Entity level F-1 scores in run_ner.py
# 🚀 Feature request

The run_ner.py script in examples/token_classification reports evaluation metrics in terms of token F-1 scores (from what I can tell by examining the code). This issue is to request entity level F-1 scores as an evaluation metric. Token level scores will evaluate the F-1 score over an input such as ["I", "work", "for", "ABC", "com", "##pany", "in", "New", "York", "City"] with the label for each token considered separately. In this example, if the ground truth labels are ["O", "O", "O", "ORG", "O", "O", "O", "GPE", "GPE", "GPE"] the label for "New" , "York" and "City" from the predictions would be compared against ground truth separately.  Entity level scores would mark a true positive if for instance all 3 tokens in the span "New York City" are labelled correctly.

## Motivation

Token level F-1 scores can be a more lenient metric compared to entity level scores since labels on sub-words/entities are considered separately.

## Contribution

I can also help implement this but I am not certain how I should get started with this. Any help is appreciated.
Oh this makes me realize this script hasn't been switched to use the datasets metrics for seqeval. This would solve the issue as it computes all scores. Will do that tomorrow.<|||||>Thank you @sgugger