---
title: cross-encoder/nli-deberta-v3-base
description: This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base. DeBERTa, 184M parameters.
canonical_url: https://superlinked.com/models/cross-encoder-nli-deberta-v3-base
last_updated: 2026-08-24
---

# cross-encoder/nli-deberta-v3-base

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base

Source: [cross-encoder/nli-deberta-v3-base on HuggingFace](https://huggingface.co/cross-encoder/nli-deberta-v3-base)
Base model: [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base)

## Overview

| Field | Value |
|-------|-------|
| Architecture | DeBERTa |
| Parameters | 184M |
| Tasks | Extract |
| Outputs | Class Labels |
| Max sequence length | 512 tokens |
| License | apache-2.0 |
| Inputs | text |
| Languages | en |

## Benchmarks

### AG News

Domain: news · Task: classification · Language: en

Topic classification of news articles into world, sports, business, and sci/tech categories

Corpus: 7,600 · Queries: 7,600

**Quality:** accuracy: 0.6497

[Reference](https://arxiv.org/abs/1509.01626)

### medical_questions_pairs

Domain: medical · Task: classification · Language: en

Classify whether two medical questions ask the same thing (question-pair similarity)

Corpus: 3,048 · Queries: 3,048

**Quality:** accuracy: 0.5197

[Reference](https://huggingface.co/datasets/medical_questions_pairs)