SPDX-FileContributor: Arthit Suriyawongkul SPDX-FileCopyrightText: 2024-present Arthit Suriyawongkul suriyawa@tcd.ie SPDX-FileType: DOCUMENTATION 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 are software bill of materials at bom.spdx3.json (written by hand) and bom-generated.spdx3.json (Pitloom-generated). Other files are given just to complete the illustration.

Not recommended for actual text classification tasks.

Updates:

• July 2026: Version 0.3.1 - SBOM fragments that were generated during the runs of evaluate/predict/preprocess/train.py are merged into the main SBOM -- shipped with the Python wheel. This gives more data/model provenance.
• March 2026: Adopted as a development reference for the Pitloom SBOM generator.
• May 2025: Added to the SPDX Usage Examples repository as ai/example02.

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-generated.spdx3.json   SBOM in SPDX 3 format (generated)
├── bom.spdx3.json             SBOM in SPDX 3 format (handwritten)
├── data                       Dataset, preprocessed and tokenized
│   ├── test.txt               Testing data
│   ├── train.txt              Training data
│   └── valid.txt              Validation 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
│       └── ...
├── src
│   ├── evaluate.py            Script to evaluate prediction performance
│   ├── model.bin              Sentiment analysis model
│   ├── predict.py             Script to predict a label of a text
│   ├── preprocess.py          Script to prepare training data
│   └── train.py               Script to build a model
└── techdocs                   Technical documentation
    ├── dataprepare.md         Data preparation
    └── instructions.md        Instruction for use

Usage

See instruction for use for how to use the application.

Data preparation

See data preparation.

Notes

• Development is in the main branch.
• 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.

The specific version information can be found in pyproject.toml.

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}
}