# Text Sentiment Analysis

A lightweight sentiment analysis model based on DistilBERT, suitable for text sentiment classification tasks

A text classification model fine-tuned on the IMDB dataset based on the distilbert-base-uncased model

A fine-tuned model based on microsoft/xtremedistil-l6-h384-uncased, specific purpose not clearly stated

A fine-tuned version of the bert-base-uncased model on a review dataset, suitable for text classification tasks.

This model is a fine-tuned version of the DistilBERT base model on the IMDB dataset, primarily used for text sentiment analysis tasks.

A pre-trained language model based on the Transformer architecture, suitable for various natural language processing tasks.

This model is fine-tuned from Google's T5-base model on an emotion recognition dataset, designed for text sentiment classification tasks, capable of classifying text into 6 basic emotions.

This model is a text classification model fine-tuned on the SST5 dataset based on the DistilBERT base model, with an accuracy of 50.45%.

A fine-tuned model based on bert-base-uncased for membership inference attack detection tasks, achieving an accuracy of 86.81% on the evaluation set.

A lightweight text sentiment classification model based on DistilBERT, fine-tuned on the emotion dataset with an accuracy of 92.4%

Text classification model fine-tuned based on distilbert-base-uncased-finetuned-sst-2-english, achieving 86.38% accuracy on the evaluation set

This model is a text classification model fine-tuned from bert-base-uncased on the SST-2 dataset, optimized through pruning with the nn_pruning library, retaining 51% of the original model's weights while achieving 91.17% accuracy.

A sentiment analysis model fine-tuned on the SST-2 dataset based on microsoft/deberta-v3-large

This model is a text classification model fine-tuned on the emotion dataset based on DistilBERT, designed for sentiment analysis tasks.