tokenizer from KBLab example

app.py

@@ -5,10 +5,9 @@ import json
 def process_swedish_text(text):
   # Models from https://huggingface.co/models
   # https://huggingface.co/KBLab/bert-base-swedish-cased-ner
-  ml_model = 'KBLab/bert-base-swedish-cased-ner'
-  ner_pipeline = pipeline(model=ml_model, task='ner', truncation=True)
+  nlp = pipeline('ner', model='KBLab/bert-base-swedish-cased-ner', tokenizer='KBLab/bert-base-swedish-cased-ner')
   # Run NER
-  pipeline_results = ner_pipeline(text)
+  pipeline_results = nlp(text)
   print('NER results:', pipeline_results)
   # Fix TypeError("'numpy.float32' object is not iterable")
   pipeline_results_adjusted = map(lambda entity: entity | { 'score': float(entity['score']) }, pipeline_results)