readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: DNABert_K6_G_quad
    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. -->
  
  # DNABert_K6_G_quad
  
  This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2424
  - Accuracy: 0.9737
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 25
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|
  | 0.0927        | 1.0   | 9375   | 0.0818          | 0.9719   |
  | 0.0681        | 2.0   | 18750  | 0.0714          | 0.9756   |
  | 0.0607        | 3.0   | 28125  | 0.0863          | 0.9734   |
  | 0.055         | 4.0   | 37500  | 0.0787          | 0.9757   |
  | 0.0496        | 5.0   | 46875  | 0.0882          | 0.9758   |
  | 0.0445        | 6.0   | 56250  | 0.0968          | 0.9752   |
  | 0.0391        | 7.0   | 65625  | 0.1024          | 0.9755   |
  | 0.0345        | 8.0   | 75000  | 0.1108          | 0.9739   |
  | 0.0304        | 9.0   | 84375  | 0.1235          | 0.9745   |
  | 0.0261        | 10.0  | 93750  | 0.1348          | 0.9730   |
  | 0.023         | 11.0  | 103125 | 0.1427          | 0.9733   |
  | 0.0197        | 12.0  | 112500 | 0.1462          | 0.9738   |
  | 0.0182        | 13.0  | 121875 | 0.1570          | 0.9730   |
  | 0.0145        | 14.0  | 131250 | 0.1757          | 0.9729   |
  | 0.0122        | 15.0  | 140625 | 0.1911          | 0.9735   |
  | 0.0108        | 16.0  | 150000 | 0.1977          | 0.9736   |
  | 0.01          | 17.0  | 159375 | 0.1993          | 0.9732   |
  | 0.0083        | 18.0  | 168750 | 0.2172          | 0.9736   |
  | 0.0074        | 19.0  | 178125 | 0.2242          | 0.9740   |
  | 0.0059        | 20.0  | 187500 | 0.2245          | 0.9732   |
  | 0.0058        | 21.0  | 196875 | 0.2306          | 0.9733   |
  | 0.0043        | 22.0  | 206250 | 0.2414          | 0.9737   |
  | 0.0044        | 23.0  | 215625 | 0.2394          | 0.9735   |
  | 0.0039        | 24.0  | 225000 | 0.2420          | 0.9736   |
  | 0.0032        | 25.0  | 234375 | 0.2424          | 0.9737   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  