transformer

https://github.com/huggingface/transformers/blob/546dc24e0883e5e9f5eb06ec8060e3e6ccc5f6d7/src/transformers/models/gpt2/modeling_gpt2.py#L698

Assertions can't be relied upon for control flow because they can be disabled, as per the following:

$ python --help
usage: python [option] ... [-c cmd | -m mod | file | -] [arg] ...
...
-O     : remove assert and __debug__-dependent statem

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

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.

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在留下您的问题时，辛苦您同步提供如下信息：

• 版本、环境信息
  1）PaddleNLP和PaddlePaddle版本：请提供您的PaddleNLP和PaddlePaddle版本号，例如PaddleNLP 2.0.4，PaddlePaddle2.1.1
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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

Hi all,

We are asking for help from our community, hoping to provide Chinese documentation to all users. It's not difficult and here is the chance to be one of the contributors of MMOCR, an open-source rising star!

Here is a list of documentation needed to be translated:

• docs/getting_started.md
• [docs/ins