run evalation after every epoch in Trainer
# 🚀 Feature request

With the current Trainer implementation:
`trainer.train(..)` is called first followed by `trainer.evaluate(..)`. It would be nice if the user can pass the flag `--run_eval` (something similar) to run evaluation after every epoch. It would be nice for users who want to see how model performs on validation set as training progresses. In some cases, this is the general norm (run evaluation after every epoch).
You should use `--evaluate_during_training` which should do mostly what you're looking for<|||||>@prajjwal1 , you should be able to achieve this with `--evaluate_during_training` provided you set `--save_steps` to  `number_of_samples/batch_size`. However, I'm currently having trouble achieving this with that option when using both `run_language_modeling.py` and `run_glue.py` as I specify in https://github.com/huggingface/transformers/issues/4630. Any ideas @julien-c ? Thanks in advance.<|||||>There's a problem with MNLI though. In the example, arguments are changed from `mnli` to `mnli-mm`, so running evaluation after each epoch will happen on MNLI and not the mismatched one with the current implementation. 