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distilbert_finetune_own_data_model

Token Classification
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distilbert
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End of training

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  1. README.md +57 -57
  2. model.safetensors +1 -1
  3. runs/Feb29_07-16-30_9879f2213cb5/events.out.tfevents.1709191010.9879f2213cb5.492.0 +3 -0
  4. runs/Feb29_07-16-30_9879f2213cb5/events.out.tfevents.1709191108.9879f2213cb5.492.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3463
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- - Precision: 0.0
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- - Recall: 0.0
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- - F1: 0.0
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- - Accuracy: 0.8333
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  ## Model description
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@@ -53,58 +53,58 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
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- | No log | 1.0 | 1 | 0.8003 | 0.0 | 0.0 | 0.0 | 0.3333 |
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- | No log | 2.0 | 2 | 0.7903 | 0.0 | 0.0 | 0.0 | 0.3333 |
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- | No log | 3.0 | 3 | 0.7861 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 4.0 | 4 | 0.7846 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 5.0 | 5 | 0.7833 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 6.0 | 6 | 0.7787 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 7.0 | 7 | 0.7700 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 8.0 | 8 | 0.7585 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 9.0 | 9 | 0.7434 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 10.0 | 10 | 0.7255 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 11.0 | 11 | 0.7045 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 12.0 | 12 | 0.6820 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 13.0 | 13 | 0.6575 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 14.0 | 14 | 0.6328 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 15.0 | 15 | 0.6085 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 16.0 | 16 | 0.5854 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 17.0 | 17 | 0.5637 | 0.0 | 0.0 | 0.0 | 0.5 |
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- | No log | 18.0 | 18 | 0.5430 | 0.0 | 0.0 | 0.0 | 0.6667 |
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- | No log | 19.0 | 19 | 0.5235 | 0.0 | 0.0 | 0.0 | 0.6667 |
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- | No log | 20.0 | 20 | 0.5059 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 21.0 | 21 | 0.4897 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 22.0 | 22 | 0.4748 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 23.0 | 23 | 0.4601 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 24.0 | 24 | 0.4465 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 25.0 | 25 | 0.4342 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 26.0 | 26 | 0.4235 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 27.0 | 27 | 0.4140 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 28.0 | 28 | 0.4053 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 29.0 | 29 | 0.3976 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 30.0 | 30 | 0.3910 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 31.0 | 31 | 0.3849 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 32.0 | 32 | 0.3793 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 33.0 | 33 | 0.3746 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 34.0 | 34 | 0.3704 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 35.0 | 35 | 0.3668 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 36.0 | 36 | 0.3637 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 37.0 | 37 | 0.3609 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 38.0 | 38 | 0.3583 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 39.0 | 39 | 0.3561 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 40.0 | 40 | 0.3543 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 41.0 | 41 | 0.3528 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 42.0 | 42 | 0.3514 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 43.0 | 43 | 0.3503 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 44.0 | 44 | 0.3493 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 45.0 | 45 | 0.3484 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 46.0 | 46 | 0.3477 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 47.0 | 47 | 0.3471 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 48.0 | 48 | 0.3467 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 49.0 | 49 | 0.3464 | 0.0 | 0.0 | 0.0 | 0.8333 |
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- | No log | 50.0 | 50 | 0.3463 | 0.0 | 0.0 | 0.0 | 0.8333 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2390
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+ - Precision: 0.7576
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+ - Recall: 0.6410
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+ - F1: 0.6944
