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yaniseuranova
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setfit-rag-hybrid-search-query-router-test

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
yaniseuranova commited on
Commit
a359ad8
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1 Parent(s): c999004

Add SetFit model

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Files changed (4) hide show
  1. README.md +325 -85
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. model_head.pkl +1 -1
README.md CHANGED
@@ -133,7 +133,7 @@ preds = model("What is the purpose of setting up a CUPS on a server?")
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  | semantic | 118 |
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  ### Training Hyperparameters
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- - batch_size: (4, 4)
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  - num_epochs: (1, 1)
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  - max_steps: -1
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  - sampling_strategy: oversampling
@@ -150,90 +150,330 @@ preds = model("What is the purpose of setting up a CUPS on a server?")
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  - load_best_model_at_end: True
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  ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:-------:|:--------:|:-------------:|:---------------:|
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- | 0.0002 | 1 | 0.4185 | - |
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- | 0.0125 | 50 | 0.3825 | - |
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- | 0.0250 | 100 | 0.1752 | - |
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- | 0.0374 | 150 | 0.1581 | - |
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- | 0.0499 | 200 | 0.2404 | - |
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- | 0.0624 | 250 | 0.1858 | - |
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- | 0.0749 | 300 | 0.3777 | - |
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- | 0.0874 | 350 | 0.0112 | - |
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- | 0.0999 | 400 | 0.0449 | - |
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- | 0.1123 | 450 | 0.0019 | - |
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- | 0.1248 | 500 | 0.0019 | - |
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- | 0.1373 | 550 | 0.0007 | - |
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- | 0.1498 | 600 | 0.0006 | - |
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- | 0.1623 | 650 | 0.0012 | - |
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- | 0.1747 | 700 | 0.0007 | - |
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- | 0.1872 | 750 | 0.001 | - |
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- | 0.1997 | 800 | 0.0004 | - |
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- | 0.2122 | 850 | 0.0001 | - |
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- | 0.2247 | 900 | 0.0002 | - |
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- | 0.2371 | 950 | 0.0001 | - |
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- | 0.2496 | 1000 | 0.0014 | - |
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- | 0.2621 | 1050 | 0.0001 | - |
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- | 0.2746 | 1100 | 0.0001 | - |
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- | 0.2871 | 1150 | 0.0001 | - |
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- | 0.2996 | 1200 | 0.0001 | - |
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- | 0.3120 | 1250 | 0.0001 | - |
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- | 0.3245 | 1300 | 0.0002 | - |
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- | 0.3370 | 1350 | 0.0001 | - |
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- | 0.3495 | 1400 | 0.0003 | - |
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- | 0.3620 | 1450 | 0.0003 | - |
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- | 0.3744 | 1500 | 0.0001 | - |
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- | 0.3869 | 1550 | 0.0005 | - |
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- | 0.3994 | 1600 | 0.0002 | - |
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- | 0.4119 | 1650 | 0.0002 | - |
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- | 0.4244 | 1700 | 0.0002 | - |
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- | 0.4368 | 1750 | 0.0001 | - |
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- | 0.4493 | 1800 | 0.0001 | - |
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- | 0.4618 | 1850 | 0.0002 | - |
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- | 0.4743 | 1900 | 0.0001 | - |
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- | 0.4868 | 1950 | 0.0002 | - |
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- | 0.4993 | 2000 | 0.0002 | - |
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- | 0.5117 | 2050 | 0.0001 | - |
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- | 0.5242 | 2100 | 0.0001 | - |
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- | 0.5367 | 2150 | 0.0001 | - |
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- | 0.5492 | 2200 | 0.0001 | - |
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- | 0.5617 | 2250 | 0.0002 | - |
