Afro Xlmr Large

🚀 afro-xlmr-large

AfroXLMR-large 是通过对 XLM-R-large 模型进行掩码语言模型（MLM）适配而创建的，该模型适配了 17 种非洲语言（阿非利卡语、阿姆哈拉语、豪萨语、伊博语、马达加斯加语、奇切瓦语、奥罗莫语、尼日利亚皮钦语、基尼亚卢旺达语、基隆迪语、绍纳语、索马里语、塞索托语、斯瓦希里语、科萨语、约鲁巴语和祖鲁语），这些语言覆盖了主要的非洲语系，同时还适配了 3 种高资源语言（阿拉伯语、法语和英语）。

✨ 主要特性

• 基于 XLM-R-large 模型进行 MLM 适配，适用于多种非洲语言和高资源语言。
• 在多种语言的评估任务中表现出色。

📚 详细文档

评估结果（MasakhaNER 数据集 F1 分数）

BibTeX 引用信息

@inproceedings{alabi-etal-2022-adapting,
    title = "Adapting Pre-trained Language Models to {A}frican Languages via Multilingual Adaptive Fine-Tuning",
    author = "Alabi, Jesujoba O.  and
      Adelani, David Ifeoluwa  and
      Mosbach, Marius  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.382",
    pages = "4336--4349",
    abstract = "Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen during pre-training, especially African languages. One of the most effective approaches to adapt to a new language is language adaptive fine-tuning (LAFT) {---} fine-tuning a multilingual PLM on monolingual texts of a language using the pre-training objective. However, adapting to target language individually takes large disk space and limits the cross-lingual transfer abilities of the resulting models because they have been specialized for a single language. In this paper, we perform multilingual adaptive fine-tuning on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent to encourage cross-lingual transfer learning. To further specialize the multilingual PLM, we removed vocabulary tokens from the embedding layer that corresponds to non-African writing scripts before MAFT, thus reducing the model size by around 50{\%}. Our evaluation on two multilingual PLMs (AfriBERTa and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to applying LAFT on individual languages while requiring significantly less disk space. Additionally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.",
}

📄 许可证

本项目采用 MIT 许可证。