Well, well, well, it's been a long day, but congratulations, you've made it.
We've gone through and curated a pristine data set, working very hard to make sure that it's got a
good sample representation of the data we want to train by.
And at the end, we of course turned it into a hugging face data set, a data set dict with the training
and test parts to it, and we uploaded it to the Hugging Face Hub.
And if you've gone through all of these instructions, which I know you will have done, you've been
following along in JupyterLab, getting comfortable with the different things that I've been doing then
now you'll be rewarded with your own data set, sitting there in the hub that you will be able to use
in the subsequent sessions.
So congratulations on on on getting that far.
Uh, so we've added to the skills that you've acquired, the understanding of the five step strategy
to solving commercial business problems with Llms, uh, weighing up the three different optimization
techniques and some real detail in data set curation, including some thorny, uh, bits of code there
that I do hope you'll look through and understand them and then use them in your projects, like sampling
from existing data sets.
So next time, next time we're going to be talking about baseline models.
We're going to be creating a traditional machine learning solution.
And we're going to be applying some traditional and advanced techniques to see what gives us good results.
And I'm excited for it.
And I will see you there.