tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
tokens
['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']
```
The addition of the special tokens [CLS] and [SEP] and subword tokenization creates a mismatch between the
input and labels. Realign the labels and tokens by:
Mapping all tokens to their corresponding word with the word_ids method.
Assigning the label -100 to the special tokens [CLS] and ``[SEP]``` so the PyTorch loss function ignores
   them.
Only labeling the first token of a given word. Assign -100 to the other subtokens from the same word.
Here is how you can create a function that will realign the labels and tokens:
