Upload README.md with huggingface_hub

README.md

@@ -5,13 +5,19 @@ language:
 - ind
 ---
 
+# indo_general_mt_en_id
+
 "In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,
+
 and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.
+
 In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and
+
 conversation,to train and benchmark some variants of transformer-based NMT models across the domains.
+
 We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models,
-and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data."
 
+and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data."
 
 ## Dataset Usage
 
@@ -19,7 +25,8 @@ Run `pip install nusacrowd` before loading the dataset through HuggingFace's `lo
 
 ## Citation
 
-```@inproceedings{guntara-etal-2020-benchmarking,
+```
+@inproceedings{guntara-etal-2020-benchmarking,
     title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
     author = "Guntara, Tri Wahyu  and
       Aji, Alham Fikri  and
@@ -42,6 +49,8 @@ Creative Commons Attribution Share-Alike 4.0 International
 
 ## Homepage
 
+[https://github.com/gunnxx/indonesian-mt-data](https://github.com/gunnxx/indonesian-mt-data)
+
 ### NusaCatalogue
 
 For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)