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Nli Deberta Base
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Nli Deberta Base

Developed by Xenova
A natural language inference (NLI) model based on the DeBERTa architecture, suitable for zero-shot classification tasks, converted to ONNX format for compatibility with Transformers.js
Text Classification
Transformers
#Zero-shot classification#Natural language inference#ONNX format
Downloads 30
Release Time : 8/8/2023

Model Overview

This model is a cross-encoder based on the DeBERTa architecture, specifically designed for natural language inference (NLI) tasks. It can understand the relationship between two text sequences and is suitable for zero-shot classification scenarios.

Model Features

ONNX format compatibility
The model has been converted to ONNX format, specifically adapted for Transformers.js, allowing direct use in web environments
Zero-shot classification capability
Can be applied to new classification tasks without fine-tuning, demonstrating strong generalization ability
Efficient cross-encoder architecture
Utilizes the DeBERTa-base architecture, excelling in understanding textual relationships

Model Capabilities

Natural language inference
Textual relationship judgment
Zero-shot text classification
Semantic similarity calculation

Use Cases

Text classification
Sentiment analysis
Determine text sentiment orientation without training data
Topic classification
Classify unseen new topics
Question answering systems
Answer relevance evaluation
Assess the matching degree between candidate answers and questions
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