# Chinese Sentiment Analysis

This is a fine-tuned sentence transformer model based on sentence-transformers/all-mpnet-base-v2, designed to map text into a 768-dimensional vector space, supporting tasks such as semantic similarity computation.

A BERT-based Chinese sentiment analysis model for determining the emotional tendency of text (positive, negative, or neutral).

A Chinese sentiment classification model fine-tuned based on mDeBERTa-v3-base, capable of recognizing 8 emotion labels

Chinese sentiment classification model fine-tuned based on xlm-roberta-large-xnli, capable of recognizing 8 emotion labels

A Chinese text emotion intensity detection model fine-tuned based on BERT architecture, capable of distinguishing between weak and strong emotional expressions

A Chinese sentiment analysis model fine-tuned based on bert-base-chinese, performing well on Chinese sentiment corpora.

A Chinese sentiment analysis model based on the MegatronBert architecture, fine-tuned on multiple sentiment analysis tasks

Optimized version fine-tuned on multiple sentiment analysis datasets based on Chinese RoBERTa-wwm-ext-large model

A fine-tuned version based on the Chinese RoBERTa-wwm-ext-base model on multiple sentiment analysis datasets

A Chinese news topic classification model fine-tuned based on RoBERTa-Base, specifically designed for categorizing Chinese news texts

Includes 5 Chinese text classification models based on RoBERTa-Base, suitable for sentiment analysis and news classification tasks across different domains

This is a Chinese RoBERTa medium model pre-trained on CLUECorpusSmall, featuring 8 layers and 512-dimensional hidden layers, suitable for various Chinese natural language processing tasks.

A text classification model trained on Chinese dialogue emotion datasets, supporting binary classification: positive and negative.

Chinese RoBERTa-Base classification models fine-tuned based on UER-py, including text classification models for 5 different datasets