multimodal generative ai for bpm下载 - multimodal generative ai for bpm源代码下载

multimodal generative ai for bpm

Ai源码

1.0.0

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多模态人工智能

关于这个项目

该存储库包含论文“ Generative AI for Business Process Management - Suitability of Modalities的源代码。目标是评估使用生成式人工智能从多模式文档创建流程模型的可行性。该存储库使用 SAP SAM 存储库中的一些代码和数据。

执照

此存储库中的示例代码的许可如下。请注意，不同的许可证适用于数据集本身！

 Copyright (c) 2024 by SAP.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

以下许可证适用于数据文件夹中的数据集。

 Copyright (c) 2024 by SAP.

SAP grants to Recipient a non-exclusive copyright license to the Model Collection to use the Model Collection for Non-Commercial Research purposes of evaluating Recipient’s algorithms or other academic research artefacts against the Model Collection. Any rights not explicitly granted herein are reserved to SAP. For the avoidance of doubt, no rights to make derivative works of the Model Collection is granted and the license granted hereunder is for Non-Commercial Research purposes only.

"Model Collection" shall mean all files in the archive (which are JSON, XML, or other representation of business process models or other models).

"Recipient" means any natural person receiving the Model Collection.

"Non-Commercial Research" means research solely for the advancement of knowledge whether by a university or other learning institution and does not include any commercial or other sales objectives.

包括第三方组件及其许可/版权信息在内的详细信息可通过 REUSE 工具获得。

要求和设置

我们提供了两个 condaenvironment.yml 文件，可用于创建新环境并安装所需的依赖项：

environment.yml ：包含抽象依赖项（pandas、numpy，...）。
environment-lock.yml ：包含所有依赖项和传递依赖项的版本，以确保可重现的结果。

您可以使用以下 conda 命令来创建环境：

conda env create -f environment.yml

或者

conda env create -f environment-lock.yml

入门

我们提供多个 Jupyter Notebook。

data_set_preparation Jupyter Notebook 提供了如何创建数据集的演练。

Explore_the_dataset Jupyter Notebook 提供了有关所创建数据集特征的见解。

bpmn_ Generation Jupyter Notebook 使用 GPT-4V 以及零次、一次和几次提示从多模式文档创建流程模型。

评估Jupyter Notebook引入了一个评估框架来计算生成的过程模型和地面实况模型的相似度分数。此外，它应用了该框架并呈现了结果。

项目组织

 ├── data
│   ├── examples          <- Some example models for illustrating main ideas.
|   └── sapsam
│       ├── cleaned       <- The created dataset.
|       ├── enriched      <- Original SAP-SAM data set enriched by some meta data
|       ├── evaluations   <- Evaluation results
|       ├── generated     <- Generated process models
|       ├── raw           <- Original SAP-SAM data set
|       └── tmp           <- Temporary data
├── notebooks             <- Jupyter notebooks.
├── src               
|   ├── multimodalgenai   <- Source code for use in this project.
│   └── sapsam            <- Adapted clone of the [SAP SAM repo](https://github.com/signavio/sap-sam)
├── LICENSE               <- License that applies to the example code in this repository.
├── README.md             <- The top-level README for developers using this project.
├── Thesis_Gen-AI-for-BPM-Modalities_Marvin-Voelter_v04_2024-03-21_final_blurred.pdf <- Thesis with more detailed explanations and thoughts
├── environment-lock.yml  <- Contains versions for all dependencies and the transitive dependencies to ensure reproducible results.
├── environment.yml       <- Contains the abstract dependencies (pandas, numpy, ...).
└── setup.py              <- Makes project pip installable (pip install -e .) such that src can be imported.