Use python read for text dataset
{'login': 'lhoestq', 'id': 42851186, 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/lhoestq', 'html_url': 'https://github.com/lhoestq', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'type': 'User', 'site_admin': False}
[]
[]
One thing though, could we try to read the files in parallel? We could but I'm not sure this would help a lot since the bottleneck is the drive IO if the files are big enough.
It could make sense for very small files. Looks like windows is not a big fan of this approach
I'm working on a fix I remember issue https://github.com/huggingface/datasets/issues/546 where this was kinda requested (but maybe IO would bottleneck). What do you think? I think it's worth testing multiprocessing. It could also be something we add to our speed benchmarks > I remember issue #546 where this was kinda requested (but maybe IO would bottleneck). What do you think?

It still would be interesting I think, especially in scenarios where IO is less of an issue (SSDs particularly) and where there are many smaller files. Wrapping this function in a `pool.map` is perhaps an easy thing to try.  Merging this one for now for the patch release
As mentioned in #622 the pandas reader used for text dataset doesn't work properly when there are \r characters in the text file.

Instead I switched to pure python using `open` and `read`.
From my benchmark on a 100MB text file, it's the same speed as the previous pandas reader.
{'url': 'https://api.github.com/repos/huggingface/datasets/issues/715/reactions', 'total_count': 3, '+1': 0, '-1': 0, 'laugh': 0, 'hooray': 0, 'confused': 0, 'heart': 0, 'rocket': 3, 'eyes': 0}
{'url': 'https://api.github.com/repos/huggingface/datasets/pulls/715', 'html_url': 'https://github.com/huggingface/datasets/pull/715', 'diff_url': 'https://github.com/huggingface/datasets/pull/715.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/715.patch', 'merged_at': '2020-10-05T13:13:16Z'}