Update README.md

README.md

@@ -39,6 +39,14 @@ model_name = "codewithdark/bert-Gomotions"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
+# Emotion labels (adjust based on your dataset)
+emotion_labels = [
+    "Admiration", "Amusement", "Anger", "Annoyance", "Approval", "Caring", "Confusion",
+    "Curiosity", "Desire", "Disappointment", "Disapproval", "Disgust", "Embarrassment",
+    "Excitement", "Fear", "Gratitude", "Grief", "Joy", "Love", "Nervousness", "Optimism",
+    "Pride", "Realization", "Relief", "Remorse", "Sadness", "Surprise", "Neutral"
+]
+
 # Example text
 text = "I'm so happy today!"
 inputs = tokenizer(text, return_tensors="pt")
@@ -46,9 +54,27 @@ inputs = tokenizer(text, return_tensors="pt")
 # Predict
 with torch.no_grad():
     outputs = model(**inputs)
-    probs = torch.sigmoid(outputs.logits)
+    probs = torch.sigmoid(outputs.logits).squeeze(0)  # Convert logits to probabilities
+
+# Get top 5 predictions
+top5_indices = torch.argsort(probs, descending=True)[:5]  # Get indices of top 5 labels
+top5_labels = [emotion_labels[i] for i in top5_indices]
+top5_probs = [probs[i].item() for i in top5_indices]
+
+# Print results
+print("Top 5 Predicted Emotions:")
+for label, prob in zip(top5_labels, top5_probs):
+    print(f"{label}: {prob:.4f}")
 
-print(probs)  # Multi-label predictions
+'''
+output:
+Top 5 Predicted Emotions:
+Joy: 0.9478
+Love: 0.7854
+Optimism: 0.6342
+Admiration: 0.5678
+Excitement: 0.5231
+'''
 ```
 
 ## 🏋️‍♂️ Training Details
@@ -64,7 +90,7 @@ print(probs)  # Multi-label predictions
 ```python
 from transformers import pipeline
 
-classifier = pipeline("text-classification", model="your-username/bert-go-emotions", top_k=None)
+classifier = pipeline("text-classification", model="codewithdark/bert-Gomotions", top_k=None)
 classifier("I'm so excited about the trip!")
 ```