# Sentiment analysis

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 a SetFit model trained on the sst2 dataset for English text classification tasks, utilizing efficient few-shot learning techniques.

ViSoBERT is the first monolingual pre-trained language model specifically designed for Vietnamese social media text, based on the XLM-R architecture, and has demonstrated excellent performance in various Vietnamese social media tasks.

This model is trained using self-supervised tuning methods and is suitable for zero-shot text classification tasks. It can perform sentiment analysis, topic classification, and other tasks without additional fine-tuning.

This model is a text classification model fine-tuned on the GLUE SST2 dataset using DeBERTa v3 small, demonstrating excellent performance in sentiment analysis tasks.

KoBART-base-v2 is a Korean pre-trained model based on the BART architecture, optimized for long-sequence semantic processing through the addition of chat data.

A text classification model fine-tuned on the GLUE SST2 dataset based on the bert-base-cased model, with an accuracy of 92.3%.

This is a versatile large language model capable of understanding and generating natural language text

This model is a BERT model trained on over 20GB of Vietnamese news datasets, suitable for tasks such as sentiment analysis, and performs excellently on the AIViVN comment dataset.

This is an adapter trained on the roberta-base model, specifically designed for sentiment analysis tasks on IMDB movie reviews.