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README.md

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 FastESM is a Huggingface compatible plug in version of ESM2-650M rewritten with a newer PyTorch attention implementation.
 
-To enhance the weights with longer context and better fp16 support, we trained ESM2-650 50000 additional steps with a traditional MLM objective (20% masking) in fp16 mixed precision on [OMGprot50](tattabio/OMG_prot50) up to sequence length of **2048**.
+To enhance the weights with longer context and better fp16 support, we trained ESM2-650 50000 additional steps with a traditional MLM objective (20% masking) in fp16 mixed precision on [OMGprot50](https://huggingface.co/datasets/tattabio/OMG_prot50) up to sequence length of **2048**.
 
 Outputting attention maps (or the contact prediction head) is not natively possible with SDPA. You can still pass ```output_attentions``` to have attention calculated manually and returned.
 Various other optimizations also make the base implementation slightly different than the one in transformers.