| # ProtBert-BFD finetuned on Rosetta 20,40,60AA dataset | |
| This model is finetuned to predict Rosetta fold energy using a dataset of 300k protein sequences: | |
| 100k of 20AA, 100k of 40AA, and 100k of 60AA | |
| Current model in this repo: `prot_bert_bfd-finetuned-032822_1323` | |
| ## Performance | |
| On a held-out eval set the performance is: | |
| - 20AA sequences (1k eval set): | |
| Metrics: 'mae': 0.100418, 'r2': 0.989028, 'mse': 0.016266, 'rmse': 0.127537 | |
| - 40AA sequences (10k eval set): | |
| Metrics: 'mae': 0.173888, 'r2': 0.963361, 'mse': 0.048218, 'rmse': 0.219587 | |
| - 60AA sequences (10k eval set): | |
| Metrics: 'mae': 0.235238, 'r2': 0.930164, 'mse': 0.088131, 'rmse': 0.2968 | |
| ## `prot_bert_bfd` from ProtTrans | |
| The starting pretrained model is from ProtTrans, trained on 2.1 billion proteins from BFD. | |
| It was trained on protein sequences using a masked language modeling (MLM) objective. It was introduced in | |
| [this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in | |
| [this repository](https://github.com/agemagician/ProtTrans). | |