Fine-tuned prajjwal1/bert-small for natural language inference (entailment / neutral / contradiction), intended for use as a zero-shot text classification model via the entailment trick (hypothesis = "This text is about {label}.").

Trained on Pankaj8922/nli-high-quality-balanced, a combined and filtered subset of MNLI, SNLI, FEVER-NLI, and ANLI: annotator-agreement filtered, deduplicated, teacher-confidence filtered, hypothesis-only artifact filtered, and class-balanced.

This is a small (~29M parameter) model, so its ceiling on zero-shot performance against novel, unseen label sets is lower than larger NLI-tuned checkpoints (e.g. DeBERTa-v3-base or -large variants). Best suited for fast inference or resource-constrained settings rather than maximum accuracy.