How to return custom `token_type_ids` or other values from a tokenizer?

I have two text features, ‘title’ and ‘abstract’. I want to tokenize these two columns in a Dataset.

checkpoint = 'jjzha/jobbert-base-cased'
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
tokenizer.model_max_len = 512

def tokenize(batch):
    tokenized_text1 = tokenizer(batch["title"], truncation=True, padding='max_length', max_length=512)
    tokenized_text2 = tokenizer(batch["abstract"], truncation=True, padding='max_length', max_length=512)

    return {'input_ids1': tokenized_text1['input_ids'], 'attention_mask1': tokenized_text1['attention_mask'],
        'token_type_ids1': tokenized_text1['token_type_ids'],
        'input_ids2': tokenized_text2['input_ids'], 'attention_mask2': tokenized_text2['attention_mask'],
        'token_type_ids2': [1] * len(tokenized_text2['token_type_ids']) #100 * [[1] * 512]
            }, batched=True)['train'].to_pandas()#['input_ids1']

In the above code, in this line ‘token_type_ids2’, I want to create the token_type_ids full of 1s for the second text feature (like next sentence prediction in BERT). But it is not working as expected. I assume ‘token_type_ids2’ should be a list of size (max_len). Is it correct?
By examining the tokenized dataset, the column “token_type_ids2” for each row, is a single value 1. But it is supposed to be a list of 1s with length 512.

How to solve this problem?
BTW, If I fix the value there to be “100 * [[1] * 512]”, it shows the correct token_type_ids. 100 is the size of the original dataset. But shouldn’t the map function applies the tokenize function to each row?