Added README huseinzol05/albert-tiny-bahasa-cased (#3746)

* add bert bahasa readme

* update readme

* update readme

* added xlnet

* added tiny-bert and fix xlnet readme

* added albert base

* added albert tiny

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

+---
+language: malay
+---
+
+# Bahasa Albert Model
+
+Pretrained Albert tiny language model for Malay and Indonesian, 85% faster execution and 50% smaller than Albert base.
+
+## Pretraining Corpus
+
+`albert-tiny-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-tiny-bahasa-cased')
+tokenizer = AlbertTokenizer.from_pretrained(
+    'huseinzol05/albert-tiny-bahasa-cased',
+    do_lower_case = False,
+)
+```
+
+## Example using AutoModelWithLMHead
+
+```python
+from transformers import AlbertTokenizer, AutoModelWithLMHead, pipeline
+
+model = AutoModelWithLMHead.from_pretrained('huseinzol05/albert-tiny-bahasa-cased')
+tokenizer = AlbertTokenizer.from_pretrained(
+    'huseinzol05/albert-tiny-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': 0.05121927708387375,
+  'token': 629},
+ {'sequence': '[CLS] makan ayam dengan sayur[SEP]',
+  'score': 0.04497420787811279,
+  'token': 1639},
+ {'sequence': '[CLS] makan ayam dengan nasi[SEP]',
+  'score': 0.039827536791563034,
+  'token': 453},
+ {'sequence': '[CLS] makan ayam dengan rendang[SEP]',
+  'score': 0.032997727394104004,
+  'token': 2451},
+ {'sequence': '[CLS] makan ayam dengan makan[SEP]',
+  'score': 0.031354598701000214,
+  'token': 129}]
+```
+
+## 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. 
+
+