Update README.md

README.md

@@ -22,6 +22,20 @@ The model was fine-tuned on 5,304 manually annotated social media posts.
 The hold-out accuracy is 86.1%. 
 For details on the training approach see Web Appendix F in Hartmann et al. (2021). 
 
+# Application
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification", model="j-hartmann/sentiment-roberta-large-english-3-classes", return_all_scores=True)
+classifier("This is so nice!")
+```
+
+```python
+Output:
+[[{'label': 'negative', 'score': 0.00016451838018838316},
+  {'label': 'neutral', 'score': 0.000174045650055632},
+  {'label': 'positive', 'score': 0.9996614456176758}]]
+```
+
 # Reference
 Please cite [this paper](https://journals.sagepub.com/doi/full/10.1177/00222437211037258) when you use our model. Feel free to reach out to [j.p.hartmann@rug.nl](mailto:j.p.hartmann@rug.nl) with any questions or feedback you may have.
 ```