Hey, everyone.
So let's continue our discussion on training your own and meeting Martin.
So in the last video, video created this model and we put product training purpose.
Now that training is completed.
Let me display this model logic.
All right.
So we got our model object in terms of work to work class.
So our model is really we can do the production and we can see some of the applications of this over
to Emory.
So the first one is we should execute this one first.
So model dot v w v function we are going to use and that function will return witness that particular
of how it is represented or what is its word vector.
So let me have Randy.
All right, so sort of a token hammy and men has been represented by this 32 numbers simply.
It has on their applications like a most similar so similar to men, which are the other 10 words,
which is very much Close's.
Now, not in terms of the actual characters contained inside those token, but in terms of the semantic
representation that man is related to, which are the 10 closest one.
So let me executing.
And you can see Roumain is quite close to man, couple, girl, boy.
So we got all those.
Which is quite similar in nature compared to what they were worthy of supply man.
So now you can understand the power of this word to work or avoid inbreeding, kind of modern, which
is a prediction based model.
Now, there is no similarity between, let's say bye and a man, but genetically try to indicate that
these are the similar voice.
So this thing just cannot be possible with a simple string matching.
But this is possible with Delp of this war, Emily.
So what inbreeding has given us power to represent?
Every single token in a hide Amundsen's space.
And the results of fixed type of Hyrum, in essence, space.
Now let's see one more application and you will be amazed to see about.
Let's see.
We'll try to do some simple vector arithmetic like I designed in subsection, so I'm just going to stop
the word vector of this king minus man, and I'm just adding to the women.
And let's see what results we get.
So Volman is close to movement itself.
Because king and man will be canceled each other, then we are union, education, religious.
So in this case, we didn't get a very good result because we are expecting no king minus man plus woman
should be like a queen because King will cancel out man the same way women will cancel out the queen.
But now why this thing happened because we're limited by the data.
Of course we are limited by the data.
Let's see one more like a Berlin is the capital of Germany as a country.
Then Paris is the capital of France.
So let's see whether we'll get to France or not.
All right.
So in this case, we got a very good result.
So if we subtract the word vector of Germany minus Berlin, plus Paris will be equal to France.
So we got excellent result in this particular case.
Now, you can imagine that why in this case, it works fine, because we're taking the data that is
avoid news data.
You can see why news data from the rally.
So was probably in those dataset, we found the words like Germany, Berlin together.
Same with Paris and France together.
That's why those kind of semantic really and see, however, vote to work model embedding technique,
it try to cattery very much easy.
Let's see about the Massee minus football.
So same Leiker Massie's the guard of the football.
Same way we are trying to find voids the guard of the cricket.
So.
Mostly district tournament is being played in a is in Pacific countries.
And let's see what will come.
So we got stalled and find a undergoes.
So we don't have enough data corresponding to this cricket might be.
And that's why this thing happened.
We can try with, let's say, hockey weather.
I'm not sure where the hockey is a token available in this.
Hello, regional leaders hope so hockey doesn't exist.
Hockey doesn't exist.
So let's just keep it as it is.
So now you can understand the power of this word to whack Mardle.
And we try to.
Playing our own Martin over the weekend, we got some very, very good result, but we are still limited
by the data set.
And of course, that will be OK.
My idea and intention is to teach you that we do.
But this didn't seem like library.
Feel your own custom data set how you can train the model.
Now, next, we are going to deal with that same word, two VEC model.
But that will be a pre train work work model and we will see the same result.
What I would say will apply those very standard updated model, which is being trained by some big companies
and those particular model will give it excellent results.
So see you in the next video.