ParsBERT: Transformer based Model for Persian Language Understanding ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT Base. Paper presenting ParsBERT: arXiv:2005.12515 All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned) Introduction This model is pre trained on a large Persian corpus with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 2M documents. A large subset of this corpus was crawled manually. As a part of ParsBERT methodology, an extensive pre processing combining POS tagging and WordPiece segmentation was carried out to bring the corpus into a proper format. This process produces more than 40M true sentences. Evaluation ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BER…
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