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+ - Accuracy: 0.8966
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 1 | 0.6390 | 0.0 | 0.0 | 0.0 | 0.6404 |
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+ | No log | 2.0 | 2 | 0.6159 | 0.0 | 0.0 | 0.0 | 0.6404 |
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+ | No log | 3.0 | 3 | 0.5974 | 0.0 | 0.0 | 0.0 | 0.6453 |
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+ | No log | 4.0 | 4 | 0.5803 | 0.0 | 0.0 | 0.0 | 0.6453 |
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+ | No log | 5.0 | 5 | 0.5629 | 0.0 | 0.0 | 0.0 | 0.6453 |
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+ | No log | 6.0 | 6 | 0.5440 | 0.0 | 0.0 | 0.0 | 0.6502 |
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+ | No log | 7.0 | 7 | 0.5235 | 0.6667 | 0.0513 | 0.0952 | 0.6700 |
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+ | No log | 8.0 | 8 | 0.5020 | 0.6667 | 0.0513 | 0.0952 | 0.6749 |
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+ | No log | 9.0 | 9 | 0.4791 | 0.5 | 0.0513 | 0.0930 | 0.6798 |
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+ | No log | 10.0 | 10 | 0.4557 | 0.5556 | 0.1282 | 0.2083 | 0.7044 |
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+ | No log | 11.0 | 11 | 0.4327 | 0.5833 | 0.1795 | 0.2745 | 0.7291 |
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+ | No log | 12.0 | 12 | 0.4097 | 0.5 | 0.2051 | 0.2909 | 0.7537 |
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+ | No log | 13.0 | 13 | 0.3872 | 0.5882 | 0.2564 | 0.3571 | 0.7635 |
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+ | No log | 14.0 | 14 | 0.3666 | 0.7059 | 0.3077 | 0.4286 | 0.7734 |
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+ | No log | 15.0 | 15 | 0.3478 | 0.6667 | 0.3590 | 0.4667 | 0.7980 |
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+ | No log | 16.0 | 16 | 0.3316 | 0.7391 | 0.4359 | 0.5484 | 0.8177 |
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+ | No log | 17.0 | 17 | 0.3179 | 0.7308 | 0.4872 | 0.5846 | 0.8325 |
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+ | No log | 18.0 | 18 | 0.3062 | 0.7037 | 0.4872 | 0.5758 | 0.8374 |
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+ | No log | 19.0 | 19 | 0.2958 | 0.75 | 0.5385 | 0.6269 | 0.8473 |
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+ | No log | 20.0 | 20 | 0.2868 | 0.7931 | 0.5897 | 0.6765 | 0.8621 |
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+ | No log | 21.0 | 21 | 0.2791 | 0.8276 | 0.6154 | 0.7059 | 0.8670 |
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+ | No log | 22.0 | 22 | 0.2727 | 0.8276 | 0.6154 | 0.7059 | 0.8670 |
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+ | No log | 23.0 | 23 | 0.2672 | 0.8333 | 0.6410 | 0.7246 | 0.8768 |
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+ | No log | 24.0 | 24 | 0.2625 | 0.7812 | 0.6410 | 0.7042 | 0.8867 |
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+ | No log | 25.0 | 25 | 0.2586 | 0.7812 | 0.6410 | 0.7042 | 0.8867 |
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+ | No log | 26.0 | 26 | 0.2553 | 0.7812 | 0.6410 | 0.7042 | 0.8867 |
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+ | No log | 27.0 | 27 | 0.2527 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 28.0 | 28 | 0.2505 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 29.0 | 29 | 0.2487 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 30.0 | 30 | 0.2472 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 31.0 | 31 | 0.2459 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 32.0 | 32 | 0.2448 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 33.0 | 33 | 0.2439 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 34.0 | 34 | 0.2431 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 35.0 | 35 | 0.2425 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 36.0 | 36 | 0.2419 | 0.7812 | 0.6410 | 0.7042 | 0.8916 |
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+ | No log | 37.0 | 37 | 0.2413 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 38.0 | 38 | 0.2409 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 39.0 | 39 | 0.2405 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 40.0 | 40 | 0.2402 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 41.0 | 41 | 0.2400 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 42.0 | 42 | 0.2398 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 43.0 | 43 | 0.2396 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 44.0 | 44 | 0.2394 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 45.0 | 45 | 0.2393 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 46.0 | 46 | 0.2392 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 47.0 | 47 | 0.2391 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 48.0 | 48 | 0.2390 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 49.0 | 49 | 0.2390 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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+ | No log | 50.0 | 50 | 0.2390 | 0.7576 | 0.6410 | 0.6944 | 0.8966 |
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  ### Framework versions
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