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--- |
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license: mit |
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library_name: unity-sentis |
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pipeline_tag: zero-shot-classification |
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--- |
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# Deberta v3 zeroshot for Unity Sentis |
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This is the [DeBERTaV3 Model](https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33) in the Sentis format to run on Unity 2023. |
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The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text. |
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There are more models of different sizes that are compatible made by [MoritzLaurer](https://huggingface.co/MoritzLaurer) |
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## How to Use |
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* Ensure Sentis version is 1.4.0-pre.3 |
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* Create a new scene in Unity 2023 |
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* Add the DebertaV3.cs file to a GameObject in the scene |
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* Assign model and vocabulary |
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* Press play, the classification scores will show in the Console |
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# Example Inputs |
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``` |
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text = "Angela Merkel is a politician in Germany and leader of the CDU" |
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classes = ["This example is about politics", "This example is about economy", "This example is about entertainment", "This example is about environment"] |
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``` |
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# Example Outputs |
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``` |
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[politics] Entailment Score: 0.9998765 |
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[economy] Entailment Score: 0.0008297313 |
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[entertainment] Entailment Score: 4.86502E-05 |
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[environment] Entailment Score: 6.163981E-05 |
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``` |
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## Unity Sentis |
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Sentis is the inference engine for Unity. More can be found about it [here](https://unity.com/products/sentis) |
