Javanese BERT Small is a masked language model based on the BERT model. It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pretrained English BERT model and is later fine-tuned on the Javanese dataset. It achieved a perplexity of 22.00 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 BERT model and their
Trainer class. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.
|Model||#params||Arch.||Training/Validation data (text)|
||110M||BERT Small||Javanese Wikipedia (319 MB of text)|
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|
from transformers import pipeline pretrained_name = "w11wo/javanese-bert-small" fill_mask = pipeline( "fill-mask", model=pretrained_name, tokenizer=pretrained_name ) fill_mask("Aku mangan sate ing [MASK] bareng konco-konco")
from transformers import BertModel, BertTokenizerFast pretrained_name = "w11wo/javanese-bert-small" model = BertModel.from_pretrained(pretrained_name) tokenizer = BertTokenizerFast.from_pretrained(pretrained_name) prompt = "Indonesia minangka negara gedhe." encoded_input = tokenizer(prompt, return_tensors='pt') output = model(**encoded_input)
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.
Javanese BERT Small was trained and evaluated by Wilson Wongso. All computation and development are done on Google Colaboratory using their free GPU access.
Select AutoNLP in the “Train” menu to fine-tune this model automatically.
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