metadata
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- bert-large-uncased
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: kopi c siew [MASK]
- text: die [MASK] must try
Model name
SingBert Large - Bert for Singlish (SG) and Manglish (MY).
Model description
Similar to SingBert but the large version, which was initialized from BERT large uncased (whole word masking), with pre-training finetuned on singlish and manglish data.
Intended uses & limitations
How to use
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert-large-sg')
>>> nlp("kopi c siew [MASK]")
[{'sequence': '[CLS] kopi c siew dai [SEP]',
'score': 0.9003700017929077,
'token': 18765,
'token_str': 'dai'},
{'sequence': '[CLS] kopi c siew mai [SEP]',
'score': 0.0779474675655365,
'token': 14736,
'token_str': 'mai'},
{'sequence': '[CLS] kopi c siew. [SEP]',
'score': 0.0032227332703769207,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] kopi c siew bao [SEP]',
'score': 0.0017727474914863706,
'token': 25945,
'token_str': 'bao'},
{'sequence': '[CLS] kopi c siew peng [SEP]',
'score': 0.0012526646023616195,
'token': 26473,
'token_str': 'peng'}]
>>> nlp("one teh c siew dai, and one kopi [MASK]")
[{'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]',
'score': 0.5249741077423096,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]',
'score': 0.27349168062210083,
'token': 1051,
'token_str': 'o'},
{'sequence': '[CLS] one teh c siew dai, and one kopi peng [SEP]',
'score': 0.057190295308828354,
'token': 26473,
'token_str': 'peng'},
{'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]',
'score': 0.04022320732474327,
'token': 1039,
'token_str': 'c'},
{'sequence': '[CLS] one teh c siew dai, and one kopi? [SEP]',
'score': 0.01191170234233141,
'token': 1029,
'token_str': '?'}]
>>> nlp("die [MASK] must try")
[{'sequence': '[CLS] die die must try [SEP]',
'score': 0.9921030402183533,
'token': 3280,
'token_str': 'die'},
{'sequence': '[CLS] die also must try [SEP]',
'score': 0.004993876442313194,
'token': 2036,
'token_str': 'also'},
{'sequence': '[CLS] die liao must try [SEP]',
'score': 0.000317625846946612,
'token': 727,
'token_str': 'liao'},
{'sequence': '[CLS] die still must try [SEP]',
'score': 0.0002260878391098231,
'token': 2145,
'token_str': 'still'},
{'sequence': '[CLS] die i must try [SEP]',
'score': 0.00016935862367972732,
'token': 1045,
'token_str': 'i'}]
>>> nlp("dont play [MASK] leh")
[{'sequence': '[CLS] dont play play leh [SEP]',
'score': 0.9079819321632385,
'token': 2377,
'token_str': 'play'},
{'sequence': '[CLS] dont play punk leh [SEP]',
'score': 0.006846973206847906,
'token': 7196,
'token_str': 'punk'},
{'sequence': '[CLS] dont play games leh [SEP]',
'score': 0.004041737411171198,
'token': 2399,
'token_str': 'games'},
{'sequence': '[CLS] dont play politics leh [SEP]',
'score': 0.003728888463228941,
'token': 4331,
'token_str': 'politics'},
{'sequence': '[CLS] dont play cheat leh [SEP]',
'score': 0.0032805048394948244,
'token': 21910,
'token_str': 'cheat'}]
>>> nlp("confirm plus [MASK]")
{'sequence': '[CLS] confirm plus chop [SEP]',
'score': 0.9749826192855835,
'token': 24494,
'token_str': 'chop'},
{'sequence': '[CLS] confirm plus chopped [SEP]',
'score': 0.017554156482219696,
'token': 24881,
'token_str': 'chopped'},
{'sequence': '[CLS] confirm plus minus [SEP]',
'score': 0.002725469646975398,
'token': 15718,
'token_str': 'minus'},
{'sequence': '[CLS] confirm plus guarantee [SEP]',
'score': 0.000900257145985961,
'token': 11302,
'token_str': 'guarantee'},
{'sequence': '[CLS] confirm plus one [SEP]',
'score': 0.0004384620988275856,
'token': 2028,
'token_str': 'one'}]
>>> nlp("catch no [MASK]")
[{'sequence': '[CLS] catch no ball [SEP]',
'score': 0.9381157159805298,
'token': 3608,
'token_str': 'ball'},
{'sequence': '[CLS] catch no balls [SEP]',
'score': 0.060842301696538925,
'token': 7395,
'token_str': 'balls'},
{'sequence': '[CLS] catch no fish [SEP]',
'score': 0.00030917322146706283,
'token': 3869,
'token_str': 'fish'},
{'sequence': '[CLS] catch no breath [SEP]',
'score': 7.552534952992573e-05,
'token': 3052,
'token_str': 'breath'},
{'sequence': '[CLS] catch no tail [SEP]',
'score': 4.208395694149658e-05,
'token': 5725,
'token_str': 'tail'}]
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('zanelim/singbert-large-sg')
model = BertModel.from_pretrained("zanelim/singbert-large-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
and in TensorFlow:
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained("zanelim/singbert-large-sg")
model = TFBertModel.from_pretrained("zanelim/singbert-large-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 bert large uncased (whole word masking) vocab and checkpoints (pre-trained weights). Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab.
Pre-training was further finetuned on training data with the following hyperparameters
- train_batch_size: 512
- max_seq_length: 128
- num_train_steps: 300000
- num_warmup_steps: 5000
- learning_rate: 2e-5
- hardware: TPU v3-8