Pre-trained language models harbor latent psychological biases inherited from training data, which remain largely unmeasured, creating risks in areas ranging from mental health support to professional decision making. In this study, the evaluation of model psychometrics and assessment of latent constructs framework is employed to evaluate psychometric biases in 334 Hugging Face natural language inference models, assessing the models’ anxiety, depression, sense of coherence, sexism, compassion, and personality. Models consistently score above zero on positively valenced constructs (e.g., compassion, sense of coherence) and below zero on negatively valenced constructs (e.g., anxiety, depression), favoring positive over negative psychological language. Architecture and size are associated with systematic differences: DeBERTa models show elevated negatively valenced constructs (e.g., PHQ δ = +0.45, GAD δ = +0.40) and reduced positively valenced constructs (e.g., Extraversion δ = −0.61), while small models show the opposite pattern (e.g., Extraversion δ = +0.30, PHQ δ = −0.22), with both effects surviving multivariate control. Among the 105 models reporting training data, fine-tuning on adversarial natural language inference was associated with elevated depression (δ = +0.65), while year of release showed no significant effects after multivariate control. These results suggest that psychometric profiling is a critical, yet missing, layer of safety auditing for NLI models.