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Nugrahasetyaardi
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SER_model_xapiens

Audio Classification
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TensorBoard
Safetensors
wav2vec2
Generated from Trainer
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  1. README.md +103 -13
  2. model.safetensors +1 -1
  3. runs/Jun05_11-35-29_DESKTOP-6VKE8JE/events.out.tfevents.1717562130.DESKTOP-6VKE8JE.40688.3 +2 -2
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6879
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- - Accuracy: 0.4050
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  ## Model description
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@@ -46,22 +46,112 @@ The following hyperparameters were used during training:
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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_ratio: 0.1
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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 9 | 1.9247 | 0.4063 |
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- | 2.0359 | 2.0 | 18 | 1.7484 | 0.4063 |
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- | 1.8346 | 3.0 | 27 | 1.7171 | 0.4063 |
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- | 1.7777 | 4.0 | 36 | 1.7039 | 0.4063 |
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- | 1.7825 | 5.0 | 45 | 1.6975 | 0.4063 |
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- | 1.7578 | 6.0 | 54 | 1.6899 | 0.4076 |
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- | 1.7549 | 7.0 | 63 | 1.6824 | 0.4181 |
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- | 1.732 | 8.0 | 72 | 1.6886 | 0.4050 |
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- | 1.7433 | 9.0 | 81 | 1.6975 | 0.3919 |
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- | 1.7299 | 10.0 | 90 | 1.6879 | 0.4050 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.6028
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+ - Accuracy: 0.2857
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  ## Model description
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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_ratio: 0.1
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+ - num_epochs: 100
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 9 | 1.6904 | 0.4050 |
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+ | 1.7287 | 2.0 | 18 | 1.6896 | 0.4050 |
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+ | 1.7165 | 3.0 | 27 | 1.7053 | 0.3932 |
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+ | 1.7139 | 4.0 | 36 | 1.6924 | 0.4063 |
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+ | 1.7197 | 5.0 | 45 | 1.6880 | 0.4089 |
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+ | 1.7001 | 6.0 | 54 | 1.7197 | 0.3879 |
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+ | 1.7189 | 7.0 | 63 | 1.6830 | 0.4142 |
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+ | 1.7085 | 8.0 | 72 | 1.6863 | 0.4155 |
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+ | 1.7037 | 9.0 | 81 | 1.6922 | 0.4024 |
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+ | 1.6801 | 10.0 | 90 | 1.7362 | 0.3906 |
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+ | 1.6801 | 11.0 | 99 | 1.6845 | 0.3997 |
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+ | 1.6703 | 12.0 | 108 | 1.7386 | 0.3840 |
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+ | 1.6618 | 13.0 | 117 | 1.7161 | 0.3644 |
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+ | 1.6553 | 14.0 | 126 | 1.7323 | 0.3893 |
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+ | 1.6675 | 15.0 | 135 | 1.7140 | 0.3984 |
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+ | 1.6092 | 16.0 | 144 | 1.7814 | 0.3499 |
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+ | 1.6335 | 17.0 | 153 | 1.7173 | 0.4115 |
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+ | 1.5963 | 18.0 | 162 | 1.7237 | 0.3945 |
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+ | 1.5934 | 19.0 | 171 | 1.7524 | 0.3893 |
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+ | 1.5604 | 20.0 | 180 | 1.7808 | 0.3696 |
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+ | 1.5604 | 21.0 | 189 | 1.7854 | 0.3696 |
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+ | 1.5403 | 22.0 | 198 | 1.7852 | 0.3604 |
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+ | 1.5435 | 23.0 | 207 | 1.8085 | 0.3578 |
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+ | 1.509 | 24.0 | 216 | 1.9569 | 0.3145 |
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+ | 1.4945 | 25.0 | 225 | 1.7921 | 0.3499 |
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+ | 1.474 | 26.0 | 234 | 1.9064 | 0.3329 |
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+ | 1.4117 | 27.0 | 243 | 1.8558 | 0.3539 |
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+ | 1.4322 | 28.0 | 252 | 1.8654 | 0.3499 |
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+ | 1.3891 | 29.0 | 261 | 1.8813 | 0.3394 |
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+ | 1.3702 | 30.0 | 270 | 1.8723 | 0.3578 |
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+ | 1.3702 | 31.0 | 279 | 1.8935 | 0.3381 |
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+ | 1.3541 | 32.0 | 288 | 2.0439 | 0.2923 |
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+ | 1.3233 | 33.0 | 297 | 1.9074 | 0.3394 |
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+ | 1.3015 | 34.0 | 306 | 1.9286 | 0.3460 |
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+ | 1.319 | 35.0 | 315 | 2.0303 | 0.3119 |
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+ | 1.2758 | 36.0 | 324 | 1.9812 | 0.3408 |
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+ | 1.2442 | 37.0 | 333 | 1.9786 | 0.3460 |
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+ | 1.2629 | 38.0 | 342 | 1.9383 | 0.3565 |
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+ | 1.2735 | 39.0 | 351 | 1.9711 | 0.3617 |
