HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection

Fuente: arXiv
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Auteurs principaux: Sani, Sani Abdullahi, Abubakar, Salim, Lawan, Falalu Ibrahim, Abubakar, Abdulhamid, Bala, Maryam
Format: Preprint
Publié: 2025
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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