Gpt2 Wechsel French

🚀 GPT2-Wechsel法語模型

GPT2-Wechsel法語模型是使用WECHSEL方法訓練的，該方法可以有效地初始化子詞嵌入，用於單語言模型的跨語言遷移。本項目提供了模型的代碼和相關研究論文，旨在為跨語言自然語言處理任務提供更高效的解決方案。

🚀 快速開始

你可以通過以下鏈接查看模型代碼和相關論文：

• 代碼：https://github.com/CPJKU/wechsel
• 論文：https://aclanthology.org/2022.naacl-main.293/

✨ 主要特性

• 跨語言遷移：通過WECHSEL方法，實現單語言模型在多種語言間的有效遷移。
• 性能提升：在多個自然語言處理任務中，相較於傳統模型有更好的表現。

📚 詳細文檔

RoBERTa模型性能對比

GPT2模型性能對比

📄 許可證

本項目採用MIT許可證。

📖 引用

如果您使用了本項目的模型或方法，請引用以下論文：

@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.",
}