention, the embedding for 'bank' evolves significantly based on its context words.