---
tags:
- generated_from_trainer

model-index:
- name: CoVBERT
  results: []

widget:
- text: "MLLTS<mask>FFALVDSTI"

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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