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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: CoVBERT |
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results: [] |
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widget: |
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- text: "MLLTS<mask>FFALVDSTI" |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# CoVBERT |
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CoVBERT is a protein language model which speaks the language of SARS-CoV-2 spike proteins! |
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Enter a sequence with mask and let CoVBERT predict the mutation at that position! |
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CoVBERT has been trained with 50K spike glycoprotein sequences scraped from [GISAID](https://gisaid.org) |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1343 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.3432 | 0.02 | 100 | 1.4642 | |
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| 1.4307 | 0.04 | 200 | 1.2907 | |
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| 1.3923 | 0.06 | 300 | 1.2445 | |
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| 1.2719 | 0.08 | 400 | 1.1913 | |
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| 1.1292 | 0.1 | 500 | 0.9962 | |
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| 0.9344 | 0.12 | 600 | 0.7351 | |
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| 0.7481 | 0.14 | 700 | 0.6377 | |
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| 0.6194 | 0.16 | 800 | 0.4843 | |
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| 0.4363 | 0.18 | 900 | 0.4043 | |
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| 0.416 | 0.2 | 1000 | 0.3693 | |
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| 0.3295 | 0.22 | 1100 | 0.3520 | |
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| 0.3416 | 0.24 | 1200 | 0.3343 | |
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| 0.3755 | 0.26 | 1300 | 0.3274 | |
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| 0.3064 | 0.28 | 1400 | 0.3127 | |
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| 0.3295 | 0.3 | 1500 | 0.2998 | |
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| 0.2928 | 0.32 | 1600 | 0.2965 | |
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| 0.3069 | 0.34 | 1700 | 0.2877 | |
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| 0.3048 | 0.36 | 1800 | 0.2850 | |
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| 0.2916 | 0.38 | 1900 | 0.2817 | |
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| 0.2979 | 0.4 | 2000 | 0.2591 | |
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| 0.2846 | 0.42 | 2100 | 0.2540 | |
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| 0.2568 | 0.44 | 2200 | 0.3389 | |
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| 0.277 | 0.46 | 2300 | 0.2369 | |
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| 0.2385 | 0.48 | 2400 | 0.2238 | |
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| 0.2477 | 0.5 | 2500 | 0.2160 | |
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| 0.2271 | 0.52 | 2600 | 0.2139 | |
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| 0.2457 | 0.54 | 2700 | 0.2024 | |
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| 0.2037 | 0.56 | 2800 | 0.2085 | |
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| 0.1865 | 0.58 | 2900 | 0.1978 | |
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| 0.2354 | 0.6 | 3000 | 0.1929 | |
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| 0.2001 | 0.62 | 3100 | 0.1865 | |
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| 0.2396 | 0.64 | 3200 | 0.1832 | |
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| 0.2197 | 0.66 | 3300 | 0.1790 | |
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| 0.1813 | 0.68 | 3400 | 0.1767 | |
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| 0.2109 | 0.7 | 3500 | 0.1970 | |
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| 0.1956 | 0.72 | 3600 | 0.1658 | |
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| 0.182 | 0.74 | 3700 | 0.1629 | |
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| 0.1916 | 0.76 | 3800 | 0.1610 | |
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| 0.1777 | 0.78 | 3900 | 0.1557 | |
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| 0.2005 | 0.8 | 4000 | 0.1492 | |
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| 0.1553 | 0.82 | 4100 | 0.1530 | |
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| 0.1631 | 0.84 | 4200 | 0.1448 | |
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| 0.1591 | 0.86 | 4300 | 0.1445 | |
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| 0.1499 | 0.88 | 4400 | 0.1427 | |
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| 0.1487 | 0.9 | 4500 | 0.1418 | |
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| 0.1638 | 0.92 | 4600 | 0.1381 | |
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| 0.1745 | 0.94 | 4700 | 0.1390 | |
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| 0.1551 | 0.96 | 4800 | 0.1366 | |
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| 0.1408 | 0.98 | 4900 | 0.1324 | |
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| 0.1254 | 1.0 | 5000 | 0.1356 | |
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### Framework versions |
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- Transformers 4.22.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Tokenizers 0.12.1 |
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