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README.md
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---
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language: jv
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tags:
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- javanese-distilbert-small-imdb
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license: mit
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datasets:
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- w11wo/imdb-javanese
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widget:
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- text: "Film favoritku yaiku Interstellar [MASK] Christopher Nolan."
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---
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## Javanese DistilBERT Small IMDB
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Javanese DistilBERT Small IMDB is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on Javanese IMDB movie reviews.
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The model was originally the pretrained [Javanese DistilBERT Small model](https://huggingface.co/w11wo/javanese-distilbert-small) and is later fine-tuned on the Javanese IMDB movie review dataset. It achieved a perplexity of 21.01 on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger).
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Hugging Face's `Trainer` class from the [Transformers]((https://huggingface.co/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.
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## Model
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| Model | #params | Arch. | Training/Validation data (text) |
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|----------------------------------|----------|----------------------|---------------------------------|
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| `javanese-distilbert-small-imdb` | 66M | DistilBERT Small | Javanese IMDB (47.5 MB of text) |
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## Evaluation Results
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The model was trained for 5 epochs and the following is the final result once the training ended.
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| train loss | valid loss | perplexity | total time |
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|------------|------------|------------|-------------|
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| 3.126 | 3.039 | 21.01 | 5:6:4 |
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## How to Use
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### As Masked Language Model
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```python
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from transformers import pipeline
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pretrained_name = "w11wo/javanese-distilbert-small-imdb"
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fill_mask = pipeline(
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"fill-mask",
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model=pretrained_name,
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tokenizer=pretrained_name
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)
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fill_mask("Aku mangan sate ing [MASK] bareng konco-konco")
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```
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### Feature Extraction in PyTorch
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```python
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from transformers import DistilBertModel, DistilBertTokenizerFast
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pretrained_name = "w11wo/javanese-distilbert-small-imdb"
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model = DistilBertModel.from_pretrained(pretrained_name)
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tokenizer = DistilBertTokenizerFast.from_pretrained(pretrained_name)
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prompt = "Indonesia minangka negara gedhe."
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encoded_input = tokenizer(prompt, return_tensors='pt')
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output = model(**encoded_input)
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```
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## Disclaimer
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Do consider the biases which came from the IMDB review that may be carried over into the results of this model.
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## Author
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Javanese DistilBERT Small was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.
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