Results 241 to 250 of about 3,331,987 (298)

PersianLLaMA: Towards Building First Persian Large Language Model

arXiv.org, 2023
Despite the widespread use of the Persian language by millions globally, limited efforts have been made in natural language processing for this language.
Mohammad Amin Abbasi, A. Ghafouri, Mahdi Firouzmandi, Hassan Naderi, B. Minaei-Bidgoli   +4 more
semanticscholar   +1 more source

Persian Language Model based on BiLSTM Model on COVID-19 Corpus

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA), 2021
Coronavirus disease 2019 (COVID-2019) appeared in China, in 2019. COVID-2019 expanded all over the world quickly and caused many deaths. COVID-19 has become one of the hottest research areas recently.
M. S. Sartakhti, M. J. M. Kahaki, Seyed Vahid Moravvej, Maedeh javadi Joortani, Alireza Bagheri   +4 more
semanticscholar   +1 more source

Translation and cultural adaptation of the Sensory Profile 2 to the Persian language

British Journal of Occupational Therapy, 2021
Introduction The purpose of this study was to translate and cross-culturally adapt the original Sensory Profile 2 questionnaires to Persian and access the psychometric properties of the adapted questionnaires.
Marjan Shahbazi, Navid Mirzakhany, Mehdi Alizadeh Zarei, F. Zayeri, A. Daryabor   +4 more
semanticscholar   +1 more source

Transfer Learning for End-to-End ASR to Deal with Low-Resource Problem in Persian Language

International Computer Society of Iran Computer Conference, 2021
End-to-end models are state of the art for Automatic Speech Recognition (ASR) systems. Despite all their advantages, they suffer a significant problem: huge amounts of training data are required to achieve excellent performance. This problem is a serious
Maryam Asadolahzade Kermanshahi, A. Akbari, B. Nasersharif   +2 more
semanticscholar   +1 more source

Old Persian language

2018
exaly   +2 more sources

FaMTEB: Massive Text Embedding Benchmark in Persian Language

Conference on Empirical Methods in Natural Language Processing
In this paper, we introduce a comprehensive benchmark for Persian (Farsi) text embeddings, built upon the Massive Text Embedding Benchmark (MTEB).
Erfan Zinvandi, Morteza Alikhani, Mehran Sarmadi, Zahra Pourbahman, Sepehr Arvin, Reza Kazemi, Arash Amini   +6 more
semanticscholar   +1 more source

ELAB: Extensive LLM Alignment Benchmark in Persian Language

arXiv.org
This paper presents a comprehensive evaluation framework for aligning Persian Large Language Models (LLMs) with critical ethical dimensions, including safety, fairness, and social norms.
Zahra Pourbahman, Fatemeh Rajabi, Mohammad Sadeghi, Omid Ghahroodi, S. Bakhshaei, Arash Amini, Reza Kazemi, M. Baghshah   +7 more
semanticscholar   +1 more source

Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?

arXiv.org
Evaluating Large Language Models (LLMs) is challenging due to their generative nature, necessitating precise evaluation methodologies. Additionally, non-English LLM evaluation lags behind English, resulting in the absence or weakness of LLMs for many ...
Omid Ghahroodi, Marzia Nouri, M. V. Sanian, Alireza Sahebi, D. Dastgheib, Ehsaneddin Asgari, M. Baghshah, M. H. Rohban   +7 more
semanticscholar   +1 more source

Affectivity in Persian language use

Culture, Medicine and Psychiatry, 1988
A sociolinguistic analysis of emotion in Iranian culture and language use is developed. The work of Friedrich, Bateson, and others is drawn on to indicate how emotion is represented though metacommunicative, paralinguistic, and stylistic elements of language use.
openaire   +2 more sources

Producing An Instagram Dataset For Persian Language Sentiment Analysis Using Crowdsourcing Method

2020 6th International Conference on Web Research (ICWR), 2020
with the rapid growth of using the internet and social media, people can easily share their opinions on these platforms. due to this fact, user's comments are considering as a rich source for natural language processing tasks such as sentiment analysis ...
Mahsa Heidari, P. Shamsinejad
semanticscholar   +1 more source