Deberta-v3-base open-source text classification model - Free for use as a benchmark in zero-shot classification tasks

Deberta V3 Base Mnli Fever Anli Ling Wanli Binary

Developed by MoritzLaurer

This model is a text classification model trained on five NLI datasets, primarily used for zero-shot classification tasks and serves as a comparative benchmark.

Text Classification

Transformers

EnglishOpen Source License:MIT #NLI benchmark model #Zero-shot classification #Multi-task training

Downloads 20

Release Time : 11/23/2023

Model Overview

Based on the DeBERTa-v3-base architecture, this model is trained on five NLI datasets and mainly used for zero-shot text classification tasks. It was created as a comparative benchmark for another more comprehensive model, suitable for scenarios that strictly adhere to the original NLI task paradigm.

Model Features

Trained on NLI datasets

Trained on five NLI datasets (MNLI, FEVER, ANLI, LING, WANLI), focusing on natural language inference tasks

Benchmark comparison purpose

Specifically created as a comparative benchmark for more comprehensive models to evaluate zero-shot classification performance

Strict NLI paradigm

Particularly suitable for application scenarios that require strict adherence to the original NLI task paradigm

Model Capabilities

Zero-shot text classification

Natural language inference

Binary classification

Use Cases

Text analysis

Zero-shot classification

Classify unseen texts without domain-specific training data

Natural language inference

Determine the logical relationship (entailment, contradiction, or neutral) between two sentences

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Deberta V3 Base Mnli Fever Anli Ling Wanli Binary

Model Overview

Model Features

Model Capabilities

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