Update README.md

README.md

@@ -28,52 +28,7 @@ Eval train split: 0.2/0.8
 - **Model type:** [Classifier]
 - **Language(s) (NLP):** [Turkish]
 - **License:** [MIT License]
-- **Finetuned from model [optional]:** [bert-base-multilingual-uncased ]
-
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-model_name = "TerminatorPower/bert-news-classif-turkish"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-model.eval()
-
-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 = "Some example news text"
-    print(f"Predicted label: {predict(text)}")
-
-
+- **Finetuned from model [optional]:** [bert-base-multilingual-uncased]
 ## Training Details
 I used rtx 3060 12gb card to tain the training took 245 minutes in total