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SimoneJLaudani
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trainer_3f

Text Classification
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Safetensors
distilbert
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End of training

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  1. README.md +53 -53
  2. model.safetensors +1 -1
  3. runs/Apr07_09-39-49_b6630175ab9e/events.out.tfevents.1712492291.b6630175ab9e.818.2 +2 -2
  4. runs/Apr07_12-35-57_b6630175ab9e/events.out.tfevents.1712493366.b6630175ab9e.818.3 +3 -0
  5. runs/Apr07_12-35-57_b6630175ab9e/events.out.tfevents.1712503671.b6630175ab9e.818.4 +3 -0
  6. 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-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7582
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- - Precision: 0.8478
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- - Recall: 0.8448
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- - F1: 0.8446
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- - Accuracy: 0.8448
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  ## Model description
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@@ -43,7 +43,7 @@ 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: 5e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -55,53 +55,53 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 1.6945 | 0.08 | 30 | 1.5762 | 0.5100 | 0.5115 | 0.5001 | 0.5115 |
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- | 1.4363 | 0.17 | 60 | 1.3360 | 0.5888 | 0.5697 | 0.5483 | 0.5697 |
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- | 1.1347 | 0.25 | 90 | 1.0684 | 0.7150 | 0.6966 | 0.6943 | 0.6966 |
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- | 0.8997 | 0.34 | 120 | 0.8797 | 0.7419 | 0.7337 | 0.7333 | 0.7337 |
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- | 0.7458 | 0.42 | 150 | 0.8047 | 0.7659 | 0.7354 | 0.7328 | 0.7354 |
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- | 0.8419 | 0.51 | 180 | 0.7057 | 0.7912 | 0.7795 | 0.7806 | 0.7795 |
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- | 0.5818 | 0.59 | 210 | 0.7138 | 0.7896 | 0.7831 | 0.7774 | 0.7831 |
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- | 0.6419 | 0.67 | 240 | 0.6906 | 0.8012 | 0.7848 | 0.7806 | 0.7848 |
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- | 0.7079 | 0.76 | 270 | 0.6524 | 0.8114 | 0.7972 | 0.7953 | 0.7972 |
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- | 0.5156 | 0.84 | 300 | 0.6335 | 0.8372 | 0.8148 | 0.8134 | 0.8148 |
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- | 0.5888 | 0.93 | 330 | 0.6472 | 0.8282 | 0.8166 | 0.8162 | 0.8166 |
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- | 0.4568 | 1.01 | 360 | 0.5767 | 0.8562 | 0.8430 | 0.8443 | 0.8430 |
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- | 0.3246 | 1.1 | 390 | 0.6753 | 0.8152 | 0.8042 | 0.8042 | 0.8042 |
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- | 0.3564 | 1.18 | 420 | 0.5988 | 0.8273 | 0.8166 | 0.8172 | 0.8166 |
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- | 0.217 | 1.26 | 450 | 0.6661 | 0.8206 | 0.8078 | 0.8084 | 0.8078 |
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- | 0.3266 | 1.35 | 480 | 0.6623 | 0.8255 | 0.8095 | 0.8099 | 0.8095 |
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- | 0.3483 | 1.43 | 510 | 0.6147 | 0.8289 | 0.8219 | 0.8220 | 0.8219 |
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- | 0.3132 | 1.52 | 540 | 0.6314 | 0.8531 | 0.8466 | 0.8469 | 0.8466 |
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- | 0.3595 | 1.6 | 570 | 0.6002 | 0.8400 | 0.8360 | 0.8367 | 0.8360 |
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- | 0.4029 | 1.69 | 600 | 0.6517 | 0.8375 | 0.8307 | 0.8311 | 0.8307 |
