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 provided by the SEPP-NLG Shared Task. 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:

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
English, Italian, French, German and Dutch	oliverguhr/fullstop-punctuation-multilingual-sonar-base
Dutch	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
Catalan	softcatala/fullstop-catalan-punctuation-prediction
Welsh	techiaith/fullstop-welsh-punctuation-prediction

You can use different models by setting the model parameter:

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

Where do I find the code and can I train my own model?

Yes you can! For complete code of the reareach project take a look at this repository.

There is also an guide on how to fine tune this model for you data / language.

References

@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}
}

oliverguhr/fullstop-punctuation-multilang-large