electra-base-irish-cased-generator-v1

gaELECTRA

gaELECTRA is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.

Limitations and bias

Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.

BibTeX entry and citation info

If you use this model in your research, please consider citing our paper:

@inproceedings{barry-etal-2022-gabert,
    title = "ga{BERT} {---} an {I}rish Language Model",
    author = "Barry, James  and
      Wagner, Joachim  and
      Cassidy, Lauren  and
      Cowap, Alan  and
      Lynn, Teresa  and
      Walsh, Abigail  and
      {\'O} Meachair, M{\'\i}che{\'a}l J.  and
      Foster, Jennifer",
    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-1.511",
    pages = "4774--4788",
    abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.",
}