# News Classification

A text classification model fine-tuned on distilbert-base-uncased, trained on the AG News dataset with an accuracy of 89.06%

This model is designed to classify news articles, helping users quickly identify and organize news content.

A text classification model trained with AutoTrain, suitable for few-shot learning scenarios, capable of efficiently classifying news articles.

A news classification model fine-tuned based on distilbert-base-uncased, focusing on the classification task of litigation-related news.

This model is obtained through distillation from a zero-shot classification pipeline for AG News, primarily demonstrating how to distill an expensive zero-shot model into a more efficient student model.

This is a news classification model fine-tuned on the BERT-Mini architecture, achieving 93% test accuracy on the ag_news dataset.

A text classification model fine-tuned on the AG News dataset based on the BERT base model, achieving an accuracy of 93.75%

A lightweight text classification model based on the DistilBERT architecture, fine-tuned on the AG News dataset using the TextAttack framework, suitable for news classification tasks.

A BERT-based text classification model, fine-tuned on the AG News dataset with a test accuracy of 94.5%.

A Bengali news article classification model fine-tuned on sahajBERT, supporting classification into 6 news categories

A Transformer-based Persian language understanding model, with a reconstructed vocabulary and fine-tuned on a new Persian corpus, providing enhanced functionality for Persian text processing.