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--- |
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datasets: |
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- AnasAlokla/multilingual_go_emotions |
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language: |
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- ar |
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- en |
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- fr |
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- es |
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- de |
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- tr |
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library_name: transformers |
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tags: |
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- emotion |
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- classification |
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- text-classification |
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- bert |
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- emojis |
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- emotions |
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- v1.0 |
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- sentiment-analysis |
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- nlp |
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- chatbot |
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- social-media |
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- mental-health |
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- short-text |
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- emotion-detection |
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- transformers |
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- expressive |
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- ai |
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- machine-learning |
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- inference |
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- edge-ai |
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- smart-replies |
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- tone-analysis |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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base_model: |
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- AnasAlokla/multilingual_go_emotions |
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new_version: AnasAlokla/multilingual_go_emotions |
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pipeline_tag: text-classification |
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--- |
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# 🌍 Multilingual GoEmotions Classifier 💬 |
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[](https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions) |
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[](https://huggingface.co/AnasAlokla/multilingual_go_emotions#key-features) |
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[](https://huggingface.co/AnasAlokla/multilingual_go_emotions#overview) |
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[](https://huggingface.co/AnasAlokla/multilingual_go_emotions) |
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## Table of Contents |
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- 📖 [Overview](#overview) |
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- ✨ [Key Features](#key-features) |
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- 💫 [Supported Emotions](#supported-emotions) |
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- 🔗 [Links](#links) |
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- ⚙️ [Installation](#installation) |
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- 🚀 [Quickstart: Emotion Detection](#quickstart-emotion-detection) |
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- 📊 [Evaluation](#evaluation) |
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- 💡 [Use Cases](#use-cases) |
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- 📚 [Trained On](#trained-on) |
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- 🔧 [Fine-Tuning Guide](#fine-tuning-guide) |
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- 🏷️ [Tags](#tags) |
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- 💬 [Support & Contact](#support--contact) |
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## Overview |
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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. |
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- **Model Name**: AnasAlokla/multilingual_go_emotions_V1.1 |
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- **Architecture**: BERT (bert-base-multilingual-cased) |
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- **Tasks**: Multi-Label Text Classification | Emotion Detection | Sentiment Analysis |
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- **Languages**: Arabic, English, French, Spanish, Dutch, Turkish |
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## Key Features |
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- 🌍 **Truly Multilingual**: Natively supports 6 major languages, making it ideal for global applications. |
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- 🏷️ **Multi-Label Classification**: Capable of detecting multiple emotions in a single piece of text, capturing complex emotional expressions. |
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- 💪 **High Performance**: Built on `bert-base-multilingual-cased`, delivering strong results across all supported languages and emotions. See the detailed [evaluation metrics](#evaluation). |
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- 🔗 **Open & Accessible**: Comes with a live demo, the full dataset, and the complete training code for full transparency and reproducibility. |
