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Create a NER example similar to the Pytorch one. It takes the same options, and can be run the same way.

Co-Authored-By: Piero Molino <w4nderlust@gmail.com>

When evaluating, shouldn't we always use the SequentialSampler instead of DistributedSampler? Evaluation only runs on 1 GPU no matter what, so if you use the DistributedSampler with N GPUs, I think you'll only evaluate on 1/N of the evaluation set. That's at least what I'm finding when I run an older/modified version of this repo.

Co-Authored-By: Rosanne Liu <mimosavvy@gmail.com>

Imrpovements: model_path renamed pretrained_model, tokenizer loaded from pretrained_model, pretrained_model set to discriminator's when discrim is specified, sample = False by default but cli parameter introduced. To obtain identical samples call the cli with --sample

First cleanup step, changing function names and passing parameters all the way through without using args. Identical output as before.

(cherry picked from commit 566468cc984c6ec7e10dfc62b5b4191781a99cd2)

Co-authored-by: piero <piero@uber.com>
Co-authored-by: Rosanne Liu <mimosavvy@gmail.com>