sahri/indonesiasentiment

Indonesian RoBERTa Base Sentiment Classifier

Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on indonlu's SmSA dataset consisting of Indonesian comments and reviews.

After training, the model achieved an evaluation accuracy of 94.36% and F1-macro of 92.42%. On the benchmark test set, the model achieved an accuracy of 93.2% and F1-macro of 91.02%.

Hugging Face's Trainer class from the Transformers library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.

Model

Model	#params	Arch.	Training/Validation data (text)
indonesian-roberta-base-sentiment-classifier	124M	RoBERTa Base	SmSA

Evaluation Results

The model was trained for 5 epochs and the best model was loaded at the end.

Epoch	Training Loss	Validation Loss	Accuracy	F1	Precision	Recall
1	0.342600	0.213551	0.928571	0.898539	0.909803	0.890694
2	0.190700	0.213466	0.934127	0.901135	0.925297	0.882757
3	0.125500	0.219539	0.942857	0.920901	0.927511	0.915193
4	0.083600	0.235232	0.943651	0.924227	0.926494	0.922048
5	0.059200	0.262473	0.942063	0.920583	0.924084	0.917351

How to Use

As Text Classifier

from transformers import pipeline

pretrained_name = "sahri/sentiment"

nlp = pipeline(
    "sentiment-analysis",
    model=pretrained_name,
    tokenizer=pretrained_name
)

nlp("tidak jelek tapi keren")

Disclaimer

Do consider the biases which come from both the pre-trained RoBERTa model and the SmSA dataset that may be carried over into the results of this model.

Author

Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [sahri ramadhan] All computation and development are done on Google Colaboratory using their free GPU access.