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README.md

@@ -1,21 +1,60 @@
+
 ---
-tags:
-- machine-translation
-language:
+language: 
 - ind
 - sun
+pretty_name: Bible Su Id
+task_categories: 
+- machine-translation
+tags: 
+- machine-translation
 ---
 
-# bible_su_id
-
 Bible Su-Id is a machine translation dataset containing Indonesian-Sundanese parallel sentences collected from the bible. As there is no existing parallel corpus for Sundanese and Indonesian, we create a new dataset for Sundanese and Indonesian translation generated from the Bible. We create a verse-aligned parallel corpus with a 75%, 10%, and 15% split for the training, validation, and test sets. The dataset is also evaluated in both directions.
 
+
+## Languages
+
+ind, sun
+
+## Supported Tasks
+
+Machine Translation
+    
 ## Dataset Usage
+### Using `datasets` library
+```
+    from datasets import load_dataset
+    dset = datasets.load_dataset("SEACrowd/bible_su_id", trust_remote_code=True)
+```
+### Using `seacrowd` library
+```import seacrowd as sc
+# Load the dataset using the default config
+    dset = sc.load_dataset("bible_su_id", schema="seacrowd")
+# Check all available subsets (config names) of the dataset
+    print(sc.available_config_names("bible_su_id"))
+# Load the dataset using a specific config
+    dset = sc.load_dataset_by_config_name(config_name="<config_name>")
+```
+    
+    More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
+    
+
+## Dataset Homepage
 
-Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
+[https://github.com/IndoNLP/indonlg](https://github.com/IndoNLP/indonlg)
+
+## Dataset Version
+
+Source: 1.0.0. SEACrowd: 2024.06.20.
+
+## Dataset License
+
+Creative Commons Attribution Share-Alike 4.0 International
 
 ## Citation
 
+If you are using the **Bible Su Id** dataloader in your work, please cite the following:
 ```
 @inproceedings{cahyawijaya-etal-2021-indonlg,
     title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation",
@@ -41,16 +80,14 @@ Run `pip install nusacrowd` before loading the dataset through HuggingFace's `lo
     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
 
-Creative Commons Attribution Share-Alike 4.0 International
-
-## Homepage
-
-[https://github.com/IndoNLP/indonlg](https://github.com/IndoNLP/indonlg)
-
-### NusaCatalogue
+@article{lovenia2024seacrowd,
+    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
+    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
+    year={2024},
+    eprint={2406.10118},
+    journal={arXiv preprint arXiv: 2406.10118}
+}
 
-For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
+```