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Bert Base Go Emotion

Developed by bhadresh-savani
A text classification model based on the BERT-base-uncased architecture, specifically designed for sentiment analysis tasks, trained on the Go Emotions dataset.
Text Classification
Transformers
 EnglishOpen Source License:Apache-2.0#Multi-label Sentiment Classification#High Accuracy#English Text Analysis
Downloads 5,500
Release Time : 3/2/2022

Model Overview

This model is a multi-label text classification model used to identify emotional categories in text. Based on the BERT architecture and fine-tuned on the Go Emotions dataset, it is suitable for sentiment analysis applications.

Model Features

Multi-label Sentiment Classification
Capable of identifying multiple emotions expressed in text simultaneously, rather than a single emotional label.
BERT-based Architecture
Utilizes BERT's powerful contextual understanding capabilities for sentiment analysis.
High Accuracy
Achieved an accuracy of 96.15% in evaluations.

Model Capabilities

Text Sentiment Analysis
Multi-label Classification
Natural Language Understanding

Use Cases

Sentiment Analysis
Social Media Sentiment Monitoring
Analyze user emotions in social media posts
Accurately identifies multiple compound emotions
Customer Feedback Analysis
Automatically classify emotional tendencies in customer feedback
Helps identify customer satisfaction issues
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