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+ | 1.2826 | 40.0 | 360 | 2.0502 | 0.3303 |
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+ | 1.2826 | 41.0 | 369 | 2.0452 | 0.3342 |
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+ | 1.2416 | 42.0 | 378 | 2.0793 | 0.3342 |
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+ | 1.1714 | 43.0 | 387 | 2.1191 | 0.3250 |
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+ | 1.1593 | 44.0 | 396 | 2.0360 | 0.3342 |
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+ | 1.1263 | 45.0 | 405 | 2.0652 | 0.3526 |
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+ | 1.143 | 46.0 | 414 | 2.1089 | 0.3080 |
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+ | 1.0774 | 47.0 | 423 | 2.1723 | 0.3132 |
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+ | 1.0518 | 48.0 | 432 | 2.1094 | 0.3316 |
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+ | 1.0804 | 49.0 | 441 | 2.1944 | 0.3028 |
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+ | 1.0304 | 50.0 | 450 | 2.1495 | 0.3028 |
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+ | 1.0304 | 51.0 | 459 | 2.1812 | 0.3198 |
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+ | 1.01 | 52.0 | 468 | 2.1290 | 0.3447 |
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+ | 0.9972 | 53.0 | 477 | 2.2778 | 0.3028 |
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+ | 0.9883 | 54.0 | 486 | 2.2143 | 0.3211 |
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+ | 0.9725 | 55.0 | 495 | 2.2285 | 0.3421 |
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+ | 0.9637 | 56.0 | 504 | 2.1786 | 0.3342 |
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+ | 0.9545 | 57.0 | 513 | 2.1761 | 0.3552 |
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+ | 0.9102 | 58.0 | 522 | 2.2526 | 0.3342 |
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+ | 0.9037 | 59.0 | 531 | 2.2311 | 0.3093 |
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+ | 0.867 | 60.0 | 540 | 2.3681 | 0.2896 |
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+ | 0.867 | 61.0 | 549 | 2.3130 | 0.3093 |
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+ | 0.8681 | 62.0 | 558 | 2.2619 | 0.3132 |
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+ | 0.8157 | 63.0 | 567 | 2.2726 | 0.3080 |
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+ | 0.8166 | 64.0 | 576 | 2.2933 | 0.3263 |
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+ | 0.8126 | 65.0 | 585 | 2.2754 | 0.3381 |
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+ | 0.8223 | 66.0 | 594 | 2.3631 | 0.3014 |
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+ | 0.7593 | 67.0 | 603 | 2.3403 | 0.3080 |
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+ | 0.774 | 68.0 | 612 | 2.3941 | 0.3001 |
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+ | 0.7347 | 69.0 | 621 | 2.3866 | 0.2896 |
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+ | 0.7397 | 70.0 | 630 | 2.4285 | 0.2896 |
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+ | 0.7397 | 71.0 | 639 | 2.3924 | 0.2936 |
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+ | 0.7556 | 72.0 | 648 | 2.3677 | 0.3067 |
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+ | 0.7019 | 73.0 | 657 | 2.4436 | 0.2844 |
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+ | 0.7186 | 74.0 | 666 | 2.4368 | 0.2883 |
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+ | 0.686 | 75.0 | 675 | 2.4293 | 0.2870 |
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+ | 0.6895 | 76.0 | 684 | 2.4057 | 0.2936 |
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+ | 0.6719 | 77.0 | 693 | 2.4716 | 0.3001 |
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+ | 0.6507 | 78.0 | 702 | 2.4922 | 0.2936 |
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+ | 0.6741 | 79.0 | 711 | 2.4093 | 0.3132 |
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+ | 0.6265 | 80.0 | 720 | 2.5002 | 0.3145 |
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+ | 0.6265 | 81.0 | 729 | 2.5818 | 0.2661 |
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+ | 0.617 | 82.0 | 738 | 2.4953 | 0.2883 |
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+ | 0.6441 | 83.0 | 747 | 2.5105 | 0.2923 |
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+ | 0.5921 | 84.0 | 756 | 2.5884 | 0.2792 |
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+ | 0.6288 | 85.0 | 765 | 2.5165 | 0.2962 |
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+ | 0.5871 | 86.0 | 774 | 2.5537 | 0.3067 |
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+ | 0.5974 | 87.0 | 783 | 2.5740 | 0.2805 |
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+ | 0.5942 | 88.0 | 792 | 2.5240 | 0.2936 |
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+ | 0.5782 | 89.0 | 801 | 2.6014 | 0.2910 |
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+ | 0.5717 | 90.0 | 810 | 2.5036 | 0.3054 |
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+ | 0.5717 | 91.0 | 819 | 2.5913 | 0.2831 |
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+ | 0.5761 | 92.0 | 828 | 2.5776 | 0.2910 |
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+ | 0.5672 | 93.0 | 837 | 2.6038 | 0.2844 |
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+ | 0.535 | 94.0 | 846 | 2.5620 | 0.3014 |
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+ | 0.5591 | 95.0 | 855 | 2.6176 | 0.2936 |
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+ | 0.558 | 96.0 | 864 | 2.5990 | 0.2988 |
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+ | 0.5319 | 97.0 | 873 | 2.5890 | 0.2936 |
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+ | 0.5266 | 98.0 | 882 | 2.6031 | 0.2844 |
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+ | 0.5337 | 99.0 | 891 | 2.6090 | 0.2805 |
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+ | 0.5252 | 100.0 | 900 | 2.6028 | 0.2857 |
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  ### Framework versions
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