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)

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Select AutoNLP in the “Train” menu to fine-tune this model automatically.

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