bert-base-irish-cased-v1

bert-base-irish-cased-v1

gaBERT is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.

Model description

Encoder-based Transformer to be used to obtain features for finetuning for downstream tasks in Irish.

Intended uses & limitations

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.

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: None
• training_precision: float32

Framework versions

• Transformers 4.20.1
• TensorFlow 2.9.1
• Datasets 2.3.2
• Tokenizers 0.12.1

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.",
}