It’s like having your own personal assistant who reads every book and article you ever mention to them so they know exactly what you’re talking about when you say “that thing with the red cover”.

Here’s how it works: first, we feed in a bunch of text (let’s call this our input) into the model. The model then breaks down that text into smaller pieces called tokens (like words or phrases). It does this by using something called tokenization, which is like cutting up a cake into slices so you can eat it more easily.

Next, we add some context to those tokens by looking at all the other words around them in the input. This helps us understand what’s going on in the text and how everything fits together. We do this using something called a window (like a frame for a movie), which lets us see a certain number of words before and after our token.

Once we have all that context, we feed it into another part of the model called an encoder. The encoder takes all those tokens with their context and turns them into numbers using something called embedding (like turning words into numbers so you can do math with them). This helps us compare different parts of the text to each other more easily.

Finally, we use a classifier to figure out whether our input is similar or dissimilar to another piece of text that we’re comparing it to. The classifier looks at all those numbers and decides which one is closer (like saying “this cake tastes better than that cake” based on how many slices you ate).

So, for example, let’s say we have two pieces of text: “I love pizza with mushrooms” and “I hate pizza with mushrooms”. If we feed those into our DPR Context Encoder model, it will break them down into tokens like this:

– I (input)
I (token)
i (embedding)
1024 (number)
– love (input)
love (token)
love (embedding)
3587 (number)
– pizza (input)
pizza (token)
pizza (embedding)
9102 (number)
– with (input)
with (token)
with (embedding)
4356 (number)
– mushrooms (input)
mushrooms (token)
mushrooms (embedding)
8721 (number)

And so on for both pieces of text. Then, we add some context to those tokens using a window size of 3:

– I love pizza with mushrooms (input)
I (token)
i (embedding)
1024 (number)
love (token)
love (embedding)
3587 (number)
pizza (token)
pizza (embedding)
9102 (number)
with (token)
with (embedding)
4356 (number)
mushrooms (token)
mushrooms (embedding)
8721 (number)
– I hate pizza with mushrooms (input)
I (token)
i (embedding)
1024 (number)
hate (token)
hate (embedding)
5638 (number)
pizza (token)
pizza (embedding)
9102 (number)
with (token)
with (embedding)
4356 (number)
mushrooms (token)
mushrooms (embedding)
8721 (number)

And so on for both pieces of text. Then, we feed all those numbers into our classifier to see if they’re similar or dissimilar:

– Similarity score between “I love pizza with mushrooms” and “I hate pizza with mushrooms”: -0.98 (like saying “this cake tastes worse than that cake”)

There you go! That’s how our DPR Context Encoder model works in a nutshell. It may sound complicated at first, but once you break it down into smaller pieces like this, it becomes much easier to understand.