This gist is part of my blogpost on BERT. Find the complete blogpost, covering both theory and hands-on part, here: https://towardsml.com/2019/09/17/bert-explained-a-complete-guide-with-theory-and-tutorial/

Classifying Yelp Reviews using BERT.ipynb

For some reason when saving that alpha (a) saves as first column and was messing up the training.
I changed that "alpha" column to "poop" and it fixed it. (think it is just saving columns alphabetically, maybe there is better fix here haha)

df_bert = pd.DataFrame({
'id':range(len(train_df)),
'label':train_df[0],
'poop':['a']*train_df.shape[0],
'text': train_df[1].replace(r'\n', ' ', regex=True)
})

Hey sir, can you tell me, how can I map words, only nouns, and adjectives to particular classes and properties of the Graph database, DO I have to create a dataset for each word and their respective classes and properties in an Ontology?

I am working on Q&A System for the graph database basically , I found research paper that do these thing but I don't think of how to achieve this.