Private/sensitive data
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[]
[]
["Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)\r\n\r\nWe're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road."
 'Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV/TSV and JSON are supported. To load a dataset in JSON format:\r\n\r\n```\r\nnlp.load_dataset(path="json", data_files={nlp.Split.TRAIN: ["path/to/train.json"], nlp.Split.TEST: ["path/to/test.json"]})\r\n```\r\n\r\nFor CSV/TSV datasets, you have to replace `json` by `csv`.'
 'Hi @julien-c @jplu,\r\nThanks for sharing this solution with me, it helps, this is what I was looking for. \r\nIf not already there and only missed by me, this could be a great addition in the docs.\r\n\r\nClosing my issue as resolved, thanks again.']
Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch. 

Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. 
Is there support/a plan to support such data with NLP, e.g. by reading it from local sources?

Use case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive/private data without the need to rethink data processing pipelines. 

Many thanks for your responses ahead of time and kind regards,
MFreidank
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