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.

How to use

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