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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
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