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- V1.1 **Improved Version**: An updated model is available that specifically improves performance on low-frequency emotion samples. |
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## Supported Emotions |
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The model is trained to classify text into 27 distinct emotion categories as well as a neutral class: |
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| Emotion | Emoji | Emotion | Emoji | |
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|----------------|-------|----------------|-------| |
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| Admiration | 🤩 | Love | ❤️ | |
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| Amusement | 😄 | Nervousness | 😰 | |
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| Anger | 😠 | Optimism | ✨ | |
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| Annoyance | 🙄 | Pride | 👑 | |
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| Approval | 👍 | Realization | 💡 | |
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| Caring | 🤗 | Relief | 😌 | |
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| Confusion | 😕 | Remorse | 😔 | |
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| Curiosity | 🤔 | Sadness | 😢 | |
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| Desire | 🔥 | Surprise | 😲 | |
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| Disappointment | 😞 | Disapproval | 👎 | |
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| Disgust | 🤢 | Gratitude | 🙏 | |
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| Embarrassment | 😳 | Grief | 😭 | |
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| Excitement | 🎉 | Joy | 😊 | |
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| Fear | 😱 | Neutral | 😐 | |
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## Links |
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* **Live Demo:** [**Hugging Face Space**](https://huggingface.co/spaces/AnasAlokla/test_emotion_chatbot) |
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* **Dataset (Supports 6 Languages):** [**multilingual_go_emotions**](https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions) |
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* **Based Model Used:** [**AnasAlokla/multilingual_go_emotions**](https://huggingface.co/AnasAlokla/multilingual_go_emotions) |
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* **GitHub Code:** [**emotion_chatbot**](https://github.com/anasAloklah/emotion_chatbot) |
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## Installation |
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Install the required libraries using pip: |
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```bash |
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pip install transformers torch |
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``` |
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## Quickstart: Emotion Detection |
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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. |
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```python |
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from transformers import pipeline |
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# Load the multilingual, multi-label emotion classification pipeline |
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emotion_classifier = pipeline( |
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"text-classification", |
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model="AnasAlokla/multilingual_go_emotions", |
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top_k=None # To return all scores for each label |
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) |
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# --- Example 1: English --- |
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text_en = "I'm so happy for you, but I'm also a little bit sad to see you go." |
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results_en = emotion_classifier(text_en) |
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print(f"Text (EN): {text_en}") |
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print(f"Predictions: {results_en}\n") |
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# --- Example 2: Spanish --- |
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text_es = "¡Qué sorpresa! No me lo esperaba para nada." |
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results_es = emotion_classifier(text_es) |
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print(f"Text (ES): {text_es}") |
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print(f"Predictions: {results_es}\n") |
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# --- Example 3: Arabic --- |
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text_ar = "أشعر بخيبة أمل وغضب بسبب ما حدث" |
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results_ar = emotion_classifier(text_ar) |
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print(f"Text (AR): {text_ar}") |
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print(f"Predictions: {results_ar}") |
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``` |
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Expected Output (structure): |
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Text (EN): I'm so happy for you, but I'm also a little bit sad to see you go. |
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Predictions: [[{'label': 'joy', 'score': 0.9...}, {'label': 'sadness', 'score': 0.8...}, {'label': 'caring', 'score': 0.5...}, ...]] |
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Text (ES): ¡Qué sorpresa! No me lo esperaba para nada. |
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Predictions: [[{'label': 'surprise', 'score': 0.9...}, {'label': 'excitement', 'score': 0.4...}, ...]] |
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Text (AR): أشعر بخيبة أمل وغضب بسبب ما حدث |
