Update README.md

README.md

@@ -42,7 +42,7 @@ It was trained on 15K Tweets that mentioned at least one of 699 brands. The Twee
 Because this is a multi-label classification problem, we use binary cross-entropy (BCE) with logits loss for the fine-tuning. We basically combine a sigmoid layer with BCELoss in a single class.
 To obtain the probabilities for each label (i.e., marketing mix variable), you need to "push" the predictions through a sigmoid function. This is already done in the accompanying python notebook.
 
-IMPORTANT: At the time of writing this description, Huggingface's pipeline did not support multi-label classifiers.
+***IMPORTANT*** At the time of writing this description, Huggingface's pipeline did not support multi-label classifiers.
 
 ### Working Paper
 Download the working paper from SSRN: ["Creating Synthetic Experts with Generative AI"](https://papers.ssrn.com/abstract_id=4542949)