How to estimate the accuracy on a large dataset?

Given that I have a deep learning model(handover from former colleague).
For some reason, the train/dev set was missing.

In my situation, I want to classify my dataset into 100 categories.
The dataset is extremely imbalanced.
The dataset size is about tens of millions

First of all, I run the model and got the prediction on the whole dataset.

Then, I sample 100 records per category(according to the prediction) and got a 10,000 test set.

Next, I labeled the ground truth of each record for the test set and calculate the precision, recall, f1 for each category and got F1-micro and F1-macro.

How to estimate the accuracy or other metrics on the whole dataset? Is it correct that I use the weighted sum of each category’s precision(the weight is the proportion of prediction on the whole) to estimate?