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- | 0.5741 | 2300 | 0.0001 | - |
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- | 0.5866 | 2350 | 0.0001 | - |
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- | 0.5991 | 2400 | 0.0001 | - |
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- | 0.6116 | 2450 | 0.0001 | - |
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- | 0.6241 | 2500 | 0.0002 | - |
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- | 0.6365 | 2550 | 0.0001 | - |
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- | 0.6490 | 2600 | 0.0001 | - |
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- | 0.6615 | 2650 | 0.0001 | - |
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- | 0.6740 | 2700 | 0.0001 | - |
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- | 0.6865 | 2750 | 0.0001 | - |
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- | 0.6990 | 2800 | 0.0001 | - |
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- | 0.7114 | 2850 | 0.0001 | - |
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- | 0.7239 | 2900 | 0.0001 | - |
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- | 0.7364 | 2950 | 0.0001 | - |
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- | 0.7489 | 3000 | 0.0001 | - |
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- | 0.7614 | 3050 | 0.0001 | - |
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- | 0.7738 | 3100 | 0.0001 | - |
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- | 0.7863 | 3150 | 0.0001 | - |
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- | 0.7988 | 3200 | 0.0002 | - |
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- | 0.8113 | 3250 | 0.0001 | - |
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- | 0.8238 | 3300 | 0.0001 | - |
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- | 0.8362 | 3350 | 0.0001 | - |
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- | 0.8487 | 3400 | 0.0001 | - |
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- | 0.8612 | 3450 | 0.0 | - |
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- | 0.8737 | 3500 | 0.0001 | - |
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- | 0.8862 | 3550 | 0.0 | - |
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- | 0.8987 | 3600 | 0.0001 | - |
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- | 0.9111 | 3650 | 0.0002 | - |
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- | 0.9236 | 3700 | 0.0001 | - |
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- | 0.9361 | 3750 | 0.0001 | - |
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- | 0.9486 | 3800 | 0.0 | - |
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- | 0.9611 | 3850 | 0.0001 | - |
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- | 0.9735 | 3900 | 0.0001 | - |
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- | 0.9860 | 3950 | 0.0001 | - |
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- | 0.9985 | 4000 | 0.0 | - |
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- | **1.0** | **4006** | **-** | **0.1768** |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  * The bold row denotes the saved checkpoint.
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  ### Framework Versions
 
133
  | semantic | 118 |
134
 
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  ### Training Hyperparameters
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+ - batch_size: (1, 1)
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  - num_epochs: (1, 1)
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  - max_steps: -1
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  - sampling_strategy: oversampling
 
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  - load_best_model_at_end: True
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  ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:---------:|:-------------:|:---------------:|
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+ | 0.0001 | 1 | 0.919 | - |
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+ | 0.0031 | 50 | 0.0006 | - |
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+ | 0.0062 | 100 | 0.0208 | - |
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+ | 0.0094 | 150 | 0.0001 | - |
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+ | 0.0125 | 200 | 0.0163 | - |
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+ | 0.0156 | 250 | 0.0043 | - |
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+ | 0.0187 | 300 | 0.0082 | - |
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+ | 0.0218 | 350 | 0.0217 | - |
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+ | 0.0250 | 400 | 0.008 | - |
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+ | 0.0281 | 450 | 0.0451 | - |
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+ | 0.0312 | 500 | 0.0179 | - |
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+ | 0.0343 | 550 | 0.0801 | - |
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+ | 0.0374 | 600 | 0.0898 | - |
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+ | 0.0406 | 650 | 0.1121 | - |
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+ | 0.0437 | 700 | 0.0165 | - |
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+ | 0.0468 | 750 | 0.0402 | - |
