readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - indonlu
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilled-optimized-indobert-classification
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: indonlu
        type: indonlu
        args: smsa
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9
      - name: F1
        type: f1
        value: 0.8994069293432798
  ---
  
  <!-- 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-optimized-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: 0.7397
  - Accuracy: 0.9
  - F1: 0.8994
  
  ## 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: 4.315104717136378e-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: 9
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.128         | 1.0   | 688  | 0.8535          | 0.8913   | 0.8917 |
  | 0.1475        | 2.0   | 1376 | 0.9171          | 0.8913   | 0.8913 |
  | 0.0997        | 3.0   | 2064 | 0.7799          | 0.8960   | 0.8951 |
  | 0.0791        | 4.0   | 2752 | 0.7179          | 0.9032   | 0.9023 |
  | 0.0577        | 5.0   | 3440 | 0.6908          | 0.9063   | 0.9055 |
  | 0.0406        | 6.0   | 4128 | 0.7613          | 0.8992   | 0.8986 |
  | 0.0275        | 7.0   | 4816 | 0.7502          | 0.8992   | 0.8989 |
  | 0.023         | 8.0   | 5504 | 0.7408          | 0.8976   | 0.8969 |
  | 0.0169        | 9.0   | 6192 | 0.7397          | 0.9      | 0.8994 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  