Migrate model card from transformers-repo

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Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/cahya/roberta-base-indonesian-522M/README.md

README.md

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+---
+language: "id"
+license: "mit"
+datasets:
+- Indonesian Wikipedia
+widget:
+- text: "Ibu ku sedang bekerja <mask> supermarket."
+---
+
+# Indonesian RoBERTa base model (uncased) 
+
+## Model description
+It is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This 
+model is uncased: it does not make a difference between indonesia and Indonesia.
+
+This is one of several other language models that have been pre-trained with indonesian datasets. More detail about 
+its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)
+
+## Intended uses & limitations
+
+### How to use
+You can use this model directly with a pipeline for masked language modeling:
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='cahya/roberta-base-indonesian-522M')
+>>> unmasker("Ibu ku sedang bekerja <mask> supermarket")
+
+```
+Here is how to use this model to get the features of a given text in PyTorch:
+```python
+from transformers import RobertaTokenizer, RobertaModel
+
+model_name='cahya/roberta-base-indonesian-522M'
+tokenizer = RobertaTokenizer.from_pretrained(model_name)
+model = RobertaModel.from_pretrained(model_name)
+text = "Silakan diganti dengan text apa saja."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+and in Tensorflow:
+```python
+from transformers import RobertaTokenizer, TFRobertaModel
+
+model_name='cahya/roberta-base-indonesian-522M'
+tokenizer = RobertaTokenizer.from_pretrained(model_name)
+model = TFRobertaModel.from_pretrained(model_name)
+text = "Silakan diganti dengan text apa saja."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+## Training data
+
+This model was pre-trained with 522MB of indonesian Wikipedia.
+The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are 
+then of the form:
+
+```<s> Sentence A </s> Sentence B </s>```