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bart_samsum_v2

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bart
text2text-generation
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End of training

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  1. README.md +78 -53
  2. model.safetensors +1 -1
  3. runs/Feb24_13-43-15_a191c644522c/events.out.tfevents.1708782200.a191c644522c.2522.1 +3 -0
  4. runs/Feb24_13-45-18_a191c644522c/events.out.tfevents.1708782322.a191c644522c.2522.2 +3 -0
  5. runs/Feb24_13-47-25_a191c644522c/events.out.tfevents.1708782446.a191c644522c.23281.0 +3 -0
  6. training_args.bin +1 -1
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 [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9055
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  ## Model description
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@@ -34,7 +34,7 @@ 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: 1e-05
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  - train_batch_size: 4
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  - eval_batch_size: 4
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  - seed: 42
@@ -42,63 +42,88 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 64
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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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- - lr_scheduler_warmup_steps: 5
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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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  | 9.4233 | 0.17 | 1 | 9.1990 |
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- | 9.5213 | 0.34 | 2 | 8.8988 |
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- | 9.1833 | 0.52 | 3 | 8.5352 |
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- | 8.9925 | 0.69 | 4 | 8.2488 |
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- | 8.4764 | 0.86 | 5 | 7.9750 |
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- | 8.1476 | 1.03 | 6 | 7.6447 |
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- | 7.8018 | 1.2 | 7 | 7.3010 |
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- | 7.4513 | 1.38 | 8 | 6.9432 |
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- | 7.0989 | 1.55 | 9 | 6.5388 |
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- | 6.6235 | 1.72 | 10 | 6.0939 |
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- | 6.4569 | 1.89 | 11 | 5.6696 |
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- | 6.1586 | 2.06 | 12 | 5.2364 |
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- | 5.5829 | 2.24 | 13 | 4.7483 |
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- | 5.1618 | 2.41 | 14 | 4.3015 |
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- | 4.6379 | 2.58 | 15 | 3.9224 |
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- | 4.3306 | 2.75 | 16 | 3.5770 |
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- | 4.0446 | 2.92 | 17 | 3.2700 |
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- | 3.721 | 3.1 | 18 | 3.0062 |
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- | 3.5989 | 3.27 | 19 | 2.7701 |
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- | 3.2333 | 3.44 | 20 | 2.5514 |
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- | 3.03 | 3.61 | 21 | 2.3574 |
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- | 2.7969 | 3.78 | 22 | 2.1917 |
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- | 2.65 | 3.96 | 23 | 2.0489 |
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- | 2.4654 | 4.13 | 24 | 1.9216 |
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- | 2.2993 | 4.3 | 25 | 1.8079 |
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- | 2.292 | 4.47 | 26 | 1.7075 |
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- | 2.1275 | 4.65 | 27 | 1.6178 |
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- | 2.0392 | 4.82 | 28 | 1.5364 |
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- | 1.9177 | 4.99 | 29 | 1.4631 |
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- | 1.8414 | 5.16 | 30 | 1.3969 |
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- | 1.823 | 5.33 | 31 | 1.3376 |
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- | 1.6799 | 5.51 | 32 | 1.2845 |
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- | 1.6504 | 5.68 | 33 | 1.2368 |
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- | 1.6111 | 5.85 | 34 | 1.1933 |
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- | 1.538 | 6.02 | 35 | 1.1531 |
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- | 1.4641 | 6.19 | 36 | 1.1163 |
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- | 1.4927 | 6.37 | 37 | 1.0832 |
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- | 1.4657 | 6.54 | 38 | 1.0536 |
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- | 1.4045 | 6.71 | 39 | 1.0276 |
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- | 1.371 | 6.88 | 40 | 1.0049 |
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- | 1.3339 | 7.05 | 41 | 0.9852 |
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- | 1.3281 | 7.23 | 42 | 0.9682 |
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- | 1.2857 | 7.4 | 43 | 0.9535 |
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- | 1.324 | 7.57 | 44 | 0.9410 |
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- | 1.2581 | 7.74 | 45 | 0.9306 |
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- | 1.2301 | 7.91 | 46 | 0.9221 |
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- | 1.2526 | 8.09 | 47 | 0.9153 |
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- | 1.2499 | 8.26 | 48 | 0.9104 |
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- | 1.2298 | 8.43 | 49 | 0.9072 |
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- | 1.195 | 8.6 | 50 | 0.9055 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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16
  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.0236
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20
  ## 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: 4
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  - eval_batch_size: 4
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  - seed: 42
 
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  - total_train_batch_size: 64
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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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+ - lr_scheduler_warmup_steps: 8
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+ - num_epochs: 15
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  ### Training results
49
 
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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  | 9.4233 | 0.17 | 1 | 9.1990 |
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+ | 9.5213 | 0.34 | 2 | 8.5394 |
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+ | 8.7467 | 0.52 | 3 | 8.1115 |
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+ | 8.4697 | 0.69 | 4 | 7.5747 |
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+ | 7.752 | 0.86 | 5 | 6.8712 |
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+ | 7.0515 | 1.03 | 6 | 5.8670 |
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+ | 6.0874 | 1.2 | 7 | 4.6814 |
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+ | 5.0408 | 1.38 | 8 | 3.8055 |
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+ | 4.14 | 1.55 | 9 | 2.6678 |
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+ | 2.9893 | 1.72 | 10 | 1.9701 |
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+ | 2.4337 | 1.89 | 11 | 1.5191 |
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+ | 1.9451 | 2.06 | 12 | 1.2105 |
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+ | 1.53 | 2.24 | 13 | 0.9714 |
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+ | 1.2369 | 2.41 | 14 | 0.7905 |
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+ | 1.0014 | 2.58 | 15 | 0.6478 |
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+ | 0.8419 | 2.75 | 16 | 0.5493 |
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+ | 0.7338 | 2.92 | 17 | 0.4770 |
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+ | 0.6393 | 3.1 | 18 | 0.4151 |
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+ | 0.5747 | 3.27 | 19 | 0.3691 |
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+ | 0.4962 | 3.44 | 20 | 0.3293 |
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+ | 0.4516 | 3.61 | 21 | 0.2935 |
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+ | 0.3995 | 3.78 | 22 | 0.2614 |
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+ | 0.3618 | 3.96 | 23 | 0.2346 |
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+ | 0.3246 | 4.13 | 24 | 0.2129 |
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+ | 0.2929 | 4.3 | 25 | 0.1938 |
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+ | 0.278 | 4.47 | 26 | 0.1770 |
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+ | 0.2493 | 4.65 | 27 | 0.1627 |
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+ | 0.2273 | 4.82 | 28 | 0.1500 |
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+ | 0.2067 | 4.99 | 29 | 0.1381 |
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+ | 0.1917 | 5.16 | 30 | 0.1274 |
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+ | 0.1805 | 5.33 | 31 | 0.1174 |
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+ | 0.1557 | 5.51 | 32 | 0.1081 |
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+ | 0.1495 | 5.68 | 33 | 0.1002 |
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+ | 0.1394 | 5.85 | 34 | 0.0933 |
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+ | 0.1261 | 6.02 | 35 | 0.0868 |
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+ | 0.1155 | 6.19 | 36 | 0.0809 |
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+ | 0.1114 | 6.37 | 37 | 0.0755 |
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+ | 0.1041 | 6.54 | 38 | 0.0705 |
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+ | 0.0952 | 6.71 | 39 | 0.0657 |
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+ | 0.0881 | 6.88 | 40 | 0.0615 |
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+ | 0.0823 | 7.05 | 41 | 0.0577 |
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+ | 0.0778 | 7.23 | 42 | 0.0545 |
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+ | 0.071 | 7.4 | 43 | 0.0515 |
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+ | 0.07 | 7.57 | 44 | 0.0487 |
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+ | 0.0625 | 7.74 | 45 | 0.0463 |
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+ | 0.0589 | 7.91 | 46 | 0.0440 |
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+ | 0.0567 | 8.09 | 47 | 0.0422 |
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+ | 0.0537 | 8.26 | 48 | 0.0411 |
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+ | 0.05 | 8.43 | 49 | 0.0398 |
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+ | 0.0472 | 8.6 | 50 | 0.0384 |
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+ | 0.0458 | 8.77 | 51 | 0.0363 |
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+ | 0.0455 | 8.95 | 52 | 0.0347 |
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+ | 0.0412 | 9.12 | 53 | 0.0340 |
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+ | 0.0414 | 9.29 | 54 | 0.0326 |
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+ | 0.0403 | 9.46 | 55 | 0.0333 |
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+ | 0.0384 | 9.63 | 56 | 0.0303 |
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+ | 0.0353 | 9.81 | 57 | 0.0298 |
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+ | 0.0348 | 9.98 | 58 | 0.0293 |
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+ | 0.0342 | 10.15 | 59 | 0.0275 |
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+ | 0.0311 | 10.32 | 60 | 0.0272 |
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+ | 0.0317 | 10.49 | 61 | 0.0270 |
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+ | 0.0315 | 10.67 | 62 | 0.0261 |
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+ | 0.0289 | 10.84 | 63 | 0.0253 |
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+ | 0.0285 | 11.01 | 64 | 0.0247 |
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+ | 0.0273 | 11.18 | 65 | 0.0244 |
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+ | 0.0277 | 11.35 | 66 | 0.0240 |
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+ | 0.0267 | 11.53 | 67 | 0.0237 |
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+ | 0.0263 | 11.7 | 68 | 0.0237 |
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+ | 0.0258 | 11.87 | 69 | 0.0237 |
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+ | 0.0254 | 12.04 | 70 | 0.0238 |
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+ | 0.0248 | 12.22 | 71 | 0.0239 |
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+ | 0.0246 | 12.39 | 72 | 0.0239 |
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+ | 0.0249 | 12.56 | 73 | 0.0237 |
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+ | 0.0239 | 12.73 | 74 | 0.0236 |
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+ | 0.0247 | 12.9 | 75 | 0.0236 |
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  ### Framework versions
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