readme: ---
  library_name: paddlenlp
  license: apache-2.0
  language:
  - zh
  tags:
  - zero-shot-classification
  ---
  
  [![paddlenlp-banner](https://user-images.githubusercontent.com/1371212/175816733-8ec25eb0-9af3-4380-9218-27c154518258.png)](https://github.com/PaddlePaddle/PaddleNLP)
  
  # PaddlePaddle/utc-large
  
  Text classification technology is widely used in various industries such as dialogue intention recognition, bill archiving, and event detection. 
  However, there are many challenges in industrial-level text classification practices, including diverse tasks, limited data availability and label transfer difficulty. 
  To address these issues, UTC models text classification as a matching task between labels and text, based on the idea of Unified Semantic Matching (USM).
  Thus, it can handle multiple classification tasks with a single model, reducing development and machine costs and achieving good zero/few-shot transfer performance.
  
  
  
  USM Paper: https://arxiv.org/abs/2301.03282
  
  PaddleNLP released UTC model for various text classification tasks which use ERNIE models as the pre-trained language models and were finetuned on a large amount of text classification data.
  
  
  ![UTC-diagram](https://user-images.githubusercontent.com/25607475/212268807-66181bcb-d3f9-4086-9d4a-de4d1d0933c2.png)
  
  
  ## Available Models
  
  |     Model Name   |        Usage Scenarios     |       Supporting Tasks        |
  | :--------------: | :------------------------- | :---------------------------- |
  | `utc-large`      | A **text classification** model supports **Chinese** | Supports intention recognition, semantic matching, natural language inference, semantic analysis, etc. |
  
  
  ## Performance on Text Dataset
  
  UTC won the 1st place on both [ZeroCLUE](https://www.cluebenchmarks.com/zeroclue.html) and [FewCLUE](https://www.cluebenchmarks.com/fewclue.html) benchmarks.
  
  ![UTC-benchmarks](https://s3.amazonaws.com/moonup/production/uploads/1675419368924-62d7bc8c63583ad7bf1d665a.png)
  
  
  
  **Detailed Info:** https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/zero_shot_text_classification
   