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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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|
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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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## How to cite us |
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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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``` |
