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End of training

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README.md CHANGED
@@ -20,12 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1830
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- - Accuracy: 0.9608
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- - Precision: 0.9618
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- - Recall: 0.9606
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- - F1: 0.9603
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- - Ratio: 0.0510
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  ## Model description
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@@ -57,11 +57,11 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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- | 1.7701 | 1.0 | 362 | 1.6423 | 0.8980 | 0.9086 | 0.8982 | 0.8983 | 0.0549 |
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- | 0.3762 | 2.0 | 724 | 0.3326 | 0.9451 | 0.9466 | 0.9449 | 0.9446 | 0.0529 |
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- | 0.1056 | 3.0 | 1086 | 0.1995 | 0.9608 | 0.9617 | 0.9608 | 0.9605 | 0.0510 |
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- | 0.0238 | 4.0 | 1448 | 0.1830 | 0.9608 | 0.9618 | 0.9606 | 0.9603 | 0.0510 |
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- | 0.1092 | 5.0 | 1810 | 0.1919 | 0.9627 | 0.9641 | 0.9627 | 0.9626 | 0.0510 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1403
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+ - Accuracy: 0.9745
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+ - Precision: 0.9751
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+ - Recall: 0.9743
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+ - F1: 0.9744
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+ - Ratio: 0.0529
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | 1.7313 | 1.0 | 362 | 1.5891 | 0.9137 | 0.9237 | 0.9138 | 0.9148 | 0.0569 |
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+ | 0.3284 | 2.0 | 724 | 0.2812 | 0.9529 | 0.9560 | 0.9528 | 0.9533 | 0.0490 |
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+ | 0.103 | 3.0 | 1086 | 0.1580 | 0.9667 | 0.9681 | 0.9665 | 0.9663 | 0.0510 |
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+ | 0.1073 | 4.0 | 1448 | 0.1532 | 0.9686 | 0.9693 | 0.9685 | 0.9686 | 0.0529 |
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+ | 0.1295 | 5.0 | 1810 | 0.1403 | 0.9745 | 0.9751 | 0.9743 | 0.9744 | 0.0529 |
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  ### Framework versions
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