readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: albert-base-v2-finetuned-ours-DS
    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-v2-finetuned-ours-DS
  
  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: 0.8481
  - Accuracy: 0.665
  - Precision: 0.6202
  - Recall: 0.6137
  - F1: 0.5973
  
  ## 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: 1.0031150633640573e-05
  - train_batch_size: 8
  - eval_batch_size: 16
  - seed: 43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.053         | 0.49  | 99   | 1.0208          | 0.56     | 0.5650    | 0.4382 | 0.3908 |
  | 0.8912        | 0.99  | 198  | 0.8648          | 0.59     | 0.3832    | 0.5279 | 0.4374 |
  | 0.777         | 1.48  | 297  | 0.7986          | 0.665    | 0.6579    | 0.5985 | 0.5704 |
  | 0.7391        | 1.98  | 396  | 0.8363          | 0.58     | 0.6389    | 0.5669 | 0.4966 |
  | 0.5861        | 2.48  | 495  | 0.8394          | 0.685    | 0.6380    | 0.6313 | 0.6312 |
  | 0.5711        | 2.97  | 594  | 0.9090          | 0.65     | 0.6102    | 0.5833 | 0.5900 |
  | 0.4296        | 3.46  | 693  | 0.8481          | 0.665    | 0.6202    | 0.6137 | 0.5973 |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  