just-another-emotion-classifier Open-source Emotion Classification Model

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Just Another Emotion Classifier

Developed by bdotloh

This is a Transformer-based emotion classification model capable of identifying 32 different emotions in input text.

Text Classification

Transformers

English#Multi-emotion Classification #Empathetic Dialogue Analysis #32-category Emotion Recognition

Downloads 92

Release Time : 9/19/2022

Model Overview

This model is designed for emotion classification tasks, predicting probability distributions across 32 predefined emotions. Built upon a fine-tuned distilbert-base-uncased model trained on the go-emotions dataset.

Model Features

Multi-emotion Classification

Capable of recognizing 32 distinct emotion categories

Transformer-based

Utilizes efficient distilbert architecture to reduce computational requirements while maintaining performance

Cross-dataset Training

Trained on combined go-emotions and empathetic dialogue datasets

Model Capabilities

Text Sentiment Analysis

Multi-label Classification

Emotion Probability Prediction

Use Cases

Customer Service

Customer Feedback Analysis

Analyze emotional tendencies in customer feedback

Helps identify dissatisfied customers for priority handling

Social Media

Public Sentiment Monitoring

Track changes in public emotions on social media

Provides real-time emotional trend analysis

🚀 Emotion Classification Transformer Model

This is an emotion classifier model that outputs a probability distribution over a set of pre - selected emotion words given an input text.

🚀 Quick Start

This section will be added if there are relevant quick - start instructions in the future.

✨ Features

Given an input text, the model outputs a probability distribution over a set of pre - selected emotion words.
Built on top of a [distilbert - base - uncased model fine - tuned on the go - emotions dataset](https://huggingface.co/bhadresh - savani/bert - base - go - emotion).

📚 Documentation

📋 Model Description

Yet another Transformer model fine - tuned for approximating another non - linear mapping between X and Y? That's right! This is your good ol' emotion classifier - given an input text, the model outputs a probability distribution over a set of pre - selected emotion words. In this case, it is 32, which is the number of emotion classes in the [Empathetic Dialogues](https://huggingface.co/datasets/bdotloh/empathetic - dialogues - contexts) dataset.

This model is built "on top of" a [distilbert - base - uncased model fine - tuned on the go - emotions dataset](https://huggingface.co/bhadresh - savani/bert - base - go - emotion). Y'all should really check out that model, it even contains a jupyter notebook file that illustrates how the model was trained (bhadresh - savani if you see this, thank you!).

📊 Training Data

This section will be expanded if there is more information about training data.

⚙️ Training Procedure

🛠️ Preprocessing

This section will be expanded if there is more information about preprocessing.

📈 Evaluation Results

This section will be expanded if there is more information about evaluation results.

⚠️ Limitations and Bias

Well where should we begin...

EmpatheticDialogues:

Unable to ascertain the degree of cultural specificity for the context that a respondent described when given an emotion label (i.e., p(description | emotion, culture))
...

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