Add Adversarial SQuAD dataset
MEMBER
the CI error was just a connection error due to all the activity on the repo this week ^^'
I re-ran it so it should be good now
2020-12-07T14:12:31Z
https://github.com/huggingface/datasets/pull/1184#issuecomment-739942811
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CONTRIBUTOR
I hadn't realized the problem with the dummies since it had passed without errors.
Suggestion: maybe we can show the user a warning based on the generated dummy size.
2020-12-07T15:55:28Z
https://github.com/huggingface/datasets/pull/1184#issuecomment-740006524
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MEMBER
Thanks for changing to configs ! Looks all good now :) 

Before we merge, can you re-lighten the dummy data please if you don't mind ? The idea is to have them weigh only a few KB (currently it's 50KB each). Feel free to remove any unnecessary files or chunk of text
2020-12-09T16:48:50Z
https://github.com/huggingface/datasets/pull/1184#issuecomment-741899546
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MEMBER
(also you can ignore the `RemoteDatasetTest ` CI errors, they're fixed on master )
2020-12-09T16:49:25Z
https://github.com/huggingface/datasets/pull/1184#issuecomment-741899892
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MEMBER
merging since the CI is fixed on master
2020-12-16T16:12:54Z
https://github.com/huggingface/datasets/pull/1184#issuecomment-746556098
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# Adversarial SQuAD

Adding the Adversarial [SQuAD](https://github.com/robinjia/adversarial-squad) dataset as part of the sprint 🎉 
This dataset adds adversarial sentences to a subset of the SQuAD dataset's dev examples. How to get the original squad example id is explained in readme->Data Instances. The whole data is intended for use in evaluation. (Which could of course be also used for training if one wants). So there is no classical train/val/test split, but a split based on the number of adversaries added.

There are 2 splits of this dataset:

- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.

(The AddAny and AddCommon datasets mentioned in the paper are dynamically generated based on model's output distribution thus are not included here)

The failing test look like some unrelated timeout thing, will probably clear if rerun.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)