🌍 Multilingual GoEmotions Classifier 💬

Table of Contents

• 📖 Overview
• ✨ Key Features
• 💫 Supported Emotions
• 🔗 Links
• ⚙️ Installation
• 🚀 Quickstart: Emotion Detection
• 📊 Evaluation
• 💡 Use Cases
• 📚 Trained On
• 🔧 Fine-Tuning Guide
• 🏷️ Tags
• 💬 Support & Contact

Overview

This repository contains a powerful multilingual, multi-label emotion classification model. It is fine-tuned from the robust bert-base-multilingual-cased model on the comprehensive multilingual_go_emotions dataset. The model is designed to analyze text and identify a wide spectrum of 27 different emotions, plus a neutral category. Its ability to detect multiple emotions simultaneously makes it highly effective for understanding nuanced text from diverse sources.

• Model Name: AnasAlokla/multilingual_go_emotions_V1.2
• Architecture: BERT (bert-base-multilingual-cased)
• Tasks: Multi-Label Text Classification | Emotion Detection | Sentiment Analysis
• Languages: Arabic, English, French, Spanish, Dutch, Turkish

Key Features

• 🌍 Truly Multilingual: Natively supports 6 major languages, making it ideal for global applications.
• 🏷️ Multi-Label Classification: Capable of detecting multiple emotions in a single piece of text, capturing complex emotional expressions.
• 💪 High Performance: Built on bert-base-multilingual-cased, delivering strong results across all supported languages and emotions. See the detailed evaluation metrics.
• 🔗 Open & Accessible: Comes with a live demo, the full dataset, and the complete training code for full transparency and reproducibility.
• V1.2 Improved Version: An updated model with an augmented dataset using LLMs.

Supported Emotions

The model is trained to classify text into 27 distinct emotion categories as well as a neutral class:

Links

• Live Demo: Hugging Face Space
• Dataset (Supports 6 Languages): the dataset augmented using LLMs model with train: 6619306 samples, test: 825643 samples, validation: 825643 samples.
• Based Model Used: AnasAlokla/multilingual_go_emotions
• GitHub Code: emotion_chatbot

Installation

Install the required libraries using pip:

pip install transformers torch

Quickstart: Emotion Detection

You can easily use this model for multi-label emotion classification with the transformers pipeline. Set top_k=None to see all predicted emotions above the model's default threshold.

from transformers import pipeline

# Load the multilingual, multi-label emotion classification pipeline
emotion_classifier = pipeline(
    "text-classification",
    model="multilingual_go_emotions_V1.2",
    top_k=None # To return all scores for each label
)

# --- Example 1: English ---
text_en = "I'm so happy for you, but I'm also a little bit sad to see you go."
results_en = emotion_classifier(text_en)
print(f"Text (EN): {text_en}")
print(f"Predictions: {results_en}\n")

# --- Example 2: Spanish ---
text_es = "¡Qué sorpresa! No me lo esperaba para nada."
results_es = emotion_classifier(text_es)
print(f"Text (ES): {text_es}")
print(f"Predictions: {results_es}\n")

# --- Example 3: Arabic ---
text_ar = "أشعر بخيبة أمل وغضب بسبب ما حدث"
results_ar = emotion_classifier(text_ar)
print(f"Text (AR): {text_ar}")
print(f"Predictions: {results_ar}")

Expected Output (structure):

Text (EN): I'm so happy for you, but I'm also a little bit sad to see you go. Predictions: [[{'label': 'joy', 'score': 0.9...}, {'label': 'sadness', 'score': 0.8...}, {'label': 'caring', 'score': 0.5...}, ...]]

Text (ES): ¡Qué sorpresa! No me lo esperaba para nada. Predictions: [[{'label': 'surprise', 'score': 0.9...}, {'label': 'excitement', 'score': 0.4...}, ...]]

Text (AR): أشعر بخيبة أمل وغضب بسبب ما حدث Predictions: [[{'label': 'disappointment', 'score': 0.9...}, {'label': 'anger', 'score': 0.9...}, ...]]

Evaluation

The model's performance was rigorously evaluated on the test set.

Test Set Performance

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

The table below shows the performance of the test model:

Performance of Test Model

Test Model Performance (Threshold = 0.5)

The table below shows the performance of the test model with a threshold of 0.5:

Use Cases

This model is ideal for applications requiring nuanced emotional understanding across different languages:

Global Customer Feedback Analysis: Analyze customer reviews, support tickets, and survey responses from around the world to gauge sentiment.

Multilingual Social Media Monitoring: Track brand perception and public mood across different regions and languages.

Advanced Chatbot Development: Build more empathetic and responsive chatbots that can understand user emotions in their native language.

Content Moderation: Automatically flag toxic, aggressive, or sensitive content on international platforms.

Market Research: Gain insights into how different cultures express emotions in text.

Trained On

Base Model: AnasAlokla/multilingual_go_emotions - A powerful pretrained model supporting 104 languages.

Dataset: specialized dataset based on the original Google GoEmotions dataset, augmented using LLMs.

Fine-Tuning Guide

To adapt this model for your own dataset or to replicate the training process, you can follow the methodology outlined in the official code repository. The repository provides a complete, end-to-end example, including data preprocessing, training scripts, and evaluation logic.

For full details, please refer to the GitHub repository: emotion_chatbot

Tags

#multilingual-nlp #emotion-classification #text-classification #multi-label #bert #transformer #natural-language-processing #sentiment-analysis #deep-learning #arabic-nlp #french-nlp #spanish-nlp #goemotions #BERT-Emotion #edge-nlp #emotion-detection #offline-nlp
#sentiment-analysis #emojis #emotions #embedded-nlp #ai-for-iot #efficient-bert #nlp2025 #context-aware #edge-ml
#smart-home-ai #emotion-aware #voice-ai #eco-ai #chatbot #social-media
#mental-health #short-text #smart-replies #tone-analysis

Support & Contact

For questions, bug reports, or collaboration inquiries, please open an issue on the Hugging Face Hub repository or contact the author directly.

Author: Anas Hamid Alokla

📬 Email: anasaloklahaaa@gmail.com