Sadeed: Advancing Arabic Diacritization Through Small Language Model
Arabic text diacritization remains a persistent challenge in natural language
processing due to the language's morphological richness. In this paper, we
introduce Sadeed, a novel approach based on a fine-tuned decoder-only language
model adapted from Kuwain 1.5B Hennara et al. [2025], a compact model
originally trained on diverse Arabic corpora. Sadeed is fine-tuned on carefully
curated, high-quality diacritized datasets, constructed through a rigorous
data-cleaning and normalization pipeline. Despite utilizing modest
computational resources, Sadeed achieves competitive results compared to
proprietary large language models and outperforms traditional models trained on
similar domains. Additionally, we highlight key limitations in current
benchmarking practices for Arabic diacritization. To address these issues, we
introduce SadeedDiac-25, a new benchmark designed to enable fairer and more
comprehensive evaluation across diverse text genres and complexity levels.
Together, Sadeed and SadeedDiac-25 provide a robust foundation for advancing
Arabic NLP applications, including machine translation, text-to-speech, and
language learning tools.
68130be55342cbe1ddefb262
Zeina Aldallal
extracted_confirmed
2025-05-04T10:24:18.261Z
65704741e1cfce1764ce652e
/avatars/9189aaf417426af4ebe381ed364a6c0e.svg
Zeina Aldallal
user
ZeinaD
68130be55342cbe1ddefb263
Sara Chrouf
extracted_confirmed
2025-05-28T13:48:03.917Z
650b64fb443b2a59d4d432e3
/avatars/12f284e007e8b5cddeadf4491a0a1fe8.svg
Sara Chrouf
user
SaraChrouf
68130be55342cbe1ddefb264
Khalil Hennara
extracted_confirmed
2025-05-01T06:55:15.787Z
65276c7911a8a521c91bc10f
https://cdn-avatars.huggingface.co/v1/production/uploads/65276c7911a8a521c91bc10f/39dbuUtqQTJERTJ_WkxSh.jpeg
Khalil Hennara
user
Hennara
68130be55342cbe1ddefb265
Mohamed Motaism Hamed
claimed_verified
2025-05-02T06:34:51.106Z
63aa7667769a10efc404fbbc
https://cdn-avatars.huggingface.co/v1/production/uploads/63aa7667769a10efc404fbbc/tn8ZxUmTEMS0Gze7_F7JL.jpeg
Mohamed Motasim Hamed
user
Moatasem444
68130be55342cbe1ddefb266
Muhammad Hreden
extracted_confirmed
2025-05-27T09:12:53.594Z
6496df4b3c64d75523a11973
https://cdn-avatars.huggingface.co/v1/production/uploads/6496df4b3c64d75523a11973/I_Qn5-3Czngle-NsGmabO.jpeg
Muhammad Hreden
user
muhammad0-0hreden
68130be55342cbe1ddefb267
Safwan AlModhayan
Sadeed: Advancing Arabic Diacritization Through Small Language Model
Arabic text diacritization remains a persistent challenge in natural language
processing due to the language's morphological richness. In this paper, we
introduce Sadeed, a novel approach based on a fine-tuned decoder-only language
model adapted from Kuwain 1.5B Hennara et al. [2025], a compact model
originally trained on diverse Arabic corpora. Sadeed is fine-tuned on carefully
curated, high-quality diacritized datasets, constructed through a rigorous
data-cleaning and normalization pipeline. Despite utilizing modest
computational resources, Sadeed achieves competitive results compared to
proprietary large language models and outperforms traditional models trained on
similar domains. Additionally, we highlight key limitations in current
benchmarking practices for Arabic diacritization. To address these issues, we
introduce SadeedDiac-25, a new benchmark designed to enable fairer and more
comprehensive evaluation across diverse text genres and complexity levels.
Together, Sadeed and SadeedDiac-25 provide a robust foundation for advancing
Arabic NLP applications, including machine translation, text-to-speech, and
language learning tools.
Sadeed, a fine-tuned decoder-only language model, enhances Arabic text diacritization using limited resources and addresses benchmarking limitations with SadeedDiac-25.
decoder-only language model fine-tuned Kuwain 1.5B data-cleaning normalization benchmarking SadeedDiac-25