Add multilingual to the language tag

README.md

@@ -4,22 +4,24 @@ language:
 - de
 - fr
 - it
+- multilingual
+license: mit
 tags:
 - punctuation prediction
 - punctuation
 datasets: wmt/europarl
-license: mit
-widget:
-- text: "Ho sentito che ti sei laureata il che mi fa molto piacere"
-  example_title: "Italian"
-- text: "Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre"
-  example_title: "French"
-- text: "Ist das eine Frage Frau Müller"
-  example_title: "German"
-- text: "Yet she blushed as if with guilt when Cynthia reading her thoughts said to her one day Molly you're very glad to get rid of us are not you"
-  example_title: "English"  
 metrics:
 - f1
+widget:
+- text: Ho sentito che ti sei laureata il che mi fa molto piacere
+  example_title: Italian
+- text: Tous les matins vers quatre heures mon p�re ouvrait la porte de ma chambre
+  example_title: French
+- text: Ist das eine Frage Frau M�ller
+  example_title: German
+- text: Yet she blushed as if with guilt when Cynthia reading her thoughts said to
+    her one day Molly you're very glad to get rid of us are not you
+  example_title: English
 ---
 
 This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. 
@@ -42,13 +44,13 @@ pip install deepmultilingualpunctuation
 from deepmultilingualpunctuation import PunctuationModel
 
 model = PunctuationModel()
-text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
+text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau M�ller"
 result = model.restore_punctuation(text)
 print(result)
 ```
 
 **output**
-> My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller?
+> My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau M�ller?
 
 
 ### Predict Labels 
@@ -56,7 +58,7 @@ print(result)
 from deepmultilingualpunctuation import PunctuationModel
 
 model = PunctuationModel()
-text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
+text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau M�ller"
 clean_text = model.preprocess(text)
 labled_words = model.predict(clean_text)
 print(labled_words)
@@ -64,7 +66,7 @@ print(labled_words)
 
 **output**
 
-> [['My', '0', 0.9999887], ['name', '0', 0.99998665], ['is', '0', 0.9998579], ['Clara', '0', 0.6752215], ['and', '0', 0.99990904], ['I', '0', 0.9999877], ['live', '0', 0.9999839], ['in', '0', 0.9999515], ['Berkeley', ',', 0.99800044], ['California', '.', 0.99534047], ['Ist', '0', 0.99998784], ['das', '0', 0.99999154], ['eine', '0', 0.9999918], ['Frage', ',', 0.99622655], ['Frau', '0', 0.9999889], ['Müller', '?', 0.99863917]]
+> [['My', '0', 0.9999887], ['name', '0', 0.99998665], ['is', '0', 0.9998579], ['Clara', '0', 0.6752215], ['and', '0', 0.99990904], ['I', '0', 0.9999877], ['live', '0', 0.9999839], ['in', '0', 0.9999515], ['Berkeley', ',', 0.99800044], ['California', '.', 0.99534047], ['Ist', '0', 0.99998784], ['das', '0', 0.99999154], ['eine', '0', 0.9999918], ['Frage', ',', 0.99622655], ['Frau', '0', 0.9999889], ['M�ller', '?', 0.99863917]]
 
 
 
@@ -112,7 +114,7 @@ model = PunctuationModel(model = "oliverguhr/fullstop-dutch-punctuation-predicti
 ```
 @article{guhr-EtAl:2021:fullstop,
   title={FullStop: Multilingual Deep Models for Punctuation Prediction},
-  author    = {Guhr, Oliver  and  Schumann, Anne-Kathrin  and  Bahrmann, Frank  and  Böhme, Hans Joachim},
+  author    = {Guhr, Oliver  and  Schumann, Anne-Kathrin  and  Bahrmann, Frank  and  B�hme, Hans Joachim},
   booktitle      = {Proceedings of the Swiss Text Analytics Conference 2021},
   month          = {June},
   year           = {2021},