--- 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 tops [ZeroCLUE](https://www.cluebenchmarks.com/zeroclue.html) and [FewCLUE](https://www.cluebenchmarks.com/fewclue.html) benchmarks, as of 2023/01/12. ![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