bert
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chooses 15% of token
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.
PositionalEmbedding
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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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文档增加tokenizer类别及样例建议
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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
关于一些具体建议
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Several tokenizers currently have no associated tests. I think that adding the test file for one of these tokenizers could be a very good way to make a first contribution to transformers.
Tokenizers concerned
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LED
RemBert
RetriBert
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