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layoutlmv2-base-uncased_finetuned_docvqa

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  1. README.md +91 -91
  2. model.safetensors +1 -1
  3. runs/Apr12_17-57-39_hammad/events.out.tfevents.1712969860.hammad.2476524.0 +2 -2
README.md CHANGED
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: nan
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  ## Model description
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@@ -46,96 +46,96 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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- | 0.0 | 0.22 | 50 | nan |
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- | 0.0 | 0.44 | 100 | nan |
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- | 0.0 | 0.66 | 150 | nan |
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- | 0.0 | 0.88 | 200 | nan |
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- | 0.0 | 1.11 | 250 | nan |
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- | 0.0 | 1.33 | 300 | nan |
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- | 0.0 | 1.55 | 350 | nan |
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- | 0.0 | 1.77 | 400 | nan |
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- | 0.0 | 1.99 | 450 | nan |
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- | 0.0 | 2.21 | 500 | nan |
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- | 0.0 | 2.43 | 550 | nan |
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- | 0.0 | 2.65 | 600 | nan |
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- | 0.0 | 2.88 | 650 | nan |
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- | 0.0 | 3.1 | 700 | nan |
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- | 0.0 | 3.32 | 750 | nan |
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- | 0.0 | 3.54 | 800 | nan |
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- | 0.0 | 3.76 | 850 | nan |
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- | 0.0 | 3.98 | 900 | nan |
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- | 0.0 | 4.2 | 950 | nan |
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- | 0.0 | 4.42 | 1000 | nan |
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- | 0.0 | 4.65 | 1050 | nan |
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- | 0.0 | 4.87 | 1100 | nan |
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- | 0.0 | 5.09 | 1150 | nan |
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- | 0.0 | 5.31 | 1200 | nan |
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- | 0.0 | 5.53 | 1250 | nan |
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- | 0.0 | 5.75 | 1300 | nan |
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- | 0.0 | 5.97 | 1350 | nan |
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- | 0.0 | 6.19 | 1400 | nan |
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- | 0.0 | 6.42 | 1450 | nan |
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- | 0.0 | 6.64 | 1500 | nan |
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- | 0.0 | 6.86 | 1550 | nan |
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- | 0.0 | 7.08 | 1600 | nan |
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- | 0.0 | 7.3 | 1650 | nan |
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- | 0.0 | 7.52 | 1700 | nan |
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- | 0.0 | 7.74 | 1750 | nan |
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- | 0.0 | 7.96 | 1800 | nan |
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- | 0.0 | 8.19 | 1850 | nan |
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- | 0.0 | 8.41 | 1900 | nan |
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- | 0.0 | 8.63 | 1950 | nan |
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- | 0.0 | 8.85 | 2000 | nan |
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- | 0.0 | 9.07 | 2050 | nan |
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- | 0.0 | 9.29 | 2100 | nan |
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- | 0.0 | 9.51 | 2150 | nan |
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- | 0.0 | 9.73 | 2200 | nan |
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- | 0.0 | 9.96 | 2250 | nan |
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- | 0.0 | 10.18 | 2300 | nan |
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- | 0.0 | 10.4 | 2350 | nan |
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- | 0.0 | 10.62 | 2400 | nan |
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- | 0.0 | 10.84 | 2450 | nan |
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- | 0.0 | 11.06 | 2500 | nan |
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- | 0.0 | 11.28 | 2550 | nan |
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- | 0.0 | 11.5 | 2600 | nan |
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- | 0.0 | 11.73 | 2650 | nan |
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- | 0.0 | 11.95 | 2700 | nan |
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- | 0.0 | 12.17 | 2750 | nan |
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- | 0.0 | 12.39 | 2800 | nan |
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- | 0.0 | 12.61 | 2850 | nan |
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- | 0.0 | 12.83 | 2900 | nan |
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- | 0.0 | 13.05 | 2950 | nan |
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- | 0.0 | 13.27 | 3000 | nan |
