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metadata
datasets:
  - AnasAlokla/multilingual_go_emotions
language:
  - ar
  - en
  - fr
  - es
  - nl
  - tr
metrics:
  - accuracy
  - f1
  - recall
base_model:
  - google-bert/bert-base-multilingual-cased
pipeline_tag: text-classification

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

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