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@@ -22,6 +22,24 @@ The model almost achieves SOTA results while being 20x smaller, on a test set co
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  | [`Guscode/DKbert-hatespeech-detection`](https://huggingface.co/Guscode/DKbert-hatespeech-detection) | 75.41% | 42.79% | 54.60% | 46.84% |
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  ## Training procedure
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  ### Training hyperparameters
 
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  | [`Guscode/DKbert-hatespeech-detection`](https://huggingface.co/Guscode/DKbert-hatespeech-detection) | 75.41% | 42.79% | 54.60% | 46.84% |
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+ ## Using the model
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+
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+ You can use the model simply by running the following:
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+ ```python
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+ >>> from transformers import pipeline
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+ >>> offensive_text_pipeline = pipeline(model="electra-small-offensive-text-detection-da")
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+ >>> offensive_text_pipeline("Din store idiot")
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+ [{'label': 'offensive', 'score': 0.9874388575553894}]
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+ ```
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+
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+ Processing multiple documents at the same time can be done as follows:
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+ ```python
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+ >>> offensive_text_pipeline(["Din store idiot", "ej hvor godt :)"])
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+ [{'label': 'offensive', 'score': 0.9874388575553894}, {'label': 'not offensive', 'score': 0.999760091304779}]
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+ ```
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+
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  ## Training procedure
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  ### Training hyperparameters