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- | 0.0 | 13.5 | 3050 | nan |
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- | 0.0 | 13.72 | 3100 | nan |
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- | 0.0 | 13.94 | 3150 | nan |
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- | 0.0 | 14.16 | 3200 | nan |
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- | 0.0 | 14.38 | 3250 | nan |
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- | 0.0 | 14.6 | 3300 | nan |
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- | 0.0 | 14.82 | 3350 | nan |
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- | 0.0 | 15.04 | 3400 | nan |
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- | 0.0 | 15.27 | 3450 | nan |
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- | 0.0 | 15.49 | 3500 | nan |
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- | 0.0 | 15.71 | 3550 | nan |
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- | 0.0 | 15.93 | 3600 | nan |
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- | 0.0 | 16.15 | 3650 | nan |
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- | 0.0 | 16.37 | 3700 | nan |
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- | 0.0 | 16.59 | 3750 | nan |
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- | 0.0 | 16.81 | 3800 | nan |
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- | 0.0 | 17.04 | 3850 | nan |
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- | 0.0 | 17.26 | 3900 | nan |
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- | 0.0 | 17.48 | 3950 | nan |
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- | 0.0 | 17.7 | 4000 | nan |
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- | 0.0 | 17.92 | 4050 | nan |
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- | 0.0 | 18.14 | 4100 | nan |
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- | 0.0 | 18.36 | 4150 | nan |
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- | 0.0 | 18.58 | 4200 | nan |
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- | 0.0 | 18.81 | 4250 | nan |
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- | 0.0 | 19.03 | 4300 | nan |
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- | 0.0 | 19.25 | 4350 | nan |
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- | 0.0 | 19.47 | 4400 | nan |
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- | 0.0 | 19.69 | 4450 | nan |
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- | 0.0 | 19.91 | 4500 | nan |
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  ### Framework versions
 
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16
  This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
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+ - Loss: 5.1691
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  ## Model description
21
 
 
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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+ | 5.2266 | 0.22 | 50 | 4.7202 |
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+ | 4.4028 | 0.44 | 100 | 4.1366 |
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+ | 4.0483 | 0.66 | 150 | 3.8471 |
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+ | 3.7553 | 0.88 | 200 | 3.5072 |
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+ | 3.4046 | 1.11 | 250 | 3.5053 |
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+ | 3.1543 | 1.33 | 300 | 3.1483 |
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+ | 3.0577 | 1.55 | 350 | 3.0745 |
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+ | 2.8076 | 1.77 | 400 | 2.8592 |
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+ | 2.456 | 1.99 | 450 | 2.7019 |
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+ | 1.9787 | 2.21 | 500 | 2.7553 |
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+ | 1.9308 | 2.43 | 550 | 2.3374 |
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+ | 1.7637 | 2.65 | 600 | 2.2109 |
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+ | 1.7719 | 2.88 | 650 | 2.9137 |
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+ | 1.6661 | 3.1 | 700 | 2.7946 |
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+ | 1.3154 | 3.32 | 750 | 2.5750 |
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+ | 1.3015 | 3.54 | 800 | 2.4883 |
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+ | 1.2136 | 3.76 | 850 | 2.1466 |
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+ | 1.253 | 3.98 | 900 | 2.2343 |
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+ | 0.96 | 4.2 | 950 | 2.6481 |
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+ | 0.9083 | 4.42 | 1000 | 2.2891 |
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+ | 0.9441 | 4.65 | 1050 | 2.8459 |
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+ | 0.9041 | 4.87 | 1100 | 3.0106 |
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+ | 0.8727 | 5.09 | 1150 | 2.7765 |
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+ | 0.6496 | 5.31 | 1200 | 3.0633 |
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+ | 0.7388 | 5.53 | 1250 | 2.7464 |
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+ | 0.5012 | 5.75 | 1300 | 3.3843 |
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+ | 0.6762 | 5.97 | 1350 | 3.6035 |
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+ | 0.4907 | 6.19 | 1400 | 3.4269 |
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+ | 0.5893 | 6.42 | 1450 | 3.2352 |
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+ | 0.4987 | 6.64 | 1500 | 3.2802 |
