readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - indonlu
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: indobert-finetuned-pos
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: indonlu
        type: indonlu
        config: posp
        split: train
        args: posp
      metrics:
      - name: Precision
        type: precision
        value: 0.9477284686897035
      - name: Recall
        type: recall
        value: 0.9477284686897035
      - name: F1
        type: f1
        value: 0.9477284686897035
      - name: Accuracy
        type: accuracy
        value: 0.9477284686897035
  ---
  
  <!-- 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-finetuned-pos
  
  This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the indonlu dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1762
  - Precision: 0.9477
  - Recall: 0.9477
  - F1: 0.9477
  - Accuracy: 0.9477
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 420  | 0.2238          | 0.9278    | 0.9278 | 0.9278 | 0.9278   |
  | 0.3621        | 2.0   | 840  | 0.1806          | 0.9437    | 0.9437 | 0.9437 | 0.9437   |
  | 0.1504        | 3.0   | 1260 | 0.1762          | 0.9477    | 0.9477 | 0.9477 | 0.9477   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  