I tried mapping back to closest token embeddings. Here's what I got:

    global_step = 1377; phase = continuous; lr = 5.00e-03; average_loss = 0.609497
  current tokens: ' Superman' '$MESS' '.");' '(sentence' '");' '.titleLabel' ' Republican' '?-'

    global_step = 1956; phase = continuous; lr = 5.00e-03; average_loss = 0.589661
  current tokens: ' Superman' 'marginLeft' 'iers' '.sensor' '";' '_one' '677' '».'

    global_step = 2468; phase = continuous; lr = 5.00e-03; average_loss = 0.027065
  current tokens: ' cited' '*>(' ' narrative' '_toggle' 'founder' '(V' '(len' ' pione'

    global_step = 4871; phase = continuous; lr = 5.00e-03; average_loss = 0.022909
  current tokens: ' bgcolor' '*>(' ' nomin' 'ust' ' She' 'NW' '(len' ' pione'

"Republican?" was kind of interesting! But most of the strings were unintelligible.

This was for classifying sentiment on yelp review polarity.