Minylm L3 Aug Sst2 Distilled

Text classification model fine-tuned on the SST-2 sentiment analysis dataset based on DistilBERT-base-uncased, with 91.3% accuracy

Multilingual detection model based on XLM-RoBERTa, supporting text classification in 20 languages

This model improves online hate detection through dynamic dataset generation, focusing on learning from worst-case scenarios to enhance detection effectiveness.

A multilingual sentiment analysis model fine-tuned based on bert-base-multilingual-uncased, supporting sentiment analysis of product reviews in 6 languages

A fine-tuned English text emotion classification model based on DistilRoBERTa-base, capable of predicting Ekman's six basic emotions and neutral category.

Spanish tweet sentiment analysis model based on RoBERTuito, supporting POS(positive)/NEG(negative)/NEU(neutral) three-class sentiment classification

FinBERT is a BERT model pre-trained on financial communication texts, specializing in the field of financial natural language processing. finbert-tone is its fine-tuned version for financial sentiment analysis tasks.

A multi-label sentiment classification model based on RoBERTa-base, trained on the go_emotions dataset, supporting recognition of 28 emotion labels.

XLM-EMO is a multilingual sentiment analysis model fine-tuned based on the XLM-T model, supporting 19 languages and specifically designed for sentiment prediction in social media texts.

DeBERTa-v3 model trained on MultiNLI, Fever-NLI, and ANLI datasets, excelling in zero-shot classification and natural language inference tasks