readme: ---
  language:
  - en
  - de
  - fr
  - it
  - multilingual
  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
  ---
  
  This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. 
  
  This multilanguage model was trained on the [Europarl Dataset](https://huggingface.co/datasets/wmt/europarl) provided by the [SEPP-NLG Shared Task](https://sites.google.com/view/sentence-segmentation). *Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.*
  
  The model restores the following punctuation markers: **"." "," "?" "-" ":"**
  ## Sample Code
  We provide a simple python package that allows you to process text of any length.
  
  ## Install 
  
  To get started install the package from [pypi](https://pypi.org/project/deepmultilingualpunctuation/):
  
  ```bash
  pip install deepmultilingualpunctuation
  ```
  ### Restore Punctuation
  ```python
  from deepmultilingualpunctuation import PunctuationModel
  
  model = PunctuationModel()
  text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
  result = model.restore_punctuation(text)
  print(result)
  ```
  
  **output**
  > My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller?
  
  
  ### Predict Labels 
  ```python
  from deepmultilingualpunctuation import PunctuationModel
  
  model = PunctuationModel()
  text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
  clean_text = model.preprocess(text)
  labled_words = model.predict(clean_text)
  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]]
  
  
  
  
  ## Results 
  
  The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either a comma or a full stop. The model achieves the following F1 scores for the different languages:
  
  | Label         | EN    | DE    | FR    | IT    |
  | ------------- | ----- | ----- | ----- | ----- |
  | 0             | 0.991 | 0.997 | 0.992 | 0.989 |
  | .             | 0.948 | 0.961 | 0.945 | 0.942 |
  | ?             | 0.890 | 0.893 | 0.871 | 0.832 |
  | ,             | 0.819 | 0.945 | 0.831 | 0.798 |
  | :             | 0.575 | 0.652 | 0.620 | 0.588 |
  | -             | 0.425 | 0.435 | 0.431 | 0.421 |
  | macro average | 0.775 | 0.814 | 0.782 | 0.762 |
  
  ## Languages
  
  ### Models
  
  | Languages                                  | Model                                                        |
  | ------------------------------------------ | ------------------------------------------------------------ |
  | English, Italian, French and German        | [oliverguhr/fullstop-punctuation-multilang-large](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large) |
  | English, Italian, French, German and Dutch | [oliverguhr/fullstop-punctuation-multilingual-sonar-base](https://huggingface.co/oliverguhr/fullstop-punctuation-multilingual-sonar-base) |
  | Dutch                                      | [oliverguhr/fullstop-dutch-sonar-punctuation-prediction](https://huggingface.co/oliverguhr/fullstop-dutch-sonar-punctuation-prediction) |
  
  ### Community Models
  
  | Languages                                  | Model                                                        |
  | ------------------------------------------ | ------------------------------------------------------------ |
  |English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian| [kredor/punctuate-all](https://huggingface.co/kredor/punctuate-all)                                                             |
  | Catalan                                    | [softcatala/fullstop-catalan-punctuation-prediction](https://huggingface.co/softcatala/fullstop-catalan-punctuation-prediction) |
  | Welsh | [techiaith/fullstop-welsh-punctuation-prediction](https://huggingface.co/techiaith/fullstop-welsh-punctuation-prediction) |
  
  You can use different models by setting the model parameter:
  
  ```python
  model = PunctuationModel(model = "oliverguhr/fullstop-dutch-punct