readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - indonlu
  metrics:
  - accuracy
  - f1
  model-index:
  - name: indobert-distilled-optimized-for-classification
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: indonlu
        type: indonlu
        args: smsa
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9023809523809524
      - name: F1
        type: f1
        value: 0.9020516403647337
  ---
  
  <!-- 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. -->
  
  # indobert-distilled-optimized-for-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: 0.5991
  - Accuracy: 0.9024
  - F1: 0.9021
  
  ## 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: 5.262995179171344e-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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 1.2938        | 1.0   | 688  | 0.8433          | 0.8484   | 0.8513 |
  | 0.711         | 2.0   | 1376 | 0.6408          | 0.8881   | 0.8878 |
  | 0.4416        | 3.0   | 2064 | 0.7964          | 0.8794   | 0.8793 |
  | 0.2907        | 4.0   | 2752 | 0.7559          | 0.8897   | 0.8900 |
  | 0.2065        | 5.0   | 3440 | 0.6892          | 0.8968   | 0.8974 |
  | 0.1574        | 6.0   | 4128 | 0.6881          | 0.8913   | 0.8906 |
  | 0.1131        | 7.0   | 4816 | 0.6224          | 0.8984   | 0.8982 |
  | 0.0865        | 8.0   | 5504 | 0.6312          | 0.8976   | 0.8970 |
  | 0.0678        | 9.0   | 6192 | 0.6187          | 0.8992   | 0.8989 |
  | 0.0526        | 10.0  | 6880 | 0.5991          | 0.9024   | 0.9021 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  