---
title: MoritzLaurer/deberta-v3-base-zeroshot-v2.0
description: Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification . DeBERTa, 184M parameters.
canonical_url: https://superlinked.com/models/moritzlaurer-deberta-v3-base-zeroshot-v2-0
last_updated: 2026-08-24
---

# MoritzLaurer/deberta-v3-base-zeroshot-v2.0

Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification without training data and run on both GPUs and CPUs. An overview of the latest zeroshot classifiers is available in my Zeroshot Classifier Collection.

Source: [MoritzLaurer/deberta-v3-base-zeroshot-v2.0 on HuggingFace](https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v2.0)
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 | mit |
| 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: 0.8814

[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: 0.4826

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