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README.md

@@ -44,16 +44,75 @@ The base model is **`FacebookAI/roberta-base`**, and the LoRA adapters have been
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained("mishrabp/roberta-sentiment-analysis-merged")
-tokenizer = AutoTokenizer.from_pretrained("mishrabp/roberta-sentiment-analysis-merged")
-
-text = "I really loved the new Batman movie!"
-inputs = tokenizer(text, return_tensors="pt")
-outputs = model(**inputs)
-label_id = torch.argmax(outputs.logits).item()
-
+# -----------------------------
+# Load merged model and tokenizer
+# -----------------------------
+model_name = "mishrabp/roberta-sentiment-analysis-merged"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Make sure model is on the correct device and in evaluation mode
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# -----------------------------
+# Label mapping (same as training)
+# -----------------------------
 id2label = {0: "NEGATIVE", 1: "POSITIVE"}
-print(id2label[label_id])
+
+# -----------------------------
+# Texts for validation
+# -----------------------------
+text_list = [
+    # Positive
+    "I loved the new Batman movie!",
+    "What an amazing experience!",
+    "The service was surprisingly good, even though the restaurant was packed.",
+    "Absolutely fantastic performance, though a bit too long for my taste.",
+    "The concert had incredible energy, yet the sound quality was pleasing.",
+    "The dessert was delightful.",
+    "I loved the artwork.",
+    "The new phone works well.",
+    "The flight was smooth.",
+    "I was thrilled by the surprise party.",
+    
+    # Negative
+    "The food at that restaurant was terrible.",
+    "I will never go back to that place again.",
+    "I was disappointed that my favorite dish was sold out.",
+    "The book was thrilling at first, but the ending left me mindblowing.",  # could be positive/negative, marking negative
+    "The hotel room looked nothing like the photos online, but the staff were friendly.",  # neutral → marking negative
+    "The movie had stunning visuals, but the plot was overly predictable.",
+    "The customer support solved my issue quickly, though I had to wait on hold for a long time.",
+    "I appreciated the thoughtful gift, but the packaging was damaged upon delivery."
+]
+
+# -----------------------------
+# Inference
+# -----------------------------
+# Tokenize all texts as a batch (avoids inconsistencies and is faster)
+inputs = tokenizer(
+    text_list,
+    return_tensors="pt",
+    truncation=True,
+    padding=True,
+    max_length=512
+)
+
+# Move all inputs to device
+inputs = {k: v.to(device) for k, v in inputs.items()}
+
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+
+# Print results
+for text, pred in zip(text_list, predictions):
+    print(f"Text: {text}")
+    print(f"Predicted Sentiment: {id2label[pred.item()]}")
+    print("-" * 50)
 ```
 
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