

# TiRoBERTa-GeezSwitch

This model is a fine-tuned version of [fgaim/tiroberta-base](https://huggingface.co/fgaim/tiroberta-base) on the [GeezSwitch](https://github.com/fgaim/geezswitch-data) 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:

```markdown
@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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transformers pytorch roberta text-classification geezlab ti license:cc-by-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #geezlab #ti #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 



# 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

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



# 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

If you use this model or the GeezSwitch model in your research, please cite as follows:
TAGS
#transformers #pytorch #roberta #text-classification #geezlab #ti #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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

If you use this model or the GeezSwitch model in your research, please cite as follows:
passage: TAGS
#transformers #pytorch #roberta #text-classification #geezlab #ti #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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

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