Multilabel text classification using BERT and Tensorflow 2

Question

I am trying to build a simple multilabel text classification pipeline using BERT; the goal is to classify the content of social media posts and any post can have more than one label (ie, a post can be labeled both "Medications" and "Physical and Mental Health"). I am very new to BERT and was trying to follow this example I found: https://towardsdatascience.com/building-a-multi-label-text-classifier-using-bert-and-tensorflow-f188e0ecdc5d I have some questions on how to set it up for this task. In my Anaconda system I have previously installed Tensorflow version 2.0. I have ran the command "pip install bert-tensorflow" and then ran the following:

import tensorflow as tf
import tensorflow_hub as hub
import bert
from bert import run_classifier
from bert import optimization
from bert import tokenization
from bert import modeling

And got this error at the step "from bert import run_classifier":

ModuleNotFoundError: No module named 'tensorflow.contrib'

I did some research and found out that Tensorflow 2.0 indeed does not have the contrib module, but earlier versions do. I just wanted some ideas on how to resolve this issue? I do not want to downgrade my Tensorflow version. And, if anyone can please point me to other examples of multilabel text classification and BERT I would greatly appreciate it (so far the ones I've seen are not very easy to follow).

Thank you

Answer 1

You can avoid this error by selecting Tensorflow version 1.x before your code:

%tensorflow_version 1.x

import tensorflow as tf
tf.__version__   # to check the tensorflow version

Output:

TensorFlow 1.x selected.
'1.15.2' 

This code line will convert Tensorflow version to 1.15 for your kernel runtime and now you can import the libraries and run your code without error:

import tensorflow as tf
import tensorflow_hub as hub
import bert
from bert import run_classifier
from bert import optimization
from bert import tokenization
from bert import modeling