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--- |
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language: de |
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widget: |
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- text: "Guten morgen, meine Liebe" |
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example_title: "NOT TOXIC 1" |
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- text: "Ich scheiß drauf." |
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example_title: "TOXIC 1" |
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- text: "Ich liebe dich" |
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example_title: "NOT TOXIC 2" |
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- text: "Ich hab die Schnauze voll von diesen Irren." |
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example_title: "TOXIC 2" |
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- text: "Ich wünsche Ihnen einen schönen Tag!" |
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example_title: "NOT TOXIC 3" |
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- text: "Nigger" |
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example_title: "TOXIC 3" |
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- text: "Du bist schon wieder zu spät!" |
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example_title: "NOT TOXIC 4" |
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- text: "Beweg deinen AArschhh hier rüber" |
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example_title: "TOXIC 4" |
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license: other |
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--- |
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This model was trained for toxicity labeling. Label_1 means TOXIC, Label_0 means NOT TOXIC |
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The model was fine-tuned based off the already existing sentiment classifier oliverguhr/german-sentiment-bert . The aforementioned classifier performed poorly (44% accuracy on my test sample), so I trained the current toxicity classifier. It was noted that the same performance achieved training on the [dbmdz/bert-base-german-cased model](https://huggingface.co/dbmdz/bert-base-german-cased). |
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The accuracy is 91% on the test split during training and 83% on a manually picked (and thus harder) sample of 200 sentences (100 label 1, 100 label 0) at the end of the training. |
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The model was finetuned on 37k sentences. The train data was the translations of the English data (around 30k sentences) from [the multilingual_detox dataset](https://github.com/s-nlp/multilingual_detox) by [Skolkovo Institute](https://huggingface.co/SkolkovoInstitute) using [the opus-mt-en-de translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) by [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) and semi-manually collected data (around 7 k) by crawling [the dict.cc web dictionary](https://www.dict.cc/) and [the Reverso Context](https://context.reverso.net/translation/). |
