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--- |
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license: cc0-1.0 |
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datasets: |
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- kairaamilanii/cyberbullying-indonesia |
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language: |
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- id |
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metrics: |
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- accuracy |
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- confusion_matrix |
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base_model: |
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- indolem/indobertweet-base-uncased |
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pipeline_tag: text-classification |
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--- |
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This model is based on a BERT model trained with a few bullying detection datasets. It is trained exclusively in the Indonesian language. |
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```python |
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from transformers import BertTokenizer, AutoModelForSequenceClassification |
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model_path = 'kairaamilanii/IndoBERT-Bullying-Classifier' |
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tokenizer = BertTokenizer.from_pretrained(model_path) |
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model = AutoModelForSequenceClassification.from_pretrained(model_path) |
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text = "KOK JELEK BANGET SIH" # Example text for prediction |
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inputs = tokenizer(text, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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predicted_class = torch.argmax(outputs.logits, dim=-1).item() |
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print(f"Predicted class: {predicted_class}") |
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if predicted_class == 1: |
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print("Prediction: Bullying") |
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else: |
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print("Prediction: Non-bullying") |
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``` |
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example output: |
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```python |
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[{'Predicted class': 1, 'Prediction': Bullying}] |
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``` |