Update README.md

README.md

@@ -59,25 +59,3 @@ result = classifier(text)
 # Output the result
 print(result)
 # Example Output: [{'label': 'LABEL_2', 'score': 0.9976001381874084}]
-
-#### OR
-
-from transformers import GPT2Tokenizer, GPT2ForSequenceClassification
-import torch
-
-# Load the model and tokenizer
-tokenizer = GPT2Tokenizer.from_pretrained("riturajpandey739/gpt2-sentiment-analysis-tweets")
-model = GPT2ForSequenceClassification.from_pretrained("riturajpandey739/gpt2-sentiment-analysis-tweets")
-
-# Tokenize the input text
-input_text = "This is a fantastic product! I highly recommend it."
-inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
-
-# Get model predictions
-with torch.no_grad():
-    logits = model(**inputs).logits
-
-# Get the predicted class (0, 1, 2 for Negative, Neutral, Positive)
-predicted_class = torch.argmax(logits, dim=-1).item()
-
-print(f"Predicted Label: {predicted_class}")