We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs
corpus, encompassing both informal and formal Persian texts. FaBERT is designed
to excel in traditional Natural Language Understanding (NLU) tasks, addressing
the intricacies of diverse sentence structures and linguistic styles prevalent
in the Persian language. In our comprehensive evaluation of FaBERT on 12
datasets in various downstream tasks, encompassing Sentiment Analysis (SA),
Named Entity Recognition (NER), Natural Language Inference (NLI), Question
Answering (QA), and Question Paraphrasing (QP), it consistently demonstrated
improved performance, all achieved within a compact model size. The findings
highlight the importance of utilizing diverse and cleaned corpora, such as
HmBlogs, to enhance the performance of language models like BERT in Persian
Natural Language Processing (NLP) applications. FaBERT is openly accessible at
this https URL

FaBERT 是一种基于波斯语的 BERT 基础模型，在 HmBlogs 语料库上进行了预训练，涵盖了波斯语的非正式和正式文本。在 12 个数据集上的综合评估中，FaBERT 在各种下游任务中持续展现出改进的性能，包括情感分析、命名实体识别、自然语言推理、问答和问题改写，且模型体积较小。研究结果凸显了利用多样化和清理过的语料库（如 HmBlogs）来提升波斯语自然语言处理应用中 BERT 等语言模型性能的重要性。可通过此网址免费访问 FaBERT。