This model is a fine-tuned version of IndoBERT Base Uncased, a BERT model pre-trained on Indonesian text data. It was fine-tuned to perform sentiment analysis on Indonesian comments and reviews.

The model was trained on indonlu (SmSA) and indonesian_sentiment datasets.

The model classifies a given Indonesian review text into one of three categories:

Negative
Neutral
Positive

Training hyperparameters

train_batch_size: 32
eval_batch_size: 32
learning_rate: 1e-4
optimizer: AdamW with betas=(0.9, 0.999), eps=1e-8, and weight_decay=0.01
epochs: 3
learning_rate_scheduler: StepLR with step_size=592, gamma=0.1

Training Results

The following table shows the training results for the model:

Epoch	Loss	Accuracy
1	0.2936	0.9310
2	0.1212	0.9526
3	0.0795	0.9569

How to Use

You can load the model and perform inference as follows:

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("taufiqdp/indonesian-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("taufiqdp/indonesian-sentiment")

class_names = ['negatif', 'netral', 'positif']

text = "Pelayanan lama dan tidak ramah"
tokenized_text = tokenizer(text, return_tensors='pt')

with torch.inference_mode():
    logits = model(**tokenized_text)['logits']

result = class_names[logits.argmax(dim=1)]
print(result)

Citation

@misc{koto2020indolem,
      title={IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP}, 
      author={Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin},
      year={2020},
      eprint={2011.00677},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@inproceedings{purwarianti2019improving,
  title={Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector},
  author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti},
  booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
  pages={1--5},
  year={2019},
  organization={IEEE}
}