readme: ---
  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
  