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---
license: llama2
datasets:
- Universal-NER/Pile-NER-type
language:
- en
pipeline_tag: text-generation
---
# SLIMER: Show Less Instruct More Entity Recognition
SLIMER is an instruction-tuned LLaMA-2-7B model for zero-shot NER.
Instructed on a reduced number of samples, it is designed to tackle never-seen-before Named Entity tags by leveraging a prompt enriched with a DEFINITION and GUIDELINES for the NE to be extracted.
<img src="https://huggingface.co/expertai/SLIMER/resolve/main/SLIMER_instruction_prompt.png" width="200">
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.
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.
<img src="https://huggingface.co/expertai/SLIMER/resolve/main/OOD_evals.png">
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