# E-commerce Review Classification

An Indonesian product review sentiment classification model fine-tuned based on IndoBERT, supporting positive and negative sentiment classification.

xiaobu-embedding is a multi-task embedding model that supports various Chinese natural language processing tasks, including text similarity calculation, classification, clustering, and retrieval.

A Vietnamese sentiment analysis model fine-tuned based on PhoBERT-base, used to classify the sentiment tendency of Vietnamese texts (positive/negative/neutral)

Korean sentiment analysis model based on KoELECTRA-v3, used to determine the positive or negative sentiment tendency of text.

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

This is a multilingual text sentiment analysis model based on DistilBERT, fine-tuned on the amazon_reviews_multi dataset, supporting sentiment classification tasks in multiple languages.

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

A multilingual text sentiment analysis model based on DistilBERT, fine-tuned on the Amazon multilingual reviews dataset

This is a small model based on the mT5 architecture, specifically designed for sentiment analysis tasks in Persian (Farsi) and multilingual contexts.

A RoBERTa-based sentiment text classification model for Indonesian language, fine-tuned on the SmSA dataset, used to analyze the sentiment tendencies of Indonesian reviews and evaluations.

An Arabic multilingual sentiment classification model fine-tuned based on XLM-Roberta-Large, supporting mixed language input

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