readme: ---
  language: tr
  ---
  # Turkish News Text Classification
  
      Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
  
  # Dataset
  
  Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classes.
  
  Dataset can be accessed at https://github.com/gurkan08/datasets/tree/master/trt_11_category.
  
      label_dict = {
          'LABEL_0': 'ekonomi',
          'LABEL_1': 'spor',
          'LABEL_2': 'saglik',
          'LABEL_3': 'kultur_sanat',
          'LABEL_4': 'bilim_teknoloji',
          'LABEL_5': 'egitim'
      }
  
  70% of the data were used for training and 30% for testing.
  
  train f1-weighted score = %97
  
  test f1-weighted score = %94
  
  # Usage
  
      from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
  
      tokenizer = AutoTokenizer.from_pretrained("gurkan08/bert-turkish-text-classification")
      model = AutoModelForSequenceClassification.from_pretrained("gurkan08/bert-turkish-text-classification")
  
      nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
  
      text = ["Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...",
      "Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti"]
  
      out = nlp(text)
      
      label_dict = {
       'LABEL_0': 'ekonomi',
       'LABEL_1': 'spor',
       'LABEL_2': 'saglik',
       'LABEL_3': 'kultur_sanat',
       'LABEL_4': 'bilim_teknoloji',
       'LABEL_5': 'egitim'
      }
  
      results = []
      for result in out:
          result['label'] = label_dict[result['label']]
          results.append(result)
      print(results)
  
      # > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]
      
      
      
  