readme: ---
  language: en
  tags:
  - singapore
  - sg
  - singlish
  - malaysia
  - ms
  - manglish
  - bert-base-uncased
  license: mit
  datasets:
  - reddit singapore, malaysia
  - hardwarezone
  widget:
  - text: "kopi c siew [MASK]"
  - text: "die [MASK] must try"
  ---
  
  # Model name
  
  SingBert - Bert for Singlish (SG) and Manglish (MY).
  
  ## Model description
  
  [BERT base uncased](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
  [singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
  
  ## Intended uses & limitations
  
  #### How to use
  
  ```python
  >>> from transformers import pipeline
  >>> nlp = pipeline('fill-mask', model='zanelim/singbert')
  >>> nlp("kopi c siew [MASK]")
  
  [{'sequence': '[CLS] kopi c siew dai [SEP]',
    'score': 0.5092713236808777,
    'token': 18765,
    'token_str': 'dai'},
   {'sequence': '[CLS] kopi c siew mai [SEP]',
    'score': 0.3515934646129608,
    'token': 14736,
    'token_str': 'mai'},
   {'sequence': '[CLS] kopi c siew bao [SEP]',
    'score': 0.05576375499367714,
    'token': 25945,
    'token_str': 'bao'},
   {'sequence': '[CLS] kopi c siew. [SEP]',
    'score': 0.006019321270287037,
    'token': 1012,
    'token_str': '.'},
   {'sequence': '[CLS] kopi c siew sai [SEP]',
    'score': 0.0038361591286957264,
    'token': 18952,
    'token_str': 'sai'}]
  
  >>> nlp("one teh c siew dai, and one kopi [MASK].")
  
  [{'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]',
    'score': 0.6176503300666809,
    'token': 1039,
    'token_str': 'c'},
   {'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]',
    'score': 0.21094971895217896,
    'token': 1051,
    'token_str': 'o'},
   {'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]',
    'score': 0.13027705252170563,
    'token': 1012,
    'token_str': '.'},
   {'sequence': '[CLS] one teh c siew dai, and one kopi! [SEP]',
    'score': 0.004680239595472813,
    'token': 999,
    'token_str': '!'},
   {'sequence': '[CLS] one teh c siew dai, and one kopi w [SEP]',
    'score': 0.002034128177911043,
    'token': 1059,
    'token_str': 'w'}]
  
  >>> nlp("dont play [MASK] leh")
  
  [{'sequence': '[CLS] dont play play leh [SEP]',
    'score': 0.9281464219093323,
    'token': 2377,
    'token_str': 'play'},
   {'sequence': '[CLS] dont play politics leh [SEP]',
    'score': 0.010990909300744534,
    'token': 4331,
    'token_str': 'politics'},
   {'sequence': '[CLS] dont play punk leh [SEP]',
    'score': 0.005583590362221003,
    'token': 7196,
    'token_str': 'punk'},
   {'sequence': '[CLS] dont play dirty leh [SEP]',
    'score': 0.0025784350000321865,
    'token': 6530,
    'token_str': 'dirty'},
   {'sequence': '[CLS] dont play cheat leh [SEP]',
    'score': 0.0025066907983273268,
    'token': 21910,
    'token_str': 'cheat'}]
  
  >>> nlp("catch no [MASK]")
  
  [{'sequence': '[CLS] catch no ball [SEP]',
    'score': 0.7922210693359375,
    'token': 3608,
    'token_str': 'ball'},
   {'sequence': '[CLS] catch no balls [SEP]',
    'score': 0.20503675937652588,
    'token': 7395,
    'token_str': 'balls'},
   {'sequence': '[CLS] catch no tail [SEP]',
    'score': 0.0006608376861549914,
    'token': 5725,
    'token_str': 'tail'},
   {'sequence': '[CLS] catch no talent [SEP]',
    'score': 0.0002158183924620971,
    'token': 5848,
    'token_str': 'talent'},
   {'sequence': '[CLS] catch no prisoners [SEP]',
    'score': 5.3481446229852736e-05,
    'token': 5895,
    'token_str': 'prisoners'}]
  
  >>> nlp("confirm plus [MASK]")
  
  [{'sequence': '[CLS] confirm plus chop [SEP]',
    'score': 0.992355227470398,
    'token': 24494,
    'token_str': 'chop'},
   {'sequence': '[CLS] confirm plus one [SEP]',
    'score': 0.0037301010452210903,
    'token': 2028,
    'token_str': 'one'},
   {'sequence': '[CLS] confirm plus minus [SEP]',
    'score': 0.0014284878270700574,
    'token': 15718,
    'token_str': 'minus'},
   {'sequence': '[CLS] confirm plus 1 [SEP]',
    'score': 0.0011354683665558696,
    'token': 1015,
    'token_str': '1'},
   {'sequence': '[CLS] confirm plus chopped [SEP]',
    'score': 0.0003804611915256828,
    'token': 24881,
    'token_str': 'chopped'}]
  
  >>> nlp("die [MASK] must try")
  
  [{'sequence': '[CLS] die die must try [SEP]',
    'score': 0.9552758932113647,
    'token': 3280,
    'token_str': 'die'},
   {'sequence': '[CLS] die also must try [SEP]',
    'score': 0.03644804656505585,
    'token': 2036,
    'token_str': 'also'},
   {'sequence': '[CLS] die liao must try [SEP]',
    'score': 0.003282855963334441,
    'token': 727,
    'token_str': 'liao'},
   {'sequence': '[CLS] die already must try [SEP]',
    'score': 0.0004937972989864647,
    'token': 2525,
    'token_str': 'already'},
   {'sequence': '[CLS] die hard must try [SEP]',
    'score': 0.0003659659414552152,
    'token': 2524,
    'token_str': 'hard'}]
  
  ```
  
  Here is how to use this model to get the features of a given text in PyTorch:
  ```python
  from transformers import BertTokenizer, BertModel
  tokenizer = BertTokenizer.from_pretrained('zanelim/singbert')
  model = BertModel.from_pretrained("zanelim/singbert")
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_inp