Updating Readme

README.md

@@ -1,3 +1,41 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text2text-generation
+tags:
+- bpmn
+- Business Process
+---
+# T5-Small Finetuned for Purchase Order Workflow Business Processes
+
+## Model Description
+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.**
+
+## Key Features
+- **Language Model Base**: T5-Small, known for its efficiency and efficacy in understanding and generating text.
+- **Specialization**: Fine-tuned specifically for BPMN generation in Purchase Order Workflows, improving accuracy and relevancy in business process modeling.
+- **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).
+
+## Applications
+- **Business Process Management**: Automates the generation of BPMN diagrams, which are crucial for documenting and improving Purchase Order Workflow Business Processes.
+- **AI Research and Development**: Provides a research basis for further exploration into the integration of NLP and business process management.
+- **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.
+
+## 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**: 
+  - **CPU**: Apple M1 MAX with 10 cores (8 performance + 2 efficiency), 3.20 GHz
+  - **GPU**: Integrated, 32 cores
+  - **RAM**: 64 GB LPDDR5
+  - **Storage**: 2 TB SSD
+  - **OS**: macOS 12.7.1 (Monterey)
+- **Training Script**: [Finetuning T5-Small BPMN](https://github.com/ofachati/Finetuning-T5small-BPMN)
+
+## Installation and Requirements
+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.
+
+## Contributors
+- **Pascal Poizat**: [Hugging Face Profile](https://huggingface.co/pascalpoizat) - Training hardware
+- **Omar El Fachati**: [Hugging Face Profile](https://huggingface.co/fachati) - Training script