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Soluki, M., Askarinejadamiri, Z., Zanjani, N.. (1403). Using GPT-2 Model and Hazm Library for Persian Text Generation. فناوری آموزش, 12(2), 295-304. doi: 10.22061/jecei.2023.10101.690
M. Soluki; Z. Askarinejadamiri; N. Zanjani. "Using GPT-2 Model and Hazm Library for Persian Text Generation". فناوری آموزش, 12, 2, 1403, 295-304. doi: 10.22061/jecei.2023.10101.690
Soluki, M., Askarinejadamiri, Z., Zanjani, N.. (1403). 'Using GPT-2 Model and Hazm Library for Persian Text Generation', فناوری آموزش, 12(2), pp. 295-304. doi: 10.22061/jecei.2023.10101.690
Soluki, M., Askarinejadamiri, Z., Zanjani, N.. Using GPT-2 Model and Hazm Library for Persian Text Generation. فناوری آموزش, 1403; 12(2): 295-304. doi: 10.22061/jecei.2023.10101.690

Using GPT-2 Model and Hazm Library for Persian Text Generation

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 1، دوره 12، شماره 2، مهر 2024، صفحه 295-304    اصل مقاله (858.43 K)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.10101.690
نویسندگان
M. Soluki؛ Z. Askarinejadamiri* ؛ N. Zanjani
Computer science of Refah University college, Tehran, Iran.
تاریخ دریافت: 19 شهریور 1402،  تاریخ بازنگری: 27 آذر 1402،  تاریخ پذیرش: 04 دی 1402 
چکیده
Background and Objectives: This article explores a method for generating Persian texts using the GPT-2 language model and the Hazm library. Researchers and writers often require tools that can assist them in the writing process and even think on their behalf in various domains. By leveraging the GPT-2 model, it becomes possible to generate acceptable and creative texts, which increases writing speed and efficiency, thus mitigating the high costs associated with article writing.
Methods: In this research, the GPT-2 model is employed to generate and predict Persian texts. The Hazm library is utilized for natural language processing and automated text generation. The results of this study are evaluated using different datasets and output representations, demonstrating that employing the Hazm library with input data exceeding 1000 yields superior outcomes compared to other text generation methods
Results: Through extensive experimentation and analysis, the study demonstrates the effectiveness of this combination in generating coherent and contextually appropriate text in the Persian language. The results highlight the potential of leveraging advanced language models and linguistic processing tools for enhancing natural language generation tasks in Persian. The findings of this research contribute to the growing field of Persian language processing and provide valuable insights for researchers and practitioners working on text generation applications in similar languages.
Conclusion: Overall, this study showcases the promising capabilities of the GPT-2 model and Hazm library in Persian text generation, underscoring their potential for future advancements in the field This research serves as a valuable guide and tool for generating Persian texts in the field of research and scientific writing, contributing to cost and time reduction in article writing
کلیدواژه‌ها
Persian text genertion؛ Hazm Library؛ GPT-2 Model
مراجع

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