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Emotion English Distilroberta Base

Developed by j-hartmann
A fine-tuned English text emotion classification model based on DistilRoBERTa-base, capable of predicting Ekman's six basic emotions and neutral category.
Downloads 1.1M
Release Time : 3/2/2022

Model Overview

This model is used for emotion classification of English texts, capable of identifying seven emotion categories: anger, disgust, fear, happiness, neutral, sadness, and surprise. Trained on six diverse datasets, it is suitable for emotion analysis across various text types.

Model Features

Multi-emotion classification
Capable of identifying seven different emotion categories, including Ekman's six basic emotions and neutral category.
Diverse training data
Trained on six different English datasets covering various text types such as Twitter tweets, Reddit posts, student self-reports, and TV show dialogues.
Efficient distilled model
Fine-tuned based on DistilRoBERTa-base, improving efficiency while maintaining performance.

Model Capabilities

English text emotion classification
Multi-category emotion prediction

Use Cases

Social media analysis
Tweet sentiment analysis
Analyzing user sentiment tendencies in Twitter tweets.
Can identify seven emotions including anger and happiness
Academic research
Psycholinguistic research
Used for analyzing emotional expressions and psychological states in texts.
Has been used in multiple academic papers
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