I have just noticed that German tokenized text looks rather strange in comparison to using the same settings for English (as shown below). For English I get one token per word but for German there are more tokens than that?
I also noticed the strip_accents 2 setting here - is this available for the encode_plus method as well or do I have to use the direct tokenizer API for that? If the later, what would be an equivalent way for implementing the settings below with strip_accents toggled such that they are not removed?
Thanx Dirk
from transformers import DistilBertTokenizer
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-multilingual-cased') 

tokens = tokenizer.encode_plus('schön gut'
                               ,max_length = 50
                               ,truncation=True
                               ,padding='max_length'
                               ,add_special_tokens=True
                               ,return_token_type_ids = False
                               ,return_attention_mask = True
                               ,return_tensors='tf')

print (tokens)


{‘input_ids’: <tf.Tensor: shape=(1, 10), dtype=int32, numpy=
array([[  101,   187, 10269, 15248, 23639,   102,     0,     0,     0,
0]], dtype=int32)>, ‘attention_mask’: <tf.Tensor: shape=(1, 10), dtype=int32, numpy=array([[1, 1, 1, 1, 1, 1, 0, 0, 0, 0]], dtype=int32)>}

tokens = tokenizer.encode_plus('good day'
                               ,max_length = 10
                               ,truncation=True
                               ,padding='max_length'
                               ,add_special_tokens=True
                               ,return_token_type_ids = False
                               ,return_attention_mask = True
                               ,return_tensors='tf')
print (tokens)


{‘input_ids’: <tf.Tensor: shape=(1, 10), dtype=int32, numpy=
array([[  101, 15198, 11940,   102,     0,     0,     0,     0,     0,
0]], dtype=int32)>, ‘attention_mask’: <tf.Tensor: shape=(1, 10), dtype=int32, numpy=array([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0]], dtype=int32)>}