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+ | 0.0499 | 800 | 0.1585 | - |
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+ | 0.0531 | 850 | 0.0028 | - |
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+ | 0.0562 | 900 | 0.0033 | - |
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+ | 0.0593 | 950 | 0.0001 | - |
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+ | 0.0624 | 1000 | 0.002 | - |
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+ | 0.0655 | 1050 | 0.0001 | - |
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+ | 0.0687 | 1100 | 0.0158 | - |
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+ | 0.0718 | 1150 | 0.0 | - |
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+ | 0.0749 | 1200 | 0.0001 | - |
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+ | 0.0780 | 1250 | 0.0044 | - |
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+ | 0.0811 | 1300 | 0.002 | - |
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+ | 0.0843 | 1350 | 0.0016 | - |
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+ | 0.0874 | 1400 | 0.0003 | - |
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+ | 0.0905 | 1450 | 0.0005 | - |
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+ | 0.0936 | 1500 | 0.001 | - |
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+ | 0.0967 | 1550 | 0.0 | - |
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+ | 0.0999 | 1600 | 0.0 | - |
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+ | 0.1030 | 1650 | 0.0001 | - |
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+ | 0.1061 | 1700 | 0.0 | - |
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+ | 0.1092 | 1750 | 0.0001 | - |
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+ | 0.1123 | 1800 | 0.0 | - |
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+ | 0.1155 | 1850 | 0.0002 | - |
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+ | 0.1186 | 1900 | 0.0004 | - |
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+ | 0.1217 | 1950 | 0.0006 | - |
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+ | 0.1248 | 2000 | 0.0001 | - |
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+ | 0.1279 | 2050 | 0.0 | - |
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+ | 0.1311 | 2100 | 0.0001 | - |
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+ | 0.1342 | 2150 | 0.0009 | - |
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+ | 0.1373 | 2200 | 0.0 | - |
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+ | 0.1404 | 2250 | 0.0001 | - |
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+ | 0.1436 | 2300 | 0.0001 | - |
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+ | 0.1467 | 2350 | 0.0 | - |
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+ | 0.1498 | 2400 | 0.0001 | - |
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+ | 0.1529 | 2450 | 0.0 | - |
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+ | 0.1560 | 2500 | 0.0 | - |
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+ | 0.1592 | 2550 | 0.0001 | - |
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+ | 0.1623 | 2600 | 0.0 | - |
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+ | 0.1654 | 2650 | 0.0003 | - |
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+ | 0.1685 | 2700 | 0.0 | - |
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+ | 0.1716 | 2750 | 0.0001 | - |
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+ | 0.1748 | 2800 | 0.0 | - |
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+ | 0.1779 | 2850 | 0.0 | - |
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+ | 0.1810 | 2900 | 0.0 | - |
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+ | 0.1841 | 2950 | 0.0 | - |
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+ | 0.1872 | 3000 | 0.0 | - |
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+ | 0.1904 | 3050 | 0.0 | - |
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+ | 0.1935 | 3100 | 0.0001 | - |
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+ | 0.1966 | 3150 | 0.0 | - |
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+ | 0.1997 | 3200 | 0.0 | - |
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+ | 0.2028 | 3250 | 0.0 | - |
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+ | 0.2060 | 3300 | 0.0 | - |
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+ | 0.2091 | 3350 | 0.0 | - |
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+ | 0.2122 | 3400 | 0.0 | - |
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+ | 0.2153 | 3450 | 0.0 | - |
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+ | 0.2184 | 3500 | 0.0005 | - |
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+ | 0.2216 | 3550 | 0.0 | - |
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+ | 0.2247 | 3600 | 0.0 | - |
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+ | 0.2278 | 3650 | 0.0 | - |
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+ | 0.2309 | 3700 | 0.0 | - |
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+ | 0.2341 | 3750 | 0.0005 | - |
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+ | 0.2372 | 3800 | 0.0 | - |
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+ | 0.2403 | 3850 | 0.0 | - |
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+ | 0.2434 | 3900 | 0.0 | - |
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+ | 0.2465 | 3950 | 0.0 | - |
