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Upload Arabic BERT hate speech detection model

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  1. README.md +94 -0
  2. config.json +25 -0
  3. model.safetensors +3 -0
  4. special_tokens_map.json +37 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +339 -0
  7. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ language: ar
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+ license: mit
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+ tags:
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+ - arabic
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+ - hate-speech-detection
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+ - bert
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+ - text-classification
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+ - pytorch
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+ datasets:
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+ - arabic-levantine-hate-speech-detection
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: arabic-bert-hate-speech-detection
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Hate Speech Detection
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+ dataset:
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+ type: arabic-levantine-hate-speech-detection
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+ name: Arabic Levantine Hate Speech Detection
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+ metrics:
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+ - type: accuracy
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+ value: 0.845
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+ name: Accuracy
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+ - type: f1
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+ value: 0.84
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+ name: F1 Score
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+ ---
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+
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+ # Arabic BERT Hate Speech Detection
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+
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+ This model is a fine-tuned version of `aubmindlab/bert-base-arabertv2` for Arabic hate speech detection.
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+
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+ ## Model Description
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+
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+ - **Base Model**: aubmindlab/bert-base-arabertv2
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+ - **Task**: Binary text classification (Normal vs Hate Speech)
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+ - **Language**: Arabic
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+ - **Accuracy**: 84.5%
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ import torch
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+
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+ # Load model and tokenizer
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+ model_name = "Ibracadabra13/arabic-bert-hate-speech-detection"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForSequenceClassification.from_pretrained(model_name)
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+
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+ # Function to predict hate speech
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+ def predict_hate_speech(text):
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+ inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
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+
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+ with torch.no_grad():
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+ outputs = model(**inputs)
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+ predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
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+ predicted_class = torch.argmax(predictions, dim=-1).item()
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+ confidence = predictions[0][predicted_class].item()
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+
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+ label_map = {0: 'Normal', 1: 'Hate Speech'}
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+ return {
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+ 'prediction': label_map[predicted_class],
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+ 'confidence': confidence,
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+ 'is_hate_speech': predicted_class == 1
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+ }
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+
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+ # Example usage
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+ result = predict_hate_speech("أنت حيوان حقير")
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+ print(result) # {'prediction': 'Hate Speech', 'confidence': 0.97, 'is_hate_speech': True}
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+ ```
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+
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+ ## Training Details
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+
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+ - **Training Data**: Arabic Levantine Hate Speech Detection Dataset
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+ - **Training Method**: Fine-tuning with manual training loop
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+ - **Epochs**: 2
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+ - **Batch Size**: 4
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+ - **Learning Rate**: 2e-5
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+ - **Optimizer**: AdamW
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+
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+ ## Performance
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+
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+ - **Accuracy**: 84.5%
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+ - **Normal Text**: 83% precision, 96% recall
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+ - **Hate Speech**: 90% precision, 65% recall
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+
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+ ## Limitations
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+
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+ This model is trained on a specific dataset and may not generalize well to all Arabic dialects or contexts. Use with caution in production environments.
config.json ADDED
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