add bl books genre dataset
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To fix the CI, feel free to run the `make style` command to format the code.

Then it also looks like the dummy_data.zip archives are all empty, which makes the tests fail. Can you try regenerating them ? They should have one file inside which is a dummy version of the file at https://bl.iro.bl.uk/downloads/36c7cd20-c8a7-4495-acbe-469b9132c6b1?locale=en
2021-11-29T15:54:53Z
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@lhoestq, thanks for that feedback. 

I should have made most of these changes now. The `--auto_generate` flag wasn't working because the file wasn't downloaded with a `.csv` extension. I used `--match_text_files "*"` to get around this. Because there is a lot of data that isn't annotated using the default line number for the dummy data causes the `annotated_raw` and the `title_genre_classifiction` configs to fail because they don't generate any examples — bumping the line numbers to `250` fixes this. This does make the dummy data a bit bigger, though. 

The total directory size for the dataset is now `150kb`. Is this okay, or do you want me to generate the dummy data manually instead? 
2021-12-01T12:45:53Z
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Hi ! yes 150kB is fine :)
Feel free to push your new dummy_data.zip files (I think the current one are still the empty ones)
2021-12-02T10:49:59Z
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@lhoestq I've pushed those dummy files now and added your other suggestions.
2021-12-02T11:15:30Z
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The CI failure is unrelated to this PR, merging :)
2021-12-02T16:07:39Z
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@lhoestq, thanks for all your help with this pull request 😀
2021-12-02T16:10:29Z
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First of all thanks for the fantastic library/collection of datasets 🤗

This pull request adds a dataset of metadata from digitised (mostly 19th Century) books from the British Library The [data](https://bl.iro.bl.uk/concern/datasets/1e1ccb46-65b4-4481-b6f8-b8129d5da053) contains various metadata about the books. In addition, a subset of the data includes 'genre' information which can be used for supervised text classification tasks. I hope that this offers easier access to a dataset for doing text classification on GLAM (galleries, libraries, archives and museums) data. 

I have tried to create three configurations that provide both an 'easy' version of the dataset if you want to use it for training a genre classification model and a more 'raw' version of the data for other potential use cases for the data. I am open to suggestions if this doesn't make sense. 

Similarly, for some of the arrow datatypes, I have had to fall back to strings since there are missing values for some fields/rows but I may have missed a more elegant way of dealing with it. 