# Natural Language Inference

This is a text classification model fine-tuned on the GLUE MNLI dataset based on google/switch-base-8, primarily used for natural language inference tasks.

An NLI model fine-tuned based on flan-t5-xxl, used to verify whether a premise supports a hypothesis, specially optimized for multi-sentence premise scenarios

Fine-tuned on the French portion of the XNLI dataset based on the Camembert-base model, supporting French zero-shot classification

A fine-tuned version of the RoBERTa-base model on the MNLI dataset, suitable for natural language inference tasks

mt0-xxl is a multilingual large model from the BLOOMZ series, supporting cross-lingual task processing in 46 languages, fine-tuned on the xP3 dataset, and excels in zero-shot cross-lingual instruction execution

This is a text classification model based on the BERT-tiny architecture, fine-tuned on the GLUE RTE task, primarily used for textual entailment recognition tasks.

A roberta-large model fine-tuned on the WANLI dataset for natural language inference tasks, outperforming roberta-large-mnli on multiple out-of-domain test sets.

Spanish sentence embedding model based on BERTIN RoBERTa, optimized for natural language inference tasks

A cross-encoder model based on microsoft/deberta-v3-large architecture, trained for natural language inference tasks on SNLI and MultiNLI datasets.

DeBERTa-XLarge-MNLI is an enhanced BERT model based on the disentangled attention mechanism, fine-tuned on the MNLI task with 750M parameters, excelling in natural language understanding tasks.

A cross-encoder based on DistilRoBERTa for natural language inference tasks, capable of determining the relationship between sentence pairs (contradiction, entailment, or neutral).

DeBERTa-v3-large model fine-tuned on GLUE MNLI dataset for natural language inference tasks, achieving 90% accuracy on the validation set

Roberta-large model trained on the MNLI dataset for natural language inference tasks.

A text classification model fine-tuned on the GLUE MNLI dataset based on bert-base-cased, designed for natural language inference tasks

This model is based on the RuPERTa-base architecture and fine-tuned on the PAWS-X-es Spanish dataset for natural language inference, specifically designed to identify paraphrase relationships between sentence pairs.

A text classification model fine-tuned on the GLUE RTE task based on the BERT base model

This model is a PyTorch pretrained model obtained by converting TensorFlow checkpoints from the official Google BERT repository, trained on the MNLI dataset for natural language inference tasks.

This is a cross-encoder model based on the ELECTRA architecture, specifically designed for natural language inference (NLI) in question-answering tasks, determining whether a given question can be answered by a specific paragraph.

SqueezeBERT is a lightweight version of BERT, optimized to reduce computational resource requirements while maintaining high performance in natural language understanding.

This model investigates generalization capabilities in Natural Language Inference (NLI) tasks, exploring how to move beyond simple heuristic methods.

BART-large-MNLI is a sequence classification model trained by Facebook, based on the BART architecture, specifically designed for natural language inference tasks.

This model focuses on the Natural Language Inference (NLI) task, aiming to surpass simple heuristic methods and improve the model's generalization capability in complex reasoning scenarios.

Enhanced BERT decoding model based on disentangled attention mechanism, fine-tuned on MNLI task

This model is a PyTorch pretrained model obtained by converting TensorFlow checkpoints from the official Google BERT repository, originating from the paper 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models,' and trained on the MNLI dataset.

This model is a text classification model fine-tuned on the MNLI task of GLUE based on distilbert-base-uncased, primarily used for natural language inference tasks.

This model is a text classification model based on the ALBERT base version (albert-base-v2) fine-tuned on the RTE task of the GLUE dataset, primarily used for textual entailment recognition tasks.

A Russian natural language inference model fine-tuned from rubert-tiny for predicting logical relationships between texts

This model is a text classification model fine-tuned on the WNLI task of GLUE based on ALBERT-xlarge-v2

A text classification model fine-tuned on the GLUE MNLI dataset based on ALBERT-base-v2, designed for natural language inference tasks

A zero-shot classifier based on SELECTRA, fine-tuned on the Spanish portion of the XNLI dataset, suitable for zero-shot classification tasks.

This model is fine-tuned from bert-base-uncased-sparse-70-unstructured on the MNLI task (GLUE benchmark), with the classifier layer removed for easier loading into other downstream tasks for training.