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--- |
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library_name: transformers |
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tags: |
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- bert |
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- berturk |
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language: |
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- tr |
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pipeline_tag: text-classification |
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--- |
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# Model Card for Model ID |
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Turkish news classifier. |
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### Model Description |
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11 classes are present: |
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'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 |
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The model is a finetuned bert-base-multilingual-uncased model. |
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The model is not originally a classifier model, so classifier weights were trained completely using the turkish dataset. 🤗 |
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Eval loss: train_loss': 0.8327703781731708 |
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Train loss:0.8896290063858032 |
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Eval train split: 0.2/0.8 |
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- **Developed by:** [Ezel Bayraktar] |
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- **Model type:** [Classifier] |
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- **Language(s) (NLP):** [Turkish] |
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- **License:** [MIT License] |
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- **Finetuned from model [optional]:** [bert-base-multilingual-uncased ] |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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model_name = "TerminatorPower/bert-news-classif-turkish" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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model.eval() |
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reverse_label_mapping = { |
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0: "label_0", |
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1: "label_1", |
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2: "label_2", |
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3: "label_3", |
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4: "label_4", |
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5: "label_5", |
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6: "label_6", |
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7: "label_7", |
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8: "label_8", |
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9: "label_9", |
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10: "label_10", |
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11: "label_11", |
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12: "siyaset" # Example: Map index 12 back to "siyaset" |
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} |
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def predict(text): |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512) |
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inputs = {key: value.to("cuda" if torch.cuda.is_available() else "cpu") for key, value in inputs.items()} |
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model.to(inputs["input_ids"].device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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predictions = torch.argmax(outputs.logits, dim=1) |
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predicted_label = reverse_label_mapping[predictions.item()] |
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return predicted_label |
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if __name__ == "__main__": |
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text = "Some example news text" |
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print(f"Predicted label: {predict(text)}") |
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## Training Details |
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I used rtx 3060 12gb card to tain the training took 245 minutes in total |
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learning_rate=5e-5, |
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per_device_train_batch_size=20, |
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per_device_eval_batch_size=20, |
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num_train_epochs=7, |
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### Training Data |
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I used the kemik 42bin haber data set which you can access from this link |
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http://www.kemik.yildiz.edu.tr/veri_kumelerimiz.html |
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## Model Card Contact |
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[email protected] |
