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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 tops ZeroCLUE and FewCLUE benchmarks, as of 2023/01/12.


Detailed Info: https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/zero_shot_text_classification

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Zero-Shot Classification
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