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Pankaj8922/bert-medium-nli

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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bert-medium-nli

Fine-tuned `prajjwal1/bert-medium` 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	0.8389	0.8389	0.8389	0.8389
Test	0.8373	0.8372	0.8372	0.8373

Training details

—Base model: prajjwal1/bert-medium
—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 (~41M 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.