Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/zanelim/singbert-large-sg/README.md

README.md

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+---
+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](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
+[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-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:
+```python
+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:
+```python
+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)](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