---
language:
- nl
tags:
- punctuation prediction
- punctuation
datasets: sonar
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: "Dutch Sample"
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 [SoNaR Dataset](http://hdl.handle.net/10032/tm-a2-h5).

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-sonar-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()
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', 0.99998724], ['van', '0', 0.9999784], ['de', '0', 0.99991274], ['zitting', '.', 0.6771242], ['ik', '0', 0.9999466], ['verklaar', '0', 0.9998566], ['de', '0', 0.9999783], ['zitting', '0', 0.9999809], ['van', '0', 0.99996245], ['het', '0', 0.99997795], ['europees', '0', 0.9999783], ['parlement', ',', 0.9908242], ['die', '0', 0.999985], ['op', '0', 0.99998224], ['vrijdag', '0', 0.9999831], ['17', '0', 0.99997985], ['december', '0', 0.9999827], ['werd', '0', 0.999982], ['onderbroken', ',', 0.9951485], ['te', '0', 0.9999677], ['zijn', '0', 0.99997723], ['hervat', '.', 0.9957053]]




## 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         | F1 Score |
| ------------- | -------- |
| 0             | 0.985816 |
| .             | 0.854380 |
| ?             | 0.684060 |
| ,             | 0.719308 |
| :             | 0.696088 |
| -             | 0.722000 |
| macro average | 0.776942 |
| micro average | 0.963427 |

## References

TBD