Update README.md

README.md

@@ -63,7 +63,7 @@ class SentimentClassifier:
         self.model = RobertaForSequenceClassification.from_pretrained(model, num_labels=3)
         self.tokenizer = RobertaTokenizerFast.from_pretrained(tokenizer, max_length=256)
 
-    def tokenize_text_bpe(self, text):
+    def tokenize_text(self, text):
         encoded_text = self.tokenizer.encode_plus(
             text.lower(), 
             max_length=256, 
@@ -73,7 +73,7 @@ class SentimentClassifier:
         )
         return encoded_text
 
-    def classify_text_with_bpe(self, encoded_text):
+    def classify_text(self, encoded_text):
         with torch.no_grad():
             output = self.model(**encoded_text)
             logits = output.logits
@@ -91,10 +91,10 @@ text_to_classify = "Kábel dodaný k SSD je krátky a veľmi zle sa ohýba, ten
 print("Text to classify: " + text_to_classify + "\n")
 
 # Tokenize the input text
-encoded_text_bpe = classifier.tokenize_text_bpe(text_to_classify)
+encoded_text = classifier.tokenize_text(text_to_classify)
 
 # Classify the sentiment of the tokenized text
-predicted_class, predicted_class_text, logits = classifier.classify_text_with_bpe(encoded_text_bpe)
+predicted_class, predicted_class_text, logits = classifier.classify_text(encoded_text)
 
 # Print the predicted class label and index
 print(f"Predicted class: {predicted_class_text} ({predicted_class})")