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biomedbert-hash-nano

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davidmezzetti commited on 23 days ago

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@@ -43,6 +43,6 @@ _Note: The original dataset was saved locally as `medclassify` the the `conditio
 | [bert-base-uncased](https://hf.co/google-bert/bert-base-uncased) | 110M | 0.6118 | 0.9712 |
 | [biomedbert-base](https://hf.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) | 110M | 0.6195 | 0.9037 |
 | [ModernBERT-base](https://hf.co/answerdotai/ModernBERT-base) | 149M | 0.5672 | 1.1079 |
-| [BioClinical-ModernBERT-base](https://hf.co/neuml/thomas-sounack/BioClinical-ModernBERT-base) | 149M | 0.5679 | 1.0915 |
 As we can see, this model performs very well against models much larger in size. This dataset is a challenging one!

 | [bert-base-uncased](https://hf.co/google-bert/bert-base-uncased) | 110M | 0.6118 | 0.9712 |
 | [biomedbert-base](https://hf.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) | 110M | 0.6195 | 0.9037 |
 | [ModernBERT-base](https://hf.co/answerdotai/ModernBERT-base) | 149M | 0.5672 | 1.1079 |
+| [BioClinical-ModernBERT-base](https://hf.co/thomas-sounack/BioClinical-ModernBERT-base) | 149M | 0.5679 | 1.0915 |
 As we can see, this model performs very well against models much larger in size. This dataset is a challenging one!