Update README.md

README.md

@@ -6,9 +6,10 @@ language:
 widget:
 - text: "In December 1903 in France the Royal Swedish Academy of Sciences awarded Pierre Curie, Marie Curie, and Henri Becquerel the Nobel Prize in Physics."
 - text: "Für Richard Phillips Feynman war es immer wichtig in New York, die unanschaulichen Gesetzmäßigkeiten der Quantenphysik Laien und Studenten nahezubringen und verständlich zu machen."
-- text: "My name is Julian and I live in montreal"
-- text: "My name is clara and I live in berkeley, california."
-- text: "My name is wolfgang and I live in berlin"
+- text: "My name is Julian and I live in Constance"
+- text: "Terence David John Pratchett est né le 28 avril 1948 à Beaconsfield dans le Buckinghamshire, en Angleterre."
+- text: "北京市，通称北京（汉语拼音：Běijīng；邮政式拼音：Peking），简称“京”，是中华人民共和国的首都及直辖市，是该国的政治、文化、科技、教育、军事和国际交往中心，是一座全球城市，是世界人口第三多的城市和人口最多的首都，具有重要的国际影响力，同時也是目前世界唯一的“双奥之城”，即唯一既主办过夏季"
+- text: "काठमाडौँ नेपालको सङ्घीय राजधानी र नेपालको सबैभन्दा बढी जनसङ्ख्या भएको सहर हो।"
 tags:
 - roberta
 license: mit
@@ -25,18 +26,22 @@ This model is limited by its training dataset of entity-annotated news articles
 
 ## Training data
 
+## Metrics
 
 ## Usage
 
 ```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification 
 
-model = RobertaForTokenClassification.from_pretrained("julian-schelb/roberta-ner-multilingual/")
+tokenizer = AutoTokenizer.from_pretrained("julian-schelb/roberta-ner-multilingual/", add_prefix_space=True)                          
+model = AutoModelForTokenClassification.from_pretrained("julian-schelb/roberta-ner-multilingual/")
 
-text = "Für Richard Phillips Feynman war es immer wichtig in New York, die unanschaulichen Gesetzmäßigkeiten der Quantenphysik Laien und Studenten nahezubringen und verständlich zu machen."
+text = "In December 1903 in France the Royal Swedish Academy of Sciences awarded Pierre Curie, Marie Curie, and Henri Becquerel the Nobel Prize in Physics."
 
 inputs = tokenizer(
     text, 
-    add_special_tokens=False, return_tensors="pt"
+    add_special_tokens=False, 
+    return_tensors="pt"
 )
 
 with torch.no_grad():