Create model cards for indonesian models (#6522)

* added model cards for indonesian gpt2-small, bert-base and roberta-base models

* removed bibtex entries

model_cards/cahya/bert-base-indonesian-522M/README.md

+---
+language: "id"
+license: "mit"
+datasets:
+- Indonesian Wikipedia
+widget:
+- text: "Ibu ku sedang bekerja [MASK] supermarket."
+---
+
+# Indonesian BERT base model (uncased) 
+
+## Model description
+It is BERT-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/bert-base-indonesian-522M')
+>>> unmasker("Ibu ku sedang bekerja [MASK] supermarket")
+
+[{'sequence': '[CLS] ibu ku sedang bekerja di supermarket [SEP]',
+  'score': 0.7983310222625732,
+  'token': 1495},
+ {'sequence': '[CLS] ibu ku sedang bekerja. supermarket [SEP]',
+  'score': 0.090003103017807,
+  'token': 17},
+ {'sequence': '[CLS] ibu ku sedang bekerja sebagai supermarket [SEP]',
+  'score': 0.025469014421105385,
+  'token': 1600},
+ {'sequence': '[CLS] ibu ku sedang bekerja dengan supermarket [SEP]',
+  'score': 0.017966199666261673,
+  'token': 1555},
+ {'sequence': '[CLS] ibu ku sedang bekerja untuk supermarket [SEP]',
+  'score': 0.016971781849861145,
+  'token': 1572}]
+```
+Here is how to use this model to get the features of a given text in PyTorch:
+```python
+from transformers import BertTokenizer, BertModel
+
+model_name='cahya/bert-base-indonesian-522M'
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertModel.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 BertTokenizer, TFBertModel
+
+model_name='cahya/bert-base-indonesian-522M'
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = TFBertModel.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:
+
+```[CLS] Sentence A [SEP] Sentence B [SEP]```

model_cards/cahya/gpt2-small-indonesian-522M/README.md

+---
+language: "id"
+license: "mit"
+datasets:
+- Indonesian Wikipedia
+widget:
+- text: "Pulau Dewata sering dikunjungi"
+---
+
+# Indonesian GPT2 small model 
+
+## Model description
+It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) 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 text generation. Since the generation relies on some randomness, 
+we set a seed for reproducibility:
+```python
+>>> from transformers import pipeline, set_seed
+>>> generator = pipeline('text-generation', model='cahya/gpt2-small-indonesian-522M')
+>>> set_seed(42)
+>>> generator("Kerajaan Majapahit adalah", max_length=30, num_return_sequences=5, num_beams=10)
+
+[{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini berdiri pada abad ke-14'}, 
+{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-16. Kerajaan ini berdiri pada abad ke-14'}, 
+{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini berdiri pada abad ke-15'}, 
+{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-16. Kerajaan ini berdiri pada abad ke-15'}, 
+{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini merupakan kelanjutan dari Kerajaan Majapahit yang'}]
+
+```
+Here is how to use this model to get the features of a given text in PyTorch:
+```python
+from transformers import GPT2Tokenizer, GPT2Model
+
+model_name='cahya/gpt2-small-indonesian-522M'
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2Model.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 GPT2Tokenizer, TFGPT2Model
+
+model_name='cahya/gpt2-small-indonesian-522M'
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = TFGPT2Model.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 tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and 
+a vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens.

model_cards/cahya/roberta-base-indonesian-522M/README.md

+---
+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>```