Created README.md; Added essential model information.

README.md

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+---
+language: ms
+tags:
+- malaysian-distilbert-small
+license: mit
+datasets:
+- oscar
+widget:
+- text: "Hari ini adalah hari yang [MASK]!"
+---
+
+## Malaysian DistilBERT Small
+Malaysian DistilBERT Small is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on the OSCAR dataset, specifically the `unshuffled_original_ms` subset.
+
+The model was originally HuggingFace's pretrained [English DistilBERT model](https://huggingface.co/distilbert-base-uncased) and is later fine-tuned on the Malaysian dataset. It achieved a perplexity of 10.33 on the validation dataset (20% of the 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).
+
+Hugging Face's [Transformers]((https://huggingface.co/transformers)) library was used to train the model -- utilizing the base DistilBERT model and their `Trainer` class. 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)        |
+|------------------------------|---------|------------------|----------------------------------------|
+| `malaysian-distilbert-small` |   66M   | DistilBERT Small | OSCAR `unshuffled_original_ms` Dataset |
+
+## Evaluation Results
+The model was trained for 1 epoch and the following is the final result once the training ended.
+
+| train loss | valid loss | perplexity | total time |
+|------------|------------|------------|------------|
+|    2.476   |    2.336   |   10.33    |   0:40:05  |
+
+## How to Use
+### As Masked Language Model
+```python
+from transformers import pipeline
+
+pretrained_name = "w11wo/malaysian-distilbert-small"
+
+fill_mask = pipeline(
+    "fill-mask",
+    model=pretrained_name,
+    tokenizer=pretrained_name
+)
+
+fill_mask("Henry adalah seorang lelaki yang tinggal di [MASK].")
+```
+### Feature Extraction in PyTorch
+```python
+from transformers import DistilBertModel, DistilBertTokenizerFast
+
+pretrained_name = "w11wo/malaysian-distilbert-small"
+model = DistilBertModel.from_pretrained(pretrained_name)
+tokenizer = DistilBertTokenizerFast.from_pretrained(pretrained_name)
+
+prompt = "Bolehkah anda [MASK] Bahasa Melayu?"
+encoded_input = tokenizer(prompt, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+## Disclaimer
+Do consider the biases which came from the OSCAR dataset that may be carried over into the results of this model.
+
+## Author
+Malaysian 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.