Synthetic Data Quality

Reference for diagnosing and improving synthetic data quality and privacy. Covers differential privacy errors, PII replacement issues, evaluation metric behavior, and score interpretation for operational use. For conceptual explanations of what SQS and DPS measure and how to read the HTML report, see Product Overview -- Evaluation. For runtime errors, OOM issues, and configuration problems, see Program Runtime. For environment variables and model caching, see Environment Variables.

There is always an inherent trade off between privacy and quality. A high level of privacy protection is achieved simply through the process of generating synthetic data, and is often a sufficient balance between privacy and utility. However, parameter tuning can often help you improve one without sacrificing the other.

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Row count

Your dataset should have at least 1,000 records (10,000 records if enabling differential privacy). If you have fewer, both quality and privacy are likely to suffer considerably.

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Differential Privacy

Differentially private (DP) training has strict requirements. Violating them produces errors that may not immediately point to the root cause.

Requirements

For the full list of DP compatibility constraints (use_unsloth, max_sequences_per_example, gradient checkpointing), see Configuration -- Differential Privacy.

Note

data_fraction and true_dataset_size must be available at runtime -- these are set automatically when running the full pipeline.

Common DP Errors

Unable to automatically determine a noise multiplier

The privacy budget (epsilon) is too low for your dataset size. Either increase privacy.epsilon or add more training records. Generally, you need a minimum of ~10,000 records to achieve reasonable quality when DP is enabled.

Discrete mean differs

The PRV accountant failed and the system is falling back to the Opacus RDP accountant. This is handled automatically -- there is no user-facing config to select the accountant. The fallback may produce slightly different privacy guarantees, since the two accountants use different composition methods: PRV uses privacy loss random variables (Gopi et al. 2021), while RDP uses Rényi divergence (Mironov 2017).

Number of entities in dataset is low

Small datasets cause poor privacy budget utilization. Consider lowering training.batch_size or adding more records.

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PII Replacement

Entity detection and classification issues during PII replacement.

PII Uses Unexpected Entity Types

If PII replacement is not detecting the entity types you expect, the column classifier may have failed silently. When the classifier fails to initialize or classify, it falls back to default entity types.

Look for the following log lines if PII replacement seems to use unexpected entity types:

Could not initialize column classifier, falling back to default entities.

or

Could not perform classify, falling back to default entities.

When NSS_INFERENCE_KEY is not set, the same log line is followed by guidance to set it (and a note that NSS_INFERENCE_ENDPOINT is optional with the default API). When the key is set, a traceback may be included to show the underlying API error.

Fix: set entity types explicitly in your config, or when using the CLI ensure NSS_INFERENCE_KEY is set (and NSS_INFERENCE_ENDPOINT if not using the default). PII classify config is deeply nested -- use YAML or SDK:

Config referenceSDK

replace_pii:
  globals:
    classify:
      enable_classify: true
      entities: ["name", "email", "phone_number"]

from nemo_safe_synthesizer.config.replace_pii import PiiReplacerConfig

pii_config = PiiReplacerConfig.get_default_config()
pii_config.globals.classify.enable_classify = True
pii_config.globals.classify.entities = ["name", "email", "phone_number"]

synthesizer = (
    SafeSynthesizer(config)
    .with_data_source("data.csv")
    .with_replace_pii(config=pii_config)
)

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Evaluation

For out-of-memory errors during evaluation, see Troubleshooting > OOM During Evaluation.

Minimum Data Requirements

Several evaluation metrics have minimum data requirements:

Metric	Minimum	Behavior if Unmet
Holdout split	200 records	Raises ValueError (pipeline stops)
Text semantic similarity	200 records	Skipped; score marked UNAVAILABLE
Attribute Inference Protection	FAISS installed + evaluation.quasi_identifier_count columns (default 3; auto-reduced for smaller datasets)	Skipped if FAISS missing; UNAVAILABLE if too few columns
Deep Structure Stability	2 columns, 2 rows	Skipped with warning; score marked UNAVAILABLE

UNAVAILABLE Metrics

UNAVAILABLE is the literal string that appears in the evaluation report when a metric could not be computed. Many evaluation components catch errors and return this grade instead of failing the pipeline.

Common reasons a metric shows UNAVAILABLE:

• Column type mismatch -- ColumnDistribution, DeepStructure (PCA), and Correlation apply only to numeric and categorical columns; TextSemanticSimilarity and TextStructureSimilarity apply only to text columns. A dataset with no text columns will show UNAVAILABLE for text metrics, and vice versa. This is by design.
• No holdout split -- TextSemanticSimilarity and MembershipInferenceProtection both require a held-out test set. If data.holdout is 0 (no holdout), these metrics are skipped and marked UNAVAILABLE.
• Too few records or columns -- see the minimums table above.
• Model download failure -- the SentenceTransformer model must be present in your Hugging Face cache ($HF_HOME, default ~/.cache/huggingface). Run once with internet access before switching to offline mode.

If the reason is not obvious, check the logs for warnings and exceptions logged during the evaluation stage.

Report Truncation

SQS reports are limited to sqs_report_columns=250 columns and sqs_report_rows=5000 rows by default. Larger datasets are silently truncated in the HTML report. Adjust these in evaluation config if needed. See Configuration -- Evaluation for the full list of evaluation fields.

Low SQS Scores

If the SQS (Synthetic Quality Score) report shows low quality scores:

1. Review column distributions in the HTML report -- large divergences indicate the model did not learn the data patterns well
2. Check that training data is representative and not too small
3. Consider increasing generation.num_records for a larger sample
4. Modify training.num_input_records_to_sample -- this controls how much data the model sees during training (analogous to training duration) and affects generation quality. Increasing it is usually the first thing to try, but note that very small input datasets can lead to over-training, so try both increasing and decreasing it if quality remains poor

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Interpreting Results

For a conceptual overview of evaluation metrics -- what SQS and DPS measure, how to read the HTML report, and what score ranges indicate -- see Product Overview -- Evaluation.