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README.md

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 [![paddlenlp-banner](https://user-images.githubusercontent.com/1371212/175816733-8ec25eb0-9af3-4380-9218-27c154518258.png)](https://github.com/PaddlePaddle/PaddleNLP)
 
-# PaddlePaddle/utc-large
+# 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.
+Specifically, UTC won the 1st place on both [ZeroCLUE](https://www.cluebenchmarks.com/zeroclue.html) and [FewCLUE](https://www.cluebenchmarks.com/fewclue.html) benchmarks.
+
+
+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]()
+
+![UTC-benchmarks]()
+
+## 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
+
+We conducted experiments on the in-house test sets of 
+
+
+
+**Detailed Info:** https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/zero_shot_text_classification