Indonesian RoBERTa Base is a masked language model based on the RoBERTa model. It was trained on the OSCAR dataset, specifically the unshuffled_deduplicated_id subset. The model was trained from scratch and achieved an evaluation loss of 1.798 and an evaluation accuracy of 62.45%.

This model was trained using HuggingFace's Flax framework and is part of the JAX/Flax Community Week organized by HuggingFace. All training was done on a TPUv3-8 VM, sponsored by the Google Cloud team.

All necessary scripts used for training could be found in the Files and versions tab, as well as the Training metrics logged via Tensorboard.

Model

Model	#params	Arch.	Training/Validation data (text)
`indonesian-roberta-base`	124M	RoBERTa	OSCAR `unshuffled_deduplicated_id` Dataset

Evaluation Results

The model was trained for 8 epochs and the following is the final result once the training ended.

train loss	valid loss	valid accuracy	total time
1.870	1.798	0.6245	18:25:39

How to Use

As Masked Language Model

python

from transformers import pipeline

pretrained_name = "flax-community/indonesian-roberta-base"

fill_mask = pipeline(
    "fill-mask",
    model=pretrained_name,
    tokenizer=pretrained_name
)

fill_mask("Budi sedang <mask> di sekolah.")

Feature Extraction in PyTorch

python

from transformers import RobertaModel, RobertaTokenizerFast

pretrained_name = "flax-community/indonesian-roberta-base"
model = RobertaModel.from_pretrained(pretrained_name)
tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)

prompt = "Budi sedang berada di sekolah."
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)

Team Members

Wilson Wongso (@w11wo)
Steven Limcorn (@stevenlimcorn)
Samsul Rahmadani (@munggok)
Chew Kok Wah (@chewkokwah)