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bert_baseline_prompt_adherence_task3_fold3

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  1. README.md +168 -38
  2. model.safetensors +1 -1
  3. runs/Aug22_10-22-58_0095ffe889f2/events.out.tfevents.1724322179.0095ffe889f2.25.0 +3 -0
  4. runs/Aug22_10-45-22_0095ffe889f2/events.out.tfevents.1724323523.0095ffe889f2.25.1 +3 -0
  5. runs/Aug22_11-07-47_0095ffe889f2/events.out.tfevents.1724324868.0095ffe889f2.25.2 +3 -0
  6. runs/Aug22_11-30-11_0095ffe889f2/events.out.tfevents.1724326212.0095ffe889f2.25.3 +3 -0
  7. training_args.bin +1 -1
README.md CHANGED
@@ -4,20 +4,20 @@ base_model: google-bert/bert-base-cased
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  tags:
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  - generated_from_trainer
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  model-index:
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- - name: bert_baseline_prompt_adherence_task3_fold3
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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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- # bert_baseline_prompt_adherence_task3_fold3
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16
  This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset.
17
  It achieves the following results on the evaluation set:
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- - Loss: 0.3708
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- - Qwk: 0.5742
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- - Mse: 0.3703
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  ## Model description
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@@ -42,44 +42,174 @@ The following hyperparameters were used during training:
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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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- - num_epochs: 1
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
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  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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- | No log | 0.0308 | 2 | 1.7065 | 0.0 | 1.7073 |
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- | No log | 0.0615 | 4 | 1.2573 | 0.0 | 1.2575 |
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- | No log | 0.0923 | 6 | 0.8716 | 0.1625 | 0.8714 |
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- | No log | 0.1231 | 8 | 0.6802 | 0.0579 | 0.6797 |
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- | No log | 0.1538 | 10 | 0.6149 | 0.0341 | 0.6142 |
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- | No log | 0.1846 | 12 | 0.5744 | 0.0342 | 0.5737 |
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- | No log | 0.2154 | 14 | 0.5532 | 0.0525 | 0.5526 |
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- | No log | 0.2462 | 16 | 0.4973 | 0.0876 | 0.4967 |
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- | No log | 0.2769 | 18 | 0.4653 | 0.2539 | 0.4647 |
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- | No log | 0.3077 | 20 | 0.4372 | 0.3747 | 0.4367 |
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- | No log | 0.3385 | 22 | 0.4579 | 0.5433 | 0.4574 |
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- | No log | 0.3692 | 24 | 0.4613 | 0.5636 | 0.4609 |
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- | No log | 0.4 | 26 | 0.4110 | 0.5405 | 0.4106 |
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- | No log | 0.4308 | 28 | 0.4063 | 0.4977 | 0.4058 |
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- | No log | 0.4615 | 30 | 0.4001 | 0.5459 | 0.3997 |
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- | No log | 0.4923 | 32 | 0.4023 | 0.5731 | 0.4018 |
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- | No log | 0.5231 | 34 | 0.4175 | 0.5705 | 0.4171 |
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- | No log | 0.5538 | 36 | 0.4286 | 0.5707 | 0.4283 |
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- | No log | 0.5846 | 38 | 0.4423 | 0.5956 | 0.4419 |
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- | No log | 0.6154 | 40 | 0.4439 | 0.5922 | 0.4436 |
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- | No log | 0.6462 | 42 | 0.4711 | 0.5928 | 0.4708 |
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- | No log | 0.6769 | 44 | 0.4735 | 0.5871 | 0.4731 |
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- | No log | 0.7077 | 46 | 0.4319 | 0.5812 | 0.4315 |
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- | No log | 0.7385 | 48 | 0.3941 | 0.5840 | 0.3936 |
