SEACrowd/xstorycloze

XStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI.

Languages

ind, mya

Supported Tasks

Commonsense Reasoning

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/xstorycloze", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("xstorycloze", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("xstorycloze"))
# 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

https://huggingface.co/datasets/juletxara/xstory_cloze

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Attribution Share Alike 4.0 (cc-by-sa-4.0)

Citation

If you are using the Xstorycloze dataloader in your work, please cite the following:

@inproceedings{lin2022fewshot,
    author       = {Xi Victoria Lin and
                    Todor Mihaylov and
                    Mikel Artetxe and
                    Tianlu Wang and
                    Shuohui Chen and
                    Daniel Simig and
                    Myle Ott and
                    Naman Goyal and
                    Shruti Bhosale and
                    Jingfei Du and
                    Ramakanth Pasunuru and
                    Sam Shleifer and
                    Punit Singh Koura and
                    Vishrav Chaudhary and
                    Brian O'Horo and
                    Jeff Wang and
                    Luke Zettlemoyer and
                    Zornitsa Kozareva and
                    Mona T. Diab and
                    Veselin Stoyanov and
                    Xian Li},
    editor       = {Yoav Goldberg and
                    Zornitsa Kozareva and
                    Yue Zhang},
    title        = {Few-shot Learning with Multilingual Generative Language Models},
    booktitle    = {Proceedings of the 2022 Conference on Empirical Methods in Natural
                    Language Processing, {EMNLP} 2022, Abu Dhabi, United Arab Emirates,
                    December 7-11, 2022},
    pages        = {9019--9052},
    publisher    = {Association for Computational Linguistics},
    year         = {2022},
    url          = {https://doi.org/10.18653/v1/2022.emnlp-main.616},
    doi          = {10.18653/V1/2022.EMNLP-MAIN.616},
}


@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}
}