Add model card for singbert lite. Update widget for singbert and singbert-large. (#6827)

model_cards/zanelim/singbert-large-sg/README.md

@@ -13,17 +13,17 @@ datasets:
 - reddit singapore, malaysia
 - hardwarezone
 widget:
-- text: "die [MASK] must try"
 - text: "kopi c siew [MASK]"
+- text: "die [MASK] must try"
 ---
 
 # Model name
 
-SingBert - Bert for Singlish (SG) and Manglish (MY).
+SingBert Large - Bert for Singlish (SG) and Manglish (MY).
 
 ## Model description
 
-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
+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

model_cards/zanelim/singbert-lite-sg/README.md

+---
+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_warmup_steps: 5000
+* learning_rate: 0.00176
+* hardware: TPU v3-8

model_cards/zanelim/singbert/README.md

@@ -13,8 +13,8 @@ datasets:
 - reddit singapore, malaysia
 - hardwarezone
 widget:
-- text: "die [MASK] must try"
 - text: "kopi c siew [MASK]"
+- text: "die [MASK] must try"
 ---
 
 # Model name