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- | No log | 0.7692 | 50 | 0.3833 | 0.5732 | 0.3828 |
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- | No log | 0.8 | 52 | 0.3777 | 0.5548 | 0.3772 |
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- | No log | 0.8308 | 54 | 0.3750 | 0.5475 | 0.3745 |
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- | No log | 0.8615 | 56 | 0.3737 | 0.5623 | 0.3732 |
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- | No log | 0.8923 | 58 | 0.3729 | 0.5723 | 0.3724 |
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- | No log | 0.9231 | 60 | 0.3711 | 0.5722 | 0.3706 |
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- | No log | 0.9538 | 62 | 0.3704 | 0.5723 | 0.3700 |
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- | No log | 0.9846 | 64 | 0.3708 | 0.5742 | 0.3703 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  tags:
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  - generated_from_trainer
6
  model-index:
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+ - name: bert_baseline_prompt_adherence_task3_fold2
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  results: []
9
  ---
10
 
11
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
  should probably proofread and complete it, then remove this comment. -->
13
 
14
+ # bert_baseline_prompt_adherence_task3_fold2
15
 
16
  This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.3675
19
+ - Qwk: 0.6721
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+ - Mse: 0.3673
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22
  ## Model description
23
 
 
42
  - seed: 42
43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
44
  - lr_scheduler_type: linear
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+ - num_epochs: 5
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47
  ### Training results
48
 
49
  | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
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  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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+ | No log | 0.0308 | 2 | 0.9258 | 0.0720 | 0.9259 |
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+ | No log | 0.0615 | 4 | 0.7457 | 0.0 | 0.7454 |
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+ | No log | 0.0923 | 6 | 0.6895 | 0.0 | 0.6889 |
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+ | No log | 0.1231 | 8 | 0.7119 | 0.0 | 0.7111 |
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+ | No log | 0.1538 | 10 | 0.7044 | 0.0 | 0.7036 |
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+ | No log | 0.1846 | 12 | 0.6409 | 0.0 | 0.6403 |
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+ | No log | 0.2154 | 14 | 0.6051 | 0.0 | 0.6045 |
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+ | No log | 0.2462 | 16 | 0.5999 | 0.0 | 0.5994 |
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+ | No log | 0.2769 | 18 | 0.5984 | 0.0 | 0.5978 |
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+ | No log | 0.3077 | 20 | 0.5739 | 0.0 | 0.5733 |
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+ | No log | 0.3385 | 22 | 0.5170 | 0.0088 | 0.5165 |
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+ | No log | 0.3692 | 24 | 0.4902 | 0.1599 | 0.4898 |
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+ | No log | 0.4 | 26 | 0.4863 | 0.4700 | 0.4860 |
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+ | No log | 0.4308 | 28 | 0.4477 | 0.5022 | 0.4476 |
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+ | No log | 0.4615 | 30 | 0.4374 | 0.5939 | 0.4375 |
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+ | No log | 0.4923 | 32 | 0.4172 | 0.5317 | 0.4174 |
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+ | No log | 0.5231 | 34 | 0.4537 | 0.4158 | 0.4539 |
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+ | No log | 0.5538 | 36 | 0.5930 | 0.2942 | 0.5930 |
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+ | No log | 0.5846 | 38 | 0.5653 | 0.3160 | 0.5653 |
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+ | No log | 0.6154 | 40 | 0.4553 | 0.4306 | 0.4555 |
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+ | No log | 0.6462 | 42 | 0.4686 | 0.4973 | 0.4688 |
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+ | No log | 0.6769 | 44 | 0.4960 | 0.5872 | 0.4961 |
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+ | No log | 0.7077 | 46 | 0.4903 | 0.6299 | 0.4904 |
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+ | No log | 0.7385 | 48 | 0.4814 | 0.6489 | 0.4813 |
