readme: ---
  language: 
    - tw
  tags:
  - albert
  - classification
  license: afl-3.0
  metrics:
  - Accuracy
  ---
  # Traditional Chinese news classification
  
  繁體中文新聞分類任務，使用ckiplab/albert-base-chinese預訓練模型，資料集只有2.6萬筆，做為課程的範例模型。
  
      from transformers import BertTokenizer, AlbertForSequenceClassification
      model_path = "clhuang/albert-news-classification"
      model = AlbertForSequenceClassification.from_pretrained(model_path)
      tokenizer = BertTokenizer.from_pretrained("bert-base-chinese")
      # Category index
      news_categories=['政治','科技','運動','證卷','產經','娛樂','生活','國際','社會','文化','兩岸']
      idx2cate = { i : item for i, item in enumerate(news_categories)}
      
      # get category probability
      def get_category_proba( text ):
          max_length = 250
          # prepare token sequence
          inputs = tokenizer([text], padding=True, truncation=True, max_length=max_length, return_tensors="pt")
          # perform inference
          outputs = model(**inputs)
          # get output probabilities by doing softmax
          probs = outputs[0].softmax(1)
      
          # executing argmax function to get the candidate label index
          label_index = probs.argmax(dim=1)[0].tolist() # convert tensor to int
          # get the label name        
          label = idx2cate[ label_index ]
      
          # get the label probability
          proba = round(float(probs.tolist()[0][label_index]),2)
      
          response = {'label': label, 'proba': proba}
      
          return response
         
      get_category_proba('俄羅斯2月24日入侵烏克蘭至今不到3個月，芬蘭已準備好扭轉奉行了75年的軍事不結盟政策，申請加入北約。芬蘭總理馬林昨天表示，「希望我們下星期能與瑞典一起提出申請」。')
      {'label': '國際', 'proba': 0.99}