Created README.md; Added essential model information.

README.md

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+---
+language: jv
+tags:
+- javanese-distilbert-small
+license: mit
+datasets:
+- wikipedia
+widget:
+- text: "Joko [MASK] wis kelas siji SMA."
+---
+
+## Javanese DistilBERT Small
+Javanese DistilBERT Small is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1810.04805). It was trained on the latest (late December 2020) Javanese Wikipedia articles.
+
+The model was originally HuggingFace's pretrained [English DistilBERT model](https://huggingface.co/distilbert-base-uncased) and is later fine-tuned on the Javanese dataset. It achieved a perplexity of 22.00 on the validation dataset (20% of the articles). 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)     |
+|-----------------------------|---------|------------------|-------------------------------------|
+| `javanese-distilbert-small` |   66M   | DistilBERT Small | Javanese Wikipedia (319 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 | perplexity | total time |
+|------------|------------|------------|------------|
+|    3.088   |    3.153   |   23.54    |   1:46:37  |
+
+## How to Use
+### As Masked Language Model
+```python
+from transformers import pipeline
+
+pretrained_name = "w11wo/javanese-distilbert-small"
+
+fill_mask = pipeline(
+    "fill-mask",
+    model=pretrained_name,
+    tokenizer=pretrained_name
+)
+
+fill_mask("Aku mangan sate ing [MASK] bareng konco-konco")
+```
+### Feature Extraction in PyTorch
+```python
+from transformers import DistilBertModel, DistilBertTokenizerFast
+
+pretrained_name = "w11wo/javanese-distilbert-small"
+model = DistilBertModel.from_pretrained(pretrained_name)
+tokenizer = DistilBertTokenizerFast.from_pretrained(pretrained_name)
+
+prompt = "Indonesia minangka negara gedhe."
+encoded_input = tokenizer(prompt, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+## Disclaimer
+Do remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results of this model.
+
+## Author
+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.