grover | Code for Defending Against Neural Fake News , https | Machine Learning library

You have a different problem then what I initially thought. You're calling the init function of the combo before you open the popup, but the entire content of the popup is created dynamically, when you open it. Which means the element you're trying to init the combo on doesn't yet exist at that time.

So you have to call the init combo function every time you open the popup, after it has rendered its contents.

Here's the fix:

Interesting problem to solve. Look at this code and see if it provides you the repeatable process to keep continuing with your quiz. The only area that I have a bit of a problem is your big if statements that check for scores and print varying responses. When the user has fewer questions, I had to add the older answered questions to the tally to stay in the same range. Otherwise, this should work.

Things I changed.

#1: Questions is a list of tuples. Each tuple is a question and answer (q1,'c') as example.

#2: Since we need to repeat the questions, I am iterating through incorrect question list each time. To start off, I set all questions as incorrect. So the incorrect questions list has values 0 thru 14.

#3: Every time the user answers correctly, I am removing the question from the incorrect question list.

#4: Since I am manipulating the list itself by removing the correctly answered question, I cannot use a for loop. Instead I am using a while loop and ensuring I am going through the list only till the max of list

#5: I am looping the Quiz function until the user decides to stop playing. To start with, I am setting the flag as yes and checking for it before I call Quiz function. I am returning the user's decision back as a return statement. That is helping the loop to keep going.

#6: Finally, I moved all the questions outside and made Questions a global variable. Since we are going to call Quiz a few times, I didn't want Questions to be defined every time. If you want to keep it inside, its your choice. It does not impact the overall solution. However, you need to make it a list of tuples. Additionally, inc_questions has to be global so you can manipulate it as many times as you need.

Below is the code. Let me know if you find any errors.

I think the type of your "values" should be Map, or at least var.

However, I suggest you build a model for your json response. There are many post written about this. This post is what I usually follow when making a model.

My questions is what parameter will effect more on performance reducing batch size or reducing max_seq_length?

1. On performance: None. It is a big misconception that batch size in any way affects the end metrics (e.g. accuracy). Although finer batch size means metrics being reported on shorter intervals giving illusion of much larger variability than there actually is. Effect is highly noticeable in case of batch size = 1 for obvious reasons. Larger batch sizes tend to report higher veracity for metrics as they are being calculated over several data points. End metrics are usually the same (with account for random initialization of weights).
2. On efficiency: Larger batch sizes means metrics being calculated less often but at the same time more space in the memory at the same time as metrics are being aggregated over a number of data points as per batch size. The same issue you were facing. So, batch size is more of a efficiency concern than a performance one. Moreover, how often you want to check model’s output.

1. On performance: Probably the most important metric for performance of language based models like Grover. Reason behind this is the perplexity of human-written text is lower than randomly sampled text, and this gap increases with sequence length. Generally, more the sequence length is, easier it is for a language model to stay consistent during the whole course of the output. So yeah it does help in model performance. However you might want to look into documentation for your particular model for “Goldilocks Zones” of sequence lengths and whether the sequences in power of 2 are more desirable than others.

2. On efficiency: Larger sequence sizes are of course require more processing power and computational memory so higher you go for the sequence lengths, more power you will need.

Also can I set the value of max_seq_length other then the power of 2 like some value between 512 and 1024?

Yeah why not? No model is designed to work with a fixed set of values. Wxperiment different sequence lengths and see whichever works for you best. Adjsuting some parameters in powers of two has been a classical practice for having a little computational advantage because of their simple binary representations but is negligible in case of large models as of today.

No, you are completely wrong json_normalize() normalize semi-structured JSON data into a flat table not to a DataFrame. That's why they use venues = results['response']['groups'][0]['items'] to get the venues. They used the function get_category_type() to get the category of the venue.

If you want to know more about json_normalize() please refer this link

You might be need something like

You can copy paste run full code below
You can use addPostFrameCallback and set bool isLoading
When isLoading is true, return CircularProgressIndicator()

link to (.*?) from (.*?)( shared (.*))?$

Use .*? for lazy repeaters (lazy = non-greedy)

Don't use VBA for this, you can easily accomplish this with a formula, just like this one: