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w11wo/javanese-bert-small-imdb
javanese-bert-small-imdb is a fill-mask model from w11wo. Use it when you need the model to fill a missing word. It is set up for transformers. The card lists the license as mit.
Javanese BERT Small IMDB is a masked language model based on the BERT model. It was trained on Javanese IMDB movie reviews.
Downloads · 30 days
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From the Hugging Face model README
Javanese BERT Small IMDB is a masked language model based on the BERT model. It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained Javanese BERT Small model and is later fine-tuned on the Javanese IMDB movie review dataset. It achieved a perplexity of 19.87 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 | #params | Arch. | Training/Validation data (text) |
|---|---|---|---|
javanese-bert-small-imdb | 110M | BERT Small | Javanese IMDB (47.5 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 |
|---|---|---|---|
| 3.070 | 2.989 | 19.87 | 3:12:33 |
from transformers import pipeline
pretrained_name = "w11wo/javanese-bert-small-imdb"
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-imdb"
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 consider the biases which came from the IMDB review that 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.
If you use any of our models in your research, please cite:
@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}
}