The dataset viewer is not available for this dataset.
Error code: ConfigNamesError Exception: ImportError Message: To be able to use SEACrowd/sap_wat, you need to install the following dependency: seacrowd. Please install it using 'pip install seacrowd' for instance. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response config_names = get_dataset_config_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1880, in dataset_module_factory return HubDatasetModuleFactoryWithScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module local_imports = _download_additional_modules( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules raise ImportError( ImportError: To be able to use SEACrowd/sap_wat, you need to install the following dependency: seacrowd. Please install it using 'pip install seacrowd' for instance.
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
The data set originates from the SAP Help Portal that contains documentation for SAP products and user assistance for product-related questions. The data has been processed in a way that makes it suitable as development and test data for machine translation purposes. The current language scope is English to Hindi, Indonesian, Japanese, Korean, Malay, Thai, Vietnamese, Simplified Chinese and Traditional Chinese. For each language pair about 4k segments are available, split into development and test data. The segments are provided in their document context and are annotated with additional metadata from the document.
Languages
eng, ind, zlm, tha, vie
Supported Tasks
Machine Translation
Dataset Usage
Using datasets
library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/sap_wat", trust_remote_code=True)
Using seacrowd
library
# Load the dataset using the default config
dset = sc.load_dataset("sap_wat", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("sap_wat"))
# 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://github.com/SAP/software-documentation-data-set-for-machine-translation
Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
Dataset License
Creative Commons Attribution Non Commercial 4.0 (cc-by-nc-4.0)
Citation
If you are using the Sap Wat dataloader in your work, please cite the following:
@inproceedings{buschbeck-exel-2020-parallel,
title = "A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation",
author = "Buschbeck, Bianka and
Exel, Miriam",
editor = "Nakazawa, Toshiaki and
Nakayama, Hideki and
Ding, Chenchen and
Dabre, Raj and
Kunchukuttan, Anoop and
Pa, Win Pa and
Bojar, Ond{{r}}ej and
Parida, Shantipriya and
Goto, Isao and
Mino, Hidaya and
Manabe, Hiroshi and
Sudoh, Katsuhito and
Kurohashi, Sadao and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 7th Workshop on Asian Translation",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wat-1.20",
pages = "160--169",
abstract = "This paper accompanies the software documentation data set for machine translation, a parallel
evaluation data set of data originating from the SAP Help Portal, that we released to the machine translation
community for research purposes. It offers the possibility to tune and evaluate machine translation systems
in the domain of corporate software documentation and contributes to the availability of a wider range of
evaluation scenarios. The data set comprises of the language pairs English to Hindi, Indonesian, Malay and
Thai, and thus also increases the test coverage for the many low-resource language pairs. Unlike most evaluation
data sets that consist of plain parallel text, the segments in this data set come with additional metadata that
describes structural information of the document context. We provide insights into the origin and creation, the
particularities and characteristics of the data set as well as machine translation results.",
}
@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}
}
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