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--- |
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language: de |
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datasets: |
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- news_commentary |
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widget: |
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- text: "Unberechenbar, gefährlich, ja, auf jeden Fall." |
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example_title: "Fluent example 1" |
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- text: "Aber hinterher... oh, oh..." |
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example_title: "Fluent example 2" |
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- text: "Nettes Haus, was? - Ja." |
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example_title: "Fluent example 3" |
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- text: "Wissqween Sisssasde, adddddqwe12was Mdddilednberg war, 122huh?" |
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example_title: "Disfluent example 1" |
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- text: "asdaojn;klL:JjJALSJD" |
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example_title: "Disfluent example 2" |
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- text: "Was dDadasdDasein erster aaaaEind2ruck?" |
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example_title: "Disfluent example 3" |
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license: other |
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--- |
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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). |
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Label_1 means ACCEPTABLE - the sentence is perfectly understandable by native speakers and has no serious grammatic and syntactic flaws. |
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Label_0 means NOT ACCEPTABLE - the sentence is flawed both orthographically and grammatically. |
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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. |
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