This is based on Oliver Guhr's work. 
The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on sixteen languages instead of four: 
English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian, Greek, Swedish, Danish, Hungarian and Romanian.

----- report -----

              precision    recall  f1-score   support

           0       0.99      0.99      0.99  98904114
           .       0.94      0.95      0.95   6331979
           ,       0.86      0.86      0.86   8390616
           ?       0.89      0.84      0.86    182114
           -       0.59      0.31      0.40    341375
           :       0.72      0.52      0.61    210519

    accuracy                           0.98 114360717
   macro avg       0.83      0.75      0.78 114360717
weighted avg       0.98      0.98      0.98 114360717


----- confusion matrix -----

     t/p      0     .     ,     ?     -     : 
        0   1.0   0.0   0.0   0.0   0.0   0.0 
        .   0.0   1.0   0.0   0.0   0.0   0.0 
        ,   0.1   0.0   0.9   0.0   0.0   0.0 
        ?   0.0   0.1   0.0   0.8   0.0   0.0 
        -   0.1   0.1   0.4   0.0   0.3   0.0 
        :   0.0   0.3   0.1   0.0   0.0   0.5 
        
{}
transformers pytorch xlm-roberta token-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us 

This is based on Oliver Guhr's work. 
The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on sixteen languages instead of four: 
English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian, Greek, Swedish, Danish, Hungarian and Romanian.

----- report -----

              precision    recall  f1-score   support

           0       0.99      0.99      0.99  98904114
           .       0.94      0.95      0.95   6331979
           ,       0.86      0.86      0.86   8390616
           ?       0.89      0.84      0.86    182114
           -       0.59      0.31      0.40    341375
           :       0.72      0.52      0.61    210519

    accuracy                           0.98 114360717
   macro avg       0.83      0.75      0.78 114360717
weighted avg       0.98      0.98      0.98 114360717


----- confusion matrix -----

     t/p      0     .     ,     ?     -     : 
        0   1.0   0.0   0.0   0.0   0.0   0.0 
        .   0.0   1.0   0.0   0.0   0.0   0.0 
        ,   0.1   0.0   0.9   0.0   0.0   0.0 
        ?   0.0   0.1   0.0   0.8   0.0   0.0 
        -   0.1   0.1   0.4   0.0   0.3   0.0 
        :   0.0   0.3   0.1   0.0   0.0   0.5 
        
TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us 
