Create predict.py

predict.py

@@ -0,0 +1,39 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the model and tokenizer
+model_name = "TerminatorPower/bert-news-classif-turkish"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+model.eval()
+
+# Load the reverse label mapping
+reverse_label_mapping = {
+    0: "label_0",
+    1: "label_1",
+    2: "label_2",
+    3: "label_3",
+    4: "label_4",
+    5: "label_5",
+    6: "label_6",
+    7: "label_7",
+    8: "label_8",
+    9: "label_9",
+    10: "label_10",
+    11: "label_11",
+    12: "siyaset"  # Example: Map index 12 back to "siyaset"
+}
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
+    inputs = {key: value.to("cuda" if torch.cuda.is_available() else "cpu") for key, value in inputs.items()}
+    model.to(inputs["input_ids"].device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=1)
+    predicted_label = reverse_label_mapping[predictions.item()]
+    return predicted_label
+
+if __name__ == "__main__":
+    text = input()
+    print(f"Predicted label: {predict(text)}")