  This paper discusses the impact of the Internet on modern trading and the
importance of data generated from these transactions for organizations to
improve their marketing efforts. The paper uses the example of Divar, an online
marketplace for buying and selling products and services in Iran, and presents
a competition to predict the percentage of a car sales ad that would be
published on the Divar website. Since the dataset provides a rich source of
Persian text data, the authors use the Hazm library, a Python library designed
for processing Persian text, and two state-of-the-art language models, mBERT
and ParsBERT, to analyze it. The paper's primary objective is to compare the
performance of mBERT and ParsBERT on the Divar dataset. The authors provide
some background on data mining, Persian language, and the two language models,
examine the dataset's composition and statistical features, and provide details
on their fine-tuning and training configurations for both approaches. They
present the results of their analysis and highlight the strengths and
weaknesses of the two language models when applied to Persian text data. The
paper offers valuable insights into the challenges and opportunities of working
with low-resource languages such as Persian and the potential of advanced
language models like BERT for analyzing such data. The paper also explains the
data mining process, including steps such as data cleaning and normalization
techniques. Finally, the paper discusses the types of machine learning
problems, such as supervised, unsupervised, and reinforcement learning, and the
pattern evaluation techniques, such as confusion matrix. Overall, the paper
provides an informative overview of the use of language models and data mining
techniques for analyzing text data in low-resource languages, using the example
of the Divar dataset.
