Struct MlModelInfo 

#[non_exhaustive]
pub struct MlModelInfo {

    pub approach: Option<String>,
    pub architecture_family: Option<String>,
    pub architecture_name: Option<String>,
    pub task: Option<String>,
    pub quantization: Option<String>,
    pub limitations: Option<String>,
    pub training_datasets: Vec<DatasetRef>,
    pub energy_kwh_training: Option<f64>,
    pub model_card_url: Option<String>,
    pub fairness: Vec<FairnessAssessment>,
    pub ethical_considerations: Vec<EthicalConsideration>,
    pub use_cases: Vec<String>,
    pub performance_metrics: Vec<MetricEntry>,
    pub data_preprocessing: Vec<String>,
}

Machine learning model metadata (CycloneDX 1.5+)

Structured information about trained ML models, including architecture, approach, quantization, and environmental impact.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

approach: Option<String>

ML approach type: “supervised”, “unsupervised”, “reinforcement-learning”, “semi-supervised”

architecture_family: Option<String>

Architecture family: “transformer”, “cnn”, “rnn”, “llm”, “gan”, etc.

architecture_name: Option<String>

Architecture name: “bert”, “gpt”, “resnet”, etc.

task: Option<String>

ML task: “nlp”, “computer-vision”, “audio”, “tabular”, etc.

quantization: Option<String>

Quantization mode: “int4”, “int8”, “fp16”, “bf16”, “fp32”, “mixed”, etc.

limitations: Option<String>

Limitations or known constraints of the model

training_datasets: Vec<DatasetRef>

Training datasets used for this model

energy_kwh_training: Option<f64>

Energy consumed during training in kWh (approximate)

model_card_url: Option<String>

URL to detailed model card (from ExternalRefType::ModelCard)

fairness: Vec<FairnessAssessment>

Fairness assessments (CycloneDX 1.5+ considerations.fairnessAssessments). SPDX 3.0 has no direct analogue; the nearest AI-profile signals are normalized into this shape so cross-format scoring is symmetric.

ethical_considerations: Vec<EthicalConsideration>

Ethical considerations. CycloneDX emits structured objects (considerations.ethicalConsiderations[]); SPDX emits free strings. Both are normalized into this single shape.

use_cases: Vec<String>

Intended use-cases (CycloneDX considerations.useCases, SPDX ai_domain / ai_informationAboutApplication).

performance_metrics: Vec<MetricEntry>

Quantitative performance metrics (CycloneDX modelCard.quantitativeAnalysis.performanceMetrics, SPDX ai_metric).

data_preprocessing: Vec<String>

Data-preprocessing steps applied to the model’s inputs (SPDX 3.0 AI profile ai_modelDataPreprocessing). Surfaced for the BSI/G7 SBOM-for-AI “Models” cluster; CycloneDX has no direct analogue.

Trait Implementations

impl Clone for MlModelInfo

fn clone(&self) -> MlModelInfo

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for MlModelInfo

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for MlModelInfo

fn default() -> MlModelInfo

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for MlModelInfo

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for MlModelInfo

fn eq(&self, other: &MlModelInfo) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for MlModelInfo

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for MlModelInfo