language-model
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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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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
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This is a documentation request in order to make it easier to find corresponding examples in the documentation.
Good first issue if you want to get acquainted with the docs and how to build docs using Sphinx!
Current issue
Here's the issue: currently, if one goes to an older documentation version to check the "examples" page, for example, [v2.6.0](https://huggin