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te-zsc-synthetic_5ep_2805

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roberta
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End of training

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  1. README.md +38 -38
  2. logs/events.out.tfevents.1716895384.a5a87f3fc98d.2577.8 +3 -0
  3. model.safetensors +1 -1
  4. training_args.bin +1 -1
README.md CHANGED
@@ -9,23 +9,23 @@ metrics:
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  - recall
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  - f1
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  model-index:
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- - name: 080524_epoch_3
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # 080524_epoch_3
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  This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3552
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- - Accuracy: 0.974
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- - Precision: 0.9742
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- - Recall: 0.974
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- - F1: 0.9740
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- - Ratio: 0.49
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  ## Model description
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@@ -61,36 +61,36 @@ 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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- | 0.324 | 0.0333 | 10 | 0.3475 | 0.976 | 0.9761 | 0.976 | 0.9760 | 0.492 |
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- | 0.3474 | 0.0667 | 20 | 0.3630 | 0.971 | 0.9714 | 0.971 | 0.9710 | 0.485 |
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- | 0.3651 | 0.1 | 30 | 0.3689 | 0.965 | 0.9657 | 0.965 | 0.9650 | 0.481 |
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- | 0.3128 | 0.1333 | 40 | 0.3649 | 0.968 | 0.9684 | 0.968 | 0.9680 | 0.486 |
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- | 0.3782 | 0.1667 | 50 | 0.3679 | 0.967 | 0.9672 | 0.967 | 0.9670 | 0.511 |
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- | 0.3346 | 0.2 | 60 | 0.3764 | 0.964 | 0.9651 | 0.964 | 0.9640 | 0.476 |
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- | 0.3165 | 0.2333 | 70 | 0.3855 | 0.962 | 0.9623 | 0.962 | 0.9620 | 0.488 |
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- | 0.3096 | 0.2667 | 80 | 0.3896 | 0.962 | 0.9620 | 0.962 | 0.9620 | 0.496 |
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- | 0.3075 | 0.3 | 90 | 0.3876 | 0.964 | 0.9641 | 0.964 | 0.9640 | 0.494 |
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- | 0.3163 | 0.3333 | 100 | 0.3844 | 0.963 | 0.9630 | 0.963 | 0.9630 | 0.497 |
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- | 0.326 | 0.3667 | 110 | 0.3930 | 0.961 | 0.9618 | 0.961 | 0.9610 | 0.479 |
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- | 0.3505 | 0.4 | 120 | 0.4156 | 0.952 | 0.9559 | 0.952 | 0.9519 | 0.454 |
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- | 0.3452 | 0.4333 | 130 | 0.3622 | 0.974 | 0.9741 | 0.974 | 0.9740 | 0.494 |
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- | 0.3387 | 0.4667 | 140 | 0.3566 | 0.974 | 0.974 | 0.974 | 0.974 | 0.5 |
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- | 0.3239 | 0.5 | 150 | 0.3567 | 0.974 | 0.9740 | 0.974 | 0.9740 | 0.496 |
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- | 0.3171 | 0.5333 | 160 | 0.3616 | 0.97 | 0.9706 | 0.97 | 0.9700 | 0.482 |
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- | 0.3715 | 0.5667 | 170 | 0.3536 | 0.974 | 0.9745 | 0.974 | 0.9740 | 0.484 |
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- | 0.333 | 0.6 | 180 | 0.3467 | 0.978 | 0.9783 | 0.978 | 0.9780 | 0.488 |
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- | 0.3435 | 0.6333 | 190 | 0.3500 | 0.974 | 0.9741 | 0.974 | 0.9740 | 0.494 |
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- | 0.3032 | 0.6667 | 200 | 0.3526 | 0.976 | 0.9762 | 0.976 | 0.9760 | 0.49 |
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- | 0.4074 | 0.7 | 210 | 0.3520 | 0.974 | 0.9742 | 0.974 | 0.9740 | 0.49 |
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- | 0.3946 | 0.7333 | 220 | 0.3485 | 0.976 | 0.976 | 0.976 | 0.976 | 0.5 |
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- | 0.3348 | 0.7667 | 230 | 0.3537 | 0.973 | 0.9731 | 0.973 | 0.9730 | 0.493 |
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- | 0.3515 | 0.8 | 240 | 0.3533 | 0.974 | 0.9741 | 0.974 | 0.9740 | 0.494 |
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- | 0.3603 | 0.8333 | 250 | 0.3538 | 0.973 | 0.9730 | 0.973 | 0.9730 | 0.495 |
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- | 0.3805 | 0.8667 | 260 | 0.3570 | 0.972 | 0.9721 | 0.972 | 0.9720 | 0.492 |
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- | 0.3661 | 0.9 | 270 | 0.3566 | 0.973 | 0.9732 | 0.973 | 0.9730 | 0.489 |
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- | 0.3245 | 0.9333 | 280 | 0.3565 | 0.973 | 0.9732 | 0.973 | 0.9730 | 0.489 |
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- | 0.3639 | 0.9667 | 290 | 0.3555 | 0.974 | 0.9742 | 0.974 | 0.9740 | 0.49 |
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- | 0.3261 | 1.0 | 300 | 0.3552 | 0.974 | 0.9742 | 0.974 | 0.9740 | 0.49 |
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  ### Framework versions
 
