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@@ -15,8 +15,8 @@ Instructed on a reduced number of samples, it is designed to tackle never-seen-b
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  <img src="https://huggingface.co/expertai/SLIMER/resolve/main/SLIMER_instruction_prompt.png" width="200">
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- Currently existing LLMs for NER fine-tune on an extensive number of entity classes (around 13K) and assess zero-shot NER capabilities on Out-Of-Distribution input domains.
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- SLIMER performs comparably to these state-of-the-art approaches on OOD input domains, while being trained only a reduced number of samples and a set of NE tags that overlap in lesser degree with test set.
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  <img src="https://huggingface.co/expertai/SLIMER/resolve/main/OOD_evals.png">
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  <img src="https://huggingface.co/expertai/SLIMER/resolve/main/SLIMER_instruction_prompt.png" width="200">
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+ Currently existing approaches fine-tune on an extensive number of entity classes (around 13K) and assess zero-shot NER capabilities on Out-Of-Distribution input domains.
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+ SLIMER performs comparably to these state-of-the-art models on OOD input domains, while being trained only a reduced number of samples and a set of NE tags that overlap in lesser degree with test set.
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  <img src="https://huggingface.co/expertai/SLIMER/resolve/main/OOD_evals.png">
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