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- | 0.2137 | 1.77 | 630 | 0.6710 | 0.8504 | 0.8448 | 0.8460 | 0.8448 |
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- | 0.3862 | 1.85 | 660 | 0.6675 | 0.8385 | 0.8307 | 0.8320 | 0.8307 |
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- | 0.2409 | 1.94 | 690 | 0.6429 | 0.8518 | 0.8466 | 0.8476 | 0.8466 |
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- | 0.2348 | 2.02 | 720 | 0.6541 | 0.8445 | 0.8360 | 0.8376 | 0.8360 |
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- | 0.059 | 2.11 | 750 | 0.6353 | 0.8591 | 0.8519 | 0.8520 | 0.8519 |
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- | 0.1164 | 2.19 | 780 | 0.7147 | 0.8364 | 0.8342 | 0.8326 | 0.8342 |
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- | 0.1661 | 2.28 | 810 | 0.6468 | 0.8584 | 0.8519 | 0.8528 | 0.8519 |
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- | 0.1886 | 2.36 | 840 | 0.6984 | 0.8597 | 0.8501 | 0.8510 | 0.8501 |
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- | 0.1226 | 2.44 | 870 | 0.7298 | 0.8405 | 0.8342 | 0.8344 | 0.8342 |
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- | 0.0909 | 2.53 | 900 | 0.7092 | 0.8485 | 0.8466 | 0.8469 | 0.8466 |
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- | 0.1675 | 2.61 | 930 | 0.6954 | 0.8528 | 0.8466 | 0.8476 | 0.8466 |
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- | 0.1521 | 2.7 | 960 | 0.7429 | 0.8453 | 0.8413 | 0.8415 | 0.8413 |
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- | 0.1005 | 2.78 | 990 | 0.7826 | 0.8384 | 0.8307 | 0.8311 | 0.8307 |
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- | 0.2409 | 2.87 | 1020 | 0.8100 | 0.8325 | 0.8219 | 0.8224 | 0.8219 |
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- | 0.0748 | 2.95 | 1050 | 0.7315 | 0.8418 | 0.8360 | 0.8358 | 0.8360 |
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- | 0.0819 | 3.03 | 1080 | 0.7462 | 0.8433 | 0.8395 | 0.8399 | 0.8395 |
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- | 0.0813 | 3.12 | 1110 | 0.7432 | 0.8460 | 0.8430 | 0.8428 | 0.8430 |
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- | 0.0396 | 3.2 | 1140 | 0.7130 | 0.8456 | 0.8430 | 0.8430 | 0.8430 |
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- | 0.0846 | 3.29 | 1170 | 0.7621 | 0.8403 | 0.8377 | 0.8376 | 0.8377 |
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- | 0.0455 | 3.37 | 1200 | 0.7667 | 0.8373 | 0.8342 | 0.8341 | 0.8342 |
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- | 0.022 | 3.46 | 1230 | 0.7427 | 0.8379 | 0.8342 | 0.8342 | 0.8342 |
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- | 0.0586 | 3.54 | 1260 | 0.7552 | 0.8432 | 0.8377 | 0.8377 | 0.8377 |
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- | 0.0423 | 3.62 | 1290 | 0.7524 | 0.8443 | 0.8395 | 0.8393 | 0.8395 |
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- | 0.0366 | 3.71 | 1320 | 0.7506 | 0.8462 | 0.8430 | 0.8429 | 0.8430 |
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- | 0.0435 | 3.79 | 1350 | 0.7439 | 0.8454 | 0.8430 | 0.8428 | 0.8430 |
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- | 0.046 | 3.88 | 1380 | 0.7534 | 0.8452 | 0.8430 | 0.8429 | 0.8430 |
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- | 0.0279 | 3.96 | 1410 | 0.7576 | 0.8445 | 0.8413 | 0.8412 | 0.8413 |
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  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
22
  It achieves the following results on the evaluation set:
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+ - Loss: 1.0618
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+ - Precision: 0.8364
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+ - Recall: 0.8325
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+ - F1: 0.8329
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+ - Accuracy: 0.8325
28
 
