用预训练模型实现豪萨语多标签情绪识别，效果显著。
HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection
- 基于AfriBERTa微调，适配豪萨语情绪分类任务。
- 验证集准确率74.00%，F1分数73.50%。
- 为低资源非洲语言情绪分析提供有效方案，适合相关研究者参考。
本文介绍了我们在SemEval-2025任务11（赛题A）中针对豪萨语的多标签情绪检测方法。豪萨语是一种低资源非洲语言。我们对基于Transformer的AfriBERTa模型进行微调，将其用于将豪萨文本分类为六种情绪：愤怒、厌恶、恐惧、喜悦、悲伤和惊讶。方法包括数据预处理、分词及使用Hugging Face Trainer API进行模型微调。系统在验证集上达到74.00%的准确率和73.50%的F1分数，表明基于Transformer的模型在低资源语言的情绪检测中具有有效性。
原文摘要 · Abstract (English)
This paper presents our approach to multi-label emotion detection in Hausa, a low-resource African language, for SemEval Track A. We fine-tuned AfriBERTa, a transformer-based model pre-trained on African languages, to classify Hausa text into six emotions: anger, disgust, fear, joy, sadness, and surprise. Our methodology involved data preprocessing, tokenization, and model fine-tuning using the Hugging Face Trainer API. The system achieved a validation accuracy of 74.00%, with an F1-score of 73.50%, demonstrating the effectiveness of transformer-based models for emotion detection in low-resource languages.
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