---

name: nlp-validation description: "Route Deepchecks NLP work for TextData, labels, metadata, properties, embeddings, and suites." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

NLP Validation

Use this sub-skill when the task is about deepchecks.nlp.TextData, NLP suites, labels, predictions, metadata, properties, embeddings, or offline-safe NLP smoke prep.

Start here

• API reference for constructor signatures, label formats, suite factories, and prediction/probability rules.
• Workflows for text classification, token classification, properties/metadata/embeddings, model evaluation, and no-download patterns.
• Troubleshooting for NLP-specific failures.

Use for

• TextData(raw_text=...) or TextData(tokenized_text=...)
• task_type and label setup for text classification, token classification, and documented multilabel text classification
• metadata, categorical_metadata, properties, categorical_properties, and embeddings
• deepchecks.nlp.suites.data_integrity, train_test_validation, model_evaluation, and full_suite
• train_predictions, test_predictions, train_probabilities, test_probabilities
• optional deepchecks[nlp] and deepchecks[nlp-properties] dependency guidance

Route elsewhere

• Tabular Dataset work → tabular-validation
• Vision VisionData work → vision-validation
• Result export, HTML/JSON, or CI gating → results-and-integrations
• Package install or global import troubleshooting → ../../references/troubleshooting.md

Safe smoke helper

• python scripts/deepchecks_nlp_smoke.py --help
• python scripts/deepchecks_nlp_smoke.py --scenario text-classification --suite model-evaluation
• python scripts/deepchecks_nlp_smoke.py --scenario token-classification --skip-run

Notes

• Prefer precomputed metadata, properties, and embeddings when you need to avoid downloads or heavy model initialization.
• Prefer tokenized_text for token classification so label alignment stays explicit.
• Use text as the runtime raw-text accessor; raw_text is the constructor input.