Update README.md

README.md

@@ -42,26 +42,128 @@ new_version: AnasAlokla/multilingual_go_emotions
 pipeline_tag: text-classification
 ---
 
+# 🌍 Multilingual GoEmotions Classifier 💬
 
-#### Overview
+[![Dataset](https://img.shields.io/badge/Dataset-multilingual_go_emotions-blue)](https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions)
+[![Languages](https://img.shields.io/badge/Languages-6-brightgreen)](https://huggingface.co/AnasAlokla/multilingual_go_emotions#key-features)
+[![Task](https://img.shields.io/badge/Task-Multi--Label%20Classification%20%7C%20Emotion%20Detection%20%7C%20Text%20Classification%20%7C%20Sentiment%20Analysis-orange)](https://huggingface.co/AnasAlokla/multilingual_go_emotions#overview)
+[![Base Model](https://img.shields.io/badge/Base%20Model-mBERT-purple)](https://huggingface.co/AnasAlokla/multilingual_go_emotions)
 
-Model trained from [bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the [multilingual_go_emotions](https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions) dataset for multi-label classification.
+## Table of Contents
+- 📖 [Overview](#overview)
+- ✨ [Key Features](#key-features)
+- 💫 [Supported Emotions](#supported-emotions)
+- 🔗 [Links](#links)
+- ⚙️ [Installation](#installation)
+- 🚀 [Quickstart: Emotion Detection](#quickstart-emotion-detection)
+- 📊 [Evaluation](#evaluation)
+- 💡 [Use Cases](#use-cases)
+- 📚 [Trained On](#trained-on)
+- 🔧 [Fine-Tuning Guide](#fine-tuning-guide)
+- 🏷️ [Tags](#tags)
+- 💬 [Support & Contact](#support--contact)
 
+## Overview
 
-# Multilingual GoEmotions Chatbot Task Report
+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.
 
-**Author:** Anas Hamid Alokla
+- **Model Name**: AnasAlokla/multilingual_go_emotions_V1.1
+- **Architecture**: BERT (bert-base-multilingual-cased)
+- **Tasks**: Multi-Label Text Classification | Emotion Detection | Sentiment Analysis
+- **Languages**: Arabic, English, French, Spanish, Dutch, Turkish
 
-This repository/space contains details about a multilingual emotion classification model and chatbot based on the GoEmotions dataset.
+## 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](#evaluation).
+- 🔗 **Open & Accessible**: Comes with a live demo, the full dataset, and the complete training code for full transparency and reproducibility.
+- V1.1 **Improved Version**: An updated model is available that specifically improves performance on low-frequency emotion samples.
+
+## 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:** [https://huggingface.co/spaces/AnasAlokla/test_emotion_chatbot](https://huggingface.co/spaces/AnasAlokla/test_emotion_chatbot)
-*   **Dataset (Supports 6 Languages):** [https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions](https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions)
-*   **Model Used:** [https://huggingface.co/AnasAlokla/multilingual_go_emotions](https://huggingface.co/AnasAlokla/multilingual_go_emotions)
-*   **GitHub Code:** [https://github.com/anasAloklah/emotion_chatbot](https://github.com/anasAloklah/emotion_chatbot)
+*   **Live Demo:** [**Hugging Face Space**](https://huggingface.co/spaces/AnasAlokla/test_emotion_chatbot)
+*   **Dataset (Supports 6 Languages):** [**multilingual_go_emotions**](https://huggingface.co/datasets/AnasAlokla/multilingual_go_emotions)
+*   **Based Model Used:** [**AnasAlokla/multilingual_go_emotions**](https://huggingface.co/AnasAlokla/multilingual_go_emotions)
+*   **GitHub Code:** [**emotion_chatbot**](https://github.com/anasAloklah/emotion_chatbot)
+
+## Installation
+
+Install the required libraries using pip:
+
+```bash
+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.
+
+```python
+from transformers import pipeline
+
+# Load the multilingual, multi-label emotion classification pipeline
+emotion_classifier = pipeline(
+    "text-classification",
+    model="AnasAlokla/multilingual_go_emotions",
+    top_k=None # To return all scores for each label
+)
 
-## Test Set Performance
+# --- 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.
 
@@ -133,3 +235,51 @@ The table below shows the performance of the test model with a threshold of 0.5:
 | sadness         | 0.968    | 0.512     | 0.408  | 0.454 | 0.441 | 1062    | 0.5       |
 | surprise        | 0.974    | 0.492     | 0.430  | 0.459 | 0.447 | 828     | 0.5       |
 | neutral         | 0.742    | 0.648     | 0.440  | 0.524 | 0.368 | 10524   | 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**](https://huggingface.co/AnasAlokla/multilingual_go_emotions)  - A powerful pretrained model supporting 104 languages.
+
+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.
+
+## 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**](https://github.com/anasAloklah/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