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+ | No log | 0.7692 | 50 | 0.3928 | 0.6107 | 0.3927 |
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+ | No log | 0.8 | 52 | 0.3770 | 0.6041 | 0.3769 |
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+ | No log | 0.8308 | 54 | 0.4179 | 0.6580 | 0.4178 |
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+ | No log | 0.8615 | 56 | 0.5136 | 0.6531 | 0.5135 |
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+ | No log | 0.8923 | 58 | 0.5321 | 0.6648 | 0.5320 |
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+ | No log | 0.9231 | 60 | 0.4586 | 0.6580 | 0.4584 |
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+ | No log | 0.9538 | 62 | 0.4075 | 0.6700 | 0.4074 |
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+ | No log | 0.9846 | 64 | 0.3998 | 0.6755 | 0.3996 |
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+ | No log | 1.0154 | 66 | 0.3726 | 0.6572 | 0.3724 |
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+ | No log | 1.0462 | 68 | 0.3925 | 0.6607 | 0.3923 |
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+ | No log | 1.0769 | 70 | 0.3873 | 0.6546 | 0.3871 |
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+ | No log | 1.1077 | 72 | 0.4220 | 0.6774 | 0.4218 |
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+ | No log | 1.1385 | 74 | 0.4552 | 0.6844 | 0.4551 |
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+ | No log | 1.1692 | 76 | 0.4136 | 0.6768 | 0.4135 |
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+ | No log | 1.2 | 78 | 0.3558 | 0.6414 | 0.3557 |
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+ | No log | 1.2308 | 80 | 0.3520 | 0.6363 | 0.3519 |
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+ | No log | 1.2615 | 82 | 0.3823 | 0.6691 | 0.3822 |
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+ | No log | 1.2923 | 84 | 0.4084 | 0.6672 | 0.4083 |
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+ | No log | 1.3231 | 86 | 0.4370 | 0.6889 | 0.4369 |
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+ | No log | 1.3538 | 88 | 0.4064 | 0.6676 | 0.4063 |
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+ | No log | 1.3846 | 90 | 0.3591 | 0.6570 | 0.3589 |
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+ | No log | 1.4154 | 92 | 0.3305 | 0.5991 | 0.3304 |
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+ | No log | 1.4462 | 94 | 0.3279 | 0.5284 | 0.3278 |
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+ | No log | 1.4769 | 96 | 0.3383 | 0.6478 | 0.3382 |
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+ | No log | 1.5077 | 98 | 0.3854 | 0.6701 | 0.3853 |
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+ | No log | 1.5385 | 100 | 0.4797 | 0.6850 | 0.4797 |
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+ | No log | 1.5692 | 102 | 0.4633 | 0.6909 | 0.4633 |
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+ | No log | 1.6 | 104 | 0.3708 | 0.6739 | 0.3707 |
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+ | No log | 1.6308 | 106 | 0.3212 | 0.5450 | 0.3211 |
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+ | No log | 1.6615 | 108 | 0.3225 | 0.5315 | 0.3224 |
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+ | No log | 1.6923 | 110 | 0.3386 | 0.6214 | 0.3385 |
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+ | No log | 1.7231 | 112 | 0.3916 | 0.6701 | 0.3916 |
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+ | No log | 1.7538 | 114 | 0.4768 | 0.6773 | 0.4768 |
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+ | No log | 1.7846 | 116 | 0.4387 | 0.6697 | 0.4388 |
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+ | No log | 1.8154 | 118 | 0.3773 | 0.6627 | 0.3773 |
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+ | No log | 1.8462 | 120 | 0.3661 | 0.6635 | 0.3661 |
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+ | No log | 1.8769 | 122 | 0.3897 | 0.6690 | 0.3897 |
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+ | No log | 1.9077 | 124 | 0.4027 | 0.6671 | 0.4026 |
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+ | No log | 1.9385 | 126 | 0.3709 | 0.6768 | 0.3707 |
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+ | No log | 1.9692 | 128 | 0.3365 | 0.5919 | 0.3364 |
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+ | No log | 2.0 | 130 | 0.3325 | 0.5673 | 0.3323 |
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+ | No log | 2.0308 | 132 | 0.3381 | 0.6250 | 0.3379 |
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+ | No log | 2.0615 | 134 | 0.3789 | 0.6834 | 0.3787 |
