msgfrom96/xlm_emo_multi

🌍 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