updated readme

README.md

@@ -8,7 +8,9 @@ language:
 tags:
 - punctuation prediction
 - punctuation
-datasets: wmt/europarl
+datasets: 
+- wmt/europarl
+- SoNaR
 license: mit
 widget:
 - text: "Ho sentito che ti sei laureata il che mi fa molto piacere"
@@ -25,4 +27,67 @@ metrics:
 - f1
 ---
 
-# Work in progress
+
+
+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 |
+
+