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Kf Deberta Base Cross Nli

Developed by deliciouscat
A Korean natural language inference model based on the DeBERTa architecture, trained on the kor-nli and klue-nli datasets, supporting zero-shot classification tasks.
Downloads 21
Release Time : 3/5/2024

Model Overview

This model is a DeBERTa-base model specifically optimized for Korean natural language inference tasks, suitable for text classification, semantic relation judgment, and other tasks.

Model Features

Korean Optimization
Specifically trained and optimized for Korean natural language processing tasks
Cross-dataset Training
Jointly trained on two Korean NLI datasets: kor-nli and klue-nli
Zero-shot Classification
Supports zero-shot classification tasks without requiring domain-specific fine-tuning

Model Capabilities

Natural Language Inference
Text Classification
Semantic Relation Judgment
Zero-shot Learning

Use Cases

Text Analysis
Customer Service Dialogue Analysis
Automatically determines the semantic relationship between user inquiries and predefined questions
Improves customer service efficiency and reduces manual classification workload
Content Moderation
Judges the relevance between user-generated content and violation standards
Assists manual review and improves moderation efficiency
Intelligent Search
Semantic Search Enhancement
Understands the semantic relationship between queries and documents to improve search relevance
Enhances search result accuracy
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