Update README.md

README.md

@@ -21,6 +21,24 @@ The model achieves SOTA on a test set consisting of 600 Facebook comments annota
 | [`DaNLP/da-electra-hatespeech-detection`](https://huggingface.co/DaNLP/da-electra-hatespeech-detection) | 86.43% | 56.28% | 68.17% | 60.50% |
 | [`Guscode/DKbert-hatespeech-detection`](https://huggingface.co/Guscode/DKbert-hatespeech-detection) | 75.41% | 42.79% | 54.60% | 46.84% |
 
+## Using the model
+
+You can use the model simply by running the following:
+
+```python
+>>> from transformers import pipeline
+>>> offensive_text_pipeline = pipeline(model="xlm-roberta-base-offensive-text-detection-da")
+>>> offensive_text_pipeline("Din store idiot")
+[{'label': 'offensive', 'score': 0.9874388575553894}]
+```
+
+Processing multiple documents at the same time can be done as follows:
+
+```python
+>>> offensive_text_pipeline(["Din store idiot", "ej hvor godt :)"])
+[{'label': 'offensive', 'score': 0.9874388575553894}, {'label': 'not offensive', 'score': 0.999760091304779}]
+```
+
 ## Training procedure
 
 ### Training hyperparameters