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+ | 0.2497 | 4000 | 0.0001 | - |
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+ | 0.2528 | 4050 | 0.0 | - |
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+ | 0.2559 | 4100 | 0.0 | - |
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+ | 0.2590 | 4150 | 0.0001 | - |
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+ | 0.2621 | 4200 | 0.0001 | - |
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+ | 0.2653 | 4250 | 0.0 | - |
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+ | 0.2684 | 4300 | 0.0001 | - |
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+ | 0.2715 | 4350 | 0.0 | - |
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+ | 0.2746 | 4400 | 0.0 | - |
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+ | 0.2777 | 4450 | 0.0 | - |
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+ | 0.2809 | 4500 | 0.0 | - |
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+ | 0.2840 | 4550 | 0.0002 | - |
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+ | 0.2871 | 4600 | 0.0 | - |
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+ | 0.2902 | 4650 | 0.0002 | - |
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+ | 0.2933 | 4700 | 0.0 | - |
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+ | 0.2965 | 4750 | 0.0 | - |
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+ | 0.2996 | 4800 | 0.0 | - |
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+ | 0.3027 | 4850 | 0.0002 | - |
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+ | 0.3058 | 4900 | 0.0 | - |
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+ | 0.3090 | 4950 | 0.0 | - |
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+ | 0.3121 | 5000 | 0.0 | - |
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+ | 0.3152 | 5050 | 0.0 | - |
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+ | 0.3183 | 5100 | 0.0001 | - |
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+ | 0.3214 | 5150 | 0.0 | - |
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+ | 0.3246 | 5200 | 0.0001 | - |
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+ | 0.3277 | 5250 | 0.0 | - |
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+ | 0.3308 | 5300 | 0.0 | - |
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+ | 0.3339 | 5350 | 0.0001 | - |
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+ | 0.3370 | 5400 | 0.0 | - |
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+ | 0.3402 | 5450 | 0.0 | - |
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+ | 0.3433 | 5500 | 0.0001 | - |
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+ | 0.3464 | 5550 | 0.0 | - |
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+ | 0.3495 | 5600 | 0.0003 | - |
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+ | 0.3526 | 5650 | 0.0 | - |
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+ | 0.3589 | 5750 | 0.0 | - |
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+ | 0.3745 | 6000 | 0.0 | - |
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+ | 0.3776 | 6050 | 0.0001 | - |
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+ | 0.3838 | 6150 | 0.0 | - |
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+ | 0.3870 | 6200 | 0.0 | - |
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+ | 0.3901 | 6250 | 0.0001 | - |
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+ | 0.3932 | 6300 | 0.0001 | - |
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+ | 0.3963 | 6350 | 0.0002 | - |
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+ | 0.3995 | 6400 | 0.0003 | - |
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+ | 0.4026 | 6450 | 0.0001 | - |
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+ | 0.4119 | 6600 | 0.0 | - |
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+ | 0.4213 | 6750 | 0.0004 | - |
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+ | 0.4244 | 6800 | 0.0 | - |
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+ | 0.4275 | 6850 | 0.0001 | - |
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+ | 0.4307 | 6900 | 0.0 | - |
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+ | 0.4338 | 6950 | 0.0 | - |
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+ | 0.4369 | 7000 | 0.0001 | - |
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+ | 0.4681 | 7500 | 0.0 | - |
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+ | 0.4712 | 7550 | 0.0001 | - |
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+ | 0.4993 | 8000 | 0.0 | - |
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+ | 0.5024 | 8050 | 0.0 | - |
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+ | 0.5056 | 8100 | 0.0001 | - |
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+ | 0.5087 | 8150 | 0.0 | - |
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+ | 0.5149 | 8250 | 0.0 | - |
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+ | 0.5180 | 8300 | 0.0001 | - |
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+ | 0.5243 | 8400 | 0.0 | - |
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+ | 0.5336 | 8550 | 0.0 | - |
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+ | 0.5368 | 8600 | 0.0001 | - |
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+ | 0.5430 | 8700 | 0.0 | - |
