Add multilingual to the language tag

#1
by lbourdois - opened
Files changed (1) hide show
  1. README.md +19 -17
README.md CHANGED
@@ -5,26 +5,28 @@ language:
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  - fr
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  - it
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  - nl
 
 
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  tags:
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  - punctuation prediction
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  - punctuation
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- datasets:
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  - wmt/europarl
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  - SoNaR
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- license: mit
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- widget:
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- - text: "Ho sentito che ti sei laureata il che mi fa molto piacere"
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- example_title: "Italian"
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- - text: "Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre"
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- example_title: "French"
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- - text: "Ist das eine Frage Frau Müller"
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- example_title: "German"
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- - text: "My name is Clara and I live in Berkeley California"
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- example_title: "English"
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- - text: "hervatting van de zitting ik verklaar de zitting van het europees parlement die op vrijdag 17 december werd onderbroken te zijn hervat"
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- example_title: "Dutch"
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  metrics:
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  - f1
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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@@ -49,13 +51,13 @@ pip install deepmultilingualpunctuation
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  from deepmultilingualpunctuation import PunctuationModel
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  model = PunctuationModel(model="oliverguhr/fullstop-punctuation-multilingual-sonar-base")
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- text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
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  result = model.restore_punctuation(text)
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  print(result)
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  ```
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  **output**
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- > My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller?
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  ### Predict Labels
@@ -63,7 +65,7 @@ print(result)
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  from deepmultilingualpunctuation import PunctuationModel
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  model = PunctuationModel(model="oliverguhr/fullstop-punctuation-multilingual-sonar-base")
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- text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
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  clean_text = model.preprocess(text)
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  labled_words = model.predict(clean_text)
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  print(labled_words)
@@ -71,7 +73,7 @@ print(labled_words)
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  **output**
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- > [['My', '0', 0.99998856], ['name', '0', 0.9999708], ['is', '0', 0.99975926], ['Clara', '0', 0.6117834], ['and', '0', 0.9999014], ['I', '0', 0.9999808], ['live', '0', 0.9999666], ['in', '0', 0.99990165], ['Berkeley', ',', 0.9941764], ['California', '.', 0.9952892], ['Ist', '0', 0.9999577], ['das', '0', 0.9999678], ['eine', '0', 0.99998224], ['Frage', ',', 0.9952265], ['Frau', '0', 0.99995995], ['Müller', '?', 0.972517]]
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  - fr
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  - it
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  - nl
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+ - multilingual
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+ license: mit
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  tags:
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  - punctuation prediction
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  - punctuation
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+ datasets:
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  - wmt/europarl
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  - SoNaR
 
 
 
 
 
 
 
 
 
 
 
 
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  metrics:
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  - f1
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+ widget:
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+ - text: Ho sentito che ti sei laureata il che mi fa molto piacere
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+ example_title: Italian
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+ - text: Tous les matins vers quatre heures mon p�re ouvrait la porte de ma chambre
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+ example_title: French
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+ - text: Ist das eine Frage Frau M�ller
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+ example_title: German
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+ - text: My name is Clara and I live in Berkeley California
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+ example_title: English
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+ - text: hervatting van de zitting ik verklaar de zitting van het europees parlement
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+ die op vrijdag 17 december werd onderbroken te zijn hervat
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+ example_title: Dutch
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  ---
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  from deepmultilingualpunctuation import PunctuationModel
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  model = PunctuationModel(model="oliverguhr/fullstop-punctuation-multilingual-sonar-base")
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+ text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau M�ller"
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  result = model.restore_punctuation(text)
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  print(result)
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  ```
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  **output**
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+ > My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau M�ller?
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  ### Predict Labels
 
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  from deepmultilingualpunctuation import PunctuationModel
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  model = PunctuationModel(model="oliverguhr/fullstop-punctuation-multilingual-sonar-base")
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+ text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau M�ller"
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  clean_text = model.preprocess(text)
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  labled_words = model.predict(clean_text)
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  print(labled_words)
 
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  **output**
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+ > [['My', '0', 0.99998856], ['name', '0', 0.9999708], ['is', '0', 0.99975926], ['Clara', '0', 0.6117834], ['and', '0', 0.9999014], ['I', '0', 0.9999808], ['live', '0', 0.9999666], ['in', '0', 0.99990165], ['Berkeley', ',', 0.9941764], ['California', '.', 0.9952892], ['Ist', '0', 0.9999577], ['das', '0', 0.9999678], ['eine', '0', 0.99998224], ['Frage', ',', 0.9952265], ['Frau', '0', 0.99995995], ['M�ller', '?', 0.972517]]
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