multilingual_go_emotions Open-Source Model - Free Support for Text Sentiment Analysis in 6 Languages

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Multilingual Go Emotions

Developed by AnasAlokla

BERT-based multilingual sentiment classification model supporting text sentiment analysis in 6 languages

Text Classification

Safetensors

Supports Multiple Languages#Multilingual Sentiment Analysis #BERT Fine-tuning #Chatbot Interaction

Downloads 319

Release Time : 3/28/2025

Model Overview

This model is fine-tuned on the multilingual_go_emotions dataset based on the bert-base-multilingual-cased pre-trained model for multi-label sentiment classification tasks.

Model Features

Multilingual Support

Supports sentiment analysis in 6 languages including Arabic, English, French, etc.

Fine-grained Sentiment Classification

Capable of recognizing 27 different emotion categories including admiration, amusement, anger, etc.

BERT Architecture

Based on the powerful BERT pre-trained model with excellent text understanding capabilities

Model Capabilities

Multilingual text sentiment analysis

Multi-label sentiment classification

Real-time emotion detection

Use Cases

Social Media Analysis

User Comment Sentiment Analysis

Analyze sentiment tendencies in user comments on social media

Can identify multiple complex emotional states

Customer Service

Customer Feedback Sentiment Analysis

Automatically analyze emotional states in customer feedback

Helps identify angry or dissatisfied customers

🚀 Multilingual GoEmotions Chatbot Task Report

This repository contains details about a multilingual emotion classification model and chatbot based on the GoEmotions dataset, offering multi - label classification capabilities.

📋 Overview

A model trained from [bert - base - multilingual - cased](https://huggingface.co/google - bert/bert - base - multilingual - cased) on the multilingual_go_emotions dataset for multi - label classification.

👨‍💻 Author

Anas Hamid Alokla

🔗 Links

Live Demo: https://huggingface.co/spaces/AnasAlokla/test_emotion_chatbot
Dataset (Supports 6 Languages): https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions
Model Used: https://huggingface.co/AnasAlokla/multilingual_go_emotions
GitHub Code: https://github.com/anasAloklah/emotion_chatbot

📊 Test Set Performance

The following table shows the performance metrics of the fine - tuned model on the test set, broken down by emotion category.

index	accuracy	precision	recall	f1	mcc	support	threshold
admiration	0.942	0.652	0.684	0.667	0.636	2790	0.4
amusement	0.973	0.735	0.817	0.774	0.76	1866	0.35
anger	0.96	0.411	0.364	0.386	0.366	1128	0.35
annoyance	0.896	0.246	0.481	0.325	0.293	1704	0.15
approval	0.91	0.329	0.383	0.354	0.307	2094	0.2
caring	0.958	0.285	0.46	0.352	0.341	816	0.15
confusion	0.965	0.444	0.401	0.421	0.404	1020	0.25
curiosity	0.935	0.433	0.74	0.546	0.535	1734	0.25
desire	0.984	0.404	0.534	0.46	0.457	414	0.25
disappointment	0.942	0.224	0.345	0.272	0.249	1014	0.15
disapproval	0.935	0.306	0.413	0.352	0.322	1398	0.25
disgust	0.975	0.343	0.418	0.377	0.366	600	0.15
embarrassment	0.99	0.28	0.242	0.26	0.255	240	0.1
excitement	0.973	0.344	0.425	0.38	0.369	624	0.15
fear	0.987	0.599	0.522	0.558	0.553	498	0.35
gratitude	0.989	0.924	0.902	0.913	0.907	2004	0.4
grief	0.999	0	0	0	0	36	0.05
joy	0.965	0.454	0.532	0.49	0.474	1032	0.25
love	0.973	0.731	0.829	0.777	0.765	1812	0.35
nervousness	0.996	0.385	0.25	0.303	0.308	120	0.1
optimism	0.973	0.588	0.525	0.555	0.542	1062	0.25
pride	0.997	0	0	0	0	84	0.05
realization	0.962	0.202	0.189	0.195	0.176	792	0.15
relief	0.996	0	0	0	0	138	0.05
remorse	0.988	0.597	0.808	0.687	0.689	516	0.15
sadness	0.97	0.548	0.434	0.484	0.473	1062	0.4
surprise	0.974	0.487	0.569	0.524	0.513	828	0.3
neutral	0.726	0.551	0.818	0.658	0.468	10524	0.2

📈 Fine - tuning Performance

The following plots visualize the model's performance during the fine - tuning process across epochs.

Loss Curves (Training vs. Validation)

Loss Curves

Accuracy Curves (Training vs. Validation)

Accuracy Curves

F1 Score Curves (Training vs. Validation)

F1 Score Curves

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