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+ | 0.5461 | 8750 | 0.0 | - |
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+ | 0.5492 | 8800 | 0.0 | - |
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+ | 0.5524 | 8850 | 0.0 | - |
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+ | 0.5555 | 8900 | 0.0 | - |
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+ | 0.5742 | 9200 | 0.0 | - |
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+ | 0.5773 | 9250 | 0.0004 | - |
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+ | 0.5805 | 9300 | 0.0 | - |
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+ | 0.5836 | 9350 | 0.0 | - |
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+ | 0.5867 | 9400 | 0.0 | - |
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+ | 0.5898 | 9450 | 0.0 | - |
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+ | 0.5961 | 9550 | 0.0 | - |
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+ | 0.5992 | 9600 | 0.0 | - |
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+ | 0.6023 | 9650 | 0.0001 | - |
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+ | 0.6085 | 9750 | 0.0 | - |
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+ | 0.6117 | 9800 | 0.0 | - |
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+ | 0.6148 | 9850 | 0.0 | - |
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+ | 0.6179 | 9900 | 0.0 | - |
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+ | 0.6210 | 9950 | 0.0 | - |
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+ | 0.6241 | 10000 | 0.0 | - |
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+ | 0.6335 | 10150 | 0.0 | - |
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+ | 0.6397 | 10250 | 0.0 | - |
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+ | 0.6460 | 10350 | 0.0001 | - |
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+ | 0.6491 | 10400 | 0.0 | - |
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+ | 0.6522 | 10450 | 0.0002 | - |
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+ | 0.6834 | 10950 | 0.0 | - |
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+ | 0.6866 | 11000 | 0.0001 | - |
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+ | 0.7490 | 12000 | 0.0001 | - |
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+ | 0.7615 | 12200 | 0.0001 | - |
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+ | 0.7927 | 12700 | 0.0 | - |
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+ | 0.7958 | 12750 | 0.0 | - |
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+ | 0.7989 | 12800 | 0.0 | - |
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+ | 0.8020 | 12850 | 0.0 | - |
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+ | 0.8051 | 12900 | 0.0 | - |
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+ | 0.8114 | 13000 | 0.0 | - |
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+ | 0.8145 | 13050 | 0.0 | - |
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+ | 0.8176 | 13100 | 0.0 | - |
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+ | 0.8207 | 13150 | 0.0 | - |
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+ | 0.8239 | 13200 | 0.0 | - |
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+ | 0.8270 | 13250 | 0.0 | - |
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+ | 0.8364 | 13400 | 0.0 | - |
424
+ | 0.8395 | 13450 | 0.0 | - |
425
+ | 0.8426 | 13500 | 0.0 | - |
426
+ | 0.8457 | 13550 | 0.0 | - |
427
+ | 0.8488 | 13600 | 0.0 | - |
428
+ | 0.8520 | 13650 | 0.0 | - |
429
+ | 0.8551 | 13700 | 0.0 | - |
430
+ | 0.8582 | 13750 | 0.0 | - |
431
+ | 0.8613 | 13800 | 0.0 | - |
432
+ | 0.8644 | 13850 | 0.0 | - |
433
+ | 0.8676 | 13900 | 0.0 | - |
434
+ | 0.8707 | 13950 | 0.0 | - |
435
+ | 0.8738 | 14000 | 0.0 | - |
436
+ | 0.8769 | 14050 | 0.0 | - |
437
+ | 0.8800 | 14100 | 0.0 | - |
438
+ | 0.8832 | 14150 | 0.0 | - |
439
+ | 0.8863 | 14200 | 0.0 | - |
440
+ | 0.8894 | 14250 | 0.0 | - |
441
+ | 0.8925 | 14300 | 0.0 | - |
442
+ | 0.8956 | 14350 | 0.0 | - |
443
+ | 0.8988 | 14400 | 0.0 | - |
444
+ | 0.9019 | 14450 | 0.0 | - |
445
+ | 0.9050 | 14500 | 0.0 | - |
446
+ | 0.9081 | 14550 | 0.0 | - |
447
+ | 0.9112 | 14600 | 0.0 | - |
448
+ | 0.9144 | 14650 | 0.0 | - |
449
+ | 0.9175 | 14700 | 0.0 | - |
450
+ | 0.9206 | 14750 | 0.0 | - |
451
+ | 0.9237 | 14800 | 0.0 | - |
452
+ | 0.9269 | 14850 | 0.0 | - |
453
+ | 0.9300 | 14900 | 0.0 | - |
454
+ | 0.9331 | 14950 | 0.0 | - |
455
+ | 0.9362 | 15000 | 0.0 | - |
456
+ | 0.9393 | 15050 | 0.0 | - |
457
+ | 0.9425 | 15100 | 0.0 | - |
458
+ | 0.9456 | 15150 | 0.0 | - |
459
+ | 0.9487 | 15200 | 0.0001 | - |
460
+ | 0.9518 | 15250 | 0.0 | - |
461
+ | 0.9549 | 15300 | 0.0 | - |
462
+ | 0.9581 | 15350 | 0.0 | - |
463
+ | 0.9612 | 15400 | 0.0 | - |
464
+ | 0.9643 | 15450 | 0.0 | - |
465
+ | 0.9674 | 15500 | 0.0 | - |
466
+ | 0.9705 | 15550 | 0.0 | - |
467
+ | 0.9737 | 15600 | 0.0 | - |
468
+ | 0.9768 | 15650 | 0.0 | - |
469
+ | 0.9799 | 15700 | 0.0 | - |
470
+ | 0.9830 | 15750 | 0.0 | - |
471
+ | 0.9861 | 15800 | 0.0 | - |
472
+ | 0.9893 | 15850 | 0.0 | - |
473
+ | 0.9924 | 15900 | 0.0 | - |
474
+ | 0.9955 | 15950 | 0.0 | - |
475
+ | 0.9986 | 16000 | 0.0 | - |
476
+ | **1.0** | **16022** | **-** | **0.1745** |
477
 
478
  * The bold row denotes the saved checkpoint.
479
  ### Framework Versions
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