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}
}
Runs of fgaim tielectra-geezswitch on huggingface.co
10
Total runs
0
24-hour runs
0
3-day runs
-2
7-day runs
-20
30-day runs
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