Corrected examples.

README.md

@@ -12,7 +12,7 @@ widget:
 
 Ternary text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the BCS Political Sentiment dataset (sentence-level data).
 
-This classifier classifies text into only three categories: Negative, Neutral, and Positive. For the binary classifier (Negative, Other) check [this model](https://huggingface.co/classla/bcms-bertic-parlasent-bcs-bi).
+This classifier classifies text into only three categories: Negative, Neutral, and Positive. For the binary classifier (Negative, Other) check [this model](https://huggingface.co/classla/bcms-bertic-parlasent-bcs-bi ).
 
 For details on the dataset and the finetuning procedure, please see [this paper](https://arxiv.org/abs/2206.00929).
 
@@ -49,17 +49,17 @@ from simpletransformers.classification import ClassificationModel
 model = ClassificationModel("electra", "classla/bcms-bertic-parlasent-bcs-ter")
 
 predictions, logits = model.predict([
-     "Đački autobusi moraju da voze svaki dan", 
-     "Vi niste normalni",
-     "Da bog da ti saksida padne na glavu",
+    "Vi niste normalni",
+    "Đački autobusi moraju da voze svaki dan",
+    "Ovo je najbolji zakon na svetu",
      ]
     )
 
 predictions
-# Output: array([1, 0, 0])
+# Output: array([0, 1, 2])
 
 [model.config.id2label[i] for i in predictions]
-# Output: ['Other', 'Negative', 'Negative']
+# Output: ['Negative', 'Neutral', 'Positive']
 ```
 
 ## Citation