Add model card for singbert. (#6674)

* Add model card for singbert.

Adding a model card for singbert- bert for singlish and manglish.

* Update README.md

Add additional tags and model name.

* Update README.md

Fix tag for malay.

* Update model_cards/zanelim/singbert/README.md

Fix language
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

* Add examples and custom widget input.

Add examples and custom widget input.
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

model_cards/zanelim/singbert/README.md

+---
+language: en
+tags:
+- singapore
+- sg
+- singlish
+- malaysia
+- ms
+- manglish
+- bert-base-uncased
+license: mit
+datasets:
+- reddit singapore, malaysia
+- hardwarezone
+widget:
+- text: "die [MASK] must try"
+- text: "kopi c siew [MASK]"
+---
+
+# 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_input)
+```
+
+and in TensorFlow:
+```python
+from transformers import BertTokenizer, TFBertModel
+tokenizer = BertTokenizer.from_pretrained("zanelim/singbert")
+model = TFBertModel.from_pretrained("zanelim/singbert")
+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 base uncased](https://github.com/google-research/bert#pre-trained-models) 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