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README.md
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@@ -23,4 +23,4 @@ SLIMER performs comparably to these state-of-the-art models on OOD input domains
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To experiment the ability of existing models on never-seen-before labels, we extend the standard zero-shot evaluations on BUSTER, which is characterized by financial entities that are rather far from the more traditional tags observed by all models during training.
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An inverse trend to the OOD table can be observed, with SLIMER instead emerging as the most effective in dealing with unseen labels, thanks to its lighter instruction tuning methodology and the use of definition and guidelines.
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<img src="https://huggingface.co/expertai/SLIMER/resolve/main/BUSTERvsOOD.png">
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To experiment the ability of existing models on never-seen-before labels, we extend the standard zero-shot evaluations on BUSTER, which is characterized by financial entities that are rather far from the more traditional tags observed by all models during training.
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| 24 |
An inverse trend to the OOD table can be observed, with SLIMER instead emerging as the most effective in dealing with unseen labels, thanks to its lighter instruction tuning methodology and the use of definition and guidelines.
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<img src="https://huggingface.co/expertai/SLIMER/resolve/main/BUSTERvsOOD.png" width="250">
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