29
  ## Model description
30
 
 
43
  ### Training hyperparameters
44
 
45
  The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
47
  - train_batch_size: 8
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  - eval_batch_size: 8
49
  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0336 | 0.08 | 30 | 0.8889 | 0.8378 | 0.8272 | 0.8274 | 0.8272 |
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+ | 0.0653 | 0.17 | 60 | 0.8057 | 0.8477 | 0.8448 | 0.8446 | 0.8448 |
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+ | 0.0424 | 0.25 | 90 | 0.8184 | 0.8437 | 0.8430 | 0.8430 | 0.8430 |
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+ | 0.0725 | 0.34 | 120 | 0.8139 | 0.8490 | 0.8430 | 0.8432 | 0.8430 |
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+ | 0.0887 | 0.42 | 150 | 0.8627 | 0.8411 | 0.8342 | 0.8341 | 0.8342 |
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+ | 0.0441 | 0.51 | 180 | 0.8865 | 0.8505 | 0.8413 | 0.8420 | 0.8413 |
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+ | 0.0085 | 0.59 | 210 | 0.8662 | 0.8458 | 0.8430 | 0.8428 | 0.8430 |
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+ | 0.0785 | 0.67 | 240 | 0.9317 | 0.8431 | 0.8395 | 0.8398 | 0.8395 |
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+ | 0.0075 | 0.76 | 270 | 0.9654 | 0.8538 | 0.8448 | 0.8446 | 0.8448 |
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+ | 0.0259 | 0.84 | 300 | 0.9987 | 0.8405 | 0.8342 | 0.8342 | 0.8342 |
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+ | 0.0672 | 0.93 | 330 | 1.0019 | 0.8394 | 0.8342 | 0.8346 | 0.8342 |
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+ | 0.017 | 1.01 | 360 | 1.0248 | 0.8384 | 0.8325 | 0.8332 | 0.8325 |
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+ | 0.0049 | 1.1 | 390 | 1.0067 | 0.8474 | 0.8377 | 0.8393 | 0.8377 |
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+ | 0.0296 | 1.18 | 420 | 1.0234 | 0.8438 | 0.8377 | 0.8385 | 0.8377 |
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+ | 0.0349 | 1.26 | 450 | 1.0185 | 0.8277 | 0.8219 | 0.8223 | 0.8219 |
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+ | 0.0025 | 1.35 | 480 | 1.0467 | 0.8239 | 0.8166 | 0.8171 | 0.8166 |
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+ | 0.0041 | 1.43 | 510 | 1.0966 | 0.8315 | 0.8236 | 0.8243 | 0.8236 |
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+ | 0.0017 | 1.52 | 540 | 1.0549 | 0.8323 | 0.8272 | 0.8279 | 0.8272 |
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+ | 0.0361 | 1.6 | 570 | 1.0055 | 0.8519 | 0.8483 | 0.8490 | 0.8483 |
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+ | 0.0644 | 1.69 | 600 | 1.1315 | 0.8371 | 0.8325 | 0.8322 | 0.8325 |
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+ | 0.0016 | 1.77 | 630 | 1.1434 | 0.8244 | 0.8183 | 0.8188 | 0.8183 |
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+ | 0.0405 | 1.85 | 660 | 1.0628 | 0.8326 | 0.8272 | 0.8276 | 0.8272 |
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+ | 0.0005 | 1.94 | 690 | 1.0394 | 0.8391 | 0.8342 | 0.8348 | 0.8342 |
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+ | 0.0324 | 2.02 | 720 | 1.1081 | 0.8316 | 0.8254 | 0.8264 | 0.8254 |
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+ | 0.0012 | 2.11 | 750 | 1.0663 | 0.8354 | 0.8325 | 0.8323 | 0.8325 |
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+ | 0.005 | 2.19 | 780 | 1.0777 | 0.8335 | 0.8307 | 0.8304 | 0.8307 |
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+ | 0.0264 | 2.28 | 810 | 1.0483 | 0.8361 | 0.8325 | 0.8326 | 0.8325 |
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+ | 0.0431 | 2.36 | 840 | 1.0193 | 0.8473 | 0.8430 | 0.8435 | 0.8430 |
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+ | 0.0004 | 2.44 | 870 | 1.0411 | 0.8457 | 0.8413 | 0.8418 | 0.8413 |
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+ | 0.0045 | 2.53 | 900 | 1.0604 | 0.8319 | 0.8289 | 0.8290 | 0.8289 |
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+ | 0.0305 | 2.61 | 930 | 1.0808 | 0.8323 | 0.8289 | 0.8292 | 0.8289 |
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+ | 0.0362 | 2.7 | 960 | 1.0466 | 0.8430 | 0.8395 | 0.8398 | 0.8395 |
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+ | 0.0004 | 2.78 | 990 | 1.0518 | 0.8429 | 0.8395 | 0.8397 | 0.8395 |
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+ | 0.0147 | 2.87 | 1020 | 1.0781 | 0.8397 | 0.8360 | 0.8361 | 0.8360 |
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+ | 0.0034 | 2.95 | 1050 | 1.0696 | 0.8377 | 0.8342 | 0.8344 | 0.8342 |
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+ | 0.0004 | 3.03 | 1080 | 1.0649 | 0.8395 | 0.8360 | 0.8362 | 0.8360 |
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+ | 0.0063 | 3.12 | 1110 | 1.0614 | 0.8347 | 0.8325 | 0.8325 | 0.8325 |
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+ | 0.0014 | 3.2 | 1140 | 1.0433 | 0.8367 | 0.8342 | 0.8345 | 0.8342 |
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+ | 0.0181 | 3.29 | 1170 | 1.0559 | 0.8339 | 0.8307 | 0.8311 | 0.8307 |
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+ | 0.0069 | 3.37 | 1200 | 1.0693 | 0.8320 | 0.8289 | 0.8293 | 0.8289 |
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+ | 0.0004 | 3.46 | 1230 | 1.0666 | 0.8320 | 0.8289 | 0.8293 | 0.8289 |
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+ | 0.0117 | 3.54 | 1260 | 1.0596 | 0.8316 | 0.8289 | 0.8292 | 0.8289 |
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+ | 0.0211 | 3.62 | 1290 | 1.0679 | 0.8326 | 0.8289 | 0.8293 | 0.8289 |
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+ | 0.0124 | 3.71 | 1320 | 1.0688 | 0.8359 | 0.8325 | 0.8328 | 0.8325 |
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+ | 0.0177 | 3.79 | 1350 | 1.0676 | 0.8346 | 0.8307 | 0.8312 | 0.8307 |
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+ | 0.0009 | 3.88 | 1380 | 1.0635 | 0.8364 | 0.8325 | 0.8329 | 0.8325 |
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+ | 0.0012 | 3.96 | 1410 | 1.0616 | 0.8364 | 0.8325 | 0.8329 | 0.8325 |
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  ### Framework versions
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