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.
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Describe the feature
I think enforcing typing in methods parameters can be helpful for robustness, readability and stability of the code.
By using mypy static type checker, we can see potential improvements for jina:
Usage:
pip install mypy
mypy --ignore-missing-imports jinaDo not get overwhelmed by the errors. Let's slowly keep improving until we can eve
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Sep 1, 2020 - JavaScript
训练数据集问题
你好,看代码使用的训练数据为Restaurants_Train.xml.seg,请问这是这是在哪里下载的吗,还是semeval14的任务4中xml文件生成的?如果是后续生成的,请问有数据生成部分的代码吗?
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Currently we have a mixture of negative and positive formulated arguments, e.g.
no_cudaandtraininghere: https://github.com/huggingface/transformers/blob/0054a48cdd64e7309184a64b399ab2c58d75d4e5/src/transformers/benchmark/benchmark_args_utils.py#L61.We should change all arguments to be positively formulated, *e.g. from
no_cudatocuda. These arguments should