Merge branch 'master' of github.com:indobenchmark/indonlu into develop

README.md

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 # IndoNLU 
 ![Pull Requests Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat) [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/indobenchmark/indonlu/blob/master/LICENSE) [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](code_of_conduct.md)
 
-IndoNLU is a collection of Natural Language Understanding (NLU) resources for Bahasa Indonesia with 12 downstream tasks. We provide the code to reproduce the results and large pre-trained models (IndoBERT and IndoBERT-lite) trained with around 4 billion word corpus (Indo4B), more than 20 GB of text data. This project was initially started by a joint collaboration between universities and industry, such as Institut Teknologi Bandung, Universitas Multimedia Nusantara, The Hong Kong University of Science and Technology, Universitas Indonesia, Gojek, and Prosa.AI.
+<b>IndoNLU</b> is a collection of Natural Language Understanding (NLU) resources for Bahasa Indonesia with 12 downstream tasks. We provide the code to reproduce the results and large pre-trained models (<b>IndoBERT</b> and <b>IndoBERT-lite</b>) trained with around 4 billion word corpus (<b>Indo4B</b>), more than 20 GB of text data. This project was initially started by a joint collaboration between universities and industry, such as Institut Teknologi Bandung, Universitas Multimedia Nusantara, The Hong Kong University of Science and Technology, Universitas Indonesia, Gojek, and Prosa.AI.
 
 ## Research Paper
 IndoNLU has been accepted by AACL-IJCNLP 2020 and you can find the details in our preprint https://arxiv.org/abs/2009.05387.
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   - Phase 2  [[Link]](https://huggingface.co/indobenchmark/indobert-lite-large-p2)
 
 ## FastText (Indo4B)
-- Will be released soon!
+- Uncased 11.9 GB Model File [[Link]](https://storage.googleapis.com/babert-pretraining/IndoNLU_finals/models/fasttext/fasttext.4B.id.300.epoch5.uncased.bin) 3.9 GB Vector File [[Link]](https://storage.googleapis.com/babert-pretraining/IndoNLU_finals/models/fasttext/fasttext.4B.id.300.epoch5.uncased.vec.zip)
 
 We provide FastText models with smaller vocabulary for each of the 12 downstream tasks
 - FastText-Indo4B [[Link]](https://storage.googleapis.com/babert-pretraining/IndoNLU_finals/models/fasttext/fasttext-4B-id-uncased.zip)