up model card examples

README.md

@@ -56,18 +56,16 @@ from transformers import pipeline
 model_path = "eevvgg/StanBERT"
 cls_task = pipeline(task = "text-classification", model = model_path, tokenizer = model_path)#, device=0 
 
-sequence = ['his rambling has no clear ideas behind it', 
-            'That has nothing to do with medical care',
-            "Turns around and shows how qualified she is because of her political career.",
-            'She has very little to gain by speaking too much']
+sequence = ["user The fact is that she still doesn’t change her ways and still stays non environmental friendly"
+            "user The criteria for these awards dont seem to be very high."]
             
 result = cls_task(sequence)
                                         
 ```
 
 Sentiment classification in multilingual data. Fine-tuned on a balanced corpus of size 8,4k, partially semi-annotated. 
-Model suited for classification of stance in short text.
-
+Model suited for classification of stance in short text. 
+*Suitable for fine-tuning on hate/offensive language detection.
 
 ## Model Sources