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TiELECTRA-GeezSwitch

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

It achieves the following results on the test set:

  • F1: 0.9844
  • Recall: 0.9844
  • Precision: 0.9845
  • Accuracy: 0.9844
  • Loss: 0.2190

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}
}
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