added new readme

README.md

@@ -1,47 +1,64 @@
----
-language:
-- nl
-tags:
-- punctuation prediction
-- punctuation
-datasets: wmt/europarl
-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: "Euro Parl"
-metrics:
-- f1
----
-
-## Performance
-
-```
-               precision recall    f1-score   support
-
-           0   0.992584  0.994595  0.993588   9627605
-           .   0.960450  0.962452  0.961450    433554
-           ,   0.816974  0.804882  0.810883    379759
-           ?   0.871368  0.826812  0.848506     13494
-           -   0.619905  0.367690  0.461591     27341
-           :   0.718636  0.602076  0.655212     18305
-
-    accuracy                       0.983874  10500058
-   macro avg   0.829986  0.759751  0.788538  10500058
-weighted avg   0.983302  0.983874  0.983492  10500058
-
-```
-
-Usage:
-
-```bash
-pip install deepmultilingualpunctuation
-```
-
-```python
-from deepmultilingualpunctuation import PunctuationModel
-
-model = PunctuationModel(model="oliverguhr/fullstop-dutch-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)
-```
+---
+language:
+- nl
+tags:
+- punctuation prediction
+- punctuation
+datasets: wmt/europarl
+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: "Euro Parl"
+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 [Europarl Dataset](https://huggingface.co/datasets/wmt/europarl).
+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-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(model="oliverguhr/fullstop-dutch-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"
+clean_text = model.preprocess(text)
+labled_words = model.predict(clean_text)
+print(labled_words)
+```
+**output**
+> [['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]]
+
+## 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         | Dutch    |
+| ------------- | -------- |
+| 0             | 0.993588 |
+| .             | 0.961450 |
+| ?             | 0.848506 |
+| ,             | 0.810883 |
+| :             | 0.655212 |
+| -             | 0.461591 |
+| macro average | 0.788538 |
+| micro average | 0.983492 |
+
+## References
+TBD