Roberta Base Wechsel Swahili

🚀 roberta-base-wechsel-swahili

该模型使用WECHSEL进行训练，可有效初始化子词嵌入，用于单语言模型的跨语言迁移。

查看代码请访问：https://github.com/CPJKU/wechsel

查看论文请访问：https://aclanthology.org/2022.naacl-main.293/

✨ 主要特性

该模型借助WECHSEL方法，能高效且有效地将预训练语言模型迁移到新的语言上，尤其在跨语言参数迁移方面表现出色，相比从头开始训练的同等规模模型，能以低至1/64的训练成本取得更优效果。

📚 详细文档

性能表现

RoBERTa模型

GPT2模型

更多详细信息请参考我们的论文。

📄 许可证

本项目采用MIT许可证。

📚 引用信息

如果您使用了本模型或相关研究，请引用以下论文：

@inproceedings{minixhofer-etal-2022-wechsel,
    title = "{WECHSEL}: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models",
    author = "Minixhofer, Benjamin  and
      Paischer, Fabian  and
      Rekabsaz, Navid",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.293",
    pages = "3992--4006",
    abstract = "Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other languages. To alleviate this problem, we introduce a novel method {--} called WECHSEL {--} to efficiently and effectively transfer pretrained LMs to new languages. WECHSEL can be applied to any model which uses subword-based tokenization and learns an embedding for each subword. The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such that they are semantically similar to the English tokens by utilizing multilingual static word embeddings covering English and the target language. We use WECHSEL to transfer the English RoBERTa and GPT-2 models to four languages (French, German, Chinese and Swahili). We also study the benefits of our method on very low-resource languages. WECHSEL improves over proposed methods for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch with up to 64x less training effort. Our method makes training large language models for new languages more accessible and less damaging to the environment. We make our code and models publicly available.",
}