SPDX-License-Identifier
CC0-1.0

Sentiment Demo: A Simple AI Application and its AI BOM Example

A simple text classification application, published solely to demonstrate a software bill of materials (SBOM) in SPDX 3.0 format.

The main content of the package is its software bill of materials at bom.spdx3.json. Other files are given just to complete the illustration.

Not recommended for actual text classification tasks.

SBOM demonstration design goals:

• Comprehensible: Small enough for a human to understand easily.
• Informative: Elaborate enough to showcase the use of various information fields within an SBOM.
• Testable: Designed to facilitate testing and evaluation against specific use case requirements.

For more information about implementing AI BOM using SPDX specification, see Karen Bennet, Gopi Krishnan Rajbahadur, Arthit Suriyawongkul, and Kate Stewart, “Implementing AI Bill of Materials (AI BOM) with SPDX 3.0: A Comprehensive Guide to Creating AI and Dataset Bill of Materials”, The Linux Foundation, October 2024.

Content

.
├── LICENSE               License information
├── README.md             This README file
├── bom.spdx3.json        Software bill of materials, in SPDX 3 format
├── data                  Dataset, preprocessed and tokenized
│   ├── test.txt          Testing data
│   ├── train.txt         Training data
│   └── valid.txt         Validation data
├── evaluate.py           A script to evaluate prediction performance
├── model.bin             A sentiment analysis model
├── predict.py            A script to predict a label of a text
├── preprocess.py         A script to prepare training data
├── rawdata               Raw dataset, before preprocessing
│   ├── test              Testing data
│   │   ├── neg.txt       Testing samples for label "neg" (negative)
│   │   ├── neu.txt       Testing samples for label "neu" (neutral)
│   │   ├── pos.txt       Testing samples for label "pos" (positive)
│   │   └── q.txt         Testing samples for label "q" (question)
│   ├── train             Training data
│   │   └── ...
│   └── valid             Validation data
│       └── ...
├── requirements.txt      List of required Python libraries
├── techdocs              Technical documentation
│   ├── dataprepare.md    Data preparation
│   └── instructions.md   Instruction for use
└── train.py              A script to build a model

Usage

See instruction for use for how to use the application.

Data preparation

See data preparation.

Notes

• Development is in the dev branch.
• Will eventually be submitted to spdx/spdx-examples repo.
• The diagram is generated from a PlantUML file: bom.spdx.puml. The PlantUML file is generated by spdx3ToGraph. To brevity, spdxIds and long strings are shortened by the shortenid.sh script in tools/, and all but one hyperparameter have been manually removed.
• The energy used by the computer during model training is tracked by energy-tracker. It measures how much energy the computer uses during the training. This means the actual energy used for training the model might be a bit less than the reported amount.
• The SPDX 3.0.1 SBOM is validated structurally against the JSON Schema at https://spdx.org/schema/3.0.1/spdx-json-schema.json and semantically against the SHACL model at https://spdx.org/rdf/3.0.1/spdx-model.ttl.
• Next steps:
  • Add external dependency relationships (e.g. dependsOn, hasProvidedDependency)
  • Get tested with an SBOM quality check tool like sbomsq (once it supports SPDX 3.0).
  • Using information requirements and obligations in the EU AI Act as a target, labeling all relevant properties and relationships with corresponding difficulty levels and support levels, based on the BOM Maturity Model.

Licenses

Apart from the data and components listed in the table below, the code and content in this repository are dedicated to the public domain under the terms of Creative Commons Zero ("CC0") 1.0 Universal, which have no copyright and related or neighboring rights worldwide to the extent allowed by law.

Component	Name	License	Notes
Training data	Wisesight Sentiment Corpus	Creative Commons Zero v1.0 Universal	Samples from the corpus are in rawdata/. Preprocessed data is in data/. See data preparation for details.
Text preprocessor	th-simple-preprocessor	Apache License 2.0
Word tokenizer	newmm-tokenizer	Apache License 2.0	Inherited the license from PyThaiNLP.
Text classifier	fastText	MIT License
Array package	NumPy	BSD License

The specific version information can be found in requirements.txt.

Citation

If you use this software, including its software bill of materials (SBOM), please cite it as follows:

Suriyawongkul, Arthit. “Sentiment Demo: A Simple AI Application and Its AI BOM Example”. Zenodo, 8 November 2024. https://doi.org/10.5281/zenodo.14055332.

BibTeX:

@software{Suriyawongkul_Sentiment_Demo_A_2024,
    author = {Suriyawongkul, Arthit},
    doi = {10.5281/zenodo.14055332},
    license = {CC0-1.0},
    month = nov,
    title = {{Sentiment Demo: A Simple AI Application and its AI BOM Example}},
    url = {https://github.com/bact/sentimentdemo/},
    version = {0.1},
    year = {2024}
}