Update README.md

README.md

@@ -23,6 +23,10 @@ This is a finetuned roberta-base model aimed at identifying the strength of emot
 Embeddings for comments can be extracted for downstream analyses
 
 ## Bias, Risks, and Limitations
+Risks: If you are truly unsure of a paragraph/comment's sentiment, seek the advice of humans. This model shows some bias toward more widely represented classes... 
+
+Caring is a somewhat confusing category. During training, comments that were annotated as "caring" if they included sympathetic content or indignace on behalf of others. This emotional category will need to be further separated into different categories such as "indignance" and "caring"
+
 Sarcasm is treated as the combination of "amusement" and "disapproval" amusement can apply to irony and humorous tone, but largely applies to sarcasm... adding a specific class for sarcasm is a much needed improvement that will be pursued later down the line
 
 not many risks... just MANY limitations. The training dataset was initially imbalanced, this was remedied with data augmentation and a weighted loss function... nontheless it struggles with sarcasm and sometimes unpredictable predictions because of dominating classes.