AnasAlokla/multilingual_go_emotions

Overview

Model trained from bert-base-multilingual-cased on the multilingual_go_emotions dataset for multi-label classification.

Multilingual GoEmotions Chatbot Task Report

Author: Anas Hamid Alokla

This repository/space contains details about a multilingual emotion classification model and chatbot based on the GoEmotions dataset.

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
• An improved version for low-frequency samples is now available: https://huggingface.co/AnasAlokla/multilingual_go_emotions_V1.1

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)

Accuracy Curves (Training vs. Validation)

F1 Score Curves (Training vs. Validation)