Upload README.md with huggingface_hub

README.md

@@ -25,16 +25,15 @@ Here is how to use this model to get the Named Entities in text:
 
 ```python
 from transformers import pipeline
-ner = pipeline('ner', model='clarin-pl/FastPDN')
+ner = pipeline('ner', model='clarin-pl/FastPDN', aggregation_strategy='simple')
 
 text = "Nazywam się Jan Kowalski i mieszkam we Wrocławiu."
 ner_results = ner(text)
 for output in ner_results:
     print(output)
 
-{'entity': 'B-nam_liv_person', 'score': 0.99957544, 'index': 4, 'word': 'Jan</w>', 'start': 12, 'end': 15}
-{'entity': 'I-nam_liv_person', 'score': 0.99963534, 'index': 5, 'word': 'Kowalski</w>', 'start': 16, 'end': 24}
-{'entity': 'B-nam_loc_gpe_city', 'score': 0.998931, 'index': 9, 'word': 'Wrocławiu</w>', 'start': 39, 'end': 48}
+{'entity_group': 'nam_liv_person', 'score': 0.9996054, 'word': 'Jan Kowalski', 'start': 12, 'end': 24}
+{'entity_group': 'nam_loc_gpe_city', 'score': 0.998931, 'word': 'Wrocławiu', 'start': 39, 'end': 48}
 ```
 
 Here is how to use this model to get the logits for every token in text: