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  ## Training procedure
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- * WIP
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  ## Training procedure
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+ Fine-tuning was done via the `Trainer` API. Here is the [Jupyter notebook](https://github.com/DominguesM/language-detection/blob/main/Language_Detector_Lora.ipynb) with the training code.
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+ ### Training hyperparameters
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 128
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - num_epochs: 2
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+ ### Training results
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+ The validation results on the `valid` split of the Language Identification dataset are summarised here below.
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 1.4403 | 1.0 | 1094 | 0.0591 | 0.9952 | 0.9952 |
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+ | 0.0256 | 2.0 | 2188 | 0.0272 | 0.9955 | 0.9955 |
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+ In short, it achieves the following results on the validation set:
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+ - Loss: 0.0298
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+ - Accuracy: 0.9946
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+ - F1: 0.9946
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  ### Framework versions
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