readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: prot_bert_classification_finetuned
    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
  
  This model is a fine-tuned version of [nepp1d0/prot_bert-finetuned-smiles-bindingDB](https://huggingface.co/nepp1d0/prot_bert-finetuned-smiles-bindingDB) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5675
  - Accuracy: 0.7299
  - F1: 0.7377
  - Precision: 0.6995
  - Recall: 0.7803
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.4221        | 1.0   | 3332  | 0.6152          | 0.6615   | 0.6711 | 0.6367    | 0.7093 |
  | 0.4133        | 2.0   | 6664  | 0.5840          | 0.6845   | 0.6718 | 0.6805    | 0.6634 |
  | 0.4293        | 3.0   | 9996  | 0.5727          | 0.7116   | 0.7094 | 0.6961    | 0.7232 |
  | 0.3098        | 4.0   | 13328 | 0.5636          | 0.7163   | 0.7220 | 0.6904    | 0.7566 |
  | 0.3881        | 5.0   | 16660 | 0.5629          | 0.7265   | 0.7377 | 0.6918    | 0.7900 |
  | 0.4943        | 6.0   | 19992 | 0.5675          | 0.7299   | 0.7377 | 0.6995    | 0.7803 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  