BERT Multi-class text classification in Google Colab
I'm working on a data set of social media comments(including youtube links) as input features and the Myers-Biggs Personality Profile as the target label: 

    type    posts
0   INFJ    'http://www.youtube.com/watch?v=qsXHcwe3krw|||...
1   ENTP    'I'm finding the lack of me in these posts ver...
2   INTP    'Good one _____ https://www.youtube.com/wat...
3   INTJ    'Dear INTP, I enjoyed our conversation the o...
4   ENTJ    'You're fired.|||That's another silly misconce...


but from what I've found, BERT wants DataFrame's to be in this format:

a   label   posts
0   a   8   'http://www.youtube.com/watch?v=qsXHcwe3krw|||...
1   a   3   'I'm finding the lack of me in these posts ver...
2   a   11  'Good one _____ https://www.youtube.com/wat...
3   a   10  'Dear INTP, I enjoyed our conversation the o...
4   a   2   'You're fired.|||That's another silly misconce...


The resulting output must be a prediction on a test set of comments split into four columns, one for each Personality Profile where, for example, 'Mind' = 1 is the label for Extrovert. Basically splitting a type like INFJ into 'Mind','Energy','Nature','Tactics', like such:

    type    post    Mind    Energy  Nature  Tactics
0   INFJ    'url-web    0   1   0   1
1   INFJ    url-web 0   1   0   1
2   INFJ    enfp and intj moments url-web sportscenter n... 0   1   0   1
3   INFJ    What has been the most life-changing experienc...   0   1   0   1
4   INFJ    url-web url-web On repeat for most of today.    0   1   0   1


I've installed pytorch-pretrained-bert using:

!pip install pytorch-pretrained-bert


I've imported the models and tried to tokenize the 'posts' column using:

import torch
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

tokenized_train = tokenizer.tokenize(train)


but receive this error:

TypeError: ord() expected a character, but string of length 5 found


I tried this based off the pytorch-pretrained-bert GitHub Repo and a Youtube vidoe. 

I am a Data Science intern with no Deep Learning experience at all. I simply want to experiment with the BERT model in the most simplest way to predict the multi-class classified output so I can compare the results to simpler text-classification models we are currently working on. I am working in Google Colab and the resulting output should be a .csv file.

I understand that this is a complicated model and all documentation and examples surrounding the model are complex (fine tuning layers etc.) but any help for a simple implementation(if in fact there is such a thing) for a beginner Data Scientist with minimal Software Engineering experience, would be much appreciated.

I recommend you start with a simple BERT classification task, for example following this excellent tutorial: https://mccormickml.com/2019/07/22/BERT-fine-tuning/ 

Then you can get into multi-label by following: https://medium.com/huggingface/multi-label-text-classification-using-bert-the-mighty-transformer-69714fa3fb3d

Only then I would recommend you try your task on your own dataset.
