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MatinaAI/matina_persian_text_corpus

66 downloads in 30 days11 likesLast modified 8 Jul 2025View on Hugging Face
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The declared licence (cc-by-nc-nd-4.0) is classified as having use restrictions.

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cc-by-nc-nd-4.0, from the tagEvidence: README.md at 5a782d5
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No licence file at the top level of the repositoryEvidence: file list at 5a782d5
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Citation text from the card

@inproceedings{hosseinbeigi-etal-2025-matina,
    title = "Matina: A Large-Scale 73{B} Token {P}ersian Text Corpus",
    author = "Hosseinbeigi, Sara Bourbour  and
      Taherinezhad, Fatemeh  and
      Faili, Heshaam  and
      Baghbani, Hamed  and
      Nadi, Fatemeh  and
      Amiri, Mostafa",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.naacl-long.462/",
    doi = "10.18653/v1/2025.naacl-long.462",
    pages = "9143--9157",
    ISBN = "979-8-89176-189-6",
    abstract = "Text corpora are essential for training models used in tasks like summarization, translation, and large language models (LLMs). While various efforts have been made to collect monolingual and multilingual datasets in many languages, Persian has often been underrepresented due to limited resources for data collection and preprocessing. Existing Persian datasets are typically small and lack content diversity, consisting mainly of weblogs and news articles. This shortage of high-quality, varied data has slowed the development of NLP models and open-source LLMs for Persian. Since model performance depends heavily

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dataset_licence({ "id": "MatinaAI/matina_persian_text_corpus" })
signal: "restrictive"
stated_licence: "cc-by-nc-nd-4.0"
sources: 0