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--- |
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language: |
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- en |
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- de |
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- fr |
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- it |
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- nl |
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- multilingual |
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tags: |
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- punctuation prediction |
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- punctuation |
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datasets: |
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- wmt/europarl |
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- SoNaR |
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license: mit |
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widget: |
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- text: "Ho sentito che ti sei laureata il che mi fa molto piacere" |
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example_title: "Italian" |
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- text: "Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre" |
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example_title: "French" |
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- text: "Ist das eine Frage Frau Müller" |
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example_title: "German" |
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- text: "My name is Clara and I live in Berkeley California" |
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example_title: "English" |
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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: "Dutch" |
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metrics: |
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- f1 |
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--- |
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This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. |
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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.* |
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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-punctuation-multilingual-sonar-base") |
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text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller" |
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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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> My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller? |
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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-punctuation-multilingual-sonar-base") |
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text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller" |
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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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> [['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]] |
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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 for the different languages: |
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| Label | English | German | French|Italian| Dutch | |
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| ------------- | -------- | ------ | ----- | ----- | ----- | |
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| 0 | 0.990 | 0.996 | 0.991 | 0.988 | 0.994 | |
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| . | 0.924 | 0.951 | 0.921 | 0.917 | 0.959 | |
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| ? | 0.825 | 0.829 | 0.800 | 0.736 | 0.817 | |
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| , | 0.798 | 0.937 | 0.811 | 0.778 | 0.813 | |
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| : | 0.535 | 0.608 | 0.578 | 0.544 | 0.657 | |
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| - | 0.345 | 0.384 | 0.353 | 0.344 | 0.464 | |
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| macro average | 0.736 | 0.784 | 0.742 | 0.718 | 0.784 | |
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| micro average | 0.975 | 0.987 | 0.977 | 0.972 | 0.983 | |
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## Languages |
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### Models |
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| Languages | Model | |
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| ------------------------------------------ | ------------------------------------------------------------ | |
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| English, Italian, French and German | [oliverguhr/fullstop-punctuation-multilang-large](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large) | |
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| English, Italian, French, German and Dutch | [oliverguhr/fullstop-punctuation-multilingual-sonar-base](https://huggingface.co/oliverguhr/fullstop-punctuation-multilingual-sonar-base) | |
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| Dutch | [oliverguhr/fullstop-dutch-sonar-punctuation-prediction](https://huggingface.co/oliverguhr/fullstop-dutch-sonar-punctuation-prediction) | |
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### Community Models |
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| Languages | Model | |
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| ------------------------------------------ | ------------------------------------------------------------ | |
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|English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian| [kredor/punctuate-all](https://huggingface.co/kredor/punctuate-all) | |
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| Catalan | [softcatala/fullstop-catalan-punctuation-prediction](https://huggingface.co/softcatala/fullstop-catalan-punctuation-prediction) | |
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You can use different models by setting the model parameter: |
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```python |
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model = PunctuationModel(model = "oliverguhr/fullstop-dutch-punctuation-prediction") |
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``` |
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## How to cite us |
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``` |
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@article{guhr-EtAl:2021:fullstop, |
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title={FullStop: Multilingual Deep Models for Punctuation Prediction}, |
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author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim}, |
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booktitle = {Proceedings of the Swiss Text Analytics Conference 2021}, |
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month = {June}, |
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year = {2021}, |
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address = {Winterthur, Switzerland}, |
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publisher = {CEUR Workshop Proceedings}, |
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url = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf} |
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} |
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``` |
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``` |
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@misc{https://doi.org/10.48550/arxiv.2301.03319, |
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doi = {10.48550/ARXIV.2301.03319}, |
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url = {https://arxiv.org/abs/2301.03319}, |
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author = {Vandeghinste, Vincent and Guhr, Oliver}, |
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keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7}, |
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title = {FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers}, |
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publisher = {arXiv}, |
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year = {2023}, |
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copyright = {Creative Commons Attribution Share Alike 4.0 International} |
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} |
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``` |
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