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Predictions: [[{'label': 'disappointment', 'score': 0.9...}, {'label': 'anger', 'score': 0.9...}, ...]] |
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## Evaluation |
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The model's performance was rigorously evaluated on the test set. |
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Test Set Performance |
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The following table shows the performance metrics of the fine-tuned model on the test set, broken down by emotion category. |
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The table below shows the performance of the test model: |
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## Performance of Test Model (using class weight) |
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| Labels | accuracy | precision | recall | f1 | mcc | support | threshold | |
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| :-------------- | :------- | :-------- | :----- | :---- | :---- | :------ | :-------- | |
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| admiration | 0.933 | 0.598 | 0.668 | 0.631 | 0.596 | 2790 | 0.15 | |
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| amusement | 0.967 | 0.682 | 0.793 | 0.733 | 0.718 | 1866 | 0.10 | |
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| anger | 0.952 | 0.327 | 0.356 | 0.341 | 0.317 | 1128 | 0.15 | |
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| annoyance | 0.908 | 0.223 | 0.301 | 0.256 | 0.211 | 1704 | 0.10 | |
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| approval | 0.920 | 0.351 | 0.288 | 0.317 | 0.276 | 2094 | 0.15 | |
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| caring | 0.970 | 0.381 | 0.303 | 0.337 | 0.325 | 816 | 0.20 | |
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| confusion | 0.959 | 0.359 | 0.390 | 0.374 | 0.353 | 1020 | 0.25 | |
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| curiosity | 0.933 | 0.405 | 0.552 | 0.467 | 0.438 | 1734 | 0.10 | |
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| desire | 0.984 | 0.385 | 0.420 | 0.402 | 0.394 | 414 | 0.30 | |
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| disappointment | 0.958 | 0.278 | 0.216 | 0.243 | 0.224 | 1014 | 0.40 | |
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| disapproval | 0.920 | 0.221 | 0.343 | 0.269 | 0.235 | 1398 | 0.10 | |
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| disgust | 0.972 | 0.302 | 0.383 | 0.338 | 0.326 | 600 | 0.15 | |
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| embarrassment | 0.991 | 0.388 | 0.346 | 0.366 | 0.362 | 240 | 0.45 | |
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| excitement | 0.968 | 0.248 | 0.333 | 0.285 | 0.272 | 624 | 0.10 | |
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| fear | 0.985 | 0.501 | 0.526 | 0.513 | 0.506 | 498 | 0.20 | |
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| gratitude | 0.988 | 0.913 | 0.894 | 0.903 | 0.897 | 2004 | 0.35 | |
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| grief | 0.999 | 0.529 | 0.250 | 0.340 | 0.363 | 36 | 0.85 | |
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| joy | 0.959 | 0.381 | 0.472 | 0.422 | 0.403 | 1032 | 0.15 | |
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| love | 0.971 | 0.715 | 0.789 | 0.750 | 0.736 | 1812 | 0.25 | |
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| nervousness | 0.996 | 0.430 | 0.283 | 0.342 | 0.347 | 120 | 0.70 | |
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| optimism | 0.971 | 0.573 | 0.423 | 0.487 | 0.478 | 1062 | 0.45 | |
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| pride | 0.997 | 0.468 | 0.262 | 0.336 | 0.349 | 84 | 0.25 | |
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| realization | 0.967 | 0.220 | 0.146 | 0.176 | 0.163 | 792 | 0.25 | |
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| relief | 0.993 | 0.117 | 0.094 | 0.104 | 0.102 | 138 | 0.10 | |
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| remorse | 0.987 | 0.586 | 0.638 | 0.611 | 0.605 | 516 | 0.20 | |
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| sadness | 0.960 | 0.415 | 0.519 | 0.461 | 0.444 | 1062 | 0.15 | |
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| surprise | 0.975 | 0.518 | 0.425 | 0.467 | 0.457 | 828 | 0.60 | |
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| neutral | 0.733 | 0.582 | 0.621 | 0.601 | 0.401 | 10524 | 0.10 | |
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### Test Model Performance (Threshold = 0.5) |
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The table below shows the performance of the test model with a threshold of 0.5: |
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| Labels | accuracy | precision | recall | f1 | mcc | support | threshold | |
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| :-------------- | :------- | :-------- | :----- | :---- | :---- | :------ | :-------- | |
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| admiration | 0.939 | 0.673 | 0.570 | 0.617 | 0.587 | 2790 | 0.5 | |
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| amusement | 0.967 | 0.735 | 0.666 | 0.699 | 0.682 | 1866 | 0.5 | |
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| anger | 0.961 | 0.400 | 0.264 | 0.318 | 0.306 | 1128 | 0.5 | |
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| annoyance | 0.940 | 0.328 | 0.137 | 0.194 | 0.185 | 1704 | 0.5 | |
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| approval | 0.931 | 0.432 | 0.211 | 0.283 | 0.269 | 2094 | 0.5 | |
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| caring | 0.973 | 0.431 | 0.246 | 0.314 | 0.313 | 816 | 0.5 | |
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| confusion | 0.963 | 0.401 | 0.337 | 0.366 | 0.349 | 1020 | 0.5 | |
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| curiosity | 0.944 | 0.463 | 0.361 | 0.406 | 0.380 | 1734 | 0.5 | |
