Javanese DistilBERT Small IMDB Classifier is a movie-classification model based on the DistilBERT model. It was trained on Javanese IMDB movie reviews.

The model was originally w11wo/javanese-distilbert-small-imdb which is then fine-tuned on the w11wo/imdb-javanese dataset consisting of Javanese IMDB movie reviews. It achieved an accuracy of 76.04% on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial notebook written by Sylvain Gugger.

Hugging Face's Trainer class from the Transformers library was used to train the model. 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-distilbert-small-imdb-classifier`	66M	DistilBERT Small	Javanese IMDB (47.5 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	accuracy	total time
0.131	1.113	0.760	1:26:4

How to Use

As Text Classifier

python

from transformers import pipeline

pretrained_name = "w11wo/javanese-distilbert-small-imdb-classifier"

nlp = pipeline(
    "sentiment-analysis",
    model=pretrained_name,
    tokenizer=pretrained_name
)

nlp("Film sing apik banget!")

Disclaimer

Do consider the biases which came from the IMDB review that may be carried over into the results of this model.

Author

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

Citation

If you use any of our models in your research, please cite:

bib

@inproceedings{wongso2021causal,
    title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures},
    author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
    booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
    pages={1--7},
    year={2021},
    organization={IEEE}
}