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+ | No log | 2.0923 | 136 | 0.3974 | 0.6824 | 0.3972 |
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+ | No log | 2.1231 | 138 | 0.3930 | 0.6945 | 0.3929 |
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+ | No log | 2.1538 | 140 | 0.3502 | 0.6881 | 0.3500 |
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+ | No log | 2.1846 | 142 | 0.3353 | 0.5822 | 0.3350 |
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+ | No log | 2.2154 | 144 | 0.3479 | 0.5595 | 0.3476 |
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+ | No log | 2.2462 | 146 | 0.3405 | 0.6078 | 0.3402 |
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+ | No log | 2.2769 | 148 | 0.4023 | 0.6887 | 0.4021 |
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+ | No log | 2.3077 | 150 | 0.5183 | 0.6849 | 0.5182 |
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+ | No log | 2.3385 | 152 | 0.5548 | 0.6704 | 0.5548 |
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+ | No log | 2.3692 | 154 | 0.4854 | 0.6956 | 0.4854 |
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+ | No log | 2.4 | 156 | 0.3881 | 0.6928 | 0.3880 |
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+ | No log | 2.4308 | 158 | 0.3439 | 0.6809 | 0.3437 |
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+ | No log | 2.4615 | 160 | 0.3473 | 0.6831 | 0.3471 |
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+ | No log | 2.4923 | 162 | 0.3449 | 0.6831 | 0.3447 |
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+ | No log | 2.5231 | 164 | 0.3544 | 0.6898 | 0.3542 |
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+ | No log | 2.5538 | 166 | 0.3833 | 0.6986 | 0.3831 |
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+ | No log | 2.5846 | 168 | 0.3695 | 0.6980 | 0.3693 |
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+ | No log | 2.6154 | 170 | 0.3538 | 0.6825 | 0.3536 |
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+ | No log | 2.6462 | 172 | 0.3324 | 0.6521 | 0.3322 |
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+ | No log | 2.6769 | 174 | 0.3331 | 0.6459 | 0.3329 |
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+ | No log | 2.7077 | 176 | 0.3507 | 0.6950 | 0.3505 |
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+ | No log | 2.7385 | 178 | 0.3741 | 0.6996 | 0.3738 |
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+ | No log | 2.7692 | 180 | 0.3587 | 0.6923 | 0.3585 |
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+ | No log | 2.8 | 182 | 0.3383 | 0.6760 | 0.3381 |
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+ | No log | 2.8308 | 184 | 0.3495 | 0.6835 | 0.3493 |
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+ | No log | 2.8615 | 186 | 0.3852 | 0.6918 | 0.3850 |
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+ | No log | 2.8923 | 188 | 0.3868 | 0.6943 | 0.3866 |
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+ | No log | 2.9231 | 190 | 0.3677 | 0.6907 | 0.3675 |
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+ | No log | 2.9538 | 192 | 0.3518 | 0.6767 | 0.3516 |
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+ | No log | 2.9846 | 194 | 0.3357 | 0.6513 | 0.3355 |
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+ | No log | 3.0154 | 196 | 0.3334 | 0.6454 | 0.3332 |
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+ | No log | 3.0462 | 198 | 0.3578 | 0.6797 | 0.3575 |
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+ | No log | 3.0769 | 200 | 0.3948 | 0.6883 | 0.3946 |
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+ | No log | 3.1077 | 202 | 0.4432 | 0.7045 | 0.4429 |
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+ | No log | 3.1385 | 204 | 0.4269 | 0.6992 | 0.4266 |
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+ | No log | 3.1692 | 206 | 0.3902 | 0.6821 | 0.3900 |
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+ | No log | 3.2 | 208 | 0.3566 | 0.6888 | 0.3563 |
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+ | No log | 3.2308 | 210 | 0.3522 | 0.6801 | 0.3520 |
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+ | No log | 3.2615 | 212 | 0.3809 | 0.6844 | 0.3807 |
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+ | No log | 3.2923 | 214 | 0.3997 | 0.6809 | 0.3996 |
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+ | No log | 3.3231 | 216 | 0.3971 | 0.6845 | 0.3969 |
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+ | No log | 3.3538 | 218 | 0.4034 | 0.6868 | 0.4032 |
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+ | No log | 3.3846 | 220 | 0.3747 | 0.6779 | 0.3745 |
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+ | No log | 3.4154 | 222 | 0.3650 | 0.6754 | 0.3649 |
