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README.md
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@@ -33,7 +33,8 @@ It has been shown that pLMs like ESM-2 contain structural information in the att
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and that single sequence masked language models like ESMFold can be used in atomically accurate predictions of folds, even outperforming
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AlphaFold2. In our approach we show a positive correlation between scaling the model size and data
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in a 1-to-1 fashion provides competative and possibly even SOTA performance, although our comparison to the SOTA models is not as fair and
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comprehensive as it could be (see [this report for more details](https://api.wandb.ai/links/amelie-schreiber-math/0asqd3hs)
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This model is a finetuned version of the 35M parameter `esm2_t12_35M_UR50D` ([see here](https://huggingface.co/facebook/esm2_t12_35M_UR50D)
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and that single sequence masked language models like ESMFold can be used in atomically accurate predictions of folds, even outperforming
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AlphaFold2. In our approach we show a positive correlation between scaling the model size and data
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in a 1-to-1 fashion provides competative and possibly even SOTA performance, although our comparison to the SOTA models is not as fair and
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comprehensive as it could be (see [this report for more details](https://api.wandb.ai/links/amelie-schreiber-math/0asqd3hs) and also
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[this repost](https://wandb.ai/amelie-schreiber-math/huggingface/reports/ESM-2-Binding-Sites-Predictor-Part-3-Scaling-Results--Vmlldzo1NDA3Nzcy?accessToken=npsm0tatgumcidfwxubzjyuhal512xu8sjmpnf11sebktjm9mheg69ja397q57ok)).
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This model is a finetuned version of the 35M parameter `esm2_t12_35M_UR50D` ([see here](https://huggingface.co/facebook/esm2_t12_35M_UR50D)
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