
# 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

- 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).

> Created by [Ladislav Rampasek](https://rampasek.github.io)

{"language": "protein", "tags": ["protein language model"], "datasets": ["BFD", "Custom Rosetta"]}
transformers pytorch bert text-classification protein language model autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #protein language model #autotrain_compatible #endpoints_compatible #region-us 


# 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

- 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 and first released in
this repository.

> Created by Ladislav Rampasek

# 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

- 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 and first released in
this repository.

> Created by Ladislav Rampasek
TAGS
#transformers #pytorch #bert #text-classification #protein language model #autotrain_compatible #endpoints_compatible #region-us 
 # 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

- 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 and first released in
this repository.

> Created by Ladislav Rampasek
passage: TAGS
#transformers #pytorch #bert #text-classification #protein language model #autotrain_compatible #endpoints_compatible #region-us 
# 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

- 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 and first released in
this repository.

> Created by Ladislav Rampasek