readme: ---
  language: id
  tags:
    - indobert
    - indobenchmark
    - indonlu
  license: mit
  inference: true
  datasets:
    - Indo4B
  ---
  
  # IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset
  
  Finetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper [IndoNLU: Benchmark and Resources for Evaluating Indonesian
  Natural Language Understanding](https://arxiv.org/pdf/2009.05387.pdf).
  
  ## How to use
  
  ```python
  from transformers import pipeline
  classifier = pipeline("text-classification", 
                        model='tyqiangz/indobert-lite-large-p2-smsa', 
                        return_all_scores=True)
  text = "Penyakit koronavirus 2019"
  prediction = classifier(text)
  prediction
  
  """
  Output:
  [[{'label': 'positive', 'score': 0.0006000096909701824},
    {'label': 'neutral', 'score': 0.01223431620746851},
    {'label': 'negative', 'score': 0.987165629863739}]]
  """
  ```
  
  **Finetuning hyperparameters:**
  - learning rate: 2e-5
  - batch size: 16
  - no. of epochs: 5
  - max sequence length: 512
  - random seed: 42
  
  **Classes:**
  - 0: positive
  - 1: neutral
  - 2: negative
  
  **Performance metrics on SmSA validation dataset**
  - Validation accuracy: 0.94
  - Validation F1: 0.91
  - Validation Recall: 0.91
  - Validation Precision: 0.93
  
  