Natural language inference model supporting 100 languages, suitable for multilingual zero-shot classification tasks

Natural Language ProcessingSafetensorsMultiple Languages

A zero-shot classification model fine-tuned from MoritzLaurer/deberta-v3-base-zeroshot-v2.0-c using the SmartShot method with an NLI framework

Natural Language ProcessingSafetensorsEnglish

A zero-shot classifier fine-tuned based on ModernBERT-base, efficient and fast with low memory usage, suitable for various text classification tasks.

Natural Language ProcessingTransformers

A zero-shot classifier fine-tuned based on ModernBERT-large, efficient and fast with low memory usage, suitable for various text classification tasks.

Natural Language ProcessingTransformers

This is the ONNX format conversion of the MoritzLaurer/roberta-base-zeroshot-v2.0-c model, suitable for zero-shot classification tasks.

Natural Language ProcessingTransformers

A model specifically designed for efficient zero-shot classification, supporting multilingual text classification tasks without requiring training data

Natural Language ProcessingTransformersOther

A multilingual zero-shot text classification model trained based on BAAI/bge-m3-retromae, specifically designed for commercial-friendly scenarios

Natural Language ProcessingTransformersOther

DeBERTa-v3-large model optimized for zero-shot classification tasks, supporting text classification without training data

Natural Language ProcessingTransformersEnglish

Zero-shot classification model based on DeBERTa-v3-base architecture, designed for text classification tasks without training data

Natural Language ProcessingTransformersEnglish

A RoBERTa-large model designed for efficient zero-shot classification, trained on commercially friendly data, capable of performing text classification tasks without training data.

Natural Language ProcessingTransformersEnglish

A zero-shot classification model based on the RoBERTa architecture, designed for text classification tasks without requiring training data, supports both GPU and CPU operation, and is trained using fully business-friendly data.

Natural Language ProcessingTransformersEnglish

A zero-shot classification model based on DeBERTa-v3-base, designed for classification tasks without training data, trained on business-friendly data

Natural Language ProcessingTransformersEnglish

A DeBERTa-v3-large model specifically designed for efficient zero-shot classification, trained on fully commercially friendly synthetic data and NLI datasets, supporting GPU/CPU inference

Natural Language ProcessingTransformersEnglish

This is a small and efficient zero-shot classification model, fine-tuned based on microsoft/deberta-v3-xsmall, specifically designed for edge devices or in-browser use cases.

Natural Language ProcessingTransformersEnglish

This is an ultra-compact, high-performance zero-shot classification model designed specifically for edge devices and browser applications.

Natural Language ProcessingTransformersEnglish

This model is a zero-shot classification model based on the DeBERTa-v3-large architecture, primarily trained on five NLI datasets, suitable for tasks that strictly follow the original NLI task format.

Natural Language ProcessingTransformersEnglish

A DeBERTa-v3-large model specifically designed for zero-shot classification, capable of reformulating any classification task as a Natural Language Inference (NLI) problem

Natural Language ProcessingTransformersEnglish

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.

Natural Language ProcessingTransformersEnglish

A DeBERTa-v3 based zero-shot classification model supporting 33 datasets and 387 categories for general text classification tasks

Natural Language ProcessingTransformersEnglish

A DeBERTa-v3 model specifically designed for zero-shot classification tasks, excelling in various classification tasks

Natural Language ProcessingTransformersEnglish