readme: ---
  language:
  - tr
  tags:
  - translation
  license: mit
  ---
  
  ## About the model
  It is a Turkish bert-based model created to determine the types of bullying that people use against each other in social media.
  Included classes;
  
  - Nötr
  - Kızdırma/Hakaret
  - Cinsiyetçilik
  - Irkçılık
  
  3388 tweets were used in the training of the model. Accordingly, the success rates in education are as follows;
  
  |        | Cinsiyetçilik | Irkçılık | Kızdırma | Nötr | 
  | ------ | ------  | ------ | ------  | ------ |
  | Precision | 0.925 | 0.878 | 0.824 | 0.915 |
  | Recall  | 0.831 | 0.896 | 0.843 | 0.935 |
  | F1 Score | 0.875 | 0.887 | 0.833 | 0.925 |
  Accuracy : 0.886
  
  ## Example
  ```sh
  from transformers import AutoTokenizer, TextClassificationPipeline, TFBertForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained("nanelimon/bert-base-turkish-bullying")
  model = TFBertForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-bullying", from_pt=True)
  pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
  
  print(pipe('Bu bir denemedir hadi sende dene!'))
  ```
  Result;
  ```sh
  [{'label': 'Nötr', 'score': 0.999175488948822}]
  ```
  - label= It shows which class the sent Turkish text belongs to according to the model.
  - score= It shows the compliance rate of the Turkish text sent to the label found.
  
  ## Authors
  - Seyma SARIGIL: seymasargil@gmail.com
  - Elif SARIGIL KARA: elifsarigil@gmail.com
  - Murat KOKLU: mkoklu@selcuk.edu.tr
  - Alaaddin Erdinç DAL: aerdincdal@icloud.com
  
  
  ## License
  
  gpl-3.0
  
  **Free Software, Hell Yeah!**