TXT2BPMN

🚀 TXT2BPMN Model

TXT2BPMN is a fine - tuned T5 - Small model that extracts BPMN diagrams from text descriptions, facilitating business process modernization and automation.

🚀 Quick Start

The TXT2BPMN model is designed for developers, researchers, and business analysts aiming to automate BPMN generation from textual descriptions. It can be integrated into business software systems or used in standalone process - modeling applications. For detailed integration and usage instructions, refer to the official documentation in the model's repository on Hugging Face.

✨ Features

• Language Model Base: T5 - Small, renowned for its efficiency and effectiveness in text understanding and generation.
• Specialization: Fine - tuned specifically for BPMN generation, enhancing accuracy and relevance in business process modeling.
• Dataset: Trained on a comprehensive dataset of 30,000 pairs of text descriptions and corresponding BPMN diagrams, ensuring exposure to diverse business process scenarios.

📦 Installation

The model can be accessed and installed via the Hugging Face model hub. Requirements for using this model include Python 3.6 or newer and a machine with sufficient computational capabilities to run inference with the T5 architecture.

📚 Documentation

Applications

• Business Process Management: Automates the generation of BPMN diagrams, which are essential for documenting and improving business workflows.
• AI Research and Development: Serves as a research foundation for further exploration of the integration of NLP and business process management.
• Educational Tool: Helps teach the concepts of BPMN and AI's role in business process automation.

Configuration

• Pre - trained Model: Google's T5 - Small
• Training Environment: Utilized a dataset from "MaD: A Dataset for Interview - based BPM in Business Process Management" for training and validation.
• Hardware Used: Intel Core i7 with 24 GB RAM and Intel HD Graphics 630.

Model Performance

The model has demonstrated promising results in terms of syntactic integrity and logical fidelity in the generated BPMN diagrams. Detailed performance metrics and evaluations are included in the full project documentation.

🔧 Technical Details

The TXT2BPMN model is a fine - tuned version of the T5 - Small model. It leverages advanced language - modeling techniques to transform natural language descriptions into BPMN models. The fine - tuning process was carried out using a dataset of 30,000 pairs of text descriptions and corresponding BPMN diagrams, enabling the model to handle various business process scenarios.

📄 License

This project is released under the MIT license.

👥 Contributions

Developed by Omar El Fachati as part of a Master's thesis. The project was supervised by academic and industry professionals.

📞 Contact Information

For further assistance or collaboration opportunities, contact me on my LinkedIn.