readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: TAPE-finetuned-viralProteins
    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. -->
  
  # TAPE-finetuned-viralProteins
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9033
  - Accuracy: 0.87
  - F1: 0.8555
  - Precision: 0.8475
  - Recall: 0.87
  
  ## 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: 1e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.8845        | 1.0   | 5000  | 0.8302          | 0.85     | 0.8060 | 0.7779    | 0.85   |
  | 0.8189        | 2.0   | 10000 | 0.6062          | 0.86     | 0.8255 | 0.8115    | 0.86   |
  | 0.806         | 3.0   | 15000 | 0.8546          | 0.85     | 0.8095 | 0.7840    | 0.85   |
  | 0.6971        | 4.0   | 20000 | 0.7660          | 0.86     | 0.8228 | 0.8027    | 0.86   |
  | 0.6269        | 5.0   | 25000 | 0.7787          | 0.85     | 0.8343 | 0.8226    | 0.85   |
  | 0.5771        | 6.0   | 30000 | 0.7965          | 0.855    | 0.8402 | 0.8290    | 0.855  |
  | 0.5433        | 7.0   | 35000 | 0.7864          | 0.875    | 0.8573 | 0.8473    | 0.875  |
  | 0.5183        | 8.0   | 40000 | 0.8292          | 0.87     | 0.8521 | 0.8425    | 0.87   |
  | 0.4396        | 9.0   | 45000 | 0.8838          | 0.875    | 0.8566 | 0.8483    | 0.875  |
  | 0.4019        | 10.0  | 50000 | 0.9033          | 0.87     | 0.8555 | 0.8475    | 0.87   |
  
  
  ### Framework versions
  
  - Transformers 4.19.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  