No problem.

I believe that if you're using a BERT-esque model, you do indeed need to do "full" tokenisation (part of which is creating the attention mask and padding) because BERT expects its input to be a list of token indices. E.g. Given the token mapping {"a": 1, "cow": 2, "cat": 3, "dog": 4}, tokenisation would turn "a cat" into [1, 3] which is in the form that BERT expects.

And since BERT comes with a token mapping (due to pre-training), if you're just putting in your own features (say, number of likes and number of retweets), they'll quite possibly just get interpreted as random tokens if their numbers match up with known token indices.

If your features are already the right kind (tokenised text, with the resultant indices matching the correct BERT token indices), I suppose you could do truncation/padding yourself and feed that input directly to BERT.

But it'll probably end up simpler and less error-prone to let BERT tokenise it for you (e.g. via HuggingFace's `AutoTokenizer.from_pretrained('bert-base')`)