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  - recall
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  - f1
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  model-index:
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+ - name: 080524_epoch_5
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  results: []
14
  ---
15
 
16
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
  should probably proofread and complete it, then remove this comment. -->
18
 
19
+ # 080524_epoch_5
20
 
21
  This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3399
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+ - Accuracy: 0.981
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+ - Precision: 0.9810
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+ - Recall: 0.981
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+ - F1: 0.9810
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+ - Ratio: 0.495
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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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+ | 0.3013 | 0.0333 | 10 | 0.3474 | 0.978 | 0.9783 | 0.978 | 0.9780 | 0.488 |
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+ | 0.3087 | 0.0667 | 20 | 0.3471 | 0.979 | 0.9790 | 0.979 | 0.9790 | 0.495 |
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+ | 0.3181 | 0.1 | 30 | 0.3527 | 0.975 | 0.9752 | 0.975 | 0.9750 | 0.489 |
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+ | 0.3134 | 0.1333 | 40 | 0.3602 | 0.971 | 0.9714 | 0.971 | 0.9710 | 0.485 |
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+ | 0.3002 | 0.1667 | 50 | 0.3481 | 0.979 | 0.9790 | 0.979 | 0.9790 | 0.501 |
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+ | 0.3226 | 0.2 | 60 | 0.3547 | 0.978 | 0.9780 | 0.978 | 0.9780 | 0.496 |
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+ | 0.2919 | 0.2333 | 70 | 0.3687 | 0.972 | 0.9724 | 0.972 | 0.9720 | 0.486 |
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+ | 0.2932 | 0.2667 | 80 | 0.3822 | 0.965 | 0.9664 | 0.965 | 0.9650 | 0.473 |
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+ | 0.3303 | 0.3 | 90 | 0.3754 | 0.969 | 0.9700 | 0.969 | 0.9690 | 0.477 |
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+ | 0.3162 | 0.3333 | 100 | 0.3557 | 0.975 | 0.9750 | 0.975 | 0.9750 | 0.505 |
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+ | 0.3012 | 0.3667 | 110 | 0.3554 | 0.974 | 0.9741 | 0.974 | 0.9740 | 0.506 |
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+ | 0.3337 | 0.4 | 120 | 0.3629 | 0.972 | 0.9725 | 0.972 | 0.9720 | 0.484 |
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+ | 0.3007 | 0.4333 | 130 | 0.3492 | 0.979 | 0.9792 | 0.979 | 0.9790 | 0.491 |
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+ | 0.3283 | 0.4667 | 140 | 0.3467 | 0.979 | 0.9790 | 0.979 | 0.9790 | 0.495 |
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+ | 0.3238 | 0.5 | 150 | 0.3410 | 0.981 | 0.9810 | 0.981 | 0.9810 | 0.497 |
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+ | 0.3076 | 0.5333 | 160 | 0.3387 | 0.982 | 0.9820 | 0.982 | 0.9820 | 0.498 |
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+ | 0.3348 | 0.5667 | 170 | 0.3375 | 0.982 | 0.9820 | 0.982 | 0.9820 | 0.498 |
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+ | 0.3258 | 0.6 | 180 | 0.3401 | 0.98 | 0.9801 | 0.98 | 0.9800 | 0.494 |
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+ | 0.3195 | 0.6333 | 190 | 0.3424 | 0.978 | 0.9781 | 0.978 | 0.9780 | 0.492 |
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+ | 0.31 | 0.6667 | 200 | 0.3392 | 0.981 | 0.9810 | 0.981 | 0.9810 | 0.495 |
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+ | 0.3407 | 0.7 | 210 | 0.3393 | 0.982 | 0.9820 | 0.982 | 0.9820 | 0.502 |
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+ | 0.3494 | 0.7333 | 220 | 0.3413 | 0.981 | 0.9810 | 0.981 | 0.9810 | 0.501 |
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+ | 0.3574 | 0.7667 | 230 | 0.3402 | 0.982 | 0.9820 | 0.982 | 0.9820 | 0.496 |
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+ | 0.3379 | 0.8 | 240 | 0.3385 | 0.982 | 0.9820 | 0.982 | 0.9820 | 0.496 |
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+ | 0.3532 | 0.8333 | 250 | 0.3385 | 0.982 | 0.9820 | 0.982 | 0.9820 | 0.496 |
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+ | 0.318 | 0.8667 | 260 | 0.3425 | 0.98 | 0.9801 | 0.98 | 0.9800 | 0.494 |
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+ | 0.3475 | 0.9 | 270 | 0.3432 | 0.98 | 0.9801 | 0.98 | 0.9800 | 0.494 |
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+ | 0.3142 | 0.9333 | 280 | 0.3408 | 0.981 | 0.9810 | 0.981 | 0.9810 | 0.495 |
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+ | 0.3421 | 0.9667 | 290 | 0.3404 | 0.981 | 0.9810 | 0.981 | 0.9810 | 0.495 |
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+ | 0.2935 | 1.0 | 300 | 0.3399 | 0.981 | 0.9810 | 0.981 | 0.9810 | 0.495 |
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  ### Framework versions
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