readme: ---
  language:
  - nl
  tags:
  - punctuation prediction
  - punctuation
  datasets: wmt/europarl
  license: mit
  widget:
  - text: "hervatting van de zitting ik verklaar de zitting van het europees parlement die op vrijdag 17 december werd onderbroken te zijn hervat"
    example_title: "Euro Parl"
  metrics:
  - f1
  ---
  
  This model predicts the punctuation of Dutch texts. We developed it to restore the punctuation of transcribed spoken language. 
  This model was trained on the [Europarl Dataset](https://huggingface.co/datasets/wmt/europarl).
  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(model="oliverguhr/fullstop-dutch-punctuation-prediction")
  text = "hervatting van de zitting ik verklaar de zitting van het europees parlement die op vrijdag 17 december werd onderbroken te zijn hervat"
  result = model.restore_punctuation(text)
  print(result)
  ```
  **output**
  > hervatting van de zitting ik verklaar de zitting van het europees parlement, die op vrijdag 17 december werd onderbroken, te zijn hervat.
  ### Predict Labels 
  ```python
  from deepmultilingualpunctuation import PunctuationModel
  
  model = PunctuationModel(model="oliverguhr/fullstop-dutch-punctuation-prediction")
  text = "hervatting van de zitting ik verklaar de zitting van het europees parlement die op vrijdag 17 december werd onderbroken te zijn hervat"
  clean_text = model.preprocess(text)
  labled_words = model.predict(clean_text)
  print(labled_words)
  ```
  **output**
  > [['hervatting', '0', 0.9999777], ['van', '0', 0.99998415], ['de', '0', 0.999987], ['zitting', '0', 0.9992779], ['ik', '0', 0.9999889], ['verklaar', '0', 0.99998295], ['de', '0', 0.99998856], ['zitting', '0', 0.9999895], ['van', '0', 0.9999902], ['het', '0', 0.999992], ['europees', '0', 0.9999924], ['parlement', ',', 0.9915131], ['die', '0', 0.99997807], ['op', '0', 0.9999882], ['vrijdag', '0', 0.9999746], ['17', '0', 0.99998784], ['december', '0', 0.99997866], ['werd', '0', 0.9999888], ['onderbroken', ',', 0.99287957], ['te', '0', 0.9999864], ['zijn', '0', 0.99998176], ['hervat', '.', 0.99762934]]
  
  ## 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:
  
  | Label         | Dutch    |
  | ------------- | -------- |
  | 0             | 0.993588 |
  | .             | 0.961450 |
  | ?             | 0.848506 |
  | ,             | 0.810883 |
  | :             | 0.655212 |
  | -             | 0.461591 |
  | macro average | 0.788538 |
  | micro average | 0.983492 |
  
  ## How to cite us
  
  ```
  @misc{https://doi.org/10.48550/arxiv.2301.03319,
    doi = {10.48550/ARXIV.2301.03319},
    url = {https://arxiv.org/abs/2301.03319},
    author = {Vandeghinste, Vincent and Guhr, Oliver},
    keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},
    title = {FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers},
    publisher = {arXiv},
    year = {2023},  
    copyright = {Creative Commons Attribution Share Alike 4.0 International}
  }
  
  ```