Added albert-base-bahasa-cased README and fixed tiny-bert-bahasa-cased README (#3613)

* add bert bahasa readme

* update readme

* update readme

* added xlnet

* added tiny-bert and fix xlnet readme

* added albert base

model_cards/huseinzol05/albert-base-bahasa-cased/README.md

+---
+language: malay
+---
+
+# Bahasa Albert Model
+
+Pretrained Albert base language model for Malay and Indonesian. 
+
+## Pretraining Corpus
+
+`albert-base-bahasa-cased` model was pretrained on ~1.8 Billion words. We trained on both standard and social media language structures, and below is list of data we trained on,
+
+1. [dumping wikipedia](https://github.com/huseinzol05/Malaya-Dataset#wikipedia-1).
+2. [local instagram](https://github.com/huseinzol05/Malaya-Dataset#instagram).
+3. [local twitter](https://github.com/huseinzol05/Malaya-Dataset#twitter-1).
+4. [local news](https://github.com/huseinzol05/Malaya-Dataset#public-news).
+5. [local parliament text](https://github.com/huseinzol05/Malaya-Dataset#parliament).
+6. [local singlish/manglish text](https://github.com/huseinzol05/Malaya-Dataset#singlish-text).
+7. [IIUM Confession](https://github.com/huseinzol05/Malaya-Dataset#iium-confession).
+8. [Wattpad](https://github.com/huseinzol05/Malaya-Dataset#wattpad).
+9. [Academia PDF](https://github.com/huseinzol05/Malaya-Dataset#academia-pdf).
+
+Preprocessing steps can reproduce from here, [Malaya/pretrained-model/preprocess](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/preprocess).
+
+## Pretraining details
+
+- This model was trained using Google Albert's github [repository](https://github.com/google-research/ALBERT) on v3-8 TPU.
+- All steps can reproduce from here, [Malaya/pretrained-model/albert](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/albert).
+
+## Load Pretrained Model
+
+You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
+
+```python
+from transformers import AlbertTokenizer, AlbertModel
+
+model = BertModel.from_pretrained('huseinzol05/albert-base-bahasa-cased')
+tokenizer = AlbertTokenizer.from_pretrained(
+    'huseinzol05/albert-base-bahasa-cased',
+    do_lower_case = False,
+)
+```
+
+## Example using AutoModelWithLMHead
+
+```python
+from transformers import AlbertTokenizer, AutoModelWithLMHead, pipeline
+
+model = AutoModelWithLMHead.from_pretrained('huseinzol05/albert-base-bahasa-cased')
+tokenizer = AlbertTokenizer.from_pretrained(
+    'huseinzol05/albert-base-bahasa-cased',
+    do_lower_case = False,
+)
+fill_mask = pipeline('fill-mask', model = model, tokenizer = tokenizer)
+print(fill_mask('makan ayam dengan [MASK]'))
+```
+
+Output is,
+
+```text
+[{'sequence': '[CLS] makan ayam dengan ayam[SEP]',
+  'score': 0.044952988624572754,
+  'token': 629},
+ {'sequence': '[CLS] makan ayam dengan sayur[SEP]',
+  'score': 0.03621877357363701,
+  'token': 1639},
+ {'sequence': '[CLS] makan ayam dengan ikan[SEP]',
+  'score': 0.034429922699928284,
+  'token': 758},
+ {'sequence': '[CLS] makan ayam dengan nasi[SEP]',
+  'score': 0.032447945326566696,
+  'token': 453},
+ {'sequence': '[CLS] makan ayam dengan rendang[SEP]',
+  'score': 0.028885239735245705,
+  'token': 2451}]
+```
+
+## Results
+
+For further details on the model performance, simply checkout accuracy page from Malaya, https://malaya.readthedocs.io/en/latest/Accuracy.html, we compared with traditional models.
+
+## Acknowledgement
+
+Thanks to [Im Big](https://www.facebook.com/imbigofficial/), [LigBlou](https://www.facebook.com/ligblou), [Mesolitica](https://mesolitica.com/) and [KeyReply](https://www.keyreply.com/) for sponsoring AWS, Google and GPU clouds to train Albert for Bahasa. 
+
+

model_cards/huseinzol05/tiny-bert-bahasa-cased/README.md

@@ -4,7 +4,7 @@ language: malay
 
 # Bahasa Tiny-BERT Model
 
-General Distilled Tiny BERT base language model for Malay and Indonesian. 
+General Distilled Tiny BERT language model for Malay and Indonesian. 
 
 ## Pretraining Corpus
 
@@ -36,7 +36,7 @@ from transformers import AlbertTokenizer, BertModel
 
 model = BertModel.from_pretrained('huseinzol05/tiny-bert-bahasa-cased')
 tokenizer = AlbertTokenizer.from_pretrained(
-    'huseinzol05/tiny-base-bahasa-cased',
+    'huseinzol05/tiny-bert-bahasa-cased',
     unk_token = '[UNK]',
     pad_token = '[PAD]',
     do_lower_case = False,
@@ -50,9 +50,9 @@ We use [google/sentencepiece](https://github.com/google/sentencepiece) to train
 ```python
 from transformers import AlbertTokenizer, AutoModelWithLMHead, pipeline
 
-model = AutoModelWithLMHead.from_pretrained('huseinzol05/tiny-base-bahasa-cased')
+model = AutoModelWithLMHead.from_pretrained('huseinzol05/tiny-bert-bahasa-cased')
 tokenizer = AlbertTokenizer.from_pretrained(
-    'huseinzol05/tiny-base-bahasa-cased',
+    'huseinzol05/tiny-bert-bahasa-cased',
     unk_token = '[UNK]',
     pad_token = '[PAD]',
     do_lower_case = False,