Gpt2 Wechsel Chinese

🚀 gpt2-wechsel-chinese

WECHSELを使用して学習されたモデルです。これは、単言語モデルのクロス言語転送のためのサブワード埋め込みの効果的な初期化手法です。

コードはこちらを参照: https://github.com/CPJKU/wechsel

論文はこちらを参照: https://aclanthology.org/2022.naacl-main.293/

🚀 クイックスタート

このモデルは、WECHSELを用いて学習され、単言語モデルのクロス言語転送におけるサブワード埋め込みの初期化を効果的に行います。

✨ 主な機能

このモデルは、WECHSEL手法を用いて、単言語モデルを複数の言語に転送することができます。具体的には、英語のRoBERTaやGPT - 2モデルをフランス語、ドイツ語、中国語、スワヒリ語に転送しています。

📚 ドキュメント

性能

RoBERTa

GPT2

詳細については論文を参照してください。

📄 ライセンス

このプロジェクトはMITライセンスの下で公開されています。

📚 引用

このモデルを使用する場合は、以下のように引用してください。

@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.",
}