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.

这篇论文探讨了互联网对现代交易的影响，以及从这些交易产生的数据对组织改善其市场营销努力的重要性。本文以伊朗的在线购买和销售产品和服务的市场 Divar 为例，并介绍了一个竞赛来预测在 Divar 网站上发布的汽车销售广告的百分比。作者在伊朗的低资源语言的背景下，使用了两种先进的语言模型，mBERT 和 ParsBERT，对这些数据进行了分析，提供了两种方法的性能比较，还详细介绍数据挖掘过程和机器学习问题类型和模式评估技术。