Add per type scores in seqeval metric
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LGTM :-) Some small suggestions to shorten the code a bit :-)  Can you put the kwargs as normal kwargs instead of a dict? (And add them to the kwargs description As well) @thom Is-it what you meant? Yes and there is a dynamically generated doc string in the metric script KWARGS DESCRIPTION
This PR add a bit more detail in the seqeval metric. Now the usage and output are:

```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
predictions =  [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
met.compute(predictions, references)

#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8}
```

It is also possible to compute scores for non IOB notations, POS tagging for example hasn't this kind of notation. Add `suffix` parameter:

```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
predictions =  [['O', 'O', 'MISC', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
met.compute(predictions, references, metrics_kwargs={"suffix": True})

#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.9}
```
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