readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: libCap_prBERTbfd_clf
    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. -->
  
  # libCap_prBERTbfd_clf
  
  This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5197
  - Accuracy: 0.7457
  - F1: 0.7940
  - Precision: 0.7567
  - Recall: 0.8352
  - Auroc: 0.7268
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 64
  - total_train_batch_size: 4096
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auroc  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
  | No log        | 0.98  | 34   | 0.6393          | 0.6396   | 0.7053 | 0.6782    | 0.7345 | 0.6197 |
  | No log        | 1.98  | 68   | 0.5713          | 0.6962   | 0.7499 | 0.7256    | 0.7759 | 0.6795 |
  | No log        | 2.98  | 102  | 0.5652          | 0.7126   | 0.7388 | 0.7918    | 0.6924 | 0.7168 |
  | No log        | 3.98  | 136  | 0.5360          | 0.7330   | 0.7896 | 0.7345    | 0.8536 | 0.7076 |
  | No log        | 4.98  | 170  | 0.5223          | 0.7423   | 0.7830 | 0.7740    | 0.7921 | 0.7318 |
  | No log        | 5.98  | 204  | 0.5180          | 0.7454   | 0.7882 | 0.7699    | 0.8075 | 0.7323 |
  | No log        | 6.98  | 238  | 0.5179          | 0.7440   | 0.7934 | 0.7537    | 0.8376 | 0.7243 |
  | No log        | 7.98  | 272  | 0.5197          | 0.7457   | 0.7940 | 0.7567    | 0.8352 | 0.7268 |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  