M

Modernbert Large Zeroshot V2.0

Developed by MoritzLaurer
A zero-shot classifier fine-tuned based on ModernBERT-large, efficient and fast with low memory usage, suitable for various text classification tasks.
Downloads 25.66k
Release Time : 12/27/2024

Model Overview

This model is fine-tuned based on ModernBERT-large, trained on the same dataset as the zeroshot-v2.0 model in the Zeroshot classifier collection, suitable for zero-shot text classification tasks.

Model Features

Efficient and fast
The model runs extremely fast with low memory usage, offering several times speed improvement and reduced memory consumption compared to DeBERTav3, supporting larger batch processing.
Performance
Slightly underperforms DeBERTav3 on average across various test tasks but offers higher overall efficiency.
Future optimizations
Plans to leverage higher-quality synthetic data to fully utilize the 8k context window advantage and update the training composition.

Model Capabilities

Zero-shot text classification
Multi-task text classification
Efficient inference

Use Cases

Text classification
Sentiment analysis
Classify sentiment tendencies in movie reviews, product reviews, etc.
Achieved an accuracy of 0.899 on the rottentomatoes dataset.
Topic classification
Classify topics in news articles, social media content, etc.
Achieved an accuracy of 0.899 on the agnews dataset.
Hate speech detection
Detect hate speech and offensive content in text.
Achieved an accuracy of 0.814 on the hatexplain dataset.
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase