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Training in progress, epoch 1

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README.md CHANGED
@@ -19,12 +19,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4065
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- - Accuracy: 0.8527
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- - F1-score: 0.7826
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- - Precision: 0.7945
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- - Recall: 0.7727
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- - Auc: 0.7727
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  ## Model description
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@@ -43,9 +43,9 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 32
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- - eval_batch_size: 32
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
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- | 0.3296 | 1.0 | 77 | 0.3456 | 0.8543 | 0.7671 | 0.8142 | 0.7408 | 0.7408 |
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- | 0.1555 | 2.0 | 154 | 0.4065 | 0.8527 | 0.7826 | 0.7945 | 0.7727 | 0.7727 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3567
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+ - Accuracy: 0.8396
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+ - F1-score: 0.7752
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+ - Precision: 0.7713
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+ - Recall: 0.7794
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+ - Auc: 0.7794
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
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+ | 0.4074 | 1.0 | 39 | 0.3802 | 0.8347 | 0.7643 | 0.7649 | 0.7636 | 0.7636 |
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+ | 0.297 | 2.0 | 78 | 0.3567 | 0.8396 | 0.7752 | 0.7713 | 0.7794 | 0.7794 |
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  ### Framework versions
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