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| desire | 0.985 | 0.409 | 0.384 | 0.396 | 0.389 | 414 | 0.5 | |
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| disappointment | 0.961 | 0.300 | 0.198 | 0.239 | 0.224 | 1014 | 0.5 | |
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| disapproval | 0.945 | 0.293 | 0.195 | 0.234 | 0.212 | 1398 | 0.5 | |
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| disgust | 0.978 | 0.376 | 0.267 | 0.312 | 0.306 | 600 | 0.5 | |
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| embarrassment | 0.991 | 0.392 | 0.333 | 0.360 | 0.357 | 240 | 0.5 | |
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| excitement | 0.977 | 0.348 | 0.204 | 0.257 | 0.255 | 624 | 0.5 | |
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| fear | 0.986 | 0.547 | 0.468 | 0.504 | 0.499 | 498 | 0.5 | |
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| gratitude | 0.988 | 0.925 | 0.879 | 0.902 | 0.896 | 2004 | 0.5 | |
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| grief | 0.999 | 0.400 | 0.278 | 0.328 | 0.333 | 36 | 0.5 | |
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| joy | 0.966 | 0.451 | 0.367 | 0.405 | 0.389 | 1032 | 0.5 | |
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| love | 0.971 | 0.742 | 0.747 | 0.744 | 0.729 | 1812 | 0.5 | |
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| nervousness | 0.996 | 0.382 | 0.283 | 0.325 | 0.327 | 120 | 0.5 | |
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| optimism | 0.971 | 0.583 | 0.413 | 0.484 | 0.477 | 1062 | 0.5 | |
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| pride | 0.997 | 0.500 | 0.190 | 0.276 | 0.308 | 84 | 0.5 | |
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| realization | 0.971 | 0.270 | 0.124 | 0.170 | 0.169 | 792 | 0.5 | |
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| relief | 0.995 | 0.125 | 0.029 | 0.047 | 0.058 | 138 | 0.5 | |
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| remorse | 0.988 | 0.644 | 0.560 | 0.599 | 0.594 | 516 | 0.5 | |
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| sadness | 0.968 | 0.512 | 0.408 | 0.454 | 0.441 | 1062 | 0.5 | |
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| surprise | 0.974 | 0.492 | 0.430 | 0.459 | 0.447 | 828 | 0.5 | |
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| neutral | 0.742 | 0.648 | 0.440 | 0.524 | 0.368 | 10524 | 0.5 | |
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## Use Cases |
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This model is ideal for applications requiring nuanced emotional understanding across different languages: |
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Global Customer Feedback Analysis: Analyze customer reviews, support tickets, and survey responses from around the world to gauge sentiment. |
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Multilingual Social Media Monitoring: Track brand perception and public mood across different regions and languages. |
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Advanced Chatbot Development: Build more empathetic and responsive chatbots that can understand user emotions in their native language. |
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Content Moderation: Automatically flag toxic, aggressive, or sensitive content on international platforms. |
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Market Research: Gain insights into how different cultures express emotions in text. |
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## Trained On |
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Base Model: [**AnasAlokla/multilingual_go_emotions**](https://huggingface.co/AnasAlokla/multilingual_go_emotions) - A powerful pretrained model supporting 104 languages. |
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Dataset: [**multilingual_go_emotions**](https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions) - A carefully translated and curated dataset for multilingual emotion analysis, based on the original Google GoEmotions dataset. |
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## Fine-Tuning Guide |
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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. |
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For full details, please refer to the GitHub repository: |
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[**emotion_chatbot**](https://github.com/anasAloklah/emotion_chatbot) |
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## Tags |
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`#multilingual-nlp` `#emotion-classification` `#text-classification` `#multi-label` `#bert` |
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`#transformer` `#natural-language-processing` `#sentiment-analysis` `#deep-learning` |
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`#arabic-nlp` `#french-nlp` `#spanish-nlp` `#goemotions` |
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`#BERT-Emotion` `#edge-nlp` `#emotion-detection` `#offline-nlp` |
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`#sentiment-analysis` `#emojis` `#emotions` `#embedded-nlp` |
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`#ai-for-iot` `#efficient-bert` `#nlp2025` `#context-aware` `#edge-ml` |
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`#smart-home-ai` `#emotion-aware` `#voice-ai` `#eco-ai` `#chatbot` `#social-media` |
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`#mental-health` `#short-text` `#smart-replies` `#tone-analysis` |
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## Support & Contact |
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For questions, bug reports, or collaboration inquiries, please open an issue on the Hugging Face Hub repository or contact the author directly. |
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Author: Anas Hamid Alokla |
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📬 Email: [email protected] |
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