Here, everyone.
So in the last video between our own way to work model and meeting Martin and we tested and somewhere
we found when a good result or so, but now we are going to use this pre train model and this pre train
model is trained by Google.
So obviously you can think about that.
The amount of data they use for the training purpose, that will be a nominal sum on top data.
So in this model, we are just going to import it and we will see how we can.
Logos model, and then we will based on our testing dataset.
And let's see what results we'll be getting on exactly.
Similar stuff.
Let me clear all the outputs and let us start working on so import all those lively Jansing tenths of
law.
One more is a Kagan library.
We need to make the Kaggle directly because same thing.
We are going to download it from the Kegl only.
Let me go to this link and you will get idea.
Next, things we need to applaud this careful dog.
Now, as we are dealing with a different notebook.
Every time we have to do this thing.
So Kagle Dargis on got uploaded.
Let me just copy.
Do not kegl for that.
And let me be severely and then let just download this data set.
Now it's a little bigger.
Martin almost drowned PTB, so it will take a little bit amount of time.
So I'm just fast forwarding my video.
Tilda's download will complete.
All right, so you can see model is successfully downloaded and it's almost around PGD, so it took
a little time.
Next phase, we need to inject this model.
Now, unzipping definitely is going to take a good amount of time.
So I ended up in the file.
We can even go to this contain.
Let me go one step up and instead of containers, start extracting all those things.
This particular bin file, we hope, downloading Jim File, VEO downloaded.
We are expecting into this bin fine.
Let me collapse.
All right, so now you can see model is completely unzip and it is available.
Think, you know, many people file for Mix Bender Dodgier.
And again, it will be me.
So it's a quite big model.
Next is we need to load this model, and for that we are going to use this vector.
We are limited to one Lack's reactor only.
We are going to load it.
Otherwise it will be too much heavy.
So let us lower fully model inside of it.
Modern variable.
And we are not doing any kind of model building.
We are just loading the existing retrain model and that is being pre trained by the global organization.
And that is Google News based on the news data available.
And they train this model.
But like, let's go with the.
Man, so let me execute it and it will give us the vector representational man.
Now you can see the size of this vector is much, much bigger.
If you try to find, let's say, land function.
Earlier case, we had a just 32 diamond set.
But this Google retrain model, that is a 300 dimension vector.
So every single token in this case will be presented by.
Three hundred different numbers.
Now let's try to find the same thing.
Like a most similar with this pre train modern and we'll try to compare what results we will get.
So in this case, also, we got the woman, but the boys first one compared to earlier, we had a couple
girls and then boy.
So a little better riding man is related to women.
And then Boyd and teenager again.
That's OK, but good results.
We are right.
But the real test will come when we test on this king minus man.
Plus remain.
So that is electric magic.
And we got the king.
So first when we got the king, but then we have a queen.
So that's quite embracing and quite good results we got.
So as the queen is to man, woman as queen, let's drive with the Germany capital, Berlin.
So Paris is the capital of France and we got, yes, France.
So in case those of you go to France.
So what this model also, it works fine.
But this particular case, we got a very good, quite impressive results.
Let me go with the last one.
Massey football and cricket.
Now we have a good amount of data.
So obviously, first one we automatically because.
This particular actor is quite close to Massey, but the second one you ought to consider like a sidewalk.
So Sela Tendulkar, Dravid.
They needed the like a king or I would say the gall of this cricket tournament or a cricket sport sameway
like a Massee in case of football kind of goes haywire.
In case of cricket.
So those of you don't know about cricket, I'm just getting idea that these two guys are the God of
the cricket.
Spots like that, then the conclusion is that this preprint model looks very much based, even though.
And one more thing I want to highlight that you just reported, one lacks reactor only.
But there are many, many more vectors is embedded inside this pretty model.
So that is all about how to use this retrain model for the embedding purpose.
See you in the next video.