readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - indonlu
  metrics:
  - accuracy
  - f1
  model-index:
  - name: indobert-classification
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: indonlu
        type: indonlu
        args: smsa
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9396825396825397
      - name: F1
        type: f1
        value: 0.9393057427148881
  ---
  
  <!-- 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-classification
  
  This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the indonlu dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3707
  - Accuracy: 0.9397
  - F1: 0.9393
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.2458        | 1.0   | 688  | 0.2229          | 0.9325   | 0.9323 |
  | 0.1258        | 2.0   | 1376 | 0.2332          | 0.9373   | 0.9369 |
  | 0.059         | 3.0   | 2064 | 0.3389          | 0.9365   | 0.9365 |
  | 0.0268        | 4.0   | 2752 | 0.3412          | 0.9421   | 0.9417 |
  | 0.0097        | 5.0   | 3440 | 0.3707          | 0.9397   | 0.9393 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  