TiRoBERTa-GeezSwitch

This model is a fine-tuned version of fgaim/tiroberta-base on the GeezSwitch dataset.

It achieves the following results on the test set:

• F1: 0.9948
• Recall: 0.9948
• Precision: 0.9948
• Accuracy: 0.9948
• Loss: 0.0222

Training

Hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0
• seed: 42

Framework versions

• Transformers 4.19.0.dev0
• Pytorch 1.11.0+cu113
• Datasets 2.1.0
• Tokenizers 0.12.1

Citation

If you use this model or the GeezSwitch model in your research, please cite as follows:

@inproceedings{fgaim2022geezswitch,
  title={GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages},
  author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
  booktitle={Proceedings of the 13th Language Resources and Evaluation Conference},
  year={2022}
}