HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918068177862656 |
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| author | Sani, Sani Abdullahi Abubakar, Salim Lawan, Falalu Ibrahim Abubakar, Abdulhamid Bala, Maryam |
| author_facet | Sani, Sani Abdullahi Abubakar, Salim Lawan, Falalu Ibrahim Abubakar, Abdulhamid Bala, Maryam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection Sani, Sani Abdullahi Abubakar, Salim Lawan, Falalu Ibrahim Abubakar, Abdulhamid Bala, Maryam Computation and Language 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. |
| title | HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.16388 |