Edit model card

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
Downloads last month
788
Hosted inference API
This model can be loaded on the Inference API on-demand.