readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: prot_bert_classification_finetuned_no_finetune
    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. -->
  
  # prot_bert_classification_finetuned_no_finetune
  
  This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6212
  - Accuracy: 0.6473
  - F1: 0.6623
  - Precision: 0.6201
  - Recall: 0.7107
  
  ## 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-06
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 3
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 5
  - num_epochs: 6
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.6494        | 1.0   | 3332  | 0.6479          | 0.6439   | 0.6679 | 0.6116    | 0.7357 |
  | 0.5357        | 2.0   | 6664  | 0.6440          | 0.6148   | 0.6459 | 0.5845    | 0.7218 |
  | 0.4661        | 3.0   | 9996  | 0.6265          | 0.6283   | 0.6414 | 0.6047    | 0.6829 |
  | 0.506         | 4.0   | 13328 | 0.6192          | 0.6439   | 0.6567 | 0.6187    | 0.6996 |
  | 0.4204        | 5.0   | 16660 | 0.6122          | 0.6567   | 0.6752 | 0.6259    | 0.7330 |
  | 0.6071        | 6.0   | 19992 | 0.6212          | 0.6473   | 0.6623 | 0.6201    | 0.7107 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  