MelayuBERT / README.md
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---
language: ms
tags:
- melayu-bert
license: mit
datasets:
- oscar
widget:
- text: "Saya [MASK] makan nasi hari ini."
---
## Melayu BERT
Melayu BERT is a masked language model based on [BERT](https://arxiv.org/abs/1810.04805). It was trained on the [OSCAR](https://huggingface.co/datasets/oscar) dataset, specifically the `unshuffled_original_ms` subset. The model used was [English BERT model](https://huggingface.co/bert-base-uncased) and fine-tuned on the Malaysian dataset. The model achieved a perplexity of 9.46 on a 20% 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), and [fine-tuning tutorial notebook](https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb) written by [Pierre Guillou](https://huggingface.co/pierreguillou). The model is available both for PyTorch and TensorFlow use.
## Model
The model was trained on 3 epochs with a learning rate of 2e-3 and achieved a training loss per steps as shown below.
| Step |Training loss|
|--------|-------------|
|500 | 5.051300 |
|1000 | 3.701700 |
|1500 | 3.288600 |
|2000 | 3.024000 |
|2500 | 2.833500 |
|3000 | 2.741600 |
|3500 | 2.637900 |
|4000 | 2.547900 |
|4500 | 2.451500 |
|5000 | 2.409600 |
|5500 | 2.388300 |
|6000 | 2.351600 |
## How to Use
### As Masked Language Model
```python
from transformers import pipeline
pretrained_name = "StevenLimcorn/MelayuBERT"
fill_mask = pipeline(
"fill-mask",
model=pretrained_name,
tokenizer=pretrained_name
)
fill_mask("Saya [MASK] makan nasi hari ini.")
```
### Import Tokenizer and Model
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("StevenLimcorn/MelayuBERT")
model = AutoModelForMaskedLM.from_pretrained("StevenLimcorn/MelayuBERT")
```
## Author
Melayu BERT was trained by [Steven Limcorn](https://github.com/stevenlimcorn) and [Wilson Wongso](https://hf.co/w11wo).