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---
library_name: paddlenlp
license: apache-2.0
language:
- zh
tags:
- zero-shot-classification
---
[](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.
## 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.
**Detailed Info:** https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/zero_shot_text_classification
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