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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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tags: |
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- bpmn |
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- Business Process |
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--- |
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# T5-Small Finetuned for Purchase Order Workflow Business Processes |
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## Model Description |
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This is a fine-tuned version of the T5-Small model, designed specifically for the extraction of BPMN (Business Process Model and Notation) diagrams from textual descriptions related to Purchase Order Workflow Business Processes. This AI-driven approach leverages advanced language modeling techniques to transform natural language descriptions into BPMN models, facilitating the modernization and automation of business processes in this specific area. **This model serves as a proof of concept and is not yet ready for real-life applications.** |
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## Key Features |
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- **Language Model Base**: T5-Small, known for its efficiency and efficacy in understanding and generating text. |
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- **Specialization**: Fine-tuned specifically for BPMN generation in Purchase Order Workflows, improving accuracy and relevancy in business process modeling. |
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- **Dataset**: Trained on the "MaD: A Dataset for Interview-based BPM in Business Process Management" dataset, cited from the research article available at [IEEE Xplore](https://ieeexplore.ieee.org/document/10191898). |
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## Applications |
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- **Business Process Management**: Automates the generation of BPMN diagrams, which are crucial for documenting and improving Purchase Order Workflow Business Processes. |
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- **AI Research and Development**: Provides a research basis for further exploration into the integration of NLP and business process management. |
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- **Educational Tool**: Assists in teaching the concepts of BPMN and AI's role in business process automation, particularly in the context of Purchase Order Workflows. |
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## Configuration |
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- **Pre-trained Model**: Google's T5-Small |
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- **Training Environment**: Utilized a dataset from "MaD: A Dataset for Interview-based BPM in Business Process Management" for training and validation. |
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- **Hardware Used**: |
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- **CPU**: Apple M1 MAX with 10 cores (8 performance + 2 efficiency), 3.20 GHz |
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- **GPU**: Integrated, 32 cores |
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- **RAM**: 64 GB LPDDR5 |
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- **Storage**: 2 TB SSD |
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- **OS**: macOS 12.7.1 (Monterey) |
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- **Training Script**: [Finetuning T5-Small BPMN](https://github.com/ofachati/Finetuning-T5small-BPMN) |
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## Installation and Requirements |
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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 access to a machine with adequate computational capabilities to run inference with the T5 architecture. |
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## Contributors |
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- **Omar El Fachati**: [Hugging Face Profile](https://huggingface.co/fachati) - Training script |
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- **Pascal Poizat**: [Hugging Face Profile](https://huggingface.co/pascalpoizat) - Training hardware |
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