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README.md

@@ -3,4 +3,65 @@ tags:
 - self-supervised-pretraining
 language:
 - ind
----
+---
+
+    Indo4B is a large-scale Indonesian self-supervised pre-training corpus
+    consists of around 3.6B words, with around 250M sentences. The corpus
+    covers both formal and colloquial Indonesian sentences compiled from 
+    12 sources, of which two cover Indonesian colloquial language, eight
+    cover formal Indonesian language, and the rest have a mixed style of
+    both colloquial and formal.
+
+
+## Dataset Usage
+
+Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
+
+## Citation
+
+```    @inproceedings{wilie-etal-2020-indonlu,
+        title = "{I}ndo{NLU}: Benchmark and Resources for Evaluating {I}ndonesian 
+            Natural Language Understanding",
+        author = "Wilie, Bryan  and
+          Vincentio, Karissa  and
+          Winata, Genta Indra  and
+          Cahyawijaya, Samuel  and
+          Li, Xiaohong  and
+          Lim, Zhi Yuan  and
+          Soleman, Sidik  and
+          Mahendra, Rahmad  and
+          Fung, Pascale  and
+          Bahar, Syafri  and
+          Purwarianti, Ayu",
+        booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the 
+                Association for Computational Linguistics and the 10th International Joint 
+                Conference on Natural Language Processing",
+        month = dec,
+        year = "2020",
+        address = "Suzhou, China",
+        publisher = "Association for Computational Linguistics",
+        url = "https://aclanthology.org/2020.aacl-main.85",
+        pages = "843--857",
+        abstract = "Although Indonesian is known to be the fourth most frequently used language 
+            over the internet, the research progress on this language in natural language processing (NLP) 
+            is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast 
+            resource for training, evaluation, and benchmarking on Indonesian natural language understanding 
+            (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to 
+            pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks 
+            lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian 
+            pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected 
+            from publicly available sources such as social media texts, blogs, news, and websites. 
+            We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, 
+            thus enabling everyone to benchmark their system performances.",
+    }
+```
+
+## License
+
+CC0
+
+## Homepage
+
+### NusaCatalogue
+
+For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)