readme: ---
  language: zh
  tags:
  - sentiment-analysis
  - pytorch
  widget:
  - text: "房间非常非常小，内窗，特别不透气，因为夜里走廊灯光是亮的，内窗对着走廊，窗帘又不能完全拉死，怎么都会有一道光射进来。"
  - text: "尽快有洗衣房就好了。"
  - text: "很好，干净整洁，交通方便。"
  - text: "干净整洁很好"
  ---
  
  # Note
  
  BERT based sentiment analysis, finetune based on https://huggingface.co/IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment .
  
  The model trained on **hotel human review chinese dataset**.
  
  # Usage
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
  
  MODEL = "tezign/Erlangshen-Sentiment-FineTune"
  
  tokenizer = AutoTokenizer.from_pretrained(MODEL)
  
  model = AutoModelForSequenceClassification.from_pretrained(MODEL, trust_remote_code=True)
  
  classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
  
  result = classifier("很好，干净整洁，交通方便。")
  
  print(result)
  
  """
  print result
  >> [{'label': 'Positive', 'score': 0.989660382270813}]
  """
  ```
  
  # Evaluate
  We compared and evaluated the performance of **Our finetune model** and the **Original Erlangshen model** on the **hotel human review test dataset**(5429 negative reviews and 1251 positive reviews).
  
  The results showed that our model substantial improved the precision and recall of positive reviews:
  
  ```text
  Our finetune model:
                precision    recall  f1-score   support
  
      Negative       0.99      0.98      0.98      5429
      Positive       0.92      0.95      0.93      1251
  
      accuracy                           0.97      6680
     macro avg       0.95      0.96      0.96      6680
  weighted avg       0.97      0.97      0.97      6680
  
  ======================================================
  
  Original Erlangshen model:
                precision    recall  f1-score   support
  
      Negative       0.81      1.00      0.90      5429
      Positive       0.00      0.00      0.00      1251
  
      accuracy                           0.81      6680
     macro avg       0.41      0.50      0.45      6680
  weighted avg       0.66      0.81      0.73      6680
  ```