AnasAlokla/multilingual_go_emotions_V1.2

🌍 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:

Emotion	Emoji	Emotion	Emoji
Admiration	🤩	Love	❤️
Amusement	😄	Nervousness	😰
Anger	😠	Optimism	✨
Annoyance	🙄	Pride	👑
Approval	👍	Realization	💡
Caring	🤗	Relief	😌
Confusion	😕	Remorse	😔
Curiosity	🤔	Sadness	😢
Desire	🔥	Surprise	😲
Disappointment	😞	Disapproval	👎
Disgust	🤢	Gratitude	🙏
Embarrassment	😳	Grief	😭
Excitement	🎉	Joy	😊
Fear	😱	Neutral	😐

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 (using class weight)

Labels	accuracy	precision	recall	f1	mcc	support	threshold
admiration	0.988	0.956	0.942	0.949	0.942	96372	0.60
amusement	0.989	0.951	0.936	0.943	0.937	81726	0.55
anger	0.990	0.938	0.904	0.921	0.915	54456	0.65
annoyance	0.976	0.891	0.856	0.873	0.860	78312	0.50
approval	0.986	0.950	0.903	0.926	0.919	79992	0.65
caring	0.992	0.958	0.936	0.947	0.943	64296	0.55
confusion	0.989	0.941	0.913	0.927	0.921	64638	0.70
curiosity	0.991	0.957	0.937	0.947	0.942	73308	0.70
desire	0.992	0.953	0.934	0.943	0.939	59862	0.55
disappointment	0.979	0.910	0.858	0.883	0.873	75726	0.60
disapproval	0.982	0.900	0.879	0.890	0.880	67158	0.50
disgust	0.992	0.954	0.919	0.936	0.932	54216	0.65
embarrassment	0.994	0.949	0.931	0.940	0.936	44316	0.60
excitement	0.988	0.922	0.899	0.910	0.904	55560	0.60
fear	0.989	0.912	0.904	0.908	0.902	50658	0.50
gratitude	0.996	0.976	0.979	0.978	0.976	74142	0.50
grief	0.992	0.927	0.909	0.918	0.914	39426	0.60
joy	0.983	0.913	0.886	0.899	0.890	72498	0.55
love	0.994	0.967	0.955	0.961	0.957	68226	0.65
nervousness	0.989	0.898	0.872	0.885	0.879	40146	0.60
optimism	0.987	0.949	0.932	0.941	0.933	90498	0.65
pride	0.997	0.955	0.952	0.954	0.952	30918	0.50
realization	0.990	0.956	0.926	0.941	0.935	73908	0.55
relief	0.997	0.966	0.955	0.960	0.959	31728	0.70
remorse	0.995	0.966	0.949	0.957	0.955	49086	0.65
sadness	0.980	0.906	0.872	0.889	0.877	77154	0.55
surprise	0.989	0.928	0.904	0.916	0.910	56130	0.60
neutral	0.981	0.920	0.876	0.898	0.887	79140	0.55

Test Model Performance (Threshold = 0.5)

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

Labels	accuracy	precision	recall	f1	mcc	support	threshold
admiration	0.988	0.949	0.948	0.949	0.942	96372	0.5
amusement	0.989	0.947	0.938	0.943	0.937	81726	0.5
anger	0.989	0.921	0.917	0.919	0.913	54456	0.5
annoyance	0.976	0.891	0.856	0.873	0.860	78312	0.5
approval	0.986	0.937	0.915	0.926	0.918	79992	0.5
caring	0.992	0.955	0.939	0.947	0.943	64296	0.5
confusion	0.988	0.924	0.927	0.926	0.919	64638	0.5
curiosity	0.990	0.943	0.946	0.945	0.939	73308	0.5
desire	0.992	0.949	0.937	0.943	0.939	59862	0.5
disappointment	0.979	0.894	0.872	0.883	0.871	75726	0.5
disapproval	0.982	0.900	0.879	0.890	0.880	67158	0.5
disgust	0.992	0.942	0.929	0.935	0.931	54216	0.5
embarrassment	0.993	0.941	0.937	0.939	0.935	44316	0.5
excitement	0.988	0.910	0.910	0.910	0.904	55560	0.5
fear	0.989	0.912	0.904	0.908	0.902	50658	0.5
gratitude	0.996	0.976	0.979	0.978	0.976	74142	0.5
grief	0.992	0.916	0.919	0.918	0.913	39426	0.5
joy	0.982	0.907	0.891	0.899	0.889	72498	0.5
love	0.993	0.960	0.960	0.960	0.957	68226	0.5
nervousness	0.989	0.881	0.886	0.884	0.878	40146	0.5
optimism	0.987	0.938	0.941	0.940	0.932	90498	0.5
pride	0.997	0.955	0.952	0.954	0.952	30918	0.5
realization	0.989	0.952	0.928	0.940	0.934	73908	0.5
relief	0.997	0.956	0.963	0.959	0.958	31728	0.5
remorse	0.995	0.957	0.956	0.956	0.953	49086	0.5
sadness	0.979	0.897	0.879	0.888	0.877	77154	0.5
surprise	0.988	0.918	0.912	0.915	0.909	56130	0.5
neutral	0.981	0.914	0.882	0.897	0.887	79140	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: [email protected]