metadata
language: id
tags:
- indonesian-roberta-base
license: mit
datasets:
- oscar
widget:
- text: Budi telat ke sekolah karena ia <mask>.
Indonesian RoBERTa Base
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
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
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)