README.md

---
language:
- en
- de
- fr
- it
- nl
- multilingual
tags:
- punctuation prediction
- punctuation
datasets: 
- wmt/europarl
- SoNaR
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: "My name is Clara and I live in Berkeley California"
  example_title: "English"  
- 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"
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) and for the Dutch language we included the [SoNaR Dataset](http://hdl.handle.net/10032/tm-a2-h5). *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(model="oliverguhr/fullstop-punctuation-multilingual-sonar-base")
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(model="oliverguhr/fullstop-punctuation-multilingual-sonar-base")
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', 0.99998856], ['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]]



## 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         | English  | German | French|Italian| Dutch |
| ------------- | -------- | ------ | ----- | ----- | ----- |
| 0             | 0.990    |  0.996 | 0.991 | 0.988 | 0.994 |
| .             | 0.924    |  0.951 | 0.921 | 0.917 | 0.959 |
| ?             | 0.825    |  0.829 | 0.800 | 0.736 | 0.817 |
| ,             | 0.798    |  0.937 | 0.811 | 0.778 | 0.813 |
| :             | 0.535    |  0.608 | 0.578 | 0.544 | 0.657 |
| -             | 0.345    |  0.384 | 0.353 | 0.344 | 0.464 |
| macro average | 0.736    |  0.784 | 0.742 | 0.718 | 0.784 |
| micro average | 0.975    |  0.987 | 0.977 | 0.972 | 0.983 |

## 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) |

You can use different models by setting the model parameter:

```python
model = PunctuationModel(model = "oliverguhr/fullstop-dutch-punctuation-prediction")
```


## How to cite us

```
@article{guhr-EtAl:2021:fullstop,
  title={FullStop: Multilingual Deep Models for Punctuation Prediction},
  author    = {Guhr, Oliver  and  Schumann, Anne-Kathrin  and  Bahrmann, Frank  and  Böhme, Hans Joachim},
  booktitle      = {Proceedings of the Swiss Text Analytics Conference 2021},
  month          = {June},
  year           = {2021},
  address        = {Winterthur, Switzerland},
  publisher      = {CEUR Workshop Proceedings},  
  url       = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf}
}

```

```
@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}
}

```