bert

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From paper, it mentioned

Instead, the training data generator chooses 15% of tokens at random, e.g., in the sentence my
dog is hairy it chooses hairy.

It means that 15% of token will be choose for sure.

From https://github.com/codertimo/BERT-pytorch/blob/master/bert_pytorch/dataset/dataset.py#L68,
for every single token, it has 15% of chance that go though the followup procedure.

Problem
Currently FARMReader will ask users to raise max_seq_length every time some samples are longer than the value set to it. However, this can be confusing if max_seq_length is already set to the maximum value allowed by the model, because raising it further will cause hard-to-read CUDA errors.

See #2177.

Solution
We should find a way to query the model for the maximum va

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  paddle版本2.0.8 paddlenlp版本2.1.0
  建议，能否在paddlenlp文档中，整理列出各个模型的tokenizer是基于什么类别的based，如bert tokenizer是word piece的，xlnet tokenizer是sentence piece的，以及对应的输入输出样例