Hi Lewis,
I got to be honest - the GLUE example is a bit out of my league right now as I just started to do some work on TensorFlow…   …but the good news is that I’ve found a Sentiment Classification 21 sample that seems to have a much lower boundary in order to get it work and make sense of it.  So, I took this and developed a MultiClass classification 140 from it (essentially setting the num_labels as discussed and using a public demo dataset as you rightfully indicated).
I see that the above solutions work with a yield mechanism so I am wondering if this implementation will scale good in terms of memory usage for larger files with more labels? What do you think?
Otherwise, I am thinking that I can develop a MultiLabel-MultiClass Classification from the MultiClass classification 140 by providing a Pandas label column with a list of values (eg ‘[0,1,0,1]’) and setting the num_labels to the length of the array in the label column. And replacing the Softmax with a Sigmoid function to yield the correct result. Would you say this is about right or am I missing something again  ?