Issue while Creating Custom Metric

Hi Team,

I am trying to create a custom metric for my training as follows, where f1 is my own metric:

```python
 def _info(self):
        # TODO: Specifies the datasets.MetricInfo object
        return datasets.MetricInfo(
            # This is the description that will appear on the metrics page.
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}),
            # Homepage of the metric for documentation
            homepage="http://metric.homepage",
            # Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_metric"],
            reference_urls=["http://path.to.reference.url/new_metric"]
        )

    def _compute(self,predictions,references,text,offset_mapping,spans):

        pred_spans = []

        for i,preds in enumerate(predictions):
            current_preds = []
            for j,token_preds in enumerate(preds):
                if (preds>0.5):
                    current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))
            pred_spans.append(current_spans)
        
        return {
            "Token Wise F1": f1_score(references,predictions,labels=[0,1]),
            "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])
        }

```

I believe this is not correct. But that's not the issue I am facing right now. I get this error :
```python
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-144-ed7349b50821> in <module>()
----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] )

2 frames
/usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch)
    802         encoded_batch = {}
    803         if set(batch) != set(self):
--> 804             print(batch)
    805             print(self)
    806             raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self)))

ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}
```
On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.

How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?

Thanks,
Gunjan

Comments:
Currently it's only possible to define the features for the two columns `references` and `predictions`.
The data for these columns can then be passed to `metric.add_batch` and `metric.compute`.
Instead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.

For example 
```python
features = datasets.Features({
    'predictions':datasets.Sequence(datasets.Value("int32")),
    "references": datasets.Sequence({
        "references_ids": datasets.Value("int32"),
        "offset_mapping": datasets.Value("int32"),
        'text': datasets.Value('string'),
        "ground": datasets.Value("int32")
    }),
})
```

Another option would be to simply have the two features like 
```python
features = datasets.Features({
    'predictions':datasets.Sequence(datasets.Value("int32")),
    "references": datasets.Sequence(datasets.Value("int32")),
})
```
and keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).


What is the metric you would like to implement ?

I'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users.

What do you think ?

Hi @lhoestq,

I am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.

Including them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.

Resolved via https://github.com/huggingface/datasets/pull/3824.