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MatinaAI/matina_persian_text_corpus
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