language-detection-fine-tuned-on-xlm-roberta-base
This model is a fine-tuned version of xlm-roberta-base on the common_language dataset. It achieves the following results on the evaluation set:
- Loss: 0.1635
- Accuracy: 0.9764
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2855 | 1.0 | 22194 | 0.1635 | 0.9764 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
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Dataset used to train alperk3003/language-detection-fine-tuned-on-xlm-roberta-base
Evaluation results
- Accuracy on common_languagetest set self-reported0.976