All right, everyone.
So in the last video, we have done all those preprocessing four of our texts in this project.
Next thing we need to build a modern.
So we are starting here like a sequential model and first layer.
We are going to place like embedding layer where our input Deming's and will be of our vocabulary size.
So every single token will be represented by a vector of size, full vocabulary size.
So wherever the values will be, when that particular number will be represented by those token s put
them and will be 50.
So that indicates that that is like in a hyper parameter for this particular neural network, how you
are going to embed your individual token into footy payments in an input line.
So I a one shot.
Before predicting your output, how many values you want to give me as a input?
So a sequence length will be over in my head.
Then I'm just providing to Alistair Lear.
So that will be a first.
Alistair Lever, having got a hundred units and then another hundred units, one more dance, Lear having
100 unit.
But in this case, the activists and I kept like a loop.
And then there will be a one dance layer.
But in this case, the unit will be vocabulary size because we are trying to predict one token.
And as I told you, those token has been represented by a vocabulary size.
So whatever that particular index number will be fired in output layer, we will predict that those
particular token has been predicted and activist unit.
We are predicting like a soft mix because then we are trying to predict the probability of each individual
neuron in a quickly.
So that is our basic model summary.
And it has closed around two point one million parameters.
So that's a too much and might be sometimes I observe that this column environment also didn't support
in terms of AVAM or so, but somehow we managed to run it.
It may happen that you won't be able to run this experiment successfully on a Google column so you can
go with Hyrum on top of Ramsell.
But many times we fail before execution of this code.
All right.
So complacent, arem, optimizer, categorical cross and Kobie, because outputs are multiclass classification
kind of problem and how we are going to find the accuracy.
So that will be a.
Accuracy, Mazarin criteria will take into consideration.
So let just run this input and output on hundred apoc and we'll take a bite size of two fifty six.
So we ran it for 100 people.
And let me go down.
So then even after running under-report with such a big modern vegard accuracy close to around thirty
four point zero percent only religious policy.
Hey.
Now the is created.
Next thing is we need to test this model that how good our model is.
So let's just take any random line.
So from our original dataset, I have taken this particular random line and this particular line.
I have taken it as a shape line.
And I'll just created one function for the prediction function.
What in this particular function will do?
That will take into consideration this sick tax after the SIP tax.
How many total votes or a token?
You want to predict our tokenized?
That is just because while prediction.
Again, you need to convert those Tolkan into kind of numbers and whatever model.
We hope you just now created tax sequence, Len.
All right.
So what we are trying to do, we are just reading over how many times we want to generate the next spread
extent.
So we want to do the Predix and for in and that's codewords now for simple ideas even here, like a
10.
Every day I take.
Those sealed techs padding those sequences and then ready to output classis the moment they predict
the output classes.
I will just create another syntax, which will be a software version of your original syntax.
Plus, your predicted output and from those predicted output, again, we're trying to predict the next
one.
And then if I just supply those things to my January tax sequence function with model tokenized, a
sequence length sequence length will be 50.
And sequence tax and 10.
So based on this particular SEIP tax code of if I help now, I'm not good at how good goes.
Grammar stuff is whatever it is been generated, but you can see it is a reasonably good accuracy.
It looks like an English.
It looks like any grammatical inglis to be steam pooling to choose a brace off.
So based on this particular sig text, it has predicted this time future token.
And that is all about the how you can do the text in recent with this l'estang recurrent neural network.
All right.
That is all about the text.
And listen.