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Emotion Trained 42
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Emotion Trained 42

Developed by marcolatella
A sentiment analysis model fine-tuned on distilbert-base-uncased, trained on the tweet_eval dataset for tweet sentiment classification
Text Classification
Transformers
Open Source License:Apache-2.0#Tweet sentiment analysis#High F1 score#DistilBERT fine-tuning
Downloads 15
Release Time : 3/2/2022

Model Overview

This model is a text classification model optimized for tweet sentiment analysis, capable of identifying emotional categories in tweets

Model Features

Efficient and lightweight
Based on the DistilBERT architecture, it reduces model size while maintaining performance
Specifically optimized for tweets
Fine-tuned on the tweet_eval dataset, making it particularly suitable for social media text analysis
Good performance
Achieves an F1 score of 0.7319 on the evaluation set

Model Capabilities

Tweet sentiment classification
English text analysis
Short text emotion recognition

Use Cases

Social media analysis
User sentiment monitoring
Analyze users' emotional reactions to specific topics on social media
Can identify basic emotions such as anger, joy, sadness, etc.
Brand reputation management
Monitor changes in user sentiment regarding brand-related tweets
Helps detect negative sentiments in a timely manner and take countermeasures
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