readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - indonlu
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilled-indobert-classification
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: indonlu
        type: indonlu
        args: smsa
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9015873015873016
      - name: F1
        type: f1
        value: 0.9014926755197933
  ---
  
  <!-- 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. -->
  
  # distilled-indobert-classification
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6015
  - Accuracy: 0.9016
  - F1: 0.9015
  
  ## 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: 6e-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
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 1.0427        | 1.0   | 688  | 0.6306          | 0.8683   | 0.8684 |
  | 0.5332        | 2.0   | 1376 | 0.5621          | 0.8794   | 0.8779 |
  | 0.3021        | 3.0   | 2064 | 0.6785          | 0.8905   | 0.8896 |
  | 0.1851        | 4.0   | 2752 | 0.6085          | 0.8968   | 0.8959 |
  | 0.1152        | 5.0   | 3440 | 0.6015          | 0.9016   | 0.9015 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  