creat app_test.py

app_test.py

@@ -0,0 +1,52 @@
+from transformers import pipeline
+
+# Load pipelines
+pipe_bert = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+pipe_roberta = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment")
+
+# Label mapping for RoBERTa
+roberta_label_mapping_dict = {
+    'LABEL_2': 'Positive',
+    'LABEL_1': 'Neutral',
+    'LABEL_0': 'Negative'
+}
+
+# Sample input - top 100 review
+top_n = 10 
+reviews = train_data['review'][:top_n]
+sentiments = train_data['sentiment'][:top_n]
+data_to_test = dict(zip(reviews, sentiments))
+
+# Print header
+print(f"{'Original':<10} | {'DistilBERT':<10} | {'RoBERTa':<10}")
+
+# Track accuracy
+bert_correct = 0
+roberta_correct = 0
+total = len(data_to_test)
+
+for text, true_label in data_to_test.items():
+    pred_bert = pipe_bert(text)[0]
+    pred_roberta = pipe_roberta(text, truncation=True)[0]
+
+    # Normalize labels
+    original = true_label.strip().capitalize()
+    bert = pred_bert["label"].capitalize()
+    roberta = roberta_label_mapping_dict.get(pred_roberta["label"], "Unknown")
+
+    # Accuracy check
+    if bert == original:
+        bert_correct += 1
+    if roberta == original:
+        roberta_correct += 1
+
+    # Print results
+    print(f"{original:<10} | {bert:<10} | {roberta:<10}")
+
+# Calculate and print accuracy
+bert_acc = (bert_correct / total) * 100
+roberta_acc = (roberta_correct / total) * 100
+
+print(f"\nAccuracy:")
+print(f"DistilBERT: {bert_acc:.2f}%")
+print(f"RoBERTa   : {roberta_acc:.2f}%")