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NuNER-v2.0

Token Classification
Transformers
Safetensors
English
roberta
feature-extraction
entity-recognition
foundation-model
RoBERTa
generic
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@@ -40,7 +40,7 @@ k=X means that as training data, we took only X examples for each class, trained
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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 v2.0 | **43.6** | **60.1** | **68.2** | **72.0** |
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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. Thus NuNER v2.0 should be even better than the 7b LLM.
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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 v2.0 | **43.6** | **61.0** | **68.2** | **72.0** |
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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. Thus NuNER v2.0 should be even better than the 7b LLM.
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