DadmaTools V2 is a comprehensive repository designed to enhance NLP capabilities for the Persian language, catering to industry practitioners seeking practical and efficient solutions. The toolkit provides extensive code examples demonstrating the integration of its models with popular NLP frameworks such as Trankit and Transformers, as well as deep learning frameworks like PyTorch. Additionally, DadmaTools supports widely used Persian embeddings and datasets, ensuring robust language processing capabilities. The latest version of DadmaTools introduces an adapter-based technique, significantly reducing memory usage by employing a shared pre-trained model across various tasks, supplemented with task-specific adapter layers. This approach eliminates the need to maintain multiple pre-trained models and optimize resource utilization. Enhancements in this version include adding new modules such as a sentiment detector, an informal-to-formal text converter, and a spell checker, further expanding the toolkit’s functionality. DadmaTools V2 thus represents a powerful, efficient, and versatile resource for advancing Persian NLP applications.

We introduce DadmaTools, an open-source Python Natural Language Processing toolkit for the Persian language. The toolkit is a neural pipeline based on spaCy for several text processing tasks, including normalization, tokenization, lemmatization, part-of-speech, dependency parsing, constituency parsing, chunking, and ezafe detecting. DadmaTools relies on fine-tuning of ParsBERT using the PerDT dataset for most of the tasks. Dataset module and embedding module are included in DadmaTools that support different Persian datasets, embeddings, and commonly used functions for them. Our evaluations show that DadmaTools can attain state-of-the-art performance on multiple NLP tasks. The source code is freely available at https://github.com/Dadmatech/DadmaTools.