readme: ---
  language: "id"
  license: "mit"
  datasets:
  - wikipedia
  widget:
  - text: "Ibu ku sedang bekerja [MASK] sawah."
  ---
  
  # 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]```
  