Datasets:

Languages:
Indonesian
ArXiv:
holylovenia commited on
Commit
fce6349
·
verified ·
1 Parent(s): 74f0a5d

Upload README.md with huggingface_hub

Browse files
Files changed (1) hide show
  1. README.md +52 -21
README.md CHANGED
@@ -1,31 +1,64 @@
 
1
  ---
2
- license: cc-by-4.0
3
- tags:
4
- - sentence-ordering
5
- language:
6
  - ind
 
 
 
 
 
7
  ---
8
 
9
- # indolem_tweet_ordering
10
-
11
  IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
12
-
13
  This task is based on the sentence ordering task of Barzilay and Lapata (2008) to assess text relatedness. We construct the data by shuffling Twitter threads (containing 3 to 5 tweets), and assessing the predicted ordering in terms of rank correlation (p) with the original. The experiment is based on 5-fold cross validation.
14
 
 
 
 
15
 
16
 
17
- Train: 4327 threads
18
 
19
- Development: 760 threads
20
 
21
- Test: 1521 threads
22
 
 
 
23
  ## Dataset Usage
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
24
 
25
- Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
 
 
 
 
 
 
 
 
 
 
26
 
27
  ## Citation
28
 
 
29
  ```
30
  @article{DBLP:journals/corr/abs-2011-00677,
31
  author = {Fajri Koto and
@@ -44,16 +77,14 @@ Run `pip install nusacrowd` before loading the dataset through HuggingFace's `lo
44
  biburl = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
45
  bibsource = {dblp computer science bibliography, https://dblp.org}
46
  }
47
- ```
48
-
49
- ## License
50
-
51
- Creative Commons Attribution 4.0
52
 
53
- ## Homepage
54
 
55
- [https://indolem.github.io/](https://indolem.github.io/)
56
-
57
- ### NusaCatalogue
 
 
 
 
58
 
59
- For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
 
1
+
2
  ---
3
+ language:
 
 
 
4
  - ind
5
+ pretty_name: Indolem Tweet Ordering
6
+ task_categories:
7
+ - sentence-ordering
8
+ tags:
9
+ - sentence-ordering
10
  ---
11
 
 
 
12
  IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
 
13
  This task is based on the sentence ordering task of Barzilay and Lapata (2008) to assess text relatedness. We construct the data by shuffling Twitter threads (containing 3 to 5 tweets), and assessing the predicted ordering in terms of rank correlation (p) with the original. The experiment is based on 5-fold cross validation.
14
 
15
+ Train: 4327 threads
16
+ Development: 760 threads
17
+ Test: 1521 threads
18
 
19
 
20
+ ## Languages
21
 
22
+ ind
23
 
24
+ ## Supported Tasks
25
 
26
+ Sentence Ordering
27
+
28
  ## Dataset Usage
29
+ ### Using `datasets` library
30
+ ```
31
+ from datasets import load_dataset
32
+ dset = datasets.load_dataset("SEACrowd/indolem_tweet_ordering", trust_remote_code=True)
33
+ ```
34
+ ### Using `seacrowd` library
35
+ ```import seacrowd as sc
36
+ # Load the dataset using the default config
37
+ dset = sc.load_dataset("indolem_tweet_ordering", schema="seacrowd")
38
+ # Check all available subsets (config names) of the dataset
39
+ print(sc.available_config_names("indolem_tweet_ordering"))
40
+ # Load the dataset using a specific config
41
+ dset = sc.load_dataset_by_config_name(config_name="<config_name>")
42
+ ```
43
+
44
+ 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).
45
+
46
 
47
+ ## Dataset Homepage
48
+
49
+ [https://indolem.github.io/](https://indolem.github.io/)
50
+
51
+ ## Dataset Version
52
+
53
+ Source: 1.0.0. SEACrowd: 2024.06.20.
54
+
55
+ ## Dataset License
56
+
57
+ Creative Commons Attribution 4.0
58
 
59
  ## Citation
60
 
61
+ If you are using the **Indolem Tweet Ordering** dataloader in your work, please cite the following:
62
  ```
63
  @article{DBLP:journals/corr/abs-2011-00677,
64
  author = {Fajri Koto and
 
77
  biburl = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
78
  bibsource = {dblp computer science bibliography, https://dblp.org}
79
  }
 
 
 
 
 
80
 
 
81
 
82
+ @article{lovenia2024seacrowd,
83
+ title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
84
+ 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},
85
+ year={2024},
86
+ eprint={2406.10118},
87
+ journal={arXiv preprint arXiv: 2406.10118}
88
+ }
89
 
90
+ ```