Update README.md

README.md

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 # **ESM-1b**
 
-ESM-1b ([paper](https://www.pnas.org/content/118/15/e2016239118#:~:text=https%3A//doi.org/10.1073/pnas.2016239118), [repository](https://github.com/facebookresearch/esm)) is a transformer protein language model, trained on protein sequence data without label supervision. The model is pretrained on Uniref50 with an unsupervised masked language modeling (MLM) objective, meaning the model is trained to predict amino acids from the surrounding sequence context. This pretraining objective allows ESM-1b to learn generally useful features which can be transferred to downstream prediction tasks. ESM-1b has been evaluated on a variety of tasks related to protein structure and function, including remote homology detection, secondary structure prediction, contact prediction, and prediction of the effect of mutations on function, producing state-of-the-art results.
+ESM-1b ([paper](https://www.pnas.org/content/118/15/e2016239118#:~:text=https%3A//doi.org/10.1073/pnas.2016239118), [repository](https://github.com/facebookresearch/esm)) is a transformer protein language model, trained on protein sequence data without label supervision. The model is pretrained on Uniref50 with an unsupervised masked language modeling (MLM) objective, meaning the model is trained to predict amino acids from the surrounding sequence context. This pretraining objective allows ESM-1b to learn generally useful features which can be transferred to downstream prediction tasks. ESM-1b has been evaluated on a variety of tasks related to protein structure and function, including remote homology detection, secondary structure prediction, contact prediction, and prediction of the effects of mutations on function, producing state-of-the-art results.
 
 
 ## **Model description**
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 The learned representations can be used as features for downstream tasks. For example if you have a dataset of measurements of protein activity you can fit a regression model on the features output by ESM-1b to predict the activity of new sequences. The model can also be fine-tuned.
 
-ESM-1b can infer information about the structure and function of proteins without further supervision, i.e. it is capable of zero-shot transfer to structure and function prediction. [Rao et al. 2020](https://openreview.net/pdf?id=fylclEqgvgd) found that the attention heads of ESM-1b directly correspond to contacts in the 3d structure of the protein. [Meier et al. 2021](https://openreview.net/pdf?id=uXc42E9ZPFs) found that ESM-1b can be used to score the effect of sequence variations on protein function.
+ESM-1b can infer information about the structure and function of proteins without further supervision, i.e. it is capable of zero-shot transfer to structure and function prediction. [Rao et al. 2020](https://openreview.net/pdf?id=fylclEqgvgd) found that the attention heads of ESM-1b directly represent contacts in the 3d structure of the protein. [Meier et al. 2021](https://openreview.net/pdf?id=uXc42E9ZPFs) found that ESM-1b can be used to score the effect of sequence variations on protein function.
 
 
 ## **Intended uses & limitations**