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README.md
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language:
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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: wmt/europarl
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license: mit
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widget:
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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: "Euro Parl"
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metrics:
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- f1
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---
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language:
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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: wmt/europarl
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license: mit
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widget:
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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: "Euro Parl"
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metrics:
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- f1
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---
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This model predicts the punctuation of Dutch texts. We developed it to restore the punctuation of transcribed spoken language.
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This model was trained on the [Europarl Dataset](https://huggingface.co/datasets/wmt/europarl).
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The model restores the following punctuation markers: **"." "," "?" "-" ":"**
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## Sample Code
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We provide a simple python package that allows you to process text of any length.
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## Install
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To get started install the package from [pypi](https://pypi.org/project/deepmultilingualpunctuation/):
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```bash
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pip install deepmultilingualpunctuation
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```
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### Restore Punctuation
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```python
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from deepmultilingualpunctuation import PunctuationModel
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model = PunctuationModel(model="oliverguhr/fullstop-dutch-punctuation-prediction")
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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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result = model.restore_punctuation(text)
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print(result)
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```
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**output**
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> 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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### Predict Labels
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```python
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from deepmultilingualpunctuation import PunctuationModel
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model = PunctuationModel(model="oliverguhr/fullstop-dutch-punctuation-prediction")
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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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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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```
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**output**
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> [['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]]
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## Results
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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:
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| Label | Dutch |
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| ------------- | -------- |
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| 0 | 0.993588 |
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| . | 0.961450 |
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| ? | 0.848506 |
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| , | 0.810883 |
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| : | 0.655212 |
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| - | 0.461591 |
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| macro average | 0.788538 |
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| micro average | 0.983492 |
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## References
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TBD
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