readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: albert-base-ours-run-4
    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. -->
  
  # albert-base-ours-run-4
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9565
  - Accuracy: 0.72
  - Precision: 0.6790
  - Recall: 0.6770
  - F1: 0.6766
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.0253        | 1.0   | 200  | 0.8974          | 0.605    | 0.7186    | 0.5341 | 0.4555 |
  | 0.8121        | 2.0   | 400  | 0.8260          | 0.675    | 0.6792    | 0.6308 | 0.6112 |
  | 0.6153        | 3.0   | 600  | 0.8504          | 0.66     | 0.6180    | 0.6026 | 0.6073 |
  | 0.441         | 4.0   | 800  | 0.8917          | 0.685    | 0.6463    | 0.6385 | 0.6403 |
  | 0.3273        | 5.0   | 1000 | 0.9384          | 0.69     | 0.6534    | 0.6602 | 0.6561 |
  | 0.2138        | 6.0   | 1200 | 1.3501          | 0.705    | 0.6573    | 0.6374 | 0.6388 |
  | 0.1435        | 7.0   | 1400 | 1.4614          | 0.71     | 0.6693    | 0.6553 | 0.6601 |
  | 0.1202        | 8.0   | 1600 | 1.5825          | 0.7      | 0.6648    | 0.6592 | 0.6530 |
  | 0.0587        | 9.0   | 1800 | 1.7755          | 0.72     | 0.6839    | 0.6849 | 0.6840 |
  | 0.0237        | 10.0  | 2000 | 1.7240          | 0.735    | 0.6960    | 0.6924 | 0.6940 |
  | 0.018         | 11.0  | 2200 | 1.7230          | 0.745    | 0.7105    | 0.7003 | 0.7026 |
  | 0.0096        | 12.0  | 2400 | 1.7812          | 0.75     | 0.7225    | 0.7142 | 0.7158 |
  | 0.006         | 13.0  | 2600 | 1.8223          | 0.75     | 0.7265    | 0.7082 | 0.7147 |
  | 0.0033        | 14.0  | 2800 | 1.9872          | 0.76     | 0.7434    | 0.7107 | 0.7188 |
  | 0.003         | 15.0  | 3000 | 1.8818          | 0.72     | 0.6778    | 0.6766 | 0.6765 |
  | 0.0027        | 16.0  | 3200 | 1.9816          | 0.75     | 0.7125    | 0.6990 | 0.7043 |
  | 0.002         | 17.0  | 3400 | 1.9268          | 0.725    | 0.6832    | 0.6834 | 0.6825 |
  | 0.0023        | 18.0  | 3600 | 1.9456          | 0.73     | 0.6913    | 0.6898 | 0.6898 |
  | 0.0025        | 19.0  | 3800 | 1.9543          | 0.72     | 0.6790    | 0.6770 | 0.6766 |
  | 0.0016        | 20.0  | 4000 | 1.9565          | 0.72     | 0.6790    | 0.6770 | 0.6766 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Tokenizers 0.13.2
  