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README.md

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-license: mit
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+🌍 XLM-R Multi-Emotion Classifier 🎭
+
+🚀 Mission Statement
+
+The XLM-R Multi-Emotion Classifier is built to understand human emotions across multiple languages, helping researchers, developers, and businesses analyze sentiment in text at scale.
+
+From social media monitoring to mental health insights, this model is designed to decode emotions with accuracy and fairness.
+
+🎯 Vision
+
+Our goal is to create an AI-powered emotion recognition model that:
+	•	🌎 Understands emotions across cultures and languages
+	•	🤖 Bridges the gap between AI and human psychology
+	•	💡 Empowers businesses, researchers, and developers to extract valuable insights from text
+
+🏗 Model Overview
+
+Model Name: msgfrom96/xlm_emo_multi
+Architecture: XLM-RoBERTa (Multi-Lingual Transformer)
+Task: Multi-label Emotion Classification
+Languages: English, Arabic
+Dataset: SemEval-2018 Task 1: Affect in Tweets
+
+The model predicts multiple emotions per text using multi-label classification. It can recognize emotions like:
+	•	🎭 Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Trust, Love, Optimism, Pessimism
+
+📦 How to Use
+
+Load Model and Tokenizer
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "msgfrom96/xlm_emo_multi"
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example text
+text = "I can't believe how amazing this is! So happy and excited!"
+
+# Tokenize input
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+# Get model predictions
+outputs = model(**inputs)
+print(outputs.logits)  # Raw emotion scores
+
+Interpreting Results
+
+The model outputs logits (raw scores) for each emotion. Apply a sigmoid activation to convert these into probabilities:
+
+import torch
+
+probs = torch.sigmoid(outputs.logits)
+print(probs)
+
+Each score represents the probability of an emotion being present in the text.
+
+⚡ Training & Fine-Tuning Details
+	•	Base Model: XLM-RoBERTa (xlm-roberta-base) 📖
+	•	Dataset: SemEval-2018 (English & Arabic Tweets) 🗂
+	•	Training Strategy: Multi-label classification 🔥
+	•	Optimizer: AdamW ⚙️
+	•	Batch Size: 16 🏋️‍♂️
+	•	Learning Rate: 2e-5 🎯
+	•	Hardware: Trained on AWS SageMaker with CUDA GPU support 🚀
+	•	Evaluation Metric: Macro-F1 & Micro-F1 📊
+	•	Best Model Selection: Auto-selected via load_best_model_at_end=True ✅
+
+📜 Citations & References
+
+If you use this model, please cite the following sources:
+
+📌 SemEval-2018 Dataset
+Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). “SemEval-2018 Task 1: Affect in Tweets.” Proceedings of SemEval-2018.
+📖 Paper Link
+
+📌 XLM-RoBERTa
+Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). “Unsupervised Cross-lingual Representation Learning at Scale.” Proceedings of ACL 2020.
+📖 Paper Link
+
+📌 Transformers Library
+Hugging Face (2020). “🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.”
+📖 Library Docs
+
+🤝 Contributing
+
+Want to improve the model? Feel free to:
+	•	Train it on more languages 🌍
+	•	Optimize for low-resource devices 🔥
+	•	Integrate it into real-world applications 💡
+	•	Submit pull requests or discussions 🚀
+
+🏆 Acknowledgments
+
+Special thanks to the Hugging Face team, SemEval organizers, and the NLP research community for providing the tools and datasets that made this model possible. 🙌
+
+🔗 Connect & Feedback
+
+💬 Questions? Issues? Create a discussion on the Hugging Face Model Hub
+📧 Email: [email protected]
+
+---
+license: mit
+---