readme: ---
  language: de        # <-- my language
  widget:
   - text: "Guten morgen, meine Liebe"
     example_title: "NOT TOXIC 1"
   - text: "Ich scheiß drauf."
     example_title: "TOXIC 1"
   - text: "Ich liebe dich"
     example_title: "NOT TOXIC 2" 
   - text: "Ich hab die Schnauze voll von diesen Irren."
     example_title: "TOXIC 2" 
   - text: "Ich wünsche Ihnen einen schönen Tag!"   
     example_title: "NOT TOXIC 3"
   - text: "Nigger"
     example_title: "TOXIC 3" 
   - text: "Du bist schon wieder zu spät!"   
     example_title: "NOT TOXIC 4"
   - text: "Beweg deinen AArschhh hier rüber"
     example_title: "TOXIC 4" 
     
  license: other
  ---
  ## Description
  NB: this version of the model is the improved version of [EIStakovskii/german_toxicity_classifier_plus](https://huggingface.co/EIStakovskii/german_toxicity_classifier_plus).
  To see the source code of training and the data please follow [the github link](https://github.com/eistakovskii/NLP_projects/tree/main/TEXT_CLASSIFICATION).
  
  This model was trained for toxicity labeling.
  
  The model was fine-tuned based off [the dbmdz/bert-base-german-cased model](https://huggingface.co/dbmdz/bert-base-german-cased).
  
  To use the model:
  
  ```python
  from transformers import pipeline
  
  classifier = pipeline("text-classification", model = 'EIStakovskii/german_toxicity_classifier_plus_v2')
  
  print(classifier("Verpiss dich von hier"))
  
  ```
  
  ## Metrics (at validation):
  
  epoch|step|eval_accuracy|eval_f1|eval_loss
  -|-|-|-|-
  0.8|1200|0.9132176234979973|0.9113535629048755|0.24135465919971466
  
  ## Comparison against Perspective
  
  This model was compared against the Google's [Perspective API](https://developers.perspectiveapi.com/s/?language=en_US) that similarly detects toxicity. 
  Two models were tested on two datasets: the size of [200 sentences](https://github.com/eistakovskii/NLP_projects/blob/main/TEXT_CLASSIFICATION/data/Toxicity_Classifiers/DE_FR/test/test_de_200.csv) and [400 sentences](https://github.com/eistakovskii/NLP_projects/blob/main/TEXT_CLASSIFICATION/data/Toxicity_Classifiers/DE_FR/test/test_de_400.csv). 
  The first one (arguably harder) was collected from the sentences of the [JigSaw](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification/data) and [DeTox](https://github.com/hdaSprachtechnologie/detox) datasets.
  The second one (easier) was collected from the combination of sources: both from JigSaw and DeTox as well as [Paradetox](https://github.com/s-nlp/multilingual_detox/tree/main/data) translations and sentences extracted from [Reverso Context](https://context.reverso.net/translation/) by keywords.
  
  # german_toxicity_classifier_plus_v2
  size|accuracy|f1 
  -|-|-
  200|0.767|0.787
  400|0.9650|0.9651
  
  # Perspective
  size|accuracy|f1 
  -|-|-
  200|0.834|0.820
  400|0.892|0.885