readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - indonlu
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: indobert-base-uncased-finetuned-indonlu-smsa
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: indonlu
        type: indonlu
        args: smsa
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9301587301587302
      - name: F1
        type: f1
        value: 0.9066105299178986
      - name: Precision
        type: precision
        value: 0.8992078788375845
      - name: Recall
        type: recall
        value: 0.9147307323234121
  ---
  
  <!-- 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-base-uncased-finetuned-indonlu-smsa
  
  This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2277
  - Accuracy: 0.9302
  - F1: 0.9066
  - Precision: 0.8992
  - Recall: 0.9147
  
  ## 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: 1e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1500
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | No log        | 1.0   | 344  | 0.3831          | 0.8476   | 0.7715 | 0.7817    | 0.7627 |
  | 0.4167        | 2.0   | 688  | 0.2809          | 0.8905   | 0.8406 | 0.8699    | 0.8185 |
  | 0.2624        | 3.0   | 1032 | 0.2254          | 0.9230   | 0.8842 | 0.9004    | 0.8714 |
  | 0.2624        | 4.0   | 1376 | 0.2378          | 0.9238   | 0.8797 | 0.9180    | 0.8594 |
  | 0.1865        | 5.0   | 1720 | 0.2277          | 0.9302   | 0.9066 | 0.8992    | 0.9147 |
  | 0.1217        | 6.0   | 2064 | 0.2444          | 0.9262   | 0.8981 | 0.9013    | 0.8957 |
  | 0.1217        | 7.0   | 2408 | 0.2985          | 0.9286   | 0.8999 | 0.9035    | 0.8971 |
  | 0.0847        | 8.0   | 2752 | 0.3397          | 0.9278   | 0.8969 | 0.9090    | 0.8871 |
  | 0.0551        | 9.0   | 3096 | 0.3542          | 0.9270   | 0.8961 | 0.9010    | 0.8924 |
  | 0.0551        | 10.0  | 3440 | 0.3862          | 0.9222   | 0.8895 | 0.8970    | 0.8846 |
  
  
  ### Framework versions
  
  - Transformers 4.14.1
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  