readme: ---
  language:
  - de
  tags:
  - Text Classification
  - sentiment
  - Simpletransformers
  - deepset/gbert-base
  ---
  
  
  
  
  This gBert-base model was finetuned on a sentiment prediction task with tweets from German politician during the German Federal Election in 2021.
  ## Model Description:
  This model was trained on ~30.000 annotated tweets in German language on its sentiment. It can predict tweets as negative, positive or neutral. It achieved an accuracy of 93% on the specific dataset.
  
  ## Model Implementation
  You can implement this model for example with Simpletransformers. First you have to unpack the file.
  
      def unpack_model(model_name=''):
        tar = tarfile.open(f"{model_name}.tar.gz", "r:gz")
        tar.extractall()
        tar.close()
        
   The hyperparameter were defined as follows:
       train_args ={"reprocess_input_data": True,
               "fp16":False,
               "num_train_epochs": 4,
               "overwrite_output_dir":True,
               "train_batch_size": 32,
               "eval_batch_size": 32}
               
    Now create the model:
      unpack_model(YOUR_DOWNLOADED_FILE_HERE)
  
      model = ClassificationModel(
          "bert", "content/outputs/",
          num_labels= 3,
          args=train_args
      )
      
  In this case for the output:
  - 0 = positive
  - 1 = negative
  - 2 = neutral
      
  Example for a positive prediction:
  
      model.predict(["Das ist gut! Wir danken dir."])
      ([0], array([[ 2.06561327, -3.57908797,  1.5340755 ]]))
  
  Example for a negative prediction:
  
      model.predict(["Ich hasse dich!"])
      ([1], array([[-3.50486898,  4.29590368, -0.9000684 ]]))
  
  Example for a neutral prediction:
  
      model.predict(["Heute ist Sonntag."])
      ([2], array([[-2.94458342, -2.91875601,  4.94414234]]))
  
  
  
  This model was created by Maximilian Weissenbacher for a project at the University of Regensburg.