Upload README.md

README.md

@@ -24,9 +24,9 @@ This model was trained on \~10k manually annotated political interpellations (
 
 **ParlBERT-Topic** was fine-tuned on a domain adapted model for topic modeling with interpellations dataset from the Comparative Agendas Project (mlm\_probability=.15). We used the HuggingFace trainer with the following hyperparameters.
 
-🤖 ** Use ** 
+🤖 **Use** 
 
-```
+```python
 from transformers import pipeline
 
 pipeline_classification_topics = pipeline("text-classification", model="chkla/parlbert-topics-german", tokenizer="bert-base-german-cased", return_all_scores=False, device=0)
@@ -70,7 +70,7 @@ The model was evaluated on an evaluation set (20%):
 
 The model can only be a starting point to dive into the exciting field of policy topic classification in political texts. But be aware. Models are often highly topic dependent. Therefore, the model may perform less well on different topics and text types not included in the training set.
 
-👥 ** Cite **
+👥 **Cite**
 ```
 @article{klamm2022frameast,
   title={FrameASt: A Framework for Second-level Agenda Setting in Parliamentary Debates through the Lense of Comparative Agenda Topics},