readme: ---
  library_name: paddlenlp
  license: apache-2.0
  language:
  - zh
  ---
  [![paddlenlp-banner](https://user-images.githubusercontent.com/1371212/175816733-8ec25eb0-9af3-4380-9218-27c154518258.png)](https://github.com/PaddlePaddle/PaddleNLP)
  
  # PaddlePaddle/uie-senta-medium
  
  Sentiment analysis is a research hotspot in recent years, aiming at analyzing, processing, summarizing and reasoning emotionally subjective texts. Sentiment analysis has a wide range of application scenarios and can be applied to consumer decision making, public opinion mining, personalized recommendation and so on.
  
  According to the analysis granularity, it can be roughly divided into three categories: document-level sentiment analysis, sentence-level sentiment analysis and aspect-level sentiment analysis. Among them, aspect-level sentiment analysis includes multiple subtasks, such as aspect term extraction, opinion term extraction, aspect-opinion-sentiment triplet extraction, etc.
  
  
  UIE-Senta is a type of Chinese sentiment analysis model, which uses UIE as backbone and further trained based on  large amount of samples related to sentiment analysis. So it has a stronger ability to understand sentiment knowledge and handle the related samples.  Currently, UIE-Senta supports most of basic sentiment analysis capabilities,  including sentiment-level sentiment classification, aspect-term extraction, opinion-term extraction, aspect-sentiment pair extraction, aspect-opinion pair extraction,  aspect-opinion-sentiment triple extraction. You could perform sentiment analysis with UIE-Senta to improve your business analysis capabilities.
  
  
  <div align="center">
      <img src="https://user-images.githubusercontent.com/35913314/199965793-f0933baa-5b82-47da-9271-ba36642119f8.png" />
  </div>
  
  
  ## Available Models
  
  |     Model Name     |           Model Config           |
  |  :---------------: |  :-----------------------------: | 
  | `uie-senta-base`   | 12-layers, 768-hidden, 12-heads  | 
  | `uie-senta-medium` | 6-layers,  768-hidden, 12-heads  | 
  | `uie-senta-mini`   | 6-layers,  384-hidden, 12-heads  | 
  | `uie-senta-micro`  | 4-layers,  384-hidden, 12-heads  |
  | `uie-senta-nano`   | 4-layers,  312-hidden, 12-heads  | 
  
  
  ## Performance on Text Dataset
  
  We conducted experiments to compare the performance different Models based on a in-house test set, which containing samples from multiple fields, such as hotel, restaurant，clothes and so. The comparison results are as follows.
  
  |      Model Name     |  Precision  |    Recall   |      F1     |
  |  :----------------: |  :--------: |  :--------: |  :--------: |
  |  `uie-senta-base`   |   0.93403   |  0.92795    |  0.93098    |  
  | `uie-senta-medium`  |   0.93146   |  0.92137    |  0.92639    |
  | `uie-senta-mini`    |   0.91799   |  0.92028    |  0.91913    |
  | `uie-senta-micro`   |   0.91542   |  0.90957    |  0.91248    |
  | `uie-senta-nano`    |   0.90817   |  0.90878    |  0.90847    |
  
  
  > Detailed Info: https://github.com/1649759610/PaddleNLP/tree/develop/applications/sentiment_analysis/unified_sentiment_extraction
  
  