## 1 Introduction  
In recent years, the field of text classification has gained increasing relevance due to the proliferation of social media, blogs, forums, and online academic libraries, alongside advancements in AI technologies. The main objective of text classification is to assign predefined labels to textual sequences. It is a fundamental task in many NLP applications, including sentiment analysis, topic labeling, and question answering. Additionally, it is widely used for spam detection, customer feedback analysis, and language identification. This makes text classification useful in a wide range of fields [1, 2, 3]. Despite the growing interest in text classification, research on Moroccan dialect text classification remains limited even though Moroccan dialect is widely used on social media and digital platforms. The increasing availability of Moroccan dialect content necessitates the deployment of effective NLP solutions for dataset processing [4]. However, classifying Moroccan dialect presents challenges due to its lexical diversity, influenced by Arabic and loanwords from languages such as French, Spanish, and Portuguese, as a result of colonization. This complexity is further compounded by the presence of Amazigh and Saharan dialects, each with distinct sub-dialects. In this sense, different linguists propose various classifications of Moroccan dialect types. Morocco's current population is nearly 40 million, distributed across 12 regions, each characterized by its own dialect. The Moroccan dialect, known as Darija, is highly diverse, encompassing urban, Amazigh, and Saharan variants. Within these dialects, there are other sub-dialects, such as Tamazight, which includes Rifia (from the Rif region) and Soussa (from the Middle and High Atlas regions), among others. Linguistic classifications of the Moroccan dialect vary according to specialists. For example, [PERSON] (2002) [PERSON] five varieties of Darija: (1) the Shamali variety in northern Morocco, (2) the Fassi variety in the central region, (3) the Rabat/Casablanca variety around these cities, (4) the Marrakshi/Agadiri variety in the south, and (5) the Hassaniya variety in the Sahara. On the other hand, researchers such as [PERSON] and [PERSON] propose a classification of four categories: Mdini (city dwellers), Jebli (mountain dwellers), Arubi (Bedouins), and Aribi (Hassani from southern Morocco) [4]. Traditional text classification models dominated from 1960 to 2010. These include such models as K-Nearest Neighbor, Support Vector Machine (SVM), and Naive Bayes. For instance, a Naive Bayesian classifier is used to classify documents as it needs less memory and computation. It is also considered the most commonly used classifier in traditional machine learning[PERSON] Although these models have clear advantages in terms of accuracy and stability, they still require extensive feature engineering, which is time-consuming[PERSON] Since 2010, text classification has advanced towards deep learning models, which omit human-designed rules and features and automatically provide representations that are semantically meaningful for text mining [1; 2]. Over the past few years, LLMs, such as BERT, RoBERTa, and OpenAI GPT, among others, have been employed for a range of text analysis tasks, such as text categorization and question answering [3; 5]. These models are usually trained on large multilingual corpora, which might contain more than a hundred languages, or on monolingual ones, particularly in English. This, however, presents the following challenge: training an LLM with large amounts of data makes it robust, but it becomes too generalized and not specialized in a particular domain. Therefore, fine-tuning LLMs for specific use cases is crucial in enhancing their performance. For example, well-known, pre-trained LLMs can be refined and applied to tasks such as text classification for more focused results. Currently, two primary architectures dominate LLM research: (1) Masked Language Models (MLM) such as BERT and (2) Causal Language Models (CLM) like GPT.