readme: ---
  language: en
  tags:
  - singapore
  - sg
  - singlish
  - malaysia
  - ms
  - manglish
  - albert-base-v2
  license: mit
  datasets:
  - reddit singapore, malaysia
  - hardwarezone
  widget:
  - text: "dont play [MASK] leh"
  - text: "die [MASK] must try"
  ---
  
  # Model name
  
  SingBert Lite - Bert for Singlish (SG) and Manglish (MY).
  
  ## Model description
  
  Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the lite-version, which was initialized from [Albert base v2](https://github.com/google-research/albert#albert), 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-lite-sg')
  >>> nlp("die [MASK] must try")
  
  [{'sequence': '[CLS] die die must try[SEP]',
    'score': 0.7731555700302124,
    'token': 1327,
    'token_str': '▁die'},
   {'sequence': '[CLS] die also must try[SEP]',
    'score': 0.04763784259557724,
    'token': 67,
    'token_str': '▁also'},
   {'sequence': '[CLS] die still must try[SEP]',
    'score': 0.01859409362077713,
    'token': 174,
    'token_str': '▁still'},
   {'sequence': '[CLS] die u must try[SEP]',
    'score': 0.015824034810066223,
    'token': 287,
    'token_str': '▁u'},
   {'sequence': '[CLS] die is must try[SEP]',
    'score': 0.011271446943283081,
    'token': 25,
    'token_str': '▁is'}]
  
  >>> nlp("dont play [MASK] leh")
  
  [{'sequence': '[CLS] dont play play leh[SEP]',
    'score': 0.4365769624710083,
    'token': 418,
    'token_str': '▁play'},
   {'sequence': '[CLS] dont play punk leh[SEP]',
    'score': 0.06880936771631241,
    'token': 6769,
    'token_str': '▁punk'},
   {'sequence': '[CLS] dont play game leh[SEP]',
    'score': 0.051739856600761414,
    'token': 250,
    'token_str': '▁game'},
   {'sequence': '[CLS] dont play games leh[SEP]',
    'score': 0.045703962445259094,
    'token': 466,
    'token_str': '▁games'},
   {'sequence': '[CLS] dont play around leh[SEP]',
    'score': 0.013458190485835075,
    'token': 140,
    'token_str': '▁around'}]
  
  >>> nlp("catch no [MASK]")
  
  [{'sequence': '[CLS] catch no ball[SEP]',
    'score': 0.6197211146354675,
    'token': 1592,
    'token_str': '▁ball'},
   {'sequence': '[CLS] catch no balls[SEP]',
    'score': 0.08441998809576035,
    'token': 7152,
    'token_str': '▁balls'},
   {'sequence': '[CLS] catch no joke[SEP]',
    'score': 0.0676785409450531,
    'token': 8186,
    'token_str': '▁joke'},
   {'sequence': '[CLS] catch no?[SEP]',
    'score': 0.040638409554958344,
    'token': 60,
    'token_str': '?'},
   {'sequence': '[CLS] catch no one[SEP]',
    'score': 0.03546864539384842,
    'token': 53,
    'token_str': '▁one'}]
  
  >>> nlp("confirm plus [MASK]")
  
  [{'sequence': '[CLS] confirm plus chop[SEP]',
    'score': 0.9608421921730042,
    'token': 17144,
    'token_str': '▁chop'},
   {'sequence': '[CLS] confirm plus guarantee[SEP]',
    'score': 0.011784233152866364,
    'token': 9120,
    'token_str': '▁guarantee'},
   {'sequence': '[CLS] confirm plus confirm[SEP]',
    'score': 0.010571340098977089,
    'token': 10265,
    'token_str': '▁confirm'},
   {'sequence': '[CLS] confirm plus egg[SEP]',
    'score': 0.0033525123726576567,
    'token': 6387,
    'token_str': '▁egg'},
   {'sequence': '[CLS] confirm plus bet[SEP]',
    'score': 0.0008760977652855217,
    'token': 5676,
    'token_str': '▁bet'}]
  
  ```
  
  Here is how to use this model to get the features of a given text in PyTorch:
  ```python
  from transformers import AlbertTokenizer, AlbertModel
  tokenizer = AlbertTokenizer.from_pretrained('zanelim/singbert-lite-sg')
  model = AlbertModel.from_pretrained("zanelim/singbert-lite-sg")
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  and in TensorFlow:
  ```python
  from transformers import AlbertTokenizer, TFAlbertModel
  tokenizer = AlbertTokenizer.from_pretrained("zanelim/singbert-lite-sg")
  model = TFAlbertModel.from_pretrained("zanelim/singbert-lite-sg")
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='tf')
  output = model(encoded_input)
  ```
  
  #### Limitations and bias
  This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
  constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.
  
  ## Training data
  Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
  corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.
  
  ## Training procedure
  
  Initialized with [albert base v2](https://github.com/google-research/albert#albert) vocab and checkpoints (pre-trained weights).
  
  Pre-training was further finetuned on training data with the following hyperparameters
  * train_batch_size: 4096
  * max_seq_length: 128
  * num_train_steps: 125000
  * num_warmu