readme: ---
  language: de        # <-- my language
  datasets:
  - news_commentary
  widget:
   - text: "Unberechenbar, gefährlich, ja, auf jeden Fall."
     example_title: "Fluent example 1"
   - text: "Aber hinterher... oh, oh..."
     example_title: "Fluent example 2"
   - text: "Nettes Haus, was? - Ja."
     example_title: "Fluent example 3"
   - text: "Wissqween Sisssasde, adddddqwe12was Mdddilednberg war, 122huh?"
     example_title: "Disfluent example 1"
   - text: "asdaojn;klL:JjJALSJD"
     example_title: "Disfluent example 2"
   - text: "Was dDadasdDasein erster aaaaEind2ruck?"
     example_title: "Disfluent example 3"      
  
  license: other
  ---
  
  This model was trained for evaluating linguistic acceptability and grammaticality. The finetuning was carried out based off [the bert-base-german-cased](https://huggingface.co/bert-base-german-cased). 
  
  Label_1 means ACCEPTABLE - the sentence is perfectly understandable by native speakers and has no serious grammatic and syntactic flaws.
  
  Label_0 means NOT ACCEPTABLE - the sentence is flawed both orthographically and grammatically.
  
  The model was trained on 50 thousand German sentences from [the news_commentary dataset](https://huggingface.co/datasets/news_commentary). Out of 50 thousand 25 thousand sentences were algorithmically corrupted using [the open source Python library](https://github.com/eistakovskii/text_corruption_plus). The library was originally developed by [aylliote](https://github.com/aylliote/corruption), but it was slightly adapted for the purposes of this model.
  