Text Classification
Transformers
Safetensors
PyTorch
English
roberta
hate-speech-and-offensive-message-detection
Instructions to use AshiniR/hate-speech-and-offensive-message-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AshiniR/hate-speech-and-offensive-message-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AshiniR/hate-speech-and-offensive-message-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AshiniR/hate-speech-and-offensive-message-classifier") model = AutoModelForSequenceClassification.from_pretrained("AshiniR/hate-speech-and-offensive-message-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -240,7 +240,7 @@ def get_inference(text: str) -> list:
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return sorted(results, key=lambda x: x["score"], reverse=True)
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# Example usage
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text = "
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predictions = get_inference(text)
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print(f"Text: '{text}'")
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print(f"Predictions: {predictions}")
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return sorted(results, key=lambda x: x["score"], reverse=True)
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# Example usage
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text = "your example message"
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predictions = get_inference(text)
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print(f"Text: '{text}'")
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print(f"Predictions: {predictions}")
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