Update README.md

README.md

@@ -37,6 +37,22 @@ The model will predict the following four entities:
 | `MISC` | Miscellaneous | A named entity of a different kind (e.g., *British Pound* or *Mona Lisa*) |
 
 
+## Use
+
+You can use this model in your scripts as follows:
+
+```python
+>>> from transformers import pipeline
+>>> import pandas as pd
+>>> ner = pipeline(task='ner', model='saattrupdan/nbailab-base-ner-scandi', aggregation_strategy='first')
+>>> result = ner('Borghild kjøper seg inn i Bunnpris')
+>>> pd.DataFrame.from_records(result)
+  entity_group     score      word  start  end
+0          PER  0.981257  Borghild      0    8
+1          ORG  0.974099  Bunnpris     26   34
+```
+
+
 ## Performance
 
 The following is the Micro-F1 NER performance on Scandinavian NER test datasets, compared with the current state-of-the-art. The models have been evaluated on the test set along with 9 bootstrapped versions of it, with the mean and 95% confidence interval shown here: