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distilbert
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End of training

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  1. README.md +83 -16
  2. model.safetensors +1 -1
  3. runs/Mar23_13-21-28_3015ac71de9b/events.out.tfevents.1711200112.3015ac71de9b.221.2 +3 -0
  4. runs/Mar23_13-21-28_3015ac71de9b/events.out.tfevents.1711200711.3015ac71de9b.221.3 +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-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6797
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- - Precision: 0.7850
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- - Recall: 0.7787
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- - F1: 0.7762
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- - Accuracy: 0.7787
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  ## Model description
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@@ -43,25 +43,92 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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- - train_batch_size: 64
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- - eval_batch_size: 64
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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: 8
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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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- | 1.8653 | 1.07 | 30 | 1.7135 | 0.3177 | 0.3501 | 0.2302 | 0.3501 |
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- | 1.5399 | 2.14 | 60 | 1.3143 | 0.6622 | 0.5490 | 0.5241 | 0.5490 |
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- | 1.1791 | 3.21 | 90 | 1.0361 | 0.7271 | 0.7115 | 0.7074 | 0.7115 |
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- | 0.891 | 4.29 | 120 | 0.8605 | 0.7451 | 0.7255 | 0.7214 | 0.7255 |
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- | 0.6817 | 5.36 | 150 | 0.7495 | 0.7595 | 0.7507 | 0.7470 | 0.7507 |
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- | 0.539 | 6.43 | 180 | 0.6965 | 0.7987 | 0.7927 | 0.7918 | 0.7927 |
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- | 0.4777 | 7.5 | 210 | 0.6830 | 0.7787 | 0.7703 | 0.7680 | 0.7703 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2397
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+ - Precision: 0.8278
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+ - Recall: 0.8235
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+ - F1: 0.8229
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+ - Accuracy: 0.8235
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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: 10
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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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+ | 1.9316 | 0.14 | 30 | 1.8468 | 0.1983 | 0.2829 | 0.2043 | 0.2829 |
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+ | 1.8133 | 0.27 | 60 | 1.6712 | 0.2291 | 0.3081 | 0.2182 | 0.3081 |
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+ | 1.5009 | 0.41 | 90 | 1.5765 | 0.4177 | 0.4426 | 0.3651 | 0.4426 |
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+ | 1.3052 | 0.54 | 120 | 1.1852 | 0.6586 | 0.6331 | 0.6178 | 0.6331 |
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+ | 1.0906 | 0.68 | 150 | 0.9284 | 0.7427 | 0.7003 | 0.6959 | 0.7003 |
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+ | 1.041 | 0.81 | 180 | 0.7793 | 0.7757 | 0.7507 | 0.7419 | 0.7507 |
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+ | 0.7168 | 0.95 | 210 | 0.7745 | 0.7805 | 0.7507 | 0.7428 | 0.7507 |
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+ | 0.6366 | 1.08 | 240 | 0.6724 | 0.8079 | 0.7983 | 0.7968 | 0.7983 |
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+ | 0.5656 | 1.22 | 270 | 0.7074 | 0.8022 | 0.7787 | 0.7769 | 0.7787 |
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+ | 0.4195 | 1.35 | 300 | 0.6338 | 0.8098 | 0.7955 | 0.7981 | 0.7955 |
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+ | 0.4036 | 1.49 | 330 | 0.6482 | 0.7850 | 0.7731 | 0.7730 | 0.7731 |
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+ | 0.4667 | 1.62 | 360 | 0.6100 | 0.8350 | 0.8179 | 0.8182 | 0.8179 |
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+ | 0.3695 | 1.76 | 390 | 0.7086 | 0.7906 | 0.7815 | 0.7739 | 0.7815 |
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+ | 0.3224 | 1.89 | 420 | 0.6846 | 0.8151 | 0.8067 | 0.8059 | 0.8067 |
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+ | 0.4226 | 2.03 | 450 | 0.7040 | 0.8013 | 0.7983 | 0.7981 | 0.7983 |
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+ | 0.2078 | 2.16 | 480 | 0.7943 | 0.8010 | 0.7983 | 0.7966 | 0.7983 |
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+ | 0.1708 | 2.3 | 510 | 0.8699 | 0.8042 | 0.7927 | 0.7927 | 0.7927 |
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+ | 0.1533 | 2.43 | 540 | 0.8414 | 0.8264 | 0.8151 | 0.8161 | 0.8151 |
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+ | 0.2079 | 2.57 | 570 | 0.8407 | 0.8166 | 0.8095 | 0.8104 | 0.8095 |
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+ | 0.2689 | 2.7 | 600 | 0.8214 | 0.8110 | 0.8067 | 0.8071 | 0.8067 |
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+ | 0.0993 | 2.84 | 630 | 0.8187 | 0.8092 | 0.8039 | 0.8048 | 0.8039 |
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+ | 0.1627 | 2.97 | 660 | 0.9606 | 0.8068 | 0.8011 | 0.8004 | 0.8011 |
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+ | 0.0926 | 3.11 | 690 | 0.8597 | 0.8351 | 0.8319 | 0.8312 | 0.8319 |
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+ | 0.0641 | 3.24 | 720 | 0.8120 | 0.8360 | 0.8319 | 0.8313 | 0.8319 |
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+ | 0.1068 | 3.38 | 750 | 0.9111 | 0.8249 | 0.8179 | 0.8179 | 0.8179 |
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+ | 0.0381 | 3.51 | 780 | 0.9949 | 0.8110 | 0.7983 | 0.7976 | 0.7983 |
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+ | 0.0673 | 3.65 | 810 | 1.0619 | 0.8272 | 0.8179 | 0.8160 | 0.8179 |
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+ | 0.0572 | 3.78 | 840 | 1.0406 | 0.8042 | 0.8011 | 0.8009 | 0.8011 |
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+ | 0.0454 | 3.92 | 870 | 1.0109 | 0.8199 | 0.8151 | 0.8151 | 0.8151 |
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+ | 0.0421 | 4.05 | 900 | 1.0379 | 0.8282 | 0.8235 | 0.8231 | 0.8235 |
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+ | 0.0046 | 4.19 | 930 | 1.1019 | 0.8342 | 0.8263 | 0.8259 | 0.8263 |
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+ | 0.0016 | 4.32 | 960 | 1.1075 | 0.8315 | 0.8235 | 0.8230 | 0.8235 |
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+ | 0.0836 | 4.46 | 990 | 1.1080 | 0.8212 | 0.8179 | 0.8173 | 0.8179 |
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+ | 0.0026 | 4.59 | 1020 | 1.1164 | 0.8256 | 0.8207 | 0.8202 | 0.8207 |
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+ | 0.0018 | 4.73 | 1050 | 1.1803 | 0.8269 | 0.8207 | 0.8206 | 0.8207 |
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+ | 0.043 | 4.86 | 1080 | 1.1563 | 0.8266 | 0.8207 | 0.8205 | 0.8207 |
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+ | 0.0303 | 5.0 | 1110 | 1.1096 | 0.8339 | 0.8291 | 0.8288 | 0.8291 |
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+ | 0.0009 | 5.14 | 1140 | 1.0963 | 0.8271 | 0.8235 | 0.8231 | 0.8235 |
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+ | 0.0016 | 5.27 | 1170 | 1.1308 | 0.8284 | 0.8235 | 0.8234 | 0.8235 |
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+ | 0.0008 | 5.41 | 1200 | 1.1495 | 0.8294 | 0.8235 | 0.8237 | 0.8235 |
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+ | 0.0288 | 5.54 | 1230 | 1.1639 | 0.8294 | 0.8235 | 0.8238 | 0.8235 |
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+ | 0.0054 | 5.68 | 1260 | 1.2040 | 0.8143 | 0.8067 | 0.8074 | 0.8067 |
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+ | 0.001 | 5.81 | 1290 | 1.1527 | 0.8105 | 0.8067 | 0.8070 | 0.8067 |
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+ | 0.0007 | 5.95 | 1320 | 1.1475 | 0.8075 | 0.8039 | 0.8040 | 0.8039 |
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+ | 0.0095 | 6.08 | 1350 | 1.1594 | 0.8188 | 0.8151 | 0.8149 | 0.8151 |
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+ | 0.0007 | 6.22 | 1380 | 1.1857 | 0.8103 | 0.8067 | 0.8063 | 0.8067 |
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+ | 0.0127 | 6.35 | 1410 | 1.2116 | 0.8297 | 0.8263 | 0.8257 | 0.8263 |
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+ | 0.0006 | 6.49 | 1440 | 1.2176 | 0.8284 | 0.8235 | 0.8231 | 0.8235 |
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+ | 0.0006 | 6.62 | 1470 | 1.2183 | 0.8303 | 0.8263 | 0.8257 | 0.8263 |
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+ | 0.0039 | 6.76 | 1500 | 1.2183 | 0.8273 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0005 | 6.89 | 1530 | 1.2273 | 0.8279 | 0.8235 | 0.8230 | 0.8235 |
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+ | 0.0005 | 7.03 | 1560 | 1.2269 | 0.8279 | 0.8235 | 0.8230 | 0.8235 |
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+ | 0.0004 | 7.16 | 1590 | 1.2290 | 0.8279 | 0.8235 | 0.8230 | 0.8235 |
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+ | 0.0005 | 7.3 | 1620 | 1.2310 | 0.8276 | 0.8235 | 0.8232 | 0.8235 |
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+ | 0.0005 | 7.43 | 1650 | 1.2280 | 0.8279 | 0.8235 | 0.8230 | 0.8235 |
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+ | 0.0004 | 7.57 | 1680 | 1.2355 | 0.8307 | 0.8263 | 0.8257 | 0.8263 |
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+ | 0.0004 | 7.7 | 1710 | 1.2396 | 0.8307 | 0.8263 | 0.8257 | 0.8263 |
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+ | 0.0005 | 7.84 | 1740 | 1.2196 | 0.8279 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0004 | 7.97 | 1770 | 1.2202 | 0.8279 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0005 | 8.11 | 1800 | 1.2283 | 0.8304 | 0.8263 | 0.8262 | 0.8263 |
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+ | 0.0005 | 8.24 | 1830 | 1.2275 | 0.8332 | 0.8291 | 0.8290 | 0.8291 |
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+ | 0.0004 | 8.38 | 1860 | 1.2252 | 0.8270 | 0.8235 | 0.8232 | 0.8235 |
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+ | 0.0004 | 8.51 | 1890 | 1.2265 | 0.8240 | 0.8207 | 0.8201 | 0.8207 |
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+ | 0.002 | 8.65 | 1920 | 1.2188 | 0.8336 | 0.8291 | 0.8285 | 0.8291 |
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+ | 0.0004 | 8.78 | 1950 | 1.2192 | 0.8336 | 0.8291 | 0.8285 | 0.8291 |
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+ | 0.0004 | 8.92 | 1980 | 1.2164 | 0.8301 | 0.8263 | 0.8257 | 0.8263 |
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+ | 0.0027 | 9.05 | 2010 | 1.2293 | 0.8278 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0004 | 9.19 | 2040 | 1.2340 | 0.8278 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0003 | 9.32 | 2070 | 1.2351 | 0.8278 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0003 | 9.46 | 2100 | 1.2358 | 0.8278 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0006 | 9.59 | 2130 | 1.2376 | 0.8278 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0004 | 9.73 | 2160 | 1.2387 | 0.8278 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0003 | 9.86 | 2190 | 1.2396 | 0.8278 | 0.8235 | 0.8229 | 0.8235 |
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+ | 0.0003 | 10.0 | 2220 | 1.2397 | 0.8278 | 0.8235 | 0.8229 | 0.8235 |
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  ### Framework versions
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