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

This model was fine-tuned using the same pipeline as described in the model card for MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33 and in this paper.

The foundation model is 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.

Usage and other details

For usage instructions and other details refer to this model card MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33 and this paper.

Metrics:

I didn't not do zeroshot evaluation for this model to save time and compute. The table below shows standard accuracy for all datasets the model was trained on (note that the NLI datasets are binary).

General takeaway: the model is much more efficient than its larger sisters, but it performs less well.

Datasets mnli_m mnli_mm fevernli anli_r1 anli_r2 anli_r3 wanli lingnli wellformedquery rottentomatoes amazonpolarity imdb yelpreviews hatexplain massive banking77 emotiondair emocontext empathetic agnews yahootopics biasframes_sex biasframes_offensive biasframes_intent financialphrasebank appreviews hateoffensive trueteacher spam wikitoxic_toxicaggregated wikitoxic_obscene wikitoxic_identityhate wikitoxic_threat wikitoxic_insult manifesto capsotu
Accuracy 0.925 0.923 0.886 0.732 0.633 0.661 0.814 0.887 0.722 0.872 0.944 0.925 0.967 0.774 0.734 0.627 0.762 0.745 0.465 0.888 0.702 0.94 0.853 0.863 0.914 0.926 0.921 0.635 0.968 0.897 0.918 0.915 0.935 0.9 0.505 0.701
Inference text/sec (A10G, batch=128) 1573.0 1630.0 683.0 1282.0 1352.0 1072.0 2325.0 2008.0 4781.0 2743.0 677.0 228.0 238.0 2357.0 5027.0 4323.0 3247.0 3129.0 941.0 1643.0 335.0 1517.0 1452.0 1498.0 2367.0 974.0 2634.0 353.0 2284.0 260.0 252.0 256.0 254.0 259.0 1941.0 2080.0
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