Icebert

🚀 IceBERT

IceBERTは、fairseqを使用してRoBERTa-baseアーキテクチャに基づいて訓練されたアイスランド語の言語モデルで、さまざまな自然言語処理の下流タスクに使用できます。

🚀 クイックスタート

このモデルは、fairseqを使用し、RoBERTa-baseアーキテクチャに基づいて訓練されています。これは、我々がアイスランド語用に訓練した多数のモデルの1つです。詳細については、以下に引用する論文を参照してください。訓練に使用されたデータは、次の表の通りです。

📚 ドキュメント

このモデルは、論文 https://arxiv.org/abs/2201.05601 で詳細に説明されています。このモデルを使用した場合は、この論文を引用してください。

@inproceedings{snaebjarnarson-etal-2022-warm,
    title = "A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models",
    author = "Sn{\ae}bjarnarson, V{\'e}steinn  and
      S{\'\i}monarson, Haukur Barri  and
      Ragnarsson, P{\'e}tur Orri  and
      Ing{\'o}lfsd{\'o}ttir, Svanhv{\'\i}t Lilja  and
      J{\'o}nsson, Haukur  and
      Thorsteinsson, Vilhjalmur  and
      Einarsson, Hafsteinn",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.464",
    pages = "4356--4366",
    abstract = "We train several language models for Icelandic, including IceBERT, that achieve state-of-the-art performance in a variety of downstream tasks, including part-of-speech tagging, named entity recognition, grammatical error detection and constituency parsing. To train the models we introduce a new corpus of Icelandic text, the Icelandic Common Crawl Corpus (IC3), a collection of high quality texts found online by targeting the Icelandic top-level-domain .is. Several other public data sources are also collected for a total of 16GB of Icelandic text. To enhance the evaluation of model performance and to raise the bar in baselines for Icelandic, we manually translate and adapt the WinoGrande commonsense reasoning dataset. Through these efforts we demonstrate that a properly cleaned crawled corpus is sufficient to achieve state-of-the-art results in NLP applications for low to medium resource languages, by comparison with models trained on a curated corpus. We further show that initializing models using existing multilingual models can lead to state-of-the-art results for some downstream tasks.",
}

📄 ライセンス

このモデルはCC BY 4.0ライセンスの下で提供されています。