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+ | No log | 3.4462 | 224 | 0.3490 | 0.6608 | 0.3489 |
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+ | No log | 3.4769 | 226 | 0.3383 | 0.6528 | 0.3382 |
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+ | No log | 3.5077 | 228 | 0.3335 | 0.6603 | 0.3334 |
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+ | No log | 3.5385 | 230 | 0.3498 | 0.6744 | 0.3497 |
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+ | No log | 3.5692 | 232 | 0.3883 | 0.6879 | 0.3882 |
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+ | No log | 3.6 | 234 | 0.3922 | 0.6898 | 0.3920 |
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+ | No log | 3.6308 | 236 | 0.3788 | 0.6837 | 0.3787 |
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+ | No log | 3.6615 | 238 | 0.3646 | 0.6837 | 0.3644 |
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+ | No log | 3.6923 | 240 | 0.3619 | 0.6837 | 0.3618 |
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+ | No log | 3.7231 | 242 | 0.3598 | 0.6829 | 0.3597 |
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+ | No log | 3.7538 | 244 | 0.3608 | 0.6768 | 0.3606 |
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+ | No log | 3.7846 | 246 | 0.3696 | 0.6847 | 0.3694 |
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+ | No log | 3.8154 | 248 | 0.3708 | 0.6805 | 0.3706 |
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+ | No log | 3.8462 | 250 | 0.3688 | 0.6833 | 0.3686 |
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+ | No log | 3.8769 | 252 | 0.3661 | 0.6739 | 0.3659 |
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+ | No log | 3.9077 | 254 | 0.3793 | 0.6901 | 0.3791 |
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+ | No log | 3.9385 | 256 | 0.3807 | 0.6815 | 0.3805 |
179
+ | No log | 3.9692 | 258 | 0.3816 | 0.6756 | 0.3813 |
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+ | No log | 4.0 | 260 | 0.3825 | 0.6756 | 0.3823 |
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+ | No log | 4.0308 | 262 | 0.3952 | 0.6844 | 0.3949 |
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+ | No log | 4.0615 | 264 | 0.3911 | 0.6814 | 0.3908 |
183
+ | No log | 4.0923 | 266 | 0.3817 | 0.6764 | 0.3814 |
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+ | No log | 4.1231 | 268 | 0.3816 | 0.6745 | 0.3814 |
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+ | No log | 4.1538 | 270 | 0.3988 | 0.6928 | 0.3986 |
186
+ | No log | 4.1846 | 272 | 0.4199 | 0.7018 | 0.4197 |
187
+ | No log | 4.2154 | 274 | 0.4594 | 0.6975 | 0.4592 |
188
+ | No log | 4.2462 | 276 | 0.4738 | 0.7068 | 0.4736 |
189
+ | No log | 4.2769 | 278 | 0.4539 | 0.6951 | 0.4536 |
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+ | No log | 4.3077 | 280 | 0.4203 | 0.6990 | 0.4201 |
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+ | No log | 4.3385 | 282 | 0.3868 | 0.6800 | 0.3866 |
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+ | No log | 4.3692 | 284 | 0.3682 | 0.6725 | 0.3680 |
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+ | No log | 4.4 | 286 | 0.3518 | 0.6431 | 0.3516 |
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+ | No log | 4.4308 | 288 | 0.3480 | 0.6392 | 0.3479 |
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+ | No log | 4.4615 | 290 | 0.3490 | 0.6410 | 0.3488 |
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+ | No log | 4.4923 | 292 | 0.3548 | 0.6683 | 0.3546 |
197
+ | No log | 4.5231 | 294 | 0.3662 | 0.6751 | 0.3660 |
198
+ | No log | 4.5538 | 296 | 0.3804 | 0.6778 | 0.3802 |
199
+ | No log | 4.5846 | 298 | 0.3937 | 0.6887 | 0.3935 |
200
+ | No log | 4.6154 | 300 | 0.4153 | 0.6981 | 0.4151 |
201
+ | No log | 4.6462 | 302 | 0.4257 | 0.7091 | 0.4255 |
202
+ | No log | 4.6769 | 304 | 0.4220 | 0.7091 | 0.4218 |
203
+ | No log | 4.7077 | 306 | 0.4101 | 0.6998 | 0.4099 |
204
+ | No log | 4.7385 | 308 | 0.4047 | 0.6935 | 0.4045 |
205
+ | No log | 4.7692 | 310 | 0.3979 | 0.6985 | 0.3977 |
206
+ | No log | 4.8 | 312 | 0.3901 | 0.6873 | 0.3899 |
207
+ | No log | 4.8308 | 314 | 0.3815 | 0.6841 | 0.3813 |
208
+ | No log | 4.8615 | 316 | 0.3744 | 0.6792 | 0.3742 |
209
+ | No log | 4.8923 | 318 | 0.3700 | 0.6757 | 0.3698 |
210
+ | No log | 4.9231 | 320 | 0.3681 | 0.6721 | 0.3679 |
211
+ | No log | 4.9538 | 322 | 0.3679 | 0.6721 | 0.3677 |
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+ | No log | 4.9846 | 324 | 0.3675 | 0.6721 | 0.3673 |
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  ### Framework versions
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