DeniableUse case · machine learning and NLP
NLP training data for repeatable model evaluation.
Machine-learning teams need examples that expose the boundary of a model, not only clean samples that confirm what it already handles. Deniable provides structured, repeatable documents for training and evaluation workflows.
The testing job
Evaluate the behaviour behind the label.
A text classifier can recognise a familiar phrase and still fail when intent, sentiment, or entity context changes. Use synthetic examples to inspect how a model handles ambiguity, mixed signals, and uneven document structure.
- Sentiment analysis and aspect-level opinion
- Intent and multi-label classification
- Named entity recognition across document categories
- Text classification with unusual or incomplete context
Useful checks
Keep training and evaluation material aligned.
Use the same documented case structure for training experiments, holdout evaluation, regression runs, and error analysis. The exact labels and fields belong to each published pack and should be reviewed before use.
Deniable approach
Human-designed variation gives the model a real question.
The local generation workflow is supported by human input and creative scenario design. Cases are shaped around a behaviour to inspect, not presented as a statistical mirror of any production population.