TerminatorPower
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bert-news-classif-turkish

@@ -1,199 +1,93 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
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 ---
 library_name: transformers
+tags:
+- bert
+- berturk
+language:
+- tr
+pipeline_tag: text-classification
 ---
 # Model Card for Model ID
+Turkish news classifier.
 ### Model Description
+11 classes are present:
+'turkiye': 0, 'ekonomi': 1, 'dunya': 2, 'spor': 3, 'magazin': 4, 'guncel': 5, 'genel': 6, 'siyaset': 7, 'saglik': 8, 'kultur-sanat': 9, 'teknoloji': 10, 'yasam': 11
+The model is a finetuned bert-base-multilingual-uncased model.
+The model is not originally a classifier model, so classifier weights were trained completely using the turkish dataset. 🤗
+Eval loss: train_loss': 0.8327703781731708
+Train loss:0.8896290063858032
+Eval train split: 0.2/0.8
+- **Developed by:** [Ezel Bayraktar]
+- **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)}")
+## Training Details
+I used rtx 3060 12gb card to tain the training took 245 minutes in total
+learning_rate=5e-5,
+per_device_train_batch_size=20,
+per_device_eval_batch_size=20,
+num_train_epochs=7,
+### Training Data
+I used the kemik 42bin haber data set which you can access from this link
+http://www.kemik.yildiz.edu.tr/veri_kumelerimiz.html
 ## Model Card Contact
+[email protected]