language:
- ind
pretty_name: Indolem Ntp
task_categories:
- next-sentence-prediction
tags:
- next-sentence-prediction
NTP (Next Tweet prediction) is one of the comprehensive Indonesian benchmarks that given a list of tweets and an option, we predict if the option is the next tweet or not. This task is similar to the next sentence prediction (NSP) task used to train BERT (Devlin et al., 2019). In NTP, each instance consists of a Twitter thread (containing 2 to 4 tweets) that we call the premise, and four possible options for the next tweet, one of which is the actual response from the original thread.
Train: 5681 threads Development: 811 threads Test: 1890 threads
Languages
ind
Supported Tasks
Next Sentence Prediction
Dataset Usage
Using datasets library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/indolem_ntp", trust_remote_code=True)
Using seacrowd library
# Load the dataset using the default config
dset = sc.load_dataset("indolem_ntp", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("indolem_ntp"))
# 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.
Dataset Homepage
Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
Dataset License
Creative Commons Attribution 4.0
Citation
If you are using the Indolem Ntp dataloader in your work, please cite the following:
@article{DBLP:journals/corr/abs-2011-00677,
author = {Fajri Koto and
Afshin Rahimi and
Jey Han Lau and
Timothy Baldwin},
title = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
Model for Indonesian {NLP}},
journal = {CoRR},
volume = {abs/2011.00677},
year = {2020},
url = {https://arxiv.org/abs/2011.00677},
eprinttype = {arXiv},
eprint = {2011.00677},
timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@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}
}