关于bertTokenizer

from transformers import BertTokenizer
import os

tokens = ['我','爱','北','京','天','安','门']

tokenizer = BertTokenizer(os.path.join('/content/drive/MyDrive/simpleNLP/model_hub/bert-base-case','vocab.txt'))
encode_dict = tokenizer.encode_plus(text=tokens,
                  max_length=256,
                  pad_to_max_length=True,
                  is_pretokenized=True,
                  return_token_type_ids=True,
                  return_attention_mask=True)
tokens = ['[CLS]'] + tokens + ['[SEP]']
print(' '.join(tokens))
print(encode_dict['input_ids'])

Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
[CLS] 我 爱 北 京 天 安 门 [SEP]
[101, 100, 100, 993, 984, 1010, 1016, 100, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2079: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).
  FutureWarning,

from transformers import BertTokenizer
import os

tokens = ['我','爱','北','京','天','安','门']

tokenizer = BertTokenizer(os.path.join('/content/drive/MyDrive/simpleNLP/model_hub/bert-base-case','vocab.txt'))
tokens_a = '我 爱 北 京 天 安 门'.split(' ')
tokens_b = '我 爱 打 英 雄 联 盟 啊 啊'.split(' ')

encode_dict = tokenizer.encode_plus(text=tokens_a,
                  text_pair=tokens_b,
                  max_length=20,
                  pad_to_max_length=True,
                  truncation_strategy='only_second',
                  is_pretokenized=True,
                  return_token_type_ids=True,
                  return_attention_mask=True)
tokens = " ".join(['[CLS]'] + tokens_a + ['[SEP]'] + tokens_b + ['[SEP]'])
token_ids = encode_dict['input_ids']
attention_masks = encode_dict['attention_mask']
token_type_ids = encode_dict['token_type_ids']

print(tokens)
print(token_ids)
print(attention_masks)
print(token_type_ids)

Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
[CLS] 我 爱 北 京 天 安 门 [SEP] 我 爱 打 英 雄 联 盟 啊 啊 [SEP]
[101, 100, 100, 993, 984, 1010, 1016, 100, 102, 100, 100, 100, 100, 100, 100, 100, 100, 100, 102, 0]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2079: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).
  FutureWarning,