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Add model card for singbert lite. Update widget for singbert and singbert-large. (#6827)
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Zane Lim
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@ -13,17 +13,17 @@ datasets:
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- reddit singapore, malaysia
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- hardwarezone
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widget:
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- text: "die [MASK] must try"
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- text: "kopi c siew [MASK]"
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- text: "die [MASK] must try"
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---
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# Model name
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SingBert - Bert for Singlish (SG) and Manglish (MY).
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SingBert Large - Bert for Singlish (SG) and Manglish (MY).
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## Model description
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Similar to [SingBert](https://huggingface.co/zanelim/singbert) but initialized from [BERT large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
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Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the large version, which was initialized from [BERT large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
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[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
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## Intended uses & limitations
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168
model_cards/zanelim/singbert-lite-sg/README.md
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168
model_cards/zanelim/singbert-lite-sg/README.md
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@ -0,0 +1,168 @@
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---
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language: en
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tags:
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- singapore
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- sg
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- singlish
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- malaysia
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- ms
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- manglish
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- albert-base-v2
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license: mit
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datasets:
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- reddit singapore, malaysia
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- hardwarezone
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widget:
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- text: "dont play [MASK] leh"
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- text: "die [MASK] must try"
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---
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# Model name
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SingBert Lite - Bert for Singlish (SG) and Manglish (MY).
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## Model description
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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
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[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
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## Intended uses & limitations
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#### How to use
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```python
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>>> from transformers import pipeline
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>>> nlp = pipeline('fill-mask', model='zanelim/singbert-lite-sg')
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>>> nlp("die [MASK] must try")
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[{'sequence': '[CLS] die die must try[SEP]',
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'score': 0.7731555700302124,
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'token': 1327,
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'token_str': '▁die'},
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{'sequence': '[CLS] die also must try[SEP]',
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'score': 0.04763784259557724,
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'token': 67,
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'token_str': '▁also'},
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{'sequence': '[CLS] die still must try[SEP]',
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'score': 0.01859409362077713,
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'token': 174,
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'token_str': '▁still'},
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{'sequence': '[CLS] die u must try[SEP]',
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'score': 0.015824034810066223,
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'token': 287,
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'token_str': '▁u'},
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{'sequence': '[CLS] die is must try[SEP]',
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'score': 0.011271446943283081,
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'token': 25,
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'token_str': '▁is'}]
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>>> nlp("dont play [MASK] leh")
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[{'sequence': '[CLS] dont play play leh[SEP]',
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'score': 0.4365769624710083,
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'token': 418,
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'token_str': '▁play'},
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{'sequence': '[CLS] dont play punk leh[SEP]',
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'score': 0.06880936771631241,
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'token': 6769,
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'token_str': '▁punk'},
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{'sequence': '[CLS] dont play game leh[SEP]',
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'score': 0.051739856600761414,
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'token': 250,
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'token_str': '▁game'},
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{'sequence': '[CLS] dont play games leh[SEP]',
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'score': 0.045703962445259094,
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'token': 466,
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'token_str': '▁games'},
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{'sequence': '[CLS] dont play around leh[SEP]',
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'score': 0.013458190485835075,
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'token': 140,
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'token_str': '▁around'}]
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>>> nlp("catch no [MASK]")
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[{'sequence': '[CLS] catch no ball[SEP]',
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'score': 0.6197211146354675,
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'token': 1592,
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'token_str': '▁ball'},
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{'sequence': '[CLS] catch no balls[SEP]',
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'score': 0.08441998809576035,
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'token': 7152,
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'token_str': '▁balls'},
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{'sequence': '[CLS] catch no joke[SEP]',
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'score': 0.0676785409450531,
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'token': 8186,
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'token_str': '▁joke'},
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{'sequence': '[CLS] catch no?[SEP]',
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'score': 0.040638409554958344,
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'token': 60,
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'token_str': '?'},
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{'sequence': '[CLS] catch no one[SEP]',
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'score': 0.03546864539384842,
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'token': 53,
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'token_str': '▁one'}]
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>>> nlp("confirm plus [MASK]")
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[{'sequence': '[CLS] confirm plus chop[SEP]',
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'score': 0.9608421921730042,
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'token': 17144,
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'token_str': '▁chop'},
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{'sequence': '[CLS] confirm plus guarantee[SEP]',
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'score': 0.011784233152866364,
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'token': 9120,
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'token_str': '▁guarantee'},
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{'sequence': '[CLS] confirm plus confirm[SEP]',
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'score': 0.010571340098977089,
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'token': 10265,
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'token_str': '▁confirm'},
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{'sequence': '[CLS] confirm plus egg[SEP]',
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'score': 0.0033525123726576567,
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'token': 6387,
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'token_str': '▁egg'},
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{'sequence': '[CLS] confirm plus bet[SEP]',
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'score': 0.0008760977652855217,
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'token': 5676,
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'token_str': '▁bet'}]
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```
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Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import AlbertTokenizer, AlbertModel
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tokenizer = AlbertTokenizer.from_pretrained('zanelim/singbert-lite-sg')
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model = AlbertModel.from_pretrained("zanelim/singbert-lite-sg")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```
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and in TensorFlow:
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```python
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from transformers import AlbertTokenizer, TFAlbertModel
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tokenizer = AlbertTokenizer.from_pretrained("zanelim/singbert-lite-sg")
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model = TFAlbertModel.from_pretrained("zanelim/singbert-lite-sg")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='tf')
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output = model(encoded_input)
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```
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#### Limitations and bias
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This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
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constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.
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## Training data
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Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
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corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.
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## Training procedure
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Initialized with [albert base v2](https://github.com/google-research/albert#albert) vocab and checkpoints (pre-trained weights).
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Pre-training was further finetuned on training data with the following hyperparameters
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* train_batch_size: 4096
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* max_seq_length: 128
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* num_train_steps: 125000
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* num_warmup_steps: 5000
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* learning_rate: 0.00176
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* hardware: TPU v3-8
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@ -13,8 +13,8 @@ datasets:
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- reddit singapore, malaysia
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- hardwarezone
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widget:
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- text: "die [MASK] must try"
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- text: "kopi c siew [MASK]"
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- text: "die [MASK] must try"
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---
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# Model name
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