Model Card Template & Example Entries

Basic metadata

Model name: [e.g., FraudDetect-v1]

Version: [semver or date-based]

Owner / contact: [team, individual, email]

Release date: [YYYY-MM-DD]

Repository / artifact links: [link to model binary, training code, README]

Short description & intended uses

Summary: One-paragraph description of what the model does.

Primary intended uses: Bullet list of permitted uses.

Contraindicated uses / warnings: Clear list of uses that are unsafe, illegal, or outside design scope.

Data provenance & lineage

Document datasets (names, owners, collection dates), preprocessing steps, synthetic data usage, and any third-party components (off-the-shelf embeddings, LLMs, retrieval indexes).

• Training data: [dataset name, URL, license, sampling method]
• Validation / test data: [dataset name, holdout strategy]
• Data transformations: [tokenization, normalization, feature selection]
• External dependencies: [libraries, pretrained models, knowledge sources]

Performance & evaluation summary

List the metrics that matter for the model's intended uses and provide numeric results with dataset context.

Example entries to include:

• Metric name and definition (e.g., F1-score on labeled holdout test set)
• Numeric result with confidence intervals (if available)
• Evaluation dataset description and sampling method
• Performance by relevant slice (demographics, class imbalance, domain, input length)

Limitations, biases, and failure modes

Describe known weaknesses (types of inputs that cause errors), fairness concerns, and examples of typical failure cases. Provide mitigation strategies where possible.

Operational readiness & monitoring hooks

Specify how the model will be monitored in production and what alerts should trigger human review or rollback.

• Production metrics to track (e.g., latency, error rate, distribution shift scores, confidence calibration, user-reported error rate)
• Thresholds and escalation paths (e.g., if weekly drift > X, notify owner)
• Feedback loop: how to collect labeled errors or human corrections
• Retraining cadence or triggers

Security, privacy, and compliance notes

Record PII exposure risk, encryption practices, access controls, data retention, and any legal constraints (HIPAA, GDPR, etc.).

Change log and versioning

Small table or list describing significant changes between versions and links to release artifacts.

Example A — Binary classification (fraud detection)

Model name: FraudDetect-v1

Intended use: Flag transactions for human review to reduce fraud-related losses. Not for automatic blacklisting without human override.

Primary metrics: F1 = 0.72 on holdout set (2024-01), precision@top5% = 0.88. Evaluation dataset: labeled transactions from 2022–2023, sampled stratified by merchant category.

Limitations: Lower recall on new merchant categories and regions underrepresented in training data. High false positive rate for rare legitimate high-risk transactions.

Monitoring hooks: Daily alert if precision drops below 0.75 over a 7-day rolling window; monthly drift score on feature distributions.

Example B — RAG-style assistant (customer support knowledge assistant)

Model name: SupportRAG-v2

Intended use: Assist support agents by surfacing relevant knowledge-base passages and suggested answer drafts. Agents must verify and edit outputs before sending to customers.

Components & lineage: Open-source LLM (base-llm-3), custom retrieval index built on KB snapshots (2024-02), and a lightweight reranker trained on historical agent responses.

Performance: Reranker NDCG@5 = 0.62 on test queries; human evaluation: 84% of suggestions rated helpful by agents in pilot.

Limitations & risks: May hallucinate unsupported facts if retrieval fails; sensitive customer data must be masked before indexing.

Operational safeguards: Masking pipeline for PII, human-in-the-loop verification required, telemetry for hallucination rate (agent marks suggestions that required major edits).