# Textual Entailment Recognition

A model fine-tuned based on bert-base-uncased for textual entailment recognition tasks, with an accuracy of 66.43%

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 RTE task based on the BERT base model

This model is a text classification model fine-tuned on the GLUE dataset's RTE task based on DistilBERT, with an accuracy of 61.73%

A lightweight model fine-tuned for French natural language inference tasks based on DistilCamemBERT, with 50% faster inference speed than the original CamemBERT

This is a text classification model based on the bert-base-uncased pre-trained model and fine-tuned on the MultiNLI dataset

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 cross-encoder model based on microsoft/deberta-v3-base, trained for natural language inference tasks, capable of determining the relationship between sentence pairs (contradiction, entailment, or neutral).