Allow empty reference summaries
# What does this PR do?

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The `run_summarization.py` has a check to skip examples where either the `text_column` or `summary_column` is `None`.  However, the way the check was written would catch any falsely values, like an empty string.

This caused all examples to be skipped for datasets where either the `text_column` or `summary_column` was the empty string (e.g. the test set of the [MS^2 dataset](https://huggingface.co/datasets/allenai/mslr2022)).

This PR just updates the check so it looks for `None` values explicitly.


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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
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- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @LysandreJik

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

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- maintained examples (not research project or legacy): @sgugger, @patil-suraj
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_The documentation is not available anymore as the PR was closed or merged._<|||||>@ydshieh, would you like to take a look at what is proposed here?<|||||>@JohnGiorgi I am not familiar with this dataset. But I am wondering if a model trained on this dataset is expected to learn to deal with empty document (this seems to strange) and/or learn to predict empty summary for some document?<|||||>@ydshieh They are just using the empty string as a placeholder. The reference summaries are held-out and not available publically. I imagine there could be other cases like this.

I guess another option is to log a warning if the inputs are empty but still proceed.<|||||>Log a warning sounds good to me. But before doing this, I am wondering what would be the benefits to process those held-out (empty string) test examples. Those examples won't be useful, right?<|||||>You may still want to generate predictions for examples even if they don’t have reference summaries (as is the case for the test set of MS^2). So another option still is to make the check just look for empty _inputs_, and ignore (but log a warning) for empty _reference summaries_
<|||||>@ydshieh Okay how's that? The only change now is that examples with empty reference summaries are still included, but a warning will be logged.<|||||>Thank you @JohnGiorgi, this LGTM overall!

The logging on each example with empty summary might be too spam. (Imagine the test dataset has 10K examples).
We can probably set a flag. If an empty summary is found -> and if flag is False -> warning -> set flag to True.

Let's wait one of the core maintainers to give a final review.<|||||>> Thank you @JohnGiorgi, this LGTM overall!
> 
> The logging on each example with empty summary might be too spam. (Imagine the test dataset has 10K examples). We can probably set a flag. If an empty summary is found -> and if flag is False -> warning -> set flag to True.
> 
> Let's wait one of the core maintainers to give a final review.

Ah very good point! Is there an example somewhere else in the codebase of something like this? I'm happy to update my change following that<|||||>Hi! Something like `deprecation_warnings` in

https://github.com/huggingface/transformers/blob/30992ef0d911bdeca425969d210771118a5cd1ac/src/transformers/tokenization_utils_base.py#L1520

could work (of course with another name), but in the training script, I think a single Boolean variable is enough.<|||||>Hi @ydshieh, is there anything blocking this that I can address?<|||||>Hi @JohnGiorgi . Sorry for being late in the review. As you can see [in this failed CI job](https://app.circleci.com/pipelines/github/huggingface/transformers/46308/workflows/5bf0bbad-5a99-47da-aa71-d6db2ed0ae37/jobs/544078), there is some issue of variable scope.

A quick solution could be

  ```python
    ...

    # A placeholder to determine whether we have already warned about empty summaries.
    empty_summary_warning = {"warned": False}

    def preprocess_function(examples):
    
        if not examples[summary_column][i] and not empty_summary_warning["warned"]:
            ...
            empty_summary_warning["warned"] = True
  ```
Let me know your opinion. Once the CIs all pass, I will request a final review from my colleagues :-)

Thanks a lot, for the PR and for the patience 🤗 <|||||>Ah, didn't look carefully at why the build was failing. Thanks, @ydshieh, your solution causes all the tests to pass!<|||||>This is too complicated for one of the example, which are just examples, not production-ready apps that should work in every case. Readability is more important than fixing this edge case IMO.<|||||>> not production-ready apps that should work in every case. Readability is more important than fixing this edge case IMO.

Fair point.

@JohnGiorgi Still thank you a lot for the PR. I am sorry that I haven't thought in another angle as a repository maintainer, and let you spend quite some time working on several of my suggestions.

You can definitely tweak the example script to meet your own needs. I am closing the issue.