readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: distilbert-undersampled
    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. -->
  
  # distilbert-undersampled
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0826
  - Accuracy: 0.9811
  - F1: 0.9810
  - Recall: 0.9811
  - Precision: 0.9812
  
  ## 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: 3e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 33
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 100
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall | Precision |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.0959        | 0.2   | 2000  | 0.0999          | 0.9651   | 0.9628 | 0.9651 | 0.9655    |
  | 0.0618        | 0.41  | 4000  | 0.0886          | 0.9717   | 0.9717 | 0.9717 | 0.9731    |
  | 0.159         | 0.61  | 6000  | 0.0884          | 0.9719   | 0.9720 | 0.9719 | 0.9728    |
  | 0.0513        | 0.81  | 8000  | 0.0785          | 0.9782   | 0.9782 | 0.9782 | 0.9788    |
  | 0.0219        | 1.01  | 10000 | 0.0680          | 0.9779   | 0.9779 | 0.9779 | 0.9783    |
  | 0.036         | 1.22  | 12000 | 0.0745          | 0.9787   | 0.9787 | 0.9787 | 0.9792    |
  | 0.0892        | 1.42  | 14000 | 0.0675          | 0.9786   | 0.9786 | 0.9786 | 0.9789    |
  | 0.0214        | 1.62  | 16000 | 0.0760          | 0.9799   | 0.9798 | 0.9799 | 0.9801    |
  | 0.0882        | 1.83  | 18000 | 0.0800          | 0.9800   | 0.9800 | 0.9800 | 0.9802    |
  | 0.0234        | 2.03  | 20000 | 0.0720          | 0.9813   | 0.9813 | 0.9813 | 0.9815    |
  | 0.0132        | 2.23  | 22000 | 0.0738          | 0.9803   | 0.9803 | 0.9803 | 0.9805    |
  | 0.0136        | 2.43  | 24000 | 0.0847          | 0.9804   | 0.9804 | 0.9804 | 0.9806    |
  | 0.0119        | 2.64  | 26000 | 0.0826          | 0.9811   | 0.9810 | 0.9811 | 0.9812    |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  