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+ | 0.3867 | 6.86 | 1550 | 3.8191 |
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+ | 0.6091 | 7.08 | 1600 | 3.4476 |
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+ | 0.4088 | 7.3 | 1650 | 3.5099 |
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+ | 0.4135 | 7.52 | 1700 | 3.4519 |
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+ | 0.3859 | 7.74 | 1750 | 3.4147 |
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+ | 0.335 | 7.96 | 1800 | 3.8082 |
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+ | 0.2068 | 8.19 | 1850 | 4.3927 |
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+ | 0.3149 | 8.41 | 1900 | 3.7065 |
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+ | 0.2526 | 8.63 | 1950 | 3.6056 |
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+ | 0.451 | 8.85 | 2000 | 3.7065 |
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+ | 0.3792 | 9.07 | 2050 | 3.8738 |
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+ | 0.2299 | 9.29 | 2100 | 3.8282 |
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+ | 0.3064 | 9.51 | 2150 | 3.6586 |
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+ | 0.26 | 9.73 | 2200 | 3.9155 |
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+ | 0.3218 | 9.96 | 2250 | 3.5863 |
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+ | 0.2826 | 10.18 | 2300 | 3.5095 |
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+ | 0.149 | 10.4 | 2350 | 3.4537 |
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+ | 0.1213 | 10.62 | 2400 | 3.8778 |
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+ | 0.2157 | 10.84 | 2450 | 3.8106 |
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+ | 0.2149 | 11.06 | 2500 | 4.2672 |
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+ | 0.1212 | 11.28 | 2550 | 4.2534 |
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+ | 0.1664 | 11.5 | 2600 | 4.3033 |
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+ | 0.1487 | 11.73 | 2650 | 3.9483 |
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+ | 0.1088 | 11.95 | 2700 | 3.9682 |
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+ | 0.0791 | 12.17 | 2750 | 4.2143 |
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+ | 0.0734 | 12.39 | 2800 | 4.2175 |
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+ | 0.128 | 12.61 | 2850 | 4.2613 |
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+ | 0.1851 | 12.83 | 2900 | 3.9094 |
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+ | 0.1215 | 13.05 | 2950 | 4.2045 |
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+ | 0.0438 | 13.27 | 3000 | 4.5802 |
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+ | 0.0107 | 13.5 | 3050 | 4.7988 |
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+ | 0.0606 | 13.72 | 3100 | 5.0228 |
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+ | 0.0819 | 13.94 | 3150 | 4.9309 |
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+ | 0.1375 | 14.16 | 3200 | 4.8995 |
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+ | 0.0729 | 14.38 | 3250 | 4.7900 |
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+ | 0.0832 | 14.6 | 3300 | 4.6417 |
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+ | 0.0687 | 14.82 | 3350 | 4.8781 |
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+ | 0.0516 | 15.04 | 3400 | 4.9231 |
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+ | 0.056 | 15.27 | 3450 | 5.1059 |
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+ | 0.0672 | 15.49 | 3500 | 5.0563 |
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+ | 0.1036 | 15.71 | 3550 | 4.5889 |
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+ | 0.1551 | 15.93 | 3600 | 4.4488 |
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+ | 0.0509 | 16.15 | 3650 | 4.8964 |
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+ | 0.0505 | 16.37 | 3700 | 4.9259 |
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+ | 0.0295 | 16.59 | 3750 | 4.8617 |
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+ | 0.0766 | 16.81 | 3800 | 4.7973 |
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+ | 0.0103 | 17.04 | 3850 | 4.9249 |
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+ | 0.0148 | 17.26 | 3900 | 4.9319 |
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+ | 0.0105 | 17.48 | 3950 | 5.1000 |
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+ | 0.0341 | 17.7 | 4000 | 4.9627 |
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+ | 0.0547 | 17.92 | 4050 | 5.0149 |
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+ | 0.0106 | 18.14 | 4100 | 5.0924 |
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+ | 0.0045 | 18.36 | 4150 | 5.1550 |
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+ | 0.0072 | 18.58 | 4200 | 5.1620 |
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+ | 0.0428 | 18.81 | 4250 | 5.1546 |
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+ | 0.0443 | 19.03 | 4300 | 5.1506 |
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+ | 0.0244 | 19.25 | 4350 | 5.1720 |
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+ | 0.0096 | 19.47 | 4400 | 5.1640 |
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+ | 0.0265 | 19.69 | 4450 | 5.1647 |
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+ | 0.0057 | 19.91 | 4500 | 5.1691 |
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  ### Framework versions
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