To get the full speed-up of FP16 training, every tensor passed through the model should have all its dimensions be a multiple of 8. In the new PyTorch examples, when using dynamic padding, the tensors are padded to the length of the biggest sentence of the batch, but that number is not necessarily a multiple of 8.

The examples should be improved to pass along the option pad_to_multiple_of=8 w

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.

Is your feature request related to a problem? Please describe.
With the new flexible Pipelines introduced in deepset-ai/haystack#596, we can build way more flexlible and complex search routes.
One common challenge that we saw in deployments: We need to distinguish between real questions and keyword queries that come in. We only want to route questions to the Reader b

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？