So, imagine we have the sentence: “The quick brown fox jumps over the lazy dog.” We want to figure out which words are nouns, verbs, adjectives, etc. To do this, we first convert each word (or token) into a number using an encoding process called one-hot encoding. This means that each unique word gets assigned a different set of 1’s and 0’s in a vector. For example:

“The”: [1, 0, 0, …] # The first position is 1 because “The” comes before all other words in the alphabet (sorted by ASCII code)
“quick”: [0, 1, 0, …] # Second word gets a different set of 1’s and 0’s based on its position in the sorted list of unique words.
“brown”: […], etc.

Now that we have our text converted into numbers (or vectors), we can feed it through a neural network to classify each token as either a noun, verb, adjective, or whatever else you want to categorize. This is where the “Transformers” part comes in instead of using traditional recurrent neural networks (RNNs) that process one word at a time, we use a more efficient method called self-attention.

Self-attention allows us to focus on specific parts of our input text and ignore others based on their relevance to the task at hand. For example, if we’re trying to classify whether each token is a noun or not, we might want to pay more attention to words that come before other nouns (like “the” in “the quick brown fox”) than those that don’t (like “jumps”).

To implement this self-attention mechanism, we use something called a Transformer Encoder. This is essentially just a stack of multiple layers (or blocks) that perform the same operations on our input vectors at each step. Each block consists of two main components: a multi-head attention layer and a feedforward neural network (FFNN).

The multi-head attention layer allows us to compute multiple different attentions simultaneously, which helps improve performance by allowing us to capture more complex relationships between words in our input text. The FFNN is used to add some nonlinearity to the output of each block and help prevent overfitting (when a model fits too closely to its training data).

Overall, “Transformers for Token Classification” is a powerful tool that can be used to classify words in any text-based application. Whether you’re working on sentiment analysis, named entity recognition, or anything else, this technique can help improve your accuracy and efficiency while reducing the amount of data needed to train your model.