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FastESM2_650

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lhallee commited on Dec 12, 2024

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@@ -59,7 +59,7 @@ _ = model.embed_dataset(
 We employ linear probing techniques on various PLMs and standard datasets, similar our previous [paper](https://www.biorxiv.org/content/10.1101/2024.07.30.605924v1), to assess the intrinsic correlation between pooled hidden states and valuable properties. FastESM performs very well.
 The plot below showcases performance normalized between the negative control (random vector embeddings) and the best performer. Classification task scores are averaged between MCC and F1 (or F1max for multilabel) and regression tasks are averaged between Spearman rho and R2.
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/4BvJwkXRFSGbMVqMksS8O.png)
 ## Comparison of half precisions
 Presumabely because we trained in mixed-precision fp16, fp16 has closer outputs to the fp32 weights then bf16. Therefore, we recommend loading in fp16.

 We employ linear probing techniques on various PLMs and standard datasets, similar our previous [paper](https://www.biorxiv.org/content/10.1101/2024.07.30.605924v1), to assess the intrinsic correlation between pooled hidden states and valuable properties. FastESM performs very well.
 The plot below showcases performance normalized between the negative control (random vector embeddings) and the best performer. Classification task scores are averaged between MCC and F1 (or F1max for multilabel) and regression tasks are averaged between Spearman rho and R2.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/d1Xi6k1Q4-9By_MtzTvdV.png)
 ## Comparison of half precisions
 Presumabely because we trained in mixed-precision fp16, fp16 has closer outputs to the fp32 weights then bf16. Therefore, we recommend loading in fp16.