Instead of using traditional machine learning techniques like logistic regression or decision trees, which can take a long time and require a lot of data preprocessing, you could use transformers for inference.

Transformers are basically fancy neural networks that have been trained on huge amounts of text data (like the entire internet) to learn how to understand language and make predictions based on what they’ve learned. They work by breaking down each sentence or paragraph into smaller pieces called tokens, which can then be fed through a series of layers to extract important features like word embeddings and contextual information.

Here’s an example: let’s say you have the following text data: “The quick brown fox jumps over the lazy dog.” You could feed this into your transformer model, which would then break it down into individual tokens (like ‘the’, ‘quick’, ‘brown’, etc.) and pass them through a series of layers to extract features like word embeddings.

At each layer, the transformer uses something called an attention mechanism to focus on specific parts of the input text that are most relevant for making predictions about what comes next (like whether or not there’s going to be another ‘fox’ in the sentence). This is where things get really cool: instead of just looking at one word at a time, like traditional machine learning models do, transformers can actually look at multiple words at once and figure out which ones are most important for making predictions.

For example, let’s say you want to predict whether or not the next sentence in your text data is going to be about animals (like “The cat sat on the mat.”). Instead of just looking at the first word (‘the’) and trying to figure out if it’s related to animals, transformers can actually look at multiple words at once (like ‘cat’, ‘sat’, and ‘mat.’) and use their attention mechanism to focus on the parts that are most relevant for making predictions about whether or not this sentence is going to be about animals.

This might sound complicated, but it’s actually pretty simple when you break it down: transformers just use a bunch of fancy math equations (like matrix multiplication and dot products) to figure out which words are most important for making predictions based on what they’ve learned from all the text data that was fed into them during training.

That’s how transformers work in a nutshell: by breaking down text data into smaller pieces, using an attention mechanism to focus on specific parts of the input text, and then making predictions based on what they’ve learned from all the other text data that was fed into them during training. It might sound complicated at first, but once you get used to it, transformers are actually pretty easy to understand (and a lot more fun than traditional machine learning models!).