Update README.md

README.md

@@ -22,6 +22,20 @@ The model was fine-tuned on 2,000 manually annotated social media posts.
 The hold-out accuracy is 95% (vs. a balanced 50% random-chance baseline). 
 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/purchase-intention-english-roberta-large", return_all_scores=True)
+classifier("I want this!")
+```
+
+```python
+Output:
+[[{'label': 'no', 'score': 0.0014553926885128021},
+  {'label': 'yes', 'score': 0.9985445737838745}]]
+```
+
 # 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.
 ```