The dataset viewer is not available for this dataset.
The dataset tries to import a module that is not installed.
Error code:   DatasetModuleNotInstalledError
Exception:    ImportError
Message:      To be able to use SEACrowd/hplt, 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 72, 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 1910, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1876, in dataset_module_factory
                  return HubDatasetModuleFactoryWithScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1498, in get_module
                  local_imports = _download_additional_modules(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 353, in _download_additional_modules
                  raise ImportError(
              ImportError: To be able to use SEACrowd/hplt, 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? Open a discussion for direct support.

YAML Metadata Warning: The task_categories "self-supervised-pretraining" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

The dataset is part of the High Performance Language Technologies project (HPLT), a 3-year EU-funded project started in September 2022. HPLT derives monolingual and bilingual datasets from the Internet Archive and CommonCrawl and builds efficient and solid machine translation (MT) as well as large language models (LLMs). HPLT aims at providing free, sustainable and reusable datasets, models and workflows at scale using high-performance computing (HPC).

Languages

ind, zlm, tha, mya, fil, vie

Supported Tasks

Self Supervised Pretraining

Dataset Usage

Using datasets library

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

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("hplt", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("hplt"))
# 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://hplt-project.org/datasets/v1.2

Dataset Version

Source: 1.2.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Zero v1.0 Universal (cc0-1.0)

Citation

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

\
@inproceedings{aulamo-etal-2023-hplt,
    title = "{HPLT}: High Performance Language Technologies",
    author = {Aulamo, Mikko  and
        Bogoychev, Nikolay  and
        Ji, Shaoxiong  and
        Nail, Graeme  and
        Ram{\'\i}rez-S{\'a}nchez, Gema  and
        Tiedemann, J{\"o}rg  and
        van der Linde, Jelmer  and
        Zaragoza, Jaume},
    editor = "Nurminen, Mary  and
        Brenner, Judith  and
        Koponen, Maarit  and
        Latomaa, Sirkku  and
        Mikhailov, Mikhail  and
        Schierl, Frederike  and
        Ranasinghe, Tharindu  and
        Vanmassenhove, Eva  and
        Vidal, Sergi Alvarez  and
        Aranberri, Nora  and
        Nunziatini, Mara  and
        Escart{\'\i}n, Carla Parra  and
        Forcada, Mikel  and
        Popovic, Maja  and
        Scarton, Carolina  and
        Moniz, Helena",
    booktitle = "Proceedings of the 24th Annual Conference of the European
    Association for Machine Translation",
    month = jun,
    year = "2023",
    address = "Tampere, Finland",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2023.eamt-1.61",
    pages = "517--518",

    abstract = "We describe the High Performance Language Technologies project
    (HPLT), a 3-year EU-funded project started in September 2022. HPLT will
    build a space combining petabytes of natural language data with large-scale
    model training. It will derive monolingual and bilingual datasets from the
    Internet Archive and CommonCrawl and build efficient and solid machine
    translation (MT) as well as large language models (LLMs). HPLT aims at
    providing free, sustainable and reusable datasets, models and workflows at
    scale using high-performance computing (HPC).",
}


@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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