# Natural language inference

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

A natural language inference model optimized through multi-task fine-tuning based on the ModernBERT-large model, excelling in zero-shot classification and NLI tasks.

A natural language inference model fine-tuned based on ModernBERT-large, specifically designed for zero-shot classification tasks

ModernBERT is a model fine-tuned on multi-task natural language inference (NLI) tasks, excelling in zero-shot classification and long-context reasoning.

This is the OpenVINO optimized version of the facebook/bart-large-mnli model for zero-shot text classification tasks.

Zion_Alpha_Instruction_Tuned_SLERP is an innovative language model trained on Hebrew, which performs excellently in tasks such as sentiment analysis and natural language inference.

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

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

This is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

Natural language inference model based on DeBERTa-v3-large architecture, ONNX weight version adapted for Transformers.js

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

Natural language inference model based on DeBERTa-v3 architecture, ONNX weight version adapted for Transformers.js

A small natural language inference model based on DeBERTa-v3, suitable for zero-shot classification tasks

This model is a fine-tuned version of intfloat/e5-base-v2 on the GLUE (MNLI) and ANLI datasets, suitable for zero-shot classification and natural language inference tasks.

A sequence classification model adapted from Flan-T5-xxl, specifically designed for natural language inference tasks with half the parameters and support for zero-shot classification

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

A multilingual natural language inference model supporting over 100 languages, suitable for zero-shot classification tasks

A multilingual natural language inference model based on XLM-V-base, supporting zero-shot classification tasks in 116 languages

A model fine-tuned on 600+ tasks based on DeBERTa-v3-base through multi-task learning, excelling in zero-shot classification and natural language inference

A natural language inference model fine-tuned from NbAiLab/nb-bert-base, supporting Danish, Norwegian Bokmål, and Swedish

A zero-shot classification natural language inference model supporting Danish, Norwegian, and Swedish

BERT-based Japanese natural language inference model, fine-tuned on the JSNLI dataset, suitable for zero-shot classification and text classification tasks.

Multilingual natural language inference model based on mDeBERTa-v3-base, supporting zero-shot classification in 100 languages

NLI model fine-tuned on DeBERTa-v3-large, achieving state-of-the-art performance on multiple NLI datasets

This model is a zero-shot classifier trained on bertin-roberta-base-spanish, suitable for Spanish text classification tasks.

A case-insensitive DistilBERT model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) dataset, optimized for zero-shot classification tasks

This model is a fine-tuned version of the uncased MobileBERT model on the Multi-Genre Natural Language Inference (MNLI) task, suitable for zero-shot classification tasks.

A cross-attention natural language inference model specifically trained for zero-shot and few-shot text classification.

A text classification model fine-tuned on the GLUE MNLI dataset based on the RoBERTa-base model, excelling in natural language inference tasks

DistilBERT is a distilled version of BERT that retains 97% of BERT's performance while being 40% smaller and 60% faster.

German zero-shot classification model based on gbert-large, fine-tuned with 847,862 machine-translated natural language inference sentence pairs

Based on the XLM-RoBERTa-large model, fine-tuned on multiple natural language inference datasets, and can be directly used for zero-shot classification tasks.

A Turkish zero-shot classification model fine-tuned based on dbmdz/distilbert-base-turkish-cased

A multi-dataset pre-trained natural language inference model based on the RoBERTa-Large architecture, integrating multiple well-known NLI datasets including SNLI, MNLI, FEVER-NLI, and ANLI.

A Turkish zero-shot classification model based on the ConvBERT architecture, fine-tuned for natural language inference tasks, suitable for multi-class text classification

A pre-trained natural language inference model based on the MiniLMv2 architecture, used to determine the relationship between sentence pairs (contradiction/entailment/neutral).

DeBERTa-v3 model trained on MultiNLI, Fever-NLI, and ANLI datasets, excelling in zero-shot classification and natural language inference tasks

Text classification model fine-tuned on the MNLI task based on the Catalan RoBERTa model

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