Hello there!
Sorry for the simple question, but consider the example below
import pandas as pd
import numpy as np

data = pd.DataFrame({'text' : ['this is good!', 'amazing stuff',
                                'not good at all', 'not very nice'],
                     'label': [1,1,0,0]})
    
data
Out[3]: 
              text  label
0    this is good!      1
1    amazing stuff      1
2  not good at all      0
3    not very nice      0

As you can see, I have a small dataset containing text and label (positive vs. negative).
I would like to understand to to train a BERT/ DistilBERT model for text classification using Huggingface and tensorflow. Is there a simple way to proceed? Can you share a minimal working example here?
Thanks!