Hey, everyone, welcome back.
So in this video, we will see how we can train our own and model.
And mostly we are going to use for our own training and with our custom data set.
We are going to use this Jansing library so first time and put it dispenser filled with Taldo.
We are not going to use this tense of law, but let me put it in this by default.
Mundos.
As a part of this club environment and defiantly, you will get this sense of low two point zero percent
now for preprocessing.
We are going to use this hand LDK library.
So I'm just going to import this and indicate and return LDK.
That is a one more package.
I am going to download it.
That is nothing but fun.
So that is basically for the organization purpose.
And we are going to use this.
Martin.
All right now.
Important parties about the Jency.
So from Jansing models, we are going to import this right to work and give to function.
So let me embody.
So, Deserto, some of the basics input.
Now, first important us, we are going to start one is a data people assessing.
And for that one database, we are going to use it.
That is available on this Kagle website.
So let us follow this Kaggle website.
And it will open a new tab.
Now, to get any Natus say inside the gaggle, you need a calm before that, you just cannot download
it.
So we are going to use this voice news dataset on ready from two thousand eight to today.
So there is a huge amount of data sets that are available for our vote embedding training model, and
that will be close to around 70 M.B.
So our first task is to get this particular data set inside the collar.
And what I mean.
All right.
And to get it inside the clip file, we are going to use this Kagle API and that is with the help of
this Kagle package module.
So let me put this Google package module with this package manager and tell this installation will complete.
First is we how to grab the token so you can go to your account, Maicon, and just scroll down and
you'll be able to see you'll be you will get this API key.
So just create new EPA token just to download this net asset into our column environment.
And I'm just going to say let me say to the download folder right now.
Next thing is we are going to create one, not Google.
Directory.
And our Kegl dot is on fire.
We are just going to cooperate and into this dark Kagle directly.
But before that, from our local environment, we have to upload it.
So applaud.
Go to download and our piggin, not just one site is available.
Now, if you open this Google, not just on fire.
That is only a user named Bill Gavin and part from user name.
There is a one API key.
All right.
So let's just copy now.
And after copying, let's just disable this particular API key.
So we are just changing the permission.
Let me call OPSEC and now we are going to download this particular dataset.
Avoid news on Reddit.
So let me go back.
Yes.
So if you click on download.
You will be able to download in your local machine on, so if you want to create this whole model on
a local machine and if you want to download on a Kagle magically into a club environment, you can use
this coming Kaggle data set now.
So it is close around seventy eight M.B and it will take little time to download.
All right, so the data got downloaded.
We can even see whether data is available yet.
Yes, data is available via news on Reddit GFI.
Now, let's just unzip it and we will keep it inside this contain vital news on a ready for you.
Let me finish it.
So our hopes.
Let's go to contain and inside the contain.
You can see the file got chipped in two nights since we filed for me.
Now, next, this.
We are going to.
Read this NAACP file into a data free object for reading, we are going to use this bindis library.
Let this play forcefield, Akos.
All right, so it's a race that has a nail.
Goodness.
And mainly we are going to use this title column for our war and reading Modern.
How many total number of records are available?
We can grab it from ship.
So we have a file X nine thousand two hundred and thirty six records are available.
That's quite good enough.
And this is the only column which contains all those title.
So we'll just grab this title column into another variable news and this called title.
All right.
If you just try to display.
Let's say first Fluticasone leaving today.
So scores killed in a Pakistan class.
Yes.
So this is the first record.
Now we are going to first tokenized this one.
So for tokenization, just like earlier or so, we use this analytical library.
So from an indicator not PAVOL and the called tokenization, we are going to do and it will do the tokenization
on every single record.
So we'll get a director like new on this.
Correct.
Now it has a good amount of datasets that are available.
So it will take a little bit amount of time.
All right, so now you can see tokenization, Fenice.
Let's display first few records.
So a new.
That's correct.
Let's just deal.
So very first record will be released.
And having our individual values will be a different voice.
What I would say in a very NLB language.
It will be kind of took.
So scores killed in Pakistan and classes.
So each individual word is considered as a token.
The next is we're going to build the actual vote work model.
And this will direct model functionalities available in this Jency library.
Right now, it is expecting this to argument ventilatory arguments.
So first one is for the text.
So tax will be a new underscore quarterback model or I would say text.
We will supply one of the two argument will be minimum gone.
So it will consider only those where we're having a minimum count will be one.
So obviously every single word or unique way it will take into consideration and the size of vector
will be 32.
Because after embedding whatever the output we get inside the model, that will be OK size thirty two
numbers in terms of timings.
And so he tries to say that every single word or a token will be represented.
Wait.
Thirty two numbers are attracted to damage that space.
So let me execute it and it will take good amount of time.
So I'm just winding up in this video at this particular time.
And in the next video, we will see after the model will be created how we can do a prediction based
on this particular model.
So see you in the next video.