final/README.md

---
language: en
license: apache-2.0
tags:
- text-classification
- zero-shot-classification
- nli
datasets:
- Pankaj8922/nli-high-quality-balanced
base_model: prajjwal1/bert-medium
metrics:
- accuracy
- f1
model-index:
- name: bert-medium-nli
  results:
  - task:
      type: text-classification
      name: Natural Language Inference
    dataset:
      name: Pankaj8922/nli-high-quality-balanced
      type: Pankaj8922/nli-high-quality-balanced
    metrics:
    - type: accuracy
      value: 0.8373
      name: Test Accuracy
    - type: f1
      value: 0.8372
      name: Test F1 (macro)
---

# bert-medium-nli

Fine-tuned [`prajjwal1/bert-medium`](https://huggingface.co/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`](https://huggingface.co/datasets/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.