MoritzLaurer
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deberta-v3-xsmall-zeroshot-v1.1-all-33

Zero-Shot Classification

text-classification

Inference Endpoints

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MoritzLaurer HF staff commited on Jan 11

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@@ -20,7 +20,7 @@ and in this [paper](https://arxiv.org/pdf/2312.17543.pdf).
 The foundation model is [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall).
 The model only has 22 million backbone parameters and 128 million vocabulary parameters.
 The backbone parameters are the main parameters active during inference, providing a significant speedup over larger models.
-The model is 241 MB small.
 This model was trained to provide a small and highly efficient zeroshot option,
 especially for edge devices or in-browser use-cases with transformers.js.

 The foundation model is [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall).
 The model only has 22 million backbone parameters and 128 million vocabulary parameters.
 The backbone parameters are the main parameters active during inference, providing a significant speedup over larger models.
+The model is 142 MB small.
 This model was trained to provide a small and highly efficient zeroshot option,
 especially for edge devices or in-browser use-cases with transformers.js.