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# CoVBERT

CoVBERT is a protein language model which speaks the language of SARS-CoV-2 spike proteins!
Enter a sequence with mask and let CoVBERT predict the mutation at that position!
CoVBERT has been trained with 50K spike glycoprotein sequences scraped from [GISAID](https://gisaid.org)


It achieves the following results on the evaluation set:
- Loss: 0.1343

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3432        | 0.02  | 100  | 1.4642          |
| 1.4307        | 0.04  | 200  | 1.2907          |
| 1.3923        | 0.06  | 300  | 1.2445          |
| 1.2719        | 0.08  | 400  | 1.1913          |
| 1.1292        | 0.1   | 500  | 0.9962          |
| 0.9344        | 0.12  | 600  | 0.7351          |
| 0.7481        | 0.14  | 700  | 0.6377          |
| 0.6194        | 0.16  | 800  | 0.4843          |
| 0.4363        | 0.18  | 900  | 0.4043          |
| 0.416         | 0.2   | 1000 | 0.3693          |
| 0.3295        | 0.22  | 1100 | 0.3520          |
| 0.3416        | 0.24  | 1200 | 0.3343          |
| 0.3755        | 0.26  | 1300 | 0.3274          |
| 0.3064        | 0.28  | 1400 | 0.3127          |
| 0.3295        | 0.3   | 1500 | 0.2998          |
| 0.2928        | 0.32  | 1600 | 0.2965          |
| 0.3069        | 0.34  | 1700 | 0.2877          |
| 0.3048        | 0.36  | 1800 | 0.2850          |
| 0.2916        | 0.38  | 1900 | 0.2817          |
| 0.2979        | 0.4   | 2000 | 0.2591          |
| 0.2846        | 0.42  | 2100 | 0.2540          |
| 0.2568        | 0.44  | 2200 | 0.3389          |
| 0.277         | 0.46  | 2300 | 0.2369          |
| 0.2385        | 0.48  | 2400 | 0.2238          |
| 0.2477        | 0.5   | 2500 | 0.2160          |
| 0.2271        | 0.52  | 2600 | 0.2139          |
| 0.2457        | 0.54  | 2700 | 0.2024          |
| 0.2037        | 0.56  | 2800 | 0.2085          |
| 0.1865        | 0.58  | 2900 | 0.1978          |
| 0.2354        | 0.6   | 3000 | 0.1929          |
| 0.2001        | 0.62  | 3100 | 0.1865          |
| 0.2396        | 0.64  | 3200 | 0.1832          |
| 0.2197        | 0.66  | 3300 | 0.1790          |
| 0.1813        | 0.68  | 3400 | 0.1767          |
| 0.2109        | 0.7   | 3500 | 0.1970          |
| 0.1956        | 0.72  | 3600 | 0.1658          |
| 0.182         | 0.74  | 3700 | 0.1629          |
| 0.1916        | 0.76  | 3800 | 0.1610          |
| 0.1777        | 0.78  | 3900 | 0.1557          |
| 0.2005        | 0.8   | 4000 | 0.1492          |
| 0.1553        | 0.82  | 4100 | 0.1530          |
| 0.1631        | 0.84  | 4200 | 0.1448          |
| 0.1591        | 0.86  | 4300 | 0.1445          |
| 0.1499        | 0.88  | 4400 | 0.1427          |
| 0.1487        | 0.9   | 4500 | 0.1418          |
| 0.1638        | 0.92  | 4600 | 0.1381          |
| 0.1745        | 0.94  | 4700 | 0.1390          |
| 0.1551        | 0.96  | 4800 | 0.1366          |
| 0.1408        | 0.98  | 4900 | 0.1324          |
| 0.1254        | 1.0   | 5000 | 0.1356          |


### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "widget": [{"text": "MLLTS<mask>FFALVDSTI"}], "model-index": [{"name": "CoVBERT", "results": []}]}
transformers pytorch safetensors roberta fill-mask generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

CoVBERT
=======


CoVBERT is a protein language model which speaks the language of SARS-CoV-2 spike proteins!
Enter a sequence with mask and let CoVBERT predict the mutation at that position!
CoVBERT has been trained with 50K spike glycoprotein sequences scraped from GISAID


It achieves the following results on the evaluation set:


* Loss: 0.1343


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1### Training results### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Tokenizers 0.12.1