Different prediction tensors on single item vs a list of items

Hi @lewtun I followed this colab notebook:

When predicting, if I do the following, this will be correct prediction:
learn.predict(“This was a really good movie, i loved it”)
But if I pass a list for the prediction like the following, the tensor values change:
test = [“This was a really good movie, i loved it”, “Wowwwwww, about an hour ago I finally finished
watching this terrible movie!!!”, “Im still a big IMDb fan, but seriously rethink this rating process
because this movie should be rated no higher than maaaybbbeee like a 3”, “I am disappointed in the
director, Sydney Pollack who gave us the classic Tootsie and other films. This one is a waste of time
and energy.”, "Wow, this movie really sucked down below the normal scale of dull, boring, and
unimaginative films I’ve seen recently. ", “Sorry. Someone has to say it. This really is/was a dull
movie. Worthy perhaps, but dull nonetheless.”, “This is a truly hilarious film and one that I have seen
many times. This is a film you could watch again and again, with a fabulous sound track! One for all
those at school in the 90’s to watch!”]
df = pd.DataFrame(test,columns=[‘information’])
final_res =
for txt in df[‘information’]:
result = learn.predict(txt)
final_res.append(result)