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NuNER-v1.0

Token Classification
Transformers
Safetensors
English
roberta
feature-extraction
entity-recognition
foundation-model
RoBERTa
generic
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@@ -44,7 +44,8 @@ k=X means that as training data for this evaluation, we took only X examples for
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  | RoBERTa-base | 24.5 | 44.7 | 58.1 | 65.4
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  | RoBERTa-base + NER-BERT pre-training | 32.3 | 50.9 | 61.9 | 67.6 |
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- | NuNER v1.0 | **39.4** | **59.6** | **67.8** | **71.5** |
 
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  NuNER v1.0 has similar performance to 7B LLMs (70 times bigger than NuNER v1.0) created specifically for the NER task.
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  |----------|----------|----------|----------|----------|
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  | RoBERTa-base | 24.5 | 44.7 | 58.1 | 65.4
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  | RoBERTa-base + NER-BERT pre-training | 32.3 | 50.9 | 61.9 | 67.6 |
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+ | NuNER v0.1 | 34.3 | 54.6 | 64.0 | 68.7 |
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+ | NuNER v1.0 | 39.4 | 59.6 | 67.8 | 71.5 |
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  NuNER v1.0 has similar performance to 7B LLMs (70 times bigger than NuNER v1.0) created specifically for the NER task.
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