Upload README.md with huggingface_hub

README.md

@@ -5,4 +5,63 @@ language:
 - ind
 - sun
 - jav
----
+---
+
+    Indo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus. 
+    Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local languages to the corpus, i.e., Sundanese and Javanese.
+    Indo4B-Plus adds 82,582,025 words (∼2.07%) of Sundanese sentences and 331,041,877 words (∼8.29%) of Javanese
+
+
+## Dataset Usage
+
+Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
+
+## Citation
+
+```    @inproceedings{cahyawijaya-etal-2021-indonlg,
+        title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation",
+        author = "Cahyawijaya, Samuel  and
+          Winata, Genta Indra  and
+          Wilie, Bryan  and
+          Vincentio, Karissa  and
+          Li, Xiaohong  and
+          Kuncoro, Adhiguna  and
+          Ruder, Sebastian  and
+          Lim, Zhi Yuan  and
+          Bahar, Syafri  and
+          Khodra, Masayu  and
+          Purwarianti, Ayu  and
+          Fung, Pascale",
+        booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+        month = nov,
+        year = "2021",
+        address = "Online and Punta Cana, Dominican Republic",
+        publisher = "Association for Computational Linguistics",
+        url = "https://aclanthology.org/2021.emnlp-main.699",
+        doi = "10.18653/v1/2021.emnlp-main.699",
+        pages = "8875--8898",
+        abstract = "Natural language generation (NLG) benchmarks provide an important avenue to measure progress 
+        and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource 
+        languages poses a challenging barrier for building NLG systems that work well for languages with limited 
+        amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG)
+        progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. 
+        Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important 
+        use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, 
+        and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, 
+        Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. 
+        We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth
+        the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes 
+        the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference 
+        at very low-resource languages like Javanese and Sundanese.",
+    }
+```
+
+## License
+
+CC0
+
+## Homepage
+
+### NusaCatalogue
+
+For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)