|
|
--- |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- f1 |
|
|
- precision |
|
|
- recall |
|
|
model-index: |
|
|
- name: protBERTbfd_AAV2_classification |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- 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. --> |
|
|
|
|
|
# protBERTbfd_AAV2_classification |
|
|
|
|
|
This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on AAV2 dataset with ~230k sequences (Bryant et al 2020). |
|
|
|
|
|
The WT sequence (aa561-588): D E E E I R T T N P V A T E Q Y G S V S T N L Q R G N R |
|
|
Maximum length: 50 |
|
|
|
|
|
It achieves the following results on the evaluation set. Note:this is result of the last epoch, I think the pushed model is loaded with best checkpoint - best val_loss, I'm not so sure though :/ |
|
|
- Loss: 0.1341 |
|
|
- Accuracy: 0.9615 |
|
|
- F1: 0.9627 |
|
|
- Precision: 0.9637 |
|
|
- Recall: 0.9618 |
|
|
- Auroc: 0.9615 |
|
|
|
|
|
## 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: 32 |
|
|
- eval_batch_size: 32 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 64 |
|
|
- total_train_batch_size: 2048 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- lr_scheduler_warmup_steps: 200 |
|
|
- num_epochs: 8 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Auroc | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| |
|
|
| No log | 1.0 | 116 | 0.2582 | 0.9064 | 0.9157 | 0.8564 | 0.9839 | 0.9038 | |
|
|
| No log | 2.0 | 232 | 0.1447 | 0.9424 | 0.9432 | 0.9618 | 0.9252 | 0.9430 | |
|
|
| No log | 3.0 | 348 | 0.1182 | 0.9542 | 0.9556 | 0.9573 | 0.9539 | 0.9542 | |
|
|
| No log | 4.0 | 464 | 0.1129 | 0.9585 | 0.9602 | 0.9520 | 0.9685 | 0.9581 | |
|
|
| 0.2162 | 5.0 | 580 | 0.1278 | 0.9553 | 0.9558 | 0.9776 | 0.9351 | 0.9561 | |
|
|
| 0.2162 | 6.0 | 696 | 0.1139 | 0.9587 | 0.9607 | 0.9465 | 0.9752 | 0.9581 | |
|
|
| 0.2162 | 7.0 | 812 | 0.1127 | 0.9620 | 0.9633 | 0.9614 | 0.9652 | 0.9619 | |
|
|
| 0.2162 | 8.0 | 928 | 0.1341 | 0.9615 | 0.9627 | 0.9637 | 0.9618 | 0.9615 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.18.0 |
|
|
- Pytorch 1.11.0+cu113 |
|
|
- Datasets 2.1.0 |
|
|
- Tokenizers 0.12.1 |
|
|
|
