  Text classification has become a crucial task in various fields, leading to a
significant amount of research on developing automated text classification
systems for national and international languages. However, there is a growing
need for automated text classification systems that can handle local languages.
This study aims to establish an automated classification system for Pashto
text. To achieve this goal, we constructed a dataset of Pashto documents and
applied various models, including statistical and neural machine learning
models such as DistilBERT-base-multilingual-cased, Multilayer Perceptron,
Support Vector Machine, K Nearest Neighbor, decision tree, Gaussian na\"ive
Bayes, multinomial na\"ive Bayes, random forest, and logistic regression, to
identify the most effective approach. We also evaluated two different feature
extraction methods, bag of words and Term Frequency Inverse Document Frequency.
The study achieved an average testing accuracy rate of 94% using the MLP
classification algorithm and TFIDF feature extraction method in single-label
multiclass classification. Similarly, MLP+TFIDF yielded the best results, with
an F1-measure of 0.81. Furthermore, the use of pre-trained language
representation models, such as DistilBERT, showed promising results for Pashto
text classification; however, the study highlights the importance of developing
a specific tokenizer for a particular language to achieve reasonable results.
