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library_name: paddlenlp
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library_name: paddlenlp
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license: apache-2.0
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language:
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- zh
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[](https://github.com/PaddlePaddle/PaddleNLP)
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# PaddlePaddle/uie-senta-base
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Sentiment analysis is a research hotspot in recent years, aiming at analyzing, processing, summarizing and reasoning emotionally subjective texts. Sentiment analysis has a wide range of application scenarios and can be applied to consumer decision-making, public opinion analysis, personalized recommendation and so on.
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According to the analysis granularity, it can be roughly divided into three categories: document-level sentiment analysis, sentence-level sentiment analysis and aspect-level sentiment analysis. Among them, aspect-level sentiment analysis includes multiple subtasks, such as aspect term extraction, opinion term extraction, aspect-opinion-sentiment triplet extraction, etc.
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UIE-Senta is a type of Chinese sentiment analysis model, which uses UIE as backbone and further trained based on large amount of samples related to sentiment analysis. So it has a stronger ability to understand sentiment knowledge and handle the related samples. Currently, UIE-Senta supports most of basic sentiment analysis capabilities, including sentiment-level sentiment classification, aspect-term extraction, opinion-term extraction, aspect-sentiment pair extraction, aspect-opinion pair extraction, aspect-opinion-sentiment triple extraction. You could perform sentiment analysis with UIE-Senta to improve your business analysis capabilities.
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<div align="center">
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<img src="https://user-images.githubusercontent.com/35913314/199965793-f0933baa-5b82-47da-9271-ba36642119f8.png" />
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</div>
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## Available Models
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| Model Name | Model Config |
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| :---------------: | :-----------------------------: |
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| `uie-senta-base` | 12-layers, 768-hidden, 12-heads |
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| `uie-senta-medium` | 6-layers, 768-hidden, 12-heads |
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| `uie-senta-mini` | 6-layers, 384-hidden, 12-heads |
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| `uie-senta-micro` | 4-layers, 384-hidden, 12-heads |
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| `uie-senta-nano` | 4-layers, 312-hidden, 12-heads |
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## Performance on Text Dataset
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We conducted experiments to compare the performance different Models based on a self-built test set, which containing samples from multiple fields, such as hotel, restaurant，clothes and so. The comparison results are as follows.
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| Model Name | Precision | Recall | F1 |
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| :----------------: | :--------: | :--------: | :--------: |
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| `uie-senta-base` | 0.93403 | 0.92795 | 0.93098 |
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| `uie-senta-medium` | 0.93146 | 0.92137 | 0.92639 |
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| `uie-senta-mini` | 0.91799 | 0.92028 | 0.91913 |
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| `uie-senta-micro` | 0.91542 | 0.90957 | 0.91248 |
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| `uie-senta-nano` | 0.90817 | 0.90878 | 0.90847 |
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> Detailed Info: https://github.com/1649759610/PaddleNLP/tree/develop/applications/sentiment_analysis/unified_sentiment_extraction
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