Javanese RoBERTa Small

Javanese RoBERTa Small is a masked language model based on the RoBERTa model. It was trained on the latest (late December 2020) Javanese Wikipedia articles.

The model was originally HuggingFace's pretrained English RoBERTa model and is later fine-tuned on the Javanese dataset. It achieved a perplexity of 33.30 on the validation dataset (20% of the articles). Many of the techniques used are based on a Hugging Face tutorial notebook written by Sylvain Gugger, and fine-tuning tutorial notebook written by Pierre Guillou.

Hugging Face's Transformers library was used to train the model -- utilizing the base RoBERTa model and their Trainer class. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.

Model

Model #params Arch. Training/Validation data (text)
javanese-roberta-small 124M RoBERTa Javanese Wikipedia (319 MB of text)

Evaluation Results

The model was trained for 5 epochs and the following is the final result once the training ended.

train loss valid loss perplexity total time
3.481 3.506 33.30 1:11:43

How to Use

As Masked Language Model

from transformers import pipeline

pretrained_name = "w11wo/javanese-roberta-small"

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

fill_mask("Meja lan kursine lagi <mask>.")

Feature Extraction in PyTorch

from transformers import RobertaModel, RobertaTokenizerFast

pretrained_name = "w11wo/javanese-roberta-small"
model = RobertaModel.from_pretrained(pretrained_name)
tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)

prompt = "Indonesia minangka negara gedhe."
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)

Disclaimer

Do remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results of this model.

Author

Javanese RoBERTa Small was trained and evaluated by Wilson Wongso. All computation and development are done on Google Colaboratory using their free GPU access.

New

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

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