| --- |
| license: mit |
| datasets: |
| - SemEvalWorkshop/sem_eval_2018_task_1 |
| language: |
| - en |
| - ar |
| base_model: |
| - FacebookAI/xlm-roberta-base |
| pipeline_tag: text-classification |
| --- |
| 🌍 XLM-R Multi-Emotion Classifier 🎭 |
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| 🚀 Mission Statement |
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| 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. |
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| From social media monitoring to mental health insights, this model is designed to decode emotions with accuracy and fairness. |
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| 🎯 Vision |
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| 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 |
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| 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 |
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| 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 |
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| Load Model and Tokenizer |
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| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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| 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) |
|
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| # 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 |
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| 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 |
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| 📌 Transformers Library |
| Hugging Face (2020). “🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.” |
| 📖 Library Docs |
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| 🤝 Contributing |
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| 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 |
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| 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. 🙌 |
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| 🔗 Connect & Feedback |
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| 💬 Questions? Issues? Create a discussion on the Hugging Face Model Hub |
| 📧 Email: gleiser2@hotmail.com |
| |
| --- |
| license: mit |
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