bert-small-nli

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.

Results

Split	Accuracy	F1 (macro)	Precision (macro)	Recall (macro)
Validation	0.8048	0.8047	0.8047	0.8048
Test	0.8045	0.8044	0.8045	0.8045

Training details

• Base model: prajjwal1/bert-small
• Epochs: 3
• Batch size: 64 (train), 128 (eval)
• Learning rate: 5e-05
• Max sequence length: 256

Labels

• 0: entailment
• 1: neutral
• 2